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CenterNessNet
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/2n/c2ng6n7nv7j6ot5s3l65cjcbcom4k45g6xz2sxctmom3kjfu2yje.py # Topologically Sorted Source Nodes: [x, norm], Original ATen: [aten.convolution, aten.native_group_norm] # Source node to ATen node mapping: # norm => var_mean # x => convolution # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True}) triton_red_fused_convolution_native_group_norm_0 = async_compile.triton('triton_red_fused_convolution_native_group_norm_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.reduction( size_hints=[512, 8192], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_convolution_native_group_norm_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 3, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_red_fused_convolution_native_group_norm_0(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr): xnumel = 512 rnumel = 8192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rbase = tl.arange(0, RBLOCK)[None, :] x4 = xindex x0 = xindex % 128 tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32) tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32) tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32) for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r5 = rindex r3 = (rindex // 4096) tmp0 = tl.load(in_out_ptr0 + (r5 + (8192*x4)), rmask & xmask, eviction_policy='evict_first', other=0.0) tmp1 = tl.load(in_ptr0 + (r3 + (2*x0)), rmask & xmask, eviction_policy='evict_last', other=0.0) tmp2 = tmp0 + tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = triton_helpers.welford_reduce( tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0 ) tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean) tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2) tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight) tl.store(in_out_ptr0 + (r5 + (8192*x4)), tmp2, rmask & xmask) tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford( tmp4_mean, tmp4_m2, tmp4_weight, 1 ) tmp4 = tmp4_tmp[:, None] tmp5 = tmp5_tmp[:, None] tmp6 = tmp6_tmp[:, None] tl.store(out_ptr0 + (x4), tmp4, xmask) tl.store(out_ptr1 + (x4), tmp5, xmask) tl.store(out_ptr2 + (x4), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/hq/chqhykjsbqdit7w6q4wokmaxashjyx3mcvrb5eht7rghwqiaatcf.py # Topologically Sorted Source Nodes: [norm], Original ATen: [aten.native_group_norm] # Source node to ATen node mapping: # norm => add, rsqrt, var_mean # Graph fragment: # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {}) # %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {}) triton_per_fused_native_group_norm_1 = async_compile.triton('triton_per_fused_native_group_norm_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[128, 4], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_group_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_native_group_norm_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 128 rnumel = 4 RBLOCK: tl.constexpr = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (4*x0)), xmask, other=0.0) tmp1 = tl.load(in_ptr1 + (r1 + (4*x0)), xmask, other=0.0) tmp2 = tl.load(in_ptr2 + (r1 + (4*x0)), xmask, other=0.0) tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp7 = tl.where(xmask, tmp3, 0) tmp8 = tl.where(xmask, tmp4, 0) tmp9 = tl.where(xmask, tmp5, 0) tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1) tmp13 = tmp10[:, None] tmp14 = tmp11[:, None] tmp15 = tmp12[:, None] tmp16 = 32768.0 tmp17 = tmp14 / tmp16 tmp18 = 1e-05 tmp19 = tmp17 + tmp18 tmp20 = libdevice.rsqrt(tmp19) tl.store(out_ptr2 + (x0), tmp20, xmask) tl.store(out_ptr0 + (x0), tmp13, xmask) tl.store(out_ptr1 + (x0), tmp14, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/q3/cq32fxmy5x7yle33r4ilakwezapd2xehsgje6if3o5knnssz3adi.py # Topologically Sorted Source Nodes: [norm, relu], Original ATen: [aten.native_group_norm, aten.relu] # Source node to ATen node mapping: # norm => add_1, mul_1 # relu => relu # Graph fragment: # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %unsqueeze_5), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_2), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_1,), kwargs = {}) triton_poi_fused_native_group_norm_relu_2 = async_compile.triton('triton_poi_fused_native_group_norm_relu_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4194304], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_group_norm_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_native_group_norm_relu_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4194304 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x4 = (xindex // 4096) x1 = (xindex // 4096) % 256 tmp0 = tl.load(in_ptr0 + (x3), None) tmp1 = tl.load(in_ptr1 + ((x4 // 8)), None, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + ((x4 // 8)), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr3 + (x1), None, eviction_policy='evict_last') tmp12 = tl.load(in_ptr4 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 - tmp1 tmp4 = 32768.0 tmp5 = tmp3 / tmp4 tmp6 = 1e-05 tmp7 = tmp5 + tmp6 tmp8 = libdevice.rsqrt(tmp7) tmp9 = tmp2 * tmp8 tmp11 = tmp9 * tmp10 tmp13 = tmp11 + tmp12 tmp14 = tl.full([1], 0, tl.int32) tmp15 = triton_helpers.maximum(tmp14, tmp13) tl.store(out_ptr0 + (x3), tmp15, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ob/cobe3cs3zdr4g3svcyqtxtcc4l4ckot56hsytcz47nu3sggotrlx.py # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution] # Source node to ATen node mapping: # input_1 => convolution_4 # Graph fragment: # %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_18, %primals_19, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), None) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tl.store(in_out_ptr0 + (x0), tmp3, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19 = args args.clear() assert_size_stride(primals_1, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_2, (256, ), (1, )) assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1)) assert_size_stride(primals_4, (256, ), (1, )) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, ), (1, )) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (256, ), (1, )) assert_size_stride(primals_12, (256, ), (1, )) assert_size_stride(primals_13, (256, ), (1, )) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256, ), (1, )) assert_size_stride(primals_16, (256, ), (1, )) assert_size_stride(primals_17, (256, ), (1, )) assert_size_stride(primals_18, (1, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 32, 1, 1, 4), (128, 4, 512, 512, 1), torch.float32) buf3 = empty_strided_cuda((4, 32, 1, 1, 4), (128, 4, 512, 512, 1), torch.float32) buf4 = empty_strided_cuda((4, 32, 1, 1, 4), (128, 4, 512, 512, 1), torch.float32) # Topologically Sorted Source Nodes: [x, norm], Original ATen: [aten.convolution, aten.native_group_norm] stream0 = get_raw_stream(0) triton_red_fused_convolution_native_group_norm_0.run(buf1, primals_2, buf2, buf3, buf4, 512, 8192, grid=grid(512), stream=stream0) del primals_2 buf5 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) buf6 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) buf8 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) # Topologically Sorted Source Nodes: [norm], Original ATen: [aten.native_group_norm] triton_per_fused_native_group_norm_1.run(buf2, buf3, buf4, buf5, buf6, buf8, 128, 4, grid=grid(128), stream=stream0) buf9 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [norm, relu], Original ATen: [aten.native_group_norm, aten.relu] triton_poi_fused_native_group_norm_relu_2.run(buf1, buf5, buf6, primals_4, primals_5, buf9, 4194304, grid=grid(4194304), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf11 = buf10; del buf10 # reuse buf12 = buf4; del buf4 # reuse buf13 = buf3; del buf3 # reuse buf14 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [x_1, norm_1], Original ATen: [aten.convolution, aten.native_group_norm] triton_red_fused_convolution_native_group_norm_0.run(buf11, primals_7, buf12, buf13, buf14, 512, 8192, grid=grid(512), stream=stream0) del primals_7 buf15 = buf6; del buf6 # reuse buf16 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) buf18 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) # Topologically Sorted Source Nodes: [norm_1], Original ATen: [aten.native_group_norm] triton_per_fused_native_group_norm_1.run(buf12, buf13, buf14, buf15, buf16, buf18, 128, 4, grid=grid(128), stream=stream0) buf19 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [norm_1, relu_2], Original ATen: [aten.native_group_norm, aten.relu] triton_poi_fused_native_group_norm_relu_2.run(buf11, buf15, buf16, primals_8, primals_9, buf19, 4194304, grid=grid(4194304), stream=stream0) del primals_9 # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution] buf20 = extern_kernels.convolution(buf19, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf21 = buf20; del buf20 # reuse buf22 = buf14; del buf14 # reuse buf23 = buf13; del buf13 # reuse buf24 = buf12; del buf12 # reuse # Topologically Sorted Source Nodes: [x_2, norm_2], Original ATen: [aten.convolution, aten.native_group_norm] triton_red_fused_convolution_native_group_norm_0.run(buf21, primals_11, buf22, buf23, buf24, 512, 8192, grid=grid(512), stream=stream0) del primals_11 buf25 = buf16; del buf16 # reuse buf26 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) buf28 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) # Topologically Sorted Source Nodes: [norm_2], Original ATen: [aten.native_group_norm] triton_per_fused_native_group_norm_1.run(buf22, buf23, buf24, buf25, buf26, buf28, 128, 4, grid=grid(128), stream=stream0) buf29 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [norm_2, relu_4], Original ATen: [aten.native_group_norm, aten.relu] triton_poi_fused_native_group_norm_relu_2.run(buf21, buf25, buf26, primals_12, primals_13, buf29, 4194304, grid=grid(4194304), stream=stream0) del primals_13 # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution] buf30 = extern_kernels.convolution(buf29, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf31 = buf30; del buf30 # reuse buf32 = buf24; del buf24 # reuse buf33 = buf23; del buf23 # reuse buf34 = buf22; del buf22 # reuse # Topologically Sorted Source Nodes: [x_3, norm_3], Original ATen: [aten.convolution, aten.native_group_norm] triton_red_fused_convolution_native_group_norm_0.run(buf31, primals_15, buf32, buf33, buf34, 512, 8192, grid=grid(512), stream=stream0) del primals_15 buf35 = buf26; del buf26 # reuse buf36 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) buf38 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch.float32) # Topologically Sorted Source Nodes: [norm_3], Original ATen: [aten.native_group_norm] triton_per_fused_native_group_norm_1.run(buf32, buf33, buf34, buf35, buf36, buf38, 128, 4, grid=grid(128), stream=stream0) del buf32 del buf33 del buf34 buf39 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) # Topologically Sorted Source Nodes: [norm_3, relu_6], Original ATen: [aten.native_group_norm, aten.relu] triton_poi_fused_native_group_norm_relu_2.run(buf31, buf35, buf36, primals_16, primals_17, buf39, 4194304, grid=grid(4194304), stream=stream0) del buf36 del primals_17 # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution] buf40 = extern_kernels.convolution(buf39, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf40, (4, 1, 64, 64), (4096, 4096, 64, 1)) buf41 = buf40; del buf40 # reuse # Topologically Sorted Source Nodes: [input_1], Original ATen: [aten.convolution] triton_poi_fused_convolution_3.run(buf41, primals_19, 16384, grid=grid(16384), stream=stream0) del primals_19 return (buf41, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf1, reinterpret_tensor(buf5, (4, 32), (32, 1), 0), reinterpret_tensor(buf8, (4, 32), (32, 1), 0), buf9, buf11, reinterpret_tensor(buf15, (4, 32), (32, 1), 0), reinterpret_tensor(buf18, (4, 32), (32, 1), 0), buf19, buf21, reinterpret_tensor(buf25, (4, 32), (32, 1), 0), reinterpret_tensor(buf28, (4, 32), (32, 1), 0), buf29, buf31, reinterpret_tensor(buf35, (4, 32), (32, 1), 0), reinterpret_tensor(buf38, (4, 32), (32, 1), 0), buf39, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 256, 64, 64), (1048576, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((1, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch import torch.nn as nn from torch.nn.modules.utils import _pair class BasicBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0): super(BasicBlock, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding) self.norm = nn.GroupNorm(num_groups=32, num_channels=256) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) norm = self.norm(x) relu = self.relu(norm) return relu class DCNv2(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, deformable_groups=1): super(DCNv2, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = _pair(kernel_size) self.stride = _pair(stride) self.padding = _pair(padding) self.dilation = _pair(dilation) self.deformable_groups = deformable_groups self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size)) self.bias = nn.Parameter(torch.Tensor(out_channels)) self.reset_parameters() def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1.0 / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.zero_() def forward(self, input, offset, mask): assert 2 * self.deformable_groups * self.kernel_size[0 ] * self.kernel_size[1] == offset.shape[1] assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[ 1] == mask.shape[1] return dcn_v2_conv(input, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.deformable_groups) class DCN(DCNv2): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, deformable_groups=1): super(DCN, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, deformable_groups) channels_ = self.deformable_groups * 3 * self.kernel_size[0 ] * self.kernel_size[1] self.conv_offset_mask = nn.Conv2d(self.in_channels, channels_, kernel_size=self.kernel_size, stride=self.stride, padding=self. padding, bias=True) self.init_offset() def init_offset(self): self.conv_offset_mask.weight.data.zero_() self.conv_offset_mask.bias.data.zero_() def forward(self, input): out = self.conv_offset_mask(input) o1, o2, mask = torch.chunk(out, 3, dim=1) offset = torch.cat((o1, o2), dim=1) mask = torch.sigmoid(mask) return dcn_v2_conv(input, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.deformable_groups) class CenterNessNet(nn.Module): def __init__(self, in_channels=256, feat_channels=256, stacked_convs=4, dcn_on_last_conv=False): super(CenterNessNet, self).__init__() self.stacked_convs = stacked_convs self.dcn_on_last_conv = dcn_on_last_conv self.in_channels = in_channels self.feat_channels = feat_channels self._init_layers() self.init_weight() def _init_layers(self): self._init_centerness_convs() self._init_centerness_predict() def normal_init(self, module, mean=0, std=1, bias=0): nn.init.normal_(module.weight, mean, std) if hasattr(module, 'bias') and module.bias is not None: nn.init.constant_(module.bias, bias) def init_weight(self): for m in self.centerness_convs.modules(): if isinstance(m, nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.GroupNorm): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): torch.nn.normal(m.weight.data, 0, 0.01) m.bias.zero_() for m in self.centerness_predict.modules(): if isinstance(m, nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: m.bias.data.zero_() def _init_centerness_convs(self): self.centerness_convs = nn.Sequential() for i in range(self.stacked_convs): chn = self.in_channels if i == 0 else self.feat_channels if self.dcn_on_last_conv and i == self.stacked_convs - 1: conv_cfg = DCN(chn, self.feat_channels, kernel_size=(3, 3), stride=1, padding=1, deformable_groups=1) else: conv_cfg = BasicBlock(chn, self.feat_channels, 3, 1, 1) self.centerness_convs.add_module(('centerness_' + str({0})). format(i), conv_cfg) def _init_centerness_predict(self): self.centerness_predict = nn.Sequential() predict = nn.Conv2d(self.feat_channels, 1, 3, padding=1) self.centerness_predict.add_module('centerness_predict', predict) def forward(self, x): convs = self.centerness_convs(x) predict = self.centerness_predict(convs) return predict def get_inputs(): return [torch.rand([4, 256, 64, 64])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import math import torch.nn as nn from torch.nn.modules.utils import _pair assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_red_fused_convolution_native_group_norm_0(in_out_ptr0, in_ptr0, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr): xnumel = 512 rnumel = 8192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rbase = tl.arange(0, RBLOCK)[None, :] x4 = xindex x0 = xindex % 128 tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32) tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32) tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32) for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r5 = rindex r3 = rindex // 4096 tmp0 = tl.load(in_out_ptr0 + (r5 + 8192 * x4), rmask & xmask, eviction_policy='evict_first', other=0.0) tmp1 = tl.load(in_ptr0 + (r3 + 2 * x0), rmask & xmask, eviction_policy='evict_last', other=0.0) tmp2 = tmp0 + tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers. welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0) ) tmp4_mean = tl.where(rmask & xmask, tmp4_mean_next, tmp4_mean) tmp4_m2 = tl.where(rmask & xmask, tmp4_m2_next, tmp4_m2) tmp4_weight = tl.where(rmask & xmask, tmp4_weight_next, tmp4_weight) tl.store(in_out_ptr0 + (r5 + 8192 * x4), tmp2, rmask & xmask) tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean, tmp4_m2, tmp4_weight, 1) tmp4 = tmp4_tmp[:, None] tmp5 = tmp5_tmp[:, None] tmp6 = tmp6_tmp[:, None] tl.store(out_ptr0 + x4, tmp4, xmask) tl.store(out_ptr1 + x4, tmp5, xmask) tl.store(out_ptr2 + x4, tmp6, xmask) @triton.jit def triton_per_fused_native_group_norm_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 128 RBLOCK: tl.constexpr = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 4 * x0), xmask, other=0.0) tmp1 = tl.load(in_ptr1 + (r1 + 4 * x0), xmask, other=0.0) tmp2 = tl.load(in_ptr2 + (r1 + 4 * x0), xmask, other=0.0) tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp7 = tl.where(xmask, tmp3, 0) tmp8 = tl.where(xmask, tmp4, 0) tmp9 = tl.where(xmask, tmp5, 0) tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1) tmp13 = tmp10[:, None] tmp14 = tmp11[:, None] tmp12[:, None] tmp16 = 32768.0 tmp17 = tmp14 / tmp16 tmp18 = 1e-05 tmp19 = tmp17 + tmp18 tmp20 = libdevice.rsqrt(tmp19) tl.store(out_ptr2 + x0, tmp20, xmask) tl.store(out_ptr0 + x0, tmp13, xmask) tl.store(out_ptr1 + x0, tmp14, xmask) @triton.jit def triton_poi_fused_native_group_norm_relu_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x4 = xindex // 4096 x1 = xindex // 4096 % 256 tmp0 = tl.load(in_ptr0 + x3, None) tmp1 = tl.load(in_ptr1 + x4 // 8, None, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + x4 // 8, None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr3 + x1, None, eviction_policy='evict_last') tmp12 = tl.load(in_ptr4 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 - tmp1 tmp4 = 32768.0 tmp5 = tmp3 / tmp4 tmp6 = 1e-05 tmp7 = tmp5 + tmp6 tmp8 = libdevice.rsqrt(tmp7) tmp9 = tmp2 * tmp8 tmp11 = tmp9 * tmp10 tmp13 = tmp11 + tmp12 tmp14 = tl.full([1], 0, tl.int32) tmp15 = triton_helpers.maximum(tmp14, tmp13) tl.store(out_ptr0 + x3, tmp15, None) @triton.jit def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, None) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tl.store(in_out_ptr0 + x0, tmp3, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19) = args args.clear() assert_size_stride(primals_1, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_2, (256,), (1,)) assert_size_stride(primals_3, (4, 256, 64, 64), (1048576, 4096, 64, 1)) assert_size_stride(primals_4, (256,), (1,)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256,), (1,)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (256,), (1,)) assert_size_stride(primals_12, (256,), (1,)) assert_size_stride(primals_13, (256,), (1,)) assert_size_stride(primals_14, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_15, (256,), (1,)) assert_size_stride(primals_16, (256,), (1,)) assert_size_stride(primals_17, (256,), (1,)) assert_size_stride(primals_18, (1, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_19, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 32, 1, 1, 4), (128, 4, 512, 512, 1), torch.float32) buf3 = empty_strided_cuda((4, 32, 1, 1, 4), (128, 4, 512, 512, 1), torch.float32) buf4 = empty_strided_cuda((4, 32, 1, 1, 4), (128, 4, 512, 512, 1), torch.float32) get_raw_stream(0) triton_red_fused_convolution_native_group_norm_0[grid(512)](buf1, primals_2, buf2, buf3, buf4, 512, 8192, XBLOCK=1, RBLOCK=1024, num_warps=16, num_stages=1) del primals_2 buf5 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) buf6 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) buf8 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) triton_per_fused_native_group_norm_1[grid(128)](buf2, buf3, buf4, buf5, buf6, buf8, 128, 4, XBLOCK=8, num_warps=2, num_stages=1) buf9 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) triton_poi_fused_native_group_norm_relu_2[grid(4194304)](buf1, buf5, buf6, primals_4, primals_5, buf9, 4194304, XBLOCK=1024, num_warps=4, num_stages=1) del primals_5 buf10 = extern_kernels.convolution(buf9, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf11 = buf10 del buf10 buf12 = buf4 del buf4 buf13 = buf3 del buf3 buf14 = buf2 del buf2 triton_red_fused_convolution_native_group_norm_0[grid(512)](buf11, primals_7, buf12, buf13, buf14, 512, 8192, XBLOCK=1, RBLOCK= 1024, num_warps=16, num_stages=1) del primals_7 buf15 = buf6 del buf6 buf16 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) buf18 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) triton_per_fused_native_group_norm_1[grid(128)](buf12, buf13, buf14, buf15, buf16, buf18, 128, 4, XBLOCK=8, num_warps=2, num_stages=1) buf19 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) triton_poi_fused_native_group_norm_relu_2[grid(4194304)](buf11, buf15, buf16, primals_8, primals_9, buf19, 4194304, XBLOCK=1024, num_warps=4, num_stages=1) del primals_9 buf20 = extern_kernels.convolution(buf19, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf20, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf21 = buf20 del buf20 buf22 = buf14 del buf14 buf23 = buf13 del buf13 buf24 = buf12 del buf12 triton_red_fused_convolution_native_group_norm_0[grid(512)](buf21, primals_11, buf22, buf23, buf24, 512, 8192, XBLOCK=1, RBLOCK= 1024, num_warps=16, num_stages=1) del primals_11 buf25 = buf16 del buf16 buf26 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) buf28 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) triton_per_fused_native_group_norm_1[grid(128)](buf22, buf23, buf24, buf25, buf26, buf28, 128, 4, XBLOCK=8, num_warps=2, num_stages=1) buf29 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) triton_poi_fused_native_group_norm_relu_2[grid(4194304)](buf21, buf25, buf26, primals_12, primals_13, buf29, 4194304, XBLOCK= 1024, num_warps=4, num_stages=1) del primals_13 buf30 = extern_kernels.convolution(buf29, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf30, (4, 256, 64, 64), (1048576, 4096, 64, 1)) buf31 = buf30 del buf30 buf32 = buf24 del buf24 buf33 = buf23 del buf23 buf34 = buf22 del buf22 triton_red_fused_convolution_native_group_norm_0[grid(512)](buf31, primals_15, buf32, buf33, buf34, 512, 8192, XBLOCK=1, RBLOCK= 1024, num_warps=16, num_stages=1) del primals_15 buf35 = buf26 del buf26 buf36 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) buf38 = empty_strided_cuda((4, 32, 1, 1), (32, 1, 128, 128), torch. float32) triton_per_fused_native_group_norm_1[grid(128)](buf32, buf33, buf34, buf35, buf36, buf38, 128, 4, XBLOCK=8, num_warps=2, num_stages=1) del buf32 del buf33 del buf34 buf39 = empty_strided_cuda((4, 256, 64, 64), (1048576, 4096, 64, 1), torch.float32) triton_poi_fused_native_group_norm_relu_2[grid(4194304)](buf31, buf35, buf36, primals_16, primals_17, buf39, 4194304, XBLOCK= 1024, num_warps=4, num_stages=1) del buf36 del primals_17 buf40 = extern_kernels.convolution(buf39, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf40, (4, 1, 64, 64), (4096, 4096, 64, 1)) buf41 = buf40 del buf40 triton_poi_fused_convolution_3[grid(16384)](buf41, primals_19, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_19 return (buf41, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, primals_16, primals_18, buf1, reinterpret_tensor(buf5, (4, 32), (32, 1), 0), reinterpret_tensor( buf8, (4, 32), (32, 1), 0), buf9, buf11, reinterpret_tensor(buf15, (4, 32), (32, 1), 0), reinterpret_tensor(buf18, (4, 32), (32, 1), 0 ), buf19, buf21, reinterpret_tensor(buf25, (4, 32), (32, 1), 0), reinterpret_tensor(buf28, (4, 32), (32, 1), 0), buf29, buf31, reinterpret_tensor(buf35, (4, 32), (32, 1), 0), reinterpret_tensor( buf38, (4, 32), (32, 1), 0), buf39) class BasicBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0): super(BasicBlock, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding) self.norm = nn.GroupNorm(num_groups=32, num_channels=256) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) norm = self.norm(x) relu = self.relu(norm) return relu class DCNv2(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, deformable_groups=1): super(DCNv2, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = _pair(kernel_size) self.stride = _pair(stride) self.padding = _pair(padding) self.dilation = _pair(dilation) self.deformable_groups = deformable_groups self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size)) self.bias = nn.Parameter(torch.Tensor(out_channels)) self.reset_parameters() def reset_parameters(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1.0 / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) self.bias.data.zero_() def forward(self, input, offset, mask): assert 2 * self.deformable_groups * self.kernel_size[0 ] * self.kernel_size[1] == offset.shape[1] assert self.deformable_groups * self.kernel_size[0] * self.kernel_size[ 1] == mask.shape[1] return dcn_v2_conv(input, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.deformable_groups) class DCN(DCNv2): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, deformable_groups=1): super(DCN, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, deformable_groups) channels_ = self.deformable_groups * 3 * self.kernel_size[0 ] * self.kernel_size[1] self.conv_offset_mask = nn.Conv2d(self.in_channels, channels_, kernel_size=self.kernel_size, stride=self.stride, padding=self. padding, bias=True) self.init_offset() def init_offset(self): self.conv_offset_mask.weight.data.zero_() self.conv_offset_mask.bias.data.zero_() def forward(self, input): out = self.conv_offset_mask(input) o1, o2, mask = torch.chunk(out, 3, dim=1) offset = torch.cat((o1, o2), dim=1) mask = torch.sigmoid(mask) return dcn_v2_conv(input, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.deformable_groups) class CenterNessNetNew(nn.Module): def __init__(self, in_channels=256, feat_channels=256, stacked_convs=4, dcn_on_last_conv=False): super(CenterNessNetNew, self).__init__() self.stacked_convs = stacked_convs self.dcn_on_last_conv = dcn_on_last_conv self.in_channels = in_channels self.feat_channels = feat_channels self._init_layers() self.init_weight() def _init_layers(self): self._init_centerness_convs() self._init_centerness_predict() def normal_init(self, module, mean=0, std=1, bias=0): nn.init.normal_(module.weight, mean, std) if hasattr(module, 'bias') and module.bias is not None: nn.init.constant_(module.bias, bias) def init_weight(self): for m in self.centerness_convs.modules(): if isinstance(m, nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.GroupNorm): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): torch.nn.normal(m.weight.data, 0, 0.01) m.bias.zero_() for m in self.centerness_predict.modules(): if isinstance(m, nn.Conv2d): torch.nn.init.xavier_normal_(m.weight.data) if m.bias is not None: m.bias.data.zero_() def _init_centerness_convs(self): self.centerness_convs = nn.Sequential() for i in range(self.stacked_convs): chn = self.in_channels if i == 0 else self.feat_channels if self.dcn_on_last_conv and i == self.stacked_convs - 1: conv_cfg = DCN(chn, self.feat_channels, kernel_size=(3, 3), stride=1, padding=1, deformable_groups=1) else: conv_cfg = BasicBlock(chn, self.feat_channels, 3, 1, 1) self.centerness_convs.add_module(('centerness_' + str({0})). format(i), conv_cfg) def _init_centerness_predict(self): self.centerness_predict = nn.Sequential() predict = nn.Conv2d(self.feat_channels, 1, 3, padding=1) self.centerness_predict.add_module('centerness_predict', predict) def forward(self, input_0): primals_1 = self.centerness_convs.centerness_0.conv.weight primals_2 = self.centerness_convs.centerness_0.conv.bias primals_4 = self.centerness_convs.centerness_0.norm.weight primals_5 = self.centerness_convs.centerness_0.norm.bias primals_6 = self.centerness_convs.centerness_1.conv.weight primals_7 = self.centerness_convs.centerness_1.conv.bias primals_8 = self.centerness_convs.centerness_1.norm.weight primals_9 = self.centerness_convs.centerness_1.norm.bias primals_10 = self.centerness_convs.centerness_2.conv.weight primals_11 = self.centerness_convs.centerness_2.conv.bias primals_12 = self.centerness_convs.centerness_2.norm.weight primals_13 = self.centerness_convs.centerness_2.norm.bias primals_14 = self.centerness_convs.centerness_3.conv.weight primals_15 = self.centerness_convs.centerness_3.conv.bias primals_16 = self.centerness_convs.centerness_3.norm.weight primals_17 = self.centerness_convs.centerness_3.norm.bias primals_18 = self.centerness_predict.centerness_predict.weight primals_19 = self.centerness_predict.centerness_predict.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19]) return output[0]
ZCDu/CenternessNet
CenterNessNet
false
9,685
[ "MIT" ]
0
03f5d01999a4e1595eaceef9f62b4450ed017843
https://github.com/ZCDu/CenternessNet/tree/03f5d01999a4e1595eaceef9f62b4450ed017843
PriorDiscriminator
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/2j/c2jdoj4tcaujecuntbzcpssdm46qqc55mrqjpjrmi7wwyblphesm.py # Topologically Sorted Source Nodes: [h], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # h => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 512 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, None) tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/dh/cdhj4aozvvzkw7stzrqoauyoij3petwtvi4g4weydesiaurrughd.py # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # h_1 => relu_1 # Graph fragment: # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 8192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, None) tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/xr/cxrxf4nkydknjv7xhdecpyrprhviagsqwicrk4lpp64qv2hkzaxp.py # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # sigmoid => sigmoid # Graph fragment: # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_5,), kwargs = {}) triton_poi_fused_sigmoid_2 = async_compile.triton('triton_poi_fused_sigmoid_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tmp4 = tl.sigmoid(tmp3) tl.store(in_out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (512, 4), (4, 1)) assert_size_stride(primals_2, (512, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (128, 512), (512, 1)) assert_size_stride(primals_5, (128, ), (1, )) assert_size_stride(primals_6, (1, 128), (128, 1)) assert_size_stride(primals_7, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 512), (512, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 512), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 512), (8192, 2048, 512, 1), 0); del buf0 # reuse buf7 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.bool) # Topologically Sorted Source Nodes: [h], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf7, 32768, grid=grid(32768), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 512), (512, 1), 0), reinterpret_tensor(primals_4, (512, 128), (1, 512), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0); del buf2 # reuse buf6 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool) # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_1.run(buf3, primals_5, buf6, 8192, grid=grid(8192), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 128), (128, 1), 0), reinterpret_tensor(primals_6, (128, 1), (1, 128), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid] triton_poi_fused_sigmoid_2.run(buf5, primals_7, 64, grid=grid(64), stream=stream0) del primals_7 return (buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 512), (512, 1), 0), reinterpret_tensor(buf3, (64, 128), (128, 1), 0), buf5, primals_6, buf6, primals_4, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((512, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((128, 512), (512, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((128, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((1, 128), (128, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim class PriorDiscriminator(nn.Module): """The prior discriminator class. This discriminate between a vector drawn from random uniform, and the vector y obtained as output of the encoder. It enforces y to be close to a uniform distribution. """ def __init__(self, y_size): super().__init__() self.l0 = nn.Linear(y_size, 512) self.l1 = nn.Linear(512, 128) self.l2 = nn.Linear(128, 1) def forward(self, x): h = F.relu(self.l0(x)) h = F.relu(self.l1(h)) return torch.sigmoid(self.l2(h)) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'y_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 512 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, None) tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 128 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, None) tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tmp4 = tl.sigmoid(tmp3) tl.store(in_out_ptr0 + x0, tmp4, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (512, 4), (4, 1)) assert_size_stride(primals_2, (512,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (128, 512), (512, 1)) assert_size_stride(primals_5, (128,), (1,)) assert_size_stride(primals_6, (1, 128), (128, 1)) assert_size_stride(primals_7, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 512), (512, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 512), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 512), (8192, 2048, 512, 1), 0 ) del buf0 buf7 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(32768)](buf1, primals_2, buf7, 32768, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 128), (128, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 512), (512, 1), 0), reinterpret_tensor(primals_4, (512, 128), (1, 512), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 128), (2048, 512, 128, 1), 0) del buf2 buf6 = empty_strided_cuda((4, 4, 4, 128), (2048, 512, 128, 1), torch.bool) triton_poi_fused_relu_threshold_backward_1[grid(8192)](buf3, primals_5, buf6, 8192, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 128), (128, 1), 0), reinterpret_tensor(primals_6, (128, 1), (1, 128), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf4 triton_poi_fused_sigmoid_2[grid(64)](buf5, primals_7, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_7 return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 512), (512, 1), 0 ), reinterpret_tensor(buf3, (64, 128), (128, 1), 0 ), buf5, primals_6, buf6, primals_4, buf7 class PriorDiscriminatorNew(nn.Module): """The prior discriminator class. This discriminate between a vector drawn from random uniform, and the vector y obtained as output of the encoder. It enforces y to be close to a uniform distribution. """ def __init__(self, y_size): super().__init__() self.l0 = nn.Linear(y_size, 512) self.l1 = nn.Linear(512, 128) self.l2 = nn.Linear(128, 1) def forward(self, input_0): primals_1 = self.l0.weight primals_2 = self.l0.bias primals_4 = self.l1.weight primals_5 = self.l1.bias primals_6 = self.l2.weight primals_7 = self.l2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
ValerioB88/self-supervised-relational-reasoning
PriorDiscriminator
false
9,686
[ "MIT" ]
0
12692b93d5c8dd3f56a31aa8b790366556e7a621
https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621
TensorMin
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/3c/c3cwnyaykszaolqgdft24txdqmia6mta3o3ubgriep3zg5waspbh.py # Topologically Sorted Source Nodes: [min_1], Original ATen: [aten.min] # Source node to ATen node mapping: # min_1 => getitem # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%min_1, 0), kwargs = {}) triton_poi_fused_min_0 = async_compile.triton('triton_poi_fused_min_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_min_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_min_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.minimum(tmp0, tmp1) tmp4 = triton_helpers.minimum(tmp2, tmp3) tmp6 = triton_helpers.minimum(tmp4, tmp5) tl.store(out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [min_1], Original ATen: [aten.min] stream0 = get_raw_stream(0) triton_poi_fused_min_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch def tensor_min(input, dim, keepdim=False): if isinstance(dim, int): return torch.min(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.min(input, dim=d, keepdim=keepdim)[0] return input class StatModule(torch.nn.Module): def __init__(self, dim, keepdim=False): if isinstance(dim, list): dim = tuple(dim) if isinstance(dim, int): dim = dim, assert isinstance(dim, tuple) self.dim = dim self.keepdim = keepdim super().__init__() class TensorMin(StatModule, torch.nn.Module): def forward(self, input): return tensor_min(input, dim=self.dim, keepdim=self.keepdim) def get_inputs(): return [torch.rand([4, 4, 4, 4, 4])] def get_init_inputs(): return [[], {'dim': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_min_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.minimum(tmp0, tmp1) tmp4 = triton_helpers.minimum(tmp2, tmp3) tmp6 = triton_helpers.minimum(tmp4, tmp5) tl.store(out_ptr0 + x0, tmp6, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_min_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, def tensor_min(input, dim, keepdim=False): if isinstance(dim, int): return torch.min(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.min(input, dim=d, keepdim=keepdim)[0] return input class StatModule(torch.nn.Module): def __init__(self, dim, keepdim=False): if isinstance(dim, list): dim = tuple(dim) if isinstance(dim, int): dim = dim, assert isinstance(dim, tuple) self.dim = dim self.keepdim = keepdim super().__init__() class TensorMinNew(StatModule, torch.nn.Module): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
Minyus/kedex
TensorMin
false
9,687
[ "Apache-2.0" ]
0
92f952eed3cb6109bc783f449051f2bd13579d2a
https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a
TensorRange
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/xw/cxwhgmdx6bmmhcpw56lx47dxsunumyc427pqdlhwf6sipvdtc44h.py # Topologically Sorted Source Nodes: [max_1, min_1, sub], Original ATen: [aten.max, aten.min, aten.sub] # Source node to ATen node mapping: # max_1 => max_1 # min_1 => min_1 # sub => sub # Graph fragment: # %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%arg0_1, 4), kwargs = {}) # %min_1 : [num_users=1] = call_function[target=torch.ops.aten.min.dim](args = (%arg0_1, 4), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem, %getitem_2), kwargs = {}) triton_poi_fused_max_min_sub_0 = async_compile.triton('triton_poi_fused_max_min_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_min_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_min_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp7 = triton_helpers.minimum(tmp0, tmp1) tmp8 = triton_helpers.minimum(tmp7, tmp3) tmp9 = triton_helpers.minimum(tmp8, tmp5) tmp10 = tmp6 - tmp9 tl.store(out_ptr0 + (x0), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_1, min_1, sub], Original ATen: [aten.max, aten.min, aten.sub] stream0 = get_raw_stream(0) triton_poi_fused_max_min_sub_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch def tensor_max(input, dim, keepdim=False): if isinstance(dim, int): return torch.max(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.max(input, dim=d, keepdim=keepdim)[0] return input def tensor_min(input, dim, keepdim=False): if isinstance(dim, int): return torch.min(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.min(input, dim=d, keepdim=keepdim)[0] return input class StatModule(torch.nn.Module): def __init__(self, dim, keepdim=False): if isinstance(dim, list): dim = tuple(dim) if isinstance(dim, int): dim = dim, assert isinstance(dim, tuple) self.dim = dim self.keepdim = keepdim super().__init__() class TensorRange(StatModule, torch.nn.Module): def forward(self, input): return tensor_max(input, dim=self.dim, keepdim=self.keepdim ) - tensor_min(input, dim=self.dim, keepdim=self.keepdim) def get_inputs(): return [torch.rand([4, 4, 4, 4, 4])] def get_init_inputs(): return [[], {'dim': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_max_min_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp7 = triton_helpers.minimum(tmp0, tmp1) tmp8 = triton_helpers.minimum(tmp7, tmp3) tmp9 = triton_helpers.minimum(tmp8, tmp5) tmp10 = tmp6 - tmp9 tl.store(out_ptr0 + x0, tmp10, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_max_min_sub_0[grid(256)](arg0_1, buf0, 256, XBLOCK =256, num_warps=4, num_stages=1) del arg0_1 return buf0, def tensor_max(input, dim, keepdim=False): if isinstance(dim, int): return torch.max(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.max(input, dim=d, keepdim=keepdim)[0] return input def tensor_min(input, dim, keepdim=False): if isinstance(dim, int): return torch.min(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.min(input, dim=d, keepdim=keepdim)[0] return input class StatModule(torch.nn.Module): def __init__(self, dim, keepdim=False): if isinstance(dim, list): dim = tuple(dim) if isinstance(dim, int): dim = dim, assert isinstance(dim, tuple) self.dim = dim self.keepdim = keepdim super().__init__() class TensorRangeNew(StatModule, torch.nn.Module): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
Minyus/kedex
TensorRange
false
9,688
[ "Apache-2.0" ]
0
92f952eed3cb6109bc783f449051f2bd13579d2a
https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a
GraphConvolution
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/wv/cwvsxo4q6wyoxpozsubbimmg6xvl34ow44hy6yl5mwa23uuy77sa.py # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # output_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%mm_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(in_out_ptr0 + (x0), tmp2, xmask) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [support], Original ATen: [aten.mm] extern_kernels.mm(primals_1, primals_2, out=buf0) del primals_2 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [output], Original ATen: [aten.mm] extern_kernels.mm(primals_3, buf0, out=buf1) del buf0 buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool) # Topologically Sorted Source Nodes: [output_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf2, buf3, 16, grid=grid(16), stream=stream0) return (buf2, buf3, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
from torch.nn import Module import torch from torch.nn import functional as F from torch.nn import Parameter import torch.utils.data import torch.multiprocessing from torch.nn.parameter import Parameter from torch.nn.modules.module import Module import torch.nn.modules.loss from scipy.sparse import * def dropout(x, drop_prob, shared_axes=[], training=False): """ Apply dropout to input tensor. Parameters ---------- input_tensor: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` Returns ------- output: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied. """ if drop_prob == 0 or drop_prob is None or not training: return x sz = list(x.size()) for i in shared_axes: sz[i] = 1 mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob) mask = mask.expand_as(x) return x * mask class GraphConvolution(Module): """ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 """ def __init__(self, in_features, out_features, dropout=0.0, act=F.relu): super(GraphConvolution, self).__init__() self.in_features = in_features self.out_features = out_features self.dropout = dropout self.act = act self.weight = Parameter(torch.FloatTensor(in_features, out_features)) self.reset_parameters() def reset_parameters(self): torch.nn.init.xavier_uniform_(self.weight) def forward(self, input, adj): input = F.dropout(input, self.dropout, self.training) support = torch.mm(input, self.weight) output = torch.spmm(adj, support) output = self.act(output) return output def __repr__(self): return self.__class__.__name__ + ' (' + str(self.in_features ) + ' -> ' + str(self.out_features) + ')' def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'in_features': 4, 'out_features': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch.nn import Module from torch.nn import functional as F from torch.nn import Parameter import torch.utils.data import torch.multiprocessing from torch.nn.parameter import Parameter from torch.nn.modules.module import Module import torch.nn.modules.loss from scipy.sparse import * assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(in_out_ptr0 + x0, tmp2, xmask) tl.store(out_ptr0 + x0, tmp4, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(primals_1, primals_2, out=buf0) del primals_2 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(primals_3, buf0, out=buf1) del buf0 buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 4), (4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(16)](buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) return buf2, buf3, reinterpret_tensor(primals_3, (4, 4), (1, 4), 0 ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0) def dropout(x, drop_prob, shared_axes=[], training=False): """ Apply dropout to input tensor. Parameters ---------- input_tensor: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` Returns ------- output: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied. """ if drop_prob == 0 or drop_prob is None or not training: return x sz = list(x.size()) for i in shared_axes: sz[i] = 1 mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob) mask = mask.expand_as(x) return x * mask class GraphConvolutionNew(Module): """ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 """ def __init__(self, in_features, out_features, dropout=0.0, act=F.relu): super(GraphConvolutionNew, self).__init__() self.in_features = in_features self.out_features = out_features self.dropout = dropout self.act = act self.weight = Parameter(torch.FloatTensor(in_features, out_features)) self.reset_parameters() def reset_parameters(self): torch.nn.init.xavier_uniform_(self.weight) def __repr__(self): return self.__class__.__name__ + ' (' + str(self.in_features ) + ' -> ' + str(self.out_features) + ')' def forward(self, input_0, input_1): primals_1 = self.weight primals_2 = input_0 primals_3 = input_1 output = call([primals_1, primals_2, primals_3]) return output[0]
LucasAPayne/graph4nlp
GraphConvolution
false
9,689
[ "Apache-2.0" ]
0
3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
GlobalDiscriminator
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ok/cok5e6xwrytpg276smpyk5pd3rehmb4nxf3fw4r3rqdnzvwf3xi2.py # Topologically Sorted Source Nodes: [conv2d, h], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d => convolution # h => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1048576], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 984064 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 3844) % 64 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/6f/c6fr4cx2vioyzlzm7pznjz6yjupxdcafrkb5pqa3msrv4wdmxh6p.py # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.convolution] # Source node to ATen node mapping: # h_1 => convolution_1 # Graph fragment: # %convolution_1 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 460800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 3600) % 32 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ia/ciabfu5dk5w373uyeecet2pkqnjtbhpm2uyxzzererpezli2tokg.py # Topologically Sorted Source Nodes: [h_2], Original ATen: [aten._adaptive_avg_pool2d] # Source node to ATen node mapping: # h_2 => _adaptive_avg_pool2d # Graph fragment: # %_adaptive_avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten._adaptive_avg_pool2d.default](args = (%convolution_1, [16, 16]), kwargs = {}) triton_poi_fused__adaptive_avg_pool2d_2 = async_compile.triton('triton_poi_fused__adaptive_avg_pool2d_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__adaptive_avg_pool2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 25, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__adaptive_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32768 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 16) % 16 x0 = xindex % 16 x2 = (xindex // 256) x4 = xindex tmp0 = ((15*x1) // 4) tmp1 = ((75 + (60*x1)) // 16) tmp2 = tmp0 < tmp1 tmp3 = ((15*x0) // 4) tmp4 = ((75 + (60*x0)) // 16) tmp5 = tmp3 < tmp4 tmp6 = tmp2 & tmp5 tmp7 = tl.load(in_ptr0 + ((60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp6, eviction_policy='evict_last', other=0.0) tmp8 = 1 + ((15*x0) // 4) tmp9 = tmp8 < tmp4 tmp10 = tmp2 & tmp9 tmp11 = tl.load(in_ptr0 + (1 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp10, eviction_policy='evict_last', other=0.0) tmp12 = tmp11 + tmp7 tmp13 = 2 + ((15*x0) // 4) tmp14 = tmp13 < tmp4 tmp15 = tmp2 & tmp14 tmp16 = tl.load(in_ptr0 + (2 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp15, eviction_policy='evict_last', other=0.0) tmp17 = tmp16 + tmp12 tmp18 = 3 + ((15*x0) // 4) tmp19 = tmp18 < tmp4 tmp20 = tmp2 & tmp19 tmp21 = tl.load(in_ptr0 + (3 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp20, eviction_policy='evict_last', other=0.0) tmp22 = tmp21 + tmp17 tmp23 = 4 + ((15*x0) // 4) tmp24 = tmp23 < tmp4 tmp25 = tmp2 & tmp24 tmp26 = tl.load(in_ptr0 + (4 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp25, eviction_policy='evict_last', other=0.0) tmp27 = tmp26 + tmp22 tmp28 = 1 + ((15*x1) // 4) tmp29 = tmp28 < tmp1 tmp30 = tmp29 & tmp5 tmp31 = tl.load(in_ptr0 + (60 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp30, eviction_policy='evict_last', other=0.0) tmp32 = tmp31 + tmp27 tmp33 = tmp29 & tmp9 tmp34 = tl.load(in_ptr0 + (61 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp33, eviction_policy='evict_last', other=0.0) tmp35 = tmp34 + tmp32 tmp36 = tmp29 & tmp14 tmp37 = tl.load(in_ptr0 + (62 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp36, eviction_policy='evict_last', other=0.0) tmp38 = tmp37 + tmp35 tmp39 = tmp29 & tmp19 tmp40 = tl.load(in_ptr0 + (63 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp39, eviction_policy='evict_last', other=0.0) tmp41 = tmp40 + tmp38 tmp42 = tmp29 & tmp24 tmp43 = tl.load(in_ptr0 + (64 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp42, eviction_policy='evict_last', other=0.0) tmp44 = tmp43 + tmp41 tmp45 = 2 + ((15*x1) // 4) tmp46 = tmp45 < tmp1 tmp47 = tmp46 & tmp5 tmp48 = tl.load(in_ptr0 + (120 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp47, eviction_policy='evict_last', other=0.0) tmp49 = tmp48 + tmp44 tmp50 = tmp46 & tmp9 tmp51 = tl.load(in_ptr0 + (121 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp50, eviction_policy='evict_last', other=0.0) tmp52 = tmp51 + tmp49 tmp53 = tmp46 & tmp14 tmp54 = tl.load(in_ptr0 + (122 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp53, eviction_policy='evict_last', other=0.0) tmp55 = tmp54 + tmp52 tmp56 = tmp46 & tmp19 tmp57 = tl.load(in_ptr0 + (123 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp56, eviction_policy='evict_last', other=0.0) tmp58 = tmp57 + tmp55 tmp59 = tmp46 & tmp24 tmp60 = tl.load(in_ptr0 + (124 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp59, eviction_policy='evict_last', other=0.0) tmp61 = tmp60 + tmp58 tmp62 = 3 + ((15*x1) // 4) tmp63 = tmp62 < tmp1 tmp64 = tmp63 & tmp5 tmp65 = tl.load(in_ptr0 + (180 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp64, eviction_policy='evict_last', other=0.0) tmp66 = tmp65 + tmp61 tmp67 = tmp63 & tmp9 tmp68 = tl.load(in_ptr0 + (181 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp67, eviction_policy='evict_last', other=0.0) tmp69 = tmp68 + tmp66 tmp70 = tmp63 & tmp14 tmp71 = tl.load(in_ptr0 + (182 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp70, eviction_policy='evict_last', other=0.0) tmp72 = tmp71 + tmp69 tmp73 = tmp63 & tmp19 tmp74 = tl.load(in_ptr0 + (183 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp73, eviction_policy='evict_last', other=0.0) tmp75 = tmp74 + tmp72 tmp76 = tmp63 & tmp24 tmp77 = tl.load(in_ptr0 + (184 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp76, eviction_policy='evict_last', other=0.0) tmp78 = tmp77 + tmp75 tmp79 = 4 + ((15*x1) // 4) tmp80 = tmp79 < tmp1 tmp81 = tmp80 & tmp5 tmp82 = tl.load(in_ptr0 + (240 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp81, eviction_policy='evict_last', other=0.0) tmp83 = tmp82 + tmp78 tmp84 = tmp80 & tmp9 tmp85 = tl.load(in_ptr0 + (241 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp84, eviction_policy='evict_last', other=0.0) tmp86 = tmp85 + tmp83 tmp87 = tmp80 & tmp14 tmp88 = tl.load(in_ptr0 + (242 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp87, eviction_policy='evict_last', other=0.0) tmp89 = tmp88 + tmp86 tmp90 = tmp80 & tmp19 tmp91 = tl.load(in_ptr0 + (243 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp90, eviction_policy='evict_last', other=0.0) tmp92 = tmp91 + tmp89 tmp93 = tmp80 & tmp24 tmp94 = tl.load(in_ptr0 + (244 + (60*((15*x1) // 4)) + (3600*x2) + ((15*x0) // 4)), tmp93, eviction_policy='evict_last', other=0.0) tmp95 = tmp94 + tmp92 tmp96 = 1.0 tmp97 = tl.full(tmp96.shape, 0.0, tmp96.dtype) tmp98 = tl.where(tmp6, tmp96, tmp97) tmp99 = tl.where(tmp10, tmp96, tmp97) tmp100 = tmp99 + tmp98 tmp101 = tl.where(tmp15, tmp96, tmp97) tmp102 = tmp101 + tmp100 tmp103 = tl.where(tmp20, tmp96, tmp97) tmp104 = tmp103 + tmp102 tmp105 = tl.where(tmp25, tmp96, tmp97) tmp106 = tmp105 + tmp104 tmp107 = tl.where(tmp30, tmp96, tmp97) tmp108 = tmp107 + tmp106 tmp109 = tl.where(tmp33, tmp96, tmp97) tmp110 = tmp109 + tmp108 tmp111 = tl.where(tmp36, tmp96, tmp97) tmp112 = tmp111 + tmp110 tmp113 = tl.where(tmp39, tmp96, tmp97) tmp114 = tmp113 + tmp112 tmp115 = tl.where(tmp42, tmp96, tmp97) tmp116 = tmp115 + tmp114 tmp117 = tl.where(tmp47, tmp96, tmp97) tmp118 = tmp117 + tmp116 tmp119 = tl.where(tmp50, tmp96, tmp97) tmp120 = tmp119 + tmp118 tmp121 = tl.where(tmp53, tmp96, tmp97) tmp122 = tmp121 + tmp120 tmp123 = tl.where(tmp56, tmp96, tmp97) tmp124 = tmp123 + tmp122 tmp125 = tl.where(tmp59, tmp96, tmp97) tmp126 = tmp125 + tmp124 tmp127 = tl.where(tmp64, tmp96, tmp97) tmp128 = tmp127 + tmp126 tmp129 = tl.where(tmp67, tmp96, tmp97) tmp130 = tmp129 + tmp128 tmp131 = tl.where(tmp70, tmp96, tmp97) tmp132 = tmp131 + tmp130 tmp133 = tl.where(tmp73, tmp96, tmp97) tmp134 = tmp133 + tmp132 tmp135 = tl.where(tmp76, tmp96, tmp97) tmp136 = tmp135 + tmp134 tmp137 = tl.where(tmp81, tmp96, tmp97) tmp138 = tmp137 + tmp136 tmp139 = tl.where(tmp84, tmp96, tmp97) tmp140 = tmp139 + tmp138 tmp141 = tl.where(tmp87, tmp96, tmp97) tmp142 = tmp141 + tmp140 tmp143 = tl.where(tmp90, tmp96, tmp97) tmp144 = tmp143 + tmp142 tmp145 = tl.where(tmp93, tmp96, tmp97) tmp146 = tmp145 + tmp144 tmp147 = tmp95 / tmp146 tl.store(out_ptr0 + (x4), tmp147, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ca/cca6z2m76x4jktokjuqmjldaccaazcofevqls2xr7donpkzkgyem.py # Topologically Sorted Source Nodes: [h_4], Original ATen: [aten.cat] # Source node to ATen node mapping: # h_4 => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_6, %view], 1), kwargs = {}) triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 32784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8196 x1 = (xindex // 8196) tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8196, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((8192*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x0 + (8224*x1)), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/f5/cf5heh7zqsknyylzco35rgq44mmqhp6toyz3sjs7jglijm2qnp5p.py # Topologically Sorted Source Nodes: [h_5], Original ATen: [aten.relu] # Source node to ATen node mapping: # h_5 => relu_1 # Graph fragment: # %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_8), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {}) triton_poi_fused_relu_4 = async_compile.triton('triton_poi_fused_relu_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args args.clear() assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1)) assert_size_stride(primals_4, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_5, (32, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (256, 8196), (8196, 1)) assert_size_stride(primals_8, (256, ), (1, )) assert_size_stride(primals_9, (256, 256), (256, 1)) assert_size_stride(primals_10, (256, ), (1, )) assert_size_stride(primals_11, (1, 256), (256, 1)) assert_size_stride(primals_12, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 64, 62, 62), (246016, 3844, 62, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv2d, h], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 984064, grid=grid(984064), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 32, 60, 60), (115200, 3600, 60, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [h_1], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf3, primals_5, 460800, grid=grid(460800), stream=stream0) del primals_5 buf4 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [h_2], Original ATen: [aten._adaptive_avg_pool2d] triton_poi_fused__adaptive_avg_pool2d_2.run(buf3, buf4, 32768, grid=grid(32768), stream=stream0) buf5 = empty_strided_cuda((4, 8196), (8224, 1), torch.float32) # Topologically Sorted Source Nodes: [h_4], Original ATen: [aten.cat] triton_poi_fused_cat_3.run(primals_6, buf4, buf5, 32784, grid=grid(32784), stream=stream0) del buf4 del primals_6 buf6 = empty_strided_cuda((4, 256), (256, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf5, reinterpret_tensor(primals_7, (8196, 256), (1, 8196), 0), out=buf6) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [h_5], Original ATen: [aten.relu] triton_poi_fused_relu_4.run(buf7, primals_8, 1024, grid=grid(1024), stream=stream0) del primals_8 buf8 = empty_strided_cuda((4, 256), (256, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf7, reinterpret_tensor(primals_9, (256, 256), (1, 256), 0), out=buf8) buf9 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [h_6], Original ATen: [aten.relu] triton_poi_fused_relu_4.run(buf9, primals_10, 1024, grid=grid(1024), stream=stream0) del primals_10 buf11 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm] extern_kernels.addmm(primals_12, buf9, reinterpret_tensor(primals_11, (256, 1), (1, 256), 0), alpha=1, beta=1, out=buf11) del primals_12 return (buf11, primals_1, primals_3, primals_4, buf1, buf3, buf5, buf7, buf9, primals_11, primals_9, primals_7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((32, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, 8196), (8196, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, 256), (256, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((1, 256), (256, 1), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim class GlobalDiscriminator(nn.Module): def __init__(self, y_size, M_channels): super().__init__() self.c0 = nn.Conv2d(M_channels, 64, kernel_size=3) self.c1 = nn.Conv2d(64, 32, kernel_size=3) self.avgpool = nn.AdaptiveAvgPool2d(16) self.l0 = nn.Linear(32 * 16 * 16 + y_size, 256) self.l1 = nn.Linear(256, 256) self.l2 = nn.Linear(256, 1) def forward(self, y, M): h = F.relu(self.c0(M)) h = self.c1(h) h = self.avgpool(h) h = h.view(M.shape[0], -1) h = torch.cat((y, h), dim=1) h = F.relu(self.l0(h)) h = F.relu(self.l1(h)) return self.l2(h) def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4, 64, 64])] def get_init_inputs(): return [[], {'y_size': 4, 'M_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 984064 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 3844 % 64 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 3600 % 32 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, None) @triton.jit def triton_poi_fused__adaptive_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 16 % 16 x0 = xindex % 16 x2 = xindex // 256 x4 = xindex tmp0 = 15 * x1 // 4 tmp1 = (75 + 60 * x1) // 16 tmp2 = tmp0 < tmp1 tmp3 = 15 * x0 // 4 tmp4 = (75 + 60 * x0) // 16 tmp5 = tmp3 < tmp4 tmp6 = tmp2 & tmp5 tmp7 = tl.load(in_ptr0 + (60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp6, eviction_policy='evict_last', other=0.0) tmp8 = 1 + 15 * x0 // 4 tmp9 = tmp8 < tmp4 tmp10 = tmp2 & tmp9 tmp11 = tl.load(in_ptr0 + (1 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp10, eviction_policy='evict_last', other=0.0) tmp12 = tmp11 + tmp7 tmp13 = 2 + 15 * x0 // 4 tmp14 = tmp13 < tmp4 tmp15 = tmp2 & tmp14 tmp16 = tl.load(in_ptr0 + (2 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp15, eviction_policy='evict_last', other=0.0) tmp17 = tmp16 + tmp12 tmp18 = 3 + 15 * x0 // 4 tmp19 = tmp18 < tmp4 tmp20 = tmp2 & tmp19 tmp21 = tl.load(in_ptr0 + (3 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp20, eviction_policy='evict_last', other=0.0) tmp22 = tmp21 + tmp17 tmp23 = 4 + 15 * x0 // 4 tmp24 = tmp23 < tmp4 tmp25 = tmp2 & tmp24 tmp26 = tl.load(in_ptr0 + (4 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp25, eviction_policy='evict_last', other=0.0) tmp27 = tmp26 + tmp22 tmp28 = 1 + 15 * x1 // 4 tmp29 = tmp28 < tmp1 tmp30 = tmp29 & tmp5 tmp31 = tl.load(in_ptr0 + (60 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp30, eviction_policy='evict_last', other=0.0) tmp32 = tmp31 + tmp27 tmp33 = tmp29 & tmp9 tmp34 = tl.load(in_ptr0 + (61 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp33, eviction_policy='evict_last', other=0.0) tmp35 = tmp34 + tmp32 tmp36 = tmp29 & tmp14 tmp37 = tl.load(in_ptr0 + (62 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp36, eviction_policy='evict_last', other=0.0) tmp38 = tmp37 + tmp35 tmp39 = tmp29 & tmp19 tmp40 = tl.load(in_ptr0 + (63 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp39, eviction_policy='evict_last', other=0.0) tmp41 = tmp40 + tmp38 tmp42 = tmp29 & tmp24 tmp43 = tl.load(in_ptr0 + (64 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp42, eviction_policy='evict_last', other=0.0) tmp44 = tmp43 + tmp41 tmp45 = 2 + 15 * x1 // 4 tmp46 = tmp45 < tmp1 tmp47 = tmp46 & tmp5 tmp48 = tl.load(in_ptr0 + (120 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp47, eviction_policy='evict_last', other=0.0) tmp49 = tmp48 + tmp44 tmp50 = tmp46 & tmp9 tmp51 = tl.load(in_ptr0 + (121 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp50, eviction_policy='evict_last', other=0.0) tmp52 = tmp51 + tmp49 tmp53 = tmp46 & tmp14 tmp54 = tl.load(in_ptr0 + (122 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp53, eviction_policy='evict_last', other=0.0) tmp55 = tmp54 + tmp52 tmp56 = tmp46 & tmp19 tmp57 = tl.load(in_ptr0 + (123 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp56, eviction_policy='evict_last', other=0.0) tmp58 = tmp57 + tmp55 tmp59 = tmp46 & tmp24 tmp60 = tl.load(in_ptr0 + (124 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp59, eviction_policy='evict_last', other=0.0) tmp61 = tmp60 + tmp58 tmp62 = 3 + 15 * x1 // 4 tmp63 = tmp62 < tmp1 tmp64 = tmp63 & tmp5 tmp65 = tl.load(in_ptr0 + (180 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp64, eviction_policy='evict_last', other=0.0) tmp66 = tmp65 + tmp61 tmp67 = tmp63 & tmp9 tmp68 = tl.load(in_ptr0 + (181 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp67, eviction_policy='evict_last', other=0.0) tmp69 = tmp68 + tmp66 tmp70 = tmp63 & tmp14 tmp71 = tl.load(in_ptr0 + (182 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp70, eviction_policy='evict_last', other=0.0) tmp72 = tmp71 + tmp69 tmp73 = tmp63 & tmp19 tmp74 = tl.load(in_ptr0 + (183 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp73, eviction_policy='evict_last', other=0.0) tmp75 = tmp74 + tmp72 tmp76 = tmp63 & tmp24 tmp77 = tl.load(in_ptr0 + (184 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp76, eviction_policy='evict_last', other=0.0) tmp78 = tmp77 + tmp75 tmp79 = 4 + 15 * x1 // 4 tmp80 = tmp79 < tmp1 tmp81 = tmp80 & tmp5 tmp82 = tl.load(in_ptr0 + (240 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp81, eviction_policy='evict_last', other=0.0) tmp83 = tmp82 + tmp78 tmp84 = tmp80 & tmp9 tmp85 = tl.load(in_ptr0 + (241 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp84, eviction_policy='evict_last', other=0.0) tmp86 = tmp85 + tmp83 tmp87 = tmp80 & tmp14 tmp88 = tl.load(in_ptr0 + (242 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp87, eviction_policy='evict_last', other=0.0) tmp89 = tmp88 + tmp86 tmp90 = tmp80 & tmp19 tmp91 = tl.load(in_ptr0 + (243 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp90, eviction_policy='evict_last', other=0.0) tmp92 = tmp91 + tmp89 tmp93 = tmp80 & tmp24 tmp94 = tl.load(in_ptr0 + (244 + 60 * (15 * x1 // 4) + 3600 * x2 + 15 * x0 // 4), tmp93, eviction_policy='evict_last', other=0.0) tmp95 = tmp94 + tmp92 tmp96 = 1.0 tmp97 = tl.full(tmp96.shape, 0.0, tmp96.dtype) tmp98 = tl.where(tmp6, tmp96, tmp97) tmp99 = tl.where(tmp10, tmp96, tmp97) tmp100 = tmp99 + tmp98 tmp101 = tl.where(tmp15, tmp96, tmp97) tmp102 = tmp101 + tmp100 tmp103 = tl.where(tmp20, tmp96, tmp97) tmp104 = tmp103 + tmp102 tmp105 = tl.where(tmp25, tmp96, tmp97) tmp106 = tmp105 + tmp104 tmp107 = tl.where(tmp30, tmp96, tmp97) tmp108 = tmp107 + tmp106 tmp109 = tl.where(tmp33, tmp96, tmp97) tmp110 = tmp109 + tmp108 tmp111 = tl.where(tmp36, tmp96, tmp97) tmp112 = tmp111 + tmp110 tmp113 = tl.where(tmp39, tmp96, tmp97) tmp114 = tmp113 + tmp112 tmp115 = tl.where(tmp42, tmp96, tmp97) tmp116 = tmp115 + tmp114 tmp117 = tl.where(tmp47, tmp96, tmp97) tmp118 = tmp117 + tmp116 tmp119 = tl.where(tmp50, tmp96, tmp97) tmp120 = tmp119 + tmp118 tmp121 = tl.where(tmp53, tmp96, tmp97) tmp122 = tmp121 + tmp120 tmp123 = tl.where(tmp56, tmp96, tmp97) tmp124 = tmp123 + tmp122 tmp125 = tl.where(tmp59, tmp96, tmp97) tmp126 = tmp125 + tmp124 tmp127 = tl.where(tmp64, tmp96, tmp97) tmp128 = tmp127 + tmp126 tmp129 = tl.where(tmp67, tmp96, tmp97) tmp130 = tmp129 + tmp128 tmp131 = tl.where(tmp70, tmp96, tmp97) tmp132 = tmp131 + tmp130 tmp133 = tl.where(tmp73, tmp96, tmp97) tmp134 = tmp133 + tmp132 tmp135 = tl.where(tmp76, tmp96, tmp97) tmp136 = tmp135 + tmp134 tmp137 = tl.where(tmp81, tmp96, tmp97) tmp138 = tmp137 + tmp136 tmp139 = tl.where(tmp84, tmp96, tmp97) tmp140 = tmp139 + tmp138 tmp141 = tl.where(tmp87, tmp96, tmp97) tmp142 = tmp141 + tmp140 tmp143 = tl.where(tmp90, tmp96, tmp97) tmp144 = tmp143 + tmp142 tmp145 = tl.where(tmp93, tmp96, tmp97) tmp146 = tmp145 + tmp144 tmp147 = tmp95 / tmp146 tl.store(out_ptr0 + x4, tmp147, None) @triton.jit def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 32784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8196 x1 = xindex // 8196 tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8196, tl.int64) tmp9 = tl.load(in_ptr1 + (8192 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x0 + 8224 * x1), tmp10, xmask) @triton.jit def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 ) = args args.clear() assert_size_stride(primals_1, (64, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1)) assert_size_stride(primals_4, (32, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_5, (32,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (256, 8196), (8196, 1)) assert_size_stride(primals_8, (256,), (1,)) assert_size_stride(primals_9, (256, 256), (256, 1)) assert_size_stride(primals_10, (256,), (1,)) assert_size_stride(primals_11, (1, 256), (256, 1)) assert_size_stride(primals_12, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 64, 62, 62), (246016, 3844, 62, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(984064)](buf1, primals_2, 984064, XBLOCK=1024, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 32, 60, 60), (115200, 3600, 60, 1)) buf3 = buf2 del buf2 triton_poi_fused_convolution_1[grid(460800)](buf3, primals_5, 460800, XBLOCK=1024, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((4, 32, 16, 16), (8192, 256, 16, 1), torch.float32) triton_poi_fused__adaptive_avg_pool2d_2[grid(32768)](buf3, buf4, 32768, XBLOCK=128, num_warps=4, num_stages=1) buf5 = empty_strided_cuda((4, 8196), (8224, 1), torch.float32) triton_poi_fused_cat_3[grid(32784)](primals_6, buf4, buf5, 32784, XBLOCK=256, num_warps=4, num_stages=1) del buf4 del primals_6 buf6 = empty_strided_cuda((4, 256), (256, 1), torch.float32) extern_kernels.mm(buf5, reinterpret_tensor(primals_7, (8196, 256), (1, 8196), 0), out=buf6) buf7 = buf6 del buf6 triton_poi_fused_relu_4[grid(1024)](buf7, primals_8, 1024, XBLOCK= 256, num_warps=4, num_stages=1) del primals_8 buf8 = empty_strided_cuda((4, 256), (256, 1), torch.float32) extern_kernels.mm(buf7, reinterpret_tensor(primals_9, (256, 256), ( 1, 256), 0), out=buf8) buf9 = buf8 del buf8 triton_poi_fused_relu_4[grid(1024)](buf9, primals_10, 1024, XBLOCK= 256, num_warps=4, num_stages=1) del primals_10 buf11 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_12, buf9, reinterpret_tensor( primals_11, (256, 1), (1, 256), 0), alpha=1, beta=1, out=buf11) del primals_12 return (buf11, primals_1, primals_3, primals_4, buf1, buf3, buf5, buf7, buf9, primals_11, primals_9, primals_7) class GlobalDiscriminatorNew(nn.Module): def __init__(self, y_size, M_channels): super().__init__() self.c0 = nn.Conv2d(M_channels, 64, kernel_size=3) self.c1 = nn.Conv2d(64, 32, kernel_size=3) self.avgpool = nn.AdaptiveAvgPool2d(16) self.l0 = nn.Linear(32 * 16 * 16 + y_size, 256) self.l1 = nn.Linear(256, 256) self.l2 = nn.Linear(256, 1) def forward(self, input_0, input_1): primals_1 = self.c0.weight primals_2 = self.c0.bias primals_4 = self.c1.weight primals_5 = self.c1.bias primals_7 = self.l0.weight primals_8 = self.l0.bias primals_9 = self.l1.weight primals_10 = self.l1.bias primals_11 = self.l2.weight primals_12 = self.l2.bias primals_6 = input_0 primals_3 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12]) return output[0]
ValerioB88/self-supervised-relational-reasoning
GlobalDiscriminator
false
9,690
[ "MIT" ]
0
12692b93d5c8dd3f56a31aa8b790366556e7a621
https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621
AdaptiveAvgPool3dOutSize1
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/tu/ctuej2j6f3oxr5p43q7juhagc3r3ncgs2ikvxemutunlnxlnvl24.py # Topologically Sorted Source Nodes: [adaptive_avg_pool3d], Original ATen: [aten.mean] # Source node to ATen node mapping: # adaptive_avg_pool3d => mean # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [-1, -2, -3], True), kwargs = {}) triton_per_fused_mean_0 = async_compile.triton('triton_per_fused_mean_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[4, 64], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 4 rnumel = 64 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, 0) tmp4 = tl.sum(tmp3, 1)[:, None] tmp5 = 64.0 tmp6 = tmp4 / tmp5 tl.debug_barrier() tl.store(in_out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32) buf1 = reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [adaptive_avg_pool3d], Original ATen: [aten.mean] stream0 = get_raw_stream(0) triton_per_fused_mean_0.run(buf1, arg0_1, 4, 64, grid=grid(4), stream=stream0) del arg0_1 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn from abc import abstractmethod from typing import Tuple import torch.utils.data import torch.nn class EfficientBlockBase(nn.Module): """ PyTorchVideo/accelerator provides a set of efficient blocks that have optimal efficiency for each target hardware device. Each efficient block has two forms: - original form: this form is for training. When efficient block is instantiated, it is in this original form. - deployable form: this form is for deployment. Once the network is ready for deploy, it can be converted into deployable form for efficient execution on target hardware. One block is transformed into deployable form by calling convert() method. By conversion to deployable form, various optimization (operator fuse, kernel optimization, etc.) are applied. EfficientBlockBase is the base class for efficient blocks. All efficient blocks should inherit this base class and implement following methods: - forward(): same as required by nn.Module - convert(): called to convert block into deployable form """ @abstractmethod def convert(self): pass @abstractmethod def forward(self): pass class AdaptiveAvgPool3dOutSize1(EfficientBlockBase): """ Implements AdaptiveAvgPool3d with output (T, H, W) = (1, 1, 1). This operator has better efficiency than AdaptiveAvgPool for mobile CPU. """ def __init__(self): super().__init__() self.pool = nn.AdaptiveAvgPool3d(1) self.convert_flag = False def convert(self, input_blob_size: 'Tuple', **kwargs): """ Converts AdaptiveAvgPool into AvgPool with constant kernel size for better efficiency. Args: input_blob_size (tuple): blob size at the input of AdaptiveAvgPool3dOutSize1 instance during forward. kwargs (any): any keyword argument (unused). """ assert self.convert_flag is False, 'AdaptiveAvgPool3dOutSize1: already converted, cannot be converted again' kernel_size = input_blob_size[2:] self.pool = nn.AvgPool3d(kernel_size) self.convert_flag = True def forward(self, x): return self.pool(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn from abc import abstractmethod from typing import Tuple import torch.utils.data import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 4 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(xmask, tmp1, 0) tmp4 = tl.sum(tmp3, 1)[:, None] tmp5 = 64.0 tmp6 = tmp4 / tmp5 tl.debug_barrier() tl.store(in_out_ptr0 + x0, tmp6, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32) buf1 = reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0) del buf0 get_raw_stream(0) triton_per_fused_mean_0[grid(4)](buf1, arg0_1, 4, 64, XBLOCK=1, num_warps=2, num_stages=1) del arg0_1 return buf1, class EfficientBlockBase(nn.Module): """ PyTorchVideo/accelerator provides a set of efficient blocks that have optimal efficiency for each target hardware device. Each efficient block has two forms: - original form: this form is for training. When efficient block is instantiated, it is in this original form. - deployable form: this form is for deployment. Once the network is ready for deploy, it can be converted into deployable form for efficient execution on target hardware. One block is transformed into deployable form by calling convert() method. By conversion to deployable form, various optimization (operator fuse, kernel optimization, etc.) are applied. EfficientBlockBase is the base class for efficient blocks. All efficient blocks should inherit this base class and implement following methods: - forward(): same as required by nn.Module - convert(): called to convert block into deployable form """ @abstractmethod def convert(self): pass @abstractmethod def forward(self): pass class AdaptiveAvgPool3dOutSize1New(EfficientBlockBase): """ Implements AdaptiveAvgPool3d with output (T, H, W) = (1, 1, 1). This operator has better efficiency than AdaptiveAvgPool for mobile CPU. """ def __init__(self): super().__init__() self.pool = nn.AdaptiveAvgPool3d(1) self.convert_flag = False def convert(self, input_blob_size: 'Tuple', **kwargs): """ Converts AdaptiveAvgPool into AvgPool with constant kernel size for better efficiency. Args: input_blob_size (tuple): blob size at the input of AdaptiveAvgPool3dOutSize1 instance during forward. kwargs (any): any keyword argument (unused). """ assert self.convert_flag is False, 'AdaptiveAvgPool3dOutSize1: already converted, cannot be converted again' kernel_size = input_blob_size[2:] self.pool = nn.AvgPool3d(kernel_size) self.convert_flag = True def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
TheShadow29/pytorchvideo
AdaptiveAvgPool3dOutSize1
false
9,691
[ "Apache-2.0" ]
0
39a3e34e33fb0e1ec142288df08f6e8c3585961a
https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a
TensorMax
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/6e/c6eujplkdmgoanbwrnlvpa2dq2cwdfnps7shcftx2nbyinknnsn4.py # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max] # Source node to ATen node mapping: # max_1 => getitem # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {}) triton_poi_fused_max_0 = async_compile.triton('triton_poi_fused_max_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tl.store(out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max] stream0 = get_raw_stream(0) triton_poi_fused_max_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch def tensor_max(input, dim, keepdim=False): if isinstance(dim, int): return torch.max(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.max(input, dim=d, keepdim=keepdim)[0] return input class StatModule(torch.nn.Module): def __init__(self, dim, keepdim=False): if isinstance(dim, list): dim = tuple(dim) if isinstance(dim, int): dim = dim, assert isinstance(dim, tuple) self.dim = dim self.keepdim = keepdim super().__init__() class TensorMax(StatModule, torch.nn.Module): def forward(self, input): return tensor_max(input, dim=self.dim, keepdim=self.keepdim) def get_inputs(): return [torch.rand([4, 4, 4, 4, 4])] def get_init_inputs(): return [[], {'dim': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_max_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tl.store(out_ptr0 + x0, tmp6, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_max_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, def tensor_max(input, dim, keepdim=False): if isinstance(dim, int): return torch.max(input, dim=dim, keepdim=keepdim)[0] else: if isinstance(dim, tuple): dim = list(dim) for d in dim: input = torch.max(input, dim=d, keepdim=keepdim)[0] return input class StatModule(torch.nn.Module): def __init__(self, dim, keepdim=False): if isinstance(dim, list): dim = tuple(dim) if isinstance(dim, int): dim = dim, assert isinstance(dim, tuple) self.dim = dim self.keepdim = keepdim super().__init__() class TensorMaxNew(StatModule, torch.nn.Module): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
Minyus/kedex
TensorMax
false
9,692
[ "Apache-2.0" ]
0
92f952eed3cb6109bc783f449051f2bd13579d2a
https://github.com/Minyus/kedex/tree/92f952eed3cb6109bc783f449051f2bd13579d2a
Affine2D
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/aa/caasf6tfx3vnqq3f7xnutefwqbvyirvxnviignj4ei2ypv32lygk.py # Topologically Sorted Source Nodes: [mul, add], Original ATen: [aten.mul, aten.add] # Source node to ATen node mapping: # add => add # mul => mul # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %primals_3), kwargs = {}) triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 4 x3 = xindex tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x3), xmask) tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp4 = tmp2 + tmp3 tl.store(out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul, add], Original ATen: [aten.mul, aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_mul_0.run(primals_1, primals_2, primals_3, buf0, 256, grid=grid(256), stream=stream0) del primals_1 del primals_3 return (buf0, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Affine2D(nn.Module): def __init__(self, cin): """ :param cin: """ super(Affine2D, self).__init__() self.weight = nn.Parameter(torch.ones(1, cin, 1, 1)) self.bias = nn.Parameter(torch.zeros(1, cin, 1, 1)) def forward(self, x): """ :param x: :return: """ return self.weight * x + self.bias def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'cin': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 4 x3 = xindex tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x3, xmask) tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp4 = tmp2 + tmp3 tl.store(out_ptr0 + x3, tmp4, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (1, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_mul_0[grid(256)](primals_1, primals_2, primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_1 del primals_3 return buf0, primals_2 class Affine2DNew(nn.Module): def __init__(self, cin): """ :param cin: """ super(Affine2DNew, self).__init__() self.weight = nn.Parameter(torch.ones(1, cin, 1, 1)) self.bias = nn.Parameter(torch.zeros(1, cin, 1, 1)) def forward(self, input_0): primals_1 = self.weight primals_3 = self.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE
Affine2D
false
9,693
[ "Apache-2.0" ]
0
13b565c20e59f204151d2dafbd221c7e1b9303c5
https://github.com/alexandre-giuly/Project-Acoustic-Scene-Classification-DCASE/tree/13b565c20e59f204151d2dafbd221c7e1b9303c5
ActorNetwork
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/nq/cnqjufcqn3ur3s7xvlb2i747nyf24md4zaiatlwgkasynplfjstu.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, None) tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/hj/chjzotk5iydxvuetxetlv36s7car7cdb24whkuqihxwcy5kkr4o2.py # Topologically Sorted Source Nodes: [tanh], Original ATen: [aten.tanh] # Source node to ATen node mapping: # tanh => tanh # Graph fragment: # %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {}) triton_poi_fused_tanh_1 = async_compile.triton('triton_poi_fused_tanh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (4, 64), (64, 1)) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse buf7 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf7, 4096, grid=grid(4096), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf2 # reuse buf6 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf6, 4096, grid=grid(4096), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [tanh], Original ATen: [aten.tanh] triton_poi_fused_tanh_1.run(buf5, primals_7, 256, grid=grid(256), stream=stream0) del primals_7 return (buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(buf3, (64, 64), (64, 1), 0), buf5, primals_6, buf6, primals_4, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn.functional as F import torch.nn as nn class ActorNetwork(nn.Module): def __init__(self, state_size, action_size, seed): super(ActorNetwork, self).__init__() torch.manual_seed(seed) hidden1 = 64 hidden2 = 64 self.fc1 = nn.Linear(state_size, hidden1) self.fc2 = nn.Linear(hidden1, hidden2) self.fc3 = nn.Linear(hidden2, action_size) def forward(self, state): x = self.fc1(state) x = F.relu(x) x = self.fc2(x) x = F.relu(x) x = self.fc3(x) return torch.tanh(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'state_size': 4, 'action_size': 4, 'seed': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, None) tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (4, 64), (64, 1)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf0 buf7 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool ) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf1, primals_2, buf7, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf2 buf6 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool ) triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf3, primals_5, buf6, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf4 triton_poi_fused_tanh_1[grid(256)](buf5, primals_7, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_7 return buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor( buf3, (64, 64), (64, 1), 0), buf5, primals_6, buf6, primals_4, buf7 class ActorNetworkNew(nn.Module): def __init__(self, state_size, action_size, seed): super(ActorNetworkNew, self).__init__() torch.manual_seed(seed) hidden1 = 64 hidden2 = 64 self.fc1 = nn.Linear(state_size, hidden1) self.fc2 = nn.Linear(hidden1, hidden2) self.fc3 = nn.Linear(hidden2, action_size) def forward(self, input_0): primals_1 = self.fc1.weight primals_2 = self.fc1.bias primals_4 = self.fc2.weight primals_5 = self.fc2.bias primals_6 = self.fc3.weight primals_7 = self.fc3.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
aishikawa/drl-impl
ActorNetwork
false
9,694
[ "MIT" ]
0
1afe7426494cd94990cb4dae247486a25dfe37bf
https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf
GRUStep
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/c4/cc4khg7fwbxxm2fufox7nnkf4gfybrmj5ir2tx3zuxfioc5b2dya.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%primals_1, %primals_2], -1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tl.load(in_ptr1 + ((4*x1) + ((-4) + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/5b/c5bkw2jxfpnk3o5xqifvptqcde6oukvmpsxncnrr4hbmq6dbwwvm.py # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat_2 => cat_2 # Graph fragment: # %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%mul, %primals_2], -1), kwargs = {}) triton_poi_fused_cat_1 = async_compile.triton('triton_poi_fused_cat_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = (xindex // 8) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tl.sigmoid(tmp5) tmp7 = tl.load(in_ptr1 + ((4*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp8 = tmp6 * tmp7 tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp4, tmp8, tmp9) tmp11 = tmp0 >= tmp3 tmp12 = tl.full([1], 8, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tl.load(in_ptr2 + ((4*x1) + ((-4) + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0) tmp15 = tl.where(tmp4, tmp10, tmp14) tl.store(out_ptr0 + (x2), tmp15, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/tn/ctn3hks2zjt4xmjlguifn25zx6whcel77cdlsr33hdq2oyumfsoc.py # Topologically Sorted Source Nodes: [z, t, sub, mul_1, mul_2, h_state], Original ATen: [aten.sigmoid, aten.tanh, aten.rsub, aten.mul, aten.add] # Source node to ATen node mapping: # h_state => add # mul_1 => mul_1 # mul_2 => mul_2 # sub => sub # t => tanh # z => sigmoid # Graph fragment: # %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {}) # %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %primals_1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %tanh), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {}) triton_poi_fused_add_mul_rsub_sigmoid_tanh_2 = async_compile.triton('triton_poi_fused_add_mul_rsub_sigmoid_tanh_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_sigmoid_tanh_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp4 = tl.load(in_ptr1 + (x0), xmask) tmp6 = tl.load(in_ptr2 + (x0), xmask) tmp1 = tl.sigmoid(tmp0) tmp2 = 1.0 tmp3 = tmp2 - tmp1 tmp5 = tmp3 * tmp4 tmp7 = libdevice.tanh(tmp6) tmp8 = tmp1 * tmp7 tmp9 = tmp5 + tmp8 tl.store(out_ptr0 + (x0), tmp9, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, 8), (8, 1)) assert_size_stride(primals_4, (4, 8), (8, 1)) assert_size_stride(primals_5, (4, 8), (8, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(primals_1, primals_2, buf0, 512, grid=grid(512), stream=stream0) buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2) del primals_4 buf3 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [cat_2], Original ATen: [aten.cat] triton_poi_fused_cat_1.run(buf2, primals_1, primals_2, buf3, 512, grid=grid(512), stream=stream0) del primals_2 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf3, (64, 8), (8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf4) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [z, t, sub, mul_1, mul_2, h_state], Original ATen: [aten.sigmoid, aten.tanh, aten.rsub, aten.mul, aten.add] triton_poi_fused_add_mul_rsub_sigmoid_tanh_2.run(buf1, primals_1, buf4, buf5, 256, grid=grid(256), stream=stream0) return (buf5, primals_1, reinterpret_tensor(buf0, (64, 8), (8, 1), 0), buf1, buf2, reinterpret_tensor(buf3, (64, 8), (8, 1), 0), buf4, primals_5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 8), (8, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data import torch.multiprocessing import torch.nn.modules.loss from scipy.sparse import * class GRUStep(nn.Module): def __init__(self, hidden_size, input_size): super(GRUStep, self).__init__() """GRU module""" self.linear_z = nn.Linear(hidden_size + input_size, hidden_size, bias=False) self.linear_r = nn.Linear(hidden_size + input_size, hidden_size, bias=False) self.linear_t = nn.Linear(hidden_size + input_size, hidden_size, bias=False) def forward(self, h_state, input): z = torch.sigmoid(self.linear_z(torch.cat([h_state, input], -1))) r = torch.sigmoid(self.linear_r(torch.cat([h_state, input], -1))) t = torch.tanh(self.linear_t(torch.cat([r * h_state, input], -1))) h_state = (1 - z) * h_state + z * t return h_state def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4, 'input_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn import torch.utils.data import torch.multiprocessing import torch.nn.modules.loss from scipy.sparse import * assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask, eviction_policy='evict_last', other=0.0) tmp10 = tl.where(tmp4, tmp5, tmp9) tl.store(out_ptr0 + x2, tmp10, xmask) @triton.jit def triton_poi_fused_cat_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 8 x1 = xindex // 8 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tl.sigmoid(tmp5) tmp7 = tl.load(in_ptr1 + (4 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp8 = tmp6 * tmp7 tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp4, tmp8, tmp9) tmp11 = tmp0 >= tmp3 tl.full([1], 8, tl.int64) tmp14 = tl.load(in_ptr2 + (4 * x1 + (-4 + x0)), tmp11 & xmask, eviction_policy='evict_last', other=0.0) tmp15 = tl.where(tmp4, tmp10, tmp14) tl.store(out_ptr0 + x2, tmp15, xmask) @triton.jit def triton_poi_fused_add_mul_rsub_sigmoid_tanh_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp4 = tl.load(in_ptr1 + x0, xmask) tmp6 = tl.load(in_ptr2 + x0, xmask) tmp1 = tl.sigmoid(tmp0) tmp2 = 1.0 tmp3 = tmp2 - tmp1 tmp5 = tmp3 * tmp4 tmp7 = libdevice.tanh(tmp6) tmp8 = tmp1 * tmp7 tmp9 = tmp5 + tmp8 tl.store(out_ptr0 + x0, tmp9, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, 8), (8, 1)) assert_size_stride(primals_4, (4, 8), (8, 1)) assert_size_stride(primals_5, (4, 8), (8, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(512)](primals_1, primals_2, buf0, 512, XBLOCK=256, num_warps=4, num_stages=1) buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_3, (8, 4), (1, 8), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf0, (64, 8), (8, 1), 0), reinterpret_tensor(primals_4, (8, 4), (1, 8), 0), out=buf2) del primals_4 buf3 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32) triton_poi_fused_cat_1[grid(512)](buf2, primals_1, primals_2, buf3, 512, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 8), (8, 1), 0), reinterpret_tensor(primals_5, (8, 4), (1, 8), 0), out=buf4) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_add_mul_rsub_sigmoid_tanh_2[grid(256)](buf1, primals_1, buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) return buf5, primals_1, reinterpret_tensor(buf0, (64, 8), (8, 1), 0 ), buf1, buf2, reinterpret_tensor(buf3, (64, 8), (8, 1), 0 ), buf4, primals_5 class GRUStepNew(nn.Module): def __init__(self, hidden_size, input_size): super(GRUStepNew, self).__init__() """GRU module""" self.linear_z = nn.Linear(hidden_size + input_size, hidden_size, bias=False) self.linear_r = nn.Linear(hidden_size + input_size, hidden_size, bias=False) self.linear_t = nn.Linear(hidden_size + input_size, hidden_size, bias=False) def forward(self, input_0, input_1): primals_3 = self.linear_z.weight primals_4 = self.linear_r.weight primals_5 = self.linear_t.weight primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
LucasAPayne/graph4nlp
GRUStep
false
9,695
[ "Apache-2.0" ]
0
3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
Network
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/jv/cjvfpvazszqsn7k2c7ac25njk43pn5fjlaxzgkwwsgomov2lqu5x.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 24 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (24, 4), (4, 1)) assert_size_stride(primals_2, (24, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (2, 24), (24, 1)) assert_size_stride(primals_5, (2, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 24), (24, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 24), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 24), (384, 96, 24, 1), 0); del buf0 # reuse buf3 = empty_strided_cuda((4, 4, 4, 24), (384, 96, 24, 1), torch.bool) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf3, 1536, grid=grid(1536), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 2), (2, 1), torch.float32) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 24), (24, 1), 0), reinterpret_tensor(primals_4, (24, 2), (1, 24), 0), alpha=1, beta=1, out=buf2) del primals_5 return (reinterpret_tensor(buf2, (4, 4, 4, 2), (32, 8, 2, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 24), (24, 1), 0), primals_4, buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((24, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((24, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((2, 24), (24, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F class Network(nn.Module): def __init__(self): nn.Module.__init__(self) self.l1 = nn.Linear(4, 24) self.l5 = nn.Linear(24, 2) def forward(self, x): x = F.relu(self.l1(x)) x = self.l5(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 24 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (24, 4), (4, 1)) assert_size_stride(primals_2, (24,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (2, 24), (24, 1)) assert_size_stride(primals_5, (2,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 24), (24, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 24), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 24), (384, 96, 24, 1), 0) del buf0 buf3 = empty_strided_cuda((4, 4, 4, 24), (384, 96, 24, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(1536)](buf1, primals_2, buf3, 1536, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 2), (2, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 24), (24, 1), 0), reinterpret_tensor(primals_4, (24, 2), (1, 24), 0), alpha=1, beta=1, out=buf2) del primals_5 return reinterpret_tensor(buf2, (4, 4, 4, 2), (32, 8, 2, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 24), (24, 1), 0), primals_4, buf3 class NetworkNew(nn.Module): def __init__(self): nn.Module.__init__(self) self.l1 = nn.Linear(4, 24) self.l5 = nn.Linear(24, 2) def forward(self, input_0): primals_1 = self.l1.weight primals_2 = self.l1.bias primals_4 = self.l5.weight primals_5 = self.l5.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
alexljenkins/reinforcement-learning-agents
Network
false
9,696
[ "MIT" ]
0
d5bdfad56c9b095d5bb0ac22ca69e19553327416
https://github.com/alexljenkins/reinforcement-learning-agents/tree/d5bdfad56c9b095d5bb0ac22ca69e19553327416
MaskedTemporalPooling
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/7l/c7lvig2zhj2iehtujvqrkzrlwayqdobfawdm2rzdswlxaokpz36v.py # Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put] # Source node to ATen node mapping: # setitem => full_default_1, index_put # Graph fragment: # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -inf), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False}) # %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%arg0_1, [%full_default], %full_default_1), kwargs = {}) triton_poi_fused_index_put_lift_fresh_0 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl.full([1], False, tl.int1) tmp2 = float("-inf") tmp3 = tl.where(tmp1, tmp2, tmp0) tl.store(out_ptr0 + (x0), tmp3, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/kd/ckdjqudl6o2qnmvu7ql4ktibvn34h6thuyobkjyytvrpbzhegrn5.py # Topologically Sorted Source Nodes: [setitem_1], Original ATen: [aten.lift_fresh, aten.index_put] # Source node to ATen node mapping: # setitem_1 => full_default_3, index_put_1 # Graph fragment: # %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False}) # %index_put_1 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%bitwise_not_1], %full_default_3), kwargs = {}) triton_poi_fused_index_put_lift_fresh_1 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_1', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp5 = tl.load(in_ptr0 + (x0), xmask) tmp0 = tl.full([1], True, tl.int1) tmp1 = tmp0 | tmp0 tmp2 = tmp1 | tmp0 tmp3 = tmp2 | tmp0 tmp4 = tmp3 == 0 tmp6 = 0.0 tmp7 = tl.where(tmp4, tmp6, tmp5) tl.store(out_ptr0 + (x0), tmp7, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/5l/c5lg2i2ofxflslf7bu6w7i56fb72x6ezjgb35d2woe4gnj5ur6ao.py # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max] # Source node to ATen node mapping: # max_1 => getitem # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {}) triton_poi_fused_max_2 = async_compile.triton('triton_poi_fused_max_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask) tmp1 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask) tmp3 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask) tmp5 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask) tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put] stream0 = get_raw_stream(0) triton_poi_fused_index_put_lift_fresh_0.run(arg0_1, arg0_1, 64, grid=grid(64), stream=stream0) # Topologically Sorted Source Nodes: [setitem_1], Original ATen: [aten.lift_fresh, aten.index_put] triton_poi_fused_index_put_lift_fresh_1.run(arg0_1, arg0_1, 64, grid=grid(64), stream=stream0) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max] triton_poi_fused_max_2.run(arg0_1, buf2, 16, grid=grid(16), stream=stream0) del arg0_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from typing import Optional import torch.utils.data import torch.nn class MaskedTemporalPooling(torch.nn.Module): """ Applies temporal pooling operations on masked inputs. For each pooling operation all masked values are ignored. """ def __init__(self, method: 'str'): """ method (str): the method of pooling to use. Options: 'max': reduces temporal dimension to each valid max value. 'avg': averages valid values in the temporal dimension. 'sum': sums valid values in the temporal dimension. Note if all batch row elements are invalid, the temporal dimension is pooled to 0 values. """ super().__init__() assert method in ('max', 'avg', 'sum') self._method = method def forward(self, x: 'torch.Tensor', mask: 'Optional[torch.Tensor]'=None ) ->torch.Tensor: """ Args: x (torch.Tensor): tensor with shape (batch_size, seq_len, feature_dim) mask (torch.Tensor): bool tensor with shape (batch_size, seq_len). Sequence elements that are False are invalid. Returns: Tensor with shape (batch_size, feature_dim) """ assert x.dim( ) == 3, 'Requires x shape (batch_size x seq_len x feature_dim)' b, t = x.shape[0], x.shape[1] if mask is None: mask = torch.ones((b, t), dtype=torch.bool) if self._method == 'max': x[~mask, :] = float('-inf') invalid_first_dim = ~mask.view(b, -1).any(dim=-1) x[invalid_first_dim, :] = 0 x = torch.max(x, dim=1)[0] elif self._method == 'avg': x = x * mask.unsqueeze(-1).float() mask = mask.view(b, t, -1).any(dim=-1) valid_lengths = mask.float().sum(dim=-1).int() x = x.sum(dim=1) x = x.div(valid_lengths.clamp(min=1).unsqueeze(-1).expand(x. size()).float()) elif self._method == 'sum': x = x * mask.unsqueeze(-1).float() x = x.sum(dim=1) else: raise NotImplementedError( f"{self._method} not available options are: 'max', 'avg', 'sum'" ) return x def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'method': 'max'}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.utils.data import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl.full([1], False, tl.int1) tmp2 = float('-inf') tmp3 = tl.where(tmp1, tmp2, tmp0) tl.store(out_ptr0 + x0, tmp3, xmask) @triton.jit def triton_poi_fused_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp5 = tl.load(in_ptr0 + x0, xmask) tmp0 = tl.full([1], True, tl.int1) tmp1 = tmp0 | tmp0 tmp2 = tmp1 | tmp0 tmp3 = tmp2 | tmp0 tmp4 = tmp3 == 0 tmp6 = 0.0 tmp7 = tl.where(tmp4, tmp6, tmp5) tl.store(out_ptr0 + x0, tmp7, xmask) @triton.jit def triton_poi_fused_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp2 = triton_helpers.maximum(tmp0, tmp1) tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp6 = triton_helpers.maximum(tmp4, tmp5) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) get_raw_stream(0) triton_poi_fused_index_put_lift_fresh_0[grid(64)](arg0_1, arg0_1, 64, XBLOCK=64, num_warps=1, num_stages=1) triton_poi_fused_index_put_lift_fresh_1[grid(64)](arg0_1, arg0_1, 64, XBLOCK=64, num_warps=1, num_stages=1) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_max_2[grid(16)](arg0_1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg0_1 return buf2, class MaskedTemporalPoolingNew(torch.nn.Module): """ Applies temporal pooling operations on masked inputs. For each pooling operation all masked values are ignored. """ def __init__(self, method: 'str'): """ method (str): the method of pooling to use. Options: 'max': reduces temporal dimension to each valid max value. 'avg': averages valid values in the temporal dimension. 'sum': sums valid values in the temporal dimension. Note if all batch row elements are invalid, the temporal dimension is pooled to 0 values. """ super().__init__() assert method in ('max', 'avg', 'sum') self._method = method def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
TheShadow29/pytorchvideo
MaskedTemporalPooling
false
9,697
[ "Apache-2.0" ]
0
39a3e34e33fb0e1ec142288df08f6e8c3585961a
https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a
InnerProductDecoder
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/mb/cmb72vxh36b4k6lvmt4562lj3nrqtpyzst2qbon2yqx22gdjfa7x.py # Topologically Sorted Source Nodes: [adj], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # adj => sigmoid # Graph fragment: # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mm,), kwargs = {}) triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.sigmoid(tmp0) tl.store(in_out_ptr0 + (x0), tmp1, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm] extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0) del arg0_1 buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [adj], Original ATen: [aten.sigmoid] stream0 = get_raw_stream(0) triton_poi_fused_sigmoid_0.run(buf1, 16, grid=grid(16), stream=stream0) return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch.nn import functional as F import torch.nn as nn import torch.utils.data import torch.multiprocessing import torch.nn.modules.loss from scipy.sparse import * def dropout(x, drop_prob, shared_axes=[], training=False): """ Apply dropout to input tensor. Parameters ---------- input_tensor: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` Returns ------- output: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied. """ if drop_prob == 0 or drop_prob is None or not training: return x sz = list(x.size()) for i in shared_axes: sz[i] = 1 mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob) mask = mask.expand_as(x) return x * mask class InnerProductDecoder(nn.Module): """Decoder for using inner product for prediction.""" def __init__(self, dropout, act=torch.sigmoid): super(InnerProductDecoder, self).__init__() self.dropout = dropout self.act = act def forward(self, z): z = F.dropout(z, self.dropout, training=self.training) adj = self.act(torch.mm(z, z.t())) return adj def get_inputs(): return [torch.rand([4, 4])] def get_init_inputs(): return [[], {'dropout': 0.5}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.utils.data import torch.multiprocessing import torch.nn.modules.loss from scipy.sparse import * assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_sigmoid_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.sigmoid(tmp0) tl.store(in_out_ptr0 + x0, tmp1, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(arg0_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4), 0), out=buf0) del arg0_1 buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_sigmoid_0[grid(16)](buf1, 16, XBLOCK=16, num_warps =1, num_stages=1) return buf1, def dropout(x, drop_prob, shared_axes=[], training=False): """ Apply dropout to input tensor. Parameters ---------- input_tensor: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` Returns ------- output: ``torch.FloatTensor`` A tensor of shape ``(batch_size, ..., num_timesteps, embedding_dim)`` with dropout applied. """ if drop_prob == 0 or drop_prob is None or not training: return x sz = list(x.size()) for i in shared_axes: sz[i] = 1 mask = x.new(*sz).bernoulli_(1.0 - drop_prob).div_(1.0 - drop_prob) mask = mask.expand_as(x) return x * mask class InnerProductDecoderNew(nn.Module): """Decoder for using inner product for prediction.""" def __init__(self, dropout, act=torch.sigmoid): super(InnerProductDecoderNew, self).__init__() self.dropout = dropout self.act = act def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
LucasAPayne/graph4nlp
InnerProductDecoder
false
9,698
[ "Apache-2.0" ]
0
3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
LearnMaskedDefault
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/kq/ckqpgjkefsaygrl3b3ktu2kqjnxztj4afoz6gn2iexdub7n3vrx2.py # Topologically Sorted Source Nodes: [mask, float_1, mul, sub, mul_1, x], Original ATen: [aten.any, aten._to_copy, aten.mul, aten.rsub, aten.add] # Source node to ATen node mapping: # float_1 => convert_element_type # mask => any_1 # mul => mul # mul_1 => mul_1 # sub => sub # x => add # Graph fragment: # %any_1 : [num_users=1] = call_function[target=torch.ops.aten.any.dim](args = (%view, -1), kwargs = {}) # %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%unsqueeze_2, torch.float32), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %convert_element_type), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %sub), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) triton_per_fused__to_copy_add_any_mul_rsub_0 = async_compile.triton('triton_per_fused__to_copy_add_any_mul_rsub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[4, 64], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_add_any_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused__to_copy_add_any_mul_rsub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 4 rnumel = 64 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex r2 = rindex % 4 tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (r1 + (64*x0)), xmask, other=0.0) tmp9 = tl.load(in_ptr2 + (r2), None, eviction_policy='evict_last') tmp1 = (tmp0 != 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp4 = tl.where(xmask, tmp2, 0) tmp5 = triton_helpers.any(tmp4, 1)[:, None] tmp7 = tmp5.to(tl.float32) tmp8 = tmp6 * tmp7 tmp10 = 1.0 tmp11 = tmp10 - tmp7 tmp12 = tmp9 * tmp11 tmp13 = tmp8 + tmp12 tl.store(out_ptr1 + (r1 + (64*x0)), tmp13, xmask) tl.store(out_ptr0 + (x0), tmp5, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, ), (1, ), torch.bool) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mask, float_1, mul, sub, mul_1, x], Original ATen: [aten.any, aten._to_copy, aten.mul, aten.rsub, aten.add] stream0 = get_raw_stream(0) triton_per_fused__to_copy_add_any_mul_rsub_0.run(primals_1, primals_2, primals_3, buf0, buf1, 4, 64, grid=grid(4), stream=stream0) del primals_1 del primals_2 del primals_3 return (buf1, reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data import torch.nn class LearnMaskedDefault(nn.Module): """ Learns default values to fill invalid entries within input tensors. The invalid entries are represented by a mask which is passed into forward alongside the input tensor. Note the default value is only used if all entries in the batch row are invalid rather than just a portion of invalid entries within each batch row. """ def __init__(self, feature_dim: 'int', init_method: 'str'='gaussian', freeze: 'bool'=False): """ Args: feature_dim (int): the size of the default value parameter, this must match the input tensor size. init_method (str): the initial default value parameter. Options: 'guassian' 'zeros' freeze (bool): If True, the learned default parameter weights are frozen. """ super().__init__() if init_method == 'zeros': self._learned_defaults = nn.Parameter(torch.zeros(feature_dim), requires_grad=not freeze) elif init_method == 'gaussian': self._learned_defaults = nn.Parameter(torch.Tensor(feature_dim), requires_grad=not freeze) nn.init.normal_(self._learned_defaults) else: raise NotImplementedError( f"{init_method} not available. Options are: 'zeros' or 'gaussian'" ) def forward(self, x: 'torch.Tensor', mask: 'torch.Tensor') ->torch.Tensor: """ Args: x (torch.Tensor): tensor of shape (batch_size, feature_dim). mask (torch.Tensor): bool tensor of shape (batch_size, seq_len) If all elements in the batch dimension are False the learned default parameter is used for that batch element. Returns: Tensor with shape (batch_size, feature_dim) """ mask = mask.view(mask.shape[0], -1).any(dim=-1) for i in range(1, x.dim()): mask = mask.unsqueeze(i) x = x * mask.float() + self._learned_defaults * (1 - mask.float()) return x def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'feature_dim': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.utils.data import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_per_fused__to_copy_add_any_mul_rsub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 4 RBLOCK: tl.constexpr = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] tl.full([XBLOCK, RBLOCK], True, tl.int1) r1 = rindex x0 = xindex r2 = rindex % 4 tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0) tmp9 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last') tmp1 = tmp0 != 0 tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK]) tmp4 = tl.where(xmask, tmp2, 0) tmp5 = triton_helpers.any(tmp4, 1)[:, None] tmp7 = tmp5.to(tl.float32) tmp8 = tmp6 * tmp7 tmp10 = 1.0 tmp11 = tmp10 - tmp7 tmp12 = tmp9 * tmp11 tmp13 = tmp8 + tmp12 tl.store(out_ptr1 + (r1 + 64 * x0), tmp13, xmask) tl.store(out_ptr0 + x0, tmp5, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4,), (1,), torch.bool) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_per_fused__to_copy_add_any_mul_rsub_0[grid(4)](primals_1, primals_2, primals_3, buf0, buf1, 4, 64, XBLOCK=1, num_warps=2, num_stages=1) del primals_1 del primals_2 del primals_3 return buf1, reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0) class LearnMaskedDefaultNew(nn.Module): """ Learns default values to fill invalid entries within input tensors. The invalid entries are represented by a mask which is passed into forward alongside the input tensor. Note the default value is only used if all entries in the batch row are invalid rather than just a portion of invalid entries within each batch row. """ def __init__(self, feature_dim: 'int', init_method: 'str'='gaussian', freeze: 'bool'=False): """ Args: feature_dim (int): the size of the default value parameter, this must match the input tensor size. init_method (str): the initial default value parameter. Options: 'guassian' 'zeros' freeze (bool): If True, the learned default parameter weights are frozen. """ super().__init__() if init_method == 'zeros': self._learned_defaults = nn.Parameter(torch.zeros(feature_dim), requires_grad=not freeze) elif init_method == 'gaussian': self._learned_defaults = nn.Parameter(torch.Tensor(feature_dim), requires_grad=not freeze) nn.init.normal_(self._learned_defaults) else: raise NotImplementedError( f"{init_method} not available. Options are: 'zeros' or 'gaussian'" ) def forward(self, input_0, input_1): primals_3 = self._learned_defaults primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3]) return output[0]
TheShadow29/pytorchvideo
LearnMaskedDefault
false
9,699
[ "Apache-2.0" ]
0
39a3e34e33fb0e1ec142288df08f6e8c3585961a
https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a
ConvGLU
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/q7/cq7qwv755rskgi3fxmqbrnzfm6sxg6uprg2cozcqvgaiyr3e5jdv.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] # Source node to ATen node mapping: # x => convolution # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [3, 3], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 8 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/mq/cmqeslewj7qjge6hbekvyub6f2jo7tbkd7w6kkeabjbjtnu6r4kr.py # Topologically Sorted Source Nodes: [sigmoid, x_1], Original ATen: [aten.sigmoid, aten.mul] # Source node to ATen node mapping: # sigmoid => sigmoid # x_1 => mul # Graph fragment: # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%slice_4,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_2, %sigmoid), kwargs = {}) triton_poi_fused_mul_sigmoid_1 = async_compile.triton('triton_poi_fused_mul_sigmoid_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_sigmoid_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = (xindex // 64) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (128*x1)), xmask) tmp1 = tl.load(in_ptr0 + (64 + x0 + (128*x1)), xmask) tmp2 = tl.sigmoid(tmp1) tmp3 = tmp0 * tmp2 tl.store(out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (8, 4, 7, 7), (196, 49, 7, 1)) assert_size_stride(primals_2, (8, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(3, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_2, 512, grid=grid(512), stream=stream0) del primals_2 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [sigmoid, x_1], Original ATen: [aten.sigmoid, aten.mul] triton_poi_fused_mul_sigmoid_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0) return (buf2, primals_1, primals_3, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((8, 4, 7, 7), (196, 49, 7, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim def str2act(txt): """Translates text to neural network activation""" return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn. Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt. lower()] class ConvGLU(nn.Module): """ A convGlu operation, used by the Degli paper's model. """ def __init__(self, in_ch, out_ch, kernel_size=(7, 7), padding=None, batchnorm=False, act='sigmoid', stride=None): super().__init__() if not padding: padding = kernel_size[0] // 2, kernel_size[1] // 2 if stride is None: self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding= padding) else: self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding= padding, stride=stride) self.weight = self.conv.weight self.bias = self.conv.bias if batchnorm: self.conv = nn.Sequential(self.conv, nn.BatchNorm2d(out_ch * 2)) self.sigmoid = str2act(act) def forward(self, x): x = self.conv(x) ch = x.shape[1] x = x[:, :ch // 2, ...] * self.sigmoid(x[:, ch // 2:, ...]) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_ch': 4, 'out_ch': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 8 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused_mul_sigmoid_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 64 x1 = xindex // 64 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1), xmask) tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1), xmask) tmp2 = tl.sigmoid(tmp1) tmp3 = tmp0 * tmp2 tl.store(out_ptr0 + x2, tmp3, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (8, 4, 7, 7), (196, 49, 7, 1)) assert_size_stride(primals_2, (8,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(3, 3), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(512)](buf1, primals_2, 512, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_mul_sigmoid_1[grid(256)](buf1, buf2, 256, XBLOCK= 256, num_warps=4, num_stages=1) return buf2, primals_1, primals_3, buf1 def str2act(txt): """Translates text to neural network activation""" return {'sigmoid': nn.Sigmoid(), 'relu': nn.ReLU(), 'none': nn. Sequential(), 'lrelu': nn.LeakyReLU(0.2), 'selu': nn.SELU()}[txt. lower()] class ConvGLUNew(nn.Module): """ A convGlu operation, used by the Degli paper's model. """ def __init__(self, in_ch, out_ch, kernel_size=(7, 7), padding=None, batchnorm=False, act='sigmoid', stride=None): super().__init__() if not padding: padding = kernel_size[0] // 2, kernel_size[1] // 2 if stride is None: self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding= padding) else: self.conv = nn.Conv2d(in_ch, out_ch * 2, kernel_size, padding= padding, stride=stride) self.weight = self.conv.weight self.bias = self.conv.bias if batchnorm: self.conv = nn.Sequential(self.conv, nn.BatchNorm2d(out_ch * 2)) self.sigmoid = str2act(act) def forward(self, input_0): primals_1 = self.weight primals_2 = self.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
Oreoluwa1234/NeMo
ConvGLU
false
9,700
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
TransposeMultiheadAttention
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ao/caoovxtqrx42gvkmjirowqmmbh6kppvfh5ebrzzv4kzkgwm2umii.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] # Source node to ATen node mapping: # multi_head_attention_forward => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1)), xmask) tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/yi/cyis4mboyae5mxe2ro5oeo66oz5rc4akmej7nfmsxeoa7ahzysuw.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] # Source node to ATen node mapping: # multi_head_attention_forward => clone_1 # Graph fragment: # %clone_1 : [num_users=3] = call_function[target=torch.ops.aten.clone.default](args = (%squeeze,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 16 x2 = (xindex // 64) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (12*x1)), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/vd/cvd6wmqfst4sb2irm2z4kur6zavvgueasfxdd7gdj23besq2ssgi.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul] # Source node to ATen node mapping: # multi_head_attention_forward => mul # Graph fragment: # %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_3, 0.5), kwargs = {}) triton_poi_fused_mul_2 = async_compile.triton('triton_poi_fused_mul_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + (x0), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7s/c7spagnqvsgjrukyw5jujzjmswxuigeuvpyhxgdob766q2gfvgzr.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax] # Source node to ATen node mapping: # multi_head_attention_forward => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/4c/c4c6p4vuakt6bwm5jq3zgmnhlhi3dsci2l7ltqlyd3droewryzhj.py # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax, aten.mean] # Source node to ATen node mapping: # multi_head_attention_forward => div, mean, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%view_8, [1]), kwargs = {}) triton_poi_fused__softmax_mean_4 = async_compile.triton('triton_poi_fused__softmax_mean_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mean_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_mean_4(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tmp9 = 1.0 tmp10 = tmp8 / tmp9 tl.store(out_ptr0 + (x2), tmp8, xmask) tl.store(out_ptr1 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (12, ), (1, )) assert_size_stride(primals_3, (12, 4), (4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0) del primals_1 buf1 = empty_strided_cuda((16, 12), (12, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 12), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((3, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf1, primals_2, buf2, 192, grid=grid(192), stream=stream0) del buf1 del primals_2 buf3 = empty_strided_cuda((4, 4, 4), (4, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul] triton_poi_fused_mul_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.mul, aten.bmm] extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 64), out=buf4) buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax] triton_poi_fused__softmax_3.run(buf4, buf5, 64, grid=grid(64), stream=stream0) buf6 = buf4; del buf4 # reuse buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten._softmax, aten.mean] triton_poi_fused__softmax_mean_4.run(buf5, buf6, buf10, 64, grid=grid(64), stream=stream0) buf7 = buf5; del buf5 # reuse # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.bmm] extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 128), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.clone] triton_poi_fused_clone_0.run(buf7, buf8, 64, grid=grid(64), stream=stream0) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [multi_head_attention_forward], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf9) del primals_5 return (reinterpret_tensor(buf9, (4, 4, 4), (4, 16, 1), 0), buf10, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_4, reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 128), reinterpret_tensor(buf3, (4, 4, 4), (4, 1, 16), 0), reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 64), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn from typing import Optional import torch.utils.data import torch.nn class TransposeMultiheadAttention(nn.Module): """ Wrapper for nn.MultiheadAttention which first transposes the input tensor from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim), then applies the attention and transposes the attention outputs back to the input shape. """ def __init__(self, feature_dim: 'int', num_heads: 'int'=1): """ Args: feature_dim (int): attention embedding dimension num_heads (int): number of attention heads """ super().__init__() self._attention = nn.MultiheadAttention(embed_dim=feature_dim, num_heads=num_heads) self._attention_weights = None @property def attention_weights(self) ->Optional[torch.Tensor]: """ Contains attention weights from last forward call. """ return self._attention_weights def forward(self, x: 'torch.Tensor', mask: 'Optional[torch.Tensor]'=None ) ->torch.Tensor: """ Args: x (torch.Tensor): tensor of shape (batch_size, seq_len, feature_dim) mask (torch.Tensor): bool tensor with shape (batch_size, seq_len). Sequence elements that are False are invalid. Returns: Tensor with shape (batch_size, seq_len, feature_dim) """ assert x.dim( ) == 3, 'Requires x shape (batch_size x seq_len x feature_dim)' if mask is not None: mask[:, 0] = True mask = ~mask x = x.transpose(0, 1) attn_output, self._attention_weights = self._attention(x, x, x, key_padding_mask=mask) attn_output = attn_output.transpose(0, 1) return attn_output def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'feature_dim': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn from typing import Optional import torch.utils.data import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask) tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 16 x2 = xindex // 64 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 12 * x1), xmask) tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused_mul_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + x0, tmp2, xmask) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_mean_4(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tmp9 = 1.0 tmp10 = tmp8 / tmp9 tl.store(out_ptr0 + x2, tmp8, xmask) tl.store(out_ptr1 + x2, tmp10, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (12,), (1,)) assert_size_stride(primals_3, (12, 4), (4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_1 buf1 = empty_strided_cuda((16, 12), (12, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 12), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((3, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_clone_1[grid(192)](buf1, primals_2, buf2, 192, XBLOCK=256, num_warps=4, num_stages=1) del buf1 del primals_2 buf3 = empty_strided_cuda((4, 4, 4), (4, 16, 1), torch.float32) triton_poi_fused_mul_2[grid(64)](buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(buf3, reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 64), out=buf4) buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_3[grid(64)](buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1) buf6 = buf4 del buf4 buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_mean_4[grid(64)](buf5, buf6, buf10, 64, XBLOCK=64, num_warps=1, num_stages=1) buf7 = buf5 del buf5 extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 128), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_clone_0[grid(64)](buf7, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0) del buf7 extern_kernels.addmm(primals_5, reinterpret_tensor(buf8, (16, 4), ( 4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf9) del primals_5 return reinterpret_tensor(buf9, (4, 4, 4), (4, 16, 1), 0 ), buf10, reinterpret_tensor(buf0, (16, 4), (4, 1), 0 ), buf6, reinterpret_tensor(buf8, (16, 4), (4, 1), 0 ), primals_4, reinterpret_tensor(buf2, (4, 4, 4), (4, 1, 16), 128 ), reinterpret_tensor(buf3, (4, 4, 4), (4, 1, 16), 0 ), reinterpret_tensor(buf2, (4, 4, 4), (4, 16, 1), 64) class TransposeMultiheadAttentionNew(nn.Module): """ Wrapper for nn.MultiheadAttention which first transposes the input tensor from (batch_size, seq_len, feature_dim) to (seq_length, batch_size, feature_dim), then applies the attention and transposes the attention outputs back to the input shape. """ def __init__(self, feature_dim: 'int', num_heads: 'int'=1): """ Args: feature_dim (int): attention embedding dimension num_heads (int): number of attention heads """ super().__init__() self._attention = nn.MultiheadAttention(embed_dim=feature_dim, num_heads=num_heads) self._attention_weights = None @property def attention_weights(self) ->Optional[torch.Tensor]: """ Contains attention weights from last forward call. """ return self._attention_weights def forward(self, input_0): primals_3 = self._attention.in_proj_weight primals_2 = self._attention.in_proj_bias primals_4 = self._attention.out_proj.weight primals_5 = self._attention.out_proj.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
TheShadow29/pytorchvideo
TransposeMultiheadAttention
false
9,701
[ "Apache-2.0" ]
0
39a3e34e33fb0e1ec142288df08f6e8c3585961a
https://github.com/TheShadow29/pytorchvideo/tree/39a3e34e33fb0e1ec142288df08f6e8c3585961a
LayerNorm
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/zf/czfnaeipqg4a3qzttb2l6zy5ng44vshk3lfmp25jc2er665hxsmw.py # Topologically Sorted Source Nodes: [mean, sub], Original ATen: [aten.mean, aten.sub] # Source node to ATen node mapping: # mean => mean # sub => sub # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1], True), kwargs = {}) # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {}) triton_poi_fused_mean_sub_0 = async_compile.triton('triton_poi_fused_mean_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = 4.0 tmp9 = tmp7 / tmp8 tmp10 = tmp0 - tmp9 tl.store(out_ptr0 + (x3), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ix/cix3agfx5ttdzebqllvt7xsyf7hguiv4c5ya7rpk6x57inkbm4xh.py # Topologically Sorted Source Nodes: [pow_1, variance, add, rsqrt, x, mul_1, x_1], Original ATen: [aten.pow, aten.mean, aten.add, aten.rsqrt, aten.mul] # Source node to ATen node mapping: # add => add # mul_1 => mul_1 # pow_1 => pow_1 # rsqrt => rsqrt # variance => mean_1 # x => mul # x_1 => add_1 # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%pow_1, [1], True), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_1, 0.0001), kwargs = {}) # %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %view), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %view_1), kwargs = {}) triton_poi_fused_add_mean_mul_pow_rsqrt_1 = async_compile.triton('triton_poi_fused_add_mean_mul_pow_rsqrt_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_mul_pow_rsqrt_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mean_mul_pow_rsqrt_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) x1 = (xindex // 16) % 4 tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp18 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = 4.0 tmp13 = tmp11 / tmp12 tmp14 = 0.0001 tmp15 = tmp13 + tmp14 tmp16 = libdevice.rsqrt(tmp15) tmp17 = tmp0 * tmp16 tmp19 = tmp17 * tmp18 tmp21 = tmp19 + tmp20 tl.store(out_ptr0 + (x3), tmp21, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean, sub], Original ATen: [aten.mean, aten.sub] stream0 = get_raw_stream(0) triton_poi_fused_mean_sub_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [pow_1, variance, add, rsqrt, x, mul_1, x_1], Original ATen: [aten.pow, aten.mean, aten.add, aten.rsqrt, aten.mul] triton_poi_fused_add_mean_mul_pow_rsqrt_1.run(buf0, primals_2, primals_3, buf1, 256, grid=grid(256), stream=stream0) del buf0 del primals_2 del primals_3 return (buf1, primals_1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda from torch import nn import torch.distributed from torch.nn import LayerNorm import torch.utils.data import torch.optim class LayerNorm(nn.Module): def __init__(self, channels, eps=0.0001): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, x): n_dims = len(x.shape) mean = torch.mean(x, 1, keepdim=True) variance = torch.mean((x - mean) ** 2, 1, keepdim=True) x = (x - mean) * torch.rsqrt(variance + self.eps) shape = [1, -1] + [1] * (n_dims - 2) x = x * self.gamma.view(*shape) + self.beta.view(*shape) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'channels': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_mean_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = 4.0 tmp9 = tmp7 / tmp8 tmp10 = tmp0 - tmp9 tl.store(out_ptr0 + x3, tmp10, xmask) @triton.jit def triton_poi_fused_add_mean_mul_pow_rsqrt_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 x1 = xindex // 16 % 4 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp18 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp20 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = 4.0 tmp13 = tmp11 / tmp12 tmp14 = 0.0001 tmp15 = tmp13 + tmp14 tmp16 = libdevice.rsqrt(tmp15) tmp17 = tmp0 * tmp16 tmp19 = tmp17 * tmp18 tmp21 = tmp19 + tmp20 tl.store(out_ptr0 + x3, tmp21, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mean_sub_0[grid(256)](primals_1, buf0, 256, XBLOCK =128, num_warps=4, num_stages=1) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_add_mean_mul_pow_rsqrt_1[grid(256)](buf0, primals_2, primals_3, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf0 del primals_2 del primals_3 return buf1, primals_1 class LayerNormNew(nn.Module): def __init__(self, channels, eps=0.0001): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = nn.Parameter(torch.zeros(channels)) def forward(self, input_0): primals_2 = self.gamma primals_3 = self.beta primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
Oreoluwa1234/NeMo
LayerNorm
false
9,702
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
JustConvBody
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ws/cws6n4tbbpmyqxjeo62c7qczm62qezonxkfcjevzwxyg67eoncus.py # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d => convolution # y => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 28800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 225) % 32 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/2i/c2iyp6dw4yq5qwlmgkb2wkqrrtoj5qkrtiaz4uf24zzsa6fwtgu4.py # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv2d_1 => convolution_1 # y_1 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 9216 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 36) % 64 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ji/cjirtqwxomnr3oj7rzservx5ihbmg6dzn372kwneqkmpevjetzhm.py # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d_2 => convolution_2 # y_2 => relu_2 # Graph fragment: # %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 64 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, None) tl.store(out_ptr0 + (x3), tmp6, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (32, 4, 8, 8), (256, 64, 8, 1)) assert_size_stride(primals_2, (32, ), (1, )) assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1)) assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 32, 15, 15), (7200, 225, 15, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv2d, y], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 28800, grid=grid(28800), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 64, 6, 6), (2304, 36, 6, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [conv2d_1, y_1], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 9216, grid=grid(9216), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1)) buf5 = buf4; del buf4 # reuse buf6 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d_2, y_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_2.run(buf5, primals_7, buf6, 4096, grid=grid(4096), stream=stream0) del primals_7 return (reinterpret_tensor(buf5, (4, 1024), (1024, 1), 0), primals_1, primals_3, primals_4, primals_6, buf1, buf3, buf6, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((32, 4, 8, 8), (256, 64, 8, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 64, 64), (16384, 4096, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((64, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F def layer_init(layer, w_scale=1.0): nn.init.orthogonal_(layer.weight.data) layer.weight.data.mul_(w_scale) nn.init.constant_(layer.bias.data, 0) return layer class JustConvBody(nn.Module): def __init__(self, in_channels=4): super(JustConvBody, self).__init__() self.feature_dim = 7 * 7 * 64 self.conv1 = layer_init(nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)) self.conv2 = layer_init(nn.Conv2d(32, 64, kernel_size=4, stride=2)) self.conv3 = layer_init(nn.Conv2d(64, 64, kernel_size=3, stride=1)) def forward(self, x): y = F.relu(self.conv1(x)) y = F.relu(self.conv2(y)) y = F.relu(self.conv3(y)) y = y.view(y.size(0), -1) return y def get_inputs(): return [torch.rand([4, 4, 64, 64])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 28800 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 225 % 32 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 9216 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 36 % 64 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 64 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, None) tl.store(out_ptr0 + x3, tmp6, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (32, 4, 8, 8), (256, 64, 8, 1)) assert_size_stride(primals_2, (32,), (1,)) assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1)) assert_size_stride(primals_4, (64, 32, 4, 4), (512, 16, 4, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (64, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 32, 15, 15), (7200, 225, 15, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(28800)](buf1, primals_2, 28800, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 64, 6, 6), (2304, 36, 6, 1)) buf3 = buf2 del buf2 triton_poi_fused_convolution_relu_1[grid(9216)](buf3, primals_5, 9216, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 64, 4, 4), (1024, 16, 4, 1)) buf5 = buf4 del buf4 buf6 = empty_strided_cuda((4, 64, 4, 4), (1024, 16, 4, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_2[grid(4096)](buf5 , primals_7, buf6, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 return reinterpret_tensor(buf5, (4, 1024), (1024, 1), 0 ), primals_1, primals_3, primals_4, primals_6, buf1, buf3, buf6 def layer_init(layer, w_scale=1.0): nn.init.orthogonal_(layer.weight.data) layer.weight.data.mul_(w_scale) nn.init.constant_(layer.bias.data, 0) return layer class JustConvBodyNew(nn.Module): def __init__(self, in_channels=4): super(JustConvBodyNew, self).__init__() self.feature_dim = 7 * 7 * 64 self.conv1 = layer_init(nn.Conv2d(in_channels, 32, kernel_size=8, stride=4)) self.conv2 = layer_init(nn.Conv2d(32, 64, kernel_size=4, stride=2)) self.conv3 = layer_init(nn.Conv2d(64, 64, kernel_size=3, stride=1)) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
Louis-Bagot/DeepRL
JustConvBody
false
9,703
[ "MIT" ]
0
0b152c52bbba90362c8276c223fee3f9a464eb32
https://github.com/Louis-Bagot/DeepRL/tree/0b152c52bbba90362c8276c223fee3f9a464eb32
Context2AnswerAttention
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/3v/c3v7n6hzyrv5pn6uojl3hf6tko347a672spakigdzmqm7ebd4zwl.py # Topologically Sorted Source Nodes: [context_fc], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # context_fc => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(in_out_ptr0 + (x0), tmp2, xmask) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/hz/chz2sqsqk26mwhf2dxhgh44jfpu2er5yqjftwkzfav5ctqtx5e7f.py # Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # prob => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_6, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_6, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/3f/c3fx6bzkalkw7u7askqdnz4rzlcoyqiec4r434sjc5x3axxgkrmr.py # Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # prob => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1) del primals_1 buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [context_fc], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf2, buf9, 256, grid=grid(256), stream=stream0) buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf1 # reuse buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [questions_fc], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf3, buf8, 256, grid=grid(256), stream=stream0) buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attention], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf4, buf5, 256, grid=grid(256), stream=stream0) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [prob], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf5, buf6, 256, grid=grid(256), stream=stream0) buf7 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5 # reuse # Topologically Sorted Source Nodes: [ques_emb], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_4, (16, 4, 4), (16, 4, 1), 0), out=buf7) del buf6 return (reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf4, reinterpret_tensor(primals_4, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), buf8, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data import torch.multiprocessing import torch.nn.modules.loss from scipy.sparse import * class Context2AnswerAttention(nn.Module): def __init__(self, dim, hidden_size): super(Context2AnswerAttention, self).__init__() self.linear_sim = nn.Linear(dim, hidden_size, bias=False) def forward(self, context, answers, out_answers, ans_mask=None): """ Parameters :context, (batch_size, L, dim) :answers, (batch_size, N, dim) :out_answers, (batch_size, N, dim) :ans_mask, (batch_size, N) Returns :ques_emb, (batch_size, L, dim) """ context_fc = torch.relu(self.linear_sim(context)) questions_fc = torch.relu(self.linear_sim(answers)) attention = torch.matmul(context_fc, questions_fc.transpose(-1, -2)) if ans_mask is not None: attention = attention.masked_fill_((1 - ans_mask).bool(). unsqueeze(1), -INF) prob = torch.softmax(attention, dim=-1) ques_emb = torch.matmul(prob, out_answers) return ques_emb def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand( [4, 4, 4, 4])] def get_init_inputs(): return [[], {'dim': 4, 'hidden_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn import torch.utils.data import torch.multiprocessing import torch.nn.modules.loss from scipy.sparse import * assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(in_out_ptr0 + x0, tmp2, xmask) tl.store(out_ptr0 + x0, tmp4, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1) del primals_1 buf2 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf2, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) buf3 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf1 buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3, buf8, 256, XBLOCK=128, num_warps=4, num_stages=1) buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__softmax_1[grid(256)](buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__softmax_2[grid(256)](buf5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1) buf7 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0) del buf5 extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_4, (16, 4, 4), (16, 4, 1), 0), out=buf7) del buf6 return reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), buf4, reinterpret_tensor(primals_4, (16, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), buf8, buf9 class Context2AnswerAttentionNew(nn.Module): def __init__(self, dim, hidden_size): super(Context2AnswerAttentionNew, self).__init__() self.linear_sim = nn.Linear(dim, hidden_size, bias=False) def forward(self, input_0, input_1, input_2): primals_1 = self.linear_sim.weight primals_2 = input_0 primals_3 = input_1 primals_4 = input_2 output = call([primals_1, primals_2, primals_3, primals_4]) return output[0]
LucasAPayne/graph4nlp
Context2AnswerAttention
false
9,704
[ "Apache-2.0" ]
0
3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
https://github.com/LucasAPayne/graph4nlp/tree/3b72308f6ed9ce04c535f78b4b21b6ae0a8f5421
MaskedInstanceNorm1d
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/hv/chvlig3vkflhzd7ivdbf6ojzalx44fop7l6z3mhozziz64jmw5ck.py # Topologically Sorted Source Nodes: [mul, sum_2, cnt, cnt_for_mu, mu, sub, sigma, mul_1, sum_3, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2], Original ATen: [aten.mul, aten.sum, aten.clamp, aten.div, aten.sub, aten.pow, aten.add, aten.sqrt] # Source node to ATen node mapping: # add => add # cnt => sum_1 # cnt_for_mu => clamp_max, clamp_min # cnt_fot_sigma => clamp_max_1, clamp_min_1 # mu => div # mul => mul # mul_1 => mul_1 # sigma => pow_1 # sigma_1 => div_1 # sigma_2 => sqrt # sub => sub # sub_1 => sub_1 # sum_2 => sum_2 # sum_3 => sum_3 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %unsqueeze), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1], True), kwargs = {}) # %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%unsqueeze, [-1], True), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_1, 1.0), kwargs = {}) # %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 100000.0), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, %clamp_max), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %div), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %unsqueeze), kwargs = {}) # %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [-1], True), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_1, 1), kwargs = {}) # %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 1.0), kwargs = {}) # %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 100000.0), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %clamp_max_1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 1e-08), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {}) triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0 = async_compile.triton('triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex % 64 x0 = xindex % 16 x2 = (xindex // 64) x4 = xindex tmp0 = tl.load(in_ptr0 + (4*x3), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + ((4*x0) + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x3)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + (4*x3)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x3)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (3 + (4*x0) + (64*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tmp15 = tmp1 + tmp4 tmp16 = tmp15 + tmp8 tmp17 = tmp16 + tmp12 tmp18 = 1.0 tmp19 = triton_helpers.maximum(tmp17, tmp18) tmp20 = 100000.0 tmp21 = triton_helpers.minimum(tmp19, tmp20) tmp22 = tmp14 / tmp21 tmp23 = tmp0 - tmp22 tmp24 = tmp23 * tmp23 tmp25 = tmp24 * tmp1 tmp26 = tmp3 - tmp22 tmp27 = tmp26 * tmp26 tmp28 = tmp27 * tmp4 tmp29 = tmp25 + tmp28 tmp30 = tmp7 - tmp22 tmp31 = tmp30 * tmp30 tmp32 = tmp31 * tmp8 tmp33 = tmp29 + tmp32 tmp34 = tmp11 - tmp22 tmp35 = tmp34 * tmp34 tmp36 = tmp35 * tmp12 tmp37 = tmp33 + tmp36 tmp38 = tmp17 - tmp18 tmp39 = triton_helpers.maximum(tmp38, tmp18) tmp40 = triton_helpers.minimum(tmp39, tmp20) tmp41 = tmp37 / tmp40 tmp42 = 1e-08 tmp43 = tmp41 + tmp42 tmp44 = libdevice.sqrt(tmp43) tl.store(out_ptr0 + (x4), tmp22, xmask) tl.store(in_out_ptr0 + (x4), tmp44, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/x7/cx7htd3xitvxaxetefd5p23n7ecwnggzfyxx3t47rzzxl37p6cg2.py # Topologically Sorted Source Nodes: [cnt, sub_2, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2, y], Original ATen: [aten.sum, aten.sub, aten.clamp, aten.div, aten.add, aten.sqrt] # Source node to ATen node mapping: # add => add # cnt => sum_1 # cnt_fot_sigma => clamp_max_1, clamp_min_1 # sigma_1 => div_1 # sigma_2 => sqrt # sub_1 => sub_1 # sub_2 => sub_2 # y => div_2 # Graph fragment: # %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%unsqueeze, [-1], True), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_1, %div), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sum_1, 1), kwargs = {}) # %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 1.0), kwargs = {}) # %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 100000.0), kwargs = {}) # %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_3, %clamp_max_1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, 1e-08), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_2, %sqrt), kwargs = {}) triton_poi_fused_add_clamp_div_sqrt_sub_sum_1 = async_compile.triton('triton_poi_fused_add_clamp_div_sqrt_sub_sum_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_div_sqrt_sub_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_clamp_div_sqrt_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex % 256 x4 = (xindex // 4) x5 = xindex tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last') tmp2 = tmp0 - tmp1 tmp4 = tmp2 / tmp3 tl.store(out_ptr0 + (x5), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32) buf1 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [mul, sum_2, cnt, cnt_for_mu, mu, sub, sigma, mul_1, sum_3, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2], Original ATen: [aten.mul, aten.sum, aten.clamp, aten.div, aten.sub, aten.pow, aten.add, aten.sqrt] stream0 = get_raw_stream(0) triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0.run(buf2, arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [cnt, sub_2, sub_1, cnt_fot_sigma, sigma_1, add, sigma_2, y], Original ATen: [aten.sum, aten.sub, aten.clamp, aten.div, aten.add, aten.sqrt] triton_poi_fused_add_clamp_div_sqrt_sub_sum_1.run(arg1_1, buf0, buf2, buf3, 1024, grid=grid(1024), stream=stream0) del arg1_1 del buf0 del buf2 return (buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim class MaskedInstanceNorm1d(nn.Module): """Instance norm + masking.""" MAX_CNT = 100000.0 def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine: 'bool'=False): super().__init__() self.d_channel = d_channel self.unbiased = unbiased self.affine = affine if self.affine: gamma = torch.ones(d_channel, dtype=torch.float) beta = torch.zeros_like(gamma) self.register_parameter('gamma', nn.Parameter(gamma)) self.register_parameter('beta', nn.Parameter(beta)) def forward(self, x: 'torch.Tensor', x_mask: 'torch.Tensor' ) ->torch.Tensor: """`x`: [B,C,T], `x_mask`: [B,T] => [B,C,T].""" x_mask = x_mask.unsqueeze(1).type_as(x) cnt = x_mask.sum(dim=-1, keepdim=True) cnt_for_mu = cnt.clamp(1.0, self.MAX_CNT) mu = (x * x_mask).sum(dim=-1, keepdim=True) / cnt_for_mu sigma = (x - mu) ** 2 cnt_fot_sigma = (cnt - int(self.unbiased)).clamp(1.0, self.MAX_CNT) sigma = (sigma * x_mask).sum(dim=-1, keepdim=True) / cnt_fot_sigma sigma = (sigma + 1e-08).sqrt() y = (x - mu) / sigma if self.affine: gamma = self.gamma.unsqueeze(0).unsqueeze(-1) beta = self.beta.unsqueeze(0).unsqueeze(-1) y = y * gamma + beta return y def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'d_channel': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex % 64 x0 = xindex % 16 x2 = xindex // 64 x4 = xindex tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4 * x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0 + 64 * x2), xmask, eviction_policy ='evict_last') tmp7 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + 4 * x0 + 64 * x2), xmask, eviction_policy ='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (3 + 4 * x0 + 64 * x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp5 = tmp3 * tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 * tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 * tmp12 tmp14 = tmp10 + tmp13 tmp15 = tmp1 + tmp4 tmp16 = tmp15 + tmp8 tmp17 = tmp16 + tmp12 tmp18 = 1.0 tmp19 = triton_helpers.maximum(tmp17, tmp18) tmp20 = 100000.0 tmp21 = triton_helpers.minimum(tmp19, tmp20) tmp22 = tmp14 / tmp21 tmp23 = tmp0 - tmp22 tmp24 = tmp23 * tmp23 tmp25 = tmp24 * tmp1 tmp26 = tmp3 - tmp22 tmp27 = tmp26 * tmp26 tmp28 = tmp27 * tmp4 tmp29 = tmp25 + tmp28 tmp30 = tmp7 - tmp22 tmp31 = tmp30 * tmp30 tmp32 = tmp31 * tmp8 tmp33 = tmp29 + tmp32 tmp34 = tmp11 - tmp22 tmp35 = tmp34 * tmp34 tmp36 = tmp35 * tmp12 tmp37 = tmp33 + tmp36 tmp38 = tmp17 - tmp18 tmp39 = triton_helpers.maximum(tmp38, tmp18) tmp40 = triton_helpers.minimum(tmp39, tmp20) tmp41 = tmp37 / tmp40 tmp42 = 1e-08 tmp43 = tmp41 + tmp42 tmp44 = libdevice.sqrt(tmp43) tl.store(out_ptr0 + x4, tmp22, xmask) tl.store(in_out_ptr0 + x4, tmp44, xmask) @triton.jit def triton_poi_fused_add_clamp_div_sqrt_sub_sum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex % 256 x4 = xindex // 4 x5 = xindex tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last') tmp2 = tmp0 - tmp1 tmp4 = tmp2 / tmp3 tl.store(out_ptr0 + x5, tmp4, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32) buf1 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256), torch.float32) buf2 = buf1 del buf1 get_raw_stream(0) triton_poi_fused_add_clamp_div_mul_pow_sqrt_sub_sum_0[grid(256)](buf2, arg1_1, arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32) triton_poi_fused_add_clamp_div_sqrt_sub_sum_1[grid(1024)](arg1_1, buf0, buf2, buf3, 1024, XBLOCK=256, num_warps=4, num_stages=1) del arg1_1 del buf0 del buf2 return buf3, class MaskedInstanceNorm1dNew(nn.Module): """Instance norm + masking.""" MAX_CNT = 100000.0 def __init__(self, d_channel: 'int', unbiased: 'bool'=True, affine: 'bool'=False): super().__init__() self.d_channel = d_channel self.unbiased = unbiased self.affine = affine if self.affine: gamma = torch.ones(d_channel, dtype=torch.float) beta = torch.zeros_like(gamma) self.register_parameter('gamma', nn.Parameter(gamma)) self.register_parameter('beta', nn.Parameter(beta)) def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
Oreoluwa1234/NeMo
MaskedInstanceNorm1d
false
9,705
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
TorchModule
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/a2/ca2wr2cvkya5clovpxidv7ia56pdcyp7uq4omtpg5m2nr7ya3ryn.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh] # Source node to ATen node mapping: # x_1 => tanh # Graph fragment: # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {}) triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ku/ckukyw44hxxcrcpyqqe6auljaf54daimtcs6kbykg5nkqzpxqi7c.py # Topologically Sorted Source Nodes: [tanh_2], Original ATen: [aten.tanh] # Source node to ATen node mapping: # tanh_2 => tanh_2 # Graph fragment: # %tanh_2 : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_5,), kwargs = {}) triton_poi_fused_tanh_1 = async_compile.triton('triton_poi_fused_tanh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = libdevice.tanh(tmp0) tl.store(out_ptr0 + (x0), tmp1, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (64, 4), (4, 1)) assert_size_stride(primals_3, (64, ), (1, )) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (4, 64), (64, 1)) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (1, 4, 4, 4, 64), (4096, 1024, 256, 64, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh] stream0 = get_raw_stream(0) triton_poi_fused_tanh_0.run(buf1, primals_3, 4096, grid=grid(4096), stream=stream0) del primals_3 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (1, 4, 4, 4, 64), (4096, 1024, 256, 64, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.tanh] triton_poi_fused_tanh_0.run(buf3, primals_5, 4096, grid=grid(4096), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf4) del primals_7 buf5 = empty_strided_cuda((1, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [tanh_2], Original ATen: [aten.tanh] triton_poi_fused_tanh_1.run(buf4, buf5, 256, grid=grid(256), stream=stream0) return (reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), buf1, buf3, buf4, primals_6, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn class TorchLinearModule(torch.nn.Module): def __init__(self, in_size, out_size): super(TorchLinearModule, self).__init__() self._linear = torch.nn.Linear(in_size, out_size) def forward(self, x): return self._linear(x) class TorchModule(torch.nn.Module): def __init__(self, in_size, out_size, dev=None, hidden_size=64): super(TorchModule, self).__init__() self._linear0 = TorchLinearModule(in_size, hidden_size) self._linear1 = TorchLinearModule(hidden_size, hidden_size) self._linear2 = TorchLinearModule(hidden_size, out_size) def forward(self, x): x = x.unsqueeze(0) x = torch.tanh(self._linear0(x)) x = torch.tanh(self._linear1(x)) return torch.tanh(self._linear2(x))[0] def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_size': 4, 'out_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, None) @triton.jit def triton_poi_fused_tanh_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = libdevice.tanh(tmp0) tl.store(out_ptr0 + x0, tmp1, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (64, 4), (4, 1)) assert_size_stride(primals_3, (64,), (1,)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (4, 64), (64, 1)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (1, 4, 4, 4, 64), (4096, 1024, 256, 64, 1), 0) del buf0 get_raw_stream(0) triton_poi_fused_tanh_0[grid(4096)](buf1, primals_3, 4096, XBLOCK= 128, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (1, 4, 4, 4, 64), (4096, 1024, 256, 64, 1), 0) del buf2 triton_poi_fused_tanh_0[grid(4096)](buf3, primals_5, 4096, XBLOCK= 128, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf4) del primals_7 buf5 = empty_strided_cuda((1, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32) triton_poi_fused_tanh_1[grid(256)](buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0 ), buf1, buf3, buf4, primals_6, primals_4 class TorchLinearModule(torch.nn.Module): def __init__(self, in_size, out_size): super(TorchLinearModule, self).__init__() self._linear = torch.nn.Linear(in_size, out_size) def forward(self, x): return self._linear(x) class TorchModuleNew(torch.nn.Module): def __init__(self, in_size, out_size, dev=None, hidden_size=64): super(TorchModuleNew, self).__init__() self._linear0 = TorchLinearModule(in_size, hidden_size) self._linear1 = TorchLinearModule(hidden_size, hidden_size) self._linear2 = TorchLinearModule(hidden_size, out_size) def forward(self, input_0): primals_2 = self._linear0._linear.weight primals_3 = self._linear0._linear.bias primals_4 = self._linear1._linear.weight primals_5 = self._linear1._linear.bias primals_6 = self._linear2._linear.weight primals_7 = self._linear2._linear.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
amit828as/ivy
TorchModule
false
9,706
[ "Apache-2.0" ]
0
fd12e513c58e337cc3775e456ad26a942a501c65
https://github.com/amit828as/ivy/tree/fd12e513c58e337cc3775e456ad26a942a501c65
ConvReLUNorm
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/cu/ccutvo2v4333pq6xhrg2zryqqwthm7dmmuqprvva2xdwiodpz5jn.py # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] # Source node to ATen node mapping: # conv1d => convolution # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [0], [1], False, [0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 4) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/gy/cgylhqrp7uo6ulwwvwcaavkdj6lb3xbmk43eobqwwqruey33hhwc.py # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm] # Source node to ATen node mapping: # layer_norm => add, clone, rsqrt, var_mean # Graph fragment: # %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone, [2]), kwargs = {correction: 0, keepdim: True}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {}) # %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {}) triton_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_native_layer_norm_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask) tmp3 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask) tmp6 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask) tmp9 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp1, tmp3) tmp5 = tmp2 + tmp4 tmp7 = triton_helpers.maximum(tmp1, tmp6) tmp8 = tmp5 + tmp7 tmp10 = triton_helpers.maximum(tmp1, tmp9) tmp11 = tmp8 + tmp10 tmp12 = 4.0 tmp13 = tmp11 / tmp12 tmp14 = tmp2 - tmp13 tmp15 = tmp14 * tmp14 tmp16 = tmp4 - tmp13 tmp17 = tmp16 * tmp16 tmp18 = tmp15 + tmp17 tmp19 = tmp7 - tmp13 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp10 - tmp13 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp24 / tmp12 tmp26 = 1e-05 tmp27 = tmp25 + tmp26 tmp28 = libdevice.rsqrt(tmp27) tl.store(out_ptr0 + (x2), tmp13, xmask) tl.store(out_ptr1 + (x2), tmp28, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/pa/cpaccjq76xvjux47qgs7wy4lqiq65radyhdwdqtrvh44sdzkani6.py # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm] # Source node to ATen node mapping: # layer_norm => add, add_1, clone, mul, mul_1, rsqrt, sub, var_mean # Graph fragment: # %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%clone, [2]), kwargs = {correction: 0, keepdim: True}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {}) # %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %getitem_1), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_4), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_5), kwargs = {}) triton_poi_fused_native_layer_norm_2 = async_compile.triton('triton_poi_fused_native_layer_norm_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (y3), ymask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + (y3), ymask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last') tmp1 = tl.full([1, 1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = tmp2 - tmp3 tmp6 = tmp4 * tmp5 tmp8 = tmp6 * tmp7 tmp10 = tmp8 + tmp9 tl.store(out_ptr0 + (x2 + (4*y3)), tmp10, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, ), (1, )) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4), (16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_2, 64, grid=grid(64), stream=stream0) del primals_2 buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm] triton_poi_fused_native_layer_norm_1.run(buf1, buf2, buf3, 16, grid=grid(16), stream=stream0) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm] triton_poi_fused_native_layer_norm_2.run(buf1, buf2, buf3, primals_4, primals_5, buf4, 16, 4, grid=grid(16, 4), stream=stream0) del buf2 del buf3 del primals_5 return (reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0), primals_1, primals_3, primals_4, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda import torch.distributed import torch.utils.data import torch.optim class ConvReLUNorm(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0): super(ConvReLUNorm, self).__init__() self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size= kernel_size, padding=kernel_size // 2) self.norm = torch.nn.LayerNorm(out_channels) self.dropout = torch.nn.Dropout(dropout) def forward(self, signal): out = torch.nn.functional.relu(self.conv(signal)) out = self.norm(out.transpose(1, 2)).transpose(1, 2) return self.dropout(out) def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.cuda import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 4 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused_native_layer_norm_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask) tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask) tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask) tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp1, tmp3) tmp5 = tmp2 + tmp4 tmp7 = triton_helpers.maximum(tmp1, tmp6) tmp8 = tmp5 + tmp7 tmp10 = triton_helpers.maximum(tmp1, tmp9) tmp11 = tmp8 + tmp10 tmp12 = 4.0 tmp13 = tmp11 / tmp12 tmp14 = tmp2 - tmp13 tmp15 = tmp14 * tmp14 tmp16 = tmp4 - tmp13 tmp17 = tmp16 * tmp16 tmp18 = tmp15 + tmp17 tmp19 = tmp7 - tmp13 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp10 - tmp13 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp24 / tmp12 tmp26 = 1e-05 tmp27 = tmp25 + tmp26 tmp28 = libdevice.rsqrt(tmp27) tl.store(out_ptr0 + x2, tmp13, xmask) tl.store(out_ptr1 + x2, tmp28, xmask) @triton.jit def triton_poi_fused_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl. constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + y3, ymask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + y3, ymask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last') tmp1 = tl.full([1, 1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = tmp2 - tmp3 tmp6 = tmp4 * tmp5 tmp8 = tmp6 * tmp7 tmp10 = tmp8 + tmp9 tl.store(out_ptr0 + (x2 + 4 * y3), tmp10, xmask & ymask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4,), (1,)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=( 0,), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4), (16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(64)](buf1, primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_2 buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) buf3 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) triton_poi_fused_native_layer_norm_1[grid(16)](buf1, buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_native_layer_norm_2[grid(16, 4)](buf1, buf2, buf3, primals_4, primals_5, buf4, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1) del buf2 del buf3 del primals_5 return reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0 ), primals_1, primals_3, primals_4, buf1 class ConvReLUNormNew(torch.nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, dropout=0.0): super(ConvReLUNormNew, self).__init__() self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size= kernel_size, padding=kernel_size // 2) self.norm = torch.nn.LayerNorm(out_channels) self.dropout = torch.nn.Dropout(dropout) def forward(self, input_0): primals_1 = self.conv.weight primals_2 = self.conv.bias primals_4 = self.norm.weight primals_5 = self.norm.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
Oreoluwa1234/NeMo
ConvReLUNorm
false
9,707
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
LeakyReLU
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/n5/cn53c6d36bm2o6wr33epyebwkqx7owzyf77kp5pts3jxdcj6obrf.py # Topologically Sorted Source Nodes: [leaky_relu], Original ATen: [aten.leaky_relu] # Source node to ATen node mapping: # leaky_relu => gt, mul, where # Graph fragment: # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 0), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.01), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %arg0_1, %mul), kwargs = {}) triton_poi_fused_leaky_relu_0 = async_compile.triton('triton_poi_fused_leaky_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.0 tmp2 = tmp0 > tmp1 tmp3 = 0.01 tmp4 = tmp0 * tmp3 tmp5 = tl.where(tmp2, tmp0, tmp4) tl.store(out_ptr0 + (x0), tmp5, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [leaky_relu], Original ATen: [aten.leaky_relu] stream0 = get_raw_stream(0) triton_poi_fused_leaky_relu_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch class Activation(torch.nn.Module): def __init__(self) ->None: super().__init__() def forward(self, inputs: 'torch.Tensor') ->torch.Tensor: raise NotImplementedError class LeakyReLU(Activation): def forward(self, inputs: 'torch.Tensor') ->torch.Tensor: return torch.nn.functional.leaky_relu(inputs) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_leaky_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.0 tmp2 = tmp0 > tmp1 tmp3 = 0.01 tmp4 = tmp0 * tmp3 tmp5 = tl.where(tmp2, tmp0, tmp4) tl.store(out_ptr0 + x0, tmp5, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_leaky_relu_0[grid(256)](arg0_1, buf0, 256, XBLOCK= 256, num_warps=4, num_stages=1) del arg0_1 return buf0, class Activation(torch.nn.Module): def __init__(self) ->None: super().__init__() def forward(self, inputs: 'torch.Tensor') ->torch.Tensor: raise NotImplementedError class LeakyReLUNew(Activation): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
altescy/xtorch
LeakyReLU
false
9,708
[ "MIT" ]
0
bcbbbe645f4d62c211af5b3555c526cc60792c32
https://github.com/altescy/xtorch/tree/bcbbbe645f4d62c211af5b3555c526cc60792c32
ELU
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ck/cck6zsxedo53nyj2po2pvkfjvrr75ansuu3rjjhu6zyrx6xzssqo.py # Topologically Sorted Source Nodes: [elu], Original ATen: [aten.elu] # Source node to ATen node mapping: # elu => expm1, gt, mul, mul_1, mul_2, where # Graph fragment: # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 0), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 1.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 1.0), kwargs = {}) # %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul_1,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {}) triton_poi_fused_elu_0 = async_compile.triton('triton_poi_fused_elu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_elu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.0 tmp2 = tmp0 > tmp1 tmp3 = 1.0 tmp4 = tmp0 * tmp3 tmp5 = libdevice.expm1(tmp4) tmp6 = tmp5 * tmp3 tmp7 = tl.where(tmp2, tmp4, tmp6) tl.store(out_ptr0 + (x0), tmp7, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [elu], Original ATen: [aten.elu] stream0 = get_raw_stream(0) triton_poi_fused_elu_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch class Activation(torch.nn.Module): def __init__(self) ->None: super().__init__() def forward(self, inputs: 'torch.Tensor') ->torch.Tensor: raise NotImplementedError class ELU(Activation): def forward(self, inputs: 'torch.Tensor') ->torch.Tensor: return torch.nn.functional.elu(inputs) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.0 tmp2 = tmp0 > tmp1 tmp3 = 1.0 tmp4 = tmp0 * tmp3 tmp5 = libdevice.expm1(tmp4) tmp6 = tmp5 * tmp3 tmp7 = tl.where(tmp2, tmp4, tmp6) tl.store(out_ptr0 + x0, tmp7, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_elu_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, class Activation(torch.nn.Module): def __init__(self) ->None: super().__init__() def forward(self, inputs: 'torch.Tensor') ->torch.Tensor: raise NotImplementedError class ELUNew(Activation): def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
altescy/xtorch
ELU
false
9,709
[ "MIT" ]
0
bcbbbe645f4d62c211af5b3555c526cc60792c32
https://github.com/altescy/xtorch/tree/bcbbbe645f4d62c211af5b3555c526cc60792c32
FocalLoss
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/mn/cmnoexayfinw3rrbjkaydy3rpcc2wfjk3illd3jankzc4n2p5omt.py # Topologically Sorted Source Nodes: [prediction_probabilities, mul, sub, sub_1, mul_1, p_t, sub_2, modulating_factor, mul_2, sub_3, mul_3, alpha_weight_factor, mul_4, per_entry_cross_ent, focal_cross_entropy_loss, mean], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add, aten.pow, aten.binary_cross_entropy_with_logits, aten.mean] # Source node to ATen node mapping: # alpha_weight_factor => add_1 # focal_cross_entropy_loss => mul_6 # mean => mean # modulating_factor => pow_1 # mul => mul_1 # mul_1 => mul_2 # mul_2 => mul_3 # mul_3 => mul_4 # mul_4 => mul_5 # p_t => add # per_entry_cross_ent => abs_1, exp, full_default, log1p, minimum, mul, neg, sub, sub_1, sub_2 # prediction_probabilities => sigmoid # sub => sub_3 # sub_1 => sub_4 # sub_2 => sub_5 # sub_3 => sub_6 # Graph fragment: # %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg1_1,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %sigmoid), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %sub_4), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {}) # %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %add), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_5, 2.0), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.25), kwargs = {}) # %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, 0.75), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {}) # %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %add_1), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg1_1), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %arg1_1), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {}) # %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sub_1), kwargs = {}) # %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %sub_2), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_6,), kwargs = {}) triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0 = async_compile.triton('triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp1 = tl.load(in_ptr1 + (r0), None) tmp2 = tl.sigmoid(tmp1) tmp3 = tmp0 * tmp2 tmp4 = 1.0 tmp5 = tmp4 - tmp0 tmp6 = tmp4 - tmp2 tmp7 = tmp5 * tmp6 tmp8 = tmp3 + tmp7 tmp9 = tmp4 - tmp8 tmp10 = tmp9 * tmp9 tmp11 = 0.25 tmp12 = tmp0 * tmp11 tmp13 = 0.75 tmp14 = tmp5 * tmp13 tmp15 = tmp12 + tmp14 tmp16 = tmp10 * tmp15 tmp17 = tmp5 * tmp1 tmp18 = 0.0 tmp19 = triton_helpers.minimum(tmp18, tmp1) tmp20 = tl_math.abs(tmp1) tmp21 = -tmp20 tmp22 = tl_math.exp(tmp21) tmp23 = libdevice.log1p(tmp22) tmp24 = tmp19 - tmp23 tmp25 = tmp17 - tmp24 tmp26 = tmp16 * tmp25 tmp27 = tl.broadcast_to(tmp26, [RBLOCK]) tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0)) tmp30 = 256.0 tmp31 = tmp29 / tmp30 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp31, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [prediction_probabilities, mul, sub, sub_1, mul_1, p_t, sub_2, modulating_factor, mul_2, sub_3, mul_3, alpha_weight_factor, mul_4, per_entry_cross_ent, focal_cross_entropy_loss, mean], Original ATen: [aten.sigmoid, aten.mul, aten.rsub, aten.add, aten.pow, aten.binary_cross_entropy_with_logits, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.optim class FocalLoss(torch.nn.Module): """Sigmoid focal cross entropy loss. Focal loss down-weights well classified examples and focusses on the hard examples. See https://arxiv.org/pdf/1708.02002.pdf for the loss definition. """ def __init__(self, gamma=2.0, alpha=0.25): """Constructor. Args: gamma: exponent of the modulating factor (1 - p_t)^gamma. alpha: optional alpha weighting factor to balance positives vs negatives, with alpha in [0, 1] for class 1 and 1-alpha for class 0. In practice alpha may be set by inverse class frequency, so that for a low number of positives, its weight is high. """ super(FocalLoss, self).__init__() self._alpha = alpha self._gamma = gamma self.BCEWithLogits = nn.BCEWithLogitsLoss(reduction='none') def forward(self, prediction_tensor, target_tensor): """Compute loss function. Args: prediction_tensor: A float tensor of shape [batch_size, num_anchors, num_classes] representing the predicted logits for each class target_tensor: A float tensor of shape [batch_size, num_anchors, num_classes] representing one-hot encoded classification targets. Returns: loss: a float tensor of shape [batch_size, num_anchors, num_classes] representing the value of the loss function. """ per_entry_cross_ent = self.BCEWithLogits(prediction_tensor, target_tensor) prediction_probabilities = torch.sigmoid(prediction_tensor) p_t = target_tensor * prediction_probabilities + (1 - target_tensor ) * (1 - prediction_probabilities) modulating_factor = 1.0 if self._gamma: modulating_factor = torch.pow(1.0 - p_t, self._gamma) alpha_weight_factor = 1.0 if self._alpha is not None: alpha_weight_factor = target_tensor * self._alpha + (1 - target_tensor) * (1 - self._alpha) focal_cross_entropy_loss = (modulating_factor * alpha_weight_factor * per_entry_cross_ent) return torch.mean(focal_cross_entropy_loss) def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0( in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp1 = tl.load(in_ptr1 + r0, None) tmp2 = tl.sigmoid(tmp1) tmp3 = tmp0 * tmp2 tmp4 = 1.0 tmp5 = tmp4 - tmp0 tmp6 = tmp4 - tmp2 tmp7 = tmp5 * tmp6 tmp8 = tmp3 + tmp7 tmp9 = tmp4 - tmp8 tmp10 = tmp9 * tmp9 tmp11 = 0.25 tmp12 = tmp0 * tmp11 tmp13 = 0.75 tmp14 = tmp5 * tmp13 tmp15 = tmp12 + tmp14 tmp16 = tmp10 * tmp15 tmp17 = tmp5 * tmp1 tmp18 = 0.0 tmp19 = triton_helpers.minimum(tmp18, tmp1) tmp20 = tl_math.abs(tmp1) tmp21 = -tmp20 tmp22 = tl_math.exp(tmp21) tmp23 = libdevice.log1p(tmp22) tmp24 = tmp19 - tmp23 tmp25 = tmp17 - tmp24 tmp26 = tmp16 * tmp25 tmp27 = tl.broadcast_to(tmp26, [RBLOCK]) tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp27, 0)) tmp30 = 256.0 tmp31 = tmp29 / tmp30 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp31, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_binary_cross_entropy_with_logits_mean_mul_pow_rsub_sigmoid_0[ grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf1, class FocalLossNew(torch.nn.Module): """Sigmoid focal cross entropy loss. Focal loss down-weights well classified examples and focusses on the hard examples. See https://arxiv.org/pdf/1708.02002.pdf for the loss definition. """ def __init__(self, gamma=2.0, alpha=0.25): """Constructor. Args: gamma: exponent of the modulating factor (1 - p_t)^gamma. alpha: optional alpha weighting factor to balance positives vs negatives, with alpha in [0, 1] for class 1 and 1-alpha for class 0. In practice alpha may be set by inverse class frequency, so that for a low number of positives, its weight is high. """ super(FocalLossNew, self).__init__() self._alpha = alpha self._gamma = gamma self.BCEWithLogits = nn.BCEWithLogitsLoss(reduction='none') def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
ValerioB88/self-supervised-relational-reasoning
FocalLoss
false
9,710
[ "MIT" ]
0
12692b93d5c8dd3f56a31aa8b790366556e7a621
https://github.com/ValerioB88/self-supervised-relational-reasoning/tree/12692b93d5c8dd3f56a31aa8b790366556e7a621
CriticNetwork
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/sm/csm4ofalq42npqq7fv6jo3il6ujywmjwqnazwa5z35h4asxel7vx.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] # Source node to ATen node mapping: # x => cat # Graph fragment: # %cat : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%relu, %primals_4], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 68 x1 = (xindex // 68) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 64, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + ((64*x1) + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tl.load(in_ptr1 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 + tmp6 tmp8 = tl.full([1], 0, tl.int32) tmp9 = triton_helpers.maximum(tmp8, tmp7) tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype) tmp11 = tl.where(tmp4, tmp9, tmp10) tmp12 = tmp0 >= tmp3 tmp13 = tl.full([1], 68, tl.int64) tmp14 = tmp0 < tmp13 tmp15 = tl.load(in_ptr2 + ((4*x1) + ((-64) + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tl.where(tmp4, tmp11, tmp15) tl.store(out_ptr0 + (x2), tmp16, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/mt/cmttmov7q7l6eww5wgel4xbdmlbbf53sgwydh2ovfk4ks65mt3ki.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu] # Source node to ATen node mapping: # x_1 => relu_1 # Graph fragment: # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_6), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {}) triton_poi_fused_relu_1 = async_compile.triton('triton_poi_fused_relu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/it/cit4qjb7wmwrbvv2rtchpn3duppvfiyliqnz2jz3tymwbqqane7m.py # Topologically Sorted Source Nodes: [xs], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # xs => relu # Graph fragment: # %add_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_1, %primals_2), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (64, 68), (68, 1)) assert_size_stride(primals_6, (64, ), (1, )) assert_size_stride(primals_7, (1, 64), (64, 1)) assert_size_stride(primals_8, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((4, 68), (68, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(buf0, primals_2, primals_4, buf1, 272, grid=grid(272), stream=stream0) del primals_4 buf2 = empty_strided_cuda((4, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (68, 64), (1, 68), 0), out=buf2) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu] triton_poi_fused_relu_1.run(buf3, primals_6, 256, grid=grid(256), stream=stream0) del primals_6 buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm] extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf5) del primals_8 buf6 = empty_strided_cuda((4, 64), (64, 1), torch.bool) # Topologically Sorted Source Nodes: [xs], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_2.run(buf0, primals_2, buf6, 256, grid=grid(256), stream=stream0) del buf0 del primals_2 return (buf5, primals_3, buf1, buf3, primals_7, primals_5, buf6, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, 68), (68, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((1, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn.functional as F import torch.nn as nn class CriticNetwork(nn.Module): def __init__(self, state_size, action_size, seed): super(CriticNetwork, self).__init__() torch.manual_seed(seed) fcs1_units = 64 fc2_units = 64 self.fcs1 = nn.Linear(state_size, fcs1_units) self.fc2 = nn.Linear(fcs1_units + action_size, fc2_units) self.fc3 = nn.Linear(fc2_units, 1) def forward(self, state, action): xs = F.relu(self.fcs1(state)) x = torch.cat((xs, action), dim=1) x = F.relu(self.fc2(x)) return self.fc3(x) def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {'state_size': 4, 'action_size': 4, 'seed': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 272 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 68 x1 = xindex // 68 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 64, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (64 * x1 + x0), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp6 = tl.load(in_ptr1 + x0, tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 + tmp6 tmp8 = tl.full([1], 0, tl.int32) tmp9 = triton_helpers.maximum(tmp8, tmp7) tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype) tmp11 = tl.where(tmp4, tmp9, tmp10) tmp12 = tmp0 >= tmp3 tl.full([1], 68, tl.int64) tmp15 = tl.load(in_ptr2 + (4 * x1 + (-64 + x0)), tmp12 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tl.where(tmp4, tmp11, tmp15) tl.store(out_ptr0 + x2, tmp16, xmask) @triton.jit def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8) = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (64, 68), (68, 1)) assert_size_stride(primals_6, (64,), (1,)) assert_size_stride(primals_7, (1, 64), (64, 1)) assert_size_stride(primals_8, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 64), (64, 1), torch.float32) extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((4, 68), (68, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(272)](buf0, primals_2, primals_4, buf1, 272, XBLOCK=128, num_warps=4, num_stages=1) del primals_4 buf2 = empty_strided_cuda((4, 64), (64, 1), torch.float32) extern_kernels.mm(buf1, reinterpret_tensor(primals_5, (68, 64), (1, 68), 0), out=buf2) buf3 = buf2 del buf2 triton_poi_fused_relu_1[grid(256)](buf3, primals_6, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_6 buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_8, buf3, reinterpret_tensor(primals_7, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf5) del primals_8 buf6 = empty_strided_cuda((4, 64), (64, 1), torch.bool) triton_poi_fused_relu_threshold_backward_2[grid(256)](buf0, primals_2, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf0 del primals_2 return buf5, primals_3, buf1, buf3, primals_7, primals_5, buf6 class CriticNetworkNew(nn.Module): def __init__(self, state_size, action_size, seed): super(CriticNetworkNew, self).__init__() torch.manual_seed(seed) fcs1_units = 64 fc2_units = 64 self.fcs1 = nn.Linear(state_size, fcs1_units) self.fc2 = nn.Linear(fcs1_units + action_size, fc2_units) self.fc3 = nn.Linear(fc2_units, 1) def forward(self, input_0, input_1): primals_1 = self.fcs1.weight primals_2 = self.fcs1.bias primals_5 = self.fc2.weight primals_6 = self.fc2.bias primals_7 = self.fc3.weight primals_8 = self.fc3.bias primals_3 = input_0 primals_4 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return output[0]
aishikawa/drl-impl
CriticNetwork
false
9,711
[ "MIT" ]
0
1afe7426494cd94990cb4dae247486a25dfe37bf
https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf
DuelingNetwork
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/nq/cnqjufcqn3ur3s7xvlb2i747nyf24md4zaiatlwgkasynplfjstu.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, None) tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7q/c7q4bwj4hxfdsged3oobb6yehaxytrqsbcvu6n6kmgwwpgq4o2zm.py # Topologically Sorted Source Nodes: [add, mean, q], Original ATen: [aten.add, aten.mean, aten.sub] # Source node to ATen node mapping: # add => add # mean => mean # q => sub # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_5, %view_9), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%view_9,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mean), kwargs = {}) triton_per_fused_add_mean_sub_1 = async_compile.triton('triton_per_fused_add_mean_sub_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_sub_1', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex r2 = (rindex // 4) tmp0 = tl.load(in_ptr0 + (r0), None) tmp4 = tl.load(in_ptr1 + (r2), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + (0)) tmp6 = tl.broadcast_to(tmp5, [RBLOCK]) tmp1 = tl.broadcast_to(tmp0, [RBLOCK]) tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0)) tmp7 = tmp4 + tmp6 tmp8 = tmp7 + tmp0 tmp9 = 256.0 tmp10 = tmp3 / tmp9 tmp11 = tmp8 - tmp10 tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp11, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (1, 64), (64, 1)) assert_size_stride(primals_7, (1, ), (1, )) assert_size_stride(primals_8, (64, 64), (64, 1)) assert_size_stride(primals_9, (64, ), (1, )) assert_size_stride(primals_10, (4, 64), (64, 1)) assert_size_stride(primals_11, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse buf12 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf12, 4096, grid=grid(4096), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf2 # reuse buf11 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [v_1], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf11, 4096, grid=grid(4096), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 1), (1, 64), 0), out=buf4) buf5 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_8, (64, 64), (1, 64), 0), out=buf5) buf6 = reinterpret_tensor(buf5, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf5 # reuse buf10 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [a_1], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf6, primals_9, buf10, 4096, grid=grid(4096), stream=stream0) del primals_9 buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [a_2], Original ATen: [aten.addmm] extern_kernels.addmm(primals_11, reinterpret_tensor(buf6, (64, 64), (64, 1), 0), reinterpret_tensor(primals_10, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf7) del primals_11 buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add, mean, q], Original ATen: [aten.add, aten.mean, aten.sub] triton_per_fused_add_mean_sub_1.run(buf7, buf4, primals_7, buf9, 1, 256, grid=grid(1), stream=stream0) del buf4 del buf7 del primals_7 return (buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(buf6, (64, 64), (64, 1), 0), primals_10, buf10, primals_8, primals_6, buf11, primals_4, buf12, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((1, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn.functional as F import torch.nn as nn class DuelingNetwork(nn.Module): def __init__(self, state_size, action_size, seed): super(DuelingNetwork, self).__init__() torch.manual_seed(seed) hidden1 = 64 hidden2 = 64 self.fc1 = nn.Linear(state_size, hidden1) self.vfc1 = nn.Linear(hidden1, hidden2) self.vfc2 = nn.Linear(hidden2, 1) self.afc1 = nn.Linear(hidden1, hidden2) self.afc2 = nn.Linear(hidden2, action_size) def forward(self, state): x = self.fc1(state) x = F.relu(x) v = self.vfc1(x) v = F.relu(v) v = self.vfc2(v) a = self.afc1(x) a = F.relu(a) a = self.afc2(a) q = v + a - a.mean() return q def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'state_size': 4, 'action_size': 4, 'seed': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, None) tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_per_fused_add_mean_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex r2 = rindex // 4 tmp0 = tl.load(in_ptr0 + r0, None) tmp4 = tl.load(in_ptr1 + r2, None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + 0) tmp6 = tl.broadcast_to(tmp5, [RBLOCK]) tmp1 = tl.broadcast_to(tmp0, [RBLOCK]) tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0)) tmp7 = tmp4 + tmp6 tmp8 = tmp7 + tmp0 tmp9 = 256.0 tmp10 = tmp3 / tmp9 tmp11 = tmp8 - tmp10 tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp11, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (1, 64), (64, 1)) assert_size_stride(primals_7, (1,), (1,)) assert_size_stride(primals_8, (64, 64), (64, 1)) assert_size_stride(primals_9, (64,), (1,)) assert_size_stride(primals_10, (4, 64), (64, 1)) assert_size_stride(primals_11, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf0 buf12 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch .bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf1, primals_2, buf12, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf2 buf11 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch .bool) triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf3, primals_5, buf11, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 1), (1, 64), 0), out=buf4) buf5 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_8, (64, 64), (1, 64), 0), out=buf5) buf6 = reinterpret_tensor(buf5, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf5 buf10 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch .bool) triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf6, primals_9, buf10, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_9 buf7 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_11, reinterpret_tensor(buf6, (64, 64), (64, 1), 0), reinterpret_tensor(primals_10, (64, 4), (1, 64), 0 ), alpha=1, beta=1, out=buf7) del primals_11 buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_per_fused_add_mean_sub_1[grid(1)](buf7, buf4, primals_7, buf9, 1, 256, num_warps=2, num_stages=1) del buf4 del buf7 del primals_7 return buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor( buf3, (64, 64), (64, 1), 0), reinterpret_tensor(buf6, (64, 64), (64, 1), 0 ), primals_10, buf10, primals_8, primals_6, buf11, primals_4, buf12 class DuelingNetworkNew(nn.Module): def __init__(self, state_size, action_size, seed): super(DuelingNetworkNew, self).__init__() torch.manual_seed(seed) hidden1 = 64 hidden2 = 64 self.fc1 = nn.Linear(state_size, hidden1) self.vfc1 = nn.Linear(hidden1, hidden2) self.vfc2 = nn.Linear(hidden2, 1) self.afc1 = nn.Linear(hidden1, hidden2) self.afc2 = nn.Linear(hidden2, action_size) def forward(self, input_0): primals_1 = self.fc1.weight primals_2 = self.fc1.bias primals_4 = self.vfc1.weight primals_5 = self.vfc1.bias primals_6 = self.vfc2.weight primals_7 = self.vfc2.bias primals_8 = self.afc1.weight primals_9 = self.afc1.bias primals_10 = self.afc2.weight primals_11 = self.afc2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]
aishikawa/drl-impl
DuelingNetwork
false
9,712
[ "MIT" ]
0
1afe7426494cd94990cb4dae247486a25dfe37bf
https://github.com/aishikawa/drl-impl/tree/1afe7426494cd94990cb4dae247486a25dfe37bf
ConvSigmoidInplace
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/bl/cblmadpxfe3fediovnwlwsnnarpsxojbblm7zuxqzbw7z2tzydhf.py # Topologically Sorted Source Nodes: [a, b, c], Original ATen: [aten.convolution, aten.sigmoid, aten.add] # Source node to ATen node mapping: # a => convolution # b => sigmoid # c => add # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %sigmoid), kwargs = {}) triton_poi_fused_add_convolution_sigmoid_0 = async_compile.triton('triton_poi_fused_add_convolution_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_sigmoid_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp3 + tmp3 tl.store(in_out_ptr0 + (x2), tmp2, xmask) tl.store(out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [a], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [a, b, c], Original ATen: [aten.convolution, aten.sigmoid, aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_convolution_sigmoid_0.run(buf1, primals_2, buf2, 16, grid=grid(16), stream=stream0) del primals_2 return (buf2, primals_1, primals_3, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized class ConvSigmoidInplace(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size): super(ConvSigmoidInplace, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) def forward(self, x): a = self.conv2d(x) b = torch.sigmoid_(a) c = torch.add(b, b) return c def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'image_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_convolution_sigmoid_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp3 + tmp3 tl.store(in_out_ptr0 + x2, tmp2, xmask) tl.store(out_ptr0 + x2, tmp4, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_convolution_sigmoid_0[grid(16)](buf1, primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 return buf2, primals_1, primals_3, buf1 class ConvSigmoidInplaceNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size): super(ConvSigmoidInplaceNew, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) def forward(self, input_0): primals_1 = self.conv2d.weight primals_2 = self.conv2d.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
XiaobingSuper/intel-extension-for-pytorch
ConvSigmoidInplace
false
9,713
[ "Apache-2.0" ]
0
b61029be10e46e6d2e13b0e700c81f8e59164df0
https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0
FocalLoss
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/hy/chyf33s66ikk3pad6dh3i4tqnggy2gywbyice7ielbtwoxupfqff.py # Topologically Sorted Source Nodes: [ce, neg, exp, sub, pow_1, mul, fc], Original ATen: [aten.binary_cross_entropy_with_logits, aten.neg, aten.exp, aten.rsub, aten.pow, aten.mul] # Source node to ATen node mapping: # ce => abs_1, exp, full_default, log1p, mean, minimum, mul, neg, sub, sub_1, sub_2 # exp => exp_1 # fc => mul_2 # mul => mul_1 # neg => neg_1 # pow_1 => pow_1 # sub => sub_3 # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg1_1), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%full_default, %arg1_1), kwargs = {}) # %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg1_1,), kwargs = {}) # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%abs_1,), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg,), kwargs = {}) # %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%exp,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%minimum, %log1p), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %sub_1), kwargs = {}) # %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.default](args = (%sub_2,), kwargs = {}) # %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {}) # %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %exp_1), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 1), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %mean), kwargs = {}) triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0 = async_compile.triton('triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp3 = tl.load(in_ptr1 + (r0), None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tmp2 * tmp3 tmp5 = 0.0 tmp6 = triton_helpers.minimum(tmp5, tmp3) tmp7 = tl_math.abs(tmp3) tmp8 = -tmp7 tmp9 = tl_math.exp(tmp8) tmp10 = libdevice.log1p(tmp9) tmp11 = tmp6 - tmp10 tmp12 = tmp4 - tmp11 tmp13 = tl.broadcast_to(tmp12, [RBLOCK]) tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0)) tmp16 = 256.0 tmp17 = tmp15 / tmp16 tmp18 = -tmp17 tmp19 = tl_math.exp(tmp18) tmp20 = tmp1 - tmp19 tmp21 = tmp20 * tmp20 tmp22 = tmp21 * tmp1 tmp23 = tmp22 * tmp17 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp23, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [ce, neg, exp, sub, pow_1, mul, fc], Original ATen: [aten.binary_cross_entropy_with_logits, aten.neg, aten.exp, aten.rsub, aten.pow, aten.mul] stream0 = get_raw_stream(0) triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0.run(buf1, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.nn.functional as F class FocalLoss(nn.Module): def __init__(self, alpha=1, gamma=2): super().__init__() self.alpha = alpha self.gamma = gamma def forward(self, x, y): ce = F.binary_cross_entropy_with_logits(x, y) fc = self.alpha * (1 - torch.exp(-ce)) ** self.gamma * ce return fc def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0( in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp3 = tl.load(in_ptr1 + r0, None) tmp1 = 1.0 tmp2 = tmp1 - tmp0 tmp4 = tmp2 * tmp3 tmp5 = 0.0 tmp6 = triton_helpers.minimum(tmp5, tmp3) tmp7 = tl_math.abs(tmp3) tmp8 = -tmp7 tmp9 = tl_math.exp(tmp8) tmp10 = libdevice.log1p(tmp9) tmp11 = tmp6 - tmp10 tmp12 = tmp4 - tmp11 tmp13 = tl.broadcast_to(tmp12, [RBLOCK]) tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0)) tmp16 = 256.0 tmp17 = tmp15 / tmp16 tmp18 = -tmp17 tmp19 = tl_math.exp(tmp18) tmp20 = tmp1 - tmp19 tmp21 = tmp20 * tmp20 tmp22 = tmp21 * tmp1 tmp23 = tmp22 * tmp17 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf1 = buf0 del buf0 get_raw_stream(0) triton_per_fused_binary_cross_entropy_with_logits_exp_mul_neg_pow_rsub_0[ grid(1)](buf1, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf1, class FocalLossNew(nn.Module): def __init__(self, alpha=1, gamma=2): super().__init__() self.alpha = alpha self.gamma = gamma def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
agrawalshubham01/FracNet
FocalLoss
false
9,714
[ "Apache-2.0" ]
0
8b912ca65651ff0ee203d9d73cf6ca18539728ac
https://github.com/agrawalshubham01/FracNet/tree/8b912ca65651ff0ee203d9d73cf6ca18539728ac
MultiLayerPerceptron
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py # Topologically Sorted Source Nodes: [output_states_2], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # output_states_2 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/g5/cg5f2rptqnpi2mrqpqc4tujqpbrrrjrse6plhgftx425znsffpfv.py # Topologically Sorted Source Nodes: [output_states_4], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # output_states_4 => amax, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [-1], True), kwargs = {}) # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {}) triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/yh/cyhogxneodczl7mcnuf7mkhxldvr2nc5wj5e42agntthff4e45p7.py # Topologically Sorted Source Nodes: [output_states_4], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # output_states_4 => exp, log, sub_1, sum_1 # Graph fragment: # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {}) triton_poi_fused__log_softmax_2 = async_compile.triton('triton_poi_fused__log_softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp2 = tl_math.exp(tmp1) tmp4 = tl_math.exp(tmp3) tmp5 = tmp2 + tmp4 tmp7 = tl_math.exp(tmp6) tmp8 = tmp5 + tmp7 tmp10 = tl_math.exp(tmp9) tmp11 = tmp8 + tmp10 tmp12 = tl_math.log(tmp11) tmp13 = tmp0 - tmp12 tl.store(out_ptr0 + (x2), tmp13, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [output_states_2], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_3, buf5, 256, grid=grid(256), stream=stream0) del primals_3 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [output_states_3], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_5 buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [output_states_4], Original ATen: [aten._log_softmax] triton_poi_fused__log_softmax_1.run(buf2, buf3, 256, grid=grid(256), stream=stream0) buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [output_states_4], Original ATen: [aten._log_softmax] triton_poi_fused__log_softmax_2.run(buf3, buf4, 256, grid=grid(256), stream=stream0) del buf3 return (buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf4, primals_4, buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda import torch.distributed import torch.utils.data import torch.optim class MultiLayerPerceptron(torch.nn.Module): """ A simple MLP that can either be used independently or put on top of pretrained models (such as BERT) and act as a classifier. Args: hidden_size (int): the size of each layer num_classes (int): number of output classes num_layers (int): number of layers activation (str): type of activations for layers in between log_softmax (bool): whether to add a log_softmax layer before output """ def __init__(self, hidden_size: 'int', num_classes: 'int', num_layers: 'int'=2, activation: 'str'='relu', log_softmax: 'bool'=True): super().__init__() self.layers = 0 for _ in range(num_layers - 1): layer = torch.nn.Linear(hidden_size, hidden_size) setattr(self, f'layer{self.layers}', layer) setattr(self, f'layer{self.layers + 1}', getattr(torch, activation) ) self.layers += 2 layer = torch.nn.Linear(hidden_size, num_classes) setattr(self, f'layer{self.layers}', layer) self.layers += 1 self.log_softmax = log_softmax @property def last_linear_layer(self): return getattr(self, f'layer{self.layers - 1}') def forward(self, hidden_states): output_states = hidden_states[:] for i in range(self.layers): output_states = getattr(self, f'layer{i}')(output_states) if self.log_softmax: output_states = torch.log_softmax(output_states, dim=-1) return output_states def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4, 'num_classes': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.cuda import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) @triton.jit def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp2 = tl_math.exp(tmp1) tmp4 = tl_math.exp(tmp3) tmp5 = tmp2 + tmp4 tmp7 = tl_math.exp(tmp6) tmp8 = tmp5 + tmp7 tmp10 = tl_math.exp(tmp9) tmp11 = tmp8 + tmp10 tmp12 = tl_math.log(tmp11) tmp13 = tmp0 - tmp12 tl.store(out_ptr0 + x2, tmp13, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_3, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), ( 4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_5 buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__log_softmax_1[grid(256)](buf2, buf3, 256, XBLOCK= 256, num_warps=4, num_stages=1) buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf2 triton_poi_fused__log_softmax_2[grid(256)](buf3, buf4, 256, XBLOCK= 256, num_warps=4, num_stages=1) del buf3 return buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf4, primals_4, buf5 class MultiLayerPerceptronNew(torch.nn.Module): """ A simple MLP that can either be used independently or put on top of pretrained models (such as BERT) and act as a classifier. Args: hidden_size (int): the size of each layer num_classes (int): number of output classes num_layers (int): number of layers activation (str): type of activations for layers in between log_softmax (bool): whether to add a log_softmax layer before output """ def __init__(self, hidden_size: 'int', num_classes: 'int', num_layers: 'int'=2, activation: 'str'='relu', log_softmax: 'bool'=True): super().__init__() self.layers = 0 for _ in range(num_layers - 1): layer = torch.nn.Linear(hidden_size, hidden_size) setattr(self, f'layer{self.layers}', layer) setattr(self, f'layer{self.layers + 1}', getattr(torch, activation) ) self.layers += 2 layer = torch.nn.Linear(hidden_size, num_classes) setattr(self, f'layer{self.layers}', layer) self.layers += 1 self.log_softmax = log_softmax @property def last_linear_layer(self): return getattr(self, f'layer{self.layers - 1}') def forward(self, input_0): primals_2 = self.layer0.weight primals_3 = self.layer0.bias primals_4 = self.layer2.weight primals_5 = self.layer2.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
Oreoluwa1234/NeMo
MultiLayerPerceptron
false
9,715
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
MLP
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/tf/ctfobpckmiv3kkga3a6gzs6unuclcnxpb4xc2h5r3udgxgix4ip5.py # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.relu] # Source node to ATen node mapping: # input_3 => relu # Graph fragment: # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_3), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {}) triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_relu_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0) del primals_3 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [input_5], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_5 return (buf2, primals_1, buf1, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn from collections import OrderedDict class MLP(nn.Module): def __init__(self, input_dims, n_hiddens, n_class): super(MLP, self).__init__() assert isinstance(input_dims, int), 'Please provide int for input_dims' self.input_dims = input_dims current_dims = input_dims layers = OrderedDict() if isinstance(n_hiddens, int): n_hiddens = [n_hiddens] else: n_hiddens = list(n_hiddens) for i, n_hidden in enumerate(n_hiddens): layers['fc{}'.format(i + 1)] = nn.Linear(current_dims, n_hidden) layers['relu{}'.format(i + 1)] = nn.ReLU() layers['drop{}'.format(i + 1)] = nn.Dropout(0.2) current_dims = n_hidden layers['out'] = nn.Linear(current_dims, n_class) self.model = nn.Sequential(layers) None def forward(self, input): input = input.view(input.size(0), -1) assert input.size(1) == self.input_dims return self.model.forward(input) def get_inputs(): return [torch.rand([4, 4])] def get_init_inputs(): return [[], {'input_dims': 4, 'n_hiddens': 4, 'n_class': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn from collections import OrderedDict assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0) del primals_2 buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_relu_0[grid(16)](buf1, primals_3, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_3 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_5 return buf2, primals_1, buf1, primals_4 class MLPNew(nn.Module): def __init__(self, input_dims, n_hiddens, n_class): super(MLPNew, self).__init__() assert isinstance(input_dims, int), 'Please provide int for input_dims' self.input_dims = input_dims current_dims = input_dims layers = OrderedDict() if isinstance(n_hiddens, int): n_hiddens = [n_hiddens] else: n_hiddens = list(n_hiddens) for i, n_hidden in enumerate(n_hiddens): layers['fc{}'.format(i + 1)] = nn.Linear(current_dims, n_hidden) layers['relu{}'.format(i + 1)] = nn.ReLU() layers['drop{}'.format(i + 1)] = nn.Dropout(0.2) current_dims = n_hidden layers['out'] = nn.Linear(current_dims, n_class) self.model = nn.Sequential(layers) None def forward(self, input_0): primals_1 = self.model.fc1.weight primals_3 = self.model.fc1.bias primals_2 = self.model.out.weight primals_5 = self.model.out.bias primals_4 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
ZhiTingXin/pytorch-playground
MLP
false
9,716
[ "MIT" ]
0
b319eaf290ad6d793e41efc488309cedf24eba96
https://github.com/ZhiTingXin/pytorch-playground/tree/b319eaf290ad6d793e41efc488309cedf24eba96
MultiHeadAttention
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/7c/c7c7bmvdtfwg2cjdph3ycnfts3mkxkveriaohpvvm4wxz2v7zwbx.py # Topologically Sorted Source Nodes: [query_1, attention_scores], Original ATen: [aten.div, aten.clone] # Source node to ATen node mapping: # attention_scores => clone # query_1 => div # Graph fragment: # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%permute_3, 1.0), kwargs = {}) # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_div_0 = async_compile.triton('triton_poi_fused_clone_div_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_div_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(out_ptr0 + (x2 + (4*y3)), tmp4, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/3v/c3vbbnaoh2ala54xhjzwr7f44xb5tmg7hvdni6ytelrhdlekfg4j.py # Topologically Sorted Source Nodes: [attention_scores_1, attention_probs], Original ATen: [aten.add, aten._softmax] # Source node to ATen node mapping: # attention_probs => amax, exp, sub, sum_1 # attention_scores_1 => add # Graph fragment: # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_11, %primals_10), kwargs = {}) # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) triton_poi_fused__softmax_add_1 = async_compile.triton('triton_poi_fused__softmax_add_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_add_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 16 tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp9 = tmp7 + tmp8 tmp10 = triton_helpers.maximum(tmp6, tmp9) tmp13 = tmp11 + tmp12 tmp14 = triton_helpers.maximum(tmp10, tmp13) tmp15 = tmp2 - tmp14 tmp16 = tl_math.exp(tmp15) tmp17 = tmp5 - tmp14 tmp18 = tl_math.exp(tmp17) tmp19 = tmp16 + tmp18 tmp20 = tmp9 - tmp14 tmp21 = tl_math.exp(tmp20) tmp22 = tmp19 + tmp21 tmp23 = tmp13 - tmp14 tmp24 = tl_math.exp(tmp23) tmp25 = tmp22 + tmp24 tl.store(out_ptr0 + (x2), tmp14, xmask) tl.store(out_ptr1 + (x2), tmp25, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/5b/c5bxb5ewlk2yffmquilpokgd2m43xu4sfizb2gbivqnyerkx3tao.py # Topologically Sorted Source Nodes: [attention_scores_1, attention_probs], Original ATen: [aten.add, aten._softmax] # Source node to ATen node mapping: # attention_probs => amax, div_2, exp, sub # attention_scores_1 => add # Graph fragment: # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_11, %primals_10), kwargs = {}) # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %div_2 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_add_2 = async_compile.triton('triton_poi_fused__softmax_add_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_add_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_add_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x4 = xindex % 64 x5 = (xindex // 4) tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 - tmp3 tmp5 = tl_math.exp(tmp4) tmp7 = tmp5 / tmp6 tl.store(in_out_ptr0 + (x3), tmp7, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/3r/c3rsks6vi53ggj2qfjmhu7vc3vqskqtyr7gc4fdp74wzt6pdrjx4.py # Topologically Sorted Source Nodes: [context], Original ATen: [aten.clone] # Source node to ATen node mapping: # context => clone_3 # Graph fragment: # %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/6t/c6t5a5ere3lqjiu7zh3uu4oxmpdoujdaqqmeunxqapgzo4m74uav.py # Topologically Sorted Source Nodes: [context_1], Original ATen: [aten.clone] # Source node to ATen node mapping: # context_1 => clone_4 # Graph fragment: # %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_7,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_11, (4, 4), (4, 1)) assert_size_stride(primals_12, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1) del primals_4 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_9, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2) del primals_7 buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [query_1, attention_scores], Original ATen: [aten.div, aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_div_0.run(buf0, primals_2, buf3, 16, 4, grid=grid(16, 4), stream=stream0) del primals_2 buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [attention_scores], Original ATen: [aten.clone] triton_poi_fused_clone_div_0.run(buf1, primals_5, buf4, 16, 4, grid=grid(16, 4), stream=stream0) del primals_5 buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attention_scores], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5) buf6 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0); del buf1 # reuse buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32) # Topologically Sorted Source Nodes: [attention_scores_1, attention_probs], Original ATen: [aten.add, aten._softmax] triton_poi_fused__softmax_add_1.run(buf5, primals_10, buf6, buf7, 64, grid=grid(64), stream=stream0) buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5 # reuse # Topologically Sorted Source Nodes: [attention_scores_1, attention_probs], Original ATen: [aten.add, aten._softmax] triton_poi_fused__softmax_add_2.run(buf8, primals_10, buf6, buf7, 256, grid=grid(256), stream=stream0) del primals_10 buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [context], Original ATen: [aten.clone] triton_poi_fused_clone_3.run(buf2, primals_8, buf9, 16, 4, grid=grid(16, 4), stream=stream0) del primals_8 buf10 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [context], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 0), 0), out=buf10) buf11 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf6 # reuse # Topologically Sorted Source Nodes: [context_1], Original ATen: [aten.clone] triton_poi_fused_clone_4.run(buf10, buf11, 16, 4, grid=grid(16, 4), stream=stream0) buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0); del buf10 # reuse # Topologically Sorted Source Nodes: [output_states], Original ATen: [aten.addmm] extern_kernels.addmm(primals_12, reinterpret_tensor(buf11, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_12 return (reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_9, (16, 4), (4, 1), 0), buf8, reinterpret_tensor(buf11, (16, 4), (4, 1), 0), primals_11, reinterpret_tensor(buf9, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim class MultiHeadAttention(nn.Module): """ Multi-head scaled dot-product attention layer. Args: hidden_size: size of the embeddings in the model, also known as d_model num_attention_heads: number of heads in multi-head attention attn_score_dropout: probability of dropout applied to attention scores attn_layer_dropout: probability of dropout applied to the output of the whole layer, but before layer normalization """ def __init__(self, hidden_size, num_attention_heads, attn_score_dropout =0.0, attn_layer_dropout=0.0): super().__init__() if hidden_size % num_attention_heads != 0: raise ValueError( 'The hidden size (%d) is not a multiple of the number of attention heads (%d)' % (hidden_size, num_attention_heads)) self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.attn_head_size = int(hidden_size / num_attention_heads) self.attn_scale = math.sqrt(math.sqrt(self.attn_head_size)) self.query_net = nn.Linear(hidden_size, hidden_size) self.key_net = nn.Linear(hidden_size, hidden_size) self.value_net = nn.Linear(hidden_size, hidden_size) self.out_projection = nn.Linear(hidden_size, hidden_size) self.attn_dropout = nn.Dropout(attn_score_dropout) self.layer_dropout = nn.Dropout(attn_layer_dropout) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self. attn_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, queries, keys, values, attention_mask): query = self.query_net(queries) key = self.key_net(keys) value = self.value_net(values) query = self.transpose_for_scores(query) / self.attn_scale key = self.transpose_for_scores(key) / self.attn_scale value = self.transpose_for_scores(value) attention_scores = torch.matmul(query, key.transpose(-1, -2)) if attention_mask is not None: attention_scores = attention_scores + attention_mask attention_probs = torch.softmax(attention_scores, dim=-1) attention_probs = self.attn_dropout(attention_probs) context = torch.matmul(attention_probs, value) context = context.permute(0, 2, 1, 3).contiguous() new_context_shape = context.size()[:-2] + (self.hidden_size,) context = context.view(*new_context_shape) output_states = self.out_projection(context) output_states = self.layer_dropout(output_states) return output_states def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4, 'num_attention_heads': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import math import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_div_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 1.0 tmp4 = tmp2 * tmp3 tl.store(out_ptr0 + (x2 + 4 * y3), tmp4, xmask & ymask) @triton.jit def triton_poi_fused__softmax_add_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 16 tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp9 = tmp7 + tmp8 tmp10 = triton_helpers.maximum(tmp6, tmp9) tmp13 = tmp11 + tmp12 tmp14 = triton_helpers.maximum(tmp10, tmp13) tmp15 = tmp2 - tmp14 tmp16 = tl_math.exp(tmp15) tmp17 = tmp5 - tmp14 tmp18 = tl_math.exp(tmp17) tmp19 = tmp16 + tmp18 tmp20 = tmp9 - tmp14 tmp21 = tl_math.exp(tmp20) tmp22 = tmp19 + tmp21 tmp23 = tmp13 - tmp14 tmp24 = tl_math.exp(tmp23) tmp25 = tmp22 + tmp24 tl.store(out_ptr0 + x2, tmp14, xmask) tl.store(out_ptr1 + x2, tmp25, xmask) @triton.jit def triton_poi_fused__softmax_add_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x4 = xindex % 64 x5 = xindex // 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 - tmp3 tmp5 = tl_math.exp(tmp4) tmp7 = tmp5 / tmp6 tl.store(in_out_ptr0 + x3, tmp7, xmask) @triton.jit def triton_poi_fused_clone_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask) @triton.jit def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 ) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_10, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_11, (4, 4), (4, 1)) assert_size_stride(primals_12, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1) del primals_4 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_9, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2) del primals_7 buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_div_0[grid(16, 4)](buf0, primals_2, buf3, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1) del primals_2 buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0) del buf0 triton_poi_fused_clone_div_0[grid(16, 4)](buf1, primals_5, buf4, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1) del primals_5 buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5) buf6 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 64), 0) del buf1 buf7 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32) triton_poi_fused__softmax_add_1[grid(64)](buf5, primals_10, buf6, buf7, 64, XBLOCK=64, num_warps=1, num_stages=1) buf8 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf5 triton_poi_fused__softmax_add_2[grid(256)](buf8, primals_10, buf6, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_10 buf9 = reinterpret_tensor(buf7, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf7 triton_poi_fused_clone_3[grid(16, 4)](buf2, primals_8, buf9, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1) del primals_8 buf10 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0) del buf2 extern_kernels.bmm(reinterpret_tensor(buf8, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf9, (16, 4, 1), (4, 1, 0), 0), out=buf10) buf11 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0) del buf6 triton_poi_fused_clone_4[grid(16, 4)](buf10, buf11, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf12 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0) del buf10 extern_kernels.addmm(primals_12, reinterpret_tensor(buf11, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf12) del primals_12 return reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0 ), reinterpret_tensor(primals_3, (16, 4), (4, 1), 0 ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0 ), reinterpret_tensor(primals_9, (16, 4), (4, 1), 0 ), buf8, reinterpret_tensor(buf11, (16, 4), (4, 1), 0 ), primals_11, reinterpret_tensor(buf9, (16, 1, 4), (4, 1, 1), 0 ), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0 ), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0) class MultiHeadAttentionNew(nn.Module): """ Multi-head scaled dot-product attention layer. Args: hidden_size: size of the embeddings in the model, also known as d_model num_attention_heads: number of heads in multi-head attention attn_score_dropout: probability of dropout applied to attention scores attn_layer_dropout: probability of dropout applied to the output of the whole layer, but before layer normalization """ def __init__(self, hidden_size, num_attention_heads, attn_score_dropout =0.0, attn_layer_dropout=0.0): super().__init__() if hidden_size % num_attention_heads != 0: raise ValueError( 'The hidden size (%d) is not a multiple of the number of attention heads (%d)' % (hidden_size, num_attention_heads)) self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.attn_head_size = int(hidden_size / num_attention_heads) self.attn_scale = math.sqrt(math.sqrt(self.attn_head_size)) self.query_net = nn.Linear(hidden_size, hidden_size) self.key_net = nn.Linear(hidden_size, hidden_size) self.value_net = nn.Linear(hidden_size, hidden_size) self.out_projection = nn.Linear(hidden_size, hidden_size) self.attn_dropout = nn.Dropout(attn_score_dropout) self.layer_dropout = nn.Dropout(attn_layer_dropout) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self. attn_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward(self, input_0, input_1, input_2, input_3): primals_1 = self.query_net.weight primals_2 = self.query_net.bias primals_4 = self.key_net.weight primals_5 = self.key_net.bias primals_7 = self.value_net.weight primals_8 = self.value_net.bias primals_11 = self.out_projection.weight primals_12 = self.out_projection.bias primals_3 = input_0 primals_6 = input_1 primals_9 = input_2 primals_10 = input_3 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12]) return output[0]
Oreoluwa1234/NeMo
MultiHeadAttention
false
9,717
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
ConvElu
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/uc/cucg2jmiutnczw7opwf7hrmwxu6suhky5tfr34lez2ccugizeilv.py # Topologically Sorted Source Nodes: [a, b, c], Original ATen: [aten.convolution, aten.elu, aten.add] # Source node to ATen node mapping: # a => convolution # b => expm1, gt, mul, mul_2, where # c => add # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {}) # %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {}) # %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, %where), kwargs = {}) triton_poi_fused_add_convolution_elu_0 = async_compile.triton('triton_poi_fused_add_convolution_elu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_elu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_elu_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tmp10 = tmp9 + tmp9 tl.store(in_out_ptr0 + (x2), tmp2, xmask) tl.store(out_ptr0 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [a], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [a, b, c], Original ATen: [aten.convolution, aten.elu, aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_convolution_elu_0.run(buf1, primals_2, buf2, 16, grid=grid(16), stream=stream0) del primals_2 return (buf2, primals_1, primals_3, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized class ConvElu(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size, inplace=False): super(ConvElu, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) self.elu = nn.ELU(inplace=inplace) def forward(self, x): a = self.conv2d(x) b = self.elu(a) c = torch.add(b, b) return c def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'image_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_convolution_elu_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tmp10 = tmp9 + tmp9 tl.store(in_out_ptr0 + x2, tmp2, xmask) tl.store(out_ptr0 + x2, tmp10, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_convolution_elu_0[grid(16)](buf1, primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 return buf2, primals_1, primals_3, buf1 class ConvEluNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size, inplace=False): super(ConvEluNew, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) self.elu = nn.ELU(inplace=inplace) def forward(self, input_0): primals_1 = self.conv2d.weight primals_2 = self.conv2d.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
XiaobingSuper/intel-extension-for-pytorch
ConvElu
false
9,718
[ "Apache-2.0" ]
0
b61029be10e46e6d2e13b0e700c81f8e59164df0
https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0
ConvSwishInplace
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/y3/cy3av6tc3latj7lsyimfuoqkkwfspnkbkuphwbwnnbdus7vidy5b.py # Topologically Sorted Source Nodes: [a, b, res], Original ATen: [aten.convolution, aten.sigmoid, aten.mul] # Source node to ATen node mapping: # a => convolution # b => sigmoid # res => mul # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, %sigmoid), kwargs = {}) triton_poi_fused_convolution_mul_sigmoid_0 = async_compile.triton('triton_poi_fused_convolution_mul_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_mul_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_mul_sigmoid_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + (x2), tmp2, xmask) tl.store(out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [a], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [a, b, res], Original ATen: [aten.convolution, aten.sigmoid, aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_convolution_mul_sigmoid_0.run(buf1, primals_2, buf2, 16, grid=grid(16), stream=stream0) del primals_2 return (buf2, primals_1, primals_3, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized class ConvSwishInplace(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size): super(ConvSwishInplace, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) def forward(self, x): a = self.conv2d(x) b = torch.sigmoid(a) res = a.mul_(b) return res def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'image_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_mul_sigmoid_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + x2, tmp2, xmask) tl.store(out_ptr0 + x2, tmp4, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_convolution_mul_sigmoid_0[grid(16)](buf1, primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 return buf2, primals_1, primals_3, buf1 class ConvSwishInplaceNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size): super(ConvSwishInplaceNew, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) def forward(self, input_0): primals_1 = self.conv2d.weight primals_2 = self.conv2d.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
XiaobingSuper/intel-extension-for-pytorch
ConvSwishInplace
false
9,719
[ "Apache-2.0" ]
0
b61029be10e46e6d2e13b0e700c81f8e59164df0
https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0
ConvSwishOutplace
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/y3/cy3av6tc3latj7lsyimfuoqkkwfspnkbkuphwbwnnbdus7vidy5b.py # Topologically Sorted Source Nodes: [a1, b1, c1], Original ATen: [aten.convolution, aten.sigmoid, aten.mul] # Source node to ATen node mapping: # a1 => convolution # b1 => sigmoid # c1 => mul # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, %sigmoid), kwargs = {}) triton_poi_fused_convolution_mul_sigmoid_0 = async_compile.triton('triton_poi_fused_convolution_mul_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_mul_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_mul_sigmoid_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + (x2), tmp2, xmask) tl.store(out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [a1], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [a1, b1, c1], Original ATen: [aten.convolution, aten.sigmoid, aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_convolution_mul_sigmoid_0.run(buf1, primals_2, buf2, 16, grid=grid(16), stream=stream0) del primals_2 return (buf2, primals_1, primals_3, buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized class ConvSwishOutplace(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size): super(ConvSwishOutplace, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) def forward(self, x): a1 = self.conv2d(x) b1 = torch.sigmoid(a1) c1 = torch.mul(a1, b1) return c1 def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'image_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_mul_sigmoid_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tmp4 = tmp2 * tmp3 tl.store(in_out_ptr0 + x2, tmp2, xmask) tl.store(out_ptr0 + x2, tmp4, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_convolution_mul_sigmoid_0[grid(16)](buf1, primals_2, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_2 return buf2, primals_1, primals_3, buf1 class ConvSwishOutplaceNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size): super(ConvSwishOutplaceNew, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) def forward(self, input_0): primals_1 = self.conv2d.weight primals_2 = self.conv2d.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
XiaobingSuper/intel-extension-for-pytorch
ConvSwishOutplace
false
9,720
[ "Apache-2.0" ]
0
b61029be10e46e6d2e13b0e700c81f8e59164df0
https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0
ConvHardtanh
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/nm/cnmow6kfcugiwss4lkkqqtfyb7frcpduk25g3yf7chs6cwjhur4v.py # Topologically Sorted Source Nodes: [a, b, c], Original ATen: [aten.convolution, aten.hardtanh, aten.add, aten.hardtanh_backward] # Source node to ATen node mapping: # a => convolution # b => clamp_max, clamp_min # c => add # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%convolution, -1.0), kwargs = {}) # %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1.0), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clamp_max, %clamp_max), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%convolution, -1.0), kwargs = {}) # %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%convolution, 1.0), kwargs = {}) # %bitwise_or : [num_users=1] = call_function[target=torch.ops.aten.bitwise_or.Tensor](args = (%le, %ge), kwargs = {}) triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0 = async_compile.triton('triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = -1.0 tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp5 = 1.0 tmp6 = triton_helpers.minimum(tmp4, tmp5) tmp7 = tmp6 + tmp6 tmp8 = tmp2 <= tmp3 tmp9 = tmp2 >= tmp5 tmp10 = tmp8 | tmp9 tl.store(out_ptr0 + (x2), tmp7, xmask) tl.store(out_ptr1 + (x2), tmp10, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [a], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool) # Topologically Sorted Source Nodes: [a, b, c], Original ATen: [aten.convolution, aten.hardtanh, aten.add, aten.hardtanh_backward] stream0 = get_raw_stream(0) triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0.run(buf0, primals_2, buf1, buf2, 16, grid=grid(16), stream=stream0) del buf0 del primals_2 return (buf1, primals_1, primals_3, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized class ConvHardtanh(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size, inplace=False): super(ConvHardtanh, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) self.hardtanh = nn.Hardtanh(inplace=inplace) def forward(self, x): a = self.conv2d(x) b = self.hardtanh(a) c = torch.add(b, b) return c def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'image_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = -1.0 tmp4 = triton_helpers.maximum(tmp2, tmp3) tmp5 = 1.0 tmp6 = triton_helpers.minimum(tmp4, tmp5) tmp7 = tmp6 + tmp6 tmp8 = tmp2 <= tmp3 tmp9 = tmp2 >= tmp5 tmp10 = tmp8 | tmp9 tl.store(out_ptr0 + x2, tmp7, xmask) tl.store(out_ptr1 + x2, tmp10, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool) get_raw_stream(0) triton_poi_fused_add_convolution_hardtanh_hardtanh_backward_0[grid(16) ](buf0, primals_2, buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1) del buf0 del primals_2 return buf1, primals_1, primals_3, buf2 class ConvHardtanhNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, image_size, inplace=False): super(ConvHardtanhNew, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, image_size) self.hardtanh = nn.Hardtanh(inplace=inplace) def forward(self, input_0): primals_1 = self.conv2d.weight primals_2 = self.conv2d.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
XiaobingSuper/intel-extension-for-pytorch
ConvHardtanh
false
9,721
[ "Apache-2.0" ]
0
b61029be10e46e6d2e13b0e700c81f8e59164df0
https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0
MultiHeadAttn
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/gu/cguak4pmui2ydemh2nfin5rax4se6fwibhum52l6wzraswotmhe6.py # Topologically Sorted Source Nodes: [q], Original ATen: [aten.clone] # Source node to ATen node mapping: # q => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) % 4 x3 = (xindex // 64) x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (48*x1) + (192*x3)), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (4*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/wt/cwtwhu7iqo5rair5bnweuo5khmb7e5l2qv2pku3hann6ogfos7mk.py # Topologically Sorted Source Nodes: [v], Original ATen: [aten.clone] # Source node to ATen node mapping: # v => clone_2 # Graph fragment: # %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) % 4 x3 = (xindex // 64) x4 = xindex tmp0 = tl.load(in_ptr0 + (32 + x0 + (4*x2) + (48*x1) + (192*x3)), xmask) tmp1 = tl.load(in_ptr1 + (32 + x0 + (4*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/v4/cv4ht4ztzq6xell45o2uwdqpaatovskqg4idcdyplfbbfxu7r6h6.py # Topologically Sorted Source Nodes: [k], Original ATen: [aten.clone] # Source node to ATen node mapping: # k => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_2,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) % 4 x3 = (xindex // 64) x4 = xindex tmp0 = tl.load(in_ptr0 + (16 + x0 + (4*x2) + (48*x1) + (192*x3)), xmask) tmp1 = tl.load(in_ptr1 + (16 + x0 + (4*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/c5/cc5335fx2gusb6qgcjpudfhou76zahyma2ckrjw26lmkw2q3zxd3.py # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_prob => exp # Graph fragment: # %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 1), kwargs = {}) # %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [2], True), kwargs = {}) # %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {}) # %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 0.5), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {}) triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + (x2), tmp17, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/zh/czh6tw7ngffcygnivwvcjex5edxy3ms4t27ymyn2hemxlpspxzq7.py # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_prob => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [2], True), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_4 = async_compile.triton('triton_poi_fused__softmax_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/my/cmyquxsupn5jxhfg2hje2gcqqg62qnzzhyxd7jixp5puholjjh5o.py # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] # Source node to ATen node mapping: # contiguous => clone_4 # Graph fragment: # %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_5,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_5 = async_compile.triton('triton_poi_fused_clone_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_5(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) % 4 x2 = (xindex // 16) x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (64*x1)), xmask) tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ns/cnspfsjjmvserkfymbru7x5vm2xumtyor5javdiv74jr3avx67rq.py # Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm] # Source node to ATen node mapping: # add => add # output => var_mean # Graph fragment: # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %view_11), kwargs = {}) # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [2]), kwargs = {correction: 0, keepdim: True}) triton_poi_fused_add_native_layer_norm_6 = async_compile.triton('triton_poi_fused_add_native_layer_norm_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = tmp27 / tmp15 tl.store(out_ptr0 + (x0), tmp16, xmask) tl.store(out_ptr1 + (x0), tmp28, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7u/c7uxwow3tztifyrr5oj6dotpbrh7qtup53xfydkt35y65ajtfwre.py # Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm] # Source node to ATen node mapping: # add => add # output => add_1, add_2, mul_1, mul_2, rsqrt, sub_1 # Graph fragment: # %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_1, %view_11), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_3, 1e-05), kwargs = {}) # %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_4), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %primals_5), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_6), kwargs = {}) triton_poi_fused_add_native_layer_norm_7 = async_compile.triton('triton_poi_fused_add_native_layer_norm_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 - tmp3 tmp6 = 1e-05 tmp7 = tmp5 + tmp6 tmp8 = libdevice.rsqrt(tmp7) tmp9 = tmp4 * tmp8 tmp11 = tmp9 * tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + (x2), tmp13, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (48, 4), (4, 1)) assert_size_stride(primals_3, (48, ), (1, )) assert_size_stride(primals_4, (4, 16), (16, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 48), (48, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 48), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [q], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(buf0, primals_3, buf1, 256, grid=grid(256), stream=stream0) buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [v], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf0, primals_3, buf2, 256, grid=grid(256), stream=stream0) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [k], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf0, primals_3, buf3, 256, grid=grid(256), stream=stream0) del buf0 del primals_3 buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_score], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4) buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] triton_poi_fused__softmax_3.run(buf4, buf5, 256, grid=grid(256), stream=stream0) buf6 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [attn_prob], Original ATen: [aten._softmax] triton_poi_fused__softmax_4.run(buf5, buf6, 256, grid=grid(256), stream=stream0) buf7 = buf5; del buf5 # reuse # Topologically Sorted Source Nodes: [attn_vec], Original ATen: [aten.bmm] extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), out=buf7) buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_5.run(buf7, buf8, 256, grid=grid(256), stream=stream0) del buf7 buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_out], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf8, (16, 16), (16, 1), 0), reinterpret_tensor(primals_4, (16, 4), (1, 16), 0), out=buf9) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) # Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm] triton_poi_fused_add_native_layer_norm_6.run(primals_1, buf9, buf10, buf11, 16, grid=grid(16), stream=stream0) buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm] triton_poi_fused_add_native_layer_norm_7.run(primals_1, buf9, buf10, buf11, primals_5, primals_6, buf12, 64, grid=grid(64), stream=stream0) del buf10 del buf11 del primals_6 return (buf12, primals_1, primals_5, buf6, reinterpret_tensor(buf8, (16, 16), (16, 1), 0), buf9, primals_4, reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((48, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((48, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda from torch.nn import functional as F from torch import nn import torch.distributed import torch.utils.data import torch.optim class MultiHeadAttn(nn.Module): def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1, pre_lnorm=False): super(MultiHeadAttn, self).__init__() self.n_head = n_head self.d_model = d_model self.d_head = d_head self.scale = 1 / d_head ** 0.5 self.pre_lnorm = pre_lnorm self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head) self.drop = nn.Dropout(dropout) self.dropatt = nn.Dropout(dropatt) self.o_net = nn.Linear(n_head * d_head, d_model, bias=False) self.layer_norm = nn.LayerNorm(d_model) def forward(self, inp, attn_mask=None): return self._forward(inp, attn_mask) def _forward(self, inp, attn_mask=None): residual = inp if self.pre_lnorm: inp = self.layer_norm(inp) n_head, d_head = self.n_head, self.d_head head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=2) head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head) head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head) head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head) q = head_q.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head) k = head_k.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head) v = head_v.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head) attn_score = torch.bmm(q, k.transpose(1, 2)) attn_score.mul_(self.scale) if attn_mask is not None: attn_mask = attn_mask.unsqueeze(1) attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1) attn_score.masked_fill_(attn_mask, -float('inf')) attn_prob = F.softmax(attn_score, dim=2) attn_prob = self.dropatt(attn_prob) attn_vec = torch.bmm(attn_prob, v) attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head) attn_vec = attn_vec.permute(1, 2, 0, 3).contiguous().view(inp.size( 0), inp.size(1), n_head * d_head) attn_out = self.o_net(attn_vec) attn_out = self.drop(attn_out) if self.pre_lnorm: output = residual + attn_out else: output = self.layer_norm(residual + attn_out) return output def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'n_head': 4, 'd_model': 4, 'd_head': 4, 'dropout': 0.5}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.cuda from torch.nn import functional as F from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 % 4 x3 = xindex // 64 x4 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 48 * x1 + 192 * x3), xmask) tmp1 = tl.load(in_ptr1 + (x0 + 4 * x2), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 % 4 x3 = xindex // 64 x4 = xindex tmp0 = tl.load(in_ptr0 + (32 + x0 + 4 * x2 + 48 * x1 + 192 * x3), xmask) tmp1 = tl.load(in_ptr1 + (32 + x0 + 4 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) @triton.jit def triton_poi_fused_clone_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 % 4 x3 = xindex // 64 x4 = xindex tmp0 = tl.load(in_ptr0 + (16 + x0 + 4 * x2 + 48 * x1 + 192 * x3), xmask) tmp1 = tl.load(in_ptr1 + (16 + x0 + 4 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + x2, tmp17, xmask) @triton.jit def triton_poi_fused__softmax_4(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_clone_5(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 % 4 x2 = xindex // 16 x3 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 64 * x1), xmask) tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused_add_native_layer_norm_6(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = tmp27 / tmp15 tl.store(out_ptr0 + x0, tmp16, xmask) tl.store(out_ptr1 + x0, tmp28, xmask) @triton.jit def triton_poi_fused_add_native_layer_norm_7(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x2, xmask) tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last') tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp4 = tmp2 - tmp3 tmp6 = 1e-05 tmp7 = tmp5 + tmp6 tmp8 = libdevice.rsqrt(tmp7) tmp9 = tmp4 * tmp8 tmp11 = tmp9 * tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + x2, tmp13, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (48, 4), (4, 1)) assert_size_stride(primals_3, (48,), (1,)) assert_size_stride(primals_4, (4, 16), (16, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 48), (48, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 48), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(256)](buf0, primals_3, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1) buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_clone_1[grid(256)](buf0, primals_3, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_clone_2[grid(256)](buf0, primals_3, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf0 del primals_3 buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (16, 4, 4), (16, 1, 4), 0), out=buf4) buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_3[grid(256)](buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) buf6 = buf4 del buf4 triton_poi_fused__softmax_4[grid(256)](buf5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1) buf7 = buf5 del buf5 extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (16, 4, 4), (16, 4, 1), 0), out=buf7) buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_clone_5[grid(256)](buf7, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf7 buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf8, (16, 16), (16, 1), 0), reinterpret_tensor(primals_4, (16, 4), (1, 16), 0), out=buf9) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32) triton_poi_fused_add_native_layer_norm_6[grid(16)](primals_1, buf9, buf10, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1) buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_add_native_layer_norm_7[grid(64)](primals_1, buf9, buf10, buf11, primals_5, primals_6, buf12, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf10 del buf11 del primals_6 return buf12, primals_1, primals_5, buf6, reinterpret_tensor(buf8, (16, 16), (16, 1), 0), buf9, primals_4, reinterpret_tensor(buf2, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf3, (16, 4, 4), (16, 4, 1), 0) class MultiHeadAttnNew(nn.Module): def __init__(self, n_head, d_model, d_head, dropout, dropatt=0.1, pre_lnorm=False): super(MultiHeadAttnNew, self).__init__() self.n_head = n_head self.d_model = d_model self.d_head = d_head self.scale = 1 / d_head ** 0.5 self.pre_lnorm = pre_lnorm self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head) self.drop = nn.Dropout(dropout) self.dropatt = nn.Dropout(dropatt) self.o_net = nn.Linear(n_head * d_head, d_model, bias=False) self.layer_norm = nn.LayerNorm(d_model) def _forward(self, inp, attn_mask=None): residual = inp if self.pre_lnorm: inp = self.layer_norm(inp) n_head, d_head = self.n_head, self.d_head head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=2) head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head) head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head) head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head) q = head_q.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head) k = head_k.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head) v = head_v.permute(0, 2, 1, 3).reshape(-1, inp.size(1), d_head) attn_score = torch.bmm(q, k.transpose(1, 2)) attn_score.mul_(self.scale) if attn_mask is not None: attn_mask = attn_mask.unsqueeze(1) attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1) attn_score.masked_fill_(attn_mask, -float('inf')) attn_prob = F.softmax(attn_score, dim=2) attn_prob = self.dropatt(attn_prob) attn_vec = torch.bmm(attn_prob, v) attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head) attn_vec = attn_vec.permute(1, 2, 0, 3).contiguous().view(inp.size( 0), inp.size(1), n_head * d_head) attn_out = self.o_net(attn_vec) attn_out = self.drop(attn_out) if self.pre_lnorm: output = residual + attn_out else: output = self.layer_norm(residual + attn_out) return output def forward(self, input_0): primals_2 = self.qkv_net.weight primals_3 = self.qkv_net.bias primals_4 = self.o_net.weight primals_5 = self.layer_norm.weight primals_6 = self.layer_norm.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6]) return output[0]
Oreoluwa1234/NeMo
MultiHeadAttn
false
9,722
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
ConvRelu
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/tp/ctpxlsxoem6rgsh674lcc52x4mj5yx6dfytpfxjlwzaisddgy4ue.py # Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv2d => convolution # relu => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 1], [4, 2], [3, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 278784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x1 = (xindex // 2112) % 33 x0 = xindex % 2112 x3 = (xindex // 2112) tmp0 = tl.load(in_out_ptr0 + (x4), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x4), tmp4, xmask) tl.store(out_ptr0 + (x0 + (2176*x3)), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (33, 16, 3, 5), (240, 15, 5, 1)) assert_size_stride(primals_2, (33, ), (1, )) assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 1), padding=(4, 2), dilation=(3, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 33, 33, 64), (69696, 2112, 64, 1)) buf1 = buf0; del buf0 # reuse buf2 = empty_strided_cuda((4, 33, 33, 64), (71808, 2176, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_threshold_backward_0.run(buf1, primals_2, buf2, 278784, grid=grid(278784), stream=stream0) del primals_2 return (buf1, primals_1, primals_3, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((33, 16, 3, 5), (240, 15, 5, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((33, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 16, 64, 64), (65536, 4096, 64, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn import torch.nn.functional as F import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized class ConvRelu(nn.Module): def __init__(self): super(ConvRelu, self).__init__() self.conv = torch.nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding= (4, 2), dilation=(3, 1)) def forward(self, x): return F.relu(self.conv(x), inplace=True) def get_inputs(): return [torch.rand([4, 16, 64, 64])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn import torch.cuda import torch.backends.cudnn import torch.backends.mkl import torch.backends.cuda import torch.backends.quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 278784 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x1 = xindex // 2112 % 33 x0 = xindex % 2112 x3 = xindex // 2112 tmp0 = tl.load(in_out_ptr0 + x4, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x4, tmp4, xmask) tl.store(out_ptr0 + (x0 + 2176 * x3), tmp6, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (33, 16, 3, 5), (240, 15, 5, 1)) assert_size_stride(primals_2, (33,), (1,)) assert_size_stride(primals_3, (4, 16, 64, 64), (65536, 4096, 64, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 1), padding=(4, 2), dilation=(3, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 33, 33, 64), (69696, 2112, 64, 1)) buf1 = buf0 del buf0 buf2 = empty_strided_cuda((4, 33, 33, 64), (71808, 2176, 64, 1), torch.bool) get_raw_stream(0) triton_poi_fused_convolution_relu_threshold_backward_0[grid(278784)]( buf1, primals_2, buf2, 278784, XBLOCK=1024, num_warps=4, num_stages=1) del primals_2 return buf1, primals_1, primals_3, buf2 class ConvReluNew(nn.Module): def __init__(self): super(ConvReluNew, self).__init__() self.conv = torch.nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding= (4, 2), dilation=(3, 1)) def forward(self, input_0): primals_1 = self.conv.weight primals_2 = self.conv.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
XiaobingSuper/intel-extension-for-pytorch
ConvRelu
false
9,723
[ "Apache-2.0" ]
0
b61029be10e46e6d2e13b0e700c81f8e59164df0
https://github.com/XiaobingSuper/intel-extension-for-pytorch/tree/b61029be10e46e6d2e13b0e700c81f8e59164df0
AttentionBlock
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/cu/ccutvo2v4333pq6xhrg2zryqqwthm7dmmuqprvva2xdwiodpz5jn.py # Topologically Sorted Source Nodes: [q], Original ATen: [aten.convolution] # Source node to ATen node mapping: # q => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [0], [1], False, [0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 4) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/s2/cs2rk3o3kmhydx4oijp6rsdb5atcrq5axy4adadrpl7gkt7scies.py # Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax] # Source node to ATen node mapping: # p_attn => exp # Graph fragment: # %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 1), kwargs = {}) # %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {}) # %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {}) # %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.0), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = tmp14 * tmp1 tmp16 = tl_math.exp(tmp15) tl.store(out_ptr0 + (x2), tmp16, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/3f/c3fx6bzkalkw7u7askqdnz4rzlcoyqiec4r434sjc5x3axxgkrmr.py # Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax] # Source node to ATen node mapping: # p_attn => div_1, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10 = args args.clear() assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_8, (4, ), (1, )) assert_size_stride(primals_9, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_10, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [q], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4), (16, 4, 1)) # Topologically Sorted Source Nodes: [k], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(primals_6, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4), (16, 4, 1)) # Topologically Sorted Source Nodes: [v], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(primals_6, primals_7, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4), (16, 4, 1)) buf3 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [q], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf3, primals_2, 64, grid=grid(64), stream=stream0) del primals_2 buf4 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [k], Original ATen: [aten.convolution] triton_poi_fused_convolution_0.run(buf4, primals_5, 64, grid=grid(64), stream=stream0) del primals_5 buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf5, buf6, 256, grid=grid(256), stream=stream0) buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5 # reuse # Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf6, buf7, 256, grid=grid(256), stream=stream0) del buf6 buf8 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [v], Original ATen: [aten.convolution] triton_poi_fused_convolution_0.run(buf8, primals_8, 64, grid=grid(64), stream=stream0) del primals_8 buf9 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf10, (4, 4, 4), (16, 4, 1)) buf11 = buf10; del buf10 # reuse # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] triton_poi_fused_convolution_0.run(buf11, primals_10, 64, grid=grid(64), stream=stream0) del primals_10 return (buf11, buf7, primals_1, primals_3, primals_4, primals_6, primals_7, primals_9, buf7, reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 1, 4), (4, 4, 1), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 4, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch import torch.cuda from torch.nn import functional as F from torch import nn import torch.distributed import torch.utils.data import torch.optim def convert_pad_shape(pad_shape): """ Used to get arguments for F.pad """ l = pad_shape[::-1] pad_shape = [item for sublist in l for item in sublist] return pad_shape class AttentionBlock(nn.Module): def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.0, block_length=None, proximal_bias= False, proximal_init=False): super().__init__() assert channels % n_heads == 0 self.channels = channels self.out_channels = out_channels self.n_heads = n_heads self.window_size = window_size self.heads_share = heads_share self.block_length = block_length self.proximal_bias = proximal_bias self.p_dropout = p_dropout self.attn = None self.k_channels = channels // n_heads self.conv_q = nn.Conv1d(channels, channels, 1) self.conv_k = nn.Conv1d(channels, channels, 1) self.conv_v = nn.Conv1d(channels, channels, 1) if window_size is not None: n_heads_rel = 1 if heads_share else n_heads rel_stddev = self.k_channels ** -0.5 self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.conv_o = nn.Conv1d(channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) nn.init.xavier_uniform_(self.conv_q.weight) nn.init.xavier_uniform_(self.conv_k.weight) if proximal_init: self.conv_k.weight.data.copy_(self.conv_q.weight.data) self.conv_k.bias.data.copy_(self.conv_q.bias.data) nn.init.xavier_uniform_(self.conv_v.weight) def forward(self, x, c, attn_mask=None): q = self.conv_q(x) k = self.conv_k(c) v = self.conv_v(c) x, self.attn = self.attention(q, k, v, mask=attn_mask) x = self.conv_o(x) return x def attention(self, query, key, value, mask=None): b, d, t_s, t_t = key.size(0), key.size(1), key.size(2), query.size(2) query = query.view(b, self.n_heads, self.k_channels, t_t).transpose( 2, 3) key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) value = value.view(b, self.n_heads, self.k_channels, t_s).transpose( 2, 3) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self .k_channels) if self.window_size is not None: assert t_s == t_t, 'Relative attention is only available for self-attention.' key_relative_embeddings = self._get_relative_embeddings(self. emb_rel_k, t_s) rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) rel_logits = self._relative_position_to_absolute_position( rel_logits) scores_local = rel_logits / math.sqrt(self.k_channels) scores = scores + scores_local if self.proximal_bias: assert t_s == t_t, 'Proximal bias is only available for self-attention.' scores = scores + self._attention_bias_proximal(t_s) if mask is not None: scores = scores.masked_fill(mask == 0, -10000.0) if self.block_length is not None: block_mask = torch.ones_like(scores).triu(-self.block_length ).tril(self.block_length) scores = scores * block_mask + -10000.0 * (1 - block_mask) p_attn = F.softmax(scores, dim=-1) p_attn = self.drop(p_attn) output = torch.matmul(p_attn, value) if self.window_size is not None: relative_weights = self._absolute_position_to_relative_position( p_attn) value_relative_embeddings = self._get_relative_embeddings(self. emb_rel_v, t_s) output = output + self._matmul_with_relative_values( relative_weights, value_relative_embeddings) output = output.transpose(2, 3).contiguous().view(b, d, t_t) return output, p_attn def _matmul_with_relative_values(self, x, y): """ x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d] """ ret = torch.matmul(x, y.unsqueeze(0)) return ret def _matmul_with_relative_keys(self, x, y): """ x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m] """ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) return ret def _get_relative_embeddings(self, relative_embeddings, length): pad_length = max(length - (self.window_size + 1), 0) slice_start_position = max(self.window_size + 1 - length, 0) slice_end_position = slice_start_position + 2 * length - 1 if pad_length > 0: padded_relative_embeddings = F.pad(relative_embeddings, convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) else: padded_relative_embeddings = relative_embeddings used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] return used_relative_embeddings def _relative_position_to_absolute_position(self, x): """ x: [b, h, l, 2*l-1] ret: [b, h, l, l] """ batch, heads, length, _ = x.size() x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) x_flat = x.view([batch, heads, length * 2 * length]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] return x_final def _absolute_position_to_relative_position(self, x): """ x: [b, h, l, l] ret: [b, h, l, 2*l-1] """ batch, heads, length, _ = x.size() x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]) ) x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] return x_final def _attention_bias_proximal(self, length): """Bias for self-attention to encourage attention to close positions. Args: length: an integer scalar. Returns: a Tensor with shape [1, 1, length, length] """ r = torch.arange(length, dtype=torch.float32) diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff) ), 0), 0) def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'channels': 4, 'out_channels': 4, 'n_heads': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import math import torch.cuda from torch.nn import functional as F from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 4 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = tmp14 * tmp1 tmp16 = tl_math.exp(tmp15) tl.store(out_ptr0 + x2, tmp16, xmask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10) = args args.clear() assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_8, (4,), (1,)) assert_size_stride(primals_9, (4, 4, 1), (4, 1, 1)) assert_size_stride(primals_10, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=( 0,), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4), (16, 4, 1)) buf1 = extern_kernels.convolution(primals_6, primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=( 0,), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4), (16, 4, 1)) buf2 = extern_kernels.convolution(primals_6, primals_7, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=( 0,), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4), (16, 4, 1)) buf3 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(64)](buf3, primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_2 buf4 = buf1 del buf1 triton_poi_fused_convolution_0[grid(64)](buf4, primals_5, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_5 buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__softmax_1[grid(256)](buf5, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1) buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf5 triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf6 buf8 = buf2 del buf2 triton_poi_fused_convolution_0[grid(64)](buf8, primals_8, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_8 buf9 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9) buf10 = extern_kernels.convolution(reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), primals_9, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf10, (4, 4, 4), (16, 4, 1)) buf11 = buf10 del buf10 triton_poi_fused_convolution_0[grid(64)](buf11, primals_10, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_10 return (buf11, buf7, primals_1, primals_3, primals_4, primals_6, primals_7, primals_9, buf7, reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 1, 4), (4, 4, 1), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 4, 1), 0), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)) def convert_pad_shape(pad_shape): """ Used to get arguments for F.pad """ l = pad_shape[::-1] pad_shape = [item for sublist in l for item in sublist] return pad_shape class AttentionBlockNew(nn.Module): def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.0, block_length=None, proximal_bias= False, proximal_init=False): super().__init__() assert channels % n_heads == 0 self.channels = channels self.out_channels = out_channels self.n_heads = n_heads self.window_size = window_size self.heads_share = heads_share self.block_length = block_length self.proximal_bias = proximal_bias self.p_dropout = p_dropout self.attn = None self.k_channels = channels // n_heads self.conv_q = nn.Conv1d(channels, channels, 1) self.conv_k = nn.Conv1d(channels, channels, 1) self.conv_v = nn.Conv1d(channels, channels, 1) if window_size is not None: n_heads_rel = 1 if heads_share else n_heads rel_stddev = self.k_channels ** -0.5 self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.conv_o = nn.Conv1d(channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) nn.init.xavier_uniform_(self.conv_q.weight) nn.init.xavier_uniform_(self.conv_k.weight) if proximal_init: self.conv_k.weight.data.copy_(self.conv_q.weight.data) self.conv_k.bias.data.copy_(self.conv_q.bias.data) nn.init.xavier_uniform_(self.conv_v.weight) def attention(self, query, key, value, mask=None): b, d, t_s, t_t = key.size(0), key.size(1), key.size(2), query.size(2) query = query.view(b, self.n_heads, self.k_channels, t_t).transpose( 2, 3) key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) value = value.view(b, self.n_heads, self.k_channels, t_s).transpose( 2, 3) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self .k_channels) if self.window_size is not None: assert t_s == t_t, 'Relative attention is only available for self-attention.' key_relative_embeddings = self._get_relative_embeddings(self. emb_rel_k, t_s) rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) rel_logits = self._relative_position_to_absolute_position( rel_logits) scores_local = rel_logits / math.sqrt(self.k_channels) scores = scores + scores_local if self.proximal_bias: assert t_s == t_t, 'Proximal bias is only available for self-attention.' scores = scores + self._attention_bias_proximal(t_s) if mask is not None: scores = scores.masked_fill(mask == 0, -10000.0) if self.block_length is not None: block_mask = torch.ones_like(scores).triu(-self.block_length ).tril(self.block_length) scores = scores * block_mask + -10000.0 * (1 - block_mask) p_attn = F.softmax(scores, dim=-1) p_attn = self.drop(p_attn) output = torch.matmul(p_attn, value) if self.window_size is not None: relative_weights = self._absolute_position_to_relative_position( p_attn) value_relative_embeddings = self._get_relative_embeddings(self. emb_rel_v, t_s) output = output + self._matmul_with_relative_values( relative_weights, value_relative_embeddings) output = output.transpose(2, 3).contiguous().view(b, d, t_t) return output, p_attn def _matmul_with_relative_values(self, x, y): """ x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d] """ ret = torch.matmul(x, y.unsqueeze(0)) return ret def _matmul_with_relative_keys(self, x, y): """ x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m] """ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) return ret def _get_relative_embeddings(self, relative_embeddings, length): pad_length = max(length - (self.window_size + 1), 0) slice_start_position = max(self.window_size + 1 - length, 0) slice_end_position = slice_start_position + 2 * length - 1 if pad_length > 0: padded_relative_embeddings = F.pad(relative_embeddings, convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])) else: padded_relative_embeddings = relative_embeddings used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position] return used_relative_embeddings def _relative_position_to_absolute_position(self, x): """ x: [b, h, l, 2*l-1] ret: [b, h, l, l] """ batch, heads, length, _ = x.size() x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) x_flat = x.view([batch, heads, length * 2 * length]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:] return x_final def _absolute_position_to_relative_position(self, x): """ x: [b, h, l, l] ret: [b, h, l, 2*l-1] """ batch, heads, length, _ = x.size() x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]) ) x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] return x_final def _attention_bias_proximal(self, length): """Bias for self-attention to encourage attention to close positions. Args: length: an integer scalar. Returns: a Tensor with shape [1, 1, length, length] """ r = torch.arange(length, dtype=torch.float32) diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff) ), 0), 0) def forward(self, input_0, input_1): primals_1 = self.conv_q.weight primals_2 = self.conv_q.bias primals_4 = self.conv_k.weight primals_5 = self.conv_k.bias primals_7 = self.conv_v.weight primals_8 = self.conv_v.bias primals_9 = self.conv_o.weight primals_10 = self.conv_o.bias primals_3 = input_0 primals_6 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10]) return output[0]
Oreoluwa1234/NeMo
AttentionBlock
false
9,724
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
InvConvNear
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/eu/ceuywbmhtjrjbdgiqzjvxg6kppccq4gu554vmz5lt2nvjjmly3vh.py # Topologically Sorted Source Nodes: [logdet], Original ATen: [aten.eq] # Source node to ATen node mapping: # logdet => eq # Graph fragment: # %eq : [num_users=2] = call_function[target=torch.ops.aten.eq.Scalar](args = (%getitem, -1.0), kwargs = {}) triton_poi_fused_eq_0 = async_compile.triton('triton_poi_fused_eq_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*i1', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) tmp0 = tl.load(in_ptr0 + (0)) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = -1.0 tmp3 = tmp1 == tmp2 tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp3, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/k6/ck6rcj2aiwjvx7ctsrajcnweqtvvjefeww6f2svf4tye2ixq4d65.py # Topologically Sorted Source Nodes: [x_len, logdet, mul_1, logdet_1], Original ATen: [aten.mul, aten.scalar_tensor, aten.where] # Source node to ATen node mapping: # logdet => full_default_1, where # logdet_1 => mul_2 # mul_1 => mul_1 # x_len => full_default # Graph fragment: # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4], 4.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default_1, %getitem_1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, 1.0), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %full_default), kwargs = {}) triton_poi_fused_mul_scalar_tensor_where_1 = async_compile.triton('triton_poi_fused_mul_scalar_tensor_where_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4], filename=__file__, triton_meta={'signature': {0: '*i1', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_scalar_tensor_where_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_scalar_tensor_where_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (0)).to(tl.int1) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = tl.load(in_ptr1 + (0)) tmp3 = tl.broadcast_to(tmp2, [XBLOCK]) tmp4 = float("nan") tmp5 = tl.where(tmp1, tmp4, tmp3) tmp6 = 1.0 tmp7 = tmp5 * tmp6 tmp8 = 4.0 tmp9 = tmp7 * tmp8 tl.store(out_ptr0 + (x0), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/y5/cy55qfc456xtrn5xgyv4h2r4osqtkteize5kc2wocd7ecb52e6fh.py # Topologically Sorted Source Nodes: [z], Original ATen: [aten.convolution] # Source node to ATen node mapping: # z => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_1, %view_2, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 4 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x1 = xindex y0 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x1)), xmask & ymask) tl.store(out_ptr0 + (x1 + (4*y0)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/6q/c6qeh4gh27f25datiqvo65rhtoulr47ssh6rvmydde7ujxxgpvwu.py # Topologically Sorted Source Nodes: [z_2], Original ATen: [aten.mul] # Source node to ATen node mapping: # z_2 => mul_3 # Graph fragment: # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_4, 1), kwargs = {}) triton_poi_fused_mul_3 = async_compile.triton('triton_poi_fused_mul_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_3(in_out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tl.store(in_out_ptr0 + (x0), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (1, 4)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [logdet], Original ATen: [aten._linalg_slogdet] buf0 = torch.ops.aten._linalg_slogdet.default(primals_2) buf1 = buf0[0] buf2 = buf0[1] buf3 = buf0[2] buf4 = buf0[3] del buf0 buf5 = empty_strided_cuda((), (), torch.bool) # Topologically Sorted Source Nodes: [logdet], Original ATen: [aten.eq] stream0 = get_raw_stream(0) triton_poi_fused_eq_0.run(buf1, buf5, 1, grid=grid(1), stream=stream0) del buf1 buf6 = empty_strided_cuda((4, ), (1, ), torch.float32) # Topologically Sorted Source Nodes: [x_len, logdet, mul_1, logdet_1], Original ATen: [aten.mul, aten.scalar_tensor, aten.where] triton_poi_fused_mul_scalar_tensor_where_1.run(buf5, buf2, buf6, 4, grid=grid(4), stream=stream0) del buf2 buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [z], Original ATen: [aten.convolution] triton_poi_fused_convolution_2.run(primals_2, buf7, 4, 4, grid=grid(4, 4), stream=stream0) # Topologically Sorted Source Nodes: [z], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 4, 1, 4), (16, 4, 4, 1), 0), buf7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 4, 1, 4), (16, 4, 4, 1)) del buf7 buf9 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0); del buf8 # reuse # Topologically Sorted Source Nodes: [z_2], Original ATen: [aten.mul] triton_poi_fused_mul_3.run(buf9, 64, grid=grid(64), stream=stream0) return (buf9, buf6, reinterpret_tensor(primals_1, (4, 4, 1, 4), (16, 4, 8, 1), 0), buf3, buf4, buf5, reinterpret_tensor(primals_2, (4, 4, 1, 1), (1, 4, 4, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (1, 4), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.cuda from torch.nn import functional as F from torch import nn import torch.distributed import torch.utils.data import torch.optim class InvConvNear(nn.Module): def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs): super().__init__() assert n_split % 2 == 0 self.channels = channels self.n_split = n_split self.no_jacobian = no_jacobian w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split). normal_())[0] if torch.det(w_init) < 0: w_init[:, 0] = -1 * w_init[:, 0] self.weight = nn.Parameter(w_init) def forward(self, x, x_mask=None, reverse=False, **kwargs): b, c, t = x.size() assert c % self.n_split == 0 if x_mask is None: x_mask = 1 x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t else: x_len = torch.sum(x_mask, [1, 2]) x = x.view(b, 2, c // self.n_split, self.n_split // 2, t) x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t) if reverse: if hasattr(self, 'weight_inv'): weight = self.weight_inv else: weight = torch.inverse(self.weight.float()) logdet = None else: weight = self.weight if self.no_jacobian: logdet = 0 else: logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len weight = weight.view(self.n_split, self.n_split, 1, 1) z = F.conv2d(x, weight) z = z.view(b, 2, self.n_split // 2, c // self.n_split, t) z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask return z, logdet def store_inverse(self): self.weight_inv = torch.inverse(self.weight.float()) def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.cuda from torch import nn import torch.distributed import torch.utils.data import torch.optim assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_eq_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) tmp0 = tl.load(in_ptr0 + 0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = -1.0 tmp3 = tmp1 == tmp2 tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp3, None) @triton.jit def triton_poi_fused_mul_scalar_tensor_where_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 4 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 0).to(tl.int1) tmp1 = tl.broadcast_to(tmp0, [XBLOCK]) tmp2 = tl.load(in_ptr1 + 0) tmp3 = tl.broadcast_to(tmp2, [XBLOCK]) tmp4 = float('nan') tmp5 = tl.where(tmp1, tmp4, tmp3) tmp6 = 1.0 tmp7 = tmp5 * tmp6 tmp8 = 4.0 tmp9 = tmp7 * tmp8 tl.store(out_ptr0 + x0, tmp9, xmask) @triton.jit def triton_poi_fused_convolution_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 4 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x1 = xindex y0 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask) tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_mul_3(in_out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tl.store(in_out_ptr0 + x0, tmp2, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (1, 4)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = torch.ops.aten._linalg_slogdet.default(primals_2) buf1 = buf0[0] buf2 = buf0[1] buf3 = buf0[2] buf4 = buf0[3] del buf0 buf5 = empty_strided_cuda((), (), torch.bool) get_raw_stream(0) triton_poi_fused_eq_0[grid(1)](buf1, buf5, 1, XBLOCK=1, num_warps=1, num_stages=1) del buf1 buf6 = empty_strided_cuda((4,), (1,), torch.float32) triton_poi_fused_mul_scalar_tensor_where_1[grid(4)](buf5, buf2, buf6, 4, XBLOCK=4, num_warps=1, num_stages=1) del buf2 buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32) triton_poi_fused_convolution_2[grid(4, 4)](primals_2, buf7, 4, 4, XBLOCK=4, YBLOCK=4, num_warps=1, num_stages=1) buf8 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 4, 1, 4), (16, 4, 4, 1), 0), buf7, stride=(1, 1), padding=(0, 0 ), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 4, 1, 4), (16, 4, 4, 1)) del buf7 buf9 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0) del buf8 triton_poi_fused_mul_3[grid(64)](buf9, 64, XBLOCK=64, num_warps=1, num_stages=1) return buf9, buf6, reinterpret_tensor(primals_1, (4, 4, 1, 4), (16, 4, 8, 1), 0), buf3, buf4, buf5, reinterpret_tensor(primals_2, (4, 4, 1, 1), (1, 4, 4, 4), 0) class InvConvNearNew(nn.Module): def __init__(self, channels, n_split=4, no_jacobian=False, **kwargs): super().__init__() assert n_split % 2 == 0 self.channels = channels self.n_split = n_split self.no_jacobian = no_jacobian w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split). normal_())[0] if torch.det(w_init) < 0: w_init[:, 0] = -1 * w_init[:, 0] self.weight = nn.Parameter(w_init) def store_inverse(self): self.weight_inv = torch.inverse(self.weight.float()) def forward(self, input_0): primals_2 = self.weight primals_1 = input_0 output = call([primals_1, primals_2]) return output[0], output[1]
Oreoluwa1234/NeMo
InvConvNear
false
9,725
[ "Apache-2.0" ]
0
b01e3ceed34efe31fd43866685dbdd19a6b30928
https://github.com/Oreoluwa1234/NeMo/tree/b01e3ceed34efe31fd43866685dbdd19a6b30928
GeLU
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/6s/c6shmuvjmq6zc4ifvdsynorwri47ra63qxa7jg3e7p6lw6xlqj5q.py # Topologically Sorted Source Nodes: [mul, truediv, erf, add, mul_1], Original ATen: [aten.mul, aten.div, aten.erf, aten.add] # Source node to ATen node mapping: # add => add # erf => erf # mul => mul # mul_1 => mul_1 # truediv => div # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.5), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, 1.4142135623730951), kwargs = {}) # %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%div,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1.0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {}) triton_poi_fused_add_div_erf_mul_0 = async_compile.triton('triton_poi_fused_add_div_erf_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_erf_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_erf_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865475 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul, truediv, erf, add, mul_1], Original ATen: [aten.mul, aten.div, aten.erf, aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_div_erf_mul_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch import torch.nn as nn def gelu(x): """Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see https://arxiv.org/abs/1606.08415 """ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) class GeLU(nn.Module): """Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see https://arxiv.org/abs/1606.08415 """ def __init__(self): super().__init__() def forward(self, x): return gelu(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_div_erf_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865475 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_div_erf_mul_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, def gelu(x): """Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see https://arxiv.org/abs/1606.08415 """ return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) class GeLUNew(nn.Module): """Implementation of the gelu activation function. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) Also see https://arxiv.org/abs/1606.08415 """ def __init__(self): super().__init__() def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
aditya10/vilbert-multi-task
GeLU
false
9,726
[ "MIT" ]
0
dda8c16187ac6cc4f6266a823fbde528f65af720
https://github.com/aditya10/vilbert-multi-task/tree/dda8c16187ac6cc4f6266a823fbde528f65af720
DiceLoss
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/pk/cpkvymr7hwn3s3mgweosxbayku2z6ah5aqss3iqwxhdocv5gzqwm.py # Topologically Sorted Source Nodes: [x, t, i, mul_1, add_1, t_1, u, add_2, dc, mean, dc_1], Original ATen: [aten.sigmoid, aten.mul, aten.sum, aten.add, aten.div, aten.mean, aten.rsub] # Source node to ATen node mapping: # add_1 => add_1 # add_2 => add_2 # dc => div # dc_1 => sub # i => sum_1 # mean => mean # mul_1 => mul_1 # t => mul # t_1 => add # u => sum_2 # x => sigmoid # Graph fragment: # %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %arg1_1), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sigmoid, %arg1_1), kwargs = {}) # %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%add,), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 1), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, %add_2), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%div,), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %mean), kwargs = {}) triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp2 = tl.load(in_ptr1 + (r0), None) tmp1 = tl.sigmoid(tmp0) tmp3 = tmp1 * tmp2 tmp4 = tl.broadcast_to(tmp3, [RBLOCK]) tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0)) tmp7 = tmp1 + tmp2 tmp8 = tl.broadcast_to(tmp7, [RBLOCK]) tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0)) tmp11 = 2.0 tmp12 = tmp6 * tmp11 tmp13 = 1.0 tmp14 = tmp12 + tmp13 tmp15 = tmp10 + tmp13 tmp16 = tmp14 / tmp15 tmp17 = tmp16 / tmp13 tmp18 = tmp13 - tmp17 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp18, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf2 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [x, t, i, mul_1, add_1, t_1, u, add_2, dc, mean, dc_1], Original ATen: [aten.sigmoid, aten.mul, aten.sum, aten.add, aten.div, aten.mean, aten.rsub] stream0 = get_raw_stream(0) triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0.run(buf2, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class DiceLoss(nn.Module): def __init__(self, image=False): super().__init__() self.image = image def forward(self, x, y): x = x.sigmoid() i, u = [(t.flatten(1).sum(1) if self.image else t.sum()) for t in [ x * y, x + y]] dc = (2 * i + 1) / (u + 1) dc = 1 - dc.mean() return dc def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp2 = tl.load(in_ptr1 + r0, None) tmp1 = tl.sigmoid(tmp0) tmp3 = tmp1 * tmp2 tmp4 = tl.broadcast_to(tmp3, [RBLOCK]) tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0)) tmp7 = tmp1 + tmp2 tmp8 = tl.broadcast_to(tmp7, [RBLOCK]) tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0)) tmp11 = 2.0 tmp12 = tmp6 * tmp11 tmp13 = 1.0 tmp14 = tmp12 + tmp13 tmp15 = tmp10 + tmp13 tmp16 = tmp14 / tmp15 tmp17 = tmp16 / tmp13 tmp18 = tmp13 - tmp17 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf2 = buf0 del buf0 get_raw_stream(0) triton_per_fused_add_div_mean_mul_rsub_sigmoid_sum_0[grid(1)](buf2, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf2, class DiceLossNew(nn.Module): def __init__(self, image=False): super().__init__() self.image = image def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
agrawalshubham01/FracNet
DiceLoss
false
9,727
[ "Apache-2.0" ]
0
8b912ca65651ff0ee203d9d73cf6ca18539728ac
https://github.com/agrawalshubham01/FracNet/tree/8b912ca65651ff0ee203d9d73cf6ca18539728ac
DQN
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/5y/c5yq7wkgmmcygrawripwacy566sggsmh2mzk5izw35wk7ferohhu.py # Topologically Sorted Source Nodes: [state], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # state => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[8192], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 6400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x0 = xindex % 100 x2 = xindex % 1600 x3 = (xindex // 1600) tmp0 = tl.load(in_out_ptr0 + (x4), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x4), tmp4, xmask) tl.store(out_ptr0 + (x2 + (1664*x3)), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (100, 4), (4, 1)) assert_size_stride(primals_2, (100, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 100), (100, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 100), (100, 1)) assert_size_stride(primals_7, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 100), (100, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 100), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 100), (1600, 400, 100, 1), 0); del buf0 # reuse buf4 = empty_strided_cuda((4, 4, 4, 100), (1664, 400, 100, 1), torch.bool) # Topologically Sorted Source Nodes: [state], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf4, 6400, grid=grid(6400), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 100), (100, 1), 0), reinterpret_tensor(primals_4, (100, 4), (1, 100), 0), alpha=1, beta=1, out=buf2) del primals_5 buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 100), (100, 1), 0), reinterpret_tensor(primals_6, (100, 4), (1, 100), 0), alpha=1, beta=1, out=buf3) del primals_7 return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 100), (100, 1), 0), primals_6, primals_4, buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((100, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((100, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 100), (100, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 100), (100, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn.functional as F import torch.nn as nn class DQN(nn.Module): """A simple deep Q network implementation. Computes Q values for each (action, object) tuple given an input state vector """ def __init__(self, state_dim, action_dim, object_dim, hidden_size=100): super(DQN, self).__init__() self.state_encoder = nn.Linear(state_dim, hidden_size) self.state2action = nn.Linear(hidden_size, action_dim) self.state2object = nn.Linear(hidden_size, object_dim) def forward(self, x): state = F.relu(self.state_encoder(x)) return self.state2action(state), self.state2object(state) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'state_dim': 4, 'action_dim': 4, 'object_dim': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 6400 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex x0 = xindex % 100 x2 = xindex % 1600 x3 = xindex // 1600 tmp0 = tl.load(in_out_ptr0 + x4, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x4, tmp4, xmask) tl.store(out_ptr0 + (x2 + 1664 * x3), tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (100, 4), (4, 1)) assert_size_stride(primals_2, (100,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 100), (100, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 100), (100, 1)) assert_size_stride(primals_7, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 100), (100, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 100), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 100), (1600, 400, 100, 1), 0) del buf0 buf4 = empty_strided_cuda((4, 4, 4, 100), (1664, 400, 100, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(6400)](buf1, primals_2, buf4, 6400, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 100), (100, 1), 0), reinterpret_tensor(primals_4, (100, 4), (1, 100), 0), alpha=1, beta=1, out=buf2) del primals_5 buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_7, reinterpret_tensor(buf1, (64, 100), (100, 1), 0), reinterpret_tensor(primals_6, (100, 4), (1, 100), 0), alpha=1, beta=1, out=buf3) del primals_7 return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 100), (100, 1), 0 ), primals_6, primals_4, buf4 class DQNNew(nn.Module): """A simple deep Q network implementation. Computes Q values for each (action, object) tuple given an input state vector """ def __init__(self, state_dim, action_dim, object_dim, hidden_size=100): super(DQNNew, self).__init__() self.state_encoder = nn.Linear(state_dim, hidden_size) self.state2action = nn.Linear(hidden_size, action_dim) self.state2object = nn.Linear(hidden_size, object_dim) def forward(self, input_0): primals_1 = self.state_encoder.weight primals_2 = self.state_encoder.bias primals_4 = self.state2action.weight primals_5 = self.state2action.bias primals_6 = self.state2object.weight primals_7 = self.state2object.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0], output[1]
arifmujib/MIT-Machine-Learning-Projects
DQN
false
9,728
[ "MIT" ]
0
445f2dddf4441bf8248166e6eb15a0716444ab21
https://github.com/arifmujib/MIT-Machine-Learning-Projects/tree/445f2dddf4441bf8248166e6eb15a0716444ab21
LblLoss
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/5y/c5y6ehc3r2lykwon2oekdgwd4xcofhs7d7mt3espstmhrqzmt7nb.py # Topologically Sorted Source Nodes: [sub, dis, wgt, setitem, mul, mean], Original ATen: [aten.sub, aten.pow, aten.ones_like, aten.lift_fresh, aten.index_put, aten.mul, aten.mean] # Source node to ATen node mapping: # dis => pow_1 # mean => mean # mul => mul # setitem => full_default_1, index_put # sub => sub # wgt => full_default # Graph fragment: # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 100.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False}) # %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%full_default, [%gt], %full_default_1), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %index_put), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul,), kwargs = {}) triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0 = async_compile.triton('triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp6 = tl.load(in_ptr1 + (r0), None) tmp1 = 0.0 tmp2 = tmp0 > tmp1 tmp3 = 100.0 tmp4 = 1.0 tmp5 = tl.where(tmp2, tmp3, tmp4) tmp7 = tmp6 - tmp0 tmp8 = tmp7 * tmp7 tmp9 = tmp8 * tmp5 tmp10 = tl.broadcast_to(tmp9, [RBLOCK]) tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0)) tmp13 = 256.0 tmp14 = tmp12 / tmp13 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp14, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf1 = empty_strided_cuda((), (), torch.float32) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [sub, dis, wgt, setitem, mul, mean], Original ATen: [aten.sub, aten.pow, aten.ones_like, aten.lift_fresh, aten.index_put, aten.mul, aten.mean] stream0 = get_raw_stream(0) triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0.run(buf2, arg1_1, arg0_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn from torchvision.models import * class LblLoss(nn.Module): def __init__(self): super().__init__() def forward(self, pred_batch, true_batch): wgt = torch.ones_like(pred_batch) wgt[true_batch > 0] = 100 dis = (pred_batch - true_batch) ** 2 return (dis * wgt).mean() def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch import nn from torchvision.models import * assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0( in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp6 = tl.load(in_ptr1 + r0, None) tmp1 = 0.0 tmp2 = tmp0 > tmp1 tmp3 = 100.0 tmp4 = 1.0 tmp5 = tl.where(tmp2, tmp3, tmp4) tmp7 = tmp6 - tmp0 tmp8 = tmp7 * tmp7 tmp9 = tmp8 * tmp5 tmp10 = tl.broadcast_to(tmp9, [RBLOCK]) tmp12 = triton_helpers.promote_to_tensor(tl.sum(tmp10, 0)) tmp13 = 256.0 tmp14 = tmp12 / tmp13 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp14, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf1 = empty_strided_cuda((), (), torch.float32) buf2 = buf1 del buf1 get_raw_stream(0) triton_per_fused_index_put_lift_fresh_mean_mul_ones_like_pow_sub_0[grid (1)](buf2, arg1_1, arg0_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf2, class LblLossNew(nn.Module): def __init__(self): super().__init__() def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
amoshyc/human-pose-estimation
LblLoss
false
9,729
[ "Apache-2.0" ]
0
8fd2962caee43b979f44637441d88d80f2ea951e
https://github.com/amoshyc/human-pose-estimation/tree/8fd2962caee43b979f44637441d88d80f2ea951e
Net
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ps/cpsdiousc6yiecsvi65sn5soumm3zhxn42wltdaifs7qz4nyhb4r.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 12000 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 3000 x1 = (xindex // 3000) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(out_ptr0 + (x0 + (3008*x1)), tmp2, xmask) tl.store(out_ptr1 + (x0 + (3072*x1)), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/xn/cxne5c6zn7w6kxaga7mqpdvaetuk3ckrwvl6awyuutq56onj2vfh.py # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.view] # Source node to ATen node mapping: # x_1 => relu # x_2 => view # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %view : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%relu, [-1, 100]), kwargs = {}) triton_poi_fused_relu_view_1 = async_compile.triton('triton_poi_fused_relu_view_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_view_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 12000 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 100 x1 = (xindex // 100) x2 = xindex tmp0 = tl.load(in_ptr0 + ((25*(((x0 + (100*x1)) // 25) % 120)) + (3008*((x0 + (100*x1)) // 3000)) + (x0 % 25)), xmask) tl.store(out_ptr0 + (x2), tmp0, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 1, 3, 8), (24, 24, 8, 1)) assert_size_stride(primals_2, (4, 1, 32, 32), (1024, 1024, 32, 1)) assert_size_stride(primals_3, (1, 100), (100, 1)) assert_size_stride(primals_4, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 30, 25), (3000, 750, 25, 1)) buf1 = empty_strided_cuda((4, 4, 30, 25), (3008, 750, 25, 1), torch.float32) buf5 = empty_strided_cuda((4, 4, 30, 25), (3072, 750, 25, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf0, buf1, buf5, 12000, grid=grid(12000), stream=stream0) buf2 = reinterpret_tensor(buf0, (120, 100), (100, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [x_1, x_2], Original ATen: [aten.relu, aten.view] triton_poi_fused_relu_view_1.run(buf1, buf2, 12000, grid=grid(12000), stream=stream0) del buf1 buf4 = empty_strided_cuda((120, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.addmm] extern_kernels.addmm(primals_4, buf2, reinterpret_tensor(primals_3, (100, 1), (1, 100), 0), alpha=1, beta=1, out=buf4) del primals_4 return (buf4, primals_1, primals_2, buf2, primals_3, buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 1, 3, 8), (24, 24, 8, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 1, 32, 32), (1024, 1024, 32, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, 100), (100, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 4, (3, 8), bias=False, stride=1) self.fc1 = nn.Linear(25 * 4, 1) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = x.view(-1, 25 * 4) x = self.fc1(x) return x def get_inputs(): return [torch.rand([4, 1, 32, 32])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 12000 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 3000 x1 = xindex // 3000 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp3 = 0.0 tmp4 = tmp2 <= tmp3 tl.store(out_ptr0 + (x0 + 3008 * x1), tmp2, xmask) tl.store(out_ptr1 + (x0 + 3072 * x1), tmp4, xmask) @triton.jit def triton_poi_fused_relu_view_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 12000 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 100 x1 = xindex // 100 x2 = xindex tmp0 = tl.load(in_ptr0 + (25 * ((x0 + 100 * x1) // 25 % 120) + 3008 * ( (x0 + 100 * x1) // 3000) + x0 % 25), xmask) tl.store(out_ptr0 + x2, tmp0, xmask) def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 1, 3, 8), (24, 24, 8, 1)) assert_size_stride(primals_2, (4, 1, 32, 32), (1024, 1024, 32, 1)) assert_size_stride(primals_3, (1, 100), (100, 1)) assert_size_stride(primals_4, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_2, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 30, 25), (3000, 750, 25, 1)) buf1 = empty_strided_cuda((4, 4, 30, 25), (3008, 750, 25, 1), torch .float32) buf5 = empty_strided_cuda((4, 4, 30, 25), (3072, 750, 25, 1), torch .bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(12000)](buf0, buf1, buf5, 12000, XBLOCK=256, num_warps=4, num_stages=1) buf2 = reinterpret_tensor(buf0, (120, 100), (100, 1), 0) del buf0 triton_poi_fused_relu_view_1[grid(12000)](buf1, buf2, 12000, XBLOCK =128, num_warps=4, num_stages=1) del buf1 buf4 = empty_strided_cuda((120, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_4, buf2, reinterpret_tensor(primals_3, (100, 1), (1, 100), 0), alpha=1, beta=1, out=buf4) del primals_4 return buf4, primals_1, primals_2, buf2, primals_3, buf5 class NetNew(nn.Module): def __init__(self): super(NetNew, self).__init__() self.conv1 = nn.Conv2d(1, 4, (3, 8), bias=False, stride=1) self.fc1 = nn.Linear(25 * 4, 1) def forward(self, input_0): primals_1 = self.conv1.weight primals_3 = self.fc1.weight primals_4 = self.fc1.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3, primals_4]) return output[0]
aoreskovic/TimeSeriesWithXNOR-Net
Net
false
9,730
[ "Apache-2.0" ]
0
5124b6c4ec19e657b49c370936efbd8adff4e60f
https://github.com/aoreskovic/TimeSeriesWithXNOR-Net/tree/5124b6c4ec19e657b49c370936efbd8adff4e60f
MultiHeadAttention
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/se/csevsploqhlaafqt4umuu5feml3ses7qj2jkxsyoy7kfy5rlinsl.py # Topologically Sorted Source Nodes: [scores_2], Original ATen: [aten._softmax] # Source node to ATen node mapping: # scores_2 => amax, clone, exp, sub, sum_1 # Graph fragment: # %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%view_11,), kwargs = {memory_format: torch.contiguous_format}) # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = (xindex // 16) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr1 + (x0 + (16*x2)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last') tmp13 = tl.load(in_ptr1 + (12 + x0 + (16*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 0.5 tmp4 = tmp2 * tmp3 tmp6 = tmp0 * tmp5 tmp7 = tmp6 * tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 * tmp9 tmp11 = tmp10 * tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 * tmp13 tmp15 = tmp14 * tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tmp17 = tmp4 - tmp16 tmp18 = tl_math.exp(tmp17) tmp19 = tmp7 - tmp16 tmp20 = tl_math.exp(tmp19) tmp21 = tmp18 + tmp20 tmp22 = tmp11 - tmp16 tmp23 = tl_math.exp(tmp22) tmp24 = tmp21 + tmp23 tmp25 = tmp15 - tmp16 tmp26 = tl_math.exp(tmp25) tmp27 = tmp24 + tmp26 tl.store(out_ptr0 + (x3), tmp16, xmask) tl.store(out_ptr1 + (x3), tmp27, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/dv/cdvddpa3xpcvcmerzvthlyccsihjoyjqxp73clitp4k7k44dwkj6.py # Topologically Sorted Source Nodes: [scores_2], Original ATen: [aten._softmax] # Source node to ATen node mapping: # scores_2 => amax, clone, div_1, exp, sub, sum_1 # Graph fragment: # %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%view_11,), kwargs = {memory_format: torch.contiguous_format}) # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = (xindex // 4) x0 = xindex % 4 x1 = (xindex // 4) % 4 x3 = (xindex // 64) x2 = (xindex // 16) % 4 tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1 + (4*x0) + (16*x3)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + (x4), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + (x4), xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 0.5 tmp4 = tmp2 * tmp3 tmp6 = tmp4 - tmp5 tmp7 = tl_math.exp(tmp6) tmp9 = tmp7 / tmp8 tl.store(out_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7k/c7kkxqo5r65gqykuvge3exgf3trgxmm4raf7gypitw4ynuylbeao.py # Topologically Sorted Source Nodes: [attention], Original ATen: [aten.clone] # Source node to ATen node mapping: # attention => clone_2 # Graph fragment: # %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_8,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_2(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/xt/cxtkkmujo4ytg6ycpz5lk5livtstr63pg5nsf5ijewjbtrfrqx6k.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone] # Source node to ATen node mapping: # out => clone_3 # Graph fragment: # %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_17,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/q4/cq4lrbjfvbivmpg2zkxhkatw7yc2rqarfj625cpqjlxqgfutfyet.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.add] # Source node to ATen node mapping: # out => add # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_19, %primals_11), kwargs = {}) triton_poi_fused_add_4 = async_compile.triton('triton_poi_fused_add_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x2), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4, ), (1, )) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_4 del primals_5 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_6 del primals_7 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf2) del primals_8 buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32) # Topologically Sorted Source Nodes: [scores_2], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf0, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [scores_2], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf0, buf1, buf3, buf4, buf5, 256, grid=grid(256), stream=stream0) buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [attention], Original ATen: [aten.clone] triton_poi_fused_clone_2.run(buf2, primals_9, buf6, 16, 4, grid=grid(16, 4), stream=stream0) del primals_9 buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [attention], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone] triton_poi_fused_clone_3.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf9) buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0); del buf9 # reuse # Topologically Sorted Source Nodes: [out], Original ATen: [aten.add] triton_poi_fused_add_4.run(buf10, primals_11, 64, grid=grid(64), stream=stream0) del primals_11 return (buf10, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_10, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch import torch.nn as nn import torch.nn.functional as F class MultiHeadAttention(nn.Module): """Multi-headed Attention for input Query, Key, Value Multi-headed Attention is a module for attention mechanisms which runs through attention in several times in parallel, then the multiple outputs are concatenated and linearly transformed Args: embed_size (int): Max embedding size num_heads (int): Number of heads in multi-headed attention; Number of splits in the embedding size dropout (float, optional): Percentage of Dropout to be applied in range 0 <= dropout <=1 batch_dim (int, optional): The dimension in which batch dimensions is """ def __init__(self, embed_size: 'int', num_heads: 'int', dropout: 'float'=0.2, batch_dim: 'int'=0): super(MultiHeadAttention, self).__init__() self.embed_size = embed_size self.num_heads = num_heads self.dropout = dropout self.batch_dim = batch_dim self.dropout_layer = nn.Dropout(dropout) self.head_size = self.embed_size // self.num_heads assert self.head_size * self.num_heads == self.embed_size, 'Heads cannot split Embedding size equally' self.Q = nn.Linear(self.embed_size, self.embed_size) self.K = nn.Linear(self.embed_size, self.embed_size) self.V = nn.Linear(self.embed_size, self.embed_size) self.linear = nn.Linear(self.embed_size, self.embed_size) def forward(self, q, k, v, mask=None): q_batch_size, q_seq_len, _q_embed_size = q.size() k_batch_size, k_seq_len, _k_embed_size = k.size() v_batch_size, v_seq_len, _v_embed_size = v.size() q = self.Q(q).reshape(q_batch_size, q_seq_len, self.num_heads, self .head_size) k = self.K(k).reshape(k_batch_size, k_seq_len, self.num_heads, self .head_size) v = self.V(v).reshape(v_batch_size, v_seq_len, self.num_heads, self .head_size) attention = self.attention(q, k, v, mask=mask) concatenated = attention.reshape(v_batch_size, -1, self.embed_size) out = self.linear(concatenated) return out def attention(self, q, k, v, mask=None): scores = torch.einsum('bqhe,bkhe->bhqk', [q, k]) if mask is not None: scores = scores.masked_fill(mask == 0, -1000000000.0) scores /= math.sqrt(self.embed_size) scores = F.softmax(scores, dim=-1) scores = self.dropout_layer(scores) attention = torch.einsum('bhql,blhd->bqhd', [scores, v]) return attention def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'embed_size': 4, 'num_heads': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import math import torch.nn as nn import torch.nn.functional as F assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 4 x2 = xindex // 16 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr1 + (x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr1 + (4 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr1 + (8 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp13 = tl.load(in_ptr1 + (12 + x0 + 16 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 * tmp1 tmp3 = 0.5 tmp4 = tmp2 * tmp3 tmp6 = tmp0 * tmp5 tmp7 = tmp6 * tmp3 tmp8 = triton_helpers.maximum(tmp4, tmp7) tmp10 = tmp0 * tmp9 tmp11 = tmp10 * tmp3 tmp12 = triton_helpers.maximum(tmp8, tmp11) tmp14 = tmp0 * tmp13 tmp15 = tmp14 * tmp3 tmp16 = triton_helpers.maximum(tmp12, tmp15) tmp17 = tmp4 - tmp16 tmp18 = tl_math.exp(tmp17) tmp19 = tmp7 - tmp16 tmp20 = tl_math.exp(tmp19) tmp21 = tmp18 + tmp20 tmp22 = tmp11 - tmp16 tmp23 = tl_math.exp(tmp22) tmp24 = tmp21 + tmp23 tmp25 = tmp15 - tmp16 tmp26 = tl_math.exp(tmp25) tmp27 = tmp24 + tmp26 tl.store(out_ptr0 + x3, tmp16, xmask) tl.store(out_ptr1 + x3, tmp27, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x4 = xindex // 4 x0 = xindex % 4 x1 = xindex // 4 % 4 x3 = xindex // 64 x2 = xindex // 16 % 4 tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x1 + 4 * x0 + 16 * x3), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last') tmp2 = tmp0 * tmp1 tmp3 = 0.5 tmp4 = tmp2 * tmp3 tmp6 = tmp4 - tmp5 tmp7 = tl_math.exp(tmp6) tmp9 = tmp7 / tmp8 tl.store(out_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), tmp9, xmask) @triton.jit def triton_poi_fused_clone_2(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask) @triton.jit def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_add_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x2, tmp2, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4,), (1,)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_4 del primals_5 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_7, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf1) del primals_6 del primals_7 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf2) del primals_8 buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 1, 4, 64), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf0, buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__softmax_1[grid(256)](buf0, buf1, buf3, buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1) buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0) del buf4 triton_poi_fused_clone_2[grid(16, 4)](buf2, primals_9, buf6, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) del primals_9 buf7 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0) del buf2 extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0) del buf3 triton_poi_fused_clone_3[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0) del buf7 extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf9) buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0) del buf9 triton_poi_fused_add_4[grid(64)](buf10, primals_11, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_11 return buf10, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0 ), buf0, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0 ), buf1, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0 ), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0 ), primals_10, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0) class MultiHeadAttentionNew(nn.Module): """Multi-headed Attention for input Query, Key, Value Multi-headed Attention is a module for attention mechanisms which runs through attention in several times in parallel, then the multiple outputs are concatenated and linearly transformed Args: embed_size (int): Max embedding size num_heads (int): Number of heads in multi-headed attention; Number of splits in the embedding size dropout (float, optional): Percentage of Dropout to be applied in range 0 <= dropout <=1 batch_dim (int, optional): The dimension in which batch dimensions is """ def __init__(self, embed_size: 'int', num_heads: 'int', dropout: 'float'=0.2, batch_dim: 'int'=0): super(MultiHeadAttentionNew, self).__init__() self.embed_size = embed_size self.num_heads = num_heads self.dropout = dropout self.batch_dim = batch_dim self.dropout_layer = nn.Dropout(dropout) self.head_size = self.embed_size // self.num_heads assert self.head_size * self.num_heads == self.embed_size, 'Heads cannot split Embedding size equally' self.Q = nn.Linear(self.embed_size, self.embed_size) self.K = nn.Linear(self.embed_size, self.embed_size) self.V = nn.Linear(self.embed_size, self.embed_size) self.linear = nn.Linear(self.embed_size, self.embed_size) def attention(self, q, k, v, mask=None): scores = torch.einsum('bqhe,bkhe->bhqk', [q, k]) if mask is not None: scores = scores.masked_fill(mask == 0, -1000000000.0) scores /= math.sqrt(self.embed_size) scores = F.softmax(scores, dim=-1) scores = self.dropout_layer(scores) attention = torch.einsum('bhql,blhd->bqhd', [scores, v]) return attention def forward(self, input_0, input_1, input_2): primals_4 = self.Q.weight primals_5 = self.Q.bias primals_6 = self.K.weight primals_7 = self.K.bias primals_8 = self.V.weight primals_9 = self.V.bias primals_10 = self.linear.weight primals_11 = self.linear.bias primals_1 = input_0 primals_2 = input_1 primals_3 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]
UdbhavPrasad072300/CPS843_Final_Project
MultiHeadAttention
false
9,731
[ "MIT" ]
0
042f0bad48c7e49b71ab8efbc4ac5a9e6a6cf31c
https://github.com/UdbhavPrasad072300/CPS843_Final_Project/tree/042f0bad48c7e49b71ab8efbc4ac5a9e6a6cf31c
VAE
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/3q/c3qwr2d2rrpjzvnddomnmdy6cwva4hjlvrn2y5epemk4ak3k2m6c.py # Topologically Sorted Source Nodes: [h1], Original ATen: [aten.relu] # Source node to ATen node mapping: # h1 => relu # Graph fragment: # %add_tensor_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default_2, %primals_3), kwargs = {}) # %relu : [num_users=3] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor_2,), kwargs = {}) triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 400 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/jd/cjdtedosfkoutmd76tpyaejxhpwspa7takf5bagemxu5kt4jquxx.py # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # sigmoid => sigmoid # Graph fragment: # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_11), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add_tensor,), kwargs = {}) triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_sigmoid_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 3136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 784 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (4, 784), (784, 1)) assert_size_stride(primals_2, (400, 784), (784, 1)) assert_size_stride(primals_3, (400, ), (1, )) assert_size_stride(primals_4, (20, 400), (400, 1)) assert_size_stride(primals_5, (20, ), (1, )) assert_size_stride(primals_6, (20, 400), (400, 1)) assert_size_stride(primals_7, (20, ), (1, )) assert_size_stride(primals_8, (400, 20), (20, 1)) assert_size_stride(primals_9, (400, ), (1, )) assert_size_stride(primals_10, (784, 400), (400, 1)) assert_size_stride(primals_11, (784, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 400), (400, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 400), (1, 784), 0), out=buf0) del primals_2 buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [h1], Original ATen: [aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_relu_0.run(buf1, primals_3, 1600, grid=grid(1600), stream=stream0) del primals_3 buf2 = empty_strided_cuda((4, 20), (20, 1), torch.float32) # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (400, 20), (1, 400), 0), alpha=1, beta=1, out=buf2) del primals_5 buf3 = empty_strided_cuda((4, 20), (20, 1), torch.float32) # Topologically Sorted Source Nodes: [logvar], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6, (400, 20), (1, 400), 0), alpha=1, beta=1, out=buf3) del primals_7 buf4 = empty_strided_cuda((4, 400), (400, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf2, reinterpret_tensor(primals_8, (20, 400), (1, 20), 0), out=buf4) buf5 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [h3], Original ATen: [aten.relu] triton_poi_fused_relu_0.run(buf5, primals_9, 1600, grid=grid(1600), stream=stream0) del primals_9 buf6 = empty_strided_cuda((4, 784), (784, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(buf5, reinterpret_tensor(primals_10, (400, 784), (1, 400), 0), out=buf6) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid] triton_poi_fused_sigmoid_1.run(buf7, primals_11, 3136, grid=grid(3136), stream=stream0) del primals_11 return (buf7, buf2, buf3, primals_1, buf1, buf2, buf5, buf7, primals_10, primals_8, primals_6, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 784), (784, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((400, 784), (784, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((20, 400), (400, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((20, 400), (400, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((400, 20), (20, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((784, 400), (400, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((784, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.parallel import torch.utils.data import torch.onnx import torch.optim import torch.utils.data.distributed import torch.nn.functional as F import torch.autograd class VAE(nn.Module): def __init__(self): super(VAE, self).__init__() self.fc1 = nn.Linear(784, 400) self.fc21 = nn.Linear(400, 20) self.fc22 = nn.Linear(400, 20) self.fc3 = nn.Linear(20, 400) self.fc4 = nn.Linear(400, 784) def encode(self, x): h1 = F.relu(self.fc1(x)) return self.fc21(h1), self.fc22(h1) def reparameterize(self, mu, logvar): if self.training: std = torch.exp(0.5 * logvar) eps = torch.randn_like(std) return eps.mul(std).add_(mu) else: return mu def decode(self, z): h3 = F.relu(self.fc3(z)) return F.sigmoid(self.fc4(h3)) def forward(self, x): mu, logvar = self.encode(x.view(-1, 784)) z = self.reparameterize(mu, logvar) return self.decode(z), mu, logvar def get_inputs(): return [torch.rand([4, 784])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.nn.parallel import torch.utils.data import torch.onnx import torch.optim import torch.utils.data.distributed import torch.nn.functional as F import torch.autograd assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 1600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 400 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, xmask) @triton.jit def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 3136 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 784 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (4, 784), (784, 1)) assert_size_stride(primals_2, (400, 784), (784, 1)) assert_size_stride(primals_3, (400,), (1,)) assert_size_stride(primals_4, (20, 400), (400, 1)) assert_size_stride(primals_5, (20,), (1,)) assert_size_stride(primals_6, (20, 400), (400, 1)) assert_size_stride(primals_7, (20,), (1,)) assert_size_stride(primals_8, (400, 20), (20, 1)) assert_size_stride(primals_9, (400,), (1,)) assert_size_stride(primals_10, (784, 400), (400, 1)) assert_size_stride(primals_11, (784,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 400), (400, 1), torch.float32) extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 400), (1, 784), 0), out=buf0) del primals_2 buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_relu_0[grid(1600)](buf1, primals_3, 1600, XBLOCK= 256, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((4, 20), (20, 1), torch.float32) extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4, (400, 20), (1, 400), 0), alpha=1, beta=1, out=buf2) del primals_5 buf3 = empty_strided_cuda((4, 20), (20, 1), torch.float32) extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6, (400, 20), (1, 400), 0), alpha=1, beta=1, out=buf3) del primals_7 buf4 = empty_strided_cuda((4, 400), (400, 1), torch.float32) extern_kernels.mm(buf2, reinterpret_tensor(primals_8, (20, 400), (1, 20), 0), out=buf4) buf5 = buf4 del buf4 triton_poi_fused_relu_0[grid(1600)](buf5, primals_9, 1600, XBLOCK= 256, num_warps=4, num_stages=1) del primals_9 buf6 = empty_strided_cuda((4, 784), (784, 1), torch.float32) extern_kernels.mm(buf5, reinterpret_tensor(primals_10, (400, 784), (1, 400), 0), out=buf6) buf7 = buf6 del buf6 triton_poi_fused_sigmoid_1[grid(3136)](buf7, primals_11, 3136, XBLOCK=256, num_warps=4, num_stages=1) del primals_11 return (buf7, buf2, buf3, primals_1, buf1, buf2, buf5, buf7, primals_10, primals_8, primals_6, primals_4) class VAENew(nn.Module): def __init__(self): super(VAENew, self).__init__() self.fc1 = nn.Linear(784, 400) self.fc21 = nn.Linear(400, 20) self.fc22 = nn.Linear(400, 20) self.fc3 = nn.Linear(20, 400) self.fc4 = nn.Linear(400, 784) def encode(self, x): h1 = F.relu(self.fc1(x)) return self.fc21(h1), self.fc22(h1) def reparameterize(self, mu, logvar): if self.training: std = torch.exp(0.5 * logvar) eps = torch.randn_like(std) return eps.mul(std).add_(mu) else: return mu def decode(self, z): h3 = F.relu(self.fc3(z)) return F.sigmoid(self.fc4(h3)) def forward(self, input_0): primals_2 = self.fc1.weight primals_3 = self.fc1.bias primals_4 = self.fc21.weight primals_5 = self.fc21.bias primals_6 = self.fc22.weight primals_7 = self.fc22.bias primals_8 = self.fc3.weight primals_9 = self.fc3.bias primals_10 = self.fc4.weight primals_11 = self.fc4.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0], output[1], output[2]
angelajiang/examples
VAE
false
9,732
[ "BSD-3-Clause" ]
0
9964d6bd97a93420f101ebcdc40f8bd540930956
https://github.com/angelajiang/examples/tree/9964d6bd97a93420f101ebcdc40f8bd540930956
QNetwork
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/nq/cnqjufcqn3ur3s7xvlb2i747nyf24md4zaiatlwgkasynplfjstu.py # Topologically Sorted Source Nodes: [state_1], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # state_1 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, None) tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/54/c546inlectt6zvbpgn5qhxi6h2mqgwz227jurnrzfeistnsnjut6.py # Topologically Sorted Source Nodes: [state_3], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # state_3 => relu_1 # Graph fragment: # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_3,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_1 = async_compile.triton('triton_poi_fused_relu_threshold_backward_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 2048 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 32 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, None) tl.store(out_ptr0 + (x2), tmp6, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/gz/cgz3rsgyyce7ybbfcrgzuaeusupxnsotqth5ok5vlppvfma4lyvv.py # Topologically Sorted Source Nodes: [state_5], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # state_5 => relu_2 # Graph fragment: # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_5,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_2, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 16 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (32, 64), (64, 1)) assert_size_stride(primals_5, (32, ), (1, )) assert_size_stride(primals_6, (16, 32), (32, 1)) assert_size_stride(primals_7, (16, ), (1, )) assert_size_stride(primals_8, (4, 16), (16, 1)) assert_size_stride(primals_9, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool) # Topologically Sorted Source Nodes: [state_1], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf9, 4096, grid=grid(4096), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 32), (32, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 32), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 32), (512, 128, 32, 1), 0); del buf2 # reuse buf8 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool) # Topologically Sorted Source Nodes: [state_3], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_1.run(buf3, primals_5, buf8, 2048, grid=grid(2048), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 16), (16, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 16), (1, 32), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 16), (256, 64, 16, 1), 0); del buf4 # reuse buf7 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool) # Topologically Sorted Source Nodes: [state_5], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_2.run(buf5, primals_7, buf7, 1024, grid=grid(1024), stream=stream0) del primals_7 buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [state_6], Original ATen: [aten.addmm] extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 16), (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf6) del primals_9 return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(buf3, (64, 32), (32, 1), 0), reinterpret_tensor(buf5, (64, 16), (16, 1), 0), primals_8, buf7, primals_6, buf8, primals_4, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((32, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((16, 32), (32, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F class QNetwork(nn.Module): """Actor (Policy) Model.""" def __init__(self, state_size, action_size, seed): """Initialize parameters and build model. Parameters: ========== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed """ super(QNetwork, self).__init__() self.seed = torch.manual_seed(seed) self.fc1 = nn.Linear(state_size, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 16) self.fc4 = nn.Linear(16, action_size) def forward(self, state): """Build a network that maps state -> action values.""" state = self.fc1(state) state = F.relu(state) state = self.fc2(state) state = F.relu(state) state = self.fc3(state) state = F.relu(state) state = self.fc4(state) return state def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'state_size': 4, 'action_size': 4, 'seed': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, None) tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_relu_threshold_backward_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 32 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, None) tl.store(out_ptr0 + x2, tmp6, None) @triton.jit def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 16 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (32, 64), (64, 1)) assert_size_stride(primals_5, (32,), (1,)) assert_size_stride(primals_6, (16, 32), (32, 1)) assert_size_stride(primals_7, (16,), (1,)) assert_size_stride(primals_8, (4, 16), (16, 1)) assert_size_stride(primals_9, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf0 buf9 = empty_strided_cuda((4, 4, 4, 64), (1024, 256, 64, 1), torch.bool ) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(4096)](buf1, primals_2, buf9, 4096, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 32), (32, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 32), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 32), (512, 128, 32, 1), 0) del buf2 buf8 = empty_strided_cuda((4, 4, 4, 32), (512, 128, 32, 1), torch.bool) triton_poi_fused_relu_threshold_backward_1[grid(2048)](buf3, primals_5, buf8, 2048, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 16), (16, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 32), (32, 1), 0), reinterpret_tensor(primals_6, (32, 16), (1, 32), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 16), (256, 64, 16, 1), 0) del buf4 buf7 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.bool) triton_poi_fused_relu_threshold_backward_2[grid(1024)](buf5, primals_7, buf7, 1024, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 16), (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf6) del primals_9 return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor( buf3, (64, 32), (32, 1), 0), reinterpret_tensor(buf5, (64, 16), (16, 1), 0), primals_8, buf7, primals_6, buf8, primals_4, buf9 class QNetworkNew(nn.Module): """Actor (Policy) Model.""" def __init__(self, state_size, action_size, seed): """Initialize parameters and build model. Parameters: ========== state_size (int): Dimension of each state action_size (int): Dimension of each action seed (int): Random seed """ super(QNetworkNew, self).__init__() self.seed = torch.manual_seed(seed) self.fc1 = nn.Linear(state_size, 64) self.fc2 = nn.Linear(64, 32) self.fc3 = nn.Linear(32, 16) self.fc4 = nn.Linear(16, action_size) def forward(self, input_0): primals_1 = self.fc1.weight primals_2 = self.fc1.bias primals_4 = self.fc2.weight primals_5 = self.fc2.bias primals_6 = self.fc3.weight primals_7 = self.fc3.bias primals_8 = self.fc4.weight primals_9 = self.fc4.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]
andreaspts/DRL_CartPole
QNetwork
false
9,733
[ "MIT" ]
0
e4f018ab4adaeeaac2902c541e14933b56957e22
https://github.com/andreaspts/DRL_CartPole/tree/e4f018ab4adaeeaac2902c541e14933b56957e22
Conv2D
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/b6/cb6fhjujjpb3x4wm4glvqtvukaf6tupfew3an36gazfbkgh2ul3p.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] # Source node to ATen node mapping: # x => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [2, 2], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[128], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 100 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tl.store(in_out_ptr0 + (x0), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (1, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 1, 5, 5), (25, 25, 5, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_2, 100, grid=grid(100), stream=stream0) del primals_2 return (buf1, primals_1, primals_3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((1, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch from torch import nn class Conv2D(nn.Module): def __init__(self, in_channels, kernel_size, last): super().__init__() if last: out_channels = 1 else: out_channels = 5 self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size= kernel_size, padding=int(math.floor(kernel_size / 2))) def forward(self, x): x = self.conv2d(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'kernel_size': 4, 'last': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 100 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tl.store(in_out_ptr0 + x0, tmp3, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (1, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 1, 5, 5), (25, 25, 5, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(100)](buf1, primals_2, 100, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 return buf1, primals_1, primals_3 class Conv2DNew(nn.Module): def __init__(self, in_channels, kernel_size, last): super().__init__() if last: out_channels = 1 else: out_channels = 5 self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size= kernel_size, padding=int(math.floor(kernel_size / 2))) def forward(self, input_0): primals_1 = self.conv2d.weight primals_2 = self.conv2d.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
Yusoi/mmdetection
Conv2D
false
9,734
[ "Apache-2.0" ]
0
cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
MultiHead
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/fz/cfzmg4qtw6jgry4nhlwopodzjz62ll3n3ykfox77hwd2crdnlh2w.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => exp # Graph fragment: # %mul_tensor_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 1), kwargs = {}) # %amax_default_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor_3, [-1], True), kwargs = {}) # %sub_tensor_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor_3, %amax_default_3), kwargs = {}) # %div_tensor_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor_3, 2.0), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor_3,), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + (x2), tmp17, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/kj/ckjtlefzavjukjsytvkak6ek26zmzexpcbnlwelx4k5kascjxlf3.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => div_1, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/mk/cmkim2hc4ksxhatli3y5cu7hoqofxcbzqrrxvnlhmswdt4cgww25.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%bmm_1, %bmm_3, %bmm_5, %bmm_7], -1), kwargs = {}) triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 1, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 2, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 3, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp0 >= tmp12 tmp17 = tl.full([1], 4, tl.int64) tmp18 = tmp0 < tmp17 tmp19 = tl.load(in_ptr3 + (x1), tmp16 & xmask, eviction_policy='evict_last', other=0.0) tmp20 = tl.where(tmp14, tmp15, tmp19) tmp21 = tl.where(tmp9, tmp10, tmp20) tmp22 = tl.where(tmp4, tmp5, tmp21) tl.store(out_ptr0 + (x2), tmp22, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [query], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [key], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [value], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2) del primals_5 buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [dot_products], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 0), out=buf3) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf4, buf5, 64, grid=grid(64), stream=stream0) buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm] extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 0), out=buf6) buf7 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [dot_products_1], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 1), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_0.run(buf7, buf8, 64, grid=grid(64), stream=stream0) buf9 = buf7; del buf7 # reuse # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf8, buf9, 64, grid=grid(64), stream=stream0) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_3], Original ATen: [aten.bmm] extern_kernels.bmm(buf9, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 1), out=buf10) buf11 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [dot_products_2], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 2), out=buf11) buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax_2], Original ATen: [aten._softmax] triton_poi_fused__softmax_0.run(buf11, buf12, 64, grid=grid(64), stream=stream0) buf13 = buf11; del buf11 # reuse # Topologically Sorted Source Nodes: [softmax_2], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf12, buf13, 64, grid=grid(64), stream=stream0) buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_5], Original ATen: [aten.bmm] extern_kernels.bmm(buf13, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 2), out=buf14) buf15 = buf12; del buf12 # reuse # Topologically Sorted Source Nodes: [dot_products_3], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 3), out=buf15) buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax_3], Original ATen: [aten._softmax] triton_poi_fused__softmax_0.run(buf15, buf16, 64, grid=grid(64), stream=stream0) buf17 = buf15; del buf15 # reuse # Topologically Sorted Source Nodes: [softmax_3], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf16, buf17, 64, grid=grid(64), stream=stream0) buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_7], Original ATen: [aten.bmm] extern_kernels.bmm(buf17, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 3), out=buf18) buf19 = buf16; del buf16 # reuse # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(buf6, buf10, buf14, buf18, buf19, 64, grid=grid(64), stream=stream0) del buf10 del buf14 del buf18 del buf6 buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf20) return (reinterpret_tensor(buf20, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), buf5, buf9, buf13, buf17, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), primals_7, reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 3), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 3), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 2), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 2), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 1), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 1), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch from torch import nn from torch.nn import functional as F class Attention(nn.Module): def __init__(self, d_key, drop_ratio, causal): super(Attention, self).__init__() self.scale = math.sqrt(d_key) self.dropout = nn.Dropout(drop_ratio) self.causal = causal def forward(self, query, key, value): dot_products = torch.bmm(query, key.transpose(1, 2)) if query.dim() == 3 and (self is None or self.causal): tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF if key.is_cuda: tri = tri dot_products.data.sub_(tri.unsqueeze(0)) return torch.bmm(self.dropout(F.softmax(dot_products / self.scale, dim=-1)), value) class MultiHead(nn.Module): def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False): super(MultiHead, self).__init__() self.attention = Attention(d_key, drop_ratio, causal=causal) self.wq = nn.Linear(d_key, d_key, bias=False) self.wk = nn.Linear(d_key, d_key, bias=False) self.wv = nn.Linear(d_value, d_value, bias=False) self.wo = nn.Linear(d_value, d_key, bias=False) self.n_heads = n_heads def forward(self, query, key, value): query, key, value = self.wq(query), self.wk(key), self.wv(value) query, key, value = (x.chunk(self.n_heads, -1) for x in (query, key, value)) return self.wo(torch.cat([self.attention(q, k, v) for q, k, v in zip(query, key, value)], -1)) def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'d_key': 4, 'd_value': 4, 'n_heads': 4, 'drop_ratio': 0.5}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import math from torch import nn from torch.nn import functional as F assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + x2, tmp17, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 1, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 2, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + x1, tmp9 & xmask, eviction_policy= 'evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 3, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + x1, tmp14 & xmask, eviction_policy= 'evict_last', other=0.0) tmp16 = tmp0 >= tmp12 tl.full([1], 4, tl.int64) tmp19 = tl.load(in_ptr3 + x1, tmp16 & xmask, eviction_policy= 'evict_last', other=0.0) tmp20 = tl.where(tmp14, tmp15, tmp19) tmp21 = tl.where(tmp9, tmp10, tmp20) tmp22 = tl.where(tmp4, tmp5, tmp21) tl.store(out_ptr0 + x2, tmp22, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2) del primals_5 buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 0), out=buf3) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = buf3 del buf3 triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1) buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 0), out=buf6) buf7 = buf4 del buf4 extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 1), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_0[grid(64)](buf7, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1) buf9 = buf7 del buf7 triton_poi_fused__softmax_1[grid(64)](buf8, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf9, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 1), out=buf10) buf11 = buf8 del buf8 extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 2), out=buf11) buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_0[grid(64)](buf11, buf12, 64, XBLOCK=64, num_warps=1, num_stages=1) buf13 = buf11 del buf11 triton_poi_fused__softmax_1[grid(64)](buf12, buf13, 64, XBLOCK=64, num_warps=1, num_stages=1) buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf13, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 2), out=buf14) buf15 = buf12 del buf12 extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 3), out=buf15) buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_0[grid(64)](buf15, buf16, 64, XBLOCK=64, num_warps=1, num_stages=1) buf17 = buf15 del buf15 triton_poi_fused__softmax_1[grid(64)](buf16, buf17, 64, XBLOCK=64, num_warps=1, num_stages=1) buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf17, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 3), out=buf18) buf19 = buf16 del buf16 triton_poi_fused_cat_2[grid(64)](buf6, buf10, buf14, buf18, buf19, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf10 del buf14 del buf18 del buf6 buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf20) return reinterpret_tensor(buf20, (4, 4, 4), (16, 4, 1), 0 ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0 ), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0 ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0 ), buf5, buf9, buf13, buf17, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), primals_7, reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 3 ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 3 ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 3 ), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 2 ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 2 ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 2 ), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 1 ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 1 ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 1 ), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 0) class Attention(nn.Module): def __init__(self, d_key, drop_ratio, causal): super(Attention, self).__init__() self.scale = math.sqrt(d_key) self.dropout = nn.Dropout(drop_ratio) self.causal = causal def forward(self, query, key, value): dot_products = torch.bmm(query, key.transpose(1, 2)) if query.dim() == 3 and (self is None or self.causal): tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF if key.is_cuda: tri = tri dot_products.data.sub_(tri.unsqueeze(0)) return torch.bmm(self.dropout(F.softmax(dot_products / self.scale, dim=-1)), value) class MultiHeadNew(nn.Module): def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False): super(MultiHeadNew, self).__init__() self.attention = Attention(d_key, drop_ratio, causal=causal) self.wq = nn.Linear(d_key, d_key, bias=False) self.wk = nn.Linear(d_key, d_key, bias=False) self.wv = nn.Linear(d_value, d_value, bias=False) self.wo = nn.Linear(d_value, d_key, bias=False) self.n_heads = n_heads def forward(self, input_0, input_1, input_2): primals_1 = self.wq.weight primals_3 = self.wk.weight primals_5 = self.wv.weight primals_7 = self.wo.weight primals_2 = input_0 primals_4 = input_1 primals_6 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
Sy-Zhang/recurrent-transformer
MultiHead
false
9,735
[ "MIT" ]
0
f66ba49a2c9ec42759d3d00d497b49ffe39e18de
https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de
EncoderImagePrecomp
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/7y/c7yvmseinx6mtn7syc332l4dh5naxbr76mdf6jwiyvy5l3xzedwc.py # Topologically Sorted Source Nodes: [pow_1, sum_1, norm, X], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.div] # Source node to ATen node mapping: # X => div # norm => sqrt # pow_1 => pow_1 # sum_1 => sum_1 # Graph fragment: # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_1, 2), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_1, %sqrt), kwargs = {}) triton_poi_fused_div_pow_sqrt_sum_0 = async_compile.triton('triton_poi_fused_div_pow_sqrt_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_pow_sqrt_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_div_pow_sqrt_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = tmp0 / tmp12 tl.store(out_ptr0 + (x3), tmp13, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [features], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [pow_1, sum_1, norm, X], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_div_pow_sqrt_sum_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0) return (buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import numpy as np from collections import OrderedDict import torch.nn as nn import torch.nn.init def l2norm(X): """L2-normalize columns of X """ norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt() X = torch.div(X, norm) return X class EncoderImagePrecomp(nn.Module): def __init__(self, img_dim, embed_size, use_abs=False, no_imgnorm=False): super(EncoderImagePrecomp, self).__init__() self.embed_size = embed_size self.no_imgnorm = no_imgnorm self.use_abs = use_abs self.fc = nn.Linear(img_dim, embed_size) self.init_weights() def init_weights(self): """Xavier initialization for the fully connected layer """ r = np.sqrt(6.0) / np.sqrt(self.fc.in_features + self.fc.out_features) self.fc.weight.data.uniform_(-r, r) self.fc.bias.data.fill_(0) def forward(self, images): """Extract image feature vectors.""" features = self.fc(images) if not self.no_imgnorm: features = l2norm(features) if self.use_abs: features = torch.abs(features) return features def load_state_dict(self, state_dict): """Copies parameters. overwritting the default one to accept state_dict from Full model """ own_state = self.state_dict() new_state = OrderedDict() for name, param in state_dict.items(): if name in own_state: new_state[name] = param super(EncoderImagePrecomp, self).load_state_dict(new_state) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'img_dim': 4, 'embed_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import numpy as np from collections import OrderedDict import torch.nn as nn import torch.nn.init assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_div_pow_sqrt_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = tmp0 / tmp12 tl.store(out_ptr0 + x3, tmp13, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_div_pow_sqrt_sum_0[grid(256)](buf0, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1) return buf1, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0 def l2norm(X): """L2-normalize columns of X """ norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt() X = torch.div(X, norm) return X class EncoderImagePrecompNew(nn.Module): def __init__(self, img_dim, embed_size, use_abs=False, no_imgnorm=False): super(EncoderImagePrecompNew, self).__init__() self.embed_size = embed_size self.no_imgnorm = no_imgnorm self.use_abs = use_abs self.fc = nn.Linear(img_dim, embed_size) self.init_weights() def init_weights(self): """Xavier initialization for the fully connected layer """ r = np.sqrt(6.0) / np.sqrt(self.fc.in_features + self.fc.out_features) self.fc.weight.data.uniform_(-r, r) self.fc.bias.data.fill_(0) def load_state_dict(self, state_dict): """Copies parameters. overwritting the default one to accept state_dict from Full model """ own_state = self.state_dict() new_state = OrderedDict() for name, param in state_dict.items(): if name in own_state: new_state[name] = param super(EncoderImagePrecompNew, self).load_state_dict(new_state) def forward(self, input_0): primals_1 = self.fc.weight primals_2 = self.fc.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
ascott02/vsepp
EncoderImagePrecomp
false
9,736
[ "Apache-2.0" ]
0
c09abd2be5f1fec237ccfe3d7f41bfdea2acfde2
https://github.com/ascott02/vsepp/tree/c09abd2be5f1fec237ccfe3d7f41bfdea2acfde2
DuplicateModel
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/au/caug6nesiygukdpkrndsclkfho3dygoeotjtbnihl4wlyyiuddug.py # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out => convolution # out_1 => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 256 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/zt/cztsnvqcgpgovnuq3top6c2aketjyfvhepwx7yn3cio7eot4p5yk.py # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] # Source node to ATen node mapping: # contiguous => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 128], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 108 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = (yindex // 16) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (1728*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (108*y3)), tmp2, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (108, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (108, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 16384, grid=grid(16384), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 16384, grid=grid(16384), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1)) buf5 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf5, primals_7, 16384, grid=grid(16384), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf7, primals_9, 16384, grid=grid(16384), stream=stream0) del primals_9 # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 108, 4, 4), (1728, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 9, 12), (1728, 432, 108, 12, 1), torch.float32) # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf8, primals_11, buf9, 64, 108, grid=grid(64, 108), stream=stream0) del buf8 del primals_11 return (reinterpret_tensor(buf9, (4, 144, 12), (1728, 12, 1), 0), primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((256, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((108, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((108, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class DuplicateModel(nn.Module): def __init__(self, num_features_in, num_anchors=9, num_classes=12, prior=0.01, feature_size=256): super(DuplicateModel, self).__init__() self.num_classes = num_classes self.num_anchors = num_anchors self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1) def forward(self, x): out = self.conv1(x) out = self.act1(out) out = self.conv2(out) out = self.act2(out) out = self.conv3(out) out = self.act3(out) out = self.conv4(out) out = self.act4(out) final_features = out out = self.output(final_features) out1 = out.permute(0, 2, 3, 1) batch_size, width, height, _channels = out1.shape out2 = out1.view(batch_size, width, height, self.num_anchors, self. num_classes) return out2.contiguous().view(x.shape[0], -1, self.num_classes) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_features_in': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 256 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, None) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 108 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = yindex // 16 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 1728 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 108 * y3), tmp2, xmask & ymask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (108, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (108,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1)) buf3 = buf2 del buf2 triton_poi_fused_convolution_relu_0[grid(16384)](buf3, primals_5, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1)) buf5 = buf4 del buf4 triton_poi_fused_convolution_relu_0[grid(16384)](buf5, primals_7, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1)) buf7 = buf6 del buf6 triton_poi_fused_convolution_relu_0[grid(16384)](buf7, primals_9, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_9 buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 108, 4, 4), (1728, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 9, 12), (1728, 432, 108, 12, 1), torch.float32) triton_poi_fused_clone_1[grid(64, 108)](buf8, primals_11, buf9, 64, 108, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1) del buf8 del primals_11 return (reinterpret_tensor(buf9, (4, 144, 12), (1728, 12, 1), 0), primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7) class DuplicateModelNew(nn.Module): def __init__(self, num_features_in, num_anchors=9, num_classes=12, prior=0.01, feature_size=256): super(DuplicateModelNew, self).__init__() self.num_classes = num_classes self.num_anchors = num_anchors self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_8 = self.conv4.weight primals_9 = self.conv4.bias primals_10 = self.output.weight primals_11 = self.output.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0]
alexrusciano/nms_free_retinanet
DuplicateModel
false
9,737
[ "Apache-2.0" ]
0
3461a86e9dea71a756b92a434c62798bbf86b52d
https://github.com/alexrusciano/nms_free_retinanet/tree/3461a86e9dea71a756b92a434c62798bbf86b52d
Threshold
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ix/cixipll2otphpgkzwvytaomybfpwjtzeqo7dfepk6crj6ta4pne6.py # Topologically Sorted Source Nodes: [result], Original ATen: [aten.threshold] # Source node to ATen node mapping: # result => full_default, le, where # Graph fragment: # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%arg0_1, 4), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%le, %full_default, %arg0_1), kwargs = {}) triton_poi_fused_threshold_0 = async_compile.triton('triton_poi_fused_threshold_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_threshold_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_threshold_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 4.0 tmp2 = tmp0 <= tmp1 tmp3 = 0.0 tmp4 = tl.where(tmp2, tmp3, tmp0) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [result], Original ATen: [aten.threshold] stream0 = get_raw_stream(0) triton_poi_fused_threshold_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class Threshold(nn.Module): def __init__(self, threshold): super(Threshold, self).__init__() self.threshold = nn.Threshold(threshold, 0.0) def forward(self, x): return self.threshold(x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'threshold': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_threshold_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 4.0 tmp2 = tmp0 <= tmp1 tmp3 = 0.0 tmp4 = tl.where(tmp2, tmp3, tmp0) tl.store(out_ptr0 + x0, tmp4, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_threshold_0[grid(256)](arg0_1, buf0, 256, XBLOCK= 256, num_warps=4, num_stages=1) del arg0_1 return buf0, class ThresholdNew(nn.Module): def __init__(self, threshold): super(ThresholdNew, self).__init__() self.threshold = nn.Threshold(threshold, 0.0) def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
Yusoi/mmdetection
Threshold
false
9,738
[ "Apache-2.0" ]
0
cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
Softmax2d
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/qz/cqza6p5fjiie2hfiu5dfjqqugrnzziwuwxzlhzy2aa7khopxjbym.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten._softmax] # Source node to ATen node mapping: # x => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [-3], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x3), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/v4/cv4nyn2kde7dd2c53ddahw4vtxyldln6pqt62jrliqindkf3sj5m.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten._softmax] # Source node to ATen node mapping: # x => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-3], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x3), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf0, buf1, 256, grid=grid(256), stream=stream0) del buf0 return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class Softmax2d(nn.Module): def __init__(self): super().__init__() self.Softmax2d = nn.Softmax2d() def forward(self, x): x = self.Softmax2d(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x3, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x3, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK= 256, num_warps=4, num_stages=1) del arg0_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__softmax_1[grid(256)](buf0, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf0 return buf1, class Softmax2dNew(nn.Module): def __init__(self): super().__init__() self.Softmax2d = nn.Softmax2d() def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
Yusoi/mmdetection
Softmax2d
false
9,739
[ "Apache-2.0" ]
0
cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
https://github.com/Yusoi/mmdetection/tree/cbb5fb00f6e124fbb2c15e7e3438d7fa76b8850a
Block
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/nh/cnhx37tsffx4r7taj3xi72s7yfpnnccem24fupfbht6b7bzliavu.py # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu] # Source node to ATen node mapping: # gelu => add, erf, mul, mul_1, mul_2 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.5), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.7071067811865476), kwargs = {}) # %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_1,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {}) # %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %add), kwargs = {}) triton_poi_fused_gelu_0 = async_compile.triton('triton_poi_fused_gelu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_gelu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865476 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/vb/cvb72aim6aofrit3ysghkm3kzwxrn4uyxrnw3xh6setjurz6rn4h.py # Topologically Sorted Source Nodes: [xhat, gelu_1], Original ATen: [aten.convolution, aten.gelu] # Source node to ATen node mapping: # gelu_1 => add_1, erf_1, mul_3, mul_4, mul_5 # xhat => convolution # Graph fragment: # %convolution : [num_users=3] = call_function[target=torch.ops.aten.convolution.default](args = (%mul_2, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.5), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 0.7071067811865476), kwargs = {}) # %erf_1 : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_4,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf_1, 1), kwargs = {}) # %mul_5 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %add_1), kwargs = {}) triton_poi_fused_convolution_gelu_1 = async_compile.triton('triton_poi_fused_convolution_gelu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_gelu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_gelu_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.5 tmp4 = tmp2 * tmp3 tmp5 = 0.7071067811865476 tmp6 = tmp2 * tmp5 tmp7 = libdevice.erf(tmp6) tmp8 = 1.0 tmp9 = tmp7 + tmp8 tmp10 = tmp4 * tmp9 tl.store(in_out_ptr0 + (x3), tmp2, xmask) tl.store(out_ptr0 + (x3), tmp10, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/w5/cw5gytijzzkwnfpq2a2axdsj4pfxgxmwiuzizuyd4bw5uwnanzw7.py # Topologically Sorted Source Nodes: [xhat_3], Original ATen: [aten.convolution] # Source node to ATen node mapping: # xhat_3 => convolution_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul_11, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_9, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [gelu], Original ATen: [aten.gelu] stream0 = get_raw_stream(0) triton_poi_fused_gelu_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [xhat], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [xhat, gelu_1], Original ATen: [aten.convolution, aten.gelu] triton_poi_fused_convolution_gelu_1.run(buf2, primals_3, buf3, 256, grid=grid(256), stream=stream0) del primals_3 # Topologically Sorted Source Nodes: [xhat_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1)) buf5 = buf4; del buf4 # reuse buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [xhat_1, gelu_2], Original ATen: [aten.convolution, aten.gelu] triton_poi_fused_convolution_gelu_1.run(buf5, primals_5, buf6, 256, grid=grid(256), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [xhat_2], Original ATen: [aten.convolution] buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1)) buf8 = buf7; del buf7 # reuse buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [xhat_2, gelu_3], Original ATen: [aten.convolution, aten.gelu] triton_poi_fused_convolution_gelu_1.run(buf8, primals_7, buf9, 256, grid=grid(256), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [xhat_3], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1)) buf11 = buf10; del buf10 # reuse # Topologically Sorted Source Nodes: [xhat_3], Original ATen: [aten.convolution] triton_poi_fused_convolution_2.run(buf11, primals_9, 256, grid=grid(256), stream=stream0) del primals_9 return (buf11, primals_2, primals_4, primals_6, primals_8, buf0, buf2, buf3, buf5, buf6, buf8, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn from torch.nn import functional as F def get_conv(in_dim, out_dim, kernel_size, stride, padding, zero_bias=True, zero_weights=False, groups=1, scaled=False): c = nn.Conv2d(in_dim, out_dim, kernel_size, stride, padding, groups=groups) if zero_bias: c.bias.data *= 0.0 if zero_weights: c.weight.data *= 0.0 return c def get_1x1(in_dim, out_dim, zero_bias=True, zero_weights=False, groups=1, scaled=False): return get_conv(in_dim, out_dim, 1, 1, 0, zero_bias, zero_weights, groups=groups, scaled=scaled) def get_3x3(in_dim, out_dim, zero_bias=True, zero_weights=False, groups=1, scaled=False): return get_conv(in_dim, out_dim, 3, 1, 1, zero_bias, zero_weights, groups=groups, scaled=scaled) class Block(nn.Module): def __init__(self, in_width, middle_width, out_width, down_rate=None, residual=False, use_3x3=True, zero_last=False): super().__init__() self.down_rate = down_rate self.residual = residual self.c1 = get_1x1(in_width, middle_width) self.c2 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1( middle_width, middle_width) self.c3 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1( middle_width, middle_width) self.c4 = get_1x1(middle_width, out_width, zero_weights=zero_last) def forward(self, x): xhat = self.c1(F.gelu(x)) xhat = self.c2(F.gelu(xhat)) xhat = self.c3(F.gelu(xhat)) xhat = self.c4(F.gelu(xhat)) out = x + xhat if self.residual else xhat if self.down_rate is not None: out = F.avg_pool2d(out, kernel_size=self.down_rate, stride=self .down_rate) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_width': 4, 'middle_width': 4, 'out_width': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_gelu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = 0.7071067811865476 tmp4 = tmp0 * tmp3 tmp5 = libdevice.erf(tmp4) tmp6 = 1.0 tmp7 = tmp5 + tmp6 tmp8 = tmp2 * tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) @triton.jit def triton_poi_fused_convolution_gelu_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.5 tmp4 = tmp2 * tmp3 tmp5 = 0.7071067811865476 tmp6 = tmp2 * tmp5 tmp7 = libdevice.erf(tmp6) tmp8 = 1.0 tmp9 = tmp7 + tmp8 tmp10 = tmp4 * tmp9 tl.store(in_out_ptr0 + x3, tmp2, xmask) tl.store(out_ptr0 + x3, tmp10, xmask) @triton.jit def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_9, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_gelu_0[grid(256)](primals_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_convolution_gelu_1[grid(256)](buf2, primals_3, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1)) buf5 = buf4 del buf4 buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_convolution_gelu_1[grid(256)](buf5, primals_5, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf7 = extern_kernels.convolution(buf6, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf7, (4, 4, 4, 4), (64, 16, 4, 1)) buf8 = buf7 del buf7 buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_convolution_gelu_1[grid(256)](buf8, primals_7, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_7 buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 4, 4, 4), (64, 16, 4, 1)) buf11 = buf10 del buf10 triton_poi_fused_convolution_2[grid(256)](buf11, primals_9, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_9 return (buf11, primals_2, primals_4, primals_6, primals_8, buf0, buf2, buf3, buf5, buf6, buf8, buf9) def get_conv(in_dim, out_dim, kernel_size, stride, padding, zero_bias=True, zero_weights=False, groups=1, scaled=False): c = nn.Conv2d(in_dim, out_dim, kernel_size, stride, padding, groups=groups) if zero_bias: c.bias.data *= 0.0 if zero_weights: c.weight.data *= 0.0 return c def get_1x1(in_dim, out_dim, zero_bias=True, zero_weights=False, groups=1, scaled=False): return get_conv(in_dim, out_dim, 1, 1, 0, zero_bias, zero_weights, groups=groups, scaled=scaled) def get_3x3(in_dim, out_dim, zero_bias=True, zero_weights=False, groups=1, scaled=False): return get_conv(in_dim, out_dim, 3, 1, 1, zero_bias, zero_weights, groups=groups, scaled=scaled) class BlockNew(nn.Module): def __init__(self, in_width, middle_width, out_width, down_rate=None, residual=False, use_3x3=True, zero_last=False): super().__init__() self.down_rate = down_rate self.residual = residual self.c1 = get_1x1(in_width, middle_width) self.c2 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1( middle_width, middle_width) self.c3 = get_3x3(middle_width, middle_width) if use_3x3 else get_1x1( middle_width, middle_width) self.c4 = get_1x1(middle_width, out_width, zero_weights=zero_last) def forward(self, input_0): primals_2 = self.c1.weight primals_3 = self.c1.bias primals_4 = self.c2.weight primals_5 = self.c2.bias primals_6 = self.c3.weight primals_7 = self.c3.bias primals_8 = self.c4.weight primals_9 = self.c4.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]
ashesh-0/vdvae
Block
false
9,740
[ "MIT" ]
0
a1ed5dfaf01a88af750413f5fcb907a5b73833a5
https://github.com/ashesh-0/vdvae/tree/a1ed5dfaf01a88af750413f5fcb907a5b73833a5
RegressionModel
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/au/caug6nesiygukdpkrndsclkfho3dygoeotjtbnihl4wlyyiuddug.py # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out => convolution # out_1 => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = (xindex // 16) % 256 tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/vm/cvmfmdis32noutvytx3ktvjzwjo476t7wge2koo4i2gxn423alal.py # Topologically Sorted Source Nodes: [contiguous, view], Original ATen: [aten.clone, aten.view] # Source node to ATen node mapping: # contiguous => clone # view => view # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) # %view : [num_users=1] = call_function[target=torch.ops.aten.reshape.default](args = (%clone, [4, -1, 4]), kwargs = {}) triton_poi_fused_clone_view_1 = async_compile.triton('triton_poi_fused_clone_view_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64, 64], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_view_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_view_1(in_out_ptr0, in_ptr0, in_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 64 xnumel = 36 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = (yindex // 16) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (576*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + (x2 + (36*y3)), tmp2, xmask & ymask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (36, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (36, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 16384, grid=grid(16384), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 16384, grid=grid(16384), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1)) buf5 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf5, primals_7, 16384, grid=grid(16384), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf7, primals_9, 16384, grid=grid(16384), stream=stream0) del primals_9 # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 36, 4, 4), (576, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch.float32) buf10 = reinterpret_tensor(buf9, (4, 144, 4), (576, 4, 1), 0); del buf9 # reuse # Topologically Sorted Source Nodes: [contiguous, view], Original ATen: [aten.clone, aten.view] triton_poi_fused_clone_view_1.run(buf10, buf8, primals_11, 64, 36, grid=grid(64, 36), stream=stream0) del buf8 del primals_11 return (buf10, buf7, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((256, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((36, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((36, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class RegressionModel(nn.Module): def __init__(self, num_features_in, num_anchors=9, feature_size=256): super(RegressionModel, self).__init__() self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size= 3, padding=1) def forward(self, x): out = self.conv1(x) out = self.act1(out) out = self.conv2(out) out = self.act2(out) out = self.conv3(out) out = self.act3(out) out = self.conv4(out) out = self.act4(out) final_features = out out = self.output(final_features) out = out.permute(0, 2, 3, 1) return out.contiguous().view(out.shape[0], -1, 4), final_features def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_features_in': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x1 = xindex // 16 % 256 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, None) @triton.jit def triton_poi_fused_clone_view_1(in_out_ptr0, in_ptr0, in_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 64 xnumel = 36 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 16 y1 = yindex // 16 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 576 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + (x2 + 36 * y3), tmp2, xmask & ymask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (36, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (36,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 256, 4, 4), (4096, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(16384)](buf1, primals_2, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 256, 4, 4), (4096, 16, 4, 1)) buf3 = buf2 del buf2 triton_poi_fused_convolution_relu_0[grid(16384)](buf3, primals_5, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf4, (4, 256, 4, 4), (4096, 16, 4, 1)) buf5 = buf4 del buf4 triton_poi_fused_convolution_relu_0[grid(16384)](buf5, primals_7, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 16, 4, 1)) buf7 = buf6 del buf6 triton_poi_fused_convolution_relu_0[grid(16384)](buf7, primals_9, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_9 buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 36, 4, 4), (576, 16, 4, 1)) buf9 = empty_strided_cuda((4, 4, 4, 36), (576, 144, 36, 1), torch. float32) buf10 = reinterpret_tensor(buf9, (4, 144, 4), (576, 4, 1), 0) del buf9 triton_poi_fused_clone_view_1[grid(64, 36)](buf10, buf8, primals_11, 64, 36, XBLOCK=64, YBLOCK=4, num_warps=4, num_stages=1) del buf8 del primals_11 return (buf10, buf7, primals_1, primals_3, primals_4, primals_6, primals_8, primals_10, buf1, buf3, buf5, buf7) class RegressionModelNew(nn.Module): def __init__(self, num_features_in, num_anchors=9, feature_size=256): super(RegressionModelNew, self).__init__() self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * 4, kernel_size= 3, padding=1) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_8 = self.conv4.weight primals_9 = self.conv4.bias primals_10 = self.output.weight primals_11 = self.output.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0], output[1]
alexrusciano/nms_free_retinanet
RegressionModel
false
9,741
[ "Apache-2.0" ]
0
3461a86e9dea71a756b92a434c62798bbf86b52d
https://github.com/alexrusciano/nms_free_retinanet/tree/3461a86e9dea71a756b92a434c62798bbf86b52d
NegativeScaledDotProduct
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/u5/cu5u6mji5tmuwjwnao33ksrbwyr5f7vlre4ywnbhgkfwjxaua7x7.py # Topologically Sorted Source Nodes: [neg, sqrt_d, truediv], Original ATen: [aten.neg, aten.sqrt, aten.div] # Source node to ATen node mapping: # neg => neg # sqrt_d => full_default # truediv => div # Graph fragment: # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mm,), kwargs = {}) # %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %full_default), kwargs = {}) triton_poi_fused_div_neg_sqrt_0 = async_compile.triton('triton_poi_fused_div_neg_sqrt_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_neg_sqrt_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_div_neg_sqrt_0(in_out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = -tmp0 tmp2 = 0.5 tmp3 = tmp1 * tmp2 tl.store(in_out_ptr0 + (x0), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm] extern_kernels.mm(arg0_1, reinterpret_tensor(arg1_1, (4, 4), (1, 4), 0), out=buf0) del arg0_1 del arg1_1 buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [neg, sqrt_d, truediv], Original ATen: [aten.neg, aten.sqrt, aten.div] stream0 = get_raw_stream(0) triton_poi_fused_div_neg_sqrt_0.run(buf1, 16, grid=grid(16), stream=stream0) return (buf1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.utils.data.dataloader import torch.nn def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False): """ Computes dot product for pairs of vectors. :param normalize: Vectors are normalized (leads to cosine similarity) :return: Matrix with res[i][j] = dot_product(a[i], b[j]) """ if len(a.shape) == 1: a = a.unsqueeze(0) if len(b.shape) == 1: b = b.unsqueeze(0) if normalize: a = torch.nn.functional.normalize(a, p=2, dim=1) b = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a, b.transpose(0, 1)) class NegativeScaledDotProduct(torch.nn.Module): def forward(self, a, b): sqrt_d = torch.sqrt(torch.tensor(a.size(-1))) return -dot_product(a, b, normalize=False) / sqrt_d def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data.dataloader import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_div_neg_sqrt_0(in_out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = -tmp0 tmp2 = 0.5 tmp3 = tmp1 * tmp2 tl.store(in_out_ptr0 + x0, tmp3, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(arg0_1, reinterpret_tensor(arg1_1, (4, 4), (1, 4), 0), out=buf0) del arg0_1 del arg1_1 buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_div_neg_sqrt_0[grid(16)](buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) return buf1, def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False): """ Computes dot product for pairs of vectors. :param normalize: Vectors are normalized (leads to cosine similarity) :return: Matrix with res[i][j] = dot_product(a[i], b[j]) """ if len(a.shape) == 1: a = a.unsqueeze(0) if len(b.shape) == 1: b = b.unsqueeze(0) if normalize: a = torch.nn.functional.normalize(a, p=2, dim=1) b = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a, b.transpose(0, 1)) class NegativeScaledDotProductNew(torch.nn.Module): def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
adriensas/flair
NegativeScaledDotProduct
false
9,742
[ "MIT" ]
0
f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
EuclideanMean
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/2c/c2caasuan6xkydnq2bvliamlyid6cl5fcz5kcz2mnyns45wtxqbs.py # Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean] # Source node to ATen node mapping: # mean => mean # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [0]), kwargs = {}) triton_poi_fused_mean_0 = async_compile.triton('triton_poi_fused_mean_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (64 + x0), xmask) tmp3 = tl.load(in_ptr0 + (128 + x0), xmask) tmp5 = tl.load(in_ptr0 + (192 + x0), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp7 = 4.0 tmp8 = tmp6 / tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean] stream0 = get_raw_stream(0) triton_poi_fused_mean_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import Tensor import torch.utils.data.dataloader from torch import nn import torch.nn class EuclideanMean(nn.Module): """Implement a EuclideanMean object.""" def forward(self, data: 'Tensor') ->Tensor: """Performs a forward pass through the network. Parameters ---------- data : torch.Tensor The input data, as a float tensor Returns ------- torch.Tensor The encoded output, as a float tensor """ return data.mean(0) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data.dataloader from torch import nn import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + (64 + x0), xmask) tmp3 = tl.load(in_ptr0 + (128 + x0), xmask) tmp5 = tl.load(in_ptr0 + (192 + x0), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp7 = 4.0 tmp8 = tmp6 / tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mean_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1) del arg0_1 return buf0, class EuclideanMeanNew(nn.Module): """Implement a EuclideanMean object.""" def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
adriensas/flair
EuclideanMean
false
9,743
[ "MIT" ]
0
f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
NegativeBinomial
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/6y/c6yfroyspngjwkhcwilotgjvk3fadnfvjohlt6mlejx3jmuibn2i.py # Topologically Sorted Source Nodes: [exp, add, log, alpha_t], Original ATen: [aten.exp, aten.add, aten.log] # Source node to ATen node mapping: # add => add # alpha_t => add_1 # exp => exp # log => log # Graph fragment: # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%addmm,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, 1e-06), kwargs = {}) triton_poi_fused_add_exp_log_0 = async_compile.triton('triton_poi_fused_add_exp_log_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_log_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_exp_log_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl_math.exp(tmp0) tmp2 = 1.0 tmp3 = tmp1 + tmp2 tmp4 = tl_math.log(tmp3) tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tl.store(out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ep/cepfqjjpb375c2ikoxz7mzns3ngmha7yyokkvupmn6gz3u77bmal.py # Topologically Sorted Source Nodes: [exp_1, add_2, mu_t], Original ATen: [aten.exp, aten.add, aten.log] # Source node to ATen node mapping: # add_2 => add_2 # exp_1 => exp_1 # mu_t => log_1 # Graph fragment: # %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%addmm_1,), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp_1, 1), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_2,), kwargs = {}) triton_poi_fused_add_exp_log_1 = async_compile.triton('triton_poi_fused_add_exp_log_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_log_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_exp_log_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl_math.exp(tmp0) tmp2 = 1.0 tmp3 = tmp1 + tmp2 tmp4 = tl_math.log(tmp3) tl.store(out_ptr0 + (x0), tmp4, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_3, primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_2 del primals_3 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [exp, add, log, alpha_t], Original ATen: [aten.exp, aten.add, aten.log] stream0 = get_raw_stream(0) triton_poi_fused_add_exp_log_0.run(buf0, buf1, 16, grid=grid(16), stream=stream0) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, primals_1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_4 del primals_5 buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [exp_1, add_2, mu_t], Original ATen: [aten.exp, aten.add, aten.log] triton_poi_fused_add_exp_log_1.run(buf2, buf3, 16, grid=grid(16), stream=stream0) return (buf3, buf1, primals_1, buf0, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class NegativeBinomial(nn.Module): def __init__(self, input_size, output_size): """ Negative Binomial Supports Positive Count Data Args: input_size (int): hidden h_{i,t} column size output_size (int): embedding size """ super(NegativeBinomial, self).__init__() self.mu_layer = nn.Linear(input_size, output_size) self.sigma_layer = nn.Linear(input_size, output_size) def forward(self, h): _, _hidden_size = h.size() alpha_t = torch.log(1 + torch.exp(self.sigma_layer(h))) + 1e-06 mu_t = torch.log(1 + torch.exp(self.mu_layer(h))) return mu_t, alpha_t def get_inputs(): return [torch.rand([4, 4])] def get_init_inputs(): return [[], {'input_size': 4, 'output_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_add_exp_log_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl_math.exp(tmp0) tmp2 = 1.0 tmp3 = tmp1 + tmp2 tmp4 = tl_math.log(tmp3) tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tl.store(out_ptr0 + x0, tmp6, xmask) @triton.jit def triton_poi_fused_add_exp_log_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl_math.exp(tmp0) tmp2 = 1.0 tmp3 = tmp1 + tmp2 tmp4 = tl_math.log(tmp3) tl.store(out_ptr0 + x0, tmp4, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_3, primals_1, reinterpret_tensor( primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_2 del primals_3 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_exp_log_0[grid(16)](buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, primals_1, reinterpret_tensor( primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_4 del primals_5 buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_add_exp_log_1[grid(16)](buf2, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) return buf3, buf1, primals_1, buf0, buf2 class NegativeBinomialNew(nn.Module): def __init__(self, input_size, output_size): """ Negative Binomial Supports Positive Count Data Args: input_size (int): hidden h_{i,t} column size output_size (int): embedding size """ super(NegativeBinomialNew, self).__init__() self.mu_layer = nn.Linear(input_size, output_size) self.sigma_layer = nn.Linear(input_size, output_size) def forward(self, input_0): primals_1 = self.mu_layer.weight primals_3 = self.mu_layer.bias primals_2 = self.sigma_layer.weight primals_5 = self.sigma_layer.bias primals_4 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0], output[1]
ashfarhangi/COVID-19_Impact
NegativeBinomial
false
9,744
[ "Apache-2.0" ]
0
7ce46616278cac95e31b3e853bb28ea7b8e58b7e
https://github.com/ashfarhangi/COVID-19_Impact/tree/7ce46616278cac95e31b3e853bb28ea7b8e58b7e
LogitCosineDistance
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/zk/czk5xfokmwnuegxn53eciq25366p2is3a6lxx47tlosf3q225vha.py # Topologically Sorted Source Nodes: [a], Original ATen: [aten.div] # Source node to ATen node mapping: # a => div # Graph fragment: # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {}) triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = 1e-12 tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp15 = tmp0 / tmp14 tl.store(out_ptr0 + (x2), tmp15, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/pu/cpuco6jpedu2iv6scsy74f4j2eos27a57ov2zo2z6uiy5rds3jue.py # Topologically Sorted Source Nodes: [mul, sub, logit], Original ATen: [aten.mul, aten.rsub, aten.logit] # Source node to ATen node mapping: # logit => clamp_max, clamp_min_2, div_2, log, sub_1 # mul => mul # sub => sub # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mm, 0.5), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.5, %mul), kwargs = {}) # %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, -1.0), kwargs = {}) # %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 2.0), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %clamp_max), kwargs = {}) # %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, %sub_1), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_2,), kwargs = {}) triton_poi_fused_logit_mul_rsub_1 = async_compile.triton('triton_poi_fused_logit_mul_rsub_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_logit_mul_rsub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_logit_mul_rsub_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = tmp1 - tmp2 tmp4 = -1.0 tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp6 = 2.0 tmp7 = triton_helpers.minimum(tmp5, tmp6) tmp8 = 1.0 tmp9 = tmp8 - tmp7 tmp10 = tmp7 / tmp9 tmp11 = tl_math.log(tmp10) tl.store(in_out_ptr0 + (x0), tmp11, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [a], Original ATen: [aten.div] stream0 = get_raw_stream(0) triton_poi_fused_div_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0) del arg0_1 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [b], Original ATen: [aten.div] triton_poi_fused_div_0.run(arg1_1, buf1, 16, grid=grid(16), stream=stream0) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [a, mm], Original ATen: [aten.div, aten.mm] extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2) del buf0 del buf1 buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [mul, sub, logit], Original ATen: [aten.mul, aten.rsub, aten.logit] triton_poi_fused_logit_mul_rsub_1.run(buf3, 16, grid=grid(16), stream=stream0) return (buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.utils.data.dataloader import torch.nn def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False): """ Computes dot product for pairs of vectors. :param normalize: Vectors are normalized (leads to cosine similarity) :return: Matrix with res[i][j] = dot_product(a[i], b[j]) """ if len(a.shape) == 1: a = a.unsqueeze(0) if len(b.shape) == 1: b = b.unsqueeze(0) if normalize: a = torch.nn.functional.normalize(a, p=2, dim=1) b = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a, b.transpose(0, 1)) class LogitCosineDistance(torch.nn.Module): def forward(self, a, b): return torch.logit(0.5 - 0.5 * dot_product(a, b, normalize=True)) def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.utils.data.dataloader import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = 1e-12 tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp15 = tmp0 / tmp14 tl.store(out_ptr0 + x2, tmp15, xmask) @triton.jit def triton_poi_fused_logit_mul_rsub_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = 0.5 tmp2 = tmp0 * tmp1 tmp3 = tmp1 - tmp2 tmp4 = -1.0 tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp6 = 2.0 tmp7 = triton_helpers.minimum(tmp5, tmp6) tmp8 = 1.0 tmp9 = tmp8 - tmp7 tmp10 = tmp7 / tmp9 tmp11 = tl_math.log(tmp10) tl.store(in_out_ptr0 + x0, tmp11, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_div_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg0_1 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_div_0[grid(16)](arg1_1, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2) del buf0 del buf1 buf3 = buf2 del buf2 triton_poi_fused_logit_mul_rsub_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) return buf3, def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False): """ Computes dot product for pairs of vectors. :param normalize: Vectors are normalized (leads to cosine similarity) :return: Matrix with res[i][j] = dot_product(a[i], b[j]) """ if len(a.shape) == 1: a = a.unsqueeze(0) if len(b.shape) == 1: b = b.unsqueeze(0) if normalize: a = torch.nn.functional.normalize(a, p=2, dim=1) b = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a, b.transpose(0, 1)) class LogitCosineDistanceNew(torch.nn.Module): def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
adriensas/flair
LogitCosineDistance
false
9,745
[ "MIT" ]
0
f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
ClassificationModel
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/cb/ccbgymnr2fvk43axzcuowohjalipdfn2nc4qqvidfjzuqhtxsj6g.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 1024 xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = (yindex // 4) tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (4*x2) + (36*y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/j5/cj5nf2owtsdm2zwcezqxpyn63iwddjyadpotkhm2ua52inoqxdcl.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = (yindex // 4) tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask) tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/co/ccosum7u5lx5fx5hf5opofiygxj2ntiq67yo5gfegevmhtkaru4r.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 65536 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/c2/cc24idh7iumu54cabqtyf4bwq723mqt6nb4chiwnswjfaoolg4us.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_3 = async_compile.triton('triton_poi_fused_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[262144, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 184320 xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = (yindex // 256) tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (256*x2) + (2304*y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/wj/cwjii5g7vcokiqucazdgsrvnsqad3q7z4gbxiwezolbw7o6ilfmr.py # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out => convolution # out_1 => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/a4/ca4c3yd6q66dwodzebdcgw5zldkpmpsgwxpgq26t7t6ntqavyjst.py # Topologically Sorted Source Nodes: [out_6, out_7, out_8], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out_6 => convolution_3 # out_7 => relu_3 # out_8 => convolution_4 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {}) # %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_relu_5 = async_compile.triton('triton_poi_fused_convolution_relu_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024, 16], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 1024 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 256 y1 = (yindex // 256) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (256*x2) + (4096*y1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x2 + (16*y3)), tmp4, xmask) tl.store(out_ptr1 + (y0 + (256*x2) + (4096*y1)), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/vb/cvbww5jxf6vavimmpspkihpe4i2dsemk3rspryyjmv55u6itomyn.py # Topologically Sorted Source Nodes: [out_8, contiguous], Original ATen: [aten.convolution, aten.clone] # Source node to ATen node mapping: # contiguous => clone # out_8 => convolution_4 # Graph fragment: # %convolution_4 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_10, %primals_11, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_convolution_6 = async_compile.triton('triton_poi_fused_clone_convolution_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_convolution_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_convolution_6(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 46080 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 720 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + (x2), tmp2, xmask) tl.store(out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256, ), (1, )) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256, ), (1, )) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256, ), (1, )) assert_size_stride(primals_10, (720, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (720, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((256, 4, 3, 3), (36, 1, 12, 4), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_1, buf0, 1024, 9, grid=grid(1024, 9), stream=stream0) del primals_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_1.run(primals_3, buf1, 16, 16, grid=grid(16, 16), stream=stream0) del primals_3 buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_4, buf2, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_4 buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_6, buf3, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_6 buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_8, buf4, 65536, 9, grid=grid(65536, 9), stream=stream0) del primals_8 buf5 = empty_strided_cuda((720, 256, 3, 3), (2304, 1, 768, 256), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_3.run(primals_10, buf5, 184320, 9, grid=grid(184320, 9), stream=stream0) del primals_10 # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf7, primals_2, 16384, grid=grid(16384), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf9 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf9, primals_5, 16384, grid=grid(16384), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf11 = buf10; del buf10 # reuse # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf11, primals_7, 16384, grid=grid(16384), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.convolution] buf12 = extern_kernels.convolution(buf11, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf13 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch.float32) buf14 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256), torch.float32) # Topologically Sorted Source Nodes: [out_6, out_7, out_8], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_5.run(buf12, primals_9, buf13, buf14, 1024, 16, grid=grid(1024, 16), stream=stream0) del buf12 del primals_9 # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution] buf15 = extern_kernels.convolution(buf14, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf15, (4, 720, 4, 4), (11520, 1, 2880, 720)) del buf14 buf16 = buf15; del buf15 # reuse buf17 = empty_strided_cuda((4, 4, 4, 9, 80), (11520, 2880, 720, 80, 1), torch.float32) # Topologically Sorted Source Nodes: [out_8, contiguous], Original ATen: [aten.convolution, aten.clone] triton_poi_fused_clone_convolution_6.run(buf16, primals_11, buf17, 46080, grid=grid(46080), stream=stream0) del primals_11 return (reinterpret_tensor(buf17, (4, 144, 80), (11520, 80, 1), 0), buf13, buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf13, buf16, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((256, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((256, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((720, 256, 3, 3), (2304, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((720, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class ClassificationModel(nn.Module): def __init__(self, num_features_in, num_anchors=9, num_classes=80, prior=0.01, feature_size=256): super(ClassificationModel, self).__init__() self.num_classes = num_classes self.num_anchors = num_anchors self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1) self.output_act = nn.Sigmoid() def forward(self, x): out = self.conv1(x) out = self.act1(out) out = self.conv2(out) out = self.act2(out) out = self.conv3(out) out = self.act3(out) out = self.conv4(out) out = self.act4(out) final_features = out out = self.output(final_features) out = self.output_act(out) out1 = out.permute(0, 2, 3, 1) batch_size, width, height, _channels = out1.shape out2 = out1.view(batch_size, width, height, self.num_anchors, self. num_classes) return out2.contiguous().view(x.shape[0], -1, self.num_classes ), final_features def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_features_in': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = yindex // 4 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 4 y1 = yindex // 4 tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask) tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 256 y1 = yindex // 256 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 256 * x2 + 2304 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 256 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_5(in_ptr0, in_ptr1, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 256 y1 = yindex // 256 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 256 * x2 + 4096 * y1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1, 1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask) tl.store(out_ptr1 + (y0 + 256 * x2 + 4096 * y1), tmp4, xmask) @triton.jit def triton_poi_fused_clone_convolution_6(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 46080 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 720 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + x2, tmp2, xmask) tl.store(out_ptr0 + x2, tmp3, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (256, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (256,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_5, (256,), (1,)) assert_size_stride(primals_6, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_7, (256,), (1,)) assert_size_stride(primals_8, (256, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_9, (256,), (1,)) assert_size_stride(primals_10, (720, 256, 3, 3), (2304, 9, 3, 1)) assert_size_stride(primals_11, (720,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((256, 4, 3, 3), (36, 1, 12, 4), torch.float32 ) get_raw_stream(0) triton_poi_fused_0[grid(1024, 9)](primals_1, buf0, 1024, 9, XBLOCK= 16, YBLOCK=64, num_warps=4, num_stages=1) del primals_1 buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32) triton_poi_fused_1[grid(16, 16)](primals_3, buf1, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_2[grid(65536, 9)](primals_4, buf2, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_4 buf3 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_2[grid(65536, 9)](primals_6, buf3, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_6 buf4 = empty_strided_cuda((256, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_2[grid(65536, 9)](primals_8, buf4, 65536, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_8 buf5 = empty_strided_cuda((720, 256, 3, 3), (2304, 1, 768, 256), torch.float32) triton_poi_fused_3[grid(184320, 9)](primals_10, buf5, 184320, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_10 buf6 = extern_kernels.convolution(buf1, buf0, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf6, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf7 = buf6 del buf6 triton_poi_fused_convolution_relu_4[grid(16384)](buf7, primals_2, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf8 = extern_kernels.convolution(buf7, buf2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf9 = buf8 del buf8 triton_poi_fused_convolution_relu_4[grid(16384)](buf9, primals_5, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf10 = extern_kernels.convolution(buf9, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf11 = buf10 del buf10 triton_poi_fused_convolution_relu_4[grid(16384)](buf11, primals_7, 16384, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 buf12 = extern_kernels.convolution(buf11, buf4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf12, (4, 256, 4, 4), (4096, 1, 1024, 256)) buf13 = empty_strided_cuda((4, 256, 4, 4), (4096, 16, 4, 1), torch. float32) buf14 = empty_strided_cuda((4, 256, 4, 4), (4096, 1, 1024, 256), torch.float32) triton_poi_fused_convolution_relu_5[grid(1024, 16)](buf12, primals_9, buf13, buf14, 1024, 16, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del buf12 del primals_9 buf15 = extern_kernels.convolution(buf14, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf15, (4, 720, 4, 4), (11520, 1, 2880, 720)) del buf14 buf16 = buf15 del buf15 buf17 = empty_strided_cuda((4, 4, 4, 9, 80), (11520, 2880, 720, 80, 1), torch.float32) triton_poi_fused_clone_convolution_6[grid(46080)](buf16, primals_11, buf17, 46080, XBLOCK=512, num_warps=4, num_stages=1) del primals_11 return (reinterpret_tensor(buf17, (4, 144, 80), (11520, 80, 1), 0), buf13, buf0, buf1, buf2, buf3, buf4, buf5, buf7, buf9, buf11, buf13, buf16) class ClassificationModelNew(nn.Module): def __init__(self, num_features_in, num_anchors=9, num_classes=80, prior=0.01, feature_size=256): super(ClassificationModelNew, self).__init__() self.num_classes = num_classes self.num_anchors = num_anchors self.conv1 = nn.Conv2d(num_features_in, feature_size, kernel_size=3, padding=1) self.act1 = nn.ReLU() self.conv2 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act2 = nn.ReLU() self.conv3 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act3 = nn.ReLU() self.conv4 = nn.Conv2d(feature_size, feature_size, kernel_size=3, padding=1) self.act4 = nn.ReLU() self.output = nn.Conv2d(feature_size, num_anchors * num_classes, kernel_size=3, padding=1) self.output_act = nn.Sigmoid() def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_8 = self.conv4.weight primals_9 = self.conv4.bias primals_10 = self.output.weight primals_11 = self.output.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0], output[1]
alexrusciano/nms_free_retinanet
ClassificationModel
false
9,746
[ "Apache-2.0" ]
0
3461a86e9dea71a756b92a434c62798bbf86b52d
https://github.com/alexrusciano/nms_free_retinanet/tree/3461a86e9dea71a756b92a434c62798bbf86b52d
GATgate_lp
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/3f/c3f32qbeh2szq3ru33przn4ijnthqvbznxay35bo5hjb6mp2zqjt.py # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul] # Source node to ATen node mapping: # mul => mul # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, %view_1), kwargs = {}) triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask) tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + (x0), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ev/cevfo4mcpjpzd6p6bxguei26icqqukw4vd6hcag7qmoe7kpvyjnu.py # Topologically Sorted Source Nodes: [h_l2_1], Original ATen: [aten.leaky_relu] # Source node to ATen node mapping: # h_l2_1 => gt, mul_1, where # Graph fragment: # %gt : [num_users=2] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_9, 0), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_9, 0.01), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %view_9, %mul_1), kwargs = {}) triton_poi_fused_leaky_relu_1 = async_compile.triton('triton_poi_fused_leaky_relu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_leaky_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 0.01 tmp6 = tmp2 * tmp5 tmp7 = tl.where(tmp4, tmp2, tmp6) tl.store(out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr1 + (x2), tmp7, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4, ), (1, )) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_l], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_p], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_4 del primals_5 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_l2], Original ATen: [aten.bmm] extern_kernels.bmm(primals_7, reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0), out=buf2) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_mul_0.run(buf2, buf0, buf3, 64, grid=grid(64), stream=stream0) buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf4) buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_l2_1], Original ATen: [aten.leaky_relu] triton_poi_fused_leaky_relu_1.run(buf4, primals_9, buf5, buf6, 64, grid=grid(64), stream=stream0) del primals_9 buf7 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0); del buf4 # reuse # Topologically Sorted Source Nodes: [h_p2], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(primals_7, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul_1], Original ATen: [aten.mul] triton_poi_fused_mul_0.run(buf7, buf1, buf8, 64, grid=grid(64), stream=stream0) buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf9) buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_p2_1], Original ATen: [aten.leaky_relu] triton_poi_fused_leaky_relu_1.run(buf9, primals_11, buf10, buf11, 64, grid=grid(64), stream=stream0) del buf9 del primals_11 return (buf6, buf11, primals_7, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), buf0, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), buf1, buf2, reinterpret_tensor(buf3, (16, 4), (4, 1), 0), buf5, buf7, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), buf10, primals_10, primals_8, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class GATgate_lp(nn.Module): def __init__(self, n_dim): super(GATgate_lp, self).__init__() self.w_l1 = nn.Linear(n_dim, n_dim) self.w_l2 = nn.Linear(n_dim, n_dim) self.w_p1 = nn.Linear(n_dim, n_dim) self.w_p2 = nn.Linear(n_dim, n_dim) self.LR = nn.LeakyReLU() def forward(self, vec_l, vec_p, adj_inter): h_l = self.w_l1(vec_l) h_p = self.w_p1(vec_p) h_l2 = torch.einsum('aij,ajk->aik', (adj_inter, h_p)) h_l2 = self.LR(self.w_l2(h_l2 * h_l)) h_p2 = torch.einsum('aij,ajk->aik', (adj_inter.transpose(-1, -2), h_l)) h_p2 = self.LR(self.w_p2(h_p2 * h_p)) return h_l2, h_p2 def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'n_dim': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr1 + x0, xmask) tmp2 = tmp0 * tmp1 tl.store(out_ptr0 + x0, tmp2, xmask) @triton.jit def triton_poi_fused_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 0.01 tmp6 = tmp2 * tmp5 tmp7 = tl.where(tmp4, tmp2, tmp6) tl.store(out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr1 + x2, tmp7, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4,), (1,)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf1) del primals_4 del primals_5 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(primals_7, reinterpret_tensor(buf1, (4, 4, 4), ( 16, 4, 1), 0), out=buf2) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mul_0[grid(64)](buf2, buf0, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf4) buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_leaky_relu_1[grid(64)](buf4, primals_9, buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_9 buf7 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0) del buf4 extern_kernels.bmm(reinterpret_tensor(primals_7, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), out =buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_mul_0[grid(64)](buf7, buf1, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1) buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf9) buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_leaky_relu_1[grid(64)](buf9, primals_11, buf10, buf11, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf9 del primals_11 return buf6, buf11, primals_7, reinterpret_tensor(primals_3, (16, 4), ( 4, 1), 0), buf0, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0 ), buf1, buf2, reinterpret_tensor(buf3, (16, 4), (4, 1), 0 ), buf5, buf7, reinterpret_tensor(buf8, (16, 4), (4, 1), 0 ), buf10, primals_10, primals_8 class GATgate_lpNew(nn.Module): def __init__(self, n_dim): super(GATgate_lpNew, self).__init__() self.w_l1 = nn.Linear(n_dim, n_dim) self.w_l2 = nn.Linear(n_dim, n_dim) self.w_p1 = nn.Linear(n_dim, n_dim) self.w_p2 = nn.Linear(n_dim, n_dim) self.LR = nn.LeakyReLU() def forward(self, input_0, input_1, input_2): primals_1 = self.w_l1.weight primals_2 = self.w_l1.bias primals_4 = self.w_l2.weight primals_5 = self.w_l2.bias primals_8 = self.w_p1.weight primals_9 = self.w_p1.bias primals_10 = self.w_p2.weight primals_11 = self.w_p2.bias primals_3 = input_0 primals_6 = input_1 primals_7 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11]) return output[0], output[1]
arwhirang/affinity_prediction_BGNN
GATgate_lp
false
9,747
[ "MIT" ]
0
b8a2a5de16a61a46dadd53856d758e7f63f9ca91
https://github.com/arwhirang/affinity_prediction_BGNN/tree/b8a2a5de16a61a46dadd53856d758e7f63f9ca91
CRF
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/hf/chfy6golfkp5hyoqxyfqo3cuiy7r25sg3yr3dcq6fpt7nsbdq6zt.py # Topologically Sorted Source Nodes: [crf_scores], Original ATen: [aten.add] # Source node to ATen node mapping: # crf_scores => add # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%expand, %unsqueeze_2), kwargs = {}) triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = (xindex // 4) x4 = xindex % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x5), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [crf_scores], Original ATen: [aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_0.run(primals_1, primals_2, buf0, 256, grid=grid(256), stream=stream0) del primals_1 del primals_2 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.utils.data.dataloader import torch.nn class CRF(torch.nn.Module): """ Conditional Random Field Implementation according to sgrvinod (https://github.com/sgrvinod). Classifier which predicts single tag / class / label for given word based on not just the word, but also on previous seen annotations. """ def __init__(self, tag_dictionary, tagset_size: 'int', init_from_state_dict: 'bool'): """ :param tag_dictionary: tag dictionary in order to find ID for start and stop tags :param tagset_size: number of tag from tag dictionary :param init_from_state_dict: whether we load pretrained model from state dict """ super(CRF, self).__init__() self.tagset_size = tagset_size self.transitions = torch.nn.Parameter(torch.randn(tagset_size, tagset_size)) if not init_from_state_dict: self.transitions.detach()[tag_dictionary.get_idx_for_item( START_TAG), :] = -10000 self.transitions.detach()[:, tag_dictionary.get_idx_for_item( STOP_TAG)] = -10000 self def forward(self, features: 'torch.Tensor') ->torch.Tensor: """ Forward propagation of Conditional Random Field. :param features: output from RNN / Linear layer in shape (batch size, seq len, hidden size) :return: CRF scores (emission scores for each token + transitions prob from previous state) in shape (batch_size, seq len, tagset size, tagset size) """ batch_size, seq_len = features.size()[:2] emission_scores = features emission_scores = emission_scores.unsqueeze(-1).expand(batch_size, seq_len, self.tagset_size, self.tagset_size) crf_scores = emission_scores + self.transitions.unsqueeze(0).unsqueeze( 0) return crf_scores def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'tag_dictionary': 4, 'tagset_size': 4, 'init_from_state_dict': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data.dataloader import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex // 4 x4 = xindex % 16 x5 = xindex tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x5, tmp2, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_0[grid(256)](primals_1, primals_2, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 del primals_2 return buf0, class CRFNew(torch.nn.Module): """ Conditional Random Field Implementation according to sgrvinod (https://github.com/sgrvinod). Classifier which predicts single tag / class / label for given word based on not just the word, but also on previous seen annotations. """ def __init__(self, tag_dictionary, tagset_size: 'int', init_from_state_dict: 'bool'): """ :param tag_dictionary: tag dictionary in order to find ID for start and stop tags :param tagset_size: number of tag from tag dictionary :param init_from_state_dict: whether we load pretrained model from state dict """ super(CRFNew, self).__init__() self.tagset_size = tagset_size self.transitions = torch.nn.Parameter(torch.randn(tagset_size, tagset_size)) if not init_from_state_dict: self.transitions.detach()[tag_dictionary.get_idx_for_item( START_TAG), :] = -10000 self.transitions.detach()[:, tag_dictionary.get_idx_for_item( STOP_TAG)] = -10000 self def forward(self, input_0): primals_2 = self.transitions primals_1 = input_0 output = call([primals_1, primals_2]) return output[0]
adriensas/flair
CRF
false
9,748
[ "MIT" ]
0
f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
EncoderLayer
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/fz/cfzmg4qtw6jgry4nhlwopodzjz62ll3n3ykfox77hwd2crdnlh2w.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => exp # Graph fragment: # %mul_tensor_3 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 1), kwargs = {}) # %amax_default_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor_3, [-1], True), kwargs = {}) # %sub_tensor_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor_3, %amax_default_3), kwargs = {}) # %div_tensor_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor_3, 2.0), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor_3,), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + (x2), tmp17, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/kj/ckjtlefzavjukjsytvkak6ek26zmzexpcbnlwelx4k5kascjxlf3.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => div_1, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/mk/cmkim2hc4ksxhatli3y5cu7hoqofxcbzqrrxvnlhmswdt4cgww25.py # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] # Source node to ATen node mapping: # cat => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%bmm_1, %bmm_3, %bmm_5, %bmm_7], -1), kwargs = {}) triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = (xindex // 4) x2 = xindex tmp0 = x0 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 1, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 2, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + (x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 3, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x1), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp0 >= tmp12 tmp17 = tl.full([1], 4, tl.int64) tmp18 = tmp0 < tmp17 tmp19 = tl.load(in_ptr3 + (x1), tmp16 & xmask, eviction_policy='evict_last', other=0.0) tmp20 = tl.where(tmp14, tmp15, tmp19) tmp21 = tl.where(tmp9, tmp10, tmp20) tmp22 = tl.where(tmp4, tmp5, tmp21) tl.store(out_ptr0 + (x2), tmp22, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7f/c7fwok6q7j5rvjs3ob32s2cth5xjbedhynzb5ozchylog57bhmxv.py # Topologically Sorted Source Nodes: [add, mean, std], Original ATen: [aten.add, aten.mean, aten.std] # Source node to ATen node mapping: # add => add # mean => mean # std => var # Graph fragment: # %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %view_7), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add, [-1], True), kwargs = {}) # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%add, [-1]), kwargs = {correction: 1.0, keepdim: True}) triton_poi_fused_add_mean_std_3 = async_compile.triton('triton_poi_fused_add_mean_std_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_std_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mean_std_3(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = 3.0 tmp29 = tmp27 / tmp28 tl.store(in_out_ptr0 + (x0), tmp29, xmask) tl.store(out_ptr0 + (x0), tmp16, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/dw/cdwd24bmovp4kvuenv3jq6ffpahgl34iziauouexc57lxivmzubp.py # Topologically Sorted Source Nodes: [add, mean, std, sub, mul, add_1, truediv_4, add_2], Original ATen: [aten.add, aten.mean, aten.std, aten.sub, aten.mul, aten.div] # Source node to ATen node mapping: # add => add # add_1 => add_1 # add_2 => add_2 # mean => mean # mul => mul # std => sqrt # sub => sub_4 # truediv_4 => div_8 # Graph fragment: # %add : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %view_7), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add, [-1], True), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {}) # %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %mean), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_6, %sub_4), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-06), kwargs = {}) # %div_8 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add_1), kwargs = {}) # %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_8, %primals_7), kwargs = {}) triton_poi_fused_add_div_mean_mul_std_sub_4 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_std_sub_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp2 = tl.load(in_ptr2 + (x2), xmask) tmp4 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 - tmp4 tmp6 = tmp0 * tmp5 tmp8 = libdevice.sqrt(tmp7) tmp9 = 1e-06 tmp10 = tmp8 + tmp9 tmp11 = tmp6 / tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + (x2), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/va/cvayouropyisaprtjrhemadbdvsels72axdjsrgmbayknhu335yc.py # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # relu => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_9,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_5 = async_compile.triton('triton_poi_fused_relu_threshold_backward_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/dg/cdg2dxfjk7prchu44e4cgkid2y4524hl5vpyijgt6dwrnsrwzz2k.py # Topologically Sorted Source Nodes: [add_3], Original ATen: [aten.add] # Source node to ATen node mapping: # add_3 => add_3 # Graph fragment: # %add_3 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %view_11), kwargs = {}) triton_poi_fused_add_6 = async_compile.triton('triton_poi_fused_add_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_out_ptr0 + (x2), xmask) tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/j4/cj4wrybpym5umgwi5ropl654n64ptcknq2hunhzirmo6b5jmhqyj.py # Topologically Sorted Source Nodes: [mean_2, std_2, sub_1, mul_1, add_4, truediv_5, add_5], Original ATen: [aten.mean, aten.std, aten.sub, aten.mul, aten.add, aten.div] # Source node to ATen node mapping: # add_4 => add_4 # add_5 => add_5 # mean_2 => mean_1 # mul_1 => mul_1 # std_2 => sqrt_1, var_1 # sub_1 => sub_5 # truediv_5 => div_9 # Graph fragment: # %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%add_3, [-1], True), kwargs = {}) # %var_1 : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%add_3, [-1]), kwargs = {correction: 1.0, keepdim: True}) # %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var_1,), kwargs = {}) # %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %mean_1), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_12, %sub_5), kwargs = {}) # %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt_1, 1e-06), kwargs = {}) # %div_9 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_4), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_9, %primals_13), kwargs = {}) triton_poi_fused_add_div_mean_mul_std_sub_7 = async_compile.triton('triton_poi_fused_add_div_mean_mul_std_sub_7', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mean_mul_std_sub_7', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_7(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x2), xmask) tmp2 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp0 * tmp11 tmp13 = tmp2 - tmp10 tmp14 = tmp13 * tmp13 tmp15 = tmp3 - tmp10 tmp16 = tmp15 * tmp15 tmp17 = tmp14 + tmp16 tmp18 = tmp5 - tmp10 tmp19 = tmp18 * tmp18 tmp20 = tmp17 + tmp19 tmp21 = tmp7 - tmp10 tmp22 = tmp21 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = 3.0 tmp25 = tmp23 / tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = 1e-06 tmp28 = tmp26 + tmp27 tmp29 = tmp12 / tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + (x2), tmp31, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, ), (1, )) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4, ), (1, )) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4, ), (1, )) assert_size_stride(primals_12, (4, ), (1, )) assert_size_stride(primals_13, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [query], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [key], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [value], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) del primals_4 buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [dot_products], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 0), out=buf3) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf4, buf5, 64, grid=grid(64), stream=stream0) buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_1], Original ATen: [aten.bmm] extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 0), out=buf6) buf7 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [dot_products_1], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 1), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_0.run(buf7, buf8, 64, grid=grid(64), stream=stream0) buf9 = buf7; del buf7 # reuse # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf8, buf9, 64, grid=grid(64), stream=stream0) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_3], Original ATen: [aten.bmm] extern_kernels.bmm(buf9, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 1), out=buf10) buf11 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [dot_products_2], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 2), out=buf11) buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax_2], Original ATen: [aten._softmax] triton_poi_fused__softmax_0.run(buf11, buf12, 64, grid=grid(64), stream=stream0) buf13 = buf11; del buf11 # reuse # Topologically Sorted Source Nodes: [softmax_2], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf12, buf13, 64, grid=grid(64), stream=stream0) buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_5], Original ATen: [aten.bmm] extern_kernels.bmm(buf13, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 2), out=buf14) buf15 = buf12; del buf12 # reuse # Topologically Sorted Source Nodes: [dot_products_3], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 3), out=buf15) buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax_3], Original ATen: [aten._softmax] triton_poi_fused__softmax_0.run(buf15, buf16, 64, grid=grid(64), stream=stream0) buf17 = buf15; del buf15 # reuse # Topologically Sorted Source Nodes: [softmax_3], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf16, buf17, 64, grid=grid(64), stream=stream0) buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [bmm_7], Original ATen: [aten.bmm] extern_kernels.bmm(buf17, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 3), out=buf18) buf19 = buf16; del buf16 # reuse # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat] triton_poi_fused_cat_2.run(buf6, buf10, buf14, buf18, buf19, 64, grid=grid(64), stream=stream0) del buf10 del buf14 buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf20) buf21 = reinterpret_tensor(buf6, (4, 4, 1), (4, 1, 16), 0); del buf6 # reuse buf22 = buf21; del buf21 # reuse buf23 = reinterpret_tensor(buf18, (4, 4, 1), (4, 1, 16), 0); del buf18 # reuse # Topologically Sorted Source Nodes: [add, mean, std], Original ATen: [aten.add, aten.mean, aten.std] triton_poi_fused_add_mean_std_3.run(buf22, primals_2, buf20, buf23, 16, grid=grid(16), stream=stream0) buf24 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add, mean, std, sub, mul, add_1, truediv_4, add_2], Original ATen: [aten.add, aten.mean, aten.std, aten.sub, aten.mul, aten.div] triton_poi_fused_add_div_mean_mul_std_sub_4.run(primals_6, primals_2, buf20, buf23, buf22, primals_7, buf24, 64, grid=grid(64), stream=stream0) del buf22 del buf23 del primals_7 buf25 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf24, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf25) buf26 = reinterpret_tensor(buf25, (4, 4, 4), (16, 4, 1), 0); del buf25 # reuse buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_5.run(buf26, primals_9, buf30, 64, grid=grid(64), stream=stream0) del primals_9 buf27 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf26, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf27) buf28 = reinterpret_tensor(buf27, (4, 4, 4), (16, 4, 1), 0); del buf27 # reuse # Topologically Sorted Source Nodes: [add_3], Original ATen: [aten.add] triton_poi_fused_add_6.run(buf28, buf24, primals_11, 64, grid=grid(64), stream=stream0) del primals_11 buf29 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mean_2, std_2, sub_1, mul_1, add_4, truediv_5, add_5], Original ATen: [aten.mean, aten.std, aten.sub, aten.mul, aten.add, aten.div] triton_poi_fused_add_div_mean_mul_std_sub_7.run(primals_12, buf28, primals_13, buf29, 64, grid=grid(64), stream=stream0) del primals_13 return (buf29, primals_2, primals_6, primals_12, buf5, buf9, buf13, buf17, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), buf20, reinterpret_tensor(buf24, (16, 4), (4, 1), 0), reinterpret_tensor(buf26, (16, 4), (4, 1), 0), buf28, primals_10, buf30, primals_8, primals_5, reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 3), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 3), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 2), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 2), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 1), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 1), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch from torch import nn from torch.nn import functional as F class LayerNorm(nn.Module): def __init__(self, d_model, eps=1e-06): super(LayerNorm, self).__init__() self.gamma = nn.Parameter(torch.ones(d_model)) self.beta = nn.Parameter(torch.zeros(d_model)) self.eps = eps def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.gamma * (x - mean) / (std + self.eps) + self.beta class ResidualBlock(nn.Module): def __init__(self, layer, d_model, drop_ratio): super(ResidualBlock, self).__init__() self.layer = layer self.dropout = nn.Dropout(drop_ratio) self.layernorm = LayerNorm(d_model) def forward(self, *x): return self.layernorm(x[0] + self.dropout(self.layer(*x))) class Attention(nn.Module): def __init__(self, d_key, drop_ratio, causal): super(Attention, self).__init__() self.scale = math.sqrt(d_key) self.dropout = nn.Dropout(drop_ratio) self.causal = causal def forward(self, query, key, value): dot_products = torch.bmm(query, key.transpose(1, 2)) if query.dim() == 3 and (self is None or self.causal): tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF if key.is_cuda: tri = tri dot_products.data.sub_(tri.unsqueeze(0)) return torch.bmm(self.dropout(F.softmax(dot_products / self.scale, dim=-1)), value) class MultiHead(nn.Module): def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False): super(MultiHead, self).__init__() self.attention = Attention(d_key, drop_ratio, causal=causal) self.wq = nn.Linear(d_key, d_key, bias=False) self.wk = nn.Linear(d_key, d_key, bias=False) self.wv = nn.Linear(d_value, d_value, bias=False) self.wo = nn.Linear(d_value, d_key, bias=False) self.n_heads = n_heads def forward(self, query, key, value): query, key, value = self.wq(query), self.wk(key), self.wv(value) query, key, value = (x.chunk(self.n_heads, -1) for x in (query, key, value)) return self.wo(torch.cat([self.attention(q, k, v) for q, k, v in zip(query, key, value)], -1)) class FeedForward(nn.Module): def __init__(self, d_model, d_hidden): super(FeedForward, self).__init__() self.linear1 = nn.Linear(d_model, d_hidden) self.linear2 = nn.Linear(d_hidden, d_model) def forward(self, x): return self.linear2(F.relu(self.linear1(x))) class EncoderLayer(nn.Module): def __init__(self, d_model, d_hidden, n_heads, drop_ratio): super(EncoderLayer, self).__init__() self.selfattn = ResidualBlock(MultiHead(d_model, d_model, n_heads, drop_ratio, causal=False), d_model, drop_ratio) self.feedforward = ResidualBlock(FeedForward(d_model, d_hidden), d_model, drop_ratio) def forward(self, x): return self.feedforward(self.selfattn(x, x, x)) def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'d_model': 4, 'd_hidden': 4, 'n_heads': 4, 'drop_ratio': 0.5}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import math from torch import nn from torch.nn import functional as F assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = 0.5 tmp16 = tmp14 * tmp15 tmp17 = tl_math.exp(tmp16) tl.store(out_ptr0 + x2, tmp17, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x1 = xindex // 4 x2 = xindex tmp0 = x0 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 1, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp6 = tmp0 >= tmp3 tmp7 = tl.full([1], 2, tl.int64) tmp8 = tmp0 < tmp7 tmp9 = tmp6 & tmp8 tmp10 = tl.load(in_ptr1 + x1, tmp9 & xmask, eviction_policy= 'evict_last', other=0.0) tmp11 = tmp0 >= tmp7 tmp12 = tl.full([1], 3, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + x1, tmp14 & xmask, eviction_policy= 'evict_last', other=0.0) tmp16 = tmp0 >= tmp12 tl.full([1], 4, tl.int64) tmp19 = tl.load(in_ptr3 + x1, tmp16 & xmask, eviction_policy= 'evict_last', other=0.0) tmp20 = tl.where(tmp14, tmp15, tmp19) tmp21 = tl.where(tmp9, tmp10, tmp20) tmp22 = tl.where(tmp4, tmp5, tmp21) tl.store(out_ptr0 + x2, tmp22, xmask) @triton.jit def triton_poi_fused_add_mean_std_3(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last' ) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tmp9 = tmp7 + tmp8 tmp10 = tmp6 + tmp9 tmp13 = tmp11 + tmp12 tmp14 = tmp10 + tmp13 tmp15 = 4.0 tmp16 = tmp14 / tmp15 tmp17 = tmp2 - tmp16 tmp18 = tmp17 * tmp17 tmp19 = tmp5 - tmp16 tmp20 = tmp19 * tmp19 tmp21 = tmp18 + tmp20 tmp22 = tmp9 - tmp16 tmp23 = tmp22 * tmp22 tmp24 = tmp21 + tmp23 tmp25 = tmp13 - tmp16 tmp26 = tmp25 * tmp25 tmp27 = tmp24 + tmp26 tmp28 = 3.0 tmp29 = tmp27 / tmp28 tl.store(in_out_ptr0 + x0, tmp29, xmask) tl.store(out_ptr0 + x0, tmp16, xmask) @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask) tmp2 = tl.load(in_ptr2 + x2, xmask) tmp4 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last') tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 - tmp4 tmp6 = tmp0 * tmp5 tmp8 = libdevice.sqrt(tmp7) tmp9 = 1e-06 tmp10 = tmp8 + tmp9 tmp11 = tmp6 / tmp10 tmp13 = tmp11 + tmp12 tl.store(out_ptr0 + x2, tmp13, xmask) @triton.jit def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) @triton.jit def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_out_ptr0 + x2, xmask) tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp4 = tmp0 + tmp3 tl.store(in_out_ptr0 + x2, tmp4, xmask) @triton.jit def triton_poi_fused_add_div_mean_mul_std_sub_7(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x2, xmask) tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp0 * tmp11 tmp13 = tmp2 - tmp10 tmp14 = tmp13 * tmp13 tmp15 = tmp3 - tmp10 tmp16 = tmp15 * tmp15 tmp17 = tmp14 + tmp16 tmp18 = tmp5 - tmp10 tmp19 = tmp18 * tmp18 tmp20 = tmp17 + tmp19 tmp21 = tmp7 - tmp10 tmp22 = tmp21 * tmp21 tmp23 = tmp20 + tmp22 tmp24 = 3.0 tmp25 = tmp23 / tmp24 tmp26 = libdevice.sqrt(tmp25) tmp27 = 1e-06 tmp28 = tmp26 + tmp27 tmp29 = tmp12 / tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + x2, tmp31, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4,), (1,)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4,), (1,)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4,), (1,)) assert_size_stride(primals_12, (4,), (1,)) assert_size_stride(primals_13, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1) del primals_3 buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) del primals_4 buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 0), out=buf3) buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = buf3 del buf3 triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1) buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 0), out=buf6) buf7 = buf4 del buf4 extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 1), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_0[grid(64)](buf7, buf8, 64, XBLOCK=64, num_warps=1, num_stages=1) buf9 = buf7 del buf7 triton_poi_fused__softmax_1[grid(64)](buf8, buf9, 64, XBLOCK=64, num_warps=1, num_stages=1) buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf9, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 1), out=buf10) buf11 = buf8 del buf8 extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 2), out=buf11) buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_0[grid(64)](buf11, buf12, 64, XBLOCK=64, num_warps=1, num_stages=1) buf13 = buf11 del buf11 triton_poi_fused__softmax_1[grid(64)](buf12, buf13, 64, XBLOCK=64, num_warps=1, num_stages=1) buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf13, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 2), out=buf14) buf15 = buf12 del buf12 extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 3), out=buf15) buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__softmax_0[grid(64)](buf15, buf16, 64, XBLOCK=64, num_warps=1, num_stages=1) buf17 = buf15 del buf15 triton_poi_fused__softmax_1[grid(64)](buf16, buf17, 64, XBLOCK=64, num_warps=1, num_stages=1) buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(buf17, reinterpret_tensor(buf2, (4, 4, 1), (16, 4, 1), 3), out=buf18) buf19 = buf16 del buf16 triton_poi_fused_cat_2[grid(64)](buf6, buf10, buf14, buf18, buf19, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf10 del buf14 buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf20) buf21 = reinterpret_tensor(buf6, (4, 4, 1), (4, 1, 16), 0) del buf6 buf22 = buf21 del buf21 buf23 = reinterpret_tensor(buf18, (4, 4, 1), (4, 1, 16), 0) del buf18 triton_poi_fused_add_mean_std_3[grid(16)](buf22, primals_2, buf20, buf23, 16, XBLOCK=16, num_warps=1, num_stages=1) buf24 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_add_div_mean_mul_std_sub_4[grid(64)](primals_6, primals_2, buf20, buf23, buf22, primals_7, buf24, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf22 del buf23 del primals_7 buf25 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf24, (16, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf25) buf26 = reinterpret_tensor(buf25, (4, 4, 4), (16, 4, 1), 0) del buf25 buf30 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_5[grid(64)](buf26, primals_9, buf30, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_9 buf27 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf26, (16, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf27) buf28 = reinterpret_tensor(buf27, (4, 4, 4), (16, 4, 1), 0) del buf27 triton_poi_fused_add_6[grid(64)](buf28, buf24, primals_11, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_11 buf29 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_add_div_mean_mul_std_sub_7[grid(64)](primals_12, buf28, primals_13, buf29, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_13 return (buf29, primals_2, primals_6, primals_12, buf5, buf9, buf13, buf17, reinterpret_tensor(buf19, (16, 4), (4, 1), 0), buf20, reinterpret_tensor(buf24, (16, 4), (4, 1), 0), reinterpret_tensor( buf26, (16, 4), (4, 1), 0), buf28, primals_10, buf30, primals_8, primals_5, reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 3), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 3), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 3), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 2), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 2), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 2), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 1), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 1), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 1), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 0), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 0)) class LayerNorm(nn.Module): def __init__(self, d_model, eps=1e-06): super(LayerNorm, self).__init__() self.gamma = nn.Parameter(torch.ones(d_model)) self.beta = nn.Parameter(torch.zeros(d_model)) self.eps = eps def forward(self, x): mean = x.mean(-1, keepdim=True) std = x.std(-1, keepdim=True) return self.gamma * (x - mean) / (std + self.eps) + self.beta class ResidualBlock(nn.Module): def __init__(self, layer, d_model, drop_ratio): super(ResidualBlock, self).__init__() self.layer = layer self.dropout = nn.Dropout(drop_ratio) self.layernorm = LayerNorm(d_model) def forward(self, *x): return self.layernorm(x[0] + self.dropout(self.layer(*x))) class Attention(nn.Module): def __init__(self, d_key, drop_ratio, causal): super(Attention, self).__init__() self.scale = math.sqrt(d_key) self.dropout = nn.Dropout(drop_ratio) self.causal = causal def forward(self, query, key, value): dot_products = torch.bmm(query, key.transpose(1, 2)) if query.dim() == 3 and (self is None or self.causal): tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF if key.is_cuda: tri = tri dot_products.data.sub_(tri.unsqueeze(0)) return torch.bmm(self.dropout(F.softmax(dot_products / self.scale, dim=-1)), value) class MultiHead(nn.Module): def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False): super(MultiHead, self).__init__() self.attention = Attention(d_key, drop_ratio, causal=causal) self.wq = nn.Linear(d_key, d_key, bias=False) self.wk = nn.Linear(d_key, d_key, bias=False) self.wv = nn.Linear(d_value, d_value, bias=False) self.wo = nn.Linear(d_value, d_key, bias=False) self.n_heads = n_heads def forward(self, query, key, value): query, key, value = self.wq(query), self.wk(key), self.wv(value) query, key, value = (x.chunk(self.n_heads, -1) for x in (query, key, value)) return self.wo(torch.cat([self.attention(q, k, v) for q, k, v in zip(query, key, value)], -1)) class FeedForward(nn.Module): def __init__(self, d_model, d_hidden): super(FeedForward, self).__init__() self.linear1 = nn.Linear(d_model, d_hidden) self.linear2 = nn.Linear(d_hidden, d_model) def forward(self, x): return self.linear2(F.relu(self.linear1(x))) class EncoderLayerNew(nn.Module): def __init__(self, d_model, d_hidden, n_heads, drop_ratio): super(EncoderLayerNew, self).__init__() self.selfattn = ResidualBlock(MultiHead(d_model, d_model, n_heads, drop_ratio, causal=False), d_model, drop_ratio) self.feedforward = ResidualBlock(FeedForward(d_model, d_hidden), d_model, drop_ratio) def forward(self, input_0): primals_1 = self.selfattn.layer.wq.weight primals_3 = self.selfattn.layer.wk.weight primals_4 = self.selfattn.layer.wv.weight primals_5 = self.selfattn.layer.wo.weight primals_6 = self.selfattn.layernorm.gamma primals_7 = self.selfattn.layernorm.beta primals_8 = self.feedforward.layer.linear1.weight primals_9 = self.feedforward.layer.linear1.bias primals_10 = self.feedforward.layer.linear2.weight primals_11 = self.feedforward.layer.linear2.bias primals_12 = self.feedforward.layernorm.gamma primals_13 = self.feedforward.layernorm.beta primals_2 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13]) return output[0]
Sy-Zhang/recurrent-transformer
EncoderLayer
false
9,749
[ "MIT" ]
0
f66ba49a2c9ec42759d3d00d497b49ffe39e18de
https://github.com/Sy-Zhang/recurrent-transformer/tree/f66ba49a2c9ec42759d3d00d497b49ffe39e18de
TenLayerNet
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le_8 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4, ), (1, )) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4, ), (1, )) assert_size_stride(primals_12, (4, 4), (4, 1)) assert_size_stride(primals_13, (4, ), (1, )) assert_size_stride(primals_14, (4, 4), (4, 1)) assert_size_stride(primals_15, (4, ), (1, )) assert_size_stride(primals_16, (4, 4), (4, 1)) assert_size_stride(primals_17, (4, ), (1, )) assert_size_stride(primals_18, (4, 4), (4, 1)) assert_size_stride(primals_19, (4, ), (1, )) assert_size_stride(primals_20, (4, 4), (4, 1)) assert_size_stride(primals_21, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf27 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf27, 256, grid=grid(256), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse buf26 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf26, 256, grid=grid(256), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf4 # reuse buf25 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf5, primals_7, buf25, 256, grid=grid(256), stream=stream0) del primals_7 buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6) buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf6 # reuse buf24 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf7, primals_9, buf24, 256, grid=grid(256), stream=stream0) del primals_9 buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf8) buf9 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf8 # reuse buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf9, primals_11, buf23, 256, grid=grid(256), stream=stream0) del primals_11 buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf10) buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf10 # reuse buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf11, primals_13, buf22, 256, grid=grid(256), stream=stream0) del primals_13 buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf12) buf13 = reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf12 # reuse buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf13, primals_15, buf21, 256, grid=grid(256), stream=stream0) del primals_15 buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(primals_16, (4, 4), (1, 4), 0), out=buf14) buf15 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf14 # reuse buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf15, primals_17, buf20, 256, grid=grid(256), stream=stream0) del primals_17 buf16 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf15, (64, 4), (4, 1), 0), reinterpret_tensor(primals_18, (4, 4), (1, 4), 0), out=buf16) buf17 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf16 # reuse buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.relu, aten.threshold_backward] triton_poi_fused_relu_threshold_backward_0.run(buf17, primals_19, buf19, 256, grid=grid(256), stream=stream0) del primals_19 buf18 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [y_pred], Original ATen: [aten.addmm] extern_kernels.addmm(primals_21, reinterpret_tensor(buf17, (64, 4), (4, 1), 0), reinterpret_tensor(primals_20, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf18) del primals_21 return (reinterpret_tensor(buf18, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(buf9, (64, 4), (4, 1), 0), reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(buf15, (64, 4), (4, 1), 0), reinterpret_tensor(buf17, (64, 4), (4, 1), 0), primals_20, buf19, primals_18, buf20, primals_16, buf21, primals_14, buf22, primals_12, buf23, primals_10, buf24, primals_8, buf25, primals_6, buf26, primals_4, buf27, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_11 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_12 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_13 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_14 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_15 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_16 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_17 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_18 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_19 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_20 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_21 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch class TenLayerNet(torch.nn.Module): def __init__(self, D_in, H, D_out): super(TenLayerNet, self).__init__() self.linear1 = torch.nn.Linear(D_in, H) self.linear2 = torch.nn.Linear(H, H) self.linear3 = torch.nn.Linear(H, H) self.linear4 = torch.nn.Linear(H, H) self.linear5 = torch.nn.Linear(H, H) self.linear6 = torch.nn.Linear(H, H) self.linear7 = torch.nn.Linear(H, H) self.linear8 = torch.nn.Linear(H, H) self.linear9 = torch.nn.Linear(H, H) self.linear10 = torch.nn.Linear(H, D_out) self.dropout = torch.nn.Dropout(p=0.5) self.relu = torch.nn.ReLU() def forward(self, x): x = self.relu(self.linear1(x)) x = self.dropout(x) x = self.relu(self.linear2(x)) x = self.dropout(x) x = self.relu(self.linear3(x)) x = self.dropout(x) x = self.relu(self.linear4(x)) x = self.dropout(x) x = self.relu(self.linear5(x)) x = self.dropout(x) x = self.relu(self.linear6(x)) x = self.dropout(x) x = self.relu(self.linear7(x)) x = self.dropout(x) x = self.relu(self.linear8(x)) x = self.dropout(x) x = self.relu(self.linear9(x)) x = self.dropout(x) y_pred = self.linear10(x) return y_pred def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'D_in': 4, 'H': 4, 'D_out': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4), (4, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4, 4), (4, 1)) assert_size_stride(primals_9, (4,), (1,)) assert_size_stride(primals_10, (4, 4), (4, 1)) assert_size_stride(primals_11, (4,), (1,)) assert_size_stride(primals_12, (4, 4), (4, 1)) assert_size_stride(primals_13, (4,), (1,)) assert_size_stride(primals_14, (4, 4), (4, 1)) assert_size_stride(primals_15, (4,), (1,)) assert_size_stride(primals_16, (4, 4), (4, 1)) assert_size_stride(primals_17, (4,), (1,)) assert_size_stride(primals_18, (4, 4), (4, 1)) assert_size_stride(primals_19, (4,), (1,)) assert_size_stride(primals_20, (4, 4), (4, 1)) assert_size_stride(primals_21, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf27 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_2, buf27, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf2 buf26 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf3, primals_5, buf26, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4) buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf4 buf25 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf5, primals_7, buf25, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf6) buf7 = reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf6 buf24 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf7, primals_9, buf24, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_9 buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf8) buf9 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf8 buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf9, primals_11, buf23, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_11 buf10 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf10) buf11 = reinterpret_tensor(buf10, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf10 buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf11, primals_13, buf22, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_13 buf12 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf11, (64, 4), (4, 1), 0), reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf12) buf13 = reinterpret_tensor(buf12, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf12 buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf13, primals_15, buf21, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_15 buf14 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(primals_16, (4, 4), (1, 4), 0), out=buf14) buf15 = reinterpret_tensor(buf14, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf14 buf20 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf15, primals_17, buf20, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_17 buf16 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf15, (64, 4), (4, 1), 0), reinterpret_tensor(primals_18, (4, 4), (1, 4), 0), out=buf16) buf17 = reinterpret_tensor(buf16, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf16 buf19 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf17, primals_19, buf19, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_19 buf18 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_21, reinterpret_tensor(buf17, (64, 4), (4, 1), 0), reinterpret_tensor(primals_20, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf18) del primals_21 return (reinterpret_tensor(buf18, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor( buf3, (64, 4), (4, 1), 0), reinterpret_tensor(buf5, (64, 4), (4, 1), 0), reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(buf9, (64, 4), (4, 1), 0), reinterpret_tensor( buf11, (64, 4), (4, 1), 0), reinterpret_tensor(buf13, (64, 4), (4, 1), 0), reinterpret_tensor(buf15, (64, 4), (4, 1), 0), reinterpret_tensor(buf17, (64, 4), (4, 1), 0), primals_20, buf19, primals_18, buf20, primals_16, buf21, primals_14, buf22, primals_12, buf23, primals_10, buf24, primals_8, buf25, primals_6, buf26, primals_4, buf27) class TenLayerNetNew(torch.nn.Module): def __init__(self, D_in, H, D_out): super(TenLayerNetNew, self).__init__() self.linear1 = torch.nn.Linear(D_in, H) self.linear2 = torch.nn.Linear(H, H) self.linear3 = torch.nn.Linear(H, H) self.linear4 = torch.nn.Linear(H, H) self.linear5 = torch.nn.Linear(H, H) self.linear6 = torch.nn.Linear(H, H) self.linear7 = torch.nn.Linear(H, H) self.linear8 = torch.nn.Linear(H, H) self.linear9 = torch.nn.Linear(H, H) self.linear10 = torch.nn.Linear(H, D_out) self.dropout = torch.nn.Dropout(p=0.5) self.relu = torch.nn.ReLU() def forward(self, input_0): primals_1 = self.linear1.weight primals_2 = self.linear1.bias primals_4 = self.linear2.weight primals_5 = self.linear2.bias primals_6 = self.linear3.weight primals_7 = self.linear3.bias primals_8 = self.linear4.weight primals_9 = self.linear4.bias primals_10 = self.linear5.weight primals_11 = self.linear5.bias primals_12 = self.linear6.weight primals_13 = self.linear6.bias primals_14 = self.linear7.weight primals_15 = self.linear7.bias primals_16 = self.linear8.weight primals_17 = self.linear8.bias primals_18 = self.linear9.weight primals_19 = self.linear9.bias primals_20 = self.linear10.weight primals_21 = self.linear10.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21]) return output[0]
anvitha-bhat/iot_final_project
TenLayerNet
false
9,750
[ "MIT" ]
0
e9301c083d5e7a228d0ad868e44cb1df3a5f7363
https://github.com/anvitha-bhat/iot_final_project/tree/e9301c083d5e7a228d0ad868e44cb1df3a5f7363
CosineDistance
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/zk/czk5xfokmwnuegxn53eciq25366p2is3a6lxx47tlosf3q225vha.py # Topologically Sorted Source Nodes: [a], Original ATen: [aten.div] # Source node to ATen node mapping: # a => div # Graph fragment: # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg0_1, %expand), kwargs = {}) triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = 1e-12 tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp15 = tmp0 / tmp14 tl.store(out_ptr0 + (x2), tmp15, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/fh/cfhxlck7uzhxtofknhjghf2xokzgxovbt22nsyc7lfq6lggpmsc6.py # Topologically Sorted Source Nodes: [neg], Original ATen: [aten.neg] # Source node to ATen node mapping: # neg => neg # Graph fragment: # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mm,), kwargs = {}) triton_poi_fused_neg_1 = async_compile.triton('triton_poi_fused_neg_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_neg_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_neg_1(in_out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = -tmp0 tl.store(in_out_ptr0 + (x0), tmp1, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [a], Original ATen: [aten.div] stream0 = get_raw_stream(0) triton_poi_fused_div_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0) del arg0_1 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [b], Original ATen: [aten.div] triton_poi_fused_div_0.run(arg1_1, buf1, 16, grid=grid(16), stream=stream0) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [a, mm], Original ATen: [aten.div, aten.mm] extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2) del buf0 del buf1 buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [neg], Original ATen: [aten.neg] triton_poi_fused_neg_1.run(buf3, 16, grid=grid(16), stream=stream0) return (buf3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.utils.data.dataloader import torch.nn def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False): """ Computes dot product for pairs of vectors. :param normalize: Vectors are normalized (leads to cosine similarity) :return: Matrix with res[i][j] = dot_product(a[i], b[j]) """ if len(a.shape) == 1: a = a.unsqueeze(0) if len(b.shape) == 1: b = b.unsqueeze(0) if normalize: a = torch.nn.functional.normalize(a, p=2, dim=1) b = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a, b.transpose(0, 1)) class CosineDistance(torch.nn.Module): def forward(self, a, b): return -dot_product(a, b, normalize=True) def get_inputs(): return [torch.rand([4, 4]), torch.rand([4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.utils.data.dataloader import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp2 = tmp1 * tmp1 tmp4 = tmp3 * tmp3 tmp5 = tmp2 + tmp4 tmp7 = tmp6 * tmp6 tmp8 = tmp5 + tmp7 tmp10 = tmp9 * tmp9 tmp11 = tmp8 + tmp10 tmp12 = libdevice.sqrt(tmp11) tmp13 = 1e-12 tmp14 = triton_helpers.maximum(tmp12, tmp13) tmp15 = tmp0 / tmp14 tl.store(out_ptr0 + x2, tmp15, xmask) @triton.jit def triton_poi_fused_neg_1(in_out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = -tmp0 tl.store(in_out_ptr0 + x0, tmp1, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4), (4, 1)) assert_size_stride(arg1_1, (4, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_div_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg0_1 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) triton_poi_fused_div_0[grid(16)](arg1_1, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) del arg1_1 buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2) del buf0 del buf1 buf3 = buf2 del buf2 triton_poi_fused_neg_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1, num_stages=1) return buf3, def dot_product(a: 'torch.Tensor', b: 'torch.Tensor', normalize=False): """ Computes dot product for pairs of vectors. :param normalize: Vectors are normalized (leads to cosine similarity) :return: Matrix with res[i][j] = dot_product(a[i], b[j]) """ if len(a.shape) == 1: a = a.unsqueeze(0) if len(b.shape) == 1: b = b.unsqueeze(0) if normalize: a = torch.nn.functional.normalize(a, p=2, dim=1) b = torch.nn.functional.normalize(b, p=2, dim=1) return torch.mm(a, b.transpose(0, 1)) class CosineDistanceNew(torch.nn.Module): def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
adriensas/flair
CosineDistance
false
9,751
[ "MIT" ]
0
f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
L1_Charbonnier_loss
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/2r/c2r4vrcqezcb7b3qaarvj7x5e62di3smzewo2zzbig52lkg5xuq4.py # Topologically Sorted Source Nodes: [neg, diff, mul, add_1, error, loss], Original ATen: [aten.neg, aten.add, aten.mul, aten.sqrt, aten.sum] # Source node to ATen node mapping: # add_1 => add_1 # diff => add # error => sqrt # loss => sum_1 # mul => mul # neg => neg # Graph fragment: # %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg0_1,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg1_1, %neg), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %add), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1e-06), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sqrt,), kwargs = {}) triton_per_fused_add_mul_neg_sqrt_sum_0 = async_compile.triton('triton_per_fused_add_mul_neg_sqrt_sum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_neg_sqrt_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_add_mul_neg_sqrt_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp1 = tl.load(in_ptr1 + (r0), None) tmp2 = -tmp1 tmp3 = tmp0 + tmp2 tmp4 = tmp3 * tmp3 tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tmp7 = libdevice.sqrt(tmp6) tmp8 = tl.broadcast_to(tmp7, [RBLOCK]) tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0)) tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp10, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) # Topologically Sorted Source Nodes: [neg, diff, mul, add_1, error, loss], Original ATen: [aten.neg, aten.add, aten.mul, aten.sqrt, aten.sum] stream0 = get_raw_stream(0) triton_per_fused_add_mul_neg_sqrt_sum_0.run(arg1_1, arg0_1, buf0, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class L1_Charbonnier_loss(nn.Module): """L1 Charbonnierloss loss function where the epsilon has been taken as 1e-3 from the paper""" def __init__(self): super(L1_Charbonnier_loss, self).__init__() self.eps = 0.001 def forward(self, X, Y): diff = torch.add(X, -Y) error = torch.sqrt(diff * diff + self.eps * self.eps) loss = torch.sum(error) return loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_add_mul_neg_sqrt_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp1 = tl.load(in_ptr1 + r0, None) tmp2 = -tmp1 tmp3 = tmp0 + tmp2 tmp4 = tmp3 * tmp3 tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tmp7 = libdevice.sqrt(tmp6) tmp8 = tl.broadcast_to(tmp7, [RBLOCK]) tmp10 = triton_helpers.promote_to_tensor(tl.sum(tmp8, 0)) tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp10, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) get_raw_stream(0) triton_per_fused_add_mul_neg_sqrt_sum_0[grid(1)](arg1_1, arg0_1, buf0, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf0, class L1_Charbonnier_lossNew(nn.Module): """L1 Charbonnierloss loss function where the epsilon has been taken as 1e-3 from the paper""" def __init__(self): super(L1_Charbonnier_lossNew, self).__init__() self.eps = 0.001 def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
ankurbhatia24/image-super-resolution
L1_Charbonnier_loss
false
9,752
[ "Apache-2.0" ]
0
7ebc2be70e1a940addb6ba886a663f88167e6007
https://github.com/ankurbhatia24/image-super-resolution/tree/7ebc2be70e1a940addb6ba886a663f88167e6007
Value
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/a2/ca2wr2cvkya5clovpxidv7ia56pdcyp7uq4omtpg5m2nr7ya3ryn.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.tanh] # Source node to ATen node mapping: # x => tanh # Graph fragment: # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {}) triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (1, 64), (64, 1)) assert_size_stride(primals_7, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [x], Original ATen: [aten.tanh] stream0 = get_raw_stream(0) triton_poi_fused_tanh_0.run(buf1, primals_2, 4096, grid=grid(4096), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh] triton_poi_fused_tanh_0.run(buf3, primals_5, 4096, grid=grid(4096), stream=stream0) del primals_5 buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [state_values], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf5) del primals_7 return (reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, buf3, primals_6, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((1, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Value(nn.Module): def __init__(self, num_inputs): super(Value, self).__init__() self.affine1 = nn.Linear(num_inputs, 64) self.affine2 = nn.Linear(64, 64) self.value_head = nn.Linear(64, 1) self.value_head.weight.data.mul_(0.1) self.value_head.bias.data.mul_(0.0) def forward(self, x): x = torch.tanh(self.affine1(x)) x = torch.tanh(self.affine2(x)) state_values = self.value_head(x) return state_values def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_inputs': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, None) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (1, 64), (64, 1)) assert_size_stride(primals_7, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf0 get_raw_stream(0) triton_poi_fused_tanh_0[grid(4096)](buf1, primals_2, 4096, XBLOCK= 128, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf2 triton_poi_fused_tanh_0[grid(4096)](buf3, primals_5, 4096, XBLOCK= 128, num_warps=4, num_stages=1) del primals_5 buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32) extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 1), (1, 64), 0), alpha=1, beta=1, out=buf5) del primals_7 return reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0 ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), buf1, buf3, primals_6, primals_4 class ValueNew(nn.Module): def __init__(self, num_inputs): super(ValueNew, self).__init__() self.affine1 = nn.Linear(num_inputs, 64) self.affine2 = nn.Linear(64, 64) self.value_head = nn.Linear(64, 1) self.value_head.weight.data.mul_(0.1) self.value_head.bias.data.mul_(0.0) def forward(self, input_0): primals_1 = self.affine1.weight primals_2 = self.affine1.bias primals_4 = self.affine2.weight primals_5 = self.affine2.bias primals_6 = self.value_head.weight primals_7 = self.value_head.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
aranganath/pytorch-trpo
Value
false
9,753
[ "MIT" ]
0
a85bc48261eb4ed5833209da706379e9dc84592f
https://github.com/aranganath/pytorch-trpo/tree/a85bc48261eb4ed5833209da706379e9dc84592f
GATgate_lp2
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/cg/ccgqaozymxqcsy7dpknosnp7h5yo22c2px3idewjh7dcughhnsyj.py # Topologically Sorted Source Nodes: [intermat_1], Original ATen: [aten.mul] # Source node to ATen node mapping: # intermat_1 => mul # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, %primals_7), kwargs = {}) triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask) tmp2 = tmp0 * tmp1 tl.store(in_out_ptr0 + (x0), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_l], Original ATen: [aten.addmm] extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [h_p], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_4 del primals_5 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [intermat], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [intermat_1], Original ATen: [aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_mul_0.run(buf3, primals_7, 64, grid=grid(64), stream=stream0) return (buf3, primals_7, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class GATgate_lp2(nn.Module): def __init__(self, n_dim): super(GATgate_lp2, self).__init__() self.w_l = nn.Linear(n_dim, n_dim) self.w_p = nn.Linear(n_dim, n_dim) self.LR = nn.LeakyReLU() def forward(self, vec_l, vec_p, adj_inter): h_l = self.w_l(vec_l) h_p = self.w_p(vec_p) intermat = torch.einsum('aij,ajk->aik', (h_l, h_p.transpose(-1, -2))) intermat = intermat * adj_inter return intermat def get_inputs(): return [torch.rand([4, 4, 4]), torch.rand([4, 4, 4]), torch.rand([4, 4, 4]) ] def get_init_inputs(): return [[], {'n_dim': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask) tmp2 = tmp0 * tmp1 tl.store(in_out_ptr0 + x0, tmp2, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7) = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_1 del primals_2 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf1) del primals_4 del primals_5 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2) buf3 = buf2 del buf2 get_raw_stream(0) triton_poi_fused_mul_0[grid(64)](buf3, primals_7, 64, XBLOCK=64, num_warps=1, num_stages=1) return buf3, primals_7, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0 ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0 ), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0 ), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0) class GATgate_lp2New(nn.Module): def __init__(self, n_dim): super(GATgate_lp2New, self).__init__() self.w_l = nn.Linear(n_dim, n_dim) self.w_p = nn.Linear(n_dim, n_dim) self.LR = nn.LeakyReLU() def forward(self, input_0, input_1, input_2): primals_1 = self.w_l.weight primals_2 = self.w_l.bias primals_4 = self.w_p.weight primals_5 = self.w_p.bias primals_3 = input_0 primals_6 = input_1 primals_7 = input_2 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7]) return output[0]
arwhirang/affinity_prediction_BGNN
GATgate_lp2
false
9,754
[ "MIT" ]
0
b8a2a5de16a61a46dadd53856d758e7f63f9ca91
https://github.com/arwhirang/affinity_prediction_BGNN/tree/b8a2a5de16a61a46dadd53856d758e7f63f9ca91
Gaussian
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/fa/cfajjzqhflrbkf33ugoaupdyb35hpbhli75wfcrl4pqthm7oahjv.py # Topologically Sorted Source Nodes: [exp, add, log, sigma_t, sigma_t_1], Original ATen: [aten.exp, aten.add, aten.log, aten.squeeze] # Source node to ATen node mapping: # add => add # exp => exp # log => log # sigma_t => add_1 # sigma_t_1 => squeeze # Graph fragment: # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%addmm,), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%log, 1e-06), kwargs = {}) # %squeeze : [num_users=1] = call_function[target=torch.ops.aten.squeeze.dim](args = (%add_1, 0), kwargs = {}) triton_poi_fused_add_exp_log_squeeze_0 = async_compile.triton('triton_poi_fused_add_exp_log_squeeze_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_exp_log_squeeze_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_exp_log_squeeze_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl_math.exp(tmp0) tmp2 = 1.0 tmp3 = tmp1 + tmp2 tmp4 = tl_math.log(tmp3) tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tl.store(out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_3, primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_2 del primals_3 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [exp, add, log, sigma_t, sigma_t_1], Original ATen: [aten.exp, aten.add, aten.log, aten.squeeze] stream0 = get_raw_stream(0) triton_poi_fused_add_exp_log_squeeze_0.run(buf0, buf1, 16, grid=grid(16), stream=stream0) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, primals_1, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_4 del primals_5 return (buf2, buf1, primals_1, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class Gaussian(nn.Module): def __init__(self, hidden_size, output_size): """ Gaussian Likelihood Supports Continuous Data Args: input_size (int): hidden h_{i,t} column size output_size (int): embedding size """ super(Gaussian, self).__init__() self.mu_layer = nn.Linear(hidden_size, output_size) self.sigma_layer = nn.Linear(hidden_size, output_size) def forward(self, h): _, _hidden_size = h.size() sigma_t = torch.log(1 + torch.exp(self.sigma_layer(h))) + 1e-06 sigma_t = sigma_t.squeeze(0) mu_t = self.mu_layer(h).squeeze(0) return mu_t, sigma_t def get_inputs(): return [torch.rand([4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4, 'output_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_add_exp_log_squeeze_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl_math.exp(tmp0) tmp2 = 1.0 tmp3 = tmp1 + tmp2 tmp4 = tl_math.log(tmp3) tmp5 = 1e-06 tmp6 = tmp4 + tmp5 tl.store(out_ptr0 + x0, tmp6, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_3, primals_1, reinterpret_tensor( primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_2 del primals_3 buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_exp_log_squeeze_0[grid(16)](buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1) buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, primals_1, reinterpret_tensor( primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2) del primals_4 del primals_5 return buf2, buf1, primals_1, buf0 class GaussianNew(nn.Module): def __init__(self, hidden_size, output_size): """ Gaussian Likelihood Supports Continuous Data Args: input_size (int): hidden h_{i,t} column size output_size (int): embedding size """ super(GaussianNew, self).__init__() self.mu_layer = nn.Linear(hidden_size, output_size) self.sigma_layer = nn.Linear(hidden_size, output_size) def forward(self, input_0): primals_1 = self.mu_layer.weight primals_3 = self.mu_layer.bias primals_2 = self.sigma_layer.weight primals_5 = self.sigma_layer.bias primals_4 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0], output[1]
ashfarhangi/COVID-19_Impact
Gaussian
false
9,755
[ "Apache-2.0" ]
0
7ce46616278cac95e31b3e853bb28ea7b8e58b7e
https://github.com/ashfarhangi/COVID-19_Impact/tree/7ce46616278cac95e31b3e853bb28ea7b8e58b7e
EuclideanDistance
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/6x/c6xkdo646iy2lk4b345vg3ymh7cacajnv357f377has4o5vkgk3j.py # Topologically Sorted Source Nodes: [dist], Original ATen: [aten.stack] # Source node to ATen node mapping: # dist => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%sum_1, %sum_2, %sum_3, %sum_4], 1), kwargs = {}) triton_poi_fused_stack_0 = async_compile.triton('triton_poi_fused_stack_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_stack_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 4) % 16 x0 = xindex % 4 x2 = (xindex // 64) x3 = xindex tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (4*x1) + (64*x2)), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (x0 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp7 = tmp5 - tmp6 tmp8 = tmp7 * tmp7 tmp9 = tl.load(in_ptr0 + (16 + x0 + (4*x1) + (64*x2)), tmp4 & xmask, other=0.0) tmp10 = tl.load(in_ptr1 + (16 + x0 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp9 - tmp10 tmp12 = tmp11 * tmp11 tmp13 = tmp8 + tmp12 tmp14 = tl.load(in_ptr0 + (32 + x0 + (4*x1) + (64*x2)), tmp4 & xmask, other=0.0) tmp15 = tl.load(in_ptr1 + (32 + x0 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp14 - tmp15 tmp17 = tmp16 * tmp16 tmp18 = tmp13 + tmp17 tmp19 = tl.load(in_ptr0 + (48 + x0 + (4*x1) + (64*x2)), tmp4 & xmask, other=0.0) tmp20 = tl.load(in_ptr1 + (48 + x0 + (4*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp21 = tmp19 - tmp20 tmp22 = tmp21 * tmp21 tmp23 = tmp18 + tmp22 tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype) tmp25 = tl.where(tmp4, tmp23, tmp24) tmp26 = tmp0 >= tmp3 tmp27 = tl.full([1], 8, tl.int64) tmp28 = tmp0 < tmp27 tmp29 = tmp26 & tmp28 tmp30 = tl.load(in_ptr0 + (x0 + (4*((-4) + x1)) + (64*x2)), tmp29 & xmask, other=0.0) tmp31 = tl.load(in_ptr1 + (64 + x0 + (4*((-4) + x1))), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp32 = tmp30 - tmp31 tmp33 = tmp32 * tmp32 tmp34 = tl.load(in_ptr0 + (16 + x0 + (4*((-4) + x1)) + (64*x2)), tmp29 & xmask, other=0.0) tmp35 = tl.load(in_ptr1 + (80 + x0 + (4*((-4) + x1))), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp36 = tmp34 - tmp35 tmp37 = tmp36 * tmp36 tmp38 = tmp33 + tmp37 tmp39 = tl.load(in_ptr0 + (32 + x0 + (4*((-4) + x1)) + (64*x2)), tmp29 & xmask, other=0.0) tmp40 = tl.load(in_ptr1 + (96 + x0 + (4*((-4) + x1))), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp41 = tmp39 - tmp40 tmp42 = tmp41 * tmp41 tmp43 = tmp38 + tmp42 tmp44 = tl.load(in_ptr0 + (48 + x0 + (4*((-4) + x1)) + (64*x2)), tmp29 & xmask, other=0.0) tmp45 = tl.load(in_ptr1 + (112 + x0 + (4*((-4) + x1))), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp46 = tmp44 - tmp45 tmp47 = tmp46 * tmp46 tmp48 = tmp43 + tmp47 tmp49 = tl.full(tmp48.shape, 0.0, tmp48.dtype) tmp50 = tl.where(tmp29, tmp48, tmp49) tmp51 = tmp0 >= tmp27 tmp52 = tl.full([1], 12, tl.int64) tmp53 = tmp0 < tmp52 tmp54 = tmp51 & tmp53 tmp55 = tl.load(in_ptr0 + (x0 + (4*((-8) + x1)) + (64*x2)), tmp54 & xmask, other=0.0) tmp56 = tl.load(in_ptr1 + (128 + x0 + (4*((-8) + x1))), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp57 = tmp55 - tmp56 tmp58 = tmp57 * tmp57 tmp59 = tl.load(in_ptr0 + (16 + x0 + (4*((-8) + x1)) + (64*x2)), tmp54 & xmask, other=0.0) tmp60 = tl.load(in_ptr1 + (144 + x0 + (4*((-8) + x1))), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp61 = tmp59 - tmp60 tmp62 = tmp61 * tmp61 tmp63 = tmp58 + tmp62 tmp64 = tl.load(in_ptr0 + (32 + x0 + (4*((-8) + x1)) + (64*x2)), tmp54 & xmask, other=0.0) tmp65 = tl.load(in_ptr1 + (160 + x0 + (4*((-8) + x1))), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp66 = tmp64 - tmp65 tmp67 = tmp66 * tmp66 tmp68 = tmp63 + tmp67 tmp69 = tl.load(in_ptr0 + (48 + x0 + (4*((-8) + x1)) + (64*x2)), tmp54 & xmask, other=0.0) tmp70 = tl.load(in_ptr1 + (176 + x0 + (4*((-8) + x1))), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp71 = tmp69 - tmp70 tmp72 = tmp71 * tmp71 tmp73 = tmp68 + tmp72 tmp74 = tl.full(tmp73.shape, 0.0, tmp73.dtype) tmp75 = tl.where(tmp54, tmp73, tmp74) tmp76 = tmp0 >= tmp52 tmp77 = tl.full([1], 16, tl.int64) tmp78 = tmp0 < tmp77 tmp79 = tl.load(in_ptr0 + (x0 + (4*((-12) + x1)) + (64*x2)), tmp76 & xmask, other=0.0) tmp80 = tl.load(in_ptr1 + (192 + x0 + (4*((-12) + x1))), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp81 = tmp79 - tmp80 tmp82 = tmp81 * tmp81 tmp83 = tl.load(in_ptr0 + (16 + x0 + (4*((-12) + x1)) + (64*x2)), tmp76 & xmask, other=0.0) tmp84 = tl.load(in_ptr1 + (208 + x0 + (4*((-12) + x1))), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp85 = tmp83 - tmp84 tmp86 = tmp85 * tmp85 tmp87 = tmp82 + tmp86 tmp88 = tl.load(in_ptr0 + (32 + x0 + (4*((-12) + x1)) + (64*x2)), tmp76 & xmask, other=0.0) tmp89 = tl.load(in_ptr1 + (224 + x0 + (4*((-12) + x1))), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp90 = tmp88 - tmp89 tmp91 = tmp90 * tmp90 tmp92 = tmp87 + tmp91 tmp93 = tl.load(in_ptr0 + (48 + x0 + (4*((-12) + x1)) + (64*x2)), tmp76 & xmask, other=0.0) tmp94 = tl.load(in_ptr1 + (240 + x0 + (4*((-12) + x1))), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp95 = tmp93 - tmp94 tmp96 = tmp95 * tmp95 tmp97 = tmp92 + tmp96 tmp98 = tl.full(tmp97.shape, 0.0, tmp97.dtype) tmp99 = tl.where(tmp76, tmp97, tmp98) tmp100 = tl.where(tmp54, tmp75, tmp99) tmp101 = tl.where(tmp29, tmp50, tmp100) tmp102 = tl.where(tmp4, tmp25, tmp101) tl.store(out_ptr0 + (x3), tmp102, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [dist], Original ATen: [aten.stack] stream0 = get_raw_stream(0) triton_poi_fused_stack_0.run(arg1_1, arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 del arg1_1 return (reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import Tensor import torch.utils.data.dataloader from torch import nn import torch.nn def arccosh(x): """Compute the arcosh, numerically stable.""" x = torch.clamp(x, min=1 + EPSILON) a = torch.log(x) b = torch.log1p(torch.sqrt(x * x - 1) / x) return a + b def mdot(x, y): """Compute the inner product.""" m = x.new_ones(1, x.size(1)) m[0, 0] = -1 return torch.sum(m * x * y, 1, keepdim=True) def dist(x, y): """Get the hyperbolic distance between x and y.""" return arccosh(-mdot(x, y)) class EuclideanDistance(nn.Module): """Implement a EuclideanDistance object.""" def forward(self, mat_1: 'Tensor', mat_2: 'Tensor') ->Tensor: """Returns the squared euclidean distance between each element in mat_1 and each element in mat_2. Parameters ---------- mat_1: torch.Tensor matrix of shape (n_1, n_features) mat_2: torch.Tensor matrix of shape (n_2, n_features) Returns ------- dist: torch.Tensor distance matrix of shape (n_1, n_2) """ _dist = [torch.sum((mat_1 - mat_2[i]) ** 2, dim=1) for i in range( mat_2.size(0))] dist = torch.stack(_dist, dim=1) return dist def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.utils.data.dataloader from torch import nn import torch.nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_stack_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 4 % 16 x0 = xindex % 4 x2 = xindex // 64 x3 = xindex tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp6 = tl.load(in_ptr1 + (x0 + 4 * x1), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp7 = tmp5 - tmp6 tmp8 = tmp7 * tmp7 tmp9 = tl.load(in_ptr0 + (16 + x0 + 4 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp10 = tl.load(in_ptr1 + (16 + x0 + 4 * x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp11 = tmp9 - tmp10 tmp12 = tmp11 * tmp11 tmp13 = tmp8 + tmp12 tmp14 = tl.load(in_ptr0 + (32 + x0 + 4 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp15 = tl.load(in_ptr1 + (32 + x0 + 4 * x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp14 - tmp15 tmp17 = tmp16 * tmp16 tmp18 = tmp13 + tmp17 tmp19 = tl.load(in_ptr0 + (48 + x0 + 4 * x1 + 64 * x2), tmp4 & xmask, other=0.0) tmp20 = tl.load(in_ptr1 + (48 + x0 + 4 * x1), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp21 = tmp19 - tmp20 tmp22 = tmp21 * tmp21 tmp23 = tmp18 + tmp22 tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype) tmp25 = tl.where(tmp4, tmp23, tmp24) tmp26 = tmp0 >= tmp3 tmp27 = tl.full([1], 8, tl.int64) tmp28 = tmp0 < tmp27 tmp29 = tmp26 & tmp28 tmp30 = tl.load(in_ptr0 + (x0 + 4 * (-4 + x1) + 64 * x2), tmp29 & xmask, other=0.0) tmp31 = tl.load(in_ptr1 + (64 + x0 + 4 * (-4 + x1)), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp32 = tmp30 - tmp31 tmp33 = tmp32 * tmp32 tmp34 = tl.load(in_ptr0 + (16 + x0 + 4 * (-4 + x1) + 64 * x2), tmp29 & xmask, other=0.0) tmp35 = tl.load(in_ptr1 + (80 + x0 + 4 * (-4 + x1)), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp36 = tmp34 - tmp35 tmp37 = tmp36 * tmp36 tmp38 = tmp33 + tmp37 tmp39 = tl.load(in_ptr0 + (32 + x0 + 4 * (-4 + x1) + 64 * x2), tmp29 & xmask, other=0.0) tmp40 = tl.load(in_ptr1 + (96 + x0 + 4 * (-4 + x1)), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp41 = tmp39 - tmp40 tmp42 = tmp41 * tmp41 tmp43 = tmp38 + tmp42 tmp44 = tl.load(in_ptr0 + (48 + x0 + 4 * (-4 + x1) + 64 * x2), tmp29 & xmask, other=0.0) tmp45 = tl.load(in_ptr1 + (112 + x0 + 4 * (-4 + x1)), tmp29 & xmask, eviction_policy='evict_last', other=0.0) tmp46 = tmp44 - tmp45 tmp47 = tmp46 * tmp46 tmp48 = tmp43 + tmp47 tmp49 = tl.full(tmp48.shape, 0.0, tmp48.dtype) tmp50 = tl.where(tmp29, tmp48, tmp49) tmp51 = tmp0 >= tmp27 tmp52 = tl.full([1], 12, tl.int64) tmp53 = tmp0 < tmp52 tmp54 = tmp51 & tmp53 tmp55 = tl.load(in_ptr0 + (x0 + 4 * (-8 + x1) + 64 * x2), tmp54 & xmask, other=0.0) tmp56 = tl.load(in_ptr1 + (128 + x0 + 4 * (-8 + x1)), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp57 = tmp55 - tmp56 tmp58 = tmp57 * tmp57 tmp59 = tl.load(in_ptr0 + (16 + x0 + 4 * (-8 + x1) + 64 * x2), tmp54 & xmask, other=0.0) tmp60 = tl.load(in_ptr1 + (144 + x0 + 4 * (-8 + x1)), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp61 = tmp59 - tmp60 tmp62 = tmp61 * tmp61 tmp63 = tmp58 + tmp62 tmp64 = tl.load(in_ptr0 + (32 + x0 + 4 * (-8 + x1) + 64 * x2), tmp54 & xmask, other=0.0) tmp65 = tl.load(in_ptr1 + (160 + x0 + 4 * (-8 + x1)), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp66 = tmp64 - tmp65 tmp67 = tmp66 * tmp66 tmp68 = tmp63 + tmp67 tmp69 = tl.load(in_ptr0 + (48 + x0 + 4 * (-8 + x1) + 64 * x2), tmp54 & xmask, other=0.0) tmp70 = tl.load(in_ptr1 + (176 + x0 + 4 * (-8 + x1)), tmp54 & xmask, eviction_policy='evict_last', other=0.0) tmp71 = tmp69 - tmp70 tmp72 = tmp71 * tmp71 tmp73 = tmp68 + tmp72 tmp74 = tl.full(tmp73.shape, 0.0, tmp73.dtype) tmp75 = tl.where(tmp54, tmp73, tmp74) tmp76 = tmp0 >= tmp52 tl.full([1], 16, tl.int64) tmp79 = tl.load(in_ptr0 + (x0 + 4 * (-12 + x1) + 64 * x2), tmp76 & xmask, other=0.0) tmp80 = tl.load(in_ptr1 + (192 + x0 + 4 * (-12 + x1)), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp81 = tmp79 - tmp80 tmp82 = tmp81 * tmp81 tmp83 = tl.load(in_ptr0 + (16 + x0 + 4 * (-12 + x1) + 64 * x2), tmp76 & xmask, other=0.0) tmp84 = tl.load(in_ptr1 + (208 + x0 + 4 * (-12 + x1)), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp85 = tmp83 - tmp84 tmp86 = tmp85 * tmp85 tmp87 = tmp82 + tmp86 tmp88 = tl.load(in_ptr0 + (32 + x0 + 4 * (-12 + x1) + 64 * x2), tmp76 & xmask, other=0.0) tmp89 = tl.load(in_ptr1 + (224 + x0 + 4 * (-12 + x1)), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp90 = tmp88 - tmp89 tmp91 = tmp90 * tmp90 tmp92 = tmp87 + tmp91 tmp93 = tl.load(in_ptr0 + (48 + x0 + 4 * (-12 + x1) + 64 * x2), tmp76 & xmask, other=0.0) tmp94 = tl.load(in_ptr1 + (240 + x0 + 4 * (-12 + x1)), tmp76 & xmask, eviction_policy='evict_last', other=0.0) tmp95 = tmp93 - tmp94 tmp96 = tmp95 * tmp95 tmp97 = tmp92 + tmp96 tmp98 = tl.full(tmp97.shape, 0.0, tmp97.dtype) tmp99 = tl.where(tmp76, tmp97, tmp98) tmp100 = tl.where(tmp54, tmp75, tmp99) tmp101 = tl.where(tmp29, tmp50, tmp100) tmp102 = tl.where(tmp4, tmp25, tmp101) tl.store(out_ptr0 + x3, tmp102, xmask) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_stack_0[grid(256)](arg1_1, arg0_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 del arg1_1 return reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), def arccosh(x): """Compute the arcosh, numerically stable.""" x = torch.clamp(x, min=1 + EPSILON) a = torch.log(x) b = torch.log1p(torch.sqrt(x * x - 1) / x) return a + b def mdot(x, y): """Compute the inner product.""" m = x.new_ones(1, x.size(1)) m[0, 0] = -1 return torch.sum(m * x * y, 1, keepdim=True) def dist(x, y): """Get the hyperbolic distance between x and y.""" return arccosh(-mdot(x, y)) class EuclideanDistanceNew(nn.Module): """Implement a EuclideanDistance object.""" def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
adriensas/flair
EuclideanDistance
false
9,756
[ "MIT" ]
0
f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
https://github.com/adriensas/flair/tree/f01b0e7ff9a87d3862acae50aeaffdc8e8b8ac21
AddReadout
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/r4/cr4a3opn2ix4j3amxdcxkqnab45qfmf7qraskl3lfiz2nl6gd6rl.py # Topologically Sorted Source Nodes: [add], Original ATen: [aten.add] # Source node to ATen node mapping: # add => add # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_3, %unsqueeze), kwargs = {}) triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 48) x3 = xindex % 48 x0 = xindex % 16 x4 = xindex tmp0 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask) tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x4), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add], Original ATen: [aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_0.run(arg0_1, buf0, 192, grid=grid(192), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data class AddReadout(nn.Module): def __init__(self, start_index=1): super(AddReadout, self).__init__() self.start_index = start_index def forward(self, x): if self.start_index == 2: readout = (x[:, 0] + x[:, 1]) / 2 else: readout = x[:, 0] return x[:, self.start_index:] + readout.unsqueeze(1) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 48 x3 = xindex % 48 x0 = xindex % 16 x4 = xindex tmp0 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask) tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + x4, tmp2, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 3, 4, 4), (48, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_0[grid(192)](arg0_1, buf0, 192, XBLOCK=128, num_warps=4, num_stages=1) del arg0_1 return buf0, class AddReadoutNew(nn.Module): def __init__(self, start_index=1): super(AddReadoutNew, self).__init__() self.start_index = start_index def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
Zacchaeus14/lang-seg
AddReadout
false
9,757
[ "MIT" ]
0
ad1196a4d33830f3219dbe2260a69364a745f094
https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094
SigmoidModel
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/r3/cr3febcwm3t44fuoitsx3ou2p6xg4sk4f7unagmmrvffasxf47te.py # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # relu => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x2), tmp4, xmask) tl.store(out_ptr0 + (x2), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/q5/cq52p2qap7uob2ddnn4qeh67r3muutkp3yhbkqpu4eqaemol3idl.py # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid] # Source node to ATen node mapping: # sigmoid => sigmoid # Graph fragment: # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_3,), kwargs = {}) triton_poi_fused_sigmoid_1 = async_compile.triton('triton_poi_fused_sigmoid_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [relu], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf4, 256, grid=grid(256), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [sigmoid], Original ATen: [aten.sigmoid] triton_poi_fused_sigmoid_1.run(buf3, primals_5, 256, grid=grid(256), stream=stream0) del primals_5 return (buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class SigmoidModel(nn.Module): """ Model architecture from: https://medium.com/coinmonks/create-a-neural-network-in -pytorch-and-make-your-life-simpler-ec5367895199 """ def __init__(self, num_in, num_hidden, num_out): super().__init__() self.num_in = num_in self.num_hidden = num_hidden self.num_out = num_out self.lin1 = nn.Linear(num_in, num_hidden) self.lin2 = nn.Linear(num_hidden, num_out) self.relu1 = nn.ReLU() self.sigmoid = nn.Sigmoid() def forward(self, input): lin1 = self.lin1(input) lin2 = self.lin2(self.relu1(lin1)) return self.sigmoid(lin2) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_in': 4, 'num_hidden': 4, 'num_out': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x2, tmp4, xmask) tl.store(out_ptr0 + x2, tmp6, xmask) @triton.jit def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.sigmoid(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4), (4, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_2, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf2 triton_poi_fused_sigmoid_1[grid(256)](buf3, primals_5, 256, XBLOCK= 128, num_warps=4, num_stages=1) del primals_5 return buf3, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf3, primals_4, buf4 class SigmoidModelNew(nn.Module): """ Model architecture from: https://medium.com/coinmonks/create-a-neural-network-in -pytorch-and-make-your-life-simpler-ec5367895199 """ def __init__(self, num_in, num_hidden, num_out): super().__init__() self.num_in = num_in self.num_hidden = num_hidden self.num_out = num_out self.lin1 = nn.Linear(num_in, num_hidden) self.lin2 = nn.Linear(num_hidden, num_out) self.relu1 = nn.ReLU() self.sigmoid = nn.Sigmoid() def forward(self, input_0): primals_1 = self.lin1.weight primals_2 = self.lin1.bias primals_4 = self.lin2.weight primals_5 = self.lin2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
archydeberker/captum
SigmoidModel
false
9,758
[ "BSD-3-Clause" ]
0
2d72a060f12f5e325c9d1c411a2ef69bf43a06fd
https://github.com/archydeberker/captum/tree/2d72a060f12f5e325c9d1c411a2ef69bf43a06fd
depthwise_clipseg_conv
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/bd/cbdohqvf5ovewdd5y6dtctx4lewezgg3jimtio6amaolbcczwafo.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] # Source node to ATen node mapping: # x => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%convolution, %convolution_1, %convolution_2, %convolution_3], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 4 x0 = xindex % 16 x2 = (xindex // 64) x3 = xindex tmp6 = tl.load(in_ptr1 + (0)) tmp7 = tl.broadcast_to(tmp6, [XBLOCK]) tmp0 = x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 1, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + (16*x2)), tmp4 & xmask, eviction_policy='evict_last', other=0.0) tmp8 = tmp5 + tmp7 tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp4, tmp8, tmp9) tmp11 = tmp0 >= tmp3 tmp12 = tl.full([1], 2, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + (16*x2)), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp15 + tmp7 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp14, tmp16, tmp17) tmp19 = tmp0 >= tmp12 tmp20 = tl.full([1], 3, tl.int64) tmp21 = tmp0 < tmp20 tmp22 = tmp19 & tmp21 tmp23 = tl.load(in_ptr3 + (x0 + (16*x2)), tmp22 & xmask, eviction_policy='evict_last', other=0.0) tmp24 = tmp23 + tmp7 tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype) tmp26 = tl.where(tmp22, tmp24, tmp25) tmp27 = tmp0 >= tmp20 tmp28 = tl.full([1], 4, tl.int64) tmp29 = tmp0 < tmp28 tmp30 = tl.load(in_ptr4 + (x0 + (16*x2)), tmp27 & xmask, eviction_policy='evict_last', other=0.0) tmp31 = tmp30 + tmp7 tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype) tmp33 = tl.where(tmp27, tmp31, tmp32) tmp34 = tl.where(tmp22, tmp26, tmp33) tmp35 = tl.where(tmp14, tmp18, tmp34) tmp36 = tl.where(tmp4, tmp10, tmp35) tl.store(out_ptr0 + (x3), tmp36, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1)) assert_size_stride(primals_3, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 0), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1)) # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 16), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1)) # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 32), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 1, 4, 4), (16, 16, 4, 1)) # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 48), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1)) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(buf0, primals_3, buf1, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0) del buf0 del buf1 del buf2 del buf3 del primals_3 return (buf4, primals_2, reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 16), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 32), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 48), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data class depthwise_clipseg_conv(nn.Module): def __init__(self): super(depthwise_clipseg_conv, self).__init__() self.depthwise = nn.Conv2d(1, 1, kernel_size=3, padding=1) def depthwise_clipseg(self, x, channels): x = torch.cat([self.depthwise(x[:, i].unsqueeze(1)) for i in range( channels)], dim=1) return x def forward(self, x): channels = x.shape[1] out = self.depthwise_clipseg(x, channels) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 4 x0 = xindex % 16 x2 = xindex // 64 x3 = xindex tmp6 = tl.load(in_ptr1 + 0) tmp7 = tl.broadcast_to(tmp6, [XBLOCK]) tmp0 = x1 tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 1, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy= 'evict_last', other=0.0) tmp8 = tmp5 + tmp7 tmp9 = tl.full(tmp8.shape, 0.0, tmp8.dtype) tmp10 = tl.where(tmp4, tmp8, tmp9) tmp11 = tmp0 >= tmp3 tmp12 = tl.full([1], 2, tl.int64) tmp13 = tmp0 < tmp12 tmp14 = tmp11 & tmp13 tmp15 = tl.load(in_ptr2 + (x0 + 16 * x2), tmp14 & xmask, eviction_policy='evict_last', other=0.0) tmp16 = tmp15 + tmp7 tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype) tmp18 = tl.where(tmp14, tmp16, tmp17) tmp19 = tmp0 >= tmp12 tmp20 = tl.full([1], 3, tl.int64) tmp21 = tmp0 < tmp20 tmp22 = tmp19 & tmp21 tmp23 = tl.load(in_ptr3 + (x0 + 16 * x2), tmp22 & xmask, eviction_policy='evict_last', other=0.0) tmp24 = tmp23 + tmp7 tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype) tmp26 = tl.where(tmp22, tmp24, tmp25) tmp27 = tmp0 >= tmp20 tl.full([1], 4, tl.int64) tmp30 = tl.load(in_ptr4 + (x0 + 16 * x2), tmp27 & xmask, eviction_policy='evict_last', other=0.0) tmp31 = tmp30 + tmp7 tmp32 = tl.full(tmp31.shape, 0.0, tmp31.dtype) tmp33 = tl.where(tmp27, tmp31, tmp32) tmp34 = tl.where(tmp22, tmp26, tmp33) tmp35 = tl.where(tmp14, tmp18, tmp34) tmp36 = tl.where(tmp4, tmp10, tmp35) tl.store(out_ptr0 + x3, tmp36, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1)) assert_size_stride(primals_3, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 0), primals_2, stride=(1, 1), padding= (1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0 ), groups=1, bias=None) assert_size_stride(buf0, (4, 1, 4, 4), (16, 16, 4, 1)) buf1 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 16), primals_2, stride=(1, 1), padding =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1)) buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 32), primals_2, stride=(1, 1), padding =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 1, 4, 4), (16, 16, 4, 1)) buf3 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 0, 4, 1), 48), primals_2, stride=(1, 1), padding =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1)) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(256)](buf0, primals_3, buf1, buf2, buf3, buf4, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf0 del buf1 del buf2 del buf3 del primals_3 return buf4, primals_2, reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 16), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 32), reinterpret_tensor(primals_1, (4, 1, 4, 4), (64, 16, 4, 1), 48 ) class depthwise_clipseg_convNew(nn.Module): def __init__(self): super(depthwise_clipseg_convNew, self).__init__() self.depthwise = nn.Conv2d(1, 1, kernel_size=3, padding=1) def depthwise_clipseg(self, x, channels): x = torch.cat([self.depthwise(x[:, i].unsqueeze(1)) for i in range( channels)], dim=1) return x def forward(self, input_0): primals_2 = self.depthwise.weight primals_3 = self.depthwise.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
Zacchaeus14/lang-seg
depthwise_clipseg_conv
false
9,759
[ "MIT" ]
0
ad1196a4d33830f3219dbe2260a69364a745f094
https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094
Policy
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/a2/ca2wr2cvkya5clovpxidv7ia56pdcyp7uq4omtpg5m2nr7ya3ryn.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.tanh] # Source node to ATen node mapping: # x => tanh # Graph fragment: # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {}) triton_poi_fused_tanh_0 = async_compile.triton('triton_poi_fused_tanh_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 4096 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/53/c5336tes3fejn37nhb2iijuur7spy3qcasflywbbqklxwgjxpcvr.py # Topologically Sorted Source Nodes: [action_std], Original ATen: [aten.exp] # Source node to ATen node mapping: # action_std => exp # Graph fragment: # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%expand,), kwargs = {}) triton_poi_fused_exp_1 = async_compile.triton('triton_poi_fused_exp_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_exp_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_exp_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp1 = tl_math.exp(tmp0) tl.store(out_ptr0 + (x2), tmp1, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64, ), (1, )) assert_size_stride(primals_6, (4, 64), (64, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (1, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [x], Original ATen: [aten.tanh] stream0 = get_raw_stream(0) triton_poi_fused_tanh_0.run(buf1, primals_2, 4096, grid=grid(4096), stream=stream0) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0); del buf2 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.tanh] triton_poi_fused_tanh_0.run(buf3, primals_5, 4096, grid=grid(4096), stream=stream0) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [action_mean], Original ATen: [aten.addmm] extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf4) del primals_7 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [action_std], Original ATen: [aten.exp] triton_poi_fused_exp_1.run(primals_8, buf5, 256, grid=grid(256), stream=stream0) return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_8, (4, 4, 4, 4), (0, 0, 0, 1), 0), buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1, buf3, buf5, primals_6, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((64, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Policy(nn.Module): def __init__(self, num_inputs, num_outputs): super(Policy, self).__init__() self.affine1 = nn.Linear(num_inputs, 64) self.affine2 = nn.Linear(64, 64) self.action_mean = nn.Linear(64, num_outputs) self.action_mean.weight.data.mul_(0.1) self.action_mean.bias.data.mul_(0.0) self.action_log_std = nn.Parameter(torch.zeros(1, num_outputs)) self.saved_actions = [] self.rewards = [] self.final_value = 0 def forward(self, x): x = torch.tanh(self.affine1(x)) x = torch.tanh(self.affine2(x)) action_mean = self.action_mean(x) action_log_std = self.action_log_std.expand_as(action_mean) action_std = torch.exp(action_log_std) return action_mean, action_log_std, action_std def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'num_inputs': 4, 'num_outputs': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_tanh_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, None) @triton.jit def triton_poi_fused_exp_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 4 x2 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp1 = tl_math.exp(tmp0) tl.store(out_ptr0 + x2, tmp1, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8) = args args.clear() assert_size_stride(primals_1, (64, 4), (4, 1)) assert_size_stride(primals_2, (64,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (64, 64), (64, 1)) assert_size_stride(primals_5, (64,), (1,)) assert_size_stride(primals_6, (4, 64), (64, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (1, 4), (4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 64), (1, 4), 0), out=buf0) del primals_1 buf1 = reinterpret_tensor(buf0, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf0 get_raw_stream(0) triton_poi_fused_tanh_0[grid(4096)](buf1, primals_2, 4096, XBLOCK= 128, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((64, 64), (64, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf1, (64, 64), (64, 1), 0), reinterpret_tensor(primals_4, (64, 64), (1, 64), 0), out=buf2) buf3 = reinterpret_tensor(buf2, (4, 4, 4, 64), (1024, 256, 64, 1), 0) del buf2 triton_poi_fused_tanh_0[grid(4096)](buf3, primals_5, 4096, XBLOCK= 128, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 64), (64, 1), 0), reinterpret_tensor(primals_6, (64, 4), (1, 64), 0), alpha=1, beta=1, out=buf4) del primals_7 buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused_exp_1[grid(256)](primals_8, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0 ), reinterpret_tensor(primals_8, (4, 4, 4, 4), (0, 0, 0, 1), 0 ), buf5, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0 ), buf1, buf3, buf5, primals_6, primals_4 class PolicyNew(nn.Module): def __init__(self, num_inputs, num_outputs): super(PolicyNew, self).__init__() self.affine1 = nn.Linear(num_inputs, 64) self.affine2 = nn.Linear(64, 64) self.action_mean = nn.Linear(64, num_outputs) self.action_mean.weight.data.mul_(0.1) self.action_mean.bias.data.mul_(0.0) self.action_log_std = nn.Parameter(torch.zeros(1, num_outputs)) self.saved_actions = [] self.rewards = [] self.final_value = 0 def forward(self, input_0): primals_8 = self.action_log_std primals_1 = self.affine1.weight primals_2 = self.affine1.bias primals_4 = self.affine2.weight primals_5 = self.affine2.bias primals_6 = self.action_mean.weight primals_7 = self.action_mean.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return output[0], output[1], output[2]
aranganath/pytorch-trpo
Policy
false
9,760
[ "MIT" ]
0
a85bc48261eb4ed5833209da706379e9dc84592f
https://github.com/aranganath/pytorch-trpo/tree/a85bc48261eb4ed5833209da706379e9dc84592f
DownBlock
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/td/ctdybbibnws4d7ukbk3fpn35zkgapxylowdhzwx7vgsllncbdrxa.py # Topologically Sorted Source Nodes: [y, y_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # y => convolution # y_1 => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/it/citiw25o2bzpowghghyesgdfdm2lebsqwdmo6qg6foemy373lm2q.py # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # y_4 => getitem, getitem_1 # Graph fragment: # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {}) # %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 2 x1 = (xindex // 2) x2 = xindex tmp0 = tl.load(in_ptr0 + ((2*x0) + (8*x1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (4 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (5 + (2*x0) + (8*x1)), xmask, eviction_policy='evict_last') tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = tmp1 > tmp0 tmp8 = tl.full([1], 1, tl.int8) tmp9 = tl.full([1], 0, tl.int8) tmp10 = tl.where(tmp7, tmp8, tmp9) tmp11 = tmp3 > tmp2 tmp12 = tl.full([1], 2, tl.int8) tmp13 = tl.where(tmp11, tmp12, tmp10) tmp14 = tmp5 > tmp4 tmp15 = tl.full([1], 3, tl.int8) tmp16 = tl.where(tmp14, tmp15, tmp13) tl.store(out_ptr0 + (x2), tmp6, xmask) tl.store(out_ptr1 + (x2), tmp16, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [y], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [y, y_1], Original ATen: [aten.convolution, aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 256, grid=grid(256), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [y_2], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [y_2, y_3], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 256, grid=grid(256), stream=stream0) del primals_5 buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32) buf5 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.int8) # Topologically Sorted Source Nodes: [y_4], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 64, grid=grid(64), stream=stream0) return (buf4, buf3, primals_1, primals_3, primals_4, buf1, buf3, buf5, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn def get_activation(activation: 'str'): if activation == 'relu': return nn.ReLU() elif activation == 'leaky': return nn.LeakyReLU(negative_slope=0.1) elif activation == 'elu': return nn.ELU() def conv_layer(dim: 'int'): if dim == 3: return nn.Conv3d elif dim == 2: return nn.Conv2d def get_conv_layer(in_channels: 'int', out_channels: 'int', kernel_size: 'int'=3, stride: 'int'=1, padding: 'int'=1, bias: 'bool'=True, dim: 'int'=2 ): return conv_layer(dim)(in_channels, out_channels, kernel_size= kernel_size, stride=stride, padding=padding, bias=bias) def maxpool_layer(dim: 'int'): if dim == 3: return nn.MaxPool3d elif dim == 2: return nn.MaxPool2d def get_maxpool_layer(kernel_size: 'int'=2, stride: 'int'=2, padding: 'int' =0, dim: 'int'=2): return maxpool_layer(dim=dim)(kernel_size=kernel_size, stride=stride, padding=padding) def get_normalization(normalization: 'str', num_channels: 'int', dim: 'int'): if normalization == 'batch': if dim == 3: return nn.BatchNorm3d(num_channels) elif dim == 2: return nn.BatchNorm2d(num_channels) elif normalization == 'instance': if dim == 3: return nn.InstanceNorm3d(num_channels) elif dim == 2: return nn.InstanceNorm2d(num_channels) elif 'group' in normalization: num_groups = int(normalization.partition('group')[-1]) return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels) class DownBlock(nn.Module): """ A helper Module that performs 2 Convolutions and 1 MaxPool. An activation follows each convolution. A normalization layer follows each convolution. """ def __init__(self, in_channels: 'int', out_channels: 'int', pooling: 'bool'=True, activation: 'str'='relu', normalization: 'str'=None, dim: 'str'=2, conv_mode: 'str'='same'): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.pooling = pooling self.normalization = normalization if conv_mode == 'same': self.padding = 1 elif conv_mode == 'valid': self.padding = 0 self.dim = dim self.activation = activation self.conv1 = get_conv_layer(self.in_channels, self.out_channels, kernel_size=3, stride=1, padding=self.padding, bias=True, dim= self.dim) self.conv2 = get_conv_layer(self.out_channels, self.out_channels, kernel_size=3, stride=1, padding=self.padding, bias=True, dim= self.dim) if self.pooling: self.pool = get_maxpool_layer(kernel_size=2, stride=2, padding= 0, dim=self.dim) self.act1 = get_activation(self.activation) self.act2 = get_activation(self.activation) if self.normalization: self.norm1 = get_normalization(normalization=self.normalization, num_channels=self.out_channels, dim=self.dim) self.norm2 = get_normalization(normalization=self.normalization, num_channels=self.out_channels, dim=self.dim) def forward(self, x): y = self.conv1(x) y = self.act1(y) if self.normalization: y = self.norm1(y) y = self.conv2(y) y = self.act2(y) if self.normalization: y = self.norm2(y) before_pooling = y if self.pooling: y = self.pool(y) return y, before_pooling def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 2 x1 = xindex // 2 x2 = xindex tmp0 = tl.load(in_ptr0 + (2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr0 + (4 + 2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr0 + (5 + 2 * x0 + 8 * x1), xmask, eviction_policy= 'evict_last') tmp2 = triton_helpers.maximum(tmp1, tmp0) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp6 = triton_helpers.maximum(tmp5, tmp4) tmp7 = tmp1 > tmp0 tmp8 = tl.full([1], 1, tl.int8) tmp9 = tl.full([1], 0, tl.int8) tmp10 = tl.where(tmp7, tmp8, tmp9) tmp11 = tmp3 > tmp2 tmp12 = tl.full([1], 2, tl.int8) tmp13 = tl.where(tmp11, tmp12, tmp10) tmp14 = tmp5 > tmp4 tmp15 = tl.full([1], 3, tl.int8) tmp16 = tl.where(tmp14, tmp15, tmp13) tl.store(out_ptr0 + x2, tmp6, xmask) tl.store(out_ptr1 + x2, tmp16, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_relu_0[grid(256)](buf1, primals_2, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1)) buf3 = buf2 del buf2 triton_poi_fused_convolution_relu_0[grid(256)](buf3, primals_5, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_5 buf4 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32) buf5 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.int8) triton_poi_fused_max_pool2d_with_indices_1[grid(64)](buf3, buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1) return buf4, buf3, primals_1, primals_3, primals_4, buf1, buf3, buf5 def get_activation(activation: 'str'): if activation == 'relu': return nn.ReLU() elif activation == 'leaky': return nn.LeakyReLU(negative_slope=0.1) elif activation == 'elu': return nn.ELU() def conv_layer(dim: 'int'): if dim == 3: return nn.Conv3d elif dim == 2: return nn.Conv2d def get_conv_layer(in_channels: 'int', out_channels: 'int', kernel_size: 'int'=3, stride: 'int'=1, padding: 'int'=1, bias: 'bool'=True, dim: 'int'=2 ): return conv_layer(dim)(in_channels, out_channels, kernel_size= kernel_size, stride=stride, padding=padding, bias=bias) def maxpool_layer(dim: 'int'): if dim == 3: return nn.MaxPool3d elif dim == 2: return nn.MaxPool2d def get_maxpool_layer(kernel_size: 'int'=2, stride: 'int'=2, padding: 'int' =0, dim: 'int'=2): return maxpool_layer(dim=dim)(kernel_size=kernel_size, stride=stride, padding=padding) def get_normalization(normalization: 'str', num_channels: 'int', dim: 'int'): if normalization == 'batch': if dim == 3: return nn.BatchNorm3d(num_channels) elif dim == 2: return nn.BatchNorm2d(num_channels) elif normalization == 'instance': if dim == 3: return nn.InstanceNorm3d(num_channels) elif dim == 2: return nn.InstanceNorm2d(num_channels) elif 'group' in normalization: num_groups = int(normalization.partition('group')[-1]) return nn.GroupNorm(num_groups=num_groups, num_channels=num_channels) class DownBlockNew(nn.Module): """ A helper Module that performs 2 Convolutions and 1 MaxPool. An activation follows each convolution. A normalization layer follows each convolution. """ def __init__(self, in_channels: 'int', out_channels: 'int', pooling: 'bool'=True, activation: 'str'='relu', normalization: 'str'=None, dim: 'str'=2, conv_mode: 'str'='same'): super().__init__() self.in_channels = in_channels self.out_channels = out_channels self.pooling = pooling self.normalization = normalization if conv_mode == 'same': self.padding = 1 elif conv_mode == 'valid': self.padding = 0 self.dim = dim self.activation = activation self.conv1 = get_conv_layer(self.in_channels, self.out_channels, kernel_size=3, stride=1, padding=self.padding, bias=True, dim= self.dim) self.conv2 = get_conv_layer(self.out_channels, self.out_channels, kernel_size=3, stride=1, padding=self.padding, bias=True, dim= self.dim) if self.pooling: self.pool = get_maxpool_layer(kernel_size=2, stride=2, padding= 0, dim=self.dim) self.act1 = get_activation(self.activation) self.act2 = get_activation(self.activation) if self.normalization: self.norm1 = get_normalization(normalization=self.normalization, num_channels=self.out_channels, dim=self.dim) self.norm2 = get_normalization(normalization=self.normalization, num_channels=self.out_channels, dim=self.dim) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0], output[1]
arshadzahangirchowdhury/TomoEncoders
DownBlock
false
9,761
[ "BSD-3-Clause" ]
0
9c2b15fd515d864079f198546821faee5d78df17
https://github.com/arshadzahangirchowdhury/TomoEncoders/tree/9c2b15fd515d864079f198546821faee5d78df17
C1Bilinear
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/aa/caau4wvw4oghvqjzcah7ewi7opq6iuwk74jnkb4l6xzk4y7rd5p7.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16384, 4096], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16384 xnumel = 4096 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 4096 y1 = (yindex // 4096) tmp0 = tl.load(in_ptr0 + (x2 + (4096*y3)), None, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (4096*x2) + (16777216*y1)), tmp0, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/a4/ca42avcba2v3rwzc4e7jjvzkmmqjzezdikufpztp7agmmmmr3j3l.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._to_copy] # Source node to ATen node mapping: # x_1 => convert_element_type_1 # Graph fragment: # %convert_element_type_1 : [num_users=5] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {}) triton_poi_fused__to_copy_1 = async_compile.triton('triton_poi_fused__to_copy_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_1(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = 0.16666666666666666 tmp5 = tmp3 * tmp4 tmp6 = tmp5 - tmp2 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp9 = tmp8.to(tl.int32) tl.store(out_ptr0 + (x0), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/f6/cf6yislaj3ayqfayvr7o47eniwfda4wyugck65vvqdlnxqsjchmd.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add, aten.clamp] # Source node to ATen node mapping: # x_1 => add_1, clamp_max # Graph fragment: # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type_1, 1), kwargs = {}) # %clamp_max : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%add_1, 63), kwargs = {}) triton_poi_fused_add_clamp_2 = async_compile.triton('triton_poi_fused_add_clamp_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*i64', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_clamp_2(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = 0.16666666666666666 tmp5 = tmp3 * tmp4 tmp6 = tmp5 - tmp2 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp9 = tmp8.to(tl.int32) tmp10 = tl.full([1], 1, tl.int64) tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 63, tl.int64) tmp13 = triton_helpers.minimum(tmp11, tmp12) tl.store(out_ptr0 + (x0), tmp13, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/gj/cgj3zqn3kr6qpqyrsszf24gwurte4p5d42s5fuy5tvlv74wnf46b.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp] # Source node to ATen node mapping: # x_1 => add, clamp_max_2, clamp_min, clamp_min_2, convert_element_type, iota, mul, sub, sub_2 # Graph fragment: # %iota : [num_users=1] = call_function[target=torch.ops.prims.iota.default](args = (384,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%iota, torch.float32), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convert_element_type, 0.5), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.16666666666666666), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 0.5), kwargs = {}) # %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub, 0.0), kwargs = {}) # %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_min, %convert_element_type_3), kwargs = {}) # %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_2, 0.0), kwargs = {}) # %clamp_max_2 : [num_users=3] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 1.0), kwargs = {}) triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3 = async_compile.triton('triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3(out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = 0.16666666666666666 tmp5 = tmp3 * tmp4 tmp6 = tmp5 - tmp2 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp9 = tmp8.to(tl.int32) tmp10 = tmp9.to(tl.float32) tmp11 = tmp8 - tmp10 tmp12 = triton_helpers.maximum(tmp11, tmp7) tmp13 = 1.0 tmp14 = triton_helpers.minimum(tmp12, tmp13) tl.store(out_ptr0 + (x0), tmp14, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/gm/cgmmfksblkq4tps6sdds4xr5bav2lwoagbkf652qjw6auh4ti5z7.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._unsafe_index, aten.sub, aten.mul, aten.add] # Source node to ATen node mapping: # x_1 => _unsafe_index, _unsafe_index_1, _unsafe_index_2, _unsafe_index_3, add_4, add_5, add_6, mul_2, mul_3, mul_4, sub_3, sub_4, sub_6 # Graph fragment: # %_unsafe_index : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %convert_element_type_1, %convert_element_type_3]), kwargs = {}) # %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %convert_element_type_1, %clamp_max_1]), kwargs = {}) # %_unsafe_index_2 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %clamp_max, %convert_element_type_3]), kwargs = {}) # %_unsafe_index_3 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%convolution, [None, None, %clamp_max, %clamp_max_1]), kwargs = {}) # %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_1, %_unsafe_index), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, %clamp_max_2), kwargs = {}) # %add_4 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index, %mul_2), kwargs = {}) # %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%_unsafe_index_3, %_unsafe_index_2), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_4, %clamp_max_2), kwargs = {}) # %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%_unsafe_index_2, %mul_3), kwargs = {}) # %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_5, %add_4), kwargs = {}) # %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_6, %clamp_max_3), kwargs = {}) # %add_6 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_4, %mul_4), kwargs = {}) triton_poi_fused__unsafe_index_add_mul_sub_4 = async_compile.triton('triton_poi_fused__unsafe_index_add_mul_sub_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[134217728], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: '*i64', 3: '*fp32', 4: '*i64', 5: '*fp32', 6: '*i64', 7: '*fp32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__unsafe_index_add_mul_sub_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__unsafe_index_add_mul_sub_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, xnumel, XBLOCK : tl.constexpr): xnumel = 88473600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x1 = (xindex // 384) % 384 x0 = xindex % 384 x2 = (xindex // 147456) % 150 x3 = (xindex // 22118400) x5 = xindex tmp0 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (x0), None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr3 + (x0), None, eviction_policy='evict_last') tmp16 = tl.load(in_ptr4 + (x0), None, eviction_policy='evict_last') tmp19 = tl.load(in_ptr5 + (x1), None, eviction_policy='evict_last') tmp29 = tl.load(in_ptr6 + (x1), None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 64, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr2 + (x2 + (150*tmp8) + (9600*tmp4) + (614400*x3)), None, eviction_policy='evict_last') tmp11 = tmp10 + tmp1 tmp12 = tmp10 < 0 tmp13 = tl.where(tmp12, tmp11, tmp10) tmp14 = tl.load(in_ptr2 + (x2 + (150*tmp13) + (9600*tmp4) + (614400*x3)), None, eviction_policy='evict_last') tmp15 = tmp14 - tmp9 tmp17 = tmp15 * tmp16 tmp18 = tmp9 + tmp17 tmp20 = tmp19 + tmp1 tmp21 = tmp19 < 0 tmp22 = tl.where(tmp21, tmp20, tmp19) tmp23 = tl.load(in_ptr2 + (x2 + (150*tmp8) + (9600*tmp22) + (614400*x3)), None, eviction_policy='evict_last') tmp24 = tl.load(in_ptr2 + (x2 + (150*tmp13) + (9600*tmp22) + (614400*x3)), None, eviction_policy='evict_last') tmp25 = tmp24 - tmp23 tmp26 = tmp25 * tmp16 tmp27 = tmp23 + tmp26 tmp28 = tmp27 - tmp18 tmp30 = tmp28 * tmp29 tmp31 = tmp18 + tmp30 tl.store(in_out_ptr0 + (x5), tmp31, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/5b/c5bokndjo3a65idte3uidrd4e6iat5kkuqp2llilshnoij4e43nj.py # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # x_2 => amax, exp, sub_7, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_6, [1], True), kwargs = {}) # %sub_7 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_6, %amax), kwargs = {}) # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_7,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) triton_red_fused__log_softmax_5 = async_compile.triton('triton_red_fused__log_softmax_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.reduction( size_hints=[1048576, 256], reduction_hint=ReductionHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__log_softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_red_fused__log_softmax_5(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr): xnumel = 589824 rnumel = 150 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1) rbase = tl.arange(0, RBLOCK)[None, :] x0 = xindex % 147456 x1 = (xindex // 147456) _tmp2 = tl.full([XBLOCK, RBLOCK], float("-inf"), tl.float32) x3 = xindex for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r2 = rindex tmp0 = tl.load(in_ptr0 + (x0 + (147456*r2) + (22118400*x1)), rmask, eviction_policy='evict_last', other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = triton_helpers.maximum(_tmp2, tmp1) _tmp2 = tl.where(rmask, tmp3, _tmp2) tmp2 = triton_helpers.max2(_tmp2, 1)[:, None] tl.store(out_ptr0 + (x3), tmp2, None) _tmp8 = tl.full([XBLOCK, RBLOCK], 0, tl.float32) for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r2 = rindex tmp4 = tl.load(in_ptr0 + (x0 + (147456*r2) + (22118400*x1)), rmask, eviction_policy='evict_first', other=0.0) tmp5 = tmp4 - tmp2 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = _tmp8 + tmp7 _tmp8 = tl.where(rmask, tmp9, _tmp8) tmp8 = tl.sum(_tmp8, 1)[:, None] tl.store(out_ptr1 + (x3), tmp8, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/wc/cwcue22ytfo4ffonjpb4j6g7czhgtcp4oncvmq6hcb4poclm24xn.py # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # x_2 => log, sub_7, sub_8 # Graph fragment: # %sub_7 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_6, %amax), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {}) # %sub_8 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_7, %log), kwargs = {}) triton_poi_fused__log_softmax_6 = async_compile.triton('triton_poi_fused__log_softmax_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[134217728], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_6(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 88473600 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x3 = xindex x0 = xindex % 147456 x2 = (xindex // 22118400) tmp0 = tl.load(in_out_ptr0 + (x3), None) tmp1 = tl.load(in_ptr0 + (x0 + (147456*x2)), None, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x0 + (147456*x2)), None, eviction_policy='evict_last') tmp2 = tmp0 - tmp1 tmp4 = tl_math.log(tmp3) tmp5 = tmp2 - tmp4 tl.store(in_out_ptr0 + (x3), tmp5, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (150, 4096, 1, 1), (4096, 1, 1, 1)) assert_size_stride(primals_2, (4, 4096, 64, 64), (16777216, 4096, 64, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4096, 64, 64), (16777216, 1, 262144, 4096), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_2, buf0, 16384, 4096, grid=grid(16384, 4096), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 150, 64, 64), (614400, 1, 9600, 150)) buf2 = empty_strided_cuda((384, 1), (1, 1), torch.int64) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._to_copy] triton_poi_fused__to_copy_1.run(buf2, 384, grid=grid(384), stream=stream0) buf3 = empty_strided_cuda((384, 1), (1, 1), torch.int64) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add, aten.clamp] triton_poi_fused_add_clamp_2.run(buf3, 384, grid=grid(384), stream=stream0) buf4 = empty_strided_cuda((384, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp] triton_poi_fused__to_copy_1.run(buf4, 384, grid=grid(384), stream=stream0) buf5 = empty_strided_cuda((384, ), (1, ), torch.int64) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add, aten.clamp] triton_poi_fused_add_clamp_2.run(buf5, 384, grid=grid(384), stream=stream0) buf6 = empty_strided_cuda((384, ), (1, ), torch.float32) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.arange, aten._to_copy, aten.add, aten.mul, aten.sub, aten.clamp] triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3.run(buf6, 384, grid=grid(384), stream=stream0) buf8 = empty_strided_cuda((384, 1), (1, 1), torch.float32) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.sub, aten.clamp] triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3.run(buf8, 384, grid=grid(384), stream=stream0) buf7 = empty_strided_cuda((4, 150, 384, 384), (22118400, 147456, 384, 1), torch.float32) buf9 = buf7; del buf7 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten._unsafe_index, aten.sub, aten.mul, aten.add] triton_poi_fused__unsafe_index_add_mul_sub_4.run(buf9, buf2, buf4, buf1, buf5, buf6, buf3, buf8, 88473600, grid=grid(88473600), stream=stream0) del buf1 buf10 = empty_strided_cuda((4, 1, 384, 384), (147456, 589824, 384, 1), torch.float32) buf11 = empty_strided_cuda((4, 1, 384, 384), (147456, 589824, 384, 1), torch.float32) # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._log_softmax] triton_red_fused__log_softmax_5.run(buf9, buf10, buf11, 589824, 150, grid=grid(589824), stream=stream0) buf12 = buf9; del buf9 # reuse # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten._log_softmax] triton_poi_fused__log_softmax_6.run(buf12, buf10, buf11, 88473600, grid=grid(88473600), stream=stream0) del buf10 del buf11 return (buf12, primals_1, buf0, buf2, buf3, buf4, buf5, buf6, buf8, buf12, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((150, 4096, 1, 1), (4096, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4096, 64, 64), (16777216, 4096, 64, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch from torch import nn class C1Bilinear(nn.Module): def __init__(self, num_class=150, fc_dim=4096, segSize=384, use_softmax =False): super(C1Bilinear, self).__init__() self.segSize = segSize self.use_softmax = use_softmax self.conv_last = nn.Conv2d(fc_dim, num_class, 1, 1, 0, bias=False) def forward(self, x, segSize=None): if segSize is None: segSize = self.segSize, self.segSize elif isinstance(segSize, int): segSize = segSize, segSize x = self.conv_last(x) if not (x.size(2) == segSize[0] and x.size(3) == segSize[1]): x = nn.functional.upsample(x, size=segSize, mode='bilinear') if self.use_softmax: x = nn.functional.softmax(x) else: x = nn.functional.log_softmax(x) return x def get_inputs(): return [torch.rand([4, 4096, 64, 64])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math from torch import nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, YBLOCK], True, tl.int1) x2 = xindex y3 = yindex y0 = yindex % 4096 y1 = yindex // 4096 tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 4096 * x2 + 16777216 * y1), tmp0, None) @triton.jit def triton_poi_fused__to_copy_1(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = 0.16666666666666666 tmp5 = tmp3 * tmp4 tmp6 = tmp5 - tmp2 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp9 = tmp8.to(tl.int32) tl.store(out_ptr0 + x0, tmp9, xmask) @triton.jit def triton_poi_fused_add_clamp_2(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = 0.16666666666666666 tmp5 = tmp3 * tmp4 tmp6 = tmp5 - tmp2 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp9 = tmp8.to(tl.int32) tmp10 = tl.full([1], 1, tl.int64) tmp11 = tmp9 + tmp10 tmp12 = tl.full([1], 63, tl.int64) tmp13 = triton_helpers.minimum(tmp11, tmp12) tl.store(out_ptr0 + x0, tmp13, xmask) @triton.jit def triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3(out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 384 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = x0 tmp1 = tmp0.to(tl.float32) tmp2 = 0.5 tmp3 = tmp1 + tmp2 tmp4 = 0.16666666666666666 tmp5 = tmp3 * tmp4 tmp6 = tmp5 - tmp2 tmp7 = 0.0 tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp9 = tmp8.to(tl.int32) tmp10 = tmp9.to(tl.float32) tmp11 = tmp8 - tmp10 tmp12 = triton_helpers.maximum(tmp11, tmp7) tmp13 = 1.0 tmp14 = triton_helpers.minimum(tmp12, tmp13) tl.store(out_ptr0 + x0, tmp14, xmask) @triton.jit def triton_poi_fused__unsafe_index_add_mul_sub_4(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x1 = xindex // 384 % 384 x0 = xindex % 384 x2 = xindex // 147456 % 150 x3 = xindex // 22118400 x5 = xindex tmp0 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + x0, None, eviction_policy='evict_last') tmp10 = tl.load(in_ptr3 + x0, None, eviction_policy='evict_last') tmp16 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last') tmp19 = tl.load(in_ptr5 + x1, None, eviction_policy='evict_last') tmp29 = tl.load(in_ptr6 + x1, None, eviction_policy='evict_last') tmp1 = tl.full([XBLOCK], 64, tl.int32) tmp2 = tmp0 + tmp1 tmp3 = tmp0 < 0 tmp4 = tl.where(tmp3, tmp2, tmp0) tmp6 = tmp5 + tmp1 tmp7 = tmp5 < 0 tmp8 = tl.where(tmp7, tmp6, tmp5) tmp9 = tl.load(in_ptr2 + (x2 + 150 * tmp8 + 9600 * tmp4 + 614400 * x3), None, eviction_policy='evict_last') tmp11 = tmp10 + tmp1 tmp12 = tmp10 < 0 tmp13 = tl.where(tmp12, tmp11, tmp10) tmp14 = tl.load(in_ptr2 + (x2 + 150 * tmp13 + 9600 * tmp4 + 614400 * x3 ), None, eviction_policy='evict_last') tmp15 = tmp14 - tmp9 tmp17 = tmp15 * tmp16 tmp18 = tmp9 + tmp17 tmp20 = tmp19 + tmp1 tmp21 = tmp19 < 0 tmp22 = tl.where(tmp21, tmp20, tmp19) tmp23 = tl.load(in_ptr2 + (x2 + 150 * tmp8 + 9600 * tmp22 + 614400 * x3 ), None, eviction_policy='evict_last') tmp24 = tl.load(in_ptr2 + (x2 + 150 * tmp13 + 9600 * tmp22 + 614400 * x3), None, eviction_policy='evict_last') tmp25 = tmp24 - tmp23 tmp26 = tmp25 * tmp16 tmp27 = tmp23 + tmp26 tmp28 = tmp27 - tmp18 tmp30 = tmp28 * tmp29 tmp31 = tmp18 + tmp30 tl.store(in_out_ptr0 + x5, tmp31, None) @triton.jit def triton_red_fused__log_softmax_5(in_ptr0, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr): rnumel = 150 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] tl.full([XBLOCK, RBLOCK], True, tl.int1) rbase = tl.arange(0, RBLOCK)[None, :] x0 = xindex % 147456 x1 = xindex // 147456 _tmp2 = tl.full([XBLOCK, RBLOCK], float('-inf'), tl.float32) x3 = xindex for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r2 = rindex tmp0 = tl.load(in_ptr0 + (x0 + 147456 * r2 + 22118400 * x1), rmask, eviction_policy='evict_last', other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = triton_helpers.maximum(_tmp2, tmp1) _tmp2 = tl.where(rmask, tmp3, _tmp2) tmp2 = triton_helpers.max2(_tmp2, 1)[:, None] tl.store(out_ptr0 + x3, tmp2, None) _tmp8 = tl.full([XBLOCK, RBLOCK], 0, tl.float32) for roffset in range(0, rnumel, RBLOCK): rindex = roffset + rbase rmask = rindex < rnumel r2 = rindex tmp4 = tl.load(in_ptr0 + (x0 + 147456 * r2 + 22118400 * x1), rmask, eviction_policy='evict_first', other=0.0) tmp5 = tmp4 - tmp2 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = _tmp8 + tmp7 _tmp8 = tl.where(rmask, tmp9, _tmp8) tmp8 = tl.sum(_tmp8, 1)[:, None] tl.store(out_ptr1 + x3, tmp8, None) @triton.jit def triton_poi_fused__log_softmax_6(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x3 = xindex x0 = xindex % 147456 x2 = xindex // 22118400 tmp0 = tl.load(in_out_ptr0 + x3, None) tmp1 = tl.load(in_ptr0 + (x0 + 147456 * x2), None, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr1 + (x0 + 147456 * x2), None, eviction_policy= 'evict_last') tmp2 = tmp0 - tmp1 tmp4 = tl_math.log(tmp3) tmp5 = tmp2 - tmp4 tl.store(in_out_ptr0 + x3, tmp5, None) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (150, 4096, 1, 1), (4096, 1, 1, 1)) assert_size_stride(primals_2, (4, 4096, 64, 64), (16777216, 4096, 64, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4096, 64, 64), (16777216, 1, 262144, 4096), torch.float32) get_raw_stream(0) triton_poi_fused_0[grid(16384, 4096)](primals_2, buf0, 16384, 4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1) del primals_2 buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 150, 64, 64), (614400, 1, 9600, 150)) buf2 = empty_strided_cuda((384, 1), (1, 1), torch.int64) triton_poi_fused__to_copy_1[grid(384)](buf2, 384, XBLOCK=256, num_warps=4, num_stages=1) buf3 = empty_strided_cuda((384, 1), (1, 1), torch.int64) triton_poi_fused_add_clamp_2[grid(384)](buf3, 384, XBLOCK=256, num_warps=4, num_stages=1) buf4 = empty_strided_cuda((384,), (1,), torch.int64) triton_poi_fused__to_copy_1[grid(384)](buf4, 384, XBLOCK=256, num_warps=4, num_stages=1) buf5 = empty_strided_cuda((384,), (1,), torch.int64) triton_poi_fused_add_clamp_2[grid(384)](buf5, 384, XBLOCK=256, num_warps=4, num_stages=1) buf6 = empty_strided_cuda((384,), (1,), torch.float32) triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3[grid(384)](buf6, 384, XBLOCK=256, num_warps=4, num_stages=1) buf8 = empty_strided_cuda((384, 1), (1, 1), torch.float32) triton_poi_fused__to_copy_add_arange_clamp_mul_sub_3[grid(384)](buf8, 384, XBLOCK=256, num_warps=4, num_stages=1) buf7 = empty_strided_cuda((4, 150, 384, 384), (22118400, 147456, 384, 1), torch.float32) buf9 = buf7 del buf7 triton_poi_fused__unsafe_index_add_mul_sub_4[grid(88473600)](buf9, buf2, buf4, buf1, buf5, buf6, buf3, buf8, 88473600, XBLOCK=512, num_warps=8, num_stages=1) del buf1 buf10 = empty_strided_cuda((4, 1, 384, 384), (147456, 589824, 384, 1), torch.float32) buf11 = empty_strided_cuda((4, 1, 384, 384), (147456, 589824, 384, 1), torch.float32) triton_red_fused__log_softmax_5[grid(589824)](buf9, buf10, buf11, 589824, 150, XBLOCK=64, RBLOCK=64, num_warps=16, num_stages=1) buf12 = buf9 del buf9 triton_poi_fused__log_softmax_6[grid(88473600)](buf12, buf10, buf11, 88473600, XBLOCK=1024, num_warps=4, num_stages=1) del buf10 del buf11 return buf12, primals_1, buf0, buf2, buf3, buf4, buf5, buf6, buf8, buf12 class C1BilinearNew(nn.Module): def __init__(self, num_class=150, fc_dim=4096, segSize=384, use_softmax =False): super(C1BilinearNew, self).__init__() self.segSize = segSize self.use_softmax = use_softmax self.conv_last = nn.Conv2d(fc_dim, num_class, 1, 1, 0, bias=False) def forward(self, input_0): primals_1 = self.conv_last.weight primals_2 = input_0 output = call([primals_1, primals_2]) return output[0]
PCIHD/Project_Daydream
C1Bilinear
false
9,762
[ "MIT" ]
0
94c75ff494e7489a4066e3f9d056a85ff768f40e
https://github.com/PCIHD/Project_Daydream/tree/94c75ff494e7489a4066e3f9d056a85ff768f40e
ResidualConvUnit
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/6q/c6q46q7lsepa4jw5qgcgbc5kiud5wm57hubk6vfo4gk47vl2tprk.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.relu] # Source node to ATen node mapping: # out => relu # Graph fragment: # %relu : [num_users=4] = call_function[target=torch.ops.aten.relu.default](args = (%primals_1,), kwargs = {}) triton_poi_fused_relu_0 = async_compile.triton('triton_poi_fused_relu_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tl.store(out_ptr0 + (x0), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/4e/c4efs56ymyev6yow4ruutakn3po5nni7rvtifmzxqreckdzecoje.py # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # out_1 => convolution # out_2 => relu_1 # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_2, %primals_3, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x3), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/li/clisyfh7uy7myv7uicl6ym42hf2x575nogmdoxa7aohhuh54uign.py # Topologically Sorted Source Nodes: [out_3, add], Original ATen: [aten.convolution, aten.add] # Source node to ATen node mapping: # add => add # out_3 => convolution_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_4, %primals_5, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %relu), kwargs = {}) # %copy_ : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%primals_1, %relu), kwargs = {}) triton_poi_fused_add_convolution_2 = async_compile.triton('triton_poi_fused_add_convolution_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_2', 'mutated_arg_names': ['in_out_ptr0', 'out_ptr0'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x3), xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tl.store(in_out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr0 + (x3), tmp3, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.relu] stream0 = get_raw_stream(0) triton_poi_fused_relu_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0) # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0) del primals_3 # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1)) buf4 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [out_3, add], Original ATen: [aten.convolution, aten.add] triton_poi_fused_add_convolution_2.run(buf4, primals_5, buf0, primals_1, 256, grid=grid(256), stream=stream0) del primals_1 del primals_5 return (buf4, primals_2, primals_4, buf0, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data class ResidualConvUnit(nn.Module): """Residual convolution module.""" def __init__(self, features): """Init. Args: features (int): number of features """ super().__init__() self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True) self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True) self.relu = nn.ReLU(inplace=True) def forward(self, x): """Forward pass. Args: x (tensor): input Returns: tensor: output """ out = self.relu(x) out = self.conv1(out) out = self.relu(out) out = self.conv2(out) return out + x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'features': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_relu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl.full([1], 0, tl.int32) tmp2 = triton_helpers.maximum(tmp1, tmp0) tl.store(out_ptr0 + x0, tmp2, xmask) @triton.jit def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x3, tmp4, xmask) @triton.jit def triton_poi_fused_add_convolution_2(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + x3, xmask) tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tl.store(in_out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr0 + x3, tmp3, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1 del buf1 triton_poi_fused_convolution_relu_1[grid(256)](buf2, primals_3, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 4), (64, 16, 4, 1)) buf4 = buf3 del buf3 triton_poi_fused_add_convolution_2[grid(256)](buf4, primals_5, buf0, primals_1, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 del primals_5 return buf4, primals_2, primals_4, buf0, buf2 class ResidualConvUnitNew(nn.Module): """Residual convolution module.""" def __init__(self, features): """Init. Args: features (int): number of features """ super().__init__() self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True) self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True) self.relu = nn.ReLU(inplace=True) def forward(self, input_0): primals_2 = self.conv1.weight primals_3 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
Zacchaeus14/lang-seg
ResidualConvUnit
false
9,763
[ "MIT" ]
0
ad1196a4d33830f3219dbe2260a69364a745f094
https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094
GlobalConvBlock
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/wu/cwu3ze25mbukaa3mrd6swwkuh6vcngueaov4bvtueedbb4fnybo7.py # Topologically Sorted Source Nodes: [x_l], Original ATen: [aten.convolution] # Source node to ATen node mapping: # x_l => convolution # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [1, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 12) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/5c/c5carxzxmk5ojmz7f5r4vdimoqgyabzcdctk7n4nv6t35zniylle.py # Topologically Sorted Source Nodes: [x_l_1, x_r_1, x], Original ATen: [aten.convolution, aten.add] # Source node to ATen node mapping: # x => add # x_l_1 => convolution_1 # x_r_1 => convolution_3 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution, %primals_4, %primals_5, [1, 1], [0, 1], [1, 1], False, [0, 0], 1), kwargs = {}) # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_2, %primals_8, %primals_9, [1, 1], [1, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_1, %convolution_3), kwargs = {}) triton_poi_fused_add_convolution_1 = async_compile.triton('triton_poi_fused_add_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr): xnumel = 144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 9) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (x3), xmask) tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tl.store(in_out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 1), (16, 4, 1, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_7, (4, ), (1, )) assert_size_stride(primals_8, (4, 4, 4, 1), (16, 4, 1, 1)) assert_size_stride(primals_9, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x_l], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 3, 4), (48, 12, 4, 1)) buf1 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [x_l], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_2, 192, grid=grid(192), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [x_l_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 3, 3), (36, 9, 3, 1)) # Topologically Sorted Source Nodes: [x_r], Original ATen: [aten.convolution] buf3 = extern_kernels.convolution(primals_3, primals_6, stride=(1, 1), padding=(0, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 3), (48, 12, 3, 1)) buf4 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [x_r], Original ATen: [aten.convolution] triton_poi_fused_convolution_0.run(buf4, primals_7, 192, grid=grid(192), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [x_r_1], Original ATen: [aten.convolution] buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1), padding=(1, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf5, (4, 4, 3, 3), (36, 9, 3, 1)) buf6 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [x_l_1, x_r_1, x], Original ATen: [aten.convolution, aten.add] triton_poi_fused_add_convolution_1.run(buf6, primals_5, buf5, primals_9, 144, grid=grid(144), stream=stream0) del buf5 del primals_5 del primals_9 return (buf6, primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 1), (16, 4, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, 4, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, 4, 4, 1), (16, 4, 1, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn from math import sqrt class GlobalConvBlock(nn.Module): def __init__(self, in_dim, out_dim, kernel_size): super(GlobalConvBlock, self).__init__() pad0 = (kernel_size[0] - 1) // 2 pad1 = (kernel_size[1] - 1) // 2 self.conv_l1 = nn.Conv2d(in_dim, out_dim, kernel_size=(kernel_size[ 0], 1), padding=(pad0, 0)) self.conv_l2 = nn.Conv2d(out_dim, out_dim, kernel_size=(1, kernel_size[1]), padding=(0, pad1)) self.conv_r1 = nn.Conv2d(in_dim, out_dim, kernel_size=(1, kernel_size[1]), padding=(0, pad1)) self.conv_r2 = nn.Conv2d(out_dim, out_dim, kernel_size=(kernel_size [0], 1), padding=(pad0, 0)) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, sqrt(2.0 / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def forward(self, x): x_l = self.conv_l1(x) x_l = self.conv_l2(x_l) x_r = self.conv_r1(x) x_r = self.conv_r2(x_r) x = x_l + x_r return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_dim': 4, 'out_dim': 4, 'kernel_size': [4, 4]}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn from math import sqrt assert_size_stride = torch._C._dynamo.guards.assert_size_stride @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 192 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 12 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) @triton.jit def triton_poi_fused_add_convolution_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr): xnumel = 144 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 9 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + x3, xmask) tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = tmp2 + tmp5 tl.store(in_out_ptr0 + x3, tmp6, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 1), (16, 4, 1, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_7, (4,), (1,)) assert_size_stride(primals_8, (4, 4, 4, 1), (16, 4, 1, 1)) assert_size_stride(primals_9, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 3, 4), (48, 12, 4, 1)) buf1 = buf0 del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(192)](buf1, primals_2, 192, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 4, 3, 3), (36, 9, 3, 1)) buf3 = extern_kernels.convolution(primals_3, primals_6, stride=(1, 1), padding=(0, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf3, (4, 4, 4, 3), (48, 12, 3, 1)) buf4 = buf3 del buf3 triton_poi_fused_convolution_0[grid(192)](buf4, primals_7, 192, XBLOCK=128, num_warps=4, num_stages=1) del primals_7 buf5 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1), padding=(1, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf5, (4, 4, 3, 3), (36, 9, 3, 1)) buf6 = buf2 del buf2 triton_poi_fused_add_convolution_1[grid(144)](buf6, primals_5, buf5, primals_9, 144, XBLOCK=128, num_warps=4, num_stages=1) del buf5 del primals_5 del primals_9 return (buf6, primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf4) class GlobalConvBlockNew(nn.Module): def __init__(self, in_dim, out_dim, kernel_size): super(GlobalConvBlockNew, self).__init__() pad0 = (kernel_size[0] - 1) // 2 pad1 = (kernel_size[1] - 1) // 2 self.conv_l1 = nn.Conv2d(in_dim, out_dim, kernel_size=(kernel_size[ 0], 1), padding=(pad0, 0)) self.conv_l2 = nn.Conv2d(out_dim, out_dim, kernel_size=(1, kernel_size[1]), padding=(0, pad1)) self.conv_r1 = nn.Conv2d(in_dim, out_dim, kernel_size=(1, kernel_size[1]), padding=(0, pad1)) self.conv_r2 = nn.Conv2d(out_dim, out_dim, kernel_size=(kernel_size [0], 1), padding=(pad0, 0)) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, sqrt(2.0 / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def forward(self, input_0): primals_1 = self.conv_l1.weight primals_2 = self.conv_l1.bias primals_4 = self.conv_l2.weight primals_5 = self.conv_l2.bias primals_6 = self.conv_r1.weight primals_7 = self.conv_r1.bias primals_8 = self.conv_r2.weight primals_9 = self.conv_r2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]
andy091045/SEGANTest
GlobalConvBlock
false
9,764
[ "MIT" ]
0
90f626461f021ed76716730f78673bc83196f0af
https://github.com/andy091045/SEGANTest/tree/90f626461f021ed76716730f78673bc83196f0af
GuidedBackpropReLUasModule
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/gi/cgi7ftd76n5op4zihzex77shvfz5h4j6ifm2vjrdncwt6cigubfx.py # Topologically Sorted Source Nodes: [output, gt, positive_mask], Original ATen: [aten.addcmul, aten.gt, aten._to_copy] # Source node to ATen node mapping: # gt => gt # output => mul, mul_1 # positive_mask => convert_element_type # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 1), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%arg0_1, 0), kwargs = {}) # %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%gt, torch.float32), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %convert_element_type), kwargs = {}) triton_poi_fused__to_copy_addcmul_gt_0 = async_compile.triton('triton_poi_fused__to_copy_addcmul_gt_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_addcmul_gt_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__to_copy_addcmul_gt_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp3 = 0.0 tmp4 = tmp0 > tmp3 tmp5 = tmp4.to(tl.float32) tmp6 = tmp2 * tmp5 tl.store(out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [output, gt, positive_mask], Original ATen: [aten.addcmul, aten.gt, aten._to_copy] stream0 = get_raw_stream(0) triton_poi_fused__to_copy_addcmul_gt_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
from torch.autograd import Function import torch class GuidedBackpropReLU(Function): @staticmethod def forward(self, input_img): positive_mask = (input_img > 0).type_as(input_img) output = torch.addcmul(torch.zeros(input_img.size()).type_as( input_img), input_img, positive_mask) self.save_for_backward(input_img, output) return output @staticmethod def backward(self, grad_output): input_img, _output = self.saved_tensors grad_input = None positive_mask_1 = (input_img > 0).type_as(grad_output) positive_mask_2 = (grad_output > 0).type_as(grad_output) grad_input = torch.addcmul(torch.zeros(input_img.size()).type_as( input_img), torch.addcmul(torch.zeros(input_img.size()).type_as (input_img), grad_output, positive_mask_1), positive_mask_2) return grad_input class GuidedBackpropReLUasModule(torch.nn.Module): def __init__(self): super(GuidedBackpropReLUasModule, self).__init__() def forward(self, input_img): return GuidedBackpropReLU.apply(input_img) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch.autograd import Function assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused__to_copy_addcmul_gt_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp3 = 0.0 tmp4 = tmp0 > tmp3 tmp5 = tmp4.to(tl.float32) tmp6 = tmp2 * tmp5 tl.store(out_ptr0 + x0, tmp6, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__to_copy_addcmul_gt_0[grid(256)](arg0_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) del arg0_1 return buf0, class GuidedBackpropReLU(Function): @staticmethod def forward(self, input_img): positive_mask = (input_img > 0).type_as(input_img) output = torch.addcmul(torch.zeros(input_img.size()).type_as( input_img), input_img, positive_mask) self.save_for_backward(input_img, output) return output @staticmethod def backward(self, grad_output): input_img, _output = self.saved_tensors grad_input = None positive_mask_1 = (input_img > 0).type_as(grad_output) positive_mask_2 = (grad_output > 0).type_as(grad_output) grad_input = torch.addcmul(torch.zeros(input_img.size()).type_as( input_img), torch.addcmul(torch.zeros(input_img.size()).type_as (input_img), grad_output, positive_mask_1), positive_mask_2) return grad_input class GuidedBackpropReLUasModuleNew(torch.nn.Module): def __init__(self): super(GuidedBackpropReLUasModuleNew, self).__init__() def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
bei2/pytorch-grad-cam
GuidedBackpropReLUasModule
false
9,765
[ "MIT" ]
0
c7f4a6cc26638fc668738c81ca35908ed6b1845b
https://github.com/bei2/pytorch-grad-cam/tree/c7f4a6cc26638fc668738c81ca35908ed6b1845b
up
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/vz/cvzodr5gcejdys7tscy26lmdqza3z2erfgo6btt332sh43xnk5w6.py # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] # Source node to ATen node mapping: # x => cat # Graph fragment: # %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%constant_pad_nd, %primals_4], 1), kwargs = {}) triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 16) % 8 x1 = (xindex // 4) % 4 x0 = xindex % 4 x3 = (xindex // 128) x6 = xindex % 16 x7 = xindex tmp0 = x2 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = 2 + x1 tmp6 = tmp5 >= tmp1 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp5 < tmp7 tmp9 = 2 + x0 tmp10 = tmp9 >= tmp1 tmp11 = tmp9 < tmp7 tmp12 = tmp6 & tmp8 tmp13 = tmp12 & tmp10 tmp14 = tmp13 & tmp11 tmp15 = tmp14 & tmp4 tmp16 = tl.load(in_ptr0 + (18 + x0 + (8*x1) + (64*x2) + (256*x3)), tmp15 & xmask, other=0.0) tmp17 = tl.load(in_ptr1 + (x2), tmp15 & xmask, eviction_policy='evict_last', other=0.0) tmp18 = tmp16 + tmp17 tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype) tmp20 = tl.where(tmp15, tmp18, tmp19) tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype) tmp22 = tl.where(tmp4, tmp20, tmp21) tmp23 = tmp0 >= tmp3 tmp24 = tmp0 < tmp7 tmp25 = tl.load(in_ptr2 + (x6 + (16*((-4) + x2)) + (64*x3)), tmp23 & xmask, other=0.0) tmp26 = tl.where(tmp4, tmp22, tmp25) tl.store(out_ptr0 + (x7), tmp26, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4, 2, 2), (16, 4, 2, 1)) assert_size_stride(primals_2, (4, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x2], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1)) buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.cat] stream0 = get_raw_stream(0) triton_poi_fused_cat_0.run(buf0, primals_2, primals_4, buf1, 512, grid=grid(512), stream=stream0) del buf0 del primals_2 del primals_4 return (buf1, primals_1, primals_3, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 2, 2), (16, 4, 2, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F class up(nn.Module): def __init__(self, in_ch, out_ch): super(up, self).__init__() self.up_scale = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2) def forward(self, x1, x2): x2 = self.up_scale(x2) diffY = x1.size()[2] - x2.size()[2] diffX = x1.size()[3] - x2.size()[3] x2 = F.pad(x2, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) x = torch.cat([x2, x1], dim=1) return x def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_ch': 4, 'out_ch': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 512 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 16 % 8 x1 = xindex // 4 % 4 x0 = xindex % 4 x3 = xindex // 128 x6 = xindex % 16 x7 = xindex tmp0 = x2 tmp1 = tl.full([1], 0, tl.int64) tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = 2 + x1 tmp6 = tmp5 >= tmp1 tmp7 = tl.full([1], 8, tl.int64) tmp8 = tmp5 < tmp7 tmp9 = 2 + x0 tmp10 = tmp9 >= tmp1 tmp11 = tmp9 < tmp7 tmp12 = tmp6 & tmp8 tmp13 = tmp12 & tmp10 tmp14 = tmp13 & tmp11 tmp15 = tmp14 & tmp4 tmp16 = tl.load(in_ptr0 + (18 + x0 + 8 * x1 + 64 * x2 + 256 * x3), tmp15 & xmask, other=0.0) tmp17 = tl.load(in_ptr1 + x2, tmp15 & xmask, eviction_policy= 'evict_last', other=0.0) tmp18 = tmp16 + tmp17 tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype) tmp20 = tl.where(tmp15, tmp18, tmp19) tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype) tmp22 = tl.where(tmp4, tmp20, tmp21) tmp23 = tmp0 >= tmp3 tmp25 = tl.load(in_ptr2 + (x6 + 16 * (-4 + x2) + 64 * x3), tmp23 & xmask, other=0.0) tmp26 = tl.where(tmp4, tmp22, tmp25) tl.store(out_ptr0 + x7, tmp26, xmask) def call(args): primals_1, primals_2, primals_3, primals_4 = args args.clear() assert_size_stride(primals_1, (4, 4, 2, 2), (16, 4, 2, 1)) assert_size_stride(primals_2, (4,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 8, 8), (256, 64, 8, 1)) buf1 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_cat_0[grid(512)](buf0, primals_2, primals_4, buf1, 512, XBLOCK=256, num_warps=4, num_stages=1) del buf0 del primals_2 del primals_4 return buf1, primals_1, primals_3 class upNew(nn.Module): def __init__(self, in_ch, out_ch): super(upNew, self).__init__() self.up_scale = nn.ConvTranspose2d(in_ch, out_ch, 2, stride=2) def forward(self, input_0, input_1): primals_1 = self.up_scale.weight primals_2 = self.up_scale.bias primals_3 = input_0 primals_4 = input_1 output = call([primals_1, primals_2, primals_3, primals_4]) return output[0]
aribryan/pytorch_task
up
false
9,766
[ "MIT" ]
0
c661f201bbf03cfd06a13deb4c1c0c61d017adb1
https://github.com/aribryan/pytorch_task/tree/c661f201bbf03cfd06a13deb4c1c0c61d017adb1
depthwise_block
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/5x/c5x7dqzlsrbbcjewbqspqqmnmhn5phgfzvac63teaxnhogxx6tll.py # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # x_3 => relu # Graph fragment: # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tmp4 = tl.full([1], 0, tl.int32) tmp5 = triton_helpers.maximum(tmp4, tmp3) tmp6 = 0.0 tmp7 = tmp5 <= tmp6 tl.store(in_out_ptr0 + (x0), tmp5, xmask) tl.store(out_ptr0 + (x0), tmp7, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1)) assert_size_stride(primals_3, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (16, 1, 4, 4), (16, 16, 4, 1), 0), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (16, 1, 4, 4), (16, 16, 4, 1)) buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_3, buf2, 256, grid=grid(256), stream=stream0) del primals_3 return (buf1, primals_2, reinterpret_tensor(primals_1, (16, 1, 4, 4), (16, 16, 4, 1), 0), buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data class depthwise_conv(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1): super(depthwise_conv, self).__init__() self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride= stride, padding=padding) def forward(self, x): C, H, W = x.shape[1:] x = x.reshape(-1, 1, H, W) x = self.depthwise(x) x = x.view(-1, C, H, W) return x class depthwise_block(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1, activation='relu'): super(depthwise_block, self).__init__() self.depthwise = depthwise_conv(kernel_size=3, stride=1, padding=1) if activation == 'relu': self.activation = nn.ReLU() elif activation == 'lrelu': self.activation = nn.LeakyReLU() elif activation == 'tanh': self.activation = nn.Tanh() def forward(self, x, act=True): x = self.depthwise(x) if act: x = self.activation(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tmp4 = tl.full([1], 0, tl.int32) tmp5 = triton_helpers.maximum(tmp4, tmp3) tmp6 = 0.0 tmp7 = tmp5 <= tmp6 tl.store(in_out_ptr0 + x0, tmp5, xmask) tl.store(out_ptr0 + x0, tmp7, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1)) assert_size_stride(primals_3, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (16, 1, 4, 4), (16, 16, 4, 1), 0), primals_2, stride=(1, 1), padding =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (16, 1, 4, 4), (16, 16, 4, 1)) buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1, primals_3, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 return buf1, primals_2, reinterpret_tensor(primals_1, (16, 1, 4, 4), ( 16, 16, 4, 1), 0), buf2 class depthwise_conv(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1): super(depthwise_conv, self).__init__() self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride= stride, padding=padding) def forward(self, x): C, H, W = x.shape[1:] x = x.reshape(-1, 1, H, W) x = self.depthwise(x) x = x.view(-1, C, H, W) return x class depthwise_blockNew(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1, activation='relu'): super(depthwise_blockNew, self).__init__() self.depthwise = depthwise_conv(kernel_size=3, stride=1, padding=1) if activation == 'relu': self.activation = nn.ReLU() elif activation == 'lrelu': self.activation = nn.LeakyReLU() elif activation == 'tanh': self.activation = nn.Tanh() def forward(self, input_0): primals_2 = self.depthwise.depthwise.weight primals_3 = self.depthwise.depthwise.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
Zacchaeus14/lang-seg
depthwise_block
false
9,767
[ "MIT" ]
0
ad1196a4d33830f3219dbe2260a69364a745f094
https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094
Attention
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/r6/cr6neze6yovkog6kjrk5k2db63h47ozkojywfys6karxe7dlumrz.py # Topologically Sorted Source Nodes: [score_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # score_1 => amax, exp, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%bmm, [-1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%bmm, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + (x2), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/kj/ckjtlefzavjukjsytvkak6ek26zmzexpcbnlwelx4k5kascjxlf3.py # Topologically Sorted Source Nodes: [score_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # score_1 => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args args.clear() assert_size_stride(primals_1, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4, ), (1, )) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4, ), (1, )) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm] extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0) del primals_3 del primals_4 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_6, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1) del primals_5 del primals_6 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [score], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), out=buf2) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [score_1], Original ATen: [aten._softmax] stream0 = get_raw_stream(0) triton_poi_fused__softmax_0.run(buf2, buf3, 64, grid=grid(64), stream=stream0) buf4 = buf2; del buf2 # reuse # Topologically Sorted Source Nodes: [score_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_1.run(buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = buf3; del buf3 # reuse # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm] extern_kernels.bmm(buf4, reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), out=buf5) buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [output_2], Original ATen: [aten.addmm] extern_kernels.addmm(primals_8, reinterpret_tensor(buf5, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6) del primals_8 return (reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0), buf4, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), buf4, reinterpret_tensor(buf5, (16, 4), (4, 1), 0), primals_7, reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 1, 4), (16, 4, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import math import torch import torch.nn.functional as F import torch.nn as nn class Attention(nn.Module): def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', dropout=0): """ Attention Mechanism :param embed_dim: :param hidden_dim: :param out_dim: :param n_head: num of head (Multi-Head Attention) :param score_function: scaled_dot_product / mlp (concat) / bi_linear (general dot) :return (?, q_len, out_dim,) """ super(Attention, self).__init__() if hidden_dim is None: hidden_dim = embed_dim // n_head if out_dim is None: out_dim = embed_dim self.embed_dim = embed_dim self.hidden_dim = hidden_dim self.n_head = n_head self.score_function = score_function self.w_k = nn.Linear(embed_dim, n_head * hidden_dim) self.w_q = nn.Linear(embed_dim, n_head * hidden_dim) self.proj = nn.Linear(n_head * hidden_dim, out_dim) self.dropout = nn.Dropout(dropout) if score_function == 'mlp': self.weight = nn.Parameter(torch.Tensor(hidden_dim * 2)) elif self.score_function == 'bi_linear': self.weight = nn.Parameter(torch.Tensor(hidden_dim, hidden_dim)) else: self.register_parameter('weight', None) self.reset_parameters() def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_dim) if self.weight is not None: self.weight.data.uniform_(-stdv, stdv) def forward(self, k, q): if len(q.shape) == 2: q = torch.unsqueeze(q, dim=1) if len(k.shape) == 2: k = torch.unsqueeze(k, dim=1) mb_size = k.shape[0] k_len = k.shape[1] q_len = q.shape[1] kx = self.w_k(k).view(mb_size, k_len, self.n_head, self.hidden_dim) kx = kx.permute(2, 0, 1, 3).contiguous().view(-1, k_len, self. hidden_dim) qx = self.w_q(q).view(mb_size, q_len, self.n_head, self.hidden_dim) qx = qx.permute(2, 0, 1, 3).contiguous().view(-1, q_len, self. hidden_dim) if self.score_function == 'dot_product': kt = kx.permute(0, 2, 1) score = torch.bmm(qx, kt) elif self.score_function == 'scaled_dot_product': kt = kx.permute(0, 2, 1) qkt = torch.bmm(qx, kt) score = torch.div(qkt, math.sqrt(self.hidden_dim)) elif self.score_function == 'mlp': kxx = torch.unsqueeze(kx, dim=1).expand(-1, q_len, -1, -1) qxx = torch.unsqueeze(qx, dim=2).expand(-1, -1, k_len, -1) kq = torch.cat((kxx, qxx), dim=-1) score = F.tanh(torch.matmul(kq, self.weight)) elif self.score_function == 'bi_linear': qw = torch.matmul(qx, self.weight) kt = kx.permute(0, 2, 1) score = torch.bmm(qw, kt) else: raise RuntimeError('invalid score_function') score = F.softmax(score, dim=-1) output = torch.bmm(score, kx) output = torch.cat(torch.split(output, mb_size, dim=0), dim=-1) output = self.proj(output) output = self.dropout(output) return output, score def get_inputs(): return [torch.rand([4, 4, 1, 4]), torch.rand([4, 4, 1, 4])] def get_init_inputs(): return [[], {'embed_dim': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tmp9 = tl_math.exp(tmp8) tl.store(out_ptr0 + x2, tmp9, xmask) @triton.jit def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8) = args args.clear() assert_size_stride(primals_1, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_2, (4, 4, 1, 4), (16, 4, 4, 1)) assert_size_stride(primals_3, (4, 4), (4, 1)) assert_size_stride(primals_4, (4,), (1,)) assert_size_stride(primals_5, (4, 4), (4, 1)) assert_size_stride(primals_6, (4,), (1,)) assert_size_stride(primals_7, (4, 4), (4, 1)) assert_size_stride(primals_8, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_4, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf0) del primals_3 del primals_4 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_6, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0 ), alpha=1, beta=1, out=buf1) del primals_5 del primals_6 buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), out=buf2) buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused__softmax_0[grid(64)](buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) buf4 = buf2 del buf2 triton_poi_fused__softmax_1[grid(64)](buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = buf3 del buf3 extern_kernels.bmm(buf4, reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), out=buf5) buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_8, reinterpret_tensor(buf5, (16, 4), ( 4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6) del primals_8 return reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0 ), buf4, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0 ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0 ), reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0 ), buf4, reinterpret_tensor(buf5, (16, 4), (4, 1), 0 ), primals_7, reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0) class AttentionNew(nn.Module): def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', dropout=0): """ Attention Mechanism :param embed_dim: :param hidden_dim: :param out_dim: :param n_head: num of head (Multi-Head Attention) :param score_function: scaled_dot_product / mlp (concat) / bi_linear (general dot) :return (?, q_len, out_dim,) """ super(AttentionNew, self).__init__() if hidden_dim is None: hidden_dim = embed_dim // n_head if out_dim is None: out_dim = embed_dim self.embed_dim = embed_dim self.hidden_dim = hidden_dim self.n_head = n_head self.score_function = score_function self.w_k = nn.Linear(embed_dim, n_head * hidden_dim) self.w_q = nn.Linear(embed_dim, n_head * hidden_dim) self.proj = nn.Linear(n_head * hidden_dim, out_dim) self.dropout = nn.Dropout(dropout) if score_function == 'mlp': self.weight = nn.Parameter(torch.Tensor(hidden_dim * 2)) elif self.score_function == 'bi_linear': self.weight = nn.Parameter(torch.Tensor(hidden_dim, hidden_dim)) else: self.register_parameter('weight', None) self.reset_parameters() def reset_parameters(self): stdv = 1.0 / math.sqrt(self.hidden_dim) if self.weight is not None: self.weight.data.uniform_(-stdv, stdv) def forward(self, input_0, input_1): primals_3 = self.w_k.weight primals_4 = self.w_k.bias primals_5 = self.w_q.weight primals_6 = self.w_q.bias primals_7 = self.proj.weight primals_8 = self.proj.bias primals_1 = input_0 primals_2 = input_1 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8]) return output[0], output[1]
aquibjaved/ABSA-PyTorch
Attention
false
9,768
[ "MIT" ]
0
fd904250ceec436e49dc50694f79891c0c67d6b1
https://github.com/aquibjaved/ABSA-PyTorch/tree/fd904250ceec436e49dc50694f79891c0c67d6b1
PatchEmbedding
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/sr/csrhhqsexdcor6gq6tz4dawxblhadgekinzxxkt33uwojltligp6.py # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] # Source node to ATen node mapping: # conv2d => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x2), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 16, 16), 0); del buf0 # reuse # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution] stream0 = get_raw_stream(0) triton_poi_fused_convolution_0.run(buf1, primals_3, 16, grid=grid(16), stream=stream0) del primals_3 return (reinterpret_tensor(buf1, (4, 1, 4), (4, 1, 1), 0), primals_1, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class PatchEmbedding(nn.Module): def __init__(self, image_size, patch_size, embed_dim, channels): super().__init__() self.image_size = image_size if image_size[0] % patch_size != 0 or image_size[1] % patch_size != 0: raise ValueError( 'image dimensions must be divisible by the patch size') self.grid_size = image_size[0] // patch_size, image_size[1 ] // patch_size self.num_patches = self.grid_size[0] * self.grid_size[1] self.patch_size = patch_size self.proj = nn.Conv2d(channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, im): _B, _C, _H, _W = im.shape x = self.proj(im).flatten(2).transpose(1, 2) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'image_size': [4, 4], 'patch_size': 4, 'embed_dim': 4, 'channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x2, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1)) buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 16, 16), 0) del buf0 get_raw_stream(0) triton_poi_fused_convolution_0[grid(16)](buf1, primals_3, 16, XBLOCK=16, num_warps=1, num_stages=1) del primals_3 return reinterpret_tensor(buf1, (4, 1, 4), (4, 1, 1), 0 ), primals_1, primals_2 class PatchEmbeddingNew(nn.Module): def __init__(self, image_size, patch_size, embed_dim, channels): super().__init__() self.image_size = image_size if image_size[0] % patch_size != 0 or image_size[1] % patch_size != 0: raise ValueError( 'image dimensions must be divisible by the patch size') self.grid_size = image_size[0] // patch_size, image_size[1 ] // patch_size self.num_patches = self.grid_size[0] * self.grid_size[1] self.patch_size = patch_size self.proj = nn.Conv2d(channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, input_0): primals_1 = self.proj.weight primals_3 = self.proj.bias primals_2 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
avniculae/segmenter
PatchEmbedding
false
9,769
[ "MIT" ]
0
ca9683399b7dae13a8ccbadc744826306b8dbf94
https://github.com/avniculae/segmenter/tree/ca9683399b7dae13a8ccbadc744826306b8dbf94
AddTensors
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/3r/c3rcleexr5nwq4qvwylgjpelwulrq7jjyvy54eszp24wxfm6tszs.py # Topologically Sorted Source Nodes: [add, add_1, add_2, add_3], Original ATen: [aten.add] # Source node to ATen node mapping: # add => add # add_1 => add_1 # add_2 => add_2 # add_3 => add_3 # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select, 0), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %select_1), kwargs = {}) # %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %select_2), kwargs = {}) # %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %select_3), kwargs = {}) triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp3 = tl.load(in_ptr0 + (64 + x0), xmask) tmp5 = tl.load(in_ptr0 + (128 + x0), xmask) tmp7 = tl.load(in_ptr0 + (192 + x0), xmask) tmp1 = 0.0 tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tl.store(out_ptr0 + (x0), tmp8, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [add, add_1, add_2, add_3], Original ATen: [aten.add] stream0 = get_raw_stream(0) triton_poi_fused_add_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0) del arg0_1 return (buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.hub class AddTensors(nn.Module): """ Adds all its inputs together. """ def forward(self, xs): return sum(xs) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn import torch.hub assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp3 = tl.load(in_ptr0 + (64 + x0), xmask) tmp5 = tl.load(in_ptr0 + (128 + x0), xmask) tmp7 = tl.load(in_ptr0 + (192 + x0), xmask) tmp1 = 0.0 tmp2 = tmp0 + tmp1 tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tl.store(out_ptr0 + x0, tmp8, xmask) def call(args): arg0_1, = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1) del arg0_1 return buf0, class AddTensorsNew(nn.Module): """ Adds all its inputs together. """ def forward(self, input_0): arg0_1 = input_0 output = call([arg0_1]) return output[0]
azavea/keras-image-segmentation
AddTensors
false
9,770
[ "Apache-2.0" ]
0
eb67d12e1c88f04387873444c7c9b05f767280e6
https://github.com/azavea/keras-image-segmentation/tree/eb67d12e1c88f04387873444c7c9b05f767280e6
ClassificationLogSoftmax
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/yy/cyya3js6wt64vdji3sfisvrqyfvqxwkwqq5mzg5bqjl2crzjs4t3.py # Topologically Sorted Source Nodes: [output_states], Original ATen: [aten.clone] # Source node to ATen node mapping: # output_states => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%select,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = (xindex // 16) x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask) tl.store(out_ptr0 + (x2), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/2g/c2gw7362i2a6wsfdx2sxyywx4o6ronjg6goebvdn44w6gpjsxpbc.py # Topologically Sorted Source Nodes: [output_states, output_states_1], Original ATen: [aten.add, aten.tanh] # Source node to ATen node mapping: # output_states => add # output_states_1 => tanh # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %primals_3), kwargs = {}) # %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {}) triton_poi_fused_add_tanh_1 = async_compile.triton('triton_poi_fused_add_tanh_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + (x2), tmp3, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/4n/c4nc446yqbrtfyayl4mzt4mxucu6lyinpbl5i77rrpgokfkjfnsn.py # Topologically Sorted Source Nodes: [log_probs], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # log_probs => amax, sub # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [-1], True), kwargs = {}) # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {}) triton_poi_fused__log_softmax_2 = async_compile.triton('triton_poi_fused__log_softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/e5/ce5suhs2e2ygw6kp4ycmsbsq4xfgw573srqfqshl4crsnmymkvfl.py # Topologically Sorted Source Nodes: [log_probs], Original ATen: [aten._log_softmax] # Source node to ATen node mapping: # log_probs => exp, log, sub_1, sum_1 # Graph fragment: # %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {}) triton_poi_fused__log_softmax_3 = async_compile.triton('triton_poi_fused__log_softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__log_softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp2 = tl_math.exp(tmp1) tmp4 = tl_math.exp(tmp3) tmp5 = tmp2 + tmp4 tmp7 = tl_math.exp(tmp6) tmp8 = tmp5 + tmp7 tmp10 = tl_math.exp(tmp9) tmp11 = tmp8 + tmp10 tmp12 = tl_math.log(tmp11) tmp13 = tmp0 - tmp12 tl.store(out_ptr0 + (x2), tmp13, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [output_states], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [output_states], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1) del primals_2 buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0); del buf1 # reuse # Topologically Sorted Source Nodes: [output_states, output_states_1], Original ATen: [aten.add, aten.tanh] triton_poi_fused_add_tanh_1.run(buf2, primals_3, 64, grid=grid(64), stream=stream0) del primals_3 buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32) # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm] extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3) del primals_5 buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [log_probs], Original ATen: [aten._log_softmax] triton_poi_fused__log_softmax_2.run(buf3, buf4, 64, grid=grid(64), stream=stream0) buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [log_probs], Original ATen: [aten._log_softmax] triton_poi_fused__log_softmax_3.run(buf4, buf5, 64, grid=grid(64), stream=stream0) del buf4 return (buf5, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf2, buf5, primals_4, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class ClassificationLogSoftmax(nn.Module): """ Classifier on top of the hidden representation of the first token, which is usually [CLS] token in BERT-like architectures. """ def __init__(self, hidden_size, num_classes): super().__init__() self.dense1 = nn.Linear(hidden_size, hidden_size) self.dense2 = nn.Linear(hidden_size, num_classes) def forward(self, hidden_states): output_states = self.dense1(hidden_states[:, 0]) output_states = torch.tanh(output_states) output_states = self.dense2(output_states).float() log_probs = torch.log_softmax(output_states, dim=-1) return log_probs def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'hidden_size': 4, 'num_classes': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 16 x1 = xindex // 16 x2 = xindex tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask) tl.store(out_ptr0 + x2, tmp0, xmask) @triton.jit def triton_poi_fused_add_tanh_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = libdevice.tanh(tmp2) tl.store(in_out_ptr0 + x2, tmp3, xmask) @triton.jit def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = triton_helpers.maximum(tmp1, tmp2) tmp5 = triton_helpers.maximum(tmp3, tmp4) tmp7 = triton_helpers.maximum(tmp5, tmp6) tmp8 = tmp0 - tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused__log_softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl. constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp2 = tl_math.exp(tmp1) tmp4 = tl_math.exp(tmp3) tmp5 = tmp2 + tmp4 tmp7 = tl_math.exp(tmp6) tmp8 = tmp5 + tmp7 tmp10 = tl_math.exp(tmp9) tmp11 = tmp8 + tmp10 tmp12 = tl_math.log(tmp11) tmp13 = tmp0 - tmp12 tl.store(out_ptr0 + x2, tmp13, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4), (4, 1)) assert_size_stride(primals_3, (4,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(64)](primals_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_1 buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1) del primals_2 buf2 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0) del buf1 triton_poi_fused_add_tanh_1[grid(64)](buf2, primals_3, 64, XBLOCK= 64, num_warps=1, num_stages=1) del primals_3 buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32) extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), ( 4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3) del primals_5 buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused__log_softmax_2[grid(64)](buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1) buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0) del buf3 triton_poi_fused__log_softmax_3[grid(64)](buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1) del buf4 return buf5, reinterpret_tensor(buf0, (16, 4), (4, 1), 0 ), buf2, buf5, primals_4 class ClassificationLogSoftmaxNew(nn.Module): """ Classifier on top of the hidden representation of the first token, which is usually [CLS] token in BERT-like architectures. """ def __init__(self, hidden_size, num_classes): super().__init__() self.dense1 = nn.Linear(hidden_size, hidden_size) self.dense2 = nn.Linear(hidden_size, num_classes) def forward(self, input_0): primals_2 = self.dense1.weight primals_3 = self.dense1.bias primals_4 = self.dense2.weight primals_5 = self.dense2.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
awesome-archive/NeMo
ClassificationLogSoftmax
false
9,771
[ "Apache-2.0" ]
0
0e566e62f0d102b725d3839564e51f7f40fa41b5
https://github.com/awesome-archive/NeMo/tree/0e566e62f0d102b725d3839564e51f7f40fa41b5
group
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/az/cazxolgp2ne6vc522yhqcdzkhjb6btel7txdrpwzpkcc5t6sm46x.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt] # Source node to ATen node mapping: # x_1 => maximum # Graph fragment: # %maximum : [num_users=2] = call_function[target=torch.ops.aten.maximum.default](args = (%getitem, %getitem_1), kwargs = {}) # %eq_2 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%getitem, %getitem_1), kwargs = {}) # %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%getitem, %getitem_1), kwargs = {}) # %lt_1 : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%getitem, %getitem_1), kwargs = {}) triton_poi_fused_eq_gt_lt_maximum_0 = async_compile.triton('triton_poi_fused_eq_gt_lt_maximum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*i1', 5: '*i1', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_gt_lt_maximum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 64) x3 = xindex % 64 x1 = (xindex // 16) % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + (128*x2)), xmask) tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (64 + x3 + (128*x2)), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp7 = tmp2 == tmp5 tmp8 = tmp2 > tmp5 tmp9 = tmp2 < tmp5 tl.store(out_ptr0 + (x4), tmp6, xmask) tl.store(out_ptr1 + (x4), tmp7, xmask) tl.store(out_ptr2 + (x4), tmp8, xmask) tl.store(out_ptr3 + (x4), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/dx/cdxsiauqixxznc5upksv4k5qv54fs7gz2sgvr4qfd5yyu72syijl.py # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt] # Source node to ATen node mapping: # x_3 => maximum_1 # Graph fragment: # %maximum_1 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%getitem_2, %getitem_3), kwargs = {}) # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%getitem_2, %getitem_3), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%getitem_2, %getitem_3), kwargs = {}) # %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%getitem_2, %getitem_3), kwargs = {}) triton_poi_fused_eq_gt_lt_maximum_1 = async_compile.triton('triton_poi_fused_eq_gt_lt_maximum_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*i1', 5: '*i1', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_gt_lt_maximum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 324) x3 = xindex % 324 x1 = (xindex // 81) % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + (648*x2)), xmask) tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (324 + x3 + (648*x2)), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp7 = tmp2 == tmp5 tmp8 = tmp2 > tmp5 tmp9 = tmp2 < tmp5 tl.store(out_ptr0 + (x4), tmp6, xmask) tl.store(out_ptr1 + (x4), tmp7, xmask) tl.store(out_ptr2 + (x4), tmp8, xmask) tl.store(out_ptr3 + (x4), tmp9, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (8, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_2, (8, ), (1, )) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (8, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (8, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1)) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt] stream0 = get_raw_stream(0) triton_poi_fused_eq_gt_lt_maximum_0.run(buf0, primals_2, buf1, buf7, buf8, buf9, 256, grid=grid(256), stream=stream0) del buf0 del primals_2 # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 8, 9, 9), (648, 81, 9, 1)) buf3 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool) buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool) buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt] triton_poi_fused_eq_gt_lt_maximum_1.run(buf2, primals_5, buf3, buf4, buf5, buf6, 1296, grid=grid(1296), stream=stream0) del buf2 del primals_5 return (buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5, buf6, buf7, buf8, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((8, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((8, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class mfm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1): super(mfm, self).__init__() self.out_channels = out_channels if type == 1: self.filter = nn.Conv2d(in_channels, 2 * out_channels, kernel_size=kernel_size, stride=stride, padding=padding) else: self.filter = nn.Linear(in_channels, 2 * out_channels) def forward(self, x): x = self.filter(x) out = torch.split(x, self.out_channels, 1) return torch.max(out[0], out[1]) class group(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding ): super(group, self).__init__() self.conv_a = mfm(in_channels, in_channels, 1, 1, 0) self.conv = mfm(in_channels, out_channels, kernel_size, stride, padding ) def forward(self, x): x = self.conv_a(x) x = self.conv(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4, 'stride': 1, 'padding': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 64 x3 = xindex % 64 x1 = xindex // 16 % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask) tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (64 + x3 + 128 * x2), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp7 = tmp2 == tmp5 tmp8 = tmp2 > tmp5 tmp9 = tmp2 < tmp5 tl.store(out_ptr0 + x4, tmp6, xmask) tl.store(out_ptr1 + x4, tmp7, xmask) tl.store(out_ptr2 + x4, tmp8, xmask) tl.store(out_ptr3 + x4, tmp9, xmask) @triton.jit def triton_poi_fused_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr): xnumel = 1296 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 324 x3 = xindex % 324 x1 = xindex // 81 % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + 648 * x2), xmask) tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (324 + x3 + 648 * x2), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp7 = tmp2 == tmp5 tmp8 = tmp2 > tmp5 tmp9 = tmp2 < tmp5 tl.store(out_ptr0 + x4, tmp6, xmask) tl.store(out_ptr1 + x4, tmp7, xmask) tl.store(out_ptr2 + x4, tmp8, xmask) tl.store(out_ptr3 + x4, tmp9, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (8, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_2, (8,), (1,)) assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_4, (8, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_5, (8,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1)) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_eq_gt_lt_maximum_0[grid(256)](buf0, primals_2, buf1, buf7, buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf0 del primals_2 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 8, 9, 9), (648, 81, 9, 1)) buf3 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool) buf5 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool) buf6 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.bool) triton_poi_fused_eq_gt_lt_maximum_1[grid(1296)](buf2, primals_5, buf3, buf4, buf5, buf6, 1296, XBLOCK=128, num_warps=4, num_stages=1 ) del buf2 del primals_5 return (buf3, primals_1, primals_3, primals_4, buf1, buf4, buf5, buf6, buf7, buf8, buf9) class mfm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1): super(mfm, self).__init__() self.out_channels = out_channels if type == 1: self.filter = nn.Conv2d(in_channels, 2 * out_channels, kernel_size=kernel_size, stride=stride, padding=padding) else: self.filter = nn.Linear(in_channels, 2 * out_channels) def forward(self, x): x = self.filter(x) out = torch.split(x, self.out_channels, 1) return torch.max(out[0], out[1]) class groupNew(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding ): super(groupNew, self).__init__() self.conv_a = mfm(in_channels, in_channels, 1, 1, 0) self.conv = mfm(in_channels, out_channels, kernel_size, stride, padding ) def forward(self, input_0): primals_1 = self.conv_a.filter.weight primals_2 = self.conv_a.filter.bias primals_4 = self.conv.filter.weight primals_5 = self.conv.filter.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
aryachiranjeev/Dependable-AI
group
false
9,772
[ "MIT" ]
0
750570572c1baaa2590a89c0982e2f71b15b48b9
https://github.com/aryachiranjeev/Dependable-AI/tree/750570572c1baaa2590a89c0982e2f71b15b48b9
ConvBlock
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/v6/cv6oewqqnsshd7he7ylh2kikzu4smtrhj2dmv6nb5csosp7g6vw5.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # out => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7r/c7rmcz7d66c7acqsst3ljub72usieb7gow6csu7nmp55tklmjx2e.py # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu] # Source node to ATen node mapping: # out_1 => convolution # out_2 => expm1, gt, mul, mul_2, where # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%convolution, 0), kwargs = {}) # %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution, 1.0), kwargs = {}) # %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {}) # %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {}) triton_poi_fused_convolution_elu_1 = async_compile.triton('triton_poi_fused_convolution_elu_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_elu_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tl.store(in_out_ptr0 + (x3), tmp9, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.convolution, aten.elu] triton_poi_fused_convolution_elu_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0) del primals_3 return (buf2, primals_2, buf0, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Conv3x3(nn.Module): """Layer to pad and convolve input """ def __init__(self, in_channels, out_channels, use_refl=True): super(Conv3x3, self).__init__() if use_refl: self.pad = nn.ReflectionPad2d(1) else: self.pad = nn.ZeroPad2d(1) self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) def forward(self, x): out = self.pad(x) out = self.conv(out) return out class ConvBlock(nn.Module): """Layer to perform a convolution followed by ELU """ def __init__(self, in_channels, out_channels): super(ConvBlock, self).__init__() self.conv = Conv3x3(in_channels, out_channels) self.nonlin = nn.ELU(inplace=True) def forward(self, x): out = self.conv(x) out = self.nonlin(out) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused_convolution_elu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = 0.0 tmp4 = tmp2 > tmp3 tmp5 = 1.0 tmp6 = tmp2 * tmp5 tmp7 = libdevice.expm1(tmp6) tmp8 = tmp7 * tmp5 tmp9 = tl.where(tmp4, tmp6, tmp8) tl.store(in_out_ptr0 + x3, tmp9, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1 del buf1 triton_poi_fused_convolution_elu_1[grid(256)](buf2, primals_3, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_3 return buf2, primals_2, buf0, buf2 class Conv3x3(nn.Module): """Layer to pad and convolve input """ def __init__(self, in_channels, out_channels, use_refl=True): super(Conv3x3, self).__init__() if use_refl: self.pad = nn.ReflectionPad2d(1) else: self.pad = nn.ZeroPad2d(1) self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) def forward(self, x): out = self.pad(x) out = self.conv(out) return out class ConvBlockNew(nn.Module): """Layer to perform a convolution followed by ELU """ def __init__(self, in_channels, out_channels): super(ConvBlockNew, self).__init__() self.conv = Conv3x3(in_channels, out_channels) self.nonlin = nn.ELU(inplace=True) def forward(self, input_0): primals_2 = self.conv.conv.weight primals_3 = self.conv.conv.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
aliasghar53/packnet-sfm
ConvBlock
false
9,773
[ "MIT" ]
0
d07dcbf026194b618a2bd9fc05b599563611f9a3
https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3
ChannelNorm2D
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/k2/ck2ahiysbz5am2kdqgxfcalsms7yveiz5dvoxtgdx6inzxd62dti.py # Topologically Sorted Source Nodes: [mu, var, sub, add, rsqrt, x_normed, mul_1, x_normed_1], Original ATen: [aten.mean, aten.var, aten.sub, aten.add, aten.rsqrt, aten.mul] # Source node to ATen node mapping: # add => add # mu => mean # mul_1 => mul_1 # rsqrt => rsqrt # sub => sub # var => var # x_normed => mul # x_normed_1 => add_1 # Graph fragment: # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [1], True), kwargs = {}) # %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_1, [1]), kwargs = {correction: 1, keepdim: True}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, 0.001), kwargs = {}) # %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {}) # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %mul), kwargs = {}) # %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_3), kwargs = {}) triton_poi_fused_add_mean_mul_rsqrt_sub_var_0 = async_compile.triton('triton_poi_fused_add_mean_mul_rsqrt_sub_var_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mean_mul_rsqrt_sub_var_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_mean_mul_rsqrt_sub_var_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 16) % 4 x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (x3), xmask) tmp2 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr1 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp30 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp2 - tmp10 tmp13 = tmp12 * tmp12 tmp14 = tmp3 - tmp10 tmp15 = tmp14 * tmp14 tmp16 = tmp13 + tmp15 tmp17 = tmp5 - tmp10 tmp18 = tmp17 * tmp17 tmp19 = tmp16 + tmp18 tmp20 = tmp7 - tmp10 tmp21 = tmp20 * tmp20 tmp22 = tmp19 + tmp21 tmp23 = 3.0 tmp24 = tmp22 / tmp23 tmp25 = 0.001 tmp26 = tmp24 + tmp25 tmp27 = libdevice.rsqrt(tmp26) tmp28 = tmp11 * tmp27 tmp29 = tmp0 * tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + (x3), tmp31, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mu, var, sub, add, rsqrt, x_normed, mul_1, x_normed_1], Original ATen: [aten.mean, aten.var, aten.sub, aten.add, aten.rsqrt, aten.mul] stream0 = get_raw_stream(0) triton_poi_fused_add_mean_mul_rsqrt_sub_var_0.run(primals_2, primals_1, primals_3, buf0, 256, grid=grid(256), stream=stream0) del primals_2 del primals_3 return (buf0, primals_1, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class ChannelNorm2D(nn.Module): """ Similar to default Torch instanceNorm2D but calculates moments over channel dimension instead of spatial dims. Expects input_dim in format (B,C,H,W) """ def __init__(self, input_channels, momentum=0.1, eps=0.001, affine=True, **kwargs): super(ChannelNorm2D, self).__init__() self.momentum = momentum self.eps = eps self.affine = affine if affine is True: self.gamma = nn.Parameter(torch.ones(1, input_channels, 1, 1)) self.beta = nn.Parameter(torch.zeros(1, input_channels, 1, 1)) def forward(self, x): """ Calculate moments over channel dim, normalize. x: Image tensor, shape (B,C,H,W) """ mu, var = torch.mean(x, dim=1, keepdim=True), torch.var(x, dim=1, keepdim=True) x_normed = (x - mu) * torch.rsqrt(var + self.eps) if self.affine is True: x_normed = self.gamma * x_normed + self.beta return x_normed def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'input_channels': 4}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_add_mean_mul_rsqrt_sub_var_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 16 % 4 x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + x3, xmask) tmp2 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp7 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp30 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last') tmp4 = tmp2 + tmp3 tmp6 = tmp4 + tmp5 tmp8 = tmp6 + tmp7 tmp9 = 4.0 tmp10 = tmp8 / tmp9 tmp11 = tmp1 - tmp10 tmp12 = tmp2 - tmp10 tmp13 = tmp12 * tmp12 tmp14 = tmp3 - tmp10 tmp15 = tmp14 * tmp14 tmp16 = tmp13 + tmp15 tmp17 = tmp5 - tmp10 tmp18 = tmp17 * tmp17 tmp19 = tmp16 + tmp18 tmp20 = tmp7 - tmp10 tmp21 = tmp20 * tmp20 tmp22 = tmp19 + tmp21 tmp23 = 3.0 tmp24 = tmp22 / tmp23 tmp25 = 0.001 tmp26 = tmp24 + tmp25 tmp27 = libdevice.rsqrt(tmp26) tmp28 = tmp11 * tmp27 tmp29 = tmp0 * tmp28 tmp31 = tmp29 + tmp30 tl.store(out_ptr0 + x3, tmp31, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 1, 1)) assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_add_mean_mul_rsqrt_sub_var_0[grid(256)](primals_2, primals_1, primals_3, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_2 del primals_3 return buf0, primals_1 class ChannelNorm2DNew(nn.Module): """ Similar to default Torch instanceNorm2D but calculates moments over channel dimension instead of spatial dims. Expects input_dim in format (B,C,H,W) """ def __init__(self, input_channels, momentum=0.1, eps=0.001, affine=True, **kwargs): super(ChannelNorm2DNew, self).__init__() self.momentum = momentum self.eps = eps self.affine = affine if affine is True: self.gamma = nn.Parameter(torch.ones(1, input_channels, 1, 1)) self.beta = nn.Parameter(torch.zeros(1, input_channels, 1, 1)) def forward(self, input_0): primals_2 = self.gamma primals_3 = self.beta primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
ali-zafari/high-fidelity-generative-compression
ChannelNorm2D
false
9,774
[ "Apache-2.0" ]
0
37ab8d6727df48f8ebf4577db0986ccd0ffe404b
https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b
Attention
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/rm/crmjcbrhesyjltwjwo2gy5ktnw7trv24ctlurkfme6ykhtfquq32.py # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] # Source node to ATen node mapping: # matmul => clone # Graph fragment: # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/rb/crbncgepp7pchewiviz2ecap4hkql77bxprjbw2ciuujmpu57s6c.py # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] # Source node to ATen node mapping: # matmul => clone_1 # Graph fragment: # %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (4 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/pq/cpqnfrogm4dnzim2vyszfmugd6fc43gfnmxicoezmiidejzudrdz.py # Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_1 => exp # Graph fragment: # %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_5, 1), kwargs = {}) # %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {}) # %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {}) # %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 1.0), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {}) triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = tmp14 * tmp1 tmp16 = tl_math.exp(tmp15) tl.store(out_ptr0 + (x2), tmp16, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ry/cryn7ntc2gpkbfzbre3xh7lffx7zkbskw6oihbzsekkgajmdbki6.py # Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax] # Source node to ATen node mapping: # attn_1 => div, sum_1 # Graph fragment: # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {}) # %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = (xindex // 4) tmp0 = tl.load(in_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + (x2), tmp8, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/bb/cbby6op7dmkjsypxm4o3urasth73g6q5oi4ddo6uk6dsuv6off2v.py # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone] # Source node to ATen node mapping: # matmul_1 => clone_3 # Graph fragment: # %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_4(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (8 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (8 + y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/we/cwe54p4p4jvwbdktkpj3wy2coheu6f3r3dgvi7ozm7xjfk4mgbwx.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.clone] # Source node to ATen node mapping: # x_1 => clone_4 # Graph fragment: # %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_9,), kwargs = {memory_format: torch.contiguous_format}) triton_poi_fused_clone_5 = async_compile.triton('triton_poi_fused_clone_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[16, 4], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = (yindex // 4) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/36/c36mdvdlmkr4g6rcquwj2pbp2ke2mrvmhx3r7akoexvrkzzfhdye.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add] # Source node to ATen node mapping: # x_1 => add # Graph fragment: # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_11, %primals_5), kwargs = {}) triton_poi_fused_add_6 = async_compile.triton('triton_poi_fused_add_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x2), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (12, 4), (4, 1)) assert_size_stride(primals_3, (12, ), (1, )) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] stream0 = get_raw_stream(0) triton_poi_fused_clone_0.run(buf0, primals_3, buf1, 16, 4, grid=grid(16, 4), stream=stream0) buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone] triton_poi_fused_clone_1.run(buf0, primals_3, buf2, 16, 4, grid=grid(16, 4), stream=stream0) buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_2.run(buf3, buf4, 256, grid=grid(256), stream=stream0) buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3 # reuse # Topologically Sorted Source Nodes: [attn_1], Original ATen: [aten._softmax] triton_poi_fused__softmax_3.run(buf4, buf5, 256, grid=grid(256), stream=stream0) del buf4 buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone] triton_poi_fused_clone_4.run(buf0, primals_3, buf6, 16, 4, grid=grid(16, 4), stream=stream0) del buf0 del primals_3 buf7 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32) # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.bmm] extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.clone] triton_poi_fused_clone_5.run(buf7, buf8, 16, 4, grid=grid(16, 4), stream=stream0) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0); del buf7 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.mm] extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf9) buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0); del buf9 # reuse # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.add] triton_poi_fused_add_6.run(buf10, primals_5, 64, grid=grid(64), stream=stream0) del primals_5 return (buf10, buf5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0), primals_4, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0), ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Attention(nn.Module): def __init__(self, dim, heads, dropout): super().__init__() self.heads = heads head_dim = dim // heads self.scale = head_dim ** -0.5 self.attn = None self.qkv = nn.Linear(dim, dim * 3) self.attn_drop = nn.Dropout(dropout) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(dropout) @property def unwrapped(self): return self def forward(self, x, mask=None): B, N, C = x.shape qkv = self.qkv(x).reshape(B, N, 3, self.heads, C // self.heads ).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] attn = q @ k.transpose(-2, -1) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B, N, C) x = self.proj(x) x = self.proj_drop(x) return x, attn def get_inputs(): return [torch.rand([4, 4, 4])] def get_init_inputs(): return [[], {'dim': 4, 'heads': 4, 'dropout': 0.5}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask) @triton.jit def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask) @triton.jit def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last' ) tmp1 = 1.0 tmp2 = tmp0 * tmp1 tmp4 = tmp3 * tmp1 tmp6 = tmp5 * tmp1 tmp7 = triton_helpers.maximum(tmp4, tmp6) tmp9 = tmp8 * tmp1 tmp10 = triton_helpers.maximum(tmp7, tmp9) tmp12 = tmp11 * tmp1 tmp13 = triton_helpers.maximum(tmp10, tmp12) tmp14 = tmp2 - tmp13 tmp15 = tmp14 * tmp1 tmp16 = tl_math.exp(tmp15) tl.store(out_ptr0 + x2, tmp16, xmask) @triton.jit def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x1 = xindex // 4 tmp0 = tl.load(in_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last') tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last') tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last') tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last') tmp3 = tmp1 + tmp2 tmp5 = tmp3 + tmp4 tmp7 = tmp5 + tmp6 tmp8 = tmp0 / tmp7 tl.store(out_ptr0 + x2, tmp8, xmask) @triton.jit def triton_poi_fused_clone_4(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (8 + y0), ymask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask) @triton.jit def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 16 xnumel = 4 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 4 y1 = yindex // 4 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 4 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x2, tmp2, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1)) assert_size_stride(primals_2, (12, 4), (4, 1)) assert_size_stride(primals_3, (12,), (1,)) assert_size_stride(primals_4, (4, 4), (4, 1)) assert_size_stride(primals_5, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), out=buf0) del primals_2 buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32) get_raw_stream(0) triton_poi_fused_clone_0[grid(16, 4)](buf0, primals_3, buf1, 16, 4, XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1) buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32) triton_poi_fused_clone_1[grid(16, 4)](buf0, primals_3, buf2, 16, 4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1) buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) triton_poi_fused__softmax_2[grid(256)](buf3, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1) buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf3 triton_poi_fused__softmax_3[grid(256)](buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf4 buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32) triton_poi_fused_clone_4[grid(16, 4)](buf0, primals_3, buf6, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) del buf0 del primals_3 buf7 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32) extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 0), out=buf7) buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32) triton_poi_fused_clone_5[grid(16, 4)](buf7, buf8, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1) buf9 = reinterpret_tensor(buf7, (16, 4), (4, 1), 0) del buf7 extern_kernels.mm(reinterpret_tensor(buf8, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf9) buf10 = reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0) del buf9 triton_poi_fused_add_6[grid(64)](buf10, primals_5, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_5 return buf10, buf5, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0 ), buf5, reinterpret_tensor(buf8, (16, 4), (4, 1), 0 ), primals_4, reinterpret_tensor(buf6, (16, 1, 4), (4, 1, 1), 0 ), reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0 ), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0) class AttentionNew(nn.Module): def __init__(self, dim, heads, dropout): super().__init__() self.heads = heads head_dim = dim // heads self.scale = head_dim ** -0.5 self.attn = None self.qkv = nn.Linear(dim, dim * 3) self.attn_drop = nn.Dropout(dropout) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(dropout) @property def unwrapped(self): return self def forward(self, input_0): primals_2 = self.qkv.weight primals_3 = self.qkv.bias primals_4 = self.proj.weight primals_5 = self.proj.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0], output[1]
avniculae/segmenter
Attention
false
9,775
[ "MIT" ]
0
ca9683399b7dae13a8ccbadc744826306b8dbf94
https://github.com/avniculae/segmenter/tree/ca9683399b7dae13a8ccbadc744826306b8dbf94
SilogLoss
# AOT ID: ['0_inference'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/su/csuls3e5t7qiiptamdgq6xvfoa2jh4fdsioco2m4khc26gapt4du.py # Topologically Sorted Source Nodes: [mul, log, mul_1, log_1, log_diff, pow_1, silog1, mean_1, pow_2, silog2, sub_1, sqrt, silog_loss], Original ATen: [aten.mul, aten.log, aten.sub, aten.pow, aten.mean, aten.sqrt] # Source node to ATen node mapping: # log => log # log_1 => log_1 # log_diff => sub # mean_1 => mean_1 # mul => mul # mul_1 => mul_1 # pow_1 => pow_1 # pow_2 => pow_2 # silog1 => mean # silog2 => mul_2 # silog_loss => mul_3 # sqrt => sqrt # sub_1 => sub_1 # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 10), kwargs = {}) # %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%mul,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 10), kwargs = {}) # %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%mul_1,), kwargs = {}) # %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%log, %log_1), kwargs = {}) # %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {}) # %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {}) # %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sub,), kwargs = {}) # %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mean_1, 2), kwargs = {}) # %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, 0.85), kwargs = {}) # %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %mul_2), kwargs = {}) # %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sub_1,), kwargs = {}) # %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, 10), kwargs = {}) triton_per_fused_log_mean_mul_pow_sqrt_sub_0 = async_compile.triton('triton_per_fused_log_mean_mul_pow_sqrt_sub_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[1, 256], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_log_mean_mul_pow_sqrt_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_log_mean_mul_pow_sqrt_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): xnumel = 1 XBLOCK: tl.constexpr = 1 rnumel = 256 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK xindex = tl.full([1], xoffset, tl.int32) xmask = tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] roffset = 0 rmask = tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + (r0), None) tmp4 = tl.load(in_ptr1 + (r0), None) tmp1 = 10.0 tmp2 = tmp0 * tmp1 tmp3 = tl_math.log(tmp2) tmp5 = tmp4 * tmp1 tmp6 = tl_math.log(tmp5) tmp7 = tmp3 - tmp6 tmp8 = tmp7 * tmp7 tmp9 = tl.broadcast_to(tmp8, [RBLOCK]) tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0)) tmp12 = tl.broadcast_to(tmp7, [RBLOCK]) tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0)) tmp15 = 256.0 tmp16 = tmp11 / tmp15 tmp17 = tmp14 / tmp15 tmp18 = tmp17 * tmp17 tmp19 = 0.85 tmp20 = tmp18 * tmp19 tmp21 = tmp16 - tmp20 tmp22 = libdevice.sqrt(tmp21) tmp23 = tmp22 * tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp23, None) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf2 = buf0; del buf0 # reuse # Topologically Sorted Source Nodes: [mul, log, mul_1, log_1, log_diff, pow_1, silog1, mean_1, pow_2, silog2, sub_1, sqrt, silog_loss], Original ATen: [aten.mul, aten.log, aten.sub, aten.pow, aten.mean, aten.sqrt] stream0 = get_raw_stream(0) triton_per_fused_log_mean_mul_pow_sqrt_sub_0.run(buf2, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0) del arg0_1 del arg1_1 return (buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([arg0_1, arg1_1]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class SilogLoss(nn.Module): def __init__(self, ratio=10, ratio2=0.85): super().__init__() self.ratio = ratio self.ratio2 = ratio2 def forward(self, pred, gt): log_diff = torch.log(pred * self.ratio) - torch.log(gt * self.ratio) silog1 = torch.mean(log_diff ** 2) silog2 = self.ratio2 * log_diff.mean() ** 2 silog_loss = torch.sqrt(silog1 - silog2) * self.ratio return silog_loss def get_inputs(): return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_per_fused_log_mean_mul_pow_sqrt_sub_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel): XBLOCK: tl.constexpr = 1 RBLOCK: tl.constexpr = 256 xoffset = tl.program_id(0) * XBLOCK tl.full([1], xoffset, tl.int32) tl.full([RBLOCK], True, tl.int1) rindex = tl.arange(0, RBLOCK)[:] tl.full([RBLOCK], True, tl.int1) r0 = rindex tmp0 = tl.load(in_ptr0 + r0, None) tmp4 = tl.load(in_ptr1 + r0, None) tmp1 = 10.0 tmp2 = tmp0 * tmp1 tmp3 = tl_math.log(tmp2) tmp5 = tmp4 * tmp1 tmp6 = tl_math.log(tmp5) tmp7 = tmp3 - tmp6 tmp8 = tmp7 * tmp7 tmp9 = tl.broadcast_to(tmp8, [RBLOCK]) tmp11 = triton_helpers.promote_to_tensor(tl.sum(tmp9, 0)) tmp12 = tl.broadcast_to(tmp7, [RBLOCK]) tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp12, 0)) tmp15 = 256.0 tmp16 = tmp11 / tmp15 tmp17 = tmp14 / tmp15 tmp18 = tmp17 * tmp17 tmp19 = 0.85 tmp20 = tmp18 * tmp19 tmp21 = tmp16 - tmp20 tmp22 = libdevice.sqrt(tmp21) tmp23 = tmp22 * tmp1 tl.debug_barrier() tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None) def call(args): arg0_1, arg1_1 = args args.clear() assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((), (), torch.float32) buf2 = buf0 del buf0 get_raw_stream(0) triton_per_fused_log_mean_mul_pow_sqrt_sub_0[grid(1)](buf2, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1) del arg0_1 del arg1_1 return buf2, class SilogLossNew(nn.Module): def __init__(self, ratio=10, ratio2=0.85): super().__init__() self.ratio = ratio self.ratio2 = ratio2 def forward(self, input_0, input_1): arg0_1 = input_0 arg1_1 = input_1 output = call([arg0_1, arg1_1]) return output[0]
aliasghar53/packnet-sfm
SilogLoss
false
9,776
[ "MIT" ]
0
d07dcbf026194b618a2bd9fc05b599563611f9a3
https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3
Swish
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/xe/cxejxkqtxrljth4tyzjublg5tugf3him5iha62nf2gwemawndksr.py # Topologically Sorted Source Nodes: [mul, sigmoid, mul_1], Original ATen: [aten.mul, aten.sigmoid] # Source node to ATen node mapping: # mul => mul # mul_1 => mul_1 # sigmoid => sigmoid # Graph fragment: # %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %primals_2), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%mul,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sigmoid), kwargs = {}) triton_poi_fused_mul_sigmoid_0 = async_compile.triton('triton_poi_fused_mul_sigmoid_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_mul_sigmoid_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr1 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp2 * tmp0 tmp4 = tl.sigmoid(tmp3) tmp5 = tmp0 * tmp4 tl.store(out_ptr0 + (x0), tmp5, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (), ()) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [mul, sigmoid, mul_1], Original ATen: [aten.mul, aten.sigmoid] stream0 = get_raw_stream(0) triton_poi_fused_mul_sigmoid_0.run(primals_2, primals_1, buf0, 256, grid=grid(256), stream=stream0) return (buf0, primals_1, primals_2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Swish(nn.Module): def __init__(self): super(Swish, self).__init__() self.beta = nn.Parameter(torch.tensor(1.0)) def forward(self, x): return x * torch.sigmoid(self.beta * x) def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_mul_sigmoid_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr1 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp2 * tmp0 tmp4 = tl.sigmoid(tmp3) tmp5 = tmp0 * tmp4 tl.store(out_ptr0 + x0, tmp5, xmask) def call(args): primals_1, primals_2 = args args.clear() assert_size_stride(primals_1, (), ()) assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) get_raw_stream(0) triton_poi_fused_mul_sigmoid_0[grid(256)](primals_2, primals_1, buf0, 256, XBLOCK=256, num_warps=4, num_stages=1) return buf0, primals_1, primals_2 class SwishNew(nn.Module): def __init__(self): super(SwishNew, self).__init__() self.beta = nn.Parameter(torch.tensor(1.0)) def forward(self, input_0): primals_1 = self.beta primals_2 = input_0 output = call([primals_1, primals_2]) return output[0]
ali-zafari/high-fidelity-generative-compression
Swish
false
9,777
[ "Apache-2.0" ]
0
37ab8d6727df48f8ebf4577db0986ccd0ffe404b
https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b
Conv3x3
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/v6/cv6oewqqnsshd7he7ylh2kikzu4smtrhj2dmv6nb5csosp7g6vw5.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d] # Source node to ATen node mapping: # out => _unsafe_index, _unsafe_index_1 # Graph fragment: # %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %sub_1, None]), kwargs = {}) # %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %sub_1]), kwargs = {}) triton_poi_fused_reflection_pad2d_0 = async_compile.triton('triton_poi_fused_reflection_pad2d_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_reflection_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = (xindex // 6) % 6 x2 = (xindex // 36) x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + ((-1)*(tl_math.abs((-3) + (tl_math.abs((-1) + x0))))) + ((-4)*(tl_math.abs((-3) + (tl_math.abs((-1) + x1))))) + (16*x2)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (x3), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/32/c32v7egt4mupqssam3gmac2qgv3ujprjybthsgweflmot256qqw7.py # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] # Source node to ATen node mapping: # out_1 => convolution # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%_unsafe_index_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 16) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x3), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.reflection_pad2d] stream0 = get_raw_stream(0) triton_poi_fused_reflection_pad2d_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1; del buf1 # reuse # Topologically Sorted Source Nodes: [out_1], Original ATen: [aten.convolution] triton_poi_fused_convolution_1.run(buf2, primals_3, 256, grid=grid(256), stream=stream0) del primals_3 return (buf2, primals_2, buf0, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((4, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Conv3x3(nn.Module): """Layer to pad and convolve input """ def __init__(self, in_channels, out_channels, use_refl=True): super(Conv3x3, self).__init__() if use_refl: self.pad = nn.ReflectionPad2d(1) else: self.pad = nn.ZeroPad2d(1) self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) def forward(self, x): out = self.pad(x) out = self.conv(out) return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_reflection_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 6 x1 = xindex // 6 % 6 x2 = xindex // 36 x3 = xindex tmp0 = tl.load(in_ptr0 + (15 + -1 * tl_math.abs(-3 + tl_math.abs(-1 + x0)) + -4 * tl_math.abs(-3 + tl_math.abs(-1 + x1)) + 16 * x2), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + x3, tmp0, xmask) @triton.jit def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 16 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x3, tmp2, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (4, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (4,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) get_raw_stream(0) triton_poi_fused_reflection_pad2d_0[grid(576)](primals_1, buf0, 576, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1)) buf2 = buf1 del buf1 triton_poi_fused_convolution_1[grid(256)](buf2, primals_3, 256, XBLOCK=256, num_warps=4, num_stages=1) del primals_3 return buf2, primals_2, buf0 class Conv3x3New(nn.Module): """Layer to pad and convolve input """ def __init__(self, in_channels, out_channels, use_refl=True): super(Conv3x3New, self).__init__() if use_refl: self.pad = nn.ReflectionPad2d(1) else: self.pad = nn.ZeroPad2d(1) self.conv = nn.Conv2d(int(in_channels), int(out_channels), 3) def forward(self, input_0): primals_2 = self.conv.weight primals_3 = self.conv.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
aliasghar53/packnet-sfm
Conv3x3
false
9,778
[ "MIT" ]
0
d07dcbf026194b618a2bd9fc05b599563611f9a3
https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3
UnpackLayerConv2d
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/st/cstyisijjjcom4h3fmnm5l3jk6rnlzjwhirorf3vpbk7do6htdzs.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # pad => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [2, 2, 2, 2], 0.0), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 8) % 8 x0 = xindex % 8 x2 = (xindex // 64) x4 = xindex tmp0 = (-2) + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = (-2) + x0 tmp6 = tmp5 >= tmp1 tmp7 = tmp5 < tmp3 tmp8 = tmp2 & tmp4 tmp9 = tmp8 & tmp6 tmp10 = tmp9 & tmp7 tmp11 = tl.load(in_ptr0 + ((-10) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0) tl.store(out_ptr0 + (x4), tmp11, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/th/cth2ocuys7pc7ywjntvhwtk6ot2bldkofkbhkq4mun25cfscmwm4.py # Topologically Sorted Source Nodes: [x, group_norm, x_2], Original ATen: [aten.convolution, aten.native_group_norm, aten.pixel_shuffle] # Source node to ATen node mapping: # group_norm => add, add_1, mul_1, rsqrt, var_mean # x => convolution # x_2 => clone # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%view, [2, 3]), kwargs = {correction: 0, keepdim: True}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {}) # %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {}) # %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %unsqueeze_5), kwargs = {}) # %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %unsqueeze_2), kwargs = {}) # %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format}) triton_per_fused_convolution_native_group_norm_pixel_shuffle_1 = async_compile.triton('triton_per_fused_convolution_native_group_norm_pixel_shuffle_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[64, 32], reduction_hint=ReductionHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_convolution_native_group_norm_pixel_shuffle_1', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused_convolution_native_group_norm_pixel_shuffle_1(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 64 rnumel = 25 RBLOCK: tl.constexpr = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = rindex < rnumel r2 = rindex x3 = xindex x0 = xindex % 16 r7 = rindex % 5 r8 = (rindex // 5) x4 = xindex % 2 x5 = (xindex // 2) % 2 x6 = (xindex // 4) tmp0 = tl.load(in_out_ptr0 + (r2 + (25*x3)), rmask & xmask, other=0.0) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp26 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tmp5 = tl.where(rmask & xmask, tmp3, 0) tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK]) tmp8 = tl.where(rmask & xmask, tmp6, 0) tmp9 = tl.sum(tmp8, 1)[:, None] tmp10 = tl.full([XBLOCK, 1], 25, tl.int32) tmp11 = tmp10.to(tl.float32) tmp12 = tmp9 / tmp11 tmp13 = tmp3 - tmp12 tmp14 = tmp13 * tmp13 tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK]) tmp17 = tl.where(rmask & xmask, tmp15, 0) tmp18 = tl.sum(tmp17, 1)[:, None] tmp19 = 25.0 tmp20 = tmp18 / tmp19 tmp21 = 1e-05 tmp22 = tmp20 + tmp21 tmp23 = libdevice.rsqrt(tmp22) tmp24 = tmp2 - tmp12 tmp25 = tmp24 * tmp23 tmp27 = tmp25 * tmp26 tmp29 = tmp27 + tmp28 tmp30 = 0.0 tmp31 = tmp29 > tmp30 tmp32 = 1.0 tmp33 = tmp29 * tmp32 tmp34 = libdevice.expm1(tmp33) tmp35 = tmp34 * tmp32 tmp36 = tl.where(tmp31, tmp33, tmp35) tl.store(in_out_ptr0 + (r2 + (25*x3)), tmp2, rmask & xmask) tl.debug_barrier() tl.store(in_out_ptr1 + (x3), tmp23, xmask) tl.store(out_ptr2 + (x4 + (2*r7) + (10*x5) + (20*r8) + (100*x6)), tmp36, rmask & xmask) tl.store(out_ptr0 + (x3), tmp12, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (16, ), (1, )) assert_size_stride(primals_4, (16, ), (1, )) assert_size_stride(primals_5, (16, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 1024, grid=grid(1024), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 5, 5), (400, 25, 5, 1)) buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.float32) buf4 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.float32) buf6 = reinterpret_tensor(buf4, (4, 16, 1, 1), (16, 1, 1, 1), 0); del buf4 # reuse buf8 = empty_strided_cuda((4, 4, 5, 2, 5, 2), (400, 100, 20, 10, 2, 1), torch.float32) # Topologically Sorted Source Nodes: [x, group_norm, x_2], Original ATen: [aten.convolution, aten.native_group_norm, aten.pixel_shuffle] triton_per_fused_convolution_native_group_norm_pixel_shuffle_1.run(buf2, buf6, primals_3, primals_4, primals_5, buf3, buf8, 64, 25, grid=grid(64), stream=stream0) del primals_3 return (reinterpret_tensor(buf8, (4, 4, 10, 10), (400, 100, 10, 1), 0), primals_2, primals_4, primals_5, buf0, buf2, buf3, buf6, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((16, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class Conv2D(nn.Module): """ 2D convolution with GroupNorm and ELU Parameters ---------- in_channels : int Number of input channels out_channels : int Number of output channels kernel_size : int Kernel size stride : int Stride """ def __init__(self, in_channels, out_channels, kernel_size, stride): super().__init__() self.kernel_size = kernel_size self.conv_base = nn.Conv2d(in_channels, out_channels, kernel_size= kernel_size, stride=stride) self.pad = nn.ConstantPad2d([kernel_size // 2] * 4, value=0) self.normalize = torch.nn.GroupNorm(16, out_channels) self.activ = nn.ELU(inplace=True) def forward(self, x): """Runs the Conv2D layer.""" x = self.conv_base(self.pad(x)) return self.activ(self.normalize(x)) class UnpackLayerConv2d(nn.Module): """ Unpacking layer with 2d convolutions. Takes a [B,C,H,W] tensor, convolves it to produce [B,(r^2)C,H,W] and then unpacks it to produce [B,C,rH,rW]. """ def __init__(self, in_channels, out_channels, kernel_size, r=2): """ Initializes a UnpackLayerConv2d object. Parameters ---------- in_channels : int Number of input channels out_channels : int Number of output channels kernel_size : int Kernel size r : int Packing ratio """ super().__init__() self.conv = Conv2D(in_channels, out_channels * r ** 2, kernel_size, 1) self.unpack = nn.PixelShuffle(r) def forward(self, x): """Runs the UnpackLayerConv2d layer.""" x = self.conv(x) x = self.unpack(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime.triton_helpers import libdevice import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 1024 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 8 % 8 x0 = xindex % 8 x2 = xindex // 64 x4 = xindex tmp0 = -2 + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = -2 + x0 tmp6 = tmp5 >= tmp1 tmp7 = tmp5 < tmp3 tmp8 = tmp2 & tmp4 tmp9 = tmp8 & tmp6 tmp10 = tmp9 & tmp7 tmp11 = tl.load(in_ptr0 + (-10 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask, other=0.0) tl.store(out_ptr0 + x4, tmp11, xmask) @triton.jit def triton_per_fused_convolution_native_group_norm_pixel_shuffle_1(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 64 rnumel = 25 RBLOCK: tl.constexpr = 32 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] rmask = rindex < rnumel r2 = rindex x3 = xindex x0 = xindex % 16 r7 = rindex % 5 r8 = rindex // 5 x4 = xindex % 2 x5 = xindex // 2 % 2 x6 = xindex // 4 tmp0 = tl.load(in_out_ptr0 + (r2 + 25 * x3), rmask & xmask, other=0.0) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp26 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last') tmp28 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK]) tl.where(rmask & xmask, tmp3, 0) tmp6 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK]) tmp8 = tl.where(rmask & xmask, tmp6, 0) tmp9 = tl.sum(tmp8, 1)[:, None] tmp10 = tl.full([XBLOCK, 1], 25, tl.int32) tmp11 = tmp10.to(tl.float32) tmp12 = tmp9 / tmp11 tmp13 = tmp3 - tmp12 tmp14 = tmp13 * tmp13 tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK]) tmp17 = tl.where(rmask & xmask, tmp15, 0) tmp18 = tl.sum(tmp17, 1)[:, None] tmp19 = 25.0 tmp20 = tmp18 / tmp19 tmp21 = 1e-05 tmp22 = tmp20 + tmp21 tmp23 = libdevice.rsqrt(tmp22) tmp24 = tmp2 - tmp12 tmp25 = tmp24 * tmp23 tmp27 = tmp25 * tmp26 tmp29 = tmp27 + tmp28 tmp30 = 0.0 tmp31 = tmp29 > tmp30 tmp32 = 1.0 tmp33 = tmp29 * tmp32 tmp34 = libdevice.expm1(tmp33) tmp35 = tmp34 * tmp32 tmp36 = tl.where(tmp31, tmp33, tmp35) tl.store(in_out_ptr0 + (r2 + 25 * x3), tmp2, rmask & xmask) tl.debug_barrier() tl.store(in_out_ptr1 + x3, tmp23, xmask) tl.store(out_ptr2 + (x4 + 2 * r7 + 10 * x5 + 20 * r8 + 100 * x6), tmp36, rmask & xmask) tl.store(out_ptr0 + x3, tmp12, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (16, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_3, (16,), (1,)) assert_size_stride(primals_4, (16,), (1,)) assert_size_stride(primals_5, (16,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 8, 8), (256, 64, 8, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(1024)](primals_1, buf0, 1024, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 16, 5, 5), (400, 25, 5, 1)) buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 1, 1), torch.float32) buf4 = empty_strided_cuda((4, 16, 1, 1), (16, 1, 64, 64), torch.float32 ) buf6 = reinterpret_tensor(buf4, (4, 16, 1, 1), (16, 1, 1, 1), 0) del buf4 buf8 = empty_strided_cuda((4, 4, 5, 2, 5, 2), (400, 100, 20, 10, 2, 1), torch.float32) triton_per_fused_convolution_native_group_norm_pixel_shuffle_1[grid(64) ](buf2, buf6, primals_3, primals_4, primals_5, buf3, buf8, 64, 25, XBLOCK=1, num_warps=2, num_stages=1) del primals_3 return reinterpret_tensor(buf8, (4, 4, 10, 10), (400, 100, 10, 1), 0 ), primals_2, primals_4, primals_5, buf0, buf2, buf3, buf6 class Conv2D(nn.Module): """ 2D convolution with GroupNorm and ELU Parameters ---------- in_channels : int Number of input channels out_channels : int Number of output channels kernel_size : int Kernel size stride : int Stride """ def __init__(self, in_channels, out_channels, kernel_size, stride): super().__init__() self.kernel_size = kernel_size self.conv_base = nn.Conv2d(in_channels, out_channels, kernel_size= kernel_size, stride=stride) self.pad = nn.ConstantPad2d([kernel_size // 2] * 4, value=0) self.normalize = torch.nn.GroupNorm(16, out_channels) self.activ = nn.ELU(inplace=True) def forward(self, x): """Runs the Conv2D layer.""" x = self.conv_base(self.pad(x)) return self.activ(self.normalize(x)) class UnpackLayerConv2dNew(nn.Module): """ Unpacking layer with 2d convolutions. Takes a [B,C,H,W] tensor, convolves it to produce [B,(r^2)C,H,W] and then unpacks it to produce [B,C,rH,rW]. """ def __init__(self, in_channels, out_channels, kernel_size, r=2): """ Initializes a UnpackLayerConv2d object. Parameters ---------- in_channels : int Number of input channels out_channels : int Number of output channels kernel_size : int Kernel size r : int Packing ratio """ super().__init__() self.conv = Conv2D(in_channels, out_channels * r ** 2, kernel_size, 1) self.unpack = nn.PixelShuffle(r) def forward(self, input_0): primals_2 = self.conv.conv_base.weight primals_3 = self.conv.conv_base.bias primals_4 = self.conv.normalize.weight primals_5 = self.conv.normalize.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
aliasghar53/packnet-sfm
UnpackLayerConv2d
false
9,779
[ "MIT" ]
0
d07dcbf026194b618a2bd9fc05b599563611f9a3
https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3
BasicModel_ConvNet_MaxPool1d
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/ni/cnii7nxq4niiy4lr34yc7mgkobmblidfnkte54pcucvd7ervvub2.py # Topologically Sorted Source Nodes: [conv1d, x], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv1d => convolution # x => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1], [0], [1], False, [0], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) # %le_2 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 496 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 62) % 2 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/du/cduq3gwruj3pcttaz7jgcybdqfx6yypblacfgntceuh2rbrk5zms.py # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # x_1 => _low_memory_max_pool2d_with_offsets, getitem_1 # Graph fragment: # %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze, [1, 2], [1, 2], [0, 0], [1, 1], False), kwargs = {}) # %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 248 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + (2*x0), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (2*x0)), xmask, eviction_policy='evict_last') tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tl.store(out_ptr0 + (x0), tmp5, xmask) tl.store(out_ptr1 + (x0), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/em/cemjfxr474cvy7lvbjzltbj2bxrokqif7efsbkfc6jqj7jow4qcg.py # Topologically Sorted Source Nodes: [conv1d_1, x_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # conv1d_1 => convolution_1 # x_2 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%squeeze, %primals_4, %primals_5, [1], [0], [1], False, [0], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) # %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {}) triton_poi_fused_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_threshold_backward_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 464 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = (xindex // 29) % 4 tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + (x3), tmp4, xmask) tl.store(out_ptr0 + (x3), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ac/cacoonmn27elu6ggvbgu4am6b355mzkwqz42prnxsccvs3o2qk62.py # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices] # Source node to ATen node mapping: # x_3 => _low_memory_max_pool2d_with_offsets_1, getitem_3 # Graph fragment: # %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze_1, [1, 2], [1, 2], [0, 0], [1, 1], False), kwargs = {}) # %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {}) triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr): xnumel = 224 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 14 x1 = (xindex // 14) x2 = xindex tmp0 = tl.load(in_ptr0 + ((2*x0) + (29*x1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (29*x1)), xmask, eviction_policy='evict_last') tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tl.store(out_ptr0 + (x2), tmp5, xmask) tl.store(out_ptr1 + (x2), tmp6, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/vw/cvwy5jmi63rkvmren5xbssec6wzlmj32pn6yk5k3v2skpobh3jvb.py # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu] # Source node to ATen node mapping: # x_5 => relu_2 # Graph fragment: # %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_7), kwargs = {}) # %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {}) triton_poi_fused_relu_4 = async_compile.triton('triton_poi_fused_relu_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[512], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 448 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 8 tmp0 = tl.load(in_out_ptr0 + (x2), xmask) tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/7g/c7gfqptsp3sddqxuwnx67i5ihjsfzdwav52gbj2otvahhhrmoacr.py # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] # Source node to ATen node mapping: # softmax => amax, div, exp, sub, sum_1 # Graph fragment: # %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm_1, [1], True), kwargs = {}) # %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm_1, %amax), kwargs = {}) # %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {}) # %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {}) triton_per_fused__softmax_5 = async_compile.triton('triton_per_fused__softmax_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.persistent_reduction( size_hints=[64, 16], reduction_hint=ReductionHint.INNER, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False} ) @triton.jit def triton_per_fused__softmax_5(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr): xnumel = 56 rnumel = 10 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] roffset = 0 rmask = rindex < rnumel r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + (10*x0)), rmask & xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(rmask & xmask, tmp1, float("-inf")) tmp4 = triton_helpers.max2(tmp3, 1)[:, None] tmp5 = tmp0 - tmp4 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.where(rmask & xmask, tmp7, 0) tmp10 = tl.sum(tmp9, 1)[:, None] tmp11 = tmp6 / tmp10 tl.store(out_ptr2 + (r1 + (10*x0)), tmp11, rmask & xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (2, 1, 3), (3, 3, 1)) assert_size_stride(primals_2, (2, ), (1, )) assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1)) assert_size_stride(primals_4, (4, 2, 3), (6, 3, 1)) assert_size_stride(primals_5, (4, ), (1, )) assert_size_stride(primals_6, (8, 4), (4, 1)) assert_size_stride(primals_7, (8, ), (1, )) assert_size_stride(primals_8, (10, 8), (8, 1)) assert_size_stride(primals_9, (10, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf0, (4, 2, 62), (124, 62, 1)) buf1 = buf0; del buf0 # reuse buf15 = empty_strided_cuda((4, 2, 62), (124, 62, 1), torch.bool) # Topologically Sorted Source Nodes: [conv1d, x], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_convolution_relu_threshold_backward_0.run(buf1, primals_2, buf15, 496, grid=grid(496), stream=stream0) del primals_2 buf2 = empty_strided_cuda((4, 2, 1, 31), (62, 31, 31, 1), torch.int8) buf3 = empty_strided_cuda((4, 2, 1, 31), (62, 31, 31, 1), torch.float32) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 248, grid=grid(248), stream=stream0) # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (4, 2, 31), (62, 31, 1), 0), primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf4, (4, 4, 29), (116, 29, 1)) buf5 = buf4; del buf4 # reuse buf14 = empty_strided_cuda((4, 4, 29), (116, 29, 1), torch.bool) # Topologically Sorted Source Nodes: [conv1d_1, x_2], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward] triton_poi_fused_convolution_relu_threshold_backward_2.run(buf5, primals_5, buf14, 464, grid=grid(464), stream=stream0) del primals_5 buf6 = empty_strided_cuda((4, 4, 1, 14), (56, 14, 14, 1), torch.int8) buf7 = empty_strided_cuda((4, 4, 1, 14), (56, 14, 14, 1), torch.float32) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.max_pool2d_with_indices] triton_poi_fused_max_pool2d_with_indices_3.run(buf5, buf6, buf7, 224, grid=grid(224), stream=stream0) buf8 = empty_strided_cuda((56, 8), (8, 1), torch.float32) # Topologically Sorted Source Nodes: [], Original ATen: [] extern_kernels.mm(reinterpret_tensor(buf7, (56, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 8), (1, 4), 0), out=buf8) buf9 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu] triton_poi_fused_relu_4.run(buf9, primals_7, 448, grid=grid(448), stream=stream0) del primals_7 buf10 = empty_strided_cuda((56, 10), (10, 1), torch.float32) # Topologically Sorted Source Nodes: [x_6], Original ATen: [aten.addmm] extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8, (8, 10), (1, 8), 0), alpha=1, beta=1, out=buf10) del primals_9 buf13 = empty_strided_cuda((56, 10), (10, 1), torch.float32) # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax] triton_per_fused__softmax_5.run(buf10, buf13, 56, 10, grid=grid(56), stream=stream0) del buf10 return (buf13, primals_1, primals_3, primals_4, reinterpret_tensor(buf1, (4, 2, 1, 62), (124, 62, 62, 1), 0), buf2, reinterpret_tensor(buf3, (4, 2, 31), (62, 31, 1), 0), reinterpret_tensor(buf5, (4, 4, 1, 29), (116, 29, 29, 1), 0), buf6, reinterpret_tensor(buf7, (56, 4), (4, 1), 0), buf9, buf13, primals_8, primals_6, buf14, buf15, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((2, 1, 3), (3, 3, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 1, 64), (64, 64, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((4, 2, 3), (6, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((10, 8), (8, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class BasicModel_ConvNet_MaxPool1d(nn.Module): """Same as above, but with the MaxPool2d replaced with a MaxPool1d. This is useful because the MaxPool modules behave differently to other modules from the perspective of the DeepLift Attributions """ def __init__(self): super().__init__() self.conv1 = nn.Conv1d(1, 2, 3) self.relu1 = nn.ReLU() self.pool1 = nn.MaxPool1d(2) self.conv2 = nn.Conv1d(2, 4, 3) self.relu2 = nn.ReLU() self.pool2 = nn.MaxPool1d(2) self.fc1 = nn.Linear(4, 8) self.relu3 = nn.ReLU() self.fc2 = nn.Linear(8, 10) self.softmax = nn.Softmax(dim=1) self.fc1.weight = nn.Parameter(torch.ones(8, 4)) self.fc2.weight = nn.Parameter(torch.ones(10, 8)) def forward(self, x): x = self.relu1(self.conv1(x)) x = self.pool1(x) x = self.relu2(self.conv2(x)) x = self.pool2(x) x = x.view(-1, 4) x = self.relu3(self.fc1(x)) x = self.fc2(x) return self.softmax(x) def get_inputs(): return [torch.rand([4, 1, 64])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers from torch._inductor.runtime.triton_helpers import math as tl_math import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 496 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 62 % 2 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr0 + x3, tmp6, xmask) @triton.jit def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 248 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_ptr0 + 2 * x0, xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr0 + (1 + 2 * x0), xmask, eviction_policy='evict_last') tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tl.store(out_ptr0 + x0, tmp5, xmask) tl.store(out_ptr1 + x0, tmp6, xmask) @triton.jit def triton_poi_fused_convolution_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 464 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x1 = xindex // 29 % 4 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tmp5 = 0.0 tmp6 = tmp4 <= tmp5 tl.store(in_out_ptr0 + x3, tmp4, xmask) tl.store(out_ptr0 + x3, tmp6, xmask) @triton.jit def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr): xnumel = 224 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex % 14 x1 = xindex // 14 x2 = xindex tmp0 = tl.load(in_ptr0 + (2 * x0 + 29 * x1), xmask, eviction_policy= 'evict_last') tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 29 * x1), xmask, eviction_policy ='evict_last') tmp2 = tmp1 > tmp0 tmp3 = tl.full([1], 1, tl.int8) tmp4 = tl.full([1], 0, tl.int8) tmp5 = tl.where(tmp2, tmp3, tmp4) tmp6 = triton_helpers.maximum(tmp1, tmp0) tl.store(out_ptr0 + x2, tmp5, xmask) tl.store(out_ptr1 + x2, tmp6, xmask) @triton.jit def triton_poi_fused_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr ): xnumel = 448 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex x0 = xindex % 8 tmp0 = tl.load(in_out_ptr0 + x2, xmask) tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, xmask) @triton.jit def triton_per_fused__softmax_5(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr): xnumel = 56 rnumel = 10 RBLOCK: tl.constexpr = 16 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel rindex = tl.arange(0, RBLOCK)[None, :] rmask = rindex < rnumel r1 = rindex x0 = xindex tmp0 = tl.load(in_ptr0 + (r1 + 10 * x0), rmask & xmask, other=0.0) tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK]) tmp3 = tl.where(rmask & xmask, tmp1, float('-inf')) tmp4 = triton_helpers.max2(tmp3, 1)[:, None] tmp5 = tmp0 - tmp4 tmp6 = tl_math.exp(tmp5) tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK]) tmp9 = tl.where(rmask & xmask, tmp7, 0) tmp10 = tl.sum(tmp9, 1)[:, None] tmp11 = tmp6 / tmp10 tl.store(out_ptr2 + (r1 + 10 * x0), tmp11, rmask & xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (2, 1, 3), (3, 3, 1)) assert_size_stride(primals_2, (2,), (1,)) assert_size_stride(primals_3, (4, 1, 64), (64, 64, 1)) assert_size_stride(primals_4, (4, 2, 3), (6, 3, 1)) assert_size_stride(primals_5, (4,), (1,)) assert_size_stride(primals_6, (8, 4), (4, 1)) assert_size_stride(primals_7, (8,), (1,)) assert_size_stride(primals_8, (10, 8), (8, 1)) assert_size_stride(primals_9, (10,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=( 0,), groups=1, bias=None) assert_size_stride(buf0, (4, 2, 62), (124, 62, 1)) buf1 = buf0 del buf0 buf15 = empty_strided_cuda((4, 2, 62), (124, 62, 1), torch.bool) get_raw_stream(0) triton_poi_fused_convolution_relu_threshold_backward_0[grid(496)](buf1, primals_2, buf15, 496, XBLOCK=256, num_warps=4, num_stages=1) del primals_2 buf2 = empty_strided_cuda((4, 2, 1, 31), (62, 31, 31, 1), torch.int8) buf3 = empty_strided_cuda((4, 2, 1, 31), (62, 31, 31, 1), torch.float32 ) triton_poi_fused_max_pool2d_with_indices_1[grid(248)](buf1, buf2, buf3, 248, XBLOCK=128, num_warps=4, num_stages=1) buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (4, 2, 31), (62, 31, 1), 0), primals_4, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None) assert_size_stride(buf4, (4, 4, 29), (116, 29, 1)) buf5 = buf4 del buf4 buf14 = empty_strided_cuda((4, 4, 29), (116, 29, 1), torch.bool) triton_poi_fused_convolution_relu_threshold_backward_2[grid(464)](buf5, primals_5, buf14, 464, XBLOCK=128, num_warps=4, num_stages=1) del primals_5 buf6 = empty_strided_cuda((4, 4, 1, 14), (56, 14, 14, 1), torch.int8) buf7 = empty_strided_cuda((4, 4, 1, 14), (56, 14, 14, 1), torch.float32 ) triton_poi_fused_max_pool2d_with_indices_3[grid(224)](buf5, buf6, buf7, 224, XBLOCK=128, num_warps=4, num_stages=1) buf8 = empty_strided_cuda((56, 8), (8, 1), torch.float32) extern_kernels.mm(reinterpret_tensor(buf7, (56, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 8), (1, 4), 0), out=buf8) buf9 = buf8 del buf8 triton_poi_fused_relu_4[grid(448)](buf9, primals_7, 448, XBLOCK=256, num_warps=4, num_stages=1) del primals_7 buf10 = empty_strided_cuda((56, 10), (10, 1), torch.float32) extern_kernels.addmm(primals_9, buf9, reinterpret_tensor(primals_8, (8, 10), (1, 8), 0), alpha=1, beta=1, out=buf10) del primals_9 buf13 = empty_strided_cuda((56, 10), (10, 1), torch.float32) triton_per_fused__softmax_5[grid(56)](buf10, buf13, 56, 10, XBLOCK= 1, num_warps=2, num_stages=1) del buf10 return buf13, primals_1, primals_3, primals_4, reinterpret_tensor(buf1, (4, 2, 1, 62), (124, 62, 62, 1), 0), buf2, reinterpret_tensor(buf3, (4, 2, 31), (62, 31, 1), 0), reinterpret_tensor(buf5, (4, 4, 1, 29), (116, 29, 29, 1), 0), buf6, reinterpret_tensor(buf7, (56, 4), (4, 1), 0 ), buf9, buf13, primals_8, primals_6, buf14, buf15 class BasicModel_ConvNet_MaxPool1dNew(nn.Module): """Same as above, but with the MaxPool2d replaced with a MaxPool1d. This is useful because the MaxPool modules behave differently to other modules from the perspective of the DeepLift Attributions """ def __init__(self): super().__init__() self.conv1 = nn.Conv1d(1, 2, 3) self.relu1 = nn.ReLU() self.pool1 = nn.MaxPool1d(2) self.conv2 = nn.Conv1d(2, 4, 3) self.relu2 = nn.ReLU() self.pool2 = nn.MaxPool1d(2) self.fc1 = nn.Linear(4, 8) self.relu3 = nn.ReLU() self.fc2 = nn.Linear(8, 10) self.softmax = nn.Softmax(dim=1) self.fc1.weight = nn.Parameter(torch.ones(8, 4)) self.fc2.weight = nn.Parameter(torch.ones(10, 8)) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.fc1.weight primals_7 = self.fc1.bias primals_8 = self.fc2.weight primals_9 = self.fc2.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]
archydeberker/captum
BasicModel_ConvNet_MaxPool1d
false
9,780
[ "BSD-3-Clause" ]
0
2d72a060f12f5e325c9d1c411a2ef69bf43a06fd
https://github.com/archydeberker/captum/tree/2d72a060f12f5e325c9d1c411a2ef69bf43a06fd
resblock
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/az/cazxolgp2ne6vc522yhqcdzkhjb6btel7txdrpwzpkcc5t6sm46x.py # Topologically Sorted Source Nodes: [out], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt] # Source node to ATen node mapping: # out => maximum # Graph fragment: # %maximum : [num_users=2] = call_function[target=torch.ops.aten.maximum.default](args = (%getitem, %getitem_1), kwargs = {}) # %eq_2 : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%getitem, %getitem_1), kwargs = {}) # %gt_1 : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%getitem, %getitem_1), kwargs = {}) # %lt_1 : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%getitem, %getitem_1), kwargs = {}) triton_poi_fused_eq_gt_lt_maximum_0 = async_compile.triton('triton_poi_fused_eq_gt_lt_maximum_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*i1', 5: '*i1', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_eq_gt_lt_maximum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 64) x3 = xindex % 64 x1 = (xindex // 16) % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + (128*x2)), xmask) tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (64 + x3 + (128*x2)), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp7 = tmp2 == tmp5 tmp8 = tmp2 > tmp5 tmp9 = tmp2 < tmp5 tl.store(out_ptr0 + (x4), tmp6, xmask) tl.store(out_ptr1 + (x4), tmp7, xmask) tl.store(out_ptr2 + (x4), tmp8, xmask) tl.store(out_ptr3 + (x4), tmp9, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/ab/cabrxc3mztaftcghxljcdmadm37r6mu5llu27nn63cpiczdivfe4.py # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.maximum, aten.add, aten.eq, aten.gt, aten.lt] # Source node to ATen node mapping: # out_1 => maximum_1 # out_2 => add # Graph fragment: # %maximum_1 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%getitem_2, %getitem_3), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%maximum_1, %primals_1), kwargs = {}) # %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%getitem_2, %getitem_3), kwargs = {}) # %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Tensor](args = (%getitem_2, %getitem_3), kwargs = {}) # %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%getitem_2, %getitem_3), kwargs = {}) triton_poi_fused_add_eq_gt_lt_maximum_1 = async_compile.triton('triton_poi_fused_add_eq_gt_lt_maximum_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*i1', 5: '*i1', 6: '*i1', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_eq_gt_lt_maximum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = (xindex // 64) x3 = xindex % 64 x1 = (xindex // 16) % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + (128*x2)), xmask) tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (64 + x3 + (128*x2)), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr2 + (x4), xmask) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp8 = tmp6 + tmp7 tmp9 = tmp2 == tmp5 tmp10 = tmp2 > tmp5 tmp11 = tmp2 < tmp5 tl.store(out_ptr0 + (x4), tmp8, xmask) tl.store(out_ptr1 + (x4), tmp9, xmask) tl.store(out_ptr2 + (x4), tmp10, xmask) tl.store(out_ptr3 + (x4), tmp11, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (8, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (8, ), (1, )) assert_size_stride(primals_4, (8, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (8, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1)) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [out], Original ATen: [aten.maximum, aten.eq, aten.gt, aten.lt] stream0 = get_raw_stream(0) triton_poi_fused_eq_gt_lt_maximum_0.run(buf0, primals_3, buf1, buf7, buf8, buf9, 256, grid=grid(256), stream=stream0) del buf0 del primals_3 # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution] buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 8, 4, 4), (128, 16, 4, 1)) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [out_1, out_2], Original ATen: [aten.maximum, aten.add, aten.eq, aten.gt, aten.lt] triton_poi_fused_add_eq_gt_lt_maximum_1.run(buf2, primals_5, primals_1, buf3, buf4, buf5, buf6, 256, grid=grid(256), stream=stream0) del buf2 del primals_5 return (buf3, primals_1, primals_2, primals_4, buf1, buf4, buf5, buf6, buf7, buf8, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((8, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((8, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class mfm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1): super(mfm, self).__init__() self.out_channels = out_channels if type == 1: self.filter = nn.Conv2d(in_channels, 2 * out_channels, kernel_size=kernel_size, stride=stride, padding=padding) else: self.filter = nn.Linear(in_channels, 2 * out_channels) def forward(self, x): x = self.filter(x) out = torch.split(x, self.out_channels, 1) return torch.max(out[0], out[1]) class resblock(nn.Module): def __init__(self, in_channels, out_channels): super(resblock, self).__init__() self.conv1 = mfm(in_channels, out_channels, kernel_size=3, stride=1, padding=1) self.conv2 = mfm(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, x): res = x out = self.conv1(x) out = self.conv2(out) out = out + res return out def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4, 'out_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_eq_gt_lt_maximum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 64 x3 = xindex % 64 x1 = xindex // 16 % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask) tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (64 + x3 + 128 * x2), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp7 = tmp2 == tmp5 tmp8 = tmp2 > tmp5 tmp9 = tmp2 < tmp5 tl.store(out_ptr0 + x4, tmp6, xmask) tl.store(out_ptr1 + x4, tmp7, xmask) tl.store(out_ptr2 + x4, tmp8, xmask) tl.store(out_ptr3 + x4, tmp9, xmask) @triton.jit def triton_poi_fused_add_eq_gt_lt_maximum_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x2 = xindex // 64 x3 = xindex % 64 x1 = xindex // 16 % 4 x4 = xindex tmp0 = tl.load(in_ptr0 + (x3 + 128 * x2), xmask) tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last') tmp3 = tl.load(in_ptr0 + (64 + x3 + 128 * x2), xmask) tmp4 = tl.load(in_ptr1 + (4 + x1), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr2 + x4, xmask) tmp2 = tmp0 + tmp1 tmp5 = tmp3 + tmp4 tmp6 = triton_helpers.maximum(tmp2, tmp5) tmp8 = tmp6 + tmp7 tmp9 = tmp2 == tmp5 tmp10 = tmp2 > tmp5 tmp11 = tmp2 < tmp5 tl.store(out_ptr0 + x4, tmp8, xmask) tl.store(out_ptr1 + x4, tmp9, xmask) tl.store(out_ptr2 + x4, tmp10, xmask) tl.store(out_ptr3 + x4, tmp11, xmask) def call(args): primals_1, primals_2, primals_3, primals_4, primals_5 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (8, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (8,), (1,)) assert_size_stride(primals_4, (8, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_5, (8,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (4, 8, 4, 4), (128, 16, 4, 1)) buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf9 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_eq_gt_lt_maximum_0[grid(256)](buf0, primals_3, buf1, buf7, buf8, buf9, 256, XBLOCK=128, num_warps=4, num_stages=1) del buf0 del primals_3 buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf2, (4, 8, 4, 4), (128, 16, 4, 1)) buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32) buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) triton_poi_fused_add_eq_gt_lt_maximum_1[grid(256)](buf2, primals_5, primals_1, buf3, buf4, buf5, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1) del buf2 del primals_5 return (buf3, primals_1, primals_2, primals_4, buf1, buf4, buf5, buf6, buf7, buf8, buf9) class mfm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1): super(mfm, self).__init__() self.out_channels = out_channels if type == 1: self.filter = nn.Conv2d(in_channels, 2 * out_channels, kernel_size=kernel_size, stride=stride, padding=padding) else: self.filter = nn.Linear(in_channels, 2 * out_channels) def forward(self, x): x = self.filter(x) out = torch.split(x, self.out_channels, 1) return torch.max(out[0], out[1]) class resblockNew(nn.Module): def __init__(self, in_channels, out_channels): super(resblockNew, self).__init__() self.conv1 = mfm(in_channels, out_channels, kernel_size=3, stride=1, padding=1) self.conv2 = mfm(in_channels, out_channels, kernel_size=3, stride=1, padding=1) def forward(self, input_0): primals_2 = self.conv1.filter.weight primals_3 = self.conv1.filter.bias primals_4 = self.conv2.filter.weight primals_5 = self.conv2.filter.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5]) return output[0]
aryachiranjeev/Dependable-AI
resblock
false
9,781
[ "MIT" ]
0
750570572c1baaa2590a89c0982e2f71b15b48b9
https://github.com/aryachiranjeev/Dependable-AI/tree/750570572c1baaa2590a89c0982e2f71b15b48b9
InvDepth
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/td/ctdv3m5a33kovvtng5iilth4k6mtnyfcota6hhwoiqm34iumu7wi.py # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd] # Source node to ATen node mapping: # pad => constant_pad_nd # Graph fragment: # %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [1, 1, 1, 1], 0.0), kwargs = {}) triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[1024], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = (xindex // 6) % 6 x0 = xindex % 6 x2 = (xindex // 36) x4 = xindex tmp0 = (-1) + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = (-1) + x0 tmp6 = tmp5 >= tmp1 tmp7 = tmp5 < tmp3 tmp8 = tmp2 & tmp4 tmp9 = tmp8 & tmp6 tmp10 = tmp9 & tmp7 tmp11 = tl.load(in_ptr0 + ((-5) + x0 + (4*x1) + (16*x2)), tmp10 & xmask, other=0.0) tl.store(out_ptr0 + (x4), tmp11, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/qj/cqjyffxbqx5v3ctgslj6o2fu3pv67cshoa7xswc2b57behdgff35.py # Topologically Sorted Source Nodes: [x, sigmoid, truediv], Original ATen: [aten.convolution, aten.sigmoid, aten.div] # Source node to ATen node mapping: # sigmoid => sigmoid # truediv => div # x => convolution # Graph fragment: # %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) # %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {}) # %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sigmoid, 0.5), kwargs = {}) triton_poi_fused_convolution_div_sigmoid_1 = async_compile.triton('triton_poi_fused_convolution_div_sigmoid_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[64], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_div_sigmoid_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_div_sigmoid_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + (x0), xmask) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tmp4 = tl.sigmoid(tmp3) tmp5 = 2.0 tmp6 = tmp4 * tmp5 tl.store(in_out_ptr0 + (x0), tmp3, xmask) tl.store(out_ptr0 + (x0), tmp6, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) # Topologically Sorted Source Nodes: [pad], Original ATen: [aten.constant_pad_nd] stream0 = get_raw_stream(0) triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 576, grid=grid(576), stream=stream0) del primals_1 # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution] buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1)) buf2 = buf1; del buf1 # reuse buf3 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32) # Topologically Sorted Source Nodes: [x, sigmoid, truediv], Original ATen: [aten.convolution, aten.sigmoid, aten.div] triton_poi_fused_convolution_div_sigmoid_1.run(buf2, primals_3, buf3, 64, grid=grid(64), stream=stream0) del primals_3 return (buf3, primals_2, buf0, buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn class InvDepth(nn.Module): """Inverse depth layer""" def __init__(self, in_channels, out_channels=1, min_depth=0.5): """ Initializes an InvDepth object. Parameters ---------- in_channels : int Number of input channels out_channels : int Number of output channels min_depth : float Minimum depth value to calculate """ super().__init__() self.min_depth = min_depth self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1) self.pad = nn.ConstantPad2d([1] * 4, value=0) self.activ = nn.Sigmoid() def forward(self, x): """Runs the InvDepth layer.""" x = self.conv1(self.pad(x)) return self.activ(x) / self.min_depth def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {'in_channels': 4}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream import torch.nn as nn assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 576 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x1 = xindex // 6 % 6 x0 = xindex % 6 x2 = xindex // 36 x4 = xindex tmp0 = -1 + x1 tmp1 = tl.full([1], 0, tl.int64) tmp2 = tmp0 >= tmp1 tmp3 = tl.full([1], 4, tl.int64) tmp4 = tmp0 < tmp3 tmp5 = -1 + x0 tmp6 = tmp5 >= tmp1 tmp7 = tmp5 < tmp3 tmp8 = tmp2 & tmp4 tmp9 = tmp8 & tmp6 tmp10 = tmp9 & tmp7 tmp11 = tl.load(in_ptr0 + (-5 + x0 + 4 * x1 + 16 * x2), tmp10 & xmask, other=0.0) tl.store(out_ptr0 + x4, tmp11, xmask) @triton.jit def triton_poi_fused_convolution_div_sigmoid_1(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 64 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x0 = xindex tmp0 = tl.load(in_out_ptr0 + x0, xmask) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp3 = tmp0 + tmp2 tmp4 = tl.sigmoid(tmp3) tmp5 = 2.0 tmp6 = tmp4 * tmp5 tl.store(in_out_ptr0 + x0, tmp3, xmask) tl.store(out_ptr0 + x0, tmp6, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 4, 3, 3), (36, 9, 3, 1)) assert_size_stride(primals_3, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((4, 4, 6, 6), (144, 36, 6, 1), torch.float32) get_raw_stream(0) triton_poi_fused_constant_pad_nd_0[grid(576)](primals_1, buf0, 576, XBLOCK=256, num_warps=4, num_stages=1) del primals_1 buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf1, (4, 1, 4, 4), (16, 16, 4, 1)) buf2 = buf1 del buf1 buf3 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32) triton_poi_fused_convolution_div_sigmoid_1[grid(64)](buf2, primals_3, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1) del primals_3 return buf3, primals_2, buf0, buf2 class InvDepthNew(nn.Module): """Inverse depth layer""" def __init__(self, in_channels, out_channels=1, min_depth=0.5): """ Initializes an InvDepth object. Parameters ---------- in_channels : int Number of input channels out_channels : int Number of output channels min_depth : float Minimum depth value to calculate """ super().__init__() self.min_depth = min_depth self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1) self.pad = nn.ConstantPad2d([1] * 4, value=0) self.activ = nn.Sigmoid() def forward(self, input_0): primals_2 = self.conv1.weight primals_3 = self.conv1.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
aliasghar53/packnet-sfm
InvDepth
false
9,782
[ "MIT" ]
0
d07dcbf026194b618a2bd9fc05b599563611f9a3
https://github.com/aliasghar53/packnet-sfm/tree/d07dcbf026194b618a2bd9fc05b599563611f9a3
bottleneck_block
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/zu/czufwylkocw24g7a2s2zvr53dauuvjo7xakshnrimfau2of7256f.py # Topologically Sorted Source Nodes: [max_1, x_3, x_4], Original ATen: [aten.max, aten.add, aten.relu, aten.threshold_backward] # Source node to ATen node mapping: # max_1 => max_1 # x_3 => add # x_4 => relu # Graph fragment: # %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%primals_1, 1, True), kwargs = {}) # %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %getitem), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {}) # %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {}) triton_poi_fused_add_max_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_add_max_relu_threshold_backward_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[256], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_max_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_add_max_relu_threshold_backward_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = (xindex // 64) tmp0 = tl.load(in_out_ptr0 + (x3), xmask) tmp1 = tl.load(in_ptr0 + (0)) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp4 = tl.load(in_ptr1 + (x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp5 = tl.load(in_ptr1 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp7 = tl.load(in_ptr1 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp9 = tl.load(in_ptr1 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last') tmp3 = tmp0 + tmp2 tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp10 = triton_helpers.maximum(tmp8, tmp9) tmp11 = tmp3 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tmp14 = 0.0 tmp15 = tmp13 <= tmp14 tl.store(in_out_ptr0 + (x3), tmp13, xmask) tl.store(out_ptr0 + (x3), tmp15, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1)) assert_size_stride(primals_3, (1, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.convolution] buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (16, 1, 4, 4), (16, 16, 4, 1), 0), primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (16, 1, 4, 4), (16, 16, 4, 1)) buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf0 # reuse buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) # Topologically Sorted Source Nodes: [max_1, x_3, x_4], Original ATen: [aten.max, aten.add, aten.relu, aten.threshold_backward] stream0 = get_raw_stream(0) triton_poi_fused_add_max_relu_threshold_backward_0.run(buf1, primals_3, primals_1, buf2, 256, grid=grid(256), stream=stream0) del primals_3 return (buf1, primals_2, reinterpret_tensor(primals_1, (16, 1, 4, 4), (16, 16, 4, 1), 0), buf2, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.utils.data class depthwise_conv(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1): super(depthwise_conv, self).__init__() self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride= stride, padding=padding) def forward(self, x): C, H, W = x.shape[1:] x = x.reshape(-1, 1, H, W) x = self.depthwise(x) x = x.view(-1, C, H, W) return x class bottleneck_block(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1, activation='relu'): super(bottleneck_block, self).__init__() self.depthwise = depthwise_conv(kernel_size=3, stride=1, padding=1) if activation == 'relu': self.activation = nn.ReLU() elif activation == 'lrelu': self.activation = nn.LeakyReLU() elif activation == 'tanh': self.activation = nn.Tanh() def forward(self, x, act=True): sum_layer = x.max(dim=1, keepdim=True)[0] x = self.depthwise(x) x = x + sum_layer if act: x = self.activation(x) return x def get_inputs(): return [torch.rand([4, 4, 4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.utils.data assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor @triton.jit def triton_poi_fused_add_max_relu_threshold_backward_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr): xnumel = 256 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = xindex < xnumel x3 = xindex x0 = xindex % 16 x2 = xindex // 64 tmp0 = tl.load(in_out_ptr0 + x3, xmask) tmp1 = tl.load(in_ptr0 + 0) tmp2 = tl.broadcast_to(tmp1, [XBLOCK]) tmp4 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp7 = tl.load(in_ptr1 + (32 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp9 = tl.load(in_ptr1 + (48 + x0 + 64 * x2), xmask, eviction_policy= 'evict_last') tmp3 = tmp0 + tmp2 tmp6 = triton_helpers.maximum(tmp4, tmp5) tmp8 = triton_helpers.maximum(tmp6, tmp7) tmp10 = triton_helpers.maximum(tmp8, tmp9) tmp11 = tmp3 + tmp10 tmp12 = tl.full([1], 0, tl.int32) tmp13 = triton_helpers.maximum(tmp12, tmp11) tmp14 = 0.0 tmp15 = tmp13 <= tmp14 tl.store(in_out_ptr0 + x3, tmp13, xmask) tl.store(out_ptr0 + x3, tmp15, xmask) def call(args): primals_1, primals_2, primals_3 = args args.clear() assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1)) assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1)) assert_size_stride(primals_3, (1,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (16, 1, 4, 4), (16, 16, 4, 1), 0), primals_2, stride=(1, 1), padding =(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf0, (16, 1, 4, 4), (16, 16, 4, 1)) buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0) del buf0 buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool) get_raw_stream(0) triton_poi_fused_add_max_relu_threshold_backward_0[grid(256)](buf1, primals_3, primals_1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1) del primals_3 return buf1, primals_2, reinterpret_tensor(primals_1, (16, 1, 4, 4), ( 16, 16, 4, 1), 0), buf2 class depthwise_conv(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1): super(depthwise_conv, self).__init__() self.depthwise = nn.Conv2d(1, 1, kernel_size=kernel_size, stride= stride, padding=padding) def forward(self, x): C, H, W = x.shape[1:] x = x.reshape(-1, 1, H, W) x = self.depthwise(x) x = x.view(-1, C, H, W) return x class bottleneck_blockNew(nn.Module): def __init__(self, kernel_size=3, stride=1, padding=1, activation='relu'): super(bottleneck_blockNew, self).__init__() self.depthwise = depthwise_conv(kernel_size=3, stride=1, padding=1) if activation == 'relu': self.activation = nn.ReLU() elif activation == 'lrelu': self.activation = nn.LeakyReLU() elif activation == 'tanh': self.activation = nn.Tanh() def forward(self, input_0): primals_2 = self.depthwise.depthwise.weight primals_3 = self.depthwise.depthwise.bias primals_1 = input_0 output = call([primals_1, primals_2, primals_3]) return output[0]
Zacchaeus14/lang-seg
bottleneck_block
false
9,783
[ "MIT" ]
0
ad1196a4d33830f3219dbe2260a69364a745f094
https://github.com/Zacchaeus14/lang-seg/tree/ad1196a4d33830f3219dbe2260a69364a745f094
HyperpriorSynthesisDLMM
# AOT ID: ['0_forward'] from ctypes import c_void_p, c_long, c_int import torch import math import random import os import tempfile from math import inf, nan from torch._inductor.hooks import run_intermediate_hooks from torch._inductor.utils import maybe_profile from torch._inductor.codegen.memory_planning import _align as align from torch import device, empty_strided from torch._inductor.async_compile import AsyncCompile from torch._inductor.select_algorithm import extern_kernels from torch._inductor.codegen.multi_kernel import MultiKernelCall import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph from torch._C import _cuda_getCurrentRawStream as get_raw_stream aten = torch.ops.aten inductor_ops = torch.ops.inductor _quantized = torch.ops._quantized assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor alloc_from_pool = torch.ops.inductor._alloc_from_pool async_compile = AsyncCompile() # kernel path: runs/run_shard_8/inductor_cache/zu/czuua4sopi7uw2j4vhwnz6siwft4q3oub6yvsst7upuvdxgbdeip.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072, 32], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 102400 xnumel = 25 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1)) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 320 y1 = (yindex // 320) tmp0 = tl.load(in_ptr0 + (x2 + (25*y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (320*x2) + (8000*y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/rs/crschneg3kvye3kuwch7myvnxivcwiau22prtweju442z2v6tr7s.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_1 = async_compile.triton('triton_poi_fused_1', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[2048, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 1280 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 320 y1 = (yindex // 320) tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (320*x2) + (5120*y1)), tmp0, xmask & ymask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/yp/cypk6shm66qwudlo2djtgfzqrzq2fyztupq3rysg7n557sbl4x4c.py # Unsorted Source Nodes: [], Original ATen: [] # Source node to ATen node mapping: triton_poi_fused_2 = async_compile.triton('triton_poi_fused_2', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[32768, 16], tile_hint=TileHint.SQUARE, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 20480 xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = (yindex // 64) tmp0 = tl.load(in_ptr0 + (x2 + (9*y3)), xmask, eviction_policy='evict_last') tl.store(out_ptr0 + (y0 + (64*x2) + (576*y1)), tmp0, xmask) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/gl/cglkcl5ykpgcv2uydrylvxb6dmvyihdwt3n735pkhiifwgzlyjgn.py # Topologically Sorted Source Nodes: [conv_transpose2d, x], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv_transpose2d => convolution # x => relu # Graph fragment: # %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [2, 2], [1, 1], True, [1, 1], 1), kwargs = {}) # %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {}) triton_poi_fused_convolution_relu_3 = async_compile.triton('triton_poi_fused_convolution_relu_3', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[131072], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 81920 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 320 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/qc/cqcifbqljbi3jwpix3usk7lyjznx6mau7gwn475jaql4t2vmad47.py # Topologically Sorted Source Nodes: [conv_transpose2d_1, x_1], Original ATen: [aten.convolution, aten.relu] # Source node to ATen node mapping: # conv_transpose2d_1 => convolution_1 # x_1 => relu_1 # Graph fragment: # %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [2, 2], [1, 1], True, [1, 1], 1), kwargs = {}) # %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {}) triton_poi_fused_convolution_relu_4 = async_compile.triton('triton_poi_fused_convolution_relu_4', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[524288], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 327680 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 320 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + (x2), tmp4, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/rr/crrnepphlcxumbxug76dqad5k6yukqznmhy2ehyblo25n7vv7jul.py # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution] # Source node to ATen node mapping: # x_2 => convolution_2 # Graph fragment: # %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_5 = async_compile.triton('triton_poi_fused_convolution_5', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[65536], filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr): xnumel = 65536 xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] xmask = tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + (x2), None) tmp1 = tl.load(in_ptr0 + (x0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + (x2), tmp2, None) ''', device_str='cuda') # kernel path: runs/run_shard_8/inductor_cache/pq/cpqkalbpw25e3evlemt6aztt5xhvq6zb3s6poshlca36wevc5gy7.py # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution] # Source node to ATen node mapping: # x_3 => convolution_3 # Graph fragment: # %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%convolution_2, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {}) triton_poi_fused_convolution_6 = async_compile.triton('triton_poi_fused_convolution_6', ''' import triton import triton.language as tl from triton.compiler.compiler import AttrsDescriptor from torch._inductor.runtime import triton_helpers, triton_heuristics from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties @triton_heuristics.pointwise( size_hints=[4096, 256], tile_hint=TileHint.DEFAULT, filename=__file__, triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]}, inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_6', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}, min_elem_per_thread=0 ) @triton.jit def triton_poi_fused_convolution_6(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr): ynumel = 3072 xnumel = 256 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 768 y1 = (yindex // 768) y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + (768*x2) + (196608*y1)), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + (y0), None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + (256*y3)), tmp2, xmask) ''', device_str='cuda') async_compile.wait(globals()) del async_compile def call(args): primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args args.clear() assert_size_stride(primals_1, (320, 320, 5, 5), (8000, 25, 5, 1)) assert_size_stride(primals_2, (320, ), (1, )) assert_size_stride(primals_3, (4, 320, 4, 4), (5120, 16, 4, 1)) assert_size_stride(primals_4, (320, 320, 5, 5), (8000, 25, 5, 1)) assert_size_stride(primals_5, (320, ), (1, )) assert_size_stride(primals_6, (320, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64, ), (1, )) assert_size_stride(primals_8, (768, 64, 1, 1), (64, 1, 1, 1)) assert_size_stride(primals_9, (768, ), (1, )) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((320, 320, 5, 5), (8000, 1, 1600, 320), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] stream0 = get_raw_stream(0) triton_poi_fused_0.run(primals_1, buf0, 102400, 25, grid=grid(102400, 25), stream=stream0) del primals_1 buf1 = empty_strided_cuda((4, 320, 4, 4), (5120, 1, 1280, 320), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_1.run(primals_3, buf1, 1280, 16, grid=grid(1280, 16), stream=stream0) del primals_3 buf2 = empty_strided_cuda((320, 320, 5, 5), (8000, 1, 1600, 320), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_0.run(primals_4, buf2, 102400, 25, grid=grid(102400, 25), stream=stream0) del primals_4 buf3 = empty_strided_cuda((320, 64, 3, 3), (576, 1, 192, 64), torch.float32) # Unsorted Source Nodes: [], Original ATen: [] triton_poi_fused_2.run(primals_6, buf3, 20480, 9, grid=grid(20480, 9), stream=stream0) del primals_6 # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution] buf4 = extern_kernels.convolution(buf1, buf0, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None) assert_size_stride(buf4, (4, 320, 8, 8), (20480, 1, 2560, 320)) buf5 = buf4; del buf4 # reuse # Topologically Sorted Source Nodes: [conv_transpose2d, x], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_3.run(buf5, primals_2, 81920, grid=grid(81920), stream=stream0) del primals_2 # Topologically Sorted Source Nodes: [conv_transpose2d_1], Original ATen: [aten.convolution] buf6 = extern_kernels.convolution(buf5, buf2, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None) assert_size_stride(buf6, (4, 320, 16, 16), (81920, 1, 5120, 320)) buf7 = buf6; del buf6 # reuse # Topologically Sorted Source Nodes: [conv_transpose2d_1, x_1], Original ATen: [aten.convolution, aten.relu] triton_poi_fused_convolution_relu_4.run(buf7, primals_5, 327680, grid=grid(327680), stream=stream0) del primals_5 # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution] buf8 = extern_kernels.convolution(buf7, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 64, 16, 16), (16384, 1, 1024, 64)) buf9 = buf8; del buf8 # reuse # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution] triton_poi_fused_convolution_5.run(buf9, primals_7, 65536, grid=grid(65536), stream=stream0) del primals_7 # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution] buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 768, 16, 16), (196608, 1, 12288, 768)) buf11 = empty_strided_cuda((4, 768, 16, 16), (196608, 256, 16, 1), torch.float32) # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution] triton_poi_fused_convolution_6.run(buf10, primals_9, buf11, 3072, 256, grid=grid(3072, 256), stream=stream0) del buf10 del primals_9 return (buf11, buf0, buf1, buf2, buf3, primals_8, buf5, buf7, buf9, ) def benchmark_compiled_module(times=10, repeat=10): from torch._dynamo.testing import rand_strided from torch._inductor.utils import print_performance primals_1 = rand_strided((320, 320, 5, 5), (8000, 25, 5, 1), device='cuda:0', dtype=torch.float32) primals_2 = rand_strided((320, ), (1, ), device='cuda:0', dtype=torch.float32) primals_3 = rand_strided((4, 320, 4, 4), (5120, 16, 4, 1), device='cuda:0', dtype=torch.float32) primals_4 = rand_strided((320, 320, 5, 5), (8000, 25, 5, 1), device='cuda:0', dtype=torch.float32) primals_5 = rand_strided((320, ), (1, ), device='cuda:0', dtype=torch.float32) primals_6 = rand_strided((320, 64, 3, 3), (576, 9, 3, 1), device='cuda:0', dtype=torch.float32) primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32) primals_8 = rand_strided((768, 64, 1, 1), (64, 1, 1, 1), device='cuda:0', dtype=torch.float32) primals_9 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.float32) fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return print_performance(fn, times=times, repeat=repeat) if __name__ == "__main__": from torch._inductor.wrapper_benchmark import compiled_module_main compiled_module_main('None', benchmark_compiled_module)
import torch import torch.nn as nn import torch.nn.functional as F def get_num_DLMM_channels(C, K=4, params=['mu', 'scale', 'mix']): """ C: Channels of latent representation (L3C uses 5). K: Number of mixture coefficients. """ return C * K * len(params) class HyperpriorSynthesisDLMM(nn.Module): """ Outputs distribution parameters of input latents, conditional on hyperlatents, assuming a discrete logistic mixture model. C: Number of output channels """ def __init__(self, C=64, N=320, activation='relu', final_activation=None): super(HyperpriorSynthesisDLMM, self).__init__() cnn_kwargs = dict(kernel_size=5, stride=2, padding=2, output_padding=1) self.activation = getattr(F, activation) self.final_activation = final_activation self.conv1 = nn.ConvTranspose2d(N, N, **cnn_kwargs) self.conv2 = nn.ConvTranspose2d(N, N, **cnn_kwargs) self.conv3 = nn.ConvTranspose2d(N, C, kernel_size=3, stride=1, padding=1) self.conv_out = nn.Conv2d(C, get_num_DLMM_channels(C), kernel_size= 1, stride=1) if self.final_activation is not None: self.final_activation = getattr(F, final_activation) def forward(self, x): x = self.activation(self.conv1(x)) x = self.activation(self.conv2(x)) x = self.conv3(x) x = self.conv_out(x) if self.final_activation is not None: x = self.final_activation(x) return x def get_inputs(): return [torch.rand([4, 320, 4, 4])] def get_init_inputs(): return [[], {}]
import torch from torch._inductor.select_algorithm import extern_kernels import triton import triton.language as tl from torch._inductor.runtime.triton_heuristics import grid from torch._C import _cuda_getCurrentRawStream as get_raw_stream from torch._inductor.runtime import triton_helpers import torch.nn as nn import torch.nn.functional as F assert_size_stride = torch._C._dynamo.guards.assert_size_stride empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda @triton.jit def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 25 yoffset = (tl.program_id(1) + tl.program_id(2) * tl.num_programs(1) ) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 320 y1 = yindex // 320 tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy= 'evict_last') tl.store(out_ptr0 + (y0 + 320 * x2 + 8000 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): ynumel = 1280 xnumel = 16 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] ymask = yindex < ynumel xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 320 y1 = yindex // 320 tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask, eviction_policy ='evict_last') tl.store(out_ptr0 + (y0 + 320 * x2 + 5120 * y1), tmp0, xmask & ymask) @triton.jit def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl. constexpr, XBLOCK: tl.constexpr): xnumel = 9 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y3 = yindex y0 = yindex % 64 y1 = yindex // 64 tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last' ) tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask) @triton.jit def triton_poi_fused_convolution_relu_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 320 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 320 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tmp3 = tl.full([1], 0, tl.int32) tmp4 = triton_helpers.maximum(tmp3, tmp2) tl.store(in_out_ptr0 + x2, tmp4, None) @triton.jit def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl .constexpr): xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:] tl.full([XBLOCK], True, tl.int1) x2 = xindex x0 = xindex % 64 tmp0 = tl.load(in_out_ptr0 + x2, None) tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(in_out_ptr0 + x2, tmp2, None) @triton.jit def triton_poi_fused_convolution_6(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr): xnumel = 256 yoffset = tl.program_id(1) * YBLOCK yindex = yoffset + tl.arange(0, YBLOCK)[None, :] tl.full([XBLOCK, YBLOCK], True, tl.int1) xoffset = tl.program_id(0) * XBLOCK xindex = xoffset + tl.arange(0, XBLOCK)[:, None] xmask = xindex < xnumel x2 = xindex y0 = yindex % 768 y1 = yindex // 768 y3 = yindex tmp0 = tl.load(in_ptr0 + (y0 + 768 * x2 + 196608 * y1), xmask, eviction_policy='evict_last') tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last') tmp2 = tmp0 + tmp1 tl.store(out_ptr0 + (x2 + 256 * y3), tmp2, xmask) def call(args): (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9) = args args.clear() assert_size_stride(primals_1, (320, 320, 5, 5), (8000, 25, 5, 1)) assert_size_stride(primals_2, (320,), (1,)) assert_size_stride(primals_3, (4, 320, 4, 4), (5120, 16, 4, 1)) assert_size_stride(primals_4, (320, 320, 5, 5), (8000, 25, 5, 1)) assert_size_stride(primals_5, (320,), (1,)) assert_size_stride(primals_6, (320, 64, 3, 3), (576, 9, 3, 1)) assert_size_stride(primals_7, (64,), (1,)) assert_size_stride(primals_8, (768, 64, 1, 1), (64, 1, 1, 1)) assert_size_stride(primals_9, (768,), (1,)) with torch.cuda._DeviceGuard(0): torch.cuda.set_device(0) buf0 = empty_strided_cuda((320, 320, 5, 5), (8000, 1, 1600, 320), torch.float32) get_raw_stream(0) triton_poi_fused_0[grid(102400, 25)](primals_1, buf0, 102400, 25, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1) del primals_1 buf1 = empty_strided_cuda((4, 320, 4, 4), (5120, 1, 1280, 320), torch.float32) triton_poi_fused_1[grid(1280, 16)](primals_3, buf1, 1280, 16, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_3 buf2 = empty_strided_cuda((320, 320, 5, 5), (8000, 1, 1600, 320), torch.float32) triton_poi_fused_0[grid(102400, 25)](primals_4, buf2, 102400, 25, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1) del primals_4 buf3 = empty_strided_cuda((320, 64, 3, 3), (576, 1, 192, 64), torch .float32) triton_poi_fused_2[grid(20480, 9)](primals_6, buf3, 20480, 9, XBLOCK=16, YBLOCK=64, num_warps=4, num_stages=1) del primals_6 buf4 = extern_kernels.convolution(buf1, buf0, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None) assert_size_stride(buf4, (4, 320, 8, 8), (20480, 1, 2560, 320)) buf5 = buf4 del buf4 triton_poi_fused_convolution_relu_3[grid(81920)](buf5, primals_2, 81920, XBLOCK=1024, num_warps=4, num_stages=1) del primals_2 buf6 = extern_kernels.convolution(buf5, buf2, stride=(2, 2), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None) assert_size_stride(buf6, (4, 320, 16, 16), (81920, 1, 5120, 320)) buf7 = buf6 del buf6 triton_poi_fused_convolution_relu_4[grid(327680)](buf7, primals_5, 327680, XBLOCK=1024, num_warps=4, num_stages=1) del primals_5 buf8 = extern_kernels.convolution(buf7, buf3, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf8, (4, 64, 16, 16), (16384, 1, 1024, 64)) buf9 = buf8 del buf8 triton_poi_fused_convolution_5[grid(65536)](buf9, primals_7, 65536, XBLOCK=512, num_warps=4, num_stages=1) del primals_7 buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None) assert_size_stride(buf10, (4, 768, 16, 16), (196608, 1, 12288, 768)) buf11 = empty_strided_cuda((4, 768, 16, 16), (196608, 256, 16, 1), torch.float32) triton_poi_fused_convolution_6[grid(3072, 256)](buf10, primals_9, buf11, 3072, 256, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1) del buf10 del primals_9 return buf11, buf0, buf1, buf2, buf3, primals_8, buf5, buf7, buf9 def get_num_DLMM_channels(C, K=4, params=['mu', 'scale', 'mix']): """ C: Channels of latent representation (L3C uses 5). K: Number of mixture coefficients. """ return C * K * len(params) class HyperpriorSynthesisDLMMNew(nn.Module): """ Outputs distribution parameters of input latents, conditional on hyperlatents, assuming a discrete logistic mixture model. C: Number of output channels """ def __init__(self, C=64, N=320, activation='relu', final_activation=None): super(HyperpriorSynthesisDLMMNew, self).__init__() cnn_kwargs = dict(kernel_size=5, stride=2, padding=2, output_padding=1) self.activation = getattr(F, activation) self.final_activation = final_activation self.conv1 = nn.ConvTranspose2d(N, N, **cnn_kwargs) self.conv2 = nn.ConvTranspose2d(N, N, **cnn_kwargs) self.conv3 = nn.ConvTranspose2d(N, C, kernel_size=3, stride=1, padding=1) self.conv_out = nn.Conv2d(C, get_num_DLMM_channels(C), kernel_size= 1, stride=1) if self.final_activation is not None: self.final_activation = getattr(F, final_activation) def forward(self, input_0): primals_1 = self.conv1.weight primals_2 = self.conv1.bias primals_4 = self.conv2.weight primals_5 = self.conv2.bias primals_6 = self.conv3.weight primals_7 = self.conv3.bias primals_8 = self.conv_out.weight primals_9 = self.conv_out.bias primals_3 = input_0 output = call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9]) return output[0]
ali-zafari/high-fidelity-generative-compression
HyperpriorSynthesisDLMM
false
9,784
[ "Apache-2.0" ]
0
37ab8d6727df48f8ebf4577db0986ccd0ffe404b
https://github.com/ali-zafari/high-fidelity-generative-compression/tree/37ab8d6727df48f8ebf4577db0986ccd0ffe404b