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import Range
from tvm.script |
import tir as T
from tvm.tir |
import stmt_functor
from tvm.tir |
import PrimFunc
from tvm.tir.usmp |
import utils as usmp_utils
from tvm.target |
import Target
from tvm |
import WorkspacePoolInfo, ConstantPoolInfo
def _replace_stmt_with_buf_var_names(buffer_info_map):
"""helper to replace tir.allocates with buffer names"""
new_buffer_info_map = dict()
for k, v in buffer_info_map.items():
new_buffer_info_map[k.name_hint] = k
return new_buffer_info_map
def _verify_conflicts(main_buf_name, conflicting_buf_names, buffer_info_map):
"""helper to check expected liveness conflicts"""
buf_info = buffer_info_map[main_buf_name]
for conflict in buf_info.conflicts:
assert conflict.name_hint in conflicting_buf_names
def _get_allocates(primfunc):
"""helper to extract all allocate nodes by name"""
allocates = dict()
def get_allocate(stmt):
if isinstance(stmt, tvm.tir.Allocate):
allocates[str(stmt.buffer_var.name)] = stmt
stmt_functor.post_order_visit(primfunc.body, get_allocate)
return allocates
def _assign_poolinfos_to_allocates_in_primfunc(primfunc, pool_infos, constant_pool_infos):
"""helper to assing poolinfos to allocate nodes in a tir.PrimFunc"""
def set_poolinfos(stmt):
if isinstance(stmt, tvm.tir.Allocate):
return tvm.tir.Allocate(
buffer_var=stmt.buffer_var,
dtype=stmt.dtype,
extents=stmt.extents,
condition=stmt.condition,
body=stmt.body,
annotations={tvm.tir.usmp.utils.CANDIDATE_MEMORY_POOL_ATTR: pool_infos},
)
elif isinstance(stmt, tvm.tir.AllocateConst):
return tvm.tir.AllocateConst(
buffer_var=stmt.buffer_var,
dtype=stmt.dtype,
extents=stmt.extents,
data_or_idx=stmt.data,
body=stmt.body,
annotations={tvm.tir.usmp.utils.CANDIDATE_MEMORY_POOL_ATTR: constant_pool_infos},
)
return primfunc.with_body(stmt_functor.ir_transform(primfunc.body, None, set_poolinfos))
def _assign_poolinfos_to_allocates_in_irmodule(mod, pool_infos, cons |
tant_pool_infos=None):
"""helper to assign poolinfos to allocate nodes in a IRModule"""
ret = tvm.IRModule()
for global_var, basefunc in mod.functions.items():
if isinstance(basefunc, tvm.tir.PrimFunc):
ret[global_var] = _assign_poolinfos_to_allocates_in_primfunc(
basefunc, pool_infos, constant_pool_infos
)
return ret
def _assign_targets_to_primfuncs_irmodule(mod, target):
"""helper to assign target for PrimFunc in a IRModule"""
ret = tvm.IRModule()
for global_var, basefunc in mod.functions.items():
if isinstance(basefunc, tvm.tir.PrimFunc):
ret[global_var] = basefunc.with_attr("target", target)
return ret
@tvm.script.ir_module
class LinearStructure:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast(placeholder_62: T.handle, placeholder_63: T.handle, placeholder_64: T.handle, T_cast_20: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", "tir.noalias": True})
placeholder_65 = T.match_buffer(pla |
ceholder_62, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_66 = T.match_buffer(placeholder_63, [9408], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_67 = T.match_buffer(placeholder_64, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_21 = T.match_buffer(T_cast_20, [289], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_7 = T.decl_buffer([157323], "int16")
for i0_i1_fused_7 in T.serial(0, 229):
for i2_7, i3_7 in T.grid(229, 3):
PaddedInput_7[(((i0_i1_fused_7*687) + (i2_7*3)) + i3_7)] = T.if_then_else(((((2 <= i0_i1_fused_7) and (i0_i1_fused_7 < 226)) and (2 <= i2_7)) and (i2_7 < 226)), placeholder_65[((((i0_i1_fused_7*672) + (i2_7*3)) + i3_7) - 1350)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_7 in T.serial(0, 12544):
Conv2dOutput_7 = T.decl_buffer([64], "int32")
for ff_3 in T.serial(0, 64):
Conv2dOutput_7[ff_3] = 0
for ry_2, rx_2, rc_7 in T.grid(7, 7, 3):
Conv2dOutput_7[ff_3] = (Conv2dOutput_7[ff_3] + (T.cast(PaddedInput_7[(((((T.floordiv(ax0_ax1_fused_ax2_fused_7, 112)*1374) + (ry_2*687)) + (T.floormod(ax0_ax1_fused_ax2_fused_7, 112)*6)) + (rx_2*3)) + rc_7)], "int32")*T.cast(placeholder_66[((((ry_2*1344) + (rx_2*192)) + (rc_7*64)) + ff_3)], "int32")))
for ax3_inner_7 in T.serial(0, 64):
T_cast_21[((ax0_ax1_fused_ax2_fused_7*64) + ax3_inner_7)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_7[ax3_inner_7] + placeholder_67[ax3_inner_7]), 1939887962, 31, -9, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_max_pool2d_cast(placeholder_28: T.handle, T_cast_6: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_max_pool2d_cast", "tir.noalias": True})
placeholder_29 = T.match_buffer(placeholder_28, [802816], dtype="uint8", elem_offset=0, |
align=64, offset_factor=1)
T_cast_7 = T.match_buffer(T_cast_6, [177], dtype="int16", elem_offset=0, align=64, offset_factor=1)
tensor_2 = T.decl_buffer([200704], "uint8")
for ax0_ax1_fused_4 in T.serial(0, 56):
for ax2_4 in T.serial(0, 56):
for ax3_init in T.serial(0, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_init)] = T.uint8(0)
for rv0_rv1_fused_1, ax3_2 in T.grid(9, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)] = T.max(tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)], T.if_then_else(((((ax0_ax1_fused_4*2) + T.floordiv(rv0_rv1_fused_1, 3)) < 112) and (((ax2_4*2) + T.floormod(rv0_rv1_fused_1, 3)) < 112)), placeholder_29[(((((ax0_ax1_fused_4*14336) + (T.floordiv(rv0_rv1_fused_1, 3)*7168)) + (ax2_4*128)) + (T.floormod(rv0_rv1_fused_1, 3)*64)) + ax3_2)], T.uint8(0), dtype="uint8"))
for ax0_ax1_fused_5 in T.serial(0, 56):
for ax2_5, ax3_3 in T.grid(56, 64):
T_cast_7[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)] = T.cast(tensor_2[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)], "int16")
@T.prim_func
def run_model(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_run_model", "runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_9 = T.allocate([301056], "int8", "global")
sid_8 = T.allocate([802816], "int8", "global")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input, T.lookup_param("p0", dtype="handle"), sid_9, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", sid_9, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_8, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_max_pool2d_cast", sid_8, output, dtype="int32"))
__tvm_ |
meta__ = None
def test_linear():
target = Target("c")
fast_memory_pool = WorkspacePoolInfo(pool_name="fast_memory", targets=[target])
slow_memory_pool = WorkspacePoolInfo(pool_name="slow_memory", targets=[target])
tir_mod = LinearStructure
tir_mod = _assign_targets_to_primfuncs_irmodule(tir_mod, target)
tir_mod = _assign_poolinfos_to_allocates_in_irmodule(
tir_mod, [fast_memory_pool, slow_memory_pool]
)
buffer_info_analysis = tvm.tir.usmp.analysis.extract_buffer_info(tir_mod["run_model"], tir_mod)
assert buffer_info_analysis.memory_pressure == 1117718
buffer_info_map = _replace_stmt_with_buf_var_names(buffer_info_analysis.buffer_info_stmts)
_verify_conflicts("PaddedInput_7", ["sid_9", "sid_8", "Conv2dOutput_7"], buffer_info_map)
_verify_conflicts("tensor_2", ["sid_8"], buffer_info_map)
_verify_conflicts("sid_9", ["PaddedInput_7"], buffer_info_map)
_verify_conflicts("sid_8", ["PaddedInput_7", "Conv2dOutput_7", "tensor_2"], buffer_info_map)
_verify_conflicts("Conv2dOutput_7", ["sid_8", "PaddedInput_7"], buffer_info_map)
assert buffer_info_map["sid_8"].size_bytes == 802816
assert buffer_info_map["Conv2dOutput_7"].size_bytes == 256
assert buffer_info_map["PaddedInput_7"].size_bytes == 314646
assert buffer_info_map["tensor_2"].size_bytes == 200704
assert buffer_info_map["sid_9"].size_bytes == 301056
assert [
pool_info.pool_name for pool_info in list(buffer_info_map["sid_8"].pool_candidates)
] == ["fast_memory", "slow_memory"]
@tvm.script.ir_module
class ParallelSerialMixedForLoops:
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1(placeholder_68: T.handle, placeholder_69: T.handle, placeholder_70: T.handle, T_cast_22: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1", "tir.noalias": True})
placeholder_71 = T.match_buffer(placeholder_68, [200704], dtype="int1 |
6", elem_offset=0, align=64, offset_factor=1)
placeholder_72 = T.match_buffer(placeholder_69, [110592], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_73 = T.match_buffer(placeholder_70, [192], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_23 = T.match_buffer(T_cast_22, [305], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_8 = T.decl_buffer([215296], "int16")
for i0_i1_fused_8 in T.serial(0, 58):
for i2_8, i3_8 in T.grid(58, 64):
PaddedInput_8[(((i0_i1_fused_8*3712) + (i2_8*64)) + i3_8)] = T.if_then_else(((((1 <= i0_i1_fused_8) and (i0_i1_fused_8 < 57)) and (1 <= i2_8)) and (i2_8 < 57)), placeholder_71[((((i0_i1_fused_8*3584) + (i2_8*64)) + i3_8) - 3648)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_8 in T.parallel(0, 3136):
dummy_allocate = T.decl_buffer([1], "int32")
for ax3_outer_4 in T.serial(0, 3):
Conv2dOutput_8 = T.decl_buffer([64], "int32")
for ff_4 in T.serial(0, 64):
Conv2dOutput_8[ff_4] = 0
for ry_3, rx_3, rc_8 in T.grid(3, 3, 64):
Conv2dOutput_8[ff_4] = (Conv2dOutput_8[ff_4] + (T.cast(PaddedInput_8[(((((T.floordiv(ax0_ax1_fused_ax2_fused_8, 56)*3712) + (ry_3*3712)) + (rx_3*64)) + (T.floormod(ax0_ax1_fused_ax2_fused_8, 56)*64)) + rc_8)], "int32")*T.cast(placeholder_72[(((((ry_3*36864) + (rx_3*12288)) + (rc_8*192)) + (ax3_outer_4*64)) + ff_4)], "int32")))
for ax3_inner_8 in T.serial(0, 64):
T_cast_23[(((ax0_ax1_fused_ax2_fused_8*192) + (ax3_outer_4*64)) + ax3_inner_8)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_8[ax3_inner_8] + placeholder_73[((ax3_outer_4*64) + ax3_inner_8)]), 1139793473, 31, -6, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def run_model(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_run_model", |
"runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1", input, T.lookup_param("p5", dtype="handle"), T.lookup_param("p6", dtype="handle"), output, dtype="int32"))
__tvm_meta__ = None
@tvm.script.ir_module
class AllSerialForLoops:
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1(placeholder_68: T.handle, placeholder_69: T.handle, placeholder_70: T.handle, T_cast_22: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1", "tir.noalias": True})
placeholder_71 = T.match_buffer(placeholder_68, [200704], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_72 = T.match_buffer(placeholder_69, [110592], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_73 = T.match_buffer(placeholder_70, [192], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_23 = T.match_buffer(T_cast_22, [305], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_8 = T.decl_buffer([215296], "int16")
for i0_i1_fused_8 in T.serial(0, 58):
for i2_8, i3_8 in T.grid(58, 64):
PaddedInput_8[(((i0_i1_fused_8*3712) + (i2_8*64)) + i3_8)] = T.if_then_else(((((1 <= i0_i1_fused_8) and (i0_i1_fused_8 < 57)) and (1 <= i2_8)) and (i2_8 < 57)), placeholder_71[((((i0_i1_fused_8*3584) + (i2_8*64)) + i3_8) - 3648)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_8 in T.serial(0, 3136):
dummy_allocate = T.decl_buffer([1], "int32")
for ax3_outer_4 in T.serial(0, 3):
Conv2dOutput_8 = T.decl_buffer([64], "int32")
for ff_4 in T.serial(0, 64):
Conv2dOutput_8[ff_4] = 0
for ry_3, rx_3, rc_8 in T.grid(3, 3, 64): |
Conv2dOutput_8[ff_4] = (Conv2dOutput_8[ff_4] + (T.cast(PaddedInput_8[(((((T.floordiv(ax0_ax1_fused_ax2_fused_8, 56)*3712) + (ry_3*3712)) + (rx_3*64)) + (T.floormod(ax0_ax1_fused_ax2_fused_8, 56)*64)) + rc_8)], "int32")*T.cast(placeholder_72[(((((ry_3*36864) + (rx_3*12288)) + (rc_8*192)) + (ax3_outer_4*64)) + ff_4)], "int32")))
for ax3_inner_8 in T.serial(0, 64):
T_cast_23[(((ax0_ax1_fused_ax2_fused_8*192) + (ax3_outer_4*64)) + ax3_inner_8)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_8[ax3_inner_8] + placeholder_73[((ax3_outer_4*64) + ax3_inner_8)]), 1139793473, 31, -6, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def run_model(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_run_model", "runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1", input, T.lookup_param("p5", dtype="handle"), T.lookup_param("p6", dtype="handle"), output, dtype="int32"))
__tvm_meta__ = None
def test_parallel_serial_mixed_for_loops():
target = Target("c")
global_ws_pool = WorkspacePoolInfo(
pool_name="global_workspace",
targets=[target],
)
all_serial_tir_mod = AllSerialForLoops
all_serial_tir_mod = _assign_targets_to_primfuncs_irmodule(all_serial_tir_mod, target)
all_serial_tir_mod = _assign_poolinfos_to_allocates_in_irmodule(
all_serial_tir_mod, [global_ws_pool]
)
main_func = all_serial_tir_mod["run_model"]
buffer_info_analysis = tvm.tir.usmp.analysis.extract_buffer_info(main_func, all_serial_tir_mod)
assert buffer_info_analysis.memory_pressure == 430848
buffer_info_map = _replace_stmt_with_buf_var_names(buffer_info_analysis.buffer_info_stmts)
assert len(buffer_info_map) == 3
for name, _ in buffer_info_map.items():
assert name in ["dummy_allocate", "Conv2d |
Output_8", "PaddedInput_8"]
parallel_serial_mixed_tir_mod = ParallelSerialMixedForLoops
parallel_serial_mixed_tir_mod = _assign_targets_to_primfuncs_irmodule(
parallel_serial_mixed_tir_mod, target
)
parallel_serial_mixed_tir_mod = _assign_poolinfos_to_allocates_in_irmodule(
parallel_serial_mixed_tir_mod, [global_ws_pool]
)
main_func = parallel_serial_mixed_tir_mod["run_model"]
buffer_info_analysis = tvm.tir.usmp.analysis.extract_buffer_info(
main_func, parallel_serial_mixed_tir_mod
)
assert buffer_info_analysis.memory_pressure == 430848
buffer_info_map = _replace_stmt_with_buf_var_names(buffer_info_analysis.buffer_info_stmts)
assert len(buffer_info_map) == 2
for name, _ in buffer_info_map.items():
assert name in ["Conv2dOutput_8", "PaddedInput_8"]
@tvm.script.ir_module
class InceptionStructure:
@T.prim_func
def tvmgen_default_fused_nn_max_pool2d(placeholder: T.handle, tensor: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_max_pool2d", "tir.noalias": True})
placeholder_1 = T.match_buffer(placeholder, [602112], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
tensor_1 = T.match_buffer(tensor, [249], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused in T.serial(0, 28):
for ax2 in T.serial(0, 28):
for ax3_outer_init, ax3_inner_init in T.grid(3, 64):
tensor_1[((((ax0_ax1_fused*5376) + (ax2*192)) + (ax3_outer_init*64)) + ax3_inner_init)] = T.uint8(0)
for rv0_rv1_fused, ax3_outer, ax3_inner in T.grid(9, 3, 64):
tensor_1[((((ax0_ax1_fused*5376) + (ax2*192)) + (ax3_outer*64)) + ax3_inner)] = T.max(tensor_1[((((ax0_ax1_fused*5376) + (ax2*192)) + (ax3_outer*64)) + ax3_inner)], T.if_then_else(((((ax0_ax1_fused*2) + T.floordiv(rv0_rv1_fused, 3)) < 56) and (((ax2*2) + T.floormod(rv0_rv1_fused, 3)) < 56)), placeholder_1[((((((ax0_ax1_fused*2 |
1504) + (T.floordiv(rv0_rv1_fused, 3)*10752)) + (ax2*384)) + (T.floormod(rv0_rv1_fused, 3)*192)) + (ax3_outer*64)) + ax3_inner)], T.uint8(0), dtype="uint8"))
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def tvmgen_default_fused_cast(placeholder_6: T.handle, T_cast: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast", "tir.noalias": True})
placeholder_7 = T.match_buffer(placeholder_6, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T_cast_1 = T.match_buffer(T_cast, [249], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_2 in T.serial(0, 28):
for ax2_2, ax3_outer_1, ax3_inner_2 in T.grid(28, 12, 16):
T_cast_1[((((ax0_ax1_fused_2*5376) + (ax2_2*192)) + (ax3_outer_1*16)) + ax3_inner_2)] = T.cast(placeholder_7[((((ax0_ax1_fused_2*5376) + (ax2_2*192)) + (ax3_outer_1*16)) + ax3_inner_2)], "int16")
@T.prim_func
def tvmgen_default_fused_concatenate(placeholder_8: T.handle, placeholder_9: T.handle, placeholder_10: T.handle, placeholder_11: T.handle, T_concat: T.handle) -> None:
T.func_attr({ |
"global_symbol": "tvmgen_default_fused_concatenate", "tir.noalias": True})
placeholder_12 = T.match_buffer(placeholder_8, [50176], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T_concat_1 = T.match_buffer(T_concat, [313], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_13 = T.match_buffer(placeholder_9, [100352], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_14 = T.match_buffer(placeholder_11, [25088], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_15 = T.match_buffer(placeholder_10, [25088], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_3 in T.serial(0, 28):
for ax2_3, ax3 in T.grid(28, 256):
T_concat_1[(((ax0_ax1_fused_3*7168) + (ax2_3*256)) + ax3)] = T.if_then_else((224 <= ax3), placeholder_14[((((ax0_ax1_fused_3*896) + (ax2_3*32)) + ax3) - 224)], T.if_then_else((192 <= ax3), placeholder_15[((((ax0_ax1_fused_3*896) + (ax2_3*32)) + ax3) - 192)], T.if_then_else((64 <= ax3), placeholder_13[((((ax0_ax1_fused_3*3584) + (ax2_3*128)) + ax3) - 64)], placeholder_12[(((ax0_ax1_fused_3*1792) + (ax2_3*64)) + ax3)], dtype="uint8"), dtype="uint8"), dtype="uint8")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast(placeholder_16: T.handle, placeholder_17: T.handle, placeholder_18: T.handle, T_cast_2: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast", "tir.noalias": True})
placeholder_19 = T.match_buffer(placeholder_16, [200704], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_20 = T.match_buffer(placeholder_17, [4096], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_21 = T.match_buffer(placeholder_18, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_3 = T.match_buffer(T_cast_2, [177], dtype="int16", elem_offset= |
0, align=64, offset_factor=1)
PaddedInput = T.decl_buffer([200704], "int16")
for i0_i1_fused in T.serial(0, 56):
for i2, i3 in T.grid(56, 64):
PaddedInput[(((i0_i1_fused*3584) + (i2*64)) + i3)] = placeholder_19[(((i0_i1_fused*3584) + (i2*64)) + i3)]
for ax0_ax1_fused_ax2_fused in T.serial(0, 3136):
Conv2dOutput = T.decl_buffer([64], "int32")
for ff in T.serial(0, 64):
Conv2dOutput[ff] = 0
for rc in T.serial(0, 64):
Conv2dOutput[ff] = (Conv2dOutput[ff] + (T.cast(PaddedInput[((ax0_ax1_fused_ax2_fused*64) + rc)], "int32")*T.cast(placeholder_20[((rc*64) + ff)], "int32")))
for ax3_inner_3 in T.serial(0, 64):
T_cast_3[((ax0_ax1_fused_ax2_fused*64) + ax3_inner_3)] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput[ax3_inner_3] + placeholder_21[ax3_inner_3]), 1191576922, 31, -4, dtype="int32"), 255), 0), "uint8"), "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1(placeholder_22: T.handle, placeholder_23: T.handle, placeholder_24: T.handle, T_cast_4: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1", "tir.noalias": True})
placeholder_25 = T.match_buffer(placeholder_22, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_26 = T.match_buffer(placeholder_23, [18432], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_27 = T.match_buffer(placeholder_24, [96], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_5 = T.match_buffer(T_cast_4, [153], dtype="int16", elem_offset=0, align=64, offset_factor=1)
PaddedInput_1 = T.decl_buffer([150528], "int16")
for i0_i1_fused_1 in T.serial(0, 28):
for i2_1, i3_1 in T.grid(28, 192):
PaddedInput_1[(((i0_i1_fused_1*5376) + (i2_1*192 |
)) + i3_1)] = placeholder_25[(((i0_i1_fused_1*5376) + (i2_1*192)) + i3_1)]
for ax0_ax1_fused_ax2_fused_1 in T.serial(0, 784):
Conv2dOutput_1 = T.decl_buffer([1], "int32")
for ax3_1 in T.serial(0, 96):
Conv2dOutput_1[0] = 0
for rc_1 in T.serial(0, 192):
Conv2dOutput_1[0] = (Conv2dOutput_1[0] + (T.cast(PaddedInput_1[((ax0_ax1_fused_ax2_fused_1*192) + rc_1)], "int32")*T.cast(placeholder_26[((rc_1*96) + ax3_1)], "int32")))
T_cast_5[((ax0_ax1_fused_ax2_fused_1*96) + ax3_1)] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_1[0] + placeholder_27[ax3_1]), 1201322342, 31, -6, dtype="int32"), 255), 0), "uint8"), "int16")
@T.prim_func
def tvmgen_default_fused_nn_max_pool2d_cast(placeholder_28: T.handle, T_cast_6: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_max_pool2d_cast", "tir.noalias": True})
placeholder_29 = T.match_buffer(placeholder_28, [802816], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T_cast_7 = T.match_buffer(T_cast_6, [177], dtype="int16", elem_offset=0, align=64, offset_factor=1)
tensor_2 = T.decl_buffer([200704], "uint8")
for ax0_ax1_fused_4 in T.serial(0, 56):
for ax2_4 in T.serial(0, 56):
for ax3_init in T.serial(0, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_init)] = T.uint8(0)
for rv0_rv1_fused_1, ax3_2 in T.grid(9, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)] = T.max(tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)], T.if_then_else(((((ax0_ax1_fused_4*2) + T.floordiv(rv0_rv1_fused_1, 3)) < 112) and (((ax2_4*2) + T.floormod(rv0_rv1_fused_1, 3)) < 112)), placeholder_29[(((((ax0_ax1_fused_4*14336) + (T.floordiv(rv0_rv1_fused_1, 3)*7168)) + (ax2_4*128)) + (T.floormod(rv0_rv1_fused_1, 3)*64)) + ax3_2)], T.uint8(0), dtype="uint8"))
for ax0_ax1_fused_5 in T.seria |
l(0, 56):
for ax2_5, ax3_3 in T.grid(56, 64):
T_cast_7[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)] = T.cast(tensor_2[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)], "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_2(placeholder_30: T.handle, placeholder_31: T.handle, placeholder_32: T.handle, T_cast_8: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_2", "tir.noalias": True})
placeholder_33 = T.match_buffer(placeholder_30, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_34 = T.match_buffer(placeholder_31, [12288], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_35 = T.match_buffer(placeholder_32, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_9 = T.match_buffer(T_cast_8, [121], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_2 = T.decl_buffer([150528], "int16")
for i0_i1_fused_2 in T.serial(0, 28):
for i2_2, i3_2 in T.grid(28, 192):
PaddedInput_2[(((i0_i1_fused_2*5376) + (i2_2*192)) + i3_2)] = placeholder_33[(((i0_i1_fused_2*5376) + (i2_2*192)) + i3_2)]
for ax0_ax1_fused_ax2_fused_2 in T.serial(0, 784):
Conv2dOutput_2 = T.decl_buffer([64], "int32")
for ff_1 in T.serial(0, 64):
Conv2dOutput_2[ff_1] = 0
for rc_2 in T.serial(0, 192):
Conv2dOutput_2[ff_1] = (Conv2dOutput_2[ff_1] + (T.cast(PaddedInput_2[((ax0_ax1_fused_ax2_fused_2*192) + rc_2)], "int32")*T.cast(placeholder_34[((rc_2*64) + ff_1)], "int32")))
for ax3_inner_4 in T.serial(0, 64):
T_cast_9[((ax0_ax1_fused_ax2_fused_2*64) + ax3_inner_4)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_2[ax3_inner_4] + placeholder_35[ax3_inner_4]), 1663316467, 31, -7, dtype="int32"), 255), 0), "uint8")
@T.pri |
m_func
def tvmgen_default_fused_nn_max_pool2d_cast_1(placeholder_36: T.handle, T_cast_10: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_max_pool2d_cast_1", "tir.noalias": True})
placeholder_37 = T.match_buffer(placeholder_36, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T_cast_11 = T.match_buffer(T_cast_10, [249], dtype="int16", elem_offset=0, align=64, offset_factor=1)
tensor_3 = T.decl_buffer([150528], "uint8")
for ax0_ax1_fused_6 in T.serial(0, 28):
for ax2_6 in T.serial(0, 28):
for ax3_outer_init_1, ax3_inner_init_1 in T.grid(3, 64):
tensor_3[((((ax0_ax1_fused_6*5376) + (ax2_6*192)) + (ax3_outer_init_1*64)) + ax3_inner_init_1)] = T.uint8(0)
for rv0_rv1_fused_2, ax3_outer_2, ax3_inner_5 in T.grid(9, 3, 64):
tensor_3[((((ax0_ax1_fused_6*5376) + (ax2_6*192)) + (ax3_outer_2*64)) + ax3_inner_5)] = T.max(tensor_3[((((ax0_ax1_fused_6*5376) + (ax2_6*192)) + (ax3_outer_2*64)) + ax3_inner_5)], T.if_then_else(((((1 <= (T.floordiv(rv0_rv1_fused_2, 3) + ax0_ax1_fused_6)) and ((T.floordiv(rv0_rv1_fused_2, 3) + ax0_ax1_fused_6) < 29)) and (1 <= (ax2_6 + T.floormod(rv0_rv1_fused_2, 3)))) and ((ax2_6 + T.floormod(rv0_rv1_fused_2, 3)) < 29)), placeholder_37[(((((((T.floordiv(rv0_rv1_fused_2, 3)*5376) + (ax0_ax1_fused_6*5376)) + (ax2_6*192)) + (T.floormod(rv0_rv1_fused_2, 3)*192)) + (ax3_outer_2*64)) + ax3_inner_5) - 5568)], T.uint8(0), dtype="uint8"))
for ax0_ax1_fused_7 in T.serial(0, 28):
for ax2_7, ax3_4 in T.grid(28, 192):
T_cast_11[(((ax0_ax1_fused_7*5376) + (ax2_7*192)) + ax3_4)] = T.cast(tensor_3[(((ax0_ax1_fused_7*5376) + (ax2_7*192)) + ax3_4)], "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320__2(placeholder_38: T.handle, placeholder_39: T.handle, placeholder_40: T.handle, T_cast_12: T.handl |
e) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320__2", "tir.noalias": True})
placeholder_41 = T.match_buffer(placeholder_38, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_42 = T.match_buffer(placeholder_39, [6144], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_43 = T.match_buffer(placeholder_40, [32], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_13 = T.match_buffer(T_cast_12, [89], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_3 = T.decl_buffer([150528], "int16")
for i0_i1_fused_3 in T.serial(0, 28):
for i2_3, i3_3 in T.grid(28, 192):
PaddedInput_3[(((i0_i1_fused_3*5376) + (i2_3*192)) + i3_3)] = placeholder_41[(((i0_i1_fused_3*5376) + (i2_3*192)) + i3_3)]
for ax0_ax1_fused_ax2_fused_3 in T.serial(0, 784):
Conv2dOutput_3 = T.decl_buffer([1], "int32")
for ax3_5 in T.serial(0, 32):
Conv2dOutput_3[0] = 0
for rc_3 in T.serial(0, 192):
Conv2dOutput_3[0] = (Conv2dOutput_3[0] + (T.cast(PaddedInput_3[((ax0_ax1_fused_ax2_fused_3*192) + rc_3)], "int32")*T.cast(placeholder_42[((rc_3*32) + ax3_5)], "int32")))
T_cast_13[((ax0_ax1_fused_ax2_fused_3*32) + ax3_5)] = T.cast(T.max(T.min(T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_3[0] + placeholder_43[ax3_5]), 1811141736, 31, -6, dtype="int32"), 255), 0), "uint8"), "int32"), 1136333842, 31, 0, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_2(placeholder_44: T.handle, placeholder_45: T.handle, placeholder_46: T.handle, T_cast_14: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast |
_2", "tir.noalias": True})
placeholder_47 = T.match_buffer(placeholder_44, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_48 = T.match_buffer(placeholder_45, [3072], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_49 = T.match_buffer(placeholder_46, [16], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_15 = T.match_buffer(T_cast_14, [73], dtype="int16", elem_offset=0, align=64, offset_factor=1)
PaddedInput_4 = T.decl_buffer([150528], "int16")
for i0_i1_fused_4 in T.serial(0, 28):
for i2_4, i3_4 in T.grid(28, 192):
PaddedInput_4[(((i0_i1_fused_4*5376) + (i2_4*192)) + i3_4)] = placeholder_47[(((i0_i1_fused_4*5376) + (i2_4*192)) + i3_4)]
for ax0_ax1_fused_ax2_fused_4 in T.serial(0, 784):
Conv2dOutput_4 = T.decl_buffer([1], "int32")
for ax3_6 in T.serial(0, 16):
Conv2dOutput_4[0] = 0
for rc_4 in T.serial(0, 192):
Conv2dOutput_4[0] = (Conv2dOutput_4[0] + (T.cast(PaddedInput_4[((ax0_ax1_fused_ax2_fused_4*192) + rc_4)], "int32")*T.cast(placeholder_48[((rc_4*16) + ax3_6)], "int32")))
T_cast_15[((ax0_ax1_fused_ax2_fused_4*16) + ax3_6)] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_4[0] + placeholder_49[ax3_6]), 1764006585, 31, -7, dtype="int32"), 255), 0), "uint8"), "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320__1(placeholder_50: T.handle, placeholder_51: T.handle, placeholder_52: T.handle, T_cast_16: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320__1", "tir.noalias": True})
placeholder_53 = T.match_buffer(placeholder_50, [12544], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_54 = T.m |
atch_buffer(placeholder_51, [4608], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_55 = T.match_buffer(placeholder_52, [32], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_17 = T.match_buffer(T_cast_16, [89], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_5 = T.decl_buffer([14400], "int16")
for i0_i1_fused_5 in T.serial(0, 30):
for i2_5, i3_5 in T.grid(30, 16):
PaddedInput_5[(((i0_i1_fused_5*480) + (i2_5*16)) + i3_5)] = T.if_then_else(((((1 <= i0_i1_fused_5) and (i0_i1_fused_5 < 29)) and (1 <= i2_5)) and (i2_5 < 29)), placeholder_53[((((i0_i1_fused_5*448) + (i2_5*16)) + i3_5) - 464)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_5 in T.serial(0, 784):
Conv2dOutput_5 = T.decl_buffer([1], "int32")
for ax3_7 in T.serial(0, 32):
Conv2dOutput_5[0] = 0
for ry, rx, rc_5 in T.grid(3, 3, 16):
Conv2dOutput_5[0] = (Conv2dOutput_5[0] + (T.cast(PaddedInput_5[(((((T.floordiv(ax0_ax1_fused_ax2_fused_5, 28)*480) + (ry*480)) + (rx*16)) + (T.floormod(ax0_ax1_fused_ax2_fused_5, 28)*16)) + rc_5)], "int32")*T.cast(placeholder_54[((((ry*1536) + (rx*512)) + (rc_5*32)) + ax3_7)], "int32")))
T_cast_17[((ax0_ax1_fused_ax2_fused_5*32) + ax3_7)] = T.cast(T.max(T.min(T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_5[0] + placeholder_55[ax3_7]), 1131968888, 31, -6, dtype="int32"), 255), 0), "uint8"), "int32"), 1900719667, 31, 0, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320_(placeholder_56: T.handle, placeholder_57: T.handle, placeholder_58: T.handle, T_cast_18: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320_", "tir |
.noalias": True})
placeholder_59 = T.match_buffer(placeholder_56, [75264], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_60 = T.match_buffer(placeholder_57, [110592], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_61 = T.match_buffer(placeholder_58, [128], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_19 = T.match_buffer(T_cast_18, [185], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_6 = T.decl_buffer([86400], "int16")
for i0_i1_fused_6 in T.serial(0, 30):
for i2_6, i3_6 in T.grid(30, 96):
PaddedInput_6[(((i0_i1_fused_6*2880) + (i2_6*96)) + i3_6)] = T.if_then_else(((((1 <= i0_i1_fused_6) and (i0_i1_fused_6 < 29)) and (1 <= i2_6)) and (i2_6 < 29)), placeholder_59[((((i0_i1_fused_6*2688) + (i2_6*96)) + i3_6) - 2784)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_6 in T.serial(0, 784):
Conv2dOutput_6 = T.decl_buffer([64], "int32")
for ax3_outer_3 in T.serial(0, 2):
for ff_2 in T.serial(0, 64):
Conv2dOutput_6[ff_2] = 0
for ry_1, rx_1, rc_6 in T.grid(3, 3, 96):
Conv2dOutput_6[ff_2] = (Conv2dOutput_6[ff_2] + (T.cast(PaddedInput_6[(((((T.floordiv(ax0_ax1_fused_ax2_fused_6, 28)*2880) + (ry_1*2880)) + (rx_1*96)) + (T.floormod(ax0_ax1_fused_ax2_fused_6, 28)*96)) + rc_6)], "int32")*T.cast(placeholder_60[(((((ry_1*36864) + (rx_1*12288)) + (rc_6*128)) + (ax3_outer_3*64)) + ff_2)], "int32")))
for ax3_inner_6 in T.serial(0, 64):
T_cast_19[(((ax0_ax1_fused_ax2_fused_6*128) + (ax3_outer_3*64)) + ax3_inner_6)] = T.cast(T.max(T.min(T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_6[ax3_inner_6] + placeholder_61[((ax3_outer_3*64) + ax3_inner_6)]), 1374050734, 31, -7, dtype="int32"), 255), 0), "uint8"), "int32"), 1544713713, 31, 0, dtype="int32"), 255), 0), "uint8")
@T.prim_fun |
c
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast(placeholder_62: T.handle, placeholder_63: T.handle, placeholder_64: T.handle, T_cast_20: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", "T.noalias": True})
placeholder_65 = T.match_buffer(placeholder_62, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_66 = T.match_buffer(placeholder_63, [9408], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_67 = T.match_buffer(placeholder_64, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_21 = T.match_buffer(T_cast_20, [289], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_7 = T.decl_buffer([157323], "int16")
for i0_i1_fused_7 in T.serial(0, 229):
for i2_7, i3_7 in T.grid(229, 3):
PaddedInput_7[(((i0_i1_fused_7*687) + (i2_7*3)) + i3_7)] = T.if_then_else(((((2 <= i0_i1_fused_7) and (i0_i1_fused_7 < 226)) and (2 <= i2_7)) and (i2_7 < 226)), placeholder_65[((((i0_i1_fused_7*672) + (i2_7*3)) + i3_7) - 1350)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_7 in T.serial(0, 12544):
Conv2dOutput_7 = T.decl_buffer([64], "int32")
for ff_3 in T.serial(0, 64):
Conv2dOutput_7[ff_3] = 0
for ry_2, rx_2, rc_7 in T.grid(7, 7, 3):
Conv2dOutput_7[ff_3] = (Conv2dOutput_7[ff_3] + (T.cast(PaddedInput_7[(((((T.floordiv(ax0_ax1_fused_ax2_fused_7, 112)*1374) + (ry_2*687)) + (T.floormod(ax0_ax1_fused_ax2_fused_7, 112)*6)) + (rx_2*3)) + rc_7)], "int32")*T.cast(placeholder_66[((((ry_2*1344) + (rx_2*192)) + (rc_7*64)) + ff_3)], "int32")))
for ax3_inner_7 in T.serial(0, 64):
T_cast_21[((ax0_ax1_fused_ax2_fused_7*64) + ax3_inner_7)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_7[ax3_inner_7] + placeholder_67[ax3_inner_7]), 1939887962, 31, -9, |
dtype="int32"), 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1(placeholder_68: T.handle, placeholder_69: T.handle, placeholder_70: T.handle, T_cast_22: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_1", "tir.noalias": True})
placeholder_71 = T.match_buffer(placeholder_68, [200704], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_72 = T.match_buffer(placeholder_69, [110592], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_73 = T.match_buffer(placeholder_70, [192], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_23 = T.match_buffer(T_cast_22, [305], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_8 = T.decl_buffer([215296], "int16")
for i0_i1_fused_8 in T.serial(0, 58):
for i2_8, i3_8 in T.grid(58, 64):
PaddedInput_8[(((i0_i1_fused_8*3712) + (i2_8*64)) + i3_8)] = T.if_then_else(((((1 <= i0_i1_fused_8) and (i0_i1_fused_8 < 57)) and (1 <= i2_8)) and (i2_8 < 57)), placeholder_71[((((i0_i1_fused_8*3584) + (i2_8*64)) + i3_8) - 3648)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_8 in T.serial(0, 3136):
Conv2dOutput_8 = T.decl_buffer([64], "int32")
for ax3_outer_4 in T.serial(0, 3):
for ff_4 in T.serial(0, 64):
Conv2dOutput_8[ff_4] = 0
for ry_3, rx_3, rc_8 in T.grid(3, 3, 64):
Conv2dOutput_8[ff_4] = (Conv2dOutput_8[ff_4] + (T.cast(PaddedInput_8[(((((T.floordiv(ax0_ax1_fused_ax2_fused_8, 56)*3712) + (ry_3*3712)) + (rx_3*64)) + (T.floormod(ax0_ax1_fused_ax2_fused_8, 56)*64)) + rc_8)], "int32")*T.cast(placeholder_72[(((((ry_3*36864) + (rx_3*12288)) + (rc_8*192)) + (ax3_outer_4*64)) + ff_4)], "int32")))
for ax3_inner_8 in T.serial(0, 64):
T_cast_23[(((ax0_ax1_ |
fused_ax2_fused_8*192) + (ax3_outer_4*64)) + ax3_inner_8)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_8[ax3_inner_8] + placeholder_73[((ax3_outer_4*64) + ax3_inner_8)]), 1139793473, 31, -6, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def run_model(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_run_model", "runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_32 = T.allocate([301056], "int8", "global")
sid_20 = T.allocate([150528], "int8", "global")
sid_6 = T.allocate([401408], "int8", "global")
sid_9 = T.allocate([301056], "int8", "global")
sid_7 = T.allocate([401408], "int8", "global")
sid_8 = T.allocate([802816], "int8", "global")
sid_2 = T.allocate([50176], "int8", "global")
sid_3 = T.allocate([301056], "int8", "global")
sid_19 = T.allocate([100352], "int8", "global")
sid_4 = T.allocate([150528], "int8", "global")
sid_5 = T.allocate([602112], "int8", "global")
sid_25 = T.allocate([25088], "int8", "global")
sid_26 = T.allocate([25088], "int8", "global")
sid_31 = T.allocate([25088], "int8", "global")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input, T.lookup_param("p0", dtype="handle"), sid_9, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", sid_9, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_8, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_max_pool2d_cast", sid_8, sid_7, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast", sid_7, T.lookup_param("p3", dtype="handle"), T.lookup_param("p4", dtype="handle"), sid_6, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cas |
t_1", sid_6, T.lookup_param("p5", dtype="handle"), T.lookup_param("p6", dtype="handle"), sid_5, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_max_pool2d", sid_5, sid_4, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_cast", sid_4, sid_3, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_2", sid_3, T.lookup_param("p7", dtype="handle"), T.lookup_param("p8", dtype="handle"), sid_2, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1", sid_3, T.lookup_param("p9", dtype="handle"), T.lookup_param("p10", dtype="handle"), sid_20, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320_", sid_20, T.lookup_param("p11", dtype="handle"), T.lookup_param("p12", dtype="handle"), sid_19, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_2", sid_3, T.lookup_param("p13", dtype="handle"), T.lookup_param("p14", dtype="handle"), sid_26, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320__1", sid_26, T.lookup_param("p15", dtype="handle"), T.lookup_param("p16", dtype="handle"), sid_25, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_max_pool2d_cast_1", sid_4, sid_32, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_fixed_point_multiply_cli_4464294615199028320__2", sid_32, T.lookup_param("p17", dtype="handle"), T.lookup_param("p18", dtype="handle"), sid_31, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_concatenate", sid_2, sid_19, sid_25, sid_31, output, dtype="int32"))
__tvm_meta__ = None
def test_inception_structure():
target = |
Target("c")
global_ws_pool = WorkspacePoolInfo(
pool_name="global_workspace",
targets=[target],
)
tir_mod = InceptionStructure
tir_mod = _assign_targets_to_primfuncs_irmodule(tir_mod, target)
tir_mod = _assign_poolinfos_to_allocates_in_irmodule(tir_mod, [global_ws_pool])
main_func = tir_mod["run_model"]
buffer_info_analysis = tvm.tir.usmp.analysis.extract_buffer_info(main_func, tir_mod)
assert buffer_info_analysis.memory_pressure == 1117718
buffer_info_map = _replace_stmt_with_buf_var_names(buffer_info_analysis.buffer_info_stmts)
_verify_conflicts(
"sid_3",
[
"sid_4",
"PaddedInput_2",
"sid_2",
"Conv2dOutput_2",
"PaddedInput_1",
"Conv2dOutput_1",
"sid_20",
"PaddedInput_6",
"Conv2dOutput_6",
"sid_19",
"PaddedInput_4",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput",
[
"sid_6",
"PaddedInput",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_7",
[
"PaddedInput_7",
"sid_8",
],
buffer_info_map,
)
_verify_conflicts(
"sid_4",
[
"sid_5",
"sid_3",
"PaddedInput_2",
"sid_2",
"Conv2dOutput_2",
"PaddedInput_1",
"Conv2dOutput_1",
"sid_20",
"PaddedInput_6",
"Conv2dOutput_6",
"sid_19",
"PaddedInput_4",
"Conv2dOutput_4",
"sid_26",
"PaddedInput_5",
"Conv2dOutput_5",
"sid_25",
"tensor_3",
],
buffer_info_map,
)
_verify_conflicts(
"sid_2",
[
"PaddedInput_2",
"sid_3",
"sid_4",
"Conv2dOutput_2",
"PaddedInput_1",
"Conv2dOutput_1", |
"sid_20",
"PaddedInput_6",
"Conv2dOutput_6",
"sid_19",
"PaddedInput_4",
"Conv2dOutput_4",
"sid_26",
"PaddedInput_5",
"Conv2dOutput_5",
"sid_25",
"tensor_3",
"sid_32",
"PaddedInput_3",
"Conv2dOutput_3",
"sid_31",
],
buffer_info_map,
)
_verify_conflicts(
"sid_19",
[
"Conv2dOutput_6",
"sid_2",
"PaddedInput_6",
"sid_3",
"sid_4",
"PaddedInput_4",
"Conv2dOutput_4",
"sid_26",
"PaddedInput_5",
"Conv2dOutput_5",
"sid_25",
"tensor_3",
"sid_32",
"PaddedInput_3",
"Conv2dOutput_3",
"sid_31",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_2",
[
"sid_3",
"sid_4",
"sid_2",
"Conv2dOutput_2",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_6",
[
"sid_2",
"PaddedInput_6",
"sid_3",
"sid_4",
"sid_19",
],
buffer_info_map,
)
_verify_conflicts(
"sid_9",
[
"PaddedInput_7",
],
buffer_info_map,
)
_verify_conflicts(
"sid_7",
[
"tensor_2",
"PaddedInput",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_4",
[
"sid_2",
"sid_19",
"sid_3",
"sid_4",
"Conv2dOutput_4",
"sid_26",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_3",
[
"sid_2",
"sid_32",
"sid_25",
"sid_19",
"Conv2dOutput_3",
"sid_31", |
],
buffer_info_map,
)
_verify_conflicts(
"sid_5",
[
"PaddedInput_8",
"Conv2dOutput_8",
"sid_4",
],
buffer_info_map,
)
_verify_conflicts(
"sid_31",
[
"Conv2dOutput_3",
"PaddedInput_3",
"sid_2",
"sid_25",
"sid_19",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput",
[
"sid_7",
"sid_6",
"Conv2dOutput",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_2",
[
"sid_2",
"PaddedInput_2",
"sid_3",
"sid_4",
],
buffer_info_map,
)
_verify_conflicts(
"sid_32",
[
"tensor_3",
"sid_2",
"sid_25",
"sid_19",
"PaddedInput_3",
],
buffer_info_map,
)
_verify_conflicts(
"tensor_2",
[
"sid_8",
"sid_7",
],
buffer_info_map,
)
_verify_conflicts(
"sid_26",
[
"Conv2dOutput_4",
"PaddedInput_4",
"sid_2",
"sid_19",
"sid_4",
"PaddedInput_5",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_3",
[
"PaddedInput_3",
"sid_2",
"sid_25",
"sid_19",
"sid_31",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_6",
[
"sid_2",
"sid_3",
"sid_20",
"sid_4",
"Conv2dOutput_6",
"sid_19",
],
buffer_info_map,
)
_verify_conflicts(
"sid_6",
[
"PaddedInput",
"Conv2dOutput",
"PaddedInput_8",
],
buffer_info_map,
)
_verify_conflicts(
"Padde |
dInput_8",
[
"sid_6",
"sid_5",
"Conv2dOutput_8",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_5",
[
"PaddedInput_5",
"sid_2",
"sid_19",
"sid_4",
"sid_25",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_1",
[
"PaddedInput_1",
"sid_2",
"sid_3",
"sid_4",
"sid_20",
],
buffer_info_map,
)
_verify_conflicts(
"tensor_3",
[
"sid_2",
"sid_25",
"sid_19",
"sid_4",
"sid_32",
],
buffer_info_map,
)
_verify_conflicts(
"sid_8",
[
"Conv2dOutput_7",
"PaddedInput_7",
"tensor_2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_20",
[
"Conv2dOutput_1",
"PaddedInput_1",
"sid_2",
"sid_3",
"sid_4",
"PaddedInput_6",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_8",
[
"sid_5",
"PaddedInput_8",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_1",
[
"sid_2",
"sid_3",
"sid_4",
"Conv2dOutput_1",
"sid_20",
],
buffer_info_map,
)
_verify_conflicts(
"Conv2dOutput_4",
[
"PaddedInput_4",
"sid_2",
"sid_19",
"sid_4",
"sid_26",
],
buffer_info_map,
)
_verify_conflicts(
"sid_25",
[
"PaddedInput_5",
"Conv2dOutput_5",
"sid_2",
"sid_19",
"sid_4",
"tensor_3",
"sid_32",
"PaddedInput_3",
"Conv2dOutput_3", |
"sid_31",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_7",
[
"sid_9",
"Conv2dOutput_7",
"sid_8",
],
buffer_info_map,
)
_verify_conflicts(
"PaddedInput_5",
[
"sid_2",
"sid_19",
"sid_26",
"sid_4",
"Conv2dOutput_5",
"sid_25",
],
buffer_info_map,
)
assert buffer_info_map["sid_20"].size_bytes == 150528
assert buffer_info_map["tensor_2"].size_bytes == 200704
assert buffer_info_map["sid_5"].size_bytes == 602112
assert buffer_info_map["sid_9"].size_bytes == 301056
assert buffer_info_map["Conv2dOutput_3"].size_bytes == 4
assert buffer_info_map["sid_26"].size_bytes == 25088
assert buffer_info_map["Conv2dOutput_2"].size_bytes == 256
assert buffer_info_map["PaddedInput_5"].size_bytes == 28800
assert buffer_info_map["sid_8"].size_bytes == 802816
assert buffer_info_map["Conv2dOutput_5"].size_bytes == 4
assert buffer_info_map["sid_3"].size_bytes == 301056
assert buffer_info_map["Conv2dOutput"].size_bytes == 256
assert buffer_info_map["PaddedInput_3"].size_bytes == 301056
assert buffer_info_map["sid_32"].size_bytes == 301056
assert buffer_info_map["PaddedInput_8"].size_bytes == 430592
assert buffer_info_map["sid_4"].size_bytes == 150528
assert buffer_info_map["PaddedInput_7"].size_bytes == 314646
assert buffer_info_map["sid_6"].size_bytes == 401408
assert buffer_info_map["Conv2dOutput_8"].size_bytes == 256
assert buffer_info_map["sid_25"].size_bytes == 25088
assert buffer_info_map["PaddedInput"].size_bytes == 401408
assert buffer_info_map["sid_7"].size_bytes == 401408
assert buffer_info_map["Conv2dOutput_1"].size_bytes == 4
assert buffer_info_map["Conv2dOutput_4"].size_bytes == 4
assert buffer_info_map["PaddedInput_2"].size_bytes == 301056
assert buffer_info_map["sid_31"].size_bytes == 25088 |
assert buffer_info_map["PaddedInput_1"].size_bytes == 301056
assert buffer_info_map["Conv2dOutput_6"].size_bytes == 256
assert buffer_info_map["PaddedInput_4"].size_bytes == 301056
assert buffer_info_map["sid_2"].size_bytes == 50176
assert buffer_info_map["tensor_3"].size_bytes == 150528
assert buffer_info_map["Conv2dOutput_7"].size_bytes == 256
assert buffer_info_map["sid_19"].size_bytes == 100352
assert buffer_info_map["PaddedInput_6"].size_bytes == 172800
@tvm.script.ir_module
class MultipleCallsToSamePrimFuncModule:
@T.prim_func
def tvmgen_default_fused_layout_transform_1(placeholder: T.handle, T_layout_trans: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_layout_transform_1", "tir.noalias": True})
placeholder_1 = T.match_buffer(placeholder, [864], dtype="float32")
T_layout_trans_1 = T.match_buffer(T_layout_trans, [41], dtype="float32")
for ax0_ax1_fused_ax2_fused, ax3, ax4_inner in T.grid(24, 12, 3):
T_layout_trans_1[ax0_ax1_fused_ax2_fused * 36 + ax3 * 3 + ax4_inner] = placeholder_1[ax4_inner * 288 + ax0_ax1_fused_ax2_fused * 12 + ax3]
@T.prim_func
def tvmgen_default_fused_nn_contrib_conv2d_NCHWc(placeholder_2: T.handle, placeholder_3: T.handle, conv2d_NCHWc: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_nn_contrib_conv2d_NCHWc", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [864], dtype="float32")
placeholder_5 = T.match_buffer(placeholder_3, [81], dtype="float32")
conv2d_NCHWc_1 = T.match_buffer(conv2d_NCHWc, [41], dtype="float32")
data_pad = T.decl_buffer([1092], "float32")
for i0_i1_fused_i2_fused, i3, i4 in T.grid(26, 14, 3):
data_pad[i0_i1_fused_i2_fused * 42 + i3 * 3 + i4] = T.if_then_else(1 <= i0_i1_fused_i2_fused and i0_i1_fused_i2_fused < 25 and 1 <= i3 and i3 < 13, placeholder |
_4[i0_i1_fused_i2_fused * 36 + i3 * 3 + i4 - 39], T.float32(0), dtype="float32")
for n_oc_chunk_fused_oh_fused in T.serial(0, 24):
conv2d_NCHWc_global = T.decl_buffer([36], "float32")
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 3] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 6] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 9] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 12] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 15] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 18] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 21] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 24] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 27] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 30] = T.float32(0)
for oc_block_c_init in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c_init + 33] = T.float32(0)
for kh, kw, ic_inner in T.grid(3, 3, 3):
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c] = conv2d_NCHWc_global[oc_block_c] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.seri |
al(0, 3):
conv2d_NCHWc_global[oc_block_c + 3] = conv2d_NCHWc_global[oc_block_c + 3] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 3] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 6] = conv2d_NCHWc_global[oc_block_c + 6] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 6] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 9] = conv2d_NCHWc_global[oc_block_c + 9] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 9] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 12] = conv2d_NCHWc_global[oc_block_c + 12] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 12] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 15] = conv2d_NCHWc_global[oc_block_c + 15] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 15] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 18] = conv2d_NCHWc_global[oc_block_c + 18] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 18] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 21] = conv2d_NCHWc_global[oc_block_c + 21] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 21] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_bl |
ock_c + 24] = conv2d_NCHWc_global[oc_block_c + 24] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 24] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 27] = conv2d_NCHWc_global[oc_block_c + 27] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 27] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 30] = conv2d_NCHWc_global[oc_block_c + 30] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 30] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for oc_block_c in T.serial(0, 3):
conv2d_NCHWc_global[oc_block_c + 33] = conv2d_NCHWc_global[oc_block_c + 33] + data_pad[kh * 42 + n_oc_chunk_fused_oh_fused * 42 + kw * 3 + ic_inner + 33] * placeholder_5[kh * 27 + kw * 9 + ic_inner * 3 + oc_block_c]
for ow_inner, oc_block in T.grid(12, 3):
conv2d_NCHWc_1[n_oc_chunk_fused_oh_fused * 36 + ow_inner * 3 + oc_block] = conv2d_NCHWc_global[ow_inner * 3 + oc_block]
@T.prim_func
def tvmgen_default_fused_nn_softmax_add_add_multiply_add(placeholder_6: T.handle, placeholder_7: T.handle, placeholder_8: T.handle, placeholder_9: T.handle, placeholder_10: T.handle, T_add: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_nn_softmax_add_add_multiply_add", "tir.noalias": True})
placeholder_11 = T.match_buffer(placeholder_6, [864], dtype="float32")
placeholder_12 = T.match_buffer(placeholder_7, [864], dtype="float32")
placeholder_13 = T.match_buffer(placeholder_8, [3], dtype="float32")
placeholder_14 = T.match_buffer(placeholder_9, [3], dtype="float32")
placeholder_15 = T.match_buffer(placeholder_10, [3], dtype="float32")
T_add_1 = T.match_buffe |
r(T_add, [864], dtype="float32")
for ax0_ax1_fused_ax2_fused in T.serial(0, 72):
T_softmax_norm = T.decl_buffer([12], "float32")
with T.decl_buffer([1], "float32") as T_softmax_maxelem:
T_softmax_maxelem[0] = T.float32(-3.4028234663852886e+38)
for k in T.serial(0, 12):
T_softmax_maxelem[0] = T.max(T_softmax_maxelem[0], placeholder_11[ax0_ax1_fused_ax2_fused * 12 + k])
T_softmax_exp = T.decl_buffer([12], "float32")
for i3 in T.serial(0, 12):
T_softmax_exp[i3] = T.exp(placeholder_11[ax0_ax1_fused_ax2_fused * 12 + i3] - T_softmax_maxelem[0], dtype="float32")
T_softmax_expsum = T.decl_buffer([1], "float32")
T_softmax_expsum[0] = T.float32(0)
for k in T.serial(0, 12):
T_softmax_expsum[0] = T_softmax_expsum[0] + T_softmax_exp[k]
for i3 in T.serial(0, 12):
T_softmax_norm[i3] = T_softmax_exp[i3] / T_softmax_expsum[0]
for ax3 in T.serial(0, 12):
T_add_1[ax0_ax1_fused_ax2_fused * 12 + ax3] = (placeholder_12[ax0_ax1_fused_ax2_fused * 12 + ax3] + T_softmax_norm[ax3] + placeholder_13[T.floordiv(ax0_ax1_fused_ax2_fused, 24)]) * placeholder_14[T.floordiv(ax0_ax1_fused_ax2_fused, 24)] + placeholder_15[T.floordiv(ax0_ax1_fused_ax2_fused, 24)]
@T.prim_func
def tvmgen_default_fused_nn_contrib_dense_pack_nn_relu(placeholder_16: T.handle, placeholder_17: T.handle, T_relu: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_nn_contrib_dense_pack_nn_relu", "tir.noalias": True})
placeholder_18 = T.match_buffer(placeholder_16, [864], dtype="float32")
placeholder_19 = T.match_buffer(placeholder_17, [144], dtype="float32")
T_relu_1 = T.match_buffer(T_relu, [864], dtype="float32")
for ax1_outer_ax0_outer_fused in T.serial(0, 18):
compute = |
T.decl_buffer([48], "float32")
with T.decl_buffer([48], "float32") as compute_global:
for x_c_init in T.serial(0, 6):
compute_global[x_c_init] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 6] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 12] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 18] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 24] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 30] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 36] = T.float32(0)
for x_c_init in T.serial(0, 6):
compute_global[x_c_init + 42] = T.float32(0)
for k_outer in T.serial(0, 12):
for x_c in T.serial(0, 6):
compute_global[x_c] = compute_global[x_c] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 6] = compute_global[x_c + 6] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer + 12] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 12] = compute_global[x_c + 12] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer + 24] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 18] = compute_global[x_c + 18] + placeholder_18[T.floormod(ax1_outer_ax |
0_outer_fused, 9) * 96 + k_outer + 36] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 24] = compute_global[x_c + 24] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer + 48] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 30] = compute_global[x_c + 30] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer + 60] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 36] = compute_global[x_c + 36] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer + 72] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_c in T.serial(0, 6):
compute_global[x_c + 42] = compute_global[x_c + 42] + placeholder_18[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + k_outer + 84] * placeholder_19[T.floordiv(ax1_outer_ax0_outer_fused, 9) * 72 + k_outer * 6 + x_c]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner] = compute_global[x_inner_inner]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 6] = compute_global[x_inner_inner + 6]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 12] = compute_global[x_inner_inner + 12]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 18] = compute_global[x_inner_inner + 18]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 24] = compute_global[x_inner_inner + 24]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 30] = |
compute_global[x_inner_inner + 30]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 36] = compute_global[x_inner_inner + 36]
for x_inner_inner in T.serial(0, 6):
compute[x_inner_inner + 42] = compute_global[x_inner_inner + 42]
for ax0_inner_inner, ax1_inner_inner in T.grid(8, 6):
T_relu_1[T.floormod(ax1_outer_ax0_outer_fused, 9) * 96 + ax0_inner_inner * 12 + T.floordiv(ax1_outer_ax0_outer_fused, 9) * 6 + ax1_inner_inner] = T.max(compute[ax0_inner_inner * 6 + ax1_inner_inner], T.float32(0))
@T.prim_func
def tvmgen_default_fused_reshape_1(placeholder_20: T.handle, T_reshape: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_reshape_1", "tir.noalias": True})
placeholder_21 = T.match_buffer(placeholder_20, [864], dtype="float32")
T_reshape_1 = T.match_buffer(T_reshape, [864], dtype="float32")
for ax0, ax1_inner in T.grid(72, 12):
T_reshape_1[ax0 * 12 + ax1_inner] = placeholder_21[ax0 * 12 + ax1_inner]
@T.prim_func
def tvmgen_default_fused_layout_transform(placeholder_22: T.handle, T_layout_trans_2: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_layout_transform", "tir.noalias": True})
placeholder_23 = T.match_buffer(placeholder_22, [864], dtype="float32")
T_layout_trans_3 = T.match_buffer(T_layout_trans_2, [864], dtype="float32")
for ax0_ax1_fused, ax2, ax3_inner in T.grid(3, 24, 12):
T_layout_trans_3[ax0_ax1_fused * 288 + ax2 * 12 + ax3_inner] = placeholder_23[ax2 * 36 + ax3_inner * 3 + ax0_ax1_fused]
@T.prim_func
def tvmgen_default_fused_reshape(placeholder_24: T.handle, T_reshape_2: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_reshape", "tir.noalias": True})
placeh |
older_25 = T.match_buffer(placeholder_24, [864], dtype="float32")
T_reshape_3 = T.match_buffer(T_reshape_2, [864], dtype="float32")
for ax0_ax1_fused, ax2, ax3_inner in T.grid(3, 24, 12):
T_reshape_3[ax0_ax1_fused * 288 + ax2 * 12 + ax3_inner] = placeholder_25[ax0_ax1_fused * 288 + ax2 * 12 + ax3_inner]
@T.prim_func
def tvmgen_default_fused_nn_softmax_add(placeholder_26: T.handle, placeholder_27: T.handle, T_add_2: T.handle) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "tvmgen_default_fused_nn_softmax_add", "tir.noalias": True})
placeholder_28 = T.match_buffer(placeholder_26, [864], dtype="float32")
placeholder_29 = T.match_buffer(placeholder_27, [864], dtype="float32")
T_add_3 = T.match_buffer(T_add_2, [864], dtype="float32")
for ax0_ax1_fused_ax2_fused in T.serial(0, 72):
T_softmax_norm = T.decl_buffer([12], "float32")
with T.decl_buffer([1], "float32") as T_softmax_maxelem:
T_softmax_maxelem[0] = T.float32(-3.4028234663852886e+38)
for k in T.serial(0, 12):
T_softmax_maxelem[0] = T.max(T_softmax_maxelem[0], placeholder_28[ax0_ax1_fused_ax2_fused * 12 + k])
T_softmax_exp= T.decl_buffer([12], "float32")
for i3 in T.serial(0, 12):
T_softmax_exp[i3] = T.exp(placeholder_28[ax0_ax1_fused_ax2_fused * 12 + i3] - T_softmax_maxelem[0], dtype="float32")
T_softmax_expsum = T.decl_buffer([1], "float32")
T_softmax_expsum[0] = T.float32(0)
for k in T.serial(0, 12):
T_softmax_expsum[0] = T_softmax_expsum[0] + T_softmax_exp[k]
for i3 in T.serial(0, 12):
T_softmax_norm[i3] = T_softmax_exp[i3] / T_softmax_expsum[0]
for ax3 in T.serial(0, 12):
T_add_3[ax0_ax1_fused_ax2_fused * 12 + ax3] = placeholder_29[ax0_ax1_fused_ax2_fused * 12 + ax3] + T_soft |
max_norm[ax3]
@T.prim_func
def run_model(data: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_run_model", "runner_function": True})
data_buffer = T.match_buffer(data, [864], dtype="float32", align=16)
output_buffer = T.match_buffer(output, [864], dtype="float32", align=16)
sid_11 = T.allocate([3456], "int8", "global.workspace")
sid_5 = T.allocate([3456], "int8", "global.workspace")
sid_10 = T.allocate([3456], "int8", "global.workspace")
sid_6 = T.allocate([3456], "int8", "global.workspace")
sid_8 = T.allocate([3456], "int8", "global.workspace")
sid_2 = T.allocate([3456], "int8", "global.workspace")
sid_7 = T.allocate([3456], "int8", "global.workspace")
sid_3 = T.allocate([3456], "int8", "global.workspace")
sid_12 = T.allocate([3456], "int8", "global.workspace")
sid_4 = T.allocate([3456], "int8", "global.workspace")
sid_18 = T.allocate([3456], "int8", "global.workspace")
sid_19 = T.allocate([3456], "int8", "global.workspace")
sid_20 = T.allocate([3456], "int8", "global.workspace")
sid_21 = T.allocate_const([0,1,2,3,4,5,6,7,8,9], "int8", [10])
sid_22 = T.allocate_const([1], "int8", [1])
sid_23 = T.allocate_const([2,1], "int8", [3456])
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_layout_transform_1", data_buffer.data, sid_23, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_nn_contrib_conv2d_NCHWc", sid_8, T.cast(T.lookup_param("p0", dtype="handle"), "handle"), sid_7, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_layout_transform", sid_7, sid_6, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_reshape_1", data_buffer.data, sid_12, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_nn_contrib_dense_pack_nn_relu", sid_12, T.cast(T.lookup_param("p1", dtype="handle"), "handle"), sid_11 |
, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_reshape", sid_11, sid_10, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_nn_softmax_add_add_multiply_add", sid_6, sid_10, T.cast(T.lookup_param("p2", dtype="handle"), "handle"), T.cast(T.lookup_param("p3", dtype="handle"), "handle"), T.cast(T.lookup_param("p4", dtype="handle"), "handle"), sid_5, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_layout_transform_1", sid_5, sid_4, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_nn_contrib_conv2d_NCHWc", sid_4, T.cast(T.lookup_param("p5", dtype="handle"), "handle"), sid_3, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_layout_transform", sid_3, sid_2, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_reshape_1", sid_5, sid_20, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_nn_contrib_dense_pack_nn_relu", sid_20, T.cast(T.lookup_param("p6", dtype="handle"), "handle"), sid_19, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_reshape", sid_19, sid_18, dtype="int32"))
T.evaluate(T.tvm_call_cpacked("tvmgen_default_fused_nn_softmax_add", sid_2, sid_18, output_buffer.data, dtype="int32"))
def test_multiple_calls_to_same_primfunc():
target = Target("c")
global_ws_pool = WorkspacePoolInfo(
pool_name="global_workspace",
targets=[target],
)
global_const_pool = ConstantPoolInfo(
pool_name="global_constants",
targets=[target],
)
tir_mod = MultipleCallsToSamePrimFuncModule
tir_mod = _assign_targets_to_primfuncs_irmodule(tir_mod, target)
tir_mod = _assign_poolinfos_to_allocates_in_irmodule(
tir_mod, [global_ws_pool], [global_const_pool]
)
main_func = tir_mod["run_model"]
buffer_info_analysis = tvm.tir.usmp.analysis.extract_buffer_info(main_func, tir_mod)
assert buffer_info_analysis.memory_pressure == 11424
buffe |
r_info_map = _replace_stmt_with_buf_var_names(buffer_info_analysis.buffer_info_stmts)
_verify_conflicts("sid_23", ["sid_22", "sid_21"], buffer_info_map)
_verify_conflicts(
"sid_6",
[
"sid_7",
"sid_12",
"compute",
"compute_global",
"sid_11",
"sid_10",
"T_softmax_exp",
"T_softmax_maxelem",
"sid_5",
"T_softmax_norm",
"T_softmax_expsum",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_exp",
[
"sid_10",
"sid_6",
"T_softmax_maxelem",
"sid_5",
"T_softmax_norm",
"T_softmax_expsum",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_expsum2",
[
"T_softmax_exp2",
"T_softmax_norm2",
"sid_18",
"T_softmax_maxelem2",
"sid_2",
],
buffer_info_map,
)
_verify_conflicts(
"compute",
[
"sid_12",
"sid_6",
"compute_global",
"sid_11",
"sid_19",
"sid_20",
"sid_2",
"compute_global",
],
buffer_info_map,
)
_verify_conflicts(
"compute_global",
[
"compute",
"sid_12",
"sid_6",
"sid_11",
"compute",
"sid_19",
"sid_20",
"sid_2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_10",
[
"sid_11",
"sid_6",
"T_softmax_exp",
"T_softmax_maxelem",
"sid_5",
"T_softmax_norm",
"T_softmax_expsum",
],
buffer_info_map,
)
_verify_conflicts(
"sid_2",
[
"sid_3",
"sid_5",
"sid_20",
"sid_19",
"compute", |
"compute_global",
"sid_18",
"T_softmax_norm2",
"T_softmax_exp2",
"T_softmax_maxelem2",
"T_softmax_expsum2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_5",
[
"T_softmax_maxelem",
"sid_10",
"T_softmax_exp",
"sid_6",
"T_softmax_norm",
"T_softmax_expsum",
"sid_4",
"data_pad",
"sid_3",
"conv2d_NCHWc_global",
"sid_2",
"sid_20",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_norm2",
[
"sid_18",
"sid_2",
"T_softmax_exp2",
"T_softmax_maxelem2",
"T_softmax_expsum2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_20",
[
"sid_2",
"sid_5",
"sid_19",
"compute",
"compute_global",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_expsum",
[
"sid_5",
"T_softmax_norm",
"T_softmax_maxelem",
"sid_10",
"T_softmax_exp",
"sid_6",
],
buffer_info_map,
)
_verify_conflicts(
"data_pad",
[
"sid_8",
"conv2d_NCHWc_global",
"sid_7",
"sid_4",
"sid_5",
"sid_3",
"conv2d_NCHWc_global",
],
buffer_info_map,
)
_verify_conflicts(
"sid_19",
[
"sid_20",
"sid_2",
"compute",
"compute_global",
"sid_18",
],
buffer_info_map,
)
_verify_conflicts(
"conv2d_NCHWc_global",
[
"data_pad",
"sid_7",
"sid_3",
"data_pad",
"sid_5",
],
buffer_info_map,
)
_verify_conflict |
s(
"sid_18",
[
"sid_19",
"sid_2",
"T_softmax_norm2",
"T_softmax_exp2",
"T_softmax_maxelem2",
"T_softmax_expsum2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_7",
[
"conv2d_NCHWc_global",
"data_pad",
"sid_6",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_exp2",
[
"T_softmax_norm2",
"sid_18",
"sid_2",
"T_softmax_maxelem2",
"T_softmax_expsum2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_4",
[
"sid_5",
"data_pad",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_maxelem",
[
"sid_10",
"T_softmax_exp",
"sid_6",
"sid_5",
"T_softmax_norm",
"T_softmax_expsum",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_maxelem2",
[
"T_softmax_exp2",
"T_softmax_norm2",
"sid_18",
"sid_2",
"T_softmax_expsum2",
],
buffer_info_map,
)
_verify_conflicts(
"sid_11",
[
"compute",
"sid_12",
"compute_global",
"sid_6",
"sid_10",
],
buffer_info_map,
)
_verify_conflicts(
"sid_12",
[
"sid_6",
"compute",
"compute_global",
"sid_11",
],
buffer_info_map,
)
_verify_conflicts(
"T_softmax_norm",
[
"sid_5",
"T_softmax_maxelem",
"sid_10",
"T_softmax_exp",
"sid_6",
"T_softmax_expsum",
],
buffer_info_map,
)
_verify_conflicts(
"sid_8",
[
"data_pad",
], |
buffer_info_map,
)
if __name__ == "__main__":
pytest.main([__file__] + sys.argv[1:]) |
import pytest |
import sys |
import tvm
from tvm.script |
import tir as T
from tvm.tir |
import stmt_functor
from tvm.tir.usmp |
import utils as usmp_utils
from tvm.target |
import Target
from tvm |
import WorkspacePoolInfo, PoolInfoProperties
def _get_primfuncs_from_module(module):
primfuncs = list()
for gv, primfunc in module.functions.items():
primfuncs.append(primfunc)
return primfuncs
def assign_poolinfos_to_allocates_in_primfunc(primfunc, pool_infos):
"""Helper to assign poolinfos to allocate nodes in a tir.PrimFunc"""
def set_poolinfos(stmt):
if isinstance(stmt, tvm.tir.Allocate):
return tvm.tir.Allocate(
buffer_var=stmt.buffer_var,
dtype=stmt.dtype,
extents=stmt.extents,
condition=stmt.condition,
body=stmt.body,
annotations={tvm.tir.usmp.utils.CANDIDATE_MEMORY_POOL_ATTR: pool_infos},
)
return primfunc.with_body(stmt_functor.ir_transform(primfunc.body, None, set_poolinfos))
def assign_poolinfos_to_allocates_in_irmodule(mod, pool_infos):
"""Helper to assign poolinfos to allocate nodes in a IRModule"""
ret = tvm.IRModule()
for global_var, basefunc in mod.functions.items():
if isinstance(basefunc, tvm.tir.PrimFunc):
ret[global_var] = assign_poolinfos_to_allocates_in_primfunc(basefunc, pool_infos)
return ret
def _assign_targets_to_primfuncs_irmodule(mod, target):
"""Helper to assign target for PrimFunc in a IRModule"""
ret = tvm.IRModule()
for global_var, basefunc in mod.functions.items():
if isinstance(basefunc, tvm.tir.PrimFunc):
ret[global_var] = basefunc.with_attr("target", target)
return ret
@tvm.script.ir_module
class LinearStructure:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(placeholder_4, [150528], dtype="u |
int8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(placeholder_5, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(T_subtract_1, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast(placeholder_62: T.handle, placeholder_63: T.handle, placeholder_64: T.handle, T_cast_20: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", "tir.noalias": True})
placeholder_65 = T.match_buffer(placeholder_62, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(placeholder_65, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_66 = T.match_buffer(placeholder_63, [9408], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(placeholder_66, [9408], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_67 = T.match_buffer(placeholder_64, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(placeholder_67, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_21 = T.match_buffer(T_cast_20, [289], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(T_cast_21, [289], dtype="uint8", elem_offset=0, align=64, offset_factor=1) |
PaddedInput_7_data = T.allocate([157323], "int16", "global")
PaddedInput_7 = T.buffer_decl(shape=[157323], dtype="int16", data=PaddedInput_7_data)
for i0_i1_fused_7 in T.serial(0, 229):
for i2_7, i3_7 in T.grid(229, 3):
PaddedInput_7[(((i0_i1_fused_7*687) + (i2_7*3)) + i3_7)] = T.if_then_else(((((2 <= i0_i1_fused_7) and (i0_i1_fused_7 < 226)) and (2 <= i2_7)) and (i2_7 < 226)), placeholder_65[((((i0_i1_fused_7*672) + (i2_7*3)) + i3_7) - 1350)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_7 in T.serial(0, 12544):
Conv2dOutput_7_data = T.allocate([64], "int32", "global")
Conv2dOutput_7 = T.buffer_decl(shape=[64], dtype="int32", data=Conv2dOutput_7_data)
for ff_3 in T.serial(0, 64):
Conv2dOutput_7[ff_3] = 0
for ry_2, rx_2, rc_7 in T.grid(7, 7, 3):
Conv2dOutput_7[ff_3] = (Conv2dOutput_7[ff_3] + (T.cast(PaddedInput_7[(((((T.floordiv(ax0_ax1_fused_ax2_fused_7, 112)*1374) + (ry_2*687)) + (T.floormod(ax0_ax1_fused_ax2_fused_7, 112)*6)) + (rx_2*3)) + rc_7)], "int32")*T.cast(placeholder_66[((((ry_2*1344) + (rx_2*192)) + (rc_7*64)) + ff_3)], "int32")))
for ax3_inner_7 in T.serial(0, 64):
T_cast_21[((ax0_ax1_fused_ax2_fused_7*64) + ax3_inner_7)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_7[ax3_inner_7] + placeholder_67[ax3_inner_7]), 1939887962, 31, -9, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_max_pool2d_cast(placeholder_28: T.handle, T_cast_6: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_max_pool2d_cast", "tir.noalias": True})
placeholder_29 = T.match_buffer(placeholder_28, [802816], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T.preflattened_buffer(placeholder_29, [802816], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T_cast_7 = T.match_buffer(T_cast_6, [177], dtype="int16", elem_offset=0 |
, align=64, offset_factor=1)
T.preflattened_buffer(T_cast_7, [177], dtype="int16", elem_offset=0, align=64, offset_factor=1)
tensor_2_data = T.allocate([200704], "uint8", "global")
tensor_2 = T.buffer_decl(shape=[200704], dtype="uint8", data=tensor_2_data)
for ax0_ax1_fused_4 in T.serial(0, 56):
for ax2_4 in T.serial(0, 56):
for ax3_init in T.serial(0, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_init)] = T.uint8(0)
for rv0_rv1_fused_1, ax3_2 in T.grid(9, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)] = T.max(tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)], T.if_then_else(((((ax0_ax1_fused_4*2) + T.floordiv(rv0_rv1_fused_1, 3)) < 112) and (((ax2_4*2) + T.floormod(rv0_rv1_fused_1, 3)) < 112)), placeholder_29[(((((ax0_ax1_fused_4*14336) + (T.floordiv(rv0_rv1_fused_1, 3)*7168)) + (ax2_4*128)) + (T.floormod(rv0_rv1_fused_1, 3)*64)) + ax3_2)], T.uint8(0), dtype="uint8"))
for ax0_ax1_fused_5 in T.serial(0, 56):
for ax2_5, ax3_3 in T.grid(56, 64):
T_cast_7[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)] = T.cast(tensor_2[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)], "int16")
@T.prim_func
def __tvm_main__(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "__tvm_main__", "runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_9 = T.allocate([301056], "int8", "global")
sid_8 = T.allocate([802816], "int8", "global")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input, T.lookup_param("p0", dtype="handle"), sid_9, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", sid_9, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_8, dtype="int32"))
T.evaluate(T.call_extern( |
"tvmgen_default_fused_nn_max_pool2d_cast", sid_8, output, dtype="int32"))
@tvm.script.ir_module
class LinearStructurePlanned:
@T.prim_func
def __tvm_main__(input: T.handle, fast_memory_0_var: T.Ptr[T.uint8], slow_memory_1_var: T.Ptr[T.uint8], output: T.handle) -> None:
fast_memory_0_buffer_var = T.match_buffer(fast_memory_0_var, [200704], dtype="uint8", strides=[1], elem_offset=0, align=16)
slow_memory_1_buffer_var = T.match_buffer(slow_memory_1_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_9_let: T.Ptr[T.int8] = T.address_of(slow_memory_1_buffer_var[1117472], dtype="handle")
sid_8_let: T.Ptr[T.int8] = T.address_of(slow_memory_1_buffer_var[0], dtype="handle")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input, T.lookup_param("p0", dtype="handle"), sid_9_let, fast_memory_0_buffer_var.data, slow_memory_1_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", sid_9_let, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_8_let, fast_memory_0_buffer_var.data, slow_memory_1_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_max_pool2d_cast", sid_8_let, output, fast_memory_0_buffer_var.data, slow_memory_1_buffer_var.data, dtype="int32"))
@T.prim_func
def tvmgen_default_fused_nn_max_pool2d_cast(placeholder_28: T.handle, T_cast_6: T.handle, fast_memory_6_var: T.Ptr[T.uint8], slow_memory_7_var: T.Ptr[T.uint8]) -> None:
placeholder_29 = T.match_buffer(placeholder_28, [802816], dtype="uint8")
T.preflattened_buffer(placeholder_29, [802816], dtype="uint8")
T_cast_7 = T.match_buffer(T_cast_6, [177], dtype="int16")
T.preflattened_buffer(T_cast_7, [177], dtype="int16")
fast_memory_6_buffer_var = T.match_buffer(fast_memory_6_var, [200704], dtype="u |
int8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(fast_memory_6_buffer_var, [200704], dtype="uint8", strides=[1], elem_offset=0, align=16)
slow_memory_7_buffer_var = T.match_buffer(slow_memory_7_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(slow_memory_7_buffer_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
tensor_2_let = T.buffer_decl([200704], dtype="uint8")
with T.let(tensor_2_let.data, T.address_of(fast_memory_6_buffer_var[0], dtype="handle")):
for ax0_ax1_fused_4, ax2_4 in T.grid(56, 56):
for ax3_init in T.serial(0, 64):
tensor_2_let[ax0_ax1_fused_4 * 3584 + ax2_4 * 64 + ax3_init] = T.uint8(0)
for rv0_rv1_fused_1, ax3_2 in T.grid(9, 64):
tensor_2_let[ax0_ax1_fused_4 * 3584 + ax2_4 * 64 + ax3_2] = T.max(tensor_2_let[ax0_ax1_fused_4 * 3584 + ax2_4 * 64 + ax3_2], T.if_then_else(ax0_ax1_fused_4 * 2 + rv0_rv1_fused_1
for ax0_ax1_fused_5, ax2_5, ax3_3 in T.grid(56, 56, 64):
T_cast_7[ax0_ax1_fused_5 * 3584 + ax2_5 * 64 + ax3_3] = T.cast(tensor_2_let[ax0_ax1_fused_5 * 3584 + ax2_5 * 64 + ax3_3], "int16")
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle, fast_memory_2_var: T.Ptr[T.uint8], slow_memory_3_var: T.Ptr[T.uint8]) -> None:
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8")
T.preflattened_buffer(placeholder_4, [150528], dtype="uint8")
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16")
T.preflattened_buffer(placeholder_5, [1], dtype="int16")
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16")
T.preflattened_buffer(T_subtract_1, [452], dtype="int16")
fast_memory_2_buffer_var = T.match_buffer(fast_memory_2_var, [200704], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.prefl |
attened_buffer(fast_memory_2_buffer_var, [200704], dtype="uint8", strides=[1], elem_offset=0, align=16)
slow_memory_3_buffer_var = T.match_buffer(slow_memory_3_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(slow_memory_3_buffer_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
for ax0_ax1_fused_1, ax2_1, ax3_inner_1 in T.grid(224, 224, 3):
T_subtract_1[ax0_ax1_fused_1 * 672 + ax2_1 * 3 + ax3_inner_1] = T.cast(placeholder_4[ax0_ax1_fused_1 * 672 + ax2_1 * 3 + ax3_inner_1], "int16") - placeholder_5[0]
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast(placeholder_62: T.handle, placeholder_63: T.handle, placeholder_64: T.handle, T_cast_20: T.handle, fast_memory_4_var: T.Ptr[T.uint8], slow_memory_5_var: T.Ptr[T.uint8]) -> None:
placeholder_65 = T.match_buffer(placeholder_62, [150528], dtype="int16")
T.preflattened_buffer(placeholder_65, [150528], dtype="int16")
placeholder_66 = T.match_buffer(placeholder_63, [9408], dtype="int16")
T.preflattened_buffer(placeholder_66, [9408], dtype="int16")
placeholder_67 = T.match_buffer(placeholder_64, [64], dtype="int32")
T.preflattened_buffer(placeholder_67, [64], dtype="int32")
T_cast_21 = T.match_buffer(T_cast_20, [289], dtype="uint8")
T.preflattened_buffer(T_cast_21, [289], dtype="uint8")
fast_memory_4_buffer_var = T.match_buffer(fast_memory_4_var, [200704], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(fast_memory_4_buffer_var, [200704], dtype="uint8", strides=[1], elem_offset=0, align=16)
slow_memory_5_buffer_var = T.match_buffer(slow_memory_5_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(slow_memory_5_buffer_var, [1418528], dtype="uint8", strides=[1], elem_offset=0, align=16)
PaddedInput_7_let = T.buffer_decl([157323], "int16")
with T.l |
et(PaddedInput_7_let.data, T.address_of(slow_memory_5_buffer_var[802816], dtype="handle")):
for i0_i1_fused_7, i2_7, i3_7 in T.grid(229, 229, 3):
PaddedInput_7_let[i0_i1_fused_7 * 687 + i2_7 * 3 + i3_7] = T.if_then_else(2 <= i0_i1_fused_7 and i0_i1_fused_7 < 226 and 2 <= i2_7 and i2_7 < 226, placeholder_65[i0_i1_fused_7 * 672 + i2_7 * 3 + i3_7 - 1350], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_7 in T.serial(0, 12544):
Conv2dOutput_7_let = T.buffer_decl([64], "int32")
with T.let(Conv2dOutput_7_let.data, T.address_of(fast_memory_4_buffer_var[0], dtype="handle")):
for ff_3 in T.serial(0, 64):
Conv2dOutput_7_let[ff_3] = 0
for ry_2, rx_2, rc_7 in T.grid(7, 7, 3):
Conv2dOutput_7_let[ff_3] = Conv2dOutput_7_let[ff_3] + T.cast(PaddedInput_7_let[ax0_ax1_fused_ax2_fused_7
for ax3_inner_7 in T.serial(0, 64):
T_cast_21[ax0_ax1_fused_ax2_fused_7 * 64 + ax3_inner_7] = T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_7_let[ax3_inner_7] + placeholder_67[ax3_inner_7], 1939887962, 31, -9, dtype="int32"), 255), 0), "uint8")
def test_mobilenet_subgraph():
target = Target("c")
fast_memory_pool = WorkspacePoolInfo(
"fast_memory",
[target],
PoolInfoProperties(size_hint_bytes=200704),
)
slow_memory_pool = WorkspacePoolInfo(
"slow_memory",
[target],
)
tir_mod = LinearStructure
tir_mod = _assign_targets_to_primfuncs_irmodule(tir_mod, target)
tir_mod = assign_poolinfos_to_allocates_in_irmodule(
tir_mod, [fast_memory_pool, slow_memory_pool]
)
main_func = tir_mod["__tvm_main__"]
buffer_analysis = tvm.tir.usmp.analysis.extract_buffer_info(main_func, tir_mod)
buffer_info_map = buffer_analysis.buffer_info_stmts
fcreate_array_bi = tvm.get_global_func("tir.usmp.CreateArrayBufferInfo")
buffer_info_arr = fcreate_array_b |
i(buffer_info_map)
fusmp_algo_greedy_by_size = tvm.get_global_func("tir.usmp.algo.greedy_by_size")
buffer_pool_allocations = fusmp_algo_greedy_by_size(
buffer_info_arr, buffer_analysis.memory_pressure
)
fassign_stmt_pool_allocations = tvm.get_global_func("tir.usmp.AssignStmtPoolAllocations")
pool_allocations = fassign_stmt_pool_allocations(buffer_info_map, buffer_pool_allocations)
tir_mod_with_offsets = tvm.tir.usmp.transform.convert_pool_allocations_to_offsets(
pool_allocations, emit_tvmscript_printable=True
)(tir_mod)
tir_mod_with_offsets_ref = LinearStructurePlanned
for gv, ref_func in tir_mod_with_offsets_ref.functions.items():
actual_func = tir_mod_with_offsets[gv.name_hint]
tvm.ir.assert_structural_equal(actual_func, ref_func)
@tvm.script.ir_module
class ResnetStructure:
@T.prim_func
def tvmgen_default_fused_cast_subtract_fixed_point_multiply_add_clip_cast_cast(placeholder: T.handle, placeholder_1: T.handle, T_cast: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract_fixed_point_multiply_add_clip_cast_cast", "tir.noalias": True})
placeholder_2 = T.match_buffer(placeholder, [360000], dtype="uint8")
T.preflattened_buffer(placeholder_2, [360000], dtype="uint8")
placeholder_3 = T.match_buffer(placeholder_1, [64], dtype="int32")
T.preflattened_buffer(placeholder_3, [64], dtype="int32")
T_cast_1 = T.match_buffer(T_cast, [215], dtype="int16")
T.preflattened_buffer(T_cast_1, [215], dtype="int16")
for ax0_ax1_fused, ax2, ax3_outer, ax3_inner in T.grid(75, 75, 4, 16):
T_cast_1[ax0_ax1_fused * 4800 + ax2 * 64 + ax3_outer * 16 + ax3_inner] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift(T.cast(placeholder_2[ax0_ax1_fused * 4800 + ax2 * 64 + ax3_outer * 16 + ax3_inner], "int32") - 94, 1843157232, 31, 1, dtype="int32") + placeholder_3[ax3_outer * 16 + ax3_inner], 255), 0), "uint8"), "int16")
@T.prim_func
def |
tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1(placeholder_10: T.handle, placeholder_11: T.handle, placeholder_12: T.handle, T_cast_4: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1", "tir.noalias": True})
placeholder_13 = T.match_buffer(placeholder_10, [360000], dtype="int16")
T.preflattened_buffer(placeholder_13, [360000], dtype="int16")
placeholder_14 = T.match_buffer(placeholder_11, [36864], dtype="int16")
T.preflattened_buffer(placeholder_14, [36864], dtype="int16")
placeholder_15 = T.match_buffer(placeholder_12, [64], dtype="int32")
T.preflattened_buffer(placeholder_15, [64], dtype="int32")
T_cast_5 = T.match_buffer(T_cast_4, [215], dtype="int16")
T.preflattened_buffer(T_cast_5, [215], dtype="int16")
PaddedInput_1_data = T.allocate([379456], "int16", "global")
PaddedInput_1 = T.buffer_decl(shape=[379456], dtype="int16", data=PaddedInput_1_data)
for i0_i1_fused_1, i2_1, i3_1 in T.grid(77, 77, 64):
PaddedInput_1[i0_i1_fused_1 * 4928 + i2_1 * 64 + i3_1] = T.if_then_else(1 <= i0_i1_fused_1 and i0_i1_fused_1 < 76 and 1 <= i2_1 and i2_1 < 76, placeholder_13[i0_i1_fused_1 * 4800 + i2_1 * 64 + i3_1 - 4864], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_1 in T.serial(0, 5625):
Conv2dOutput_1_data = T.allocate([64], "int32", "global")
Conv2dOutput_1 = T.buffer_decl(shape=[64], dtype="int32", data=Conv2dOutput_1_data)
for ff_1 in T.serial(0, 64):
Conv2dOutput_1[ff_1] = 0
for ry, rx, rc_1 in T.grid(3, 3, 64):
Conv2dOutput_1[ff_1] = Conv2dOutput_1[ff_1] + T.cast(PaddedInput_1[T.floordiv(ax0_ax1_fused_ax2_fused_1, 75) * 4928 + ry * 4928 + rx * 64 + T.floormod(ax0_ax1_fused_ax2_fused_1, 75) * 64 + rc_1], "int32") * T.cast(placeholder_14[ry * 12288 + rx * 4096 + rc_1 * 64 + ff_1], "int32") |
for ax3_inner_2 in T.serial(0, 64):
T_cast_5[ax0_ax1_fused_ax2_fused_1 * 64 + ax3_inner_2] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_1[ax3_inner_2] + placeholder_15[ax3_inner_2], 1608879842, 31, -7, dtype="int32"), 255), 0), "uint8"), "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_15934180698220515269_(placeholder_16: T.handle, placeholder_17: T.handle, placeholder_18: T.handle, T_add: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_15934180698220515269_", "tir.noalias": True})
placeholder_19 = T.match_buffer(placeholder_16, [360000], dtype="int16")
T.preflattened_buffer(placeholder_19, [360000], dtype="int16")
placeholder_20 = T.match_buffer(placeholder_17, [16384], dtype="int16")
T.preflattened_buffer(placeholder_20, [16384], dtype="int16")
placeholder_21 = T.match_buffer(placeholder_18, [256], dtype="int32")
T.preflattened_buffer(placeholder_21, [256], dtype="int32")
T_add_1 = T.match_buffer(T_add, [407], dtype="int32")
T.preflattened_buffer(T_add_1, [407], dtype="int32")
PaddedInput_2_data = T.allocate([360000], "int16", "global")
PaddedInput_2 = T.buffer_decl(shape=[360000], dtype="int16", data=PaddedInput_2_data)
for i0_i1_fused_2, i2_2, i3_2 in T.grid(75, 75, 64):
PaddedInput_2[i0_i1_fused_2 * 4800 + i2_2 * 64 + i3_2] = placeholder_19[i0_i1_fused_2 * 4800 + i2_2 * 64 + i3_2]
for ax0_ax1_fused_ax2_fused_2 in T.serial(0, 5625):
Conv2dOutput_2_data = T.allocate([64], "int32", "global")
Conv2dOutput_2 = T.buffer_decl(shape=[64], dtype="int32", data=Conv2dOutput_2_data)
for ax3_outer_1 in T.serial(0, 4):
for ff_2 in T.serial(0, 64):
Conv2dOutput_2[ff_2] = 0
for rc_2 in T. |
serial(0, 64):
Conv2dOutput_2[ff_2] = Conv2dOutput_2[ff_2] + T.cast(PaddedInput_2[ax0_ax1_fused_ax2_fused_2 * 64 + rc_2], "int32") * T.cast(placeholder_20[rc_2 * 256 + ax3_outer_1 * 64 + ff_2], "int32")
for ax3_inner_3 in T.serial(0, 64):
T_add_1[ax0_ax1_fused_ax2_fused_2 * 256 + ax3_outer_1 * 64 + ax3_inner_3] = T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_2[ax3_inner_3] + placeholder_21[ax3_outer_1 * 64 + ax3_inner_3], 1711626602, 31, -8, dtype="int32") + 132, 255), 0), "uint8"), "int32") - 132, 2094289803, 31, -2, dtype="int32") + 136
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_4200876283395191415_(placeholder_22: T.handle, placeholder_23: T.handle, placeholder_24: T.handle, placeholder_25: T.handle, T_cast_6: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_4200876283395191415_", "tir.noalias": True})
placeholder_29 = T.match_buffer(placeholder_22, [360000], dtype="int16")
T.preflattened_buffer(placeholder_29, [360000], dtype="int16")
placeholder_27 = T.match_buffer(placeholder_23, [16384], dtype="int16")
T.preflattened_buffer(placeholder_27, [16384], dtype="int16")
placeholder_26 = T.match_buffer(placeholder_24, [256], dtype="int32")
T.preflattened_buffer(placeholder_26, [256], dtype="int32")
placeholder_28 = T.match_buffer(placeholder_25, [1440000], dtype="int32")
T.preflattened_buffer(placeholder_28, [1440000], dtype="int32")
T_cast_7 = T.match_buffer(T_cast_6, [407], dtype="uint8")
T.preflattened_buffer(T_cast_7, [407], dtype="uint8")
PaddedInput_3_data = T.allocate([360000], "int16", "global")
PaddedInput_3 = T.buffer_decl(shape=[360000], dtype="int16", data=PaddedInput_3_data)
for i0_i1_fused_3, i2_3, i3_3 in T.gr |
id(75, 75, 64):
PaddedInput_3[i0_i1_fused_3 * 4800 + i2_3 * 64 + i3_3] = placeholder_29[i0_i1_fused_3 * 4800 + i2_3 * 64 + i3_3]
for ax0_ax1_fused_ax2_fused_3 in T.serial(0, 5625):
Conv2dOutput_3_data = T.allocate([64], "int32", "global")
Conv2dOutput_3 = T.buffer_decl(shape=[64], dtype="int32", data=Conv2dOutput_3_data)
for ax3_outer_2 in T.serial(0, 4):
for ff_3 in T.serial(0, 64):
Conv2dOutput_3[ff_3] = 0
for rc_3 in T.serial(0, 64):
Conv2dOutput_3[ff_3] = Conv2dOutput_3[ff_3] + T.cast(PaddedInput_3[ax0_ax1_fused_ax2_fused_3 * 64 + rc_3], "int32") * T.cast(placeholder_27[rc_3 * 256 + ax3_outer_2 * 64 + ff_3], "int32")
for ax3_inner_4 in T.serial(0, 64):
T_cast_7[ax0_ax1_fused_ax2_fused_3 * 256 + ax3_outer_2 * 64 + ax3_inner_4] = T.cast(T.max(T.min(T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_3[ax3_inner_4] + placeholder_26[ax3_outer_2 * 64 + ax3_inner_4], 1343014664, 31, -8, dtype="int32") + 136, 255), 0), "uint8"), "int32") - 136, 1073903788, 31, 1, dtype="int32") + placeholder_28[ax0_ax1_fused_ax2_fused_3 * 256 + ax3_outer_2 * 64 + ax3_inner_4], 255), 0), "uint8")
@T.prim_func
def __tvm_main__(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "__tvm_main__", "runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_2 = T.allocate([720000], "int8", "global")
sid_6 = T.allocate([5760000], "int8", "global")
sid_7 = T.allocate([720000], "int8", "global")
sid_8 = T.allocate([720000], "int8", "global")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract_fixed_point_multiply_add_clip_cast_cast", input, T.lookup_param("p0", dtype="handle"), sid_2, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_c |
lip_cast_cast", sid_2, T.lookup_param("p3", dtype="handle"), T.lookup_param("p4", dtype="handle"), sid_8, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1", sid_8, T.lookup_param("p5", dtype="handle"), T.lookup_param("p6", dtype="handle"), sid_7, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_15934180698220515269_", sid_7, T.lookup_param("p7", dtype="handle"), T.lookup_param("p8", dtype="handle"), sid_6, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_4200876283395191415_", sid_2, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_6, output, dtype="int32"))
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast(placeholder_4: T.handle, placeholder_5: T.handle, placeholder_6: T.handle, T_cast_2: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast", "tir.noalias": True})
placeholder_7 = T.match_buffer(placeholder_4, [360000], dtype="int16")
T.preflattened_buffer(placeholder_7, [360000], dtype="int16")
placeholder_8 = T.match_buffer(placeholder_5, [4096], dtype="int16")
T.preflattened_buffer(placeholder_8, [4096], dtype="int16")
placeholder_9 = T.match_buffer(placeholder_6, [64], dtype="int32")
T.preflattened_buffer(placeholder_9, [64], dtype="int32")
T_cast_3 = T.match_buffer(T_cast_2, [215], dtype="int16")
T.preflattened_buffer(T_cast_3, [215], dtype="int16")
PaddedInput_data = T.allocate([360000], "int16", "global")
PaddedInput = T.buffer_decl([360000], "int16", data=PaddedInput_data)
for i0_i1_fused, i2, i3 in T.grid(75, 75, 64):
PaddedInput[i0_i1_fused * 4800 + i2 * 64 + i3] = placeho |
lder_7[i0_i1_fused * 4800 + i2 * 64 + i3]
for ax0_ax1_fused_ax2_fused in T.serial(0, 5625):
Conv2dOutput_data = T.allocate([64], "int32", "global")
Conv2dOutput = T.buffer_decl([64], "int32", data=Conv2dOutput_data)
for ff in T.serial(0, 64):
Conv2dOutput[ff] = 0
for rc in T.serial(0, 64):
Conv2dOutput[ff] = Conv2dOutput[ff] + T.cast(PaddedInput[ax0_ax1_fused_ax2_fused * 64 + rc], "int32") * T.cast(placeholder_8[rc * 64 + ff], "int32")
for ax3_inner_1 in T.serial(0, 64):
T_cast_3[ax0_ax1_fused_ax2_fused * 64 + ax3_inner_1] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput[ax3_inner_1] + placeholder_9[ax3_inner_1], 1843106743, 31, -6, dtype="int32"), 255), 0), "uint8"), "int16")
@tvm.script.ir_module
class ResnetStructurePlanned:
@T.prim_func
def tvmgen_default_fused_cast_subtract_fixed_point_multiply_add_clip_cast_cast(placeholder: T.handle, placeholder_1: T.handle, T_cast: T.handle, global_workspace_1_var: T.Ptr[T.uint8]) -> None:
placeholder_2 = T.match_buffer(placeholder, [360000], dtype="uint8")
T.preflattened_buffer(placeholder_2, [360000], dtype="uint8")
placeholder_3 = T.match_buffer(placeholder_1, [64], dtype="int32")
T.preflattened_buffer(placeholder_3, [64], dtype="int32")
T_cast_1 = T.match_buffer(T_cast, [215], dtype="int16")
T.preflattened_buffer(T_cast_1, [215], dtype="int16")
global_workspace_1_buffer_var = T.match_buffer(global_workspace_1_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(global_workspace_1_buffer_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
for ax0_ax1_fused, ax2, ax3_outer, ax3_inner in T.grid(75, 75, 4, 16):
T_cast_1[ax0_ax1_fused * 4800 + ax2 * 64 + ax3_outer * 16 + ax3_inner] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift(T.cast(placeholder_2[ax0_ax1_fused * 4800 + ax2 * 64 + a |
x3_outer * 16 + ax3_inner], "int32") - 94, 1843157232, 31, 1, dtype="int32") + placeholder_3[ax3_outer * 16 + ax3_inner], 255), 0), "uint8"), "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_4200876283395191415_(placeholder_22: T.handle, placeholder_23: T.handle, placeholder_24: T.handle, placeholder_25: T.handle, T_cast_6: T.handle, global_workspace_5_var: T.Ptr[T.uint8]) -> None:
placeholder_29 = T.match_buffer(placeholder_22, [360000], dtype="int16")
T.preflattened_buffer(placeholder_29, [360000], dtype="int16")
placeholder_27 = T.match_buffer(placeholder_23, [16384], dtype="int16")
T.preflattened_buffer(placeholder_27, [16384], dtype="int16")
placeholder_26 = T.match_buffer(placeholder_24, [256], dtype="int32")
T.preflattened_buffer(placeholder_26, [256], dtype="int32")
placeholder_28 = T.match_buffer(placeholder_25, [1440000], dtype="int32")
T.preflattened_buffer(placeholder_28, [1440000], dtype="int32")
T_cast_7 = T.match_buffer(T_cast_6, [407], dtype="uint8")
T.preflattened_buffer(T_cast_7, [407], dtype="uint8")
global_workspace_5_buffer_var = T.match_buffer(global_workspace_5_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(global_workspace_5_buffer_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
PaddedInput_3_let = T.buffer_decl([360000], 'int16')
with T.let(PaddedInput_3_let.data, T.address_of(global_workspace_5_buffer_var[6480000], dtype="handle")):
for i0_i1_fused_3, i2_3, i3_3 in T.grid(75, 75, 64):
PaddedInput_3_let[i0_i1_fused_3 * 4800 + i2_3 * 64 + i3_3] = placeholder_29[i0_i1_fused_3 * 4800 + i2_3 * 64 + i3_3]
for ax0_ax1_fused_ax2_fused_3 in T.serial(0, 5625):
Conv2dOutput_3_let = T.buffer_decl([64], 'int32')
with T.let(Conv2dOutput_3_let.data, T.address_of(global |
_workspace_5_buffer_var[7200000], dtype="handle")):
for ax3_outer_2 in T.serial(0, 4):
for ff_3 in T.serial(0, 64):
Conv2dOutput_3_let[ff_3] = 0
for rc_3 in T.serial(0, 64):
Conv2dOutput_3_let[ff_3] = Conv2dOutput_3_let[ff_3] + T.cast(PaddedInput_3_let[ax0_ax1_fused_ax2_fused_3 * 64 + rc_3], "int32") * T.cast(placeholder_27[rc_3 * 256 + ax3_outer_2 * 64 + ff_3], "int32")
for ax3_inner_4 in T.serial(0, 64):
T_cast_7[ax0_ax1_fused_ax2_fused_3 * 256 + ax3_outer_2 * 64 + ax3_inner_4] = T.cast(T.max(T.min(T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_3_let[ax3_inner_4] + placeholder_26[ax3_outer_2 * 64 + ax3_inner_4], 1343014664, 31, -8, dtype="int32") + 136, 255), 0), "uint8"), "int32") - 136, 1073903788, 31, 1, dtype="int32") + placeholder_28[ax0_ax1_fused_ax2_fused_3 * 256 + ax3_outer_2 * 64 + ax3_inner_4], 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_15934180698220515269_(placeholder_16: T.handle, placeholder_17: T.handle, placeholder_18: T.handle, T_add: T.handle, global_workspace_4_var: T.Ptr[T.uint8]) -> None:
placeholder_19 = T.match_buffer(placeholder_16, [360000], dtype="int16")
T.preflattened_buffer(placeholder_19, [360000], dtype="int16")
placeholder_20 = T.match_buffer(placeholder_17, [16384], dtype="int16")
T.preflattened_buffer(placeholder_20, [16384], dtype="int16")
placeholder_21 = T.match_buffer(placeholder_18, [256], dtype="int32")
T.preflattened_buffer(placeholder_21, [256], dtype="int32")
T_add_1 = T.match_buffer(T_add, [407], dtype="int32")
T.preflattened_buffer(T_add_1, [407], dtype="int32")
global_workspace_4_buffer_var = T.match_buffer(global_workspace_4_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align |
=16)
T.preflattened_buffer(global_workspace_4_buffer_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
PaddedInput_2_let = T.buffer_decl([360000], "int16")
with T.let(PaddedInput_2_let.data, T.address_of(global_workspace_4_buffer_var[7200000], dtype="handle")):
for i0_i1_fused_2, i2_2, i3_2 in T.grid(75, 75, 64):
PaddedInput_2_let[i0_i1_fused_2 * 4800 + i2_2 * 64 + i3_2] = placeholder_19[i0_i1_fused_2 * 4800 + i2_2 * 64 + i3_2]
for ax0_ax1_fused_ax2_fused_2 in T.serial(0, 5625):
Conv2dOutput_2_let = T.buffer_decl([64], 'int32')
with T.let(Conv2dOutput_2_let.data, T.address_of(global_workspace_4_buffer_var[7920000], dtype="handle")):
for ax3_outer_1 in T.serial(0, 4):
for ff_2 in T.serial(0, 64):
Conv2dOutput_2_let[ff_2] = 0
for rc_2 in T.serial(0, 64):
Conv2dOutput_2_let[ff_2] = Conv2dOutput_2_let[ff_2] + T.cast(PaddedInput_2_let[ax0_ax1_fused_ax2_fused_2 * 64 + rc_2], "int32") * T.cast(placeholder_20[rc_2 * 256 + ax3_outer_1 * 64 + ff_2], "int32")
for ax3_inner_3 in T.serial(0, 64):
T_add_1[ax0_ax1_fused_ax2_fused_2 * 256 + ax3_outer_1 * 64 + ax3_inner_3] = T.q_multiply_shift(T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_2_let[ax3_inner_3] + placeholder_21[ax3_outer_1 * 64 + ax3_inner_3], 1711626602, 31, -8, dtype="int32") + 132, 255), 0), "uint8"), "int32") - 132, 2094289803, 31, -2, dtype="int32") + 136
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast(placeholder_4: T.handle, placeholder_5: T.handle, placeholder_6: T.handle, T_cast_2: T.handle, global_workspace_2_var: T.Ptr[T.uint8]) -> None:
placeholder_7 = T.match_buffer(placeholder_4, [360000], dtype="int16")
T.preflattened_buffer(placeholder_7, [360000], dtype="int16")
pl |
aceholder_8 = T.match_buffer(placeholder_5, [4096], dtype="int16")
T.preflattened_buffer(placeholder_8, [4096], dtype="int16")
placeholder_9 = T.match_buffer(placeholder_6, [64], dtype="int32")
T.preflattened_buffer(placeholder_9, [64], dtype="int32")
T_cast_3 = T.match_buffer(T_cast_2, [215], dtype="int16")
T.preflattened_buffer(T_cast_3, [215], dtype="int16")
global_workspace_2_buffer_var = T.match_buffer(global_workspace_2_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(global_workspace_2_buffer_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
PaddedInput_let = T.buffer_decl([360000], "int16")
with T.let(PaddedInput_let.data, T.address_of(global_workspace_2_buffer_var[7200000], dtype="handle")):
for i0_i1_fused, i2, i3 in T.grid(75, 75, 64):
PaddedInput_let[i0_i1_fused * 4800 + i2 * 64 + i3] = placeholder_7[i0_i1_fused * 4800 + i2 * 64 + i3]
for ax0_ax1_fused_ax2_fused in T.serial(0, 5625):
Conv2dOutput_let = T.buffer_decl([64], "int32")
with T.let(Conv2dOutput_let.data, T.address_of(global_workspace_2_buffer_var[7920000], dtype="handle")):
for ff in T.serial(0, 64):
Conv2dOutput_let[ff] = 0
for rc in T.serial(0, 64):
Conv2dOutput_let[ff] = Conv2dOutput_let[ff] + T.cast(PaddedInput_let[ax0_ax1_fused_ax2_fused * 64 + rc], "int32") * T.cast(placeholder_8[rc * 64 + ff], "int32")
for ax3_inner_1 in T.serial(0, 64):
T_cast_3[ax0_ax1_fused_ax2_fused * 64 + ax3_inner_1] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_let[ax3_inner_1] + placeholder_9[ax3_inner_1], 1843106743, 31, -6, dtype="int32"), 255), 0), "uint8"), "int16")
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1(placeholder_10: T.handle, placeh |
older_11: T.handle, placeholder_12: T.handle, T_cast_4: T.handle, global_workspace_3_var: T.Ptr[T.uint8]) -> None:
placeholder_13 = T.match_buffer(placeholder_10, [360000], dtype="int16")
T.preflattened_buffer(placeholder_13, [360000], dtype="int16")
placeholder_14 = T.match_buffer(placeholder_11, [36864], dtype="int16")
T.preflattened_buffer(placeholder_14, [36864], dtype="int16")
placeholder_15 = T.match_buffer(placeholder_12, [64], dtype="int32")
T.preflattened_buffer(placeholder_15, [64], dtype="int32")
T_cast_5 = T.match_buffer(T_cast_4, [215], dtype="int16")
T.preflattened_buffer(T_cast_5, [215], dtype="int16")
global_workspace_3_buffer_var = T.match_buffer(global_workspace_3_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.preflattened_buffer(global_workspace_3_buffer_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
PaddedInput_1_let = T.buffer_decl([379456], "int16")
with T.let(PaddedInput_1_let.data, T.address_of(global_workspace_3_buffer_var[0], dtype="handle")):
for i0_i1_fused_1, i2_1, i3_1 in T.grid(77, 77, 64):
PaddedInput_1_let[i0_i1_fused_1 * 4928 + i2_1 * 64 + i3_1] = T.if_then_else(1 <= i0_i1_fused_1 and i0_i1_fused_1 < 76 and 1 <= i2_1 and i2_1 < 76, placeholder_13[i0_i1_fused_1 * 4800 + i2_1 * 64 + i3_1 - 4864], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_1 in T.serial(0, 5625):
Conv2dOutput_1_let = T.buffer_decl([64], "int32")
with T.let(Conv2dOutput_1_let.data, T.address_of(global_workspace_3_buffer_var[7200000], dtype="handle")):
for ff_1 in T.serial(0, 64):
Conv2dOutput_1_let[ff_1] = 0
for ry, rx, rc_1 in T.grid(3, 3, 64):
Conv2dOutput_1_let[ff_1] = Conv2dOutput_1_let[ff_1] + T.cast(PaddedInput_1_let[ax0_ax1_fused_ax2_fused_1
for ax3_inner_2 in T.se |
rial(0, 64):
T_cast_5[ax0_ax1_fused_ax2_fused_1 * 64 + ax3_inner_2] = T.cast(T.cast(T.max(T.min(T.q_multiply_shift(Conv2dOutput_1_let[ax3_inner_2] + placeholder_15[ax3_inner_2], 1608879842, 31, -7, dtype="int32"), 255), 0), "uint8"), "int16")
@T.prim_func
def __tvm_main__(input: T.handle, global_workspace_0_var: T.Ptr[T.uint8], output: T.handle) -> None:
global_workspace_0_buffer_var = T.match_buffer(global_workspace_0_var, [7920256], dtype="uint8", strides=[1], elem_offset=0, align=16)
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_2_let: T.Ptr[T.int8] = T.address_of(global_workspace_0_buffer_var[5760000], dtype="handle")
sid_6_let: T.Ptr[T.int8] = T.address_of(global_workspace_0_buffer_var[0], dtype="handle")
sid_7_let: T.Ptr[T.int8] = T.address_of(global_workspace_0_buffer_var[6480000], dtype="handle")
sid_8_let: T.Ptr[T.int8] = T.address_of(global_workspace_0_buffer_var[6480000], dtype="handle")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract_fixed_point_multiply_add_clip_cast_cast", input, T.lookup_param("p0", dtype="handle"), sid_2_let, global_workspace_0_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast", sid_2_let, T.lookup_param("p3", dtype="handle"), T.lookup_param("p4", dtype="handle"), sid_8_let, global_workspace_0_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast_cast_1", sid_8_let, T.lookup_param("p5", dtype="handle"), T.lookup_param("p6", dtype="handle"), sid_7_let, global_workspace_0_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_15934180698220515269_", sid_7_let, T.lookup_param("p7", dtype="handle"), T.lookup_param("p8", dtype="handle"), sid_6_let, global_workspace_0_buff |
er_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_add_clip_cast_cast_subtract_fixed_point_4200876283395191415_", sid_2_let, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_6_let, output, global_workspace_0_buffer_var.data, dtype="int32"))
__tvm_meta__ = None
def test_resnet_subgraph():
target = Target("c")
global_workspace_pool = WorkspacePoolInfo(
"global_workspace",
[target],
)
tir_mod = ResnetStructure
tir_mod = _assign_targets_to_primfuncs_irmodule(tir_mod, target)
tir_mod = assign_poolinfos_to_allocates_in_irmodule(tir_mod, [global_workspace_pool])
main_func = tir_mod["__tvm_main__"]
buffer_analysis = tvm.tir.usmp.analysis.extract_buffer_info(main_func, tir_mod)
buffer_info_map = buffer_analysis.buffer_info_stmts
fcreate_array_bi = tvm.get_global_func("tir.usmp.CreateArrayBufferInfo")
buffer_info_arr = fcreate_array_bi(buffer_info_map)
fusmp_algo_greedy_by_size = tvm.get_global_func("tir.usmp.algo.greedy_by_size")
buffer_pool_allocations = fusmp_algo_greedy_by_size(
buffer_info_arr, buffer_analysis.memory_pressure
)
fassign_stmt_pool_allocations = tvm.get_global_func("tir.usmp.AssignStmtPoolAllocations")
pool_allocations = fassign_stmt_pool_allocations(buffer_info_map, buffer_pool_allocations)
tir_mod_with_offsets = tvm.tir.usmp.transform.convert_pool_allocations_to_offsets(
pool_allocations, emit_tvmscript_printable=True
)(tir_mod)
tir_mod_with_offsets_ref = ResnetStructurePlanned
for gv, ref_func in tir_mod_with_offsets_ref.functions.items():
actual_func = tir_mod_with_offsets[gv.name_hint]
tvm.ir.assert_structural_equal(actual_func, ref_func)
@tvm.script.ir_module
class TensorIntrinStructure:
@T.prim_func
def tensor_intrin_primfunc() -> None:
dense_data = T.allocate([10], "int32", "global")
T.evaluate(
T.call_extern( |
"intrin_function",
T.tvm_access_ptr(
T.type_annotation(dtype="int32"), dense_data, 0, 1, 2, dtype="handle"
),
dtype="int32",
)
)
dense = T.buffer_decl([10], "int32", data=dense_data)
dense[0] = T.q_multiply_shift(dense[0], 1608879842, 31, -7, dtype="int32")
@T.prim_func
def __tvm_main__(input: T.handle, output: T.handle) -> None:
T.evaluate(T.call_extern("tensor_intrin_primfunc", dtype="int32"))
@tvm.script.ir_module
class TensorIntrinStructurePlanned:
@T.prim_func
def tensor_intrin_primfunc(global_workspace_1_var: T.Ptr[T.uint8]) -> None:
global_workspace_1_buffer_var = T.match_buffer(
global_workspace_1_var, [40], dtype="uint8", strides=[1], elem_offset=0, align=16
)
T.preflattened_buffer(
global_workspace_1_buffer_var, [40], dtype="uint8", strides=[1], elem_offset=0, align=16
)
dense_let = T.buffer_decl([10], "int32")
with T.let(dense_let.data, T.address_of(global_workspace_1_buffer_var[0], dtype="handle")):
T.evaluate(
T.call_extern(
"intrin_function",
T.tvm_access_ptr(
T.type_annotation(dtype="int32"), dense_let.data, 0, 1, 2, dtype="handle"
),
dtype="int32",
)
)
dense_let[0] = T.q_multiply_shift(dense_let[0], 1608879842, 31, -7, dtype="int32")
@T.prim_func
def __tvm_main__(
input: T.handle, global_workspace_1_var: T.Ptr[T.uint8], output: T.handle
) -> None:
global_workspace_1_buffer_var = T.match_buffer(
global_workspace_1_var, [40], dtype="uint8", strides=[1], elem_offset=0, align=16
)
T.evaluate(
T.call_extern(
"tensor_intrin_primfunc", global_workspace_1_buffer_var.data, dtype="int32"
)
)
def test_tensor_intrin():
target = Tar |
get("c")
global_workspace_pool = WorkspacePoolInfo(
"global_workspace",
[target],
)
tir_mod = TensorIntrinStructure
tir_mod = _assign_targets_to_primfuncs_irmodule(tir_mod, target)
tir_mod = assign_poolinfos_to_allocates_in_irmodule(tir_mod, [global_workspace_pool])
main_func = tir_mod["__tvm_main__"]
buffer_analysis = tvm.tir.usmp.analysis.extract_buffer_info(main_func, tir_mod)
buffer_info_map = buffer_analysis.buffer_info_stmts
fcreate_array_bi = tvm.get_global_func("tir.usmp.CreateArrayBufferInfo")
buffer_info_arr = fcreate_array_bi(buffer_info_map)
fusmp_algo_greedy_by_size = tvm.get_global_func("tir.usmp.algo.greedy_by_size")
buffer_pool_allocations = fusmp_algo_greedy_by_size(
buffer_info_arr, buffer_analysis.memory_pressure
)
fassign_stmt_pool_allocations = tvm.get_global_func("tir.usmp.AssignStmtPoolAllocations")
pool_allocations = fassign_stmt_pool_allocations(buffer_info_map, buffer_pool_allocations)
tir_mod_with_offsets = tvm.tir.usmp.transform.convert_pool_allocations_to_offsets(
pool_allocations, emit_tvmscript_printable=True
)(tir_mod)
expected = TensorIntrinStructurePlanned
for gv, ref_func in expected.functions.items():
actual_func = tir_mod_with_offsets[gv.name_hint]
tvm.ir.assert_structural_equal(actual_func, ref_func)
if __name__ == "__main__":
pytest.main([__file__] + sys.argv[1:]) |
import pytest
from typing |
import NamedTuple, List |
import tvm
from tvm.script |
import tir as T
@tvm.script.ir_module
class SingleInputSingleOutput:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def __tvm_main__(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "__tvm_main__", "runner_function": True})
input_buffer_var = T.match_buffer(input, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
output_buffer_var = T.match_buffer(output, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input_buffer_var.data, T.lookup_param("p0", dtype="handle"), output_buffer_var.data, dtype="int32"))
@tvm.script.ir_module
class TwoInputSingleOutput:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int |
16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def __tvm_main__(input1: T.handle, input2: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "__tvm_main__", "runner_function": True})
input1_buffer_var = T.match_buffer(input1, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
input2_buffer_var = T.match_buffer(input2, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
output_buffer_var = T.match_buffer(output, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input1_buffer_var.data, input2_buffer_var.data, output_buffer_var.data, dtype="int32"))
@tvm.script.ir_module
class TwoInputTwoOutput:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fu |
sed_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def __tvm_main__(input1: T.handle, input2: T.handle, output1: T.handle, output2: T.handle) -> None:
T.func_attr({"global_symbol": "__tvm_main__", "runner_function": True})
input1_buffer_var = T.match_buffer(input1, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
input2_buffer_var = T.match_buffer(input2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
output1_buffer_var = T.match_buffer(output1, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
output2_buffer_var = T.match_buffer(output2, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input1_buffer_var.data, T.lookup_param("p0", dtype="handle"), output1_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input2_buffer_var.data, T.lookup_param("p1", dtype="handle"), output2_buffer_var.data, dtype="int32"))
@tvm.script.ir_module
class SingleInputTwoOutput:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - plac |
eholder_5[0])
@T.prim_func
def __tvm_main__(input: T.handle, output1: T.handle, output2: T.handle) -> None:
T.func_attr({"global_symbol": "__tvm_main__", "runner_function": True})
input_buffer_var = T.match_buffer(input, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
output1_buffer_var = T.match_buffer(output1, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
output2_buffer_var = T.match_buffer(output2, [452], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input_buffer_var.data, T.lookup_param("p0", dtype="handle"), output1_buffer_var.data, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input_buffer_var.data, T.lookup_param("p1", dtype="handle"), output2_buffer_var.data, dtype="int32")) |
class IOInfo(NamedTuple):
"""A data structure to hold test outputs per I/O tensor"""
name: str
shape: list
dtype: str
def check_io_allocations(mod: tvm.IRModule, inputs: List[IOInfo], outputs: List[IOInfo]):
"""This function checks whether outer most allocates correspond to I/O tensors"""
found_non_io_allocate_node = False
input_name_to_info = {}
for input in inputs:
input_name_to_info[input.name] = input
output_name_to_info = {}
for output in outputs:
output_name_to_info[output.name] = output
def _visit(stmt):
nonlocal found_non_io_allocate_node
if isinstance(stmt, tvm.tir.Allocate) and not found_non_io_allocate_node:
allocate = stmt
if dict(allocate.annotations).get("input_tensor"):
input_tensor_name = str(dict(allocate.annotations).get("input_tensor"))
assert input_tensor_name in input_name_to_info.keys()
assert input_name_to_info[input_tensor_name].shape == list(allocate.extents)
assert input_name_to_info[input_tensor_name].dtype == str(allocate.dtype)
del input_name_to_info[input_tensor_name]
if dict(allocate.annotations).get("output_tensor"):
output_tensor_name = str(dict(allocate.annotations).get("output_tensor"))
assert output_tensor_name in output_name_to_info.keys()
assert output_name_to_info[output_tensor_name].shape == list(allocate.extents)
assert output_name_to_info[output_tensor_name].dtype == str(allocate.dtype)
del output_name_to_info[output_tensor_name]
else:
found_non_io_allocate_node = True
main = mod["__tvm_main__"]
tvm.tir.stmt_functor.ir_transform(main.body, _visit, None, ["tir.Allocate", "tir.Call"])
assert len(input_name_to_info) == 0
assert len(output_name_to_info) == 0
@pytest.mark.parametrize(
"test_mod, input_names, output_names",
[
(
SingleInputS |
ingleOutput,
[IOInfo("input", [150528], "uint8")],
[IOInfo("output", [452], "int16")],
),
(
SingleInputTwoOutput,
[IOInfo("input", [150528], "uint8")],
[IOInfo("output1", [452], "int16"), IOInfo("output2", [452], "int16")],
),
(
TwoInputSingleOutput,
[IOInfo("input1", [150528], "uint8"), IOInfo("input2", [1], "int16")],
[IOInfo("output", [452], "int16")],
),
(
TwoInputTwoOutput,
[IOInfo("input1", [150528], "uint8"), IOInfo("input2", [150528], "uint8")],
[IOInfo("output1", [452], "int16"), IOInfo("output2", [452], "int16")],
),
],
)
def test_mobilenet_subgraph(test_mod, input_names, output_names):
CreateAllocatesForIO = tvm.get_global_func("tir.usmp.transform.CreateAllocatesForIO")
test_mod = CreateAllocatesForIO()(test_mod)
check_io_allocations(test_mod, input_names, output_names) |
import pytest |
import sys |
import tvm
from tvm.script |
import tir as T
from tvm.tir |
import stmt_functor
from tvm.tir.usmp |
import utils as usmp_utils
from tvm.target |
import Target
from tvm |
import WorkspacePoolInfo, PoolInfoProperties
@tvm.script.ir_module
class LinearStructure:
@T.prim_func
def tvmgen_default_fused_cast_subtract(placeholder_2: T.handle, placeholder_3: T.handle, T_subtract: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_cast_subtract", "tir.noalias": True})
placeholder_4 = T.match_buffer(placeholder_2, [150528], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
placeholder_5 = T.match_buffer(placeholder_3, [1], dtype="int16", elem_offset=0, align=64, offset_factor=1)
T_subtract_1 = T.match_buffer(T_subtract, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
for ax0_ax1_fused_1 in T.serial(0, 224):
for ax2_1, ax3_inner_1 in T.grid(224, 3):
T_subtract_1[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)] = (T.cast(placeholder_4[(((ax0_ax1_fused_1*672) + (ax2_1*3)) + ax3_inner_1)], "int16") - placeholder_5[0])
@T.prim_func
def tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast(placeholder_62: T.handle, placeholder_63: T.handle, placeholder_64: T.handle, T_cast_20: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", "tir.noalias": True})
placeholder_65 = T.match_buffer(placeholder_62, [150528], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_66 = T.match_buffer(placeholder_63, [9408], dtype="int16", elem_offset=0, align=64, offset_factor=1)
placeholder_67 = T.match_buffer(placeholder_64, [64], dtype="int32", elem_offset=0, align=64, offset_factor=1)
T_cast_21 = T.match_buffer(T_cast_20, [289], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
PaddedInput_7 = T.decl_buffer([157323], "int16")
for i0_i1_fused_7 in T.serial(0, 229):
for i2_7, i3_7 in T.grid(229, 3):
PaddedInput_7[(((i0_i1_fused_7*687) + (i2_7*3)) + i3_7)] = T.if_the |
n_else(((((2 <= i0_i1_fused_7) and (i0_i1_fused_7 < 226)) and (2 <= i2_7)) and (i2_7 < 226)), placeholder_65[((((i0_i1_fused_7*672) + (i2_7*3)) + i3_7) - 1350)], T.int16(0), dtype="int16")
for ax0_ax1_fused_ax2_fused_7 in T.serial(0, 12544):
Conv2dOutput_7 = T.decl_buffer([64], "int32")
for ff_3 in T.serial(0, 64):
Conv2dOutput_7[ff_3] = 0
for ry_2, rx_2, rc_7 in T.grid(7, 7, 3):
Conv2dOutput_7[ff_3] = (Conv2dOutput_7[ff_3] + (T.cast(PaddedInput_7[(((((T.floordiv(ax0_ax1_fused_ax2_fused_7, 112)*1374) + (ry_2*687)) + (T.floormod(ax0_ax1_fused_ax2_fused_7, 112)*6)) + (rx_2*3)) + rc_7)], "int32")*T.cast(placeholder_66[((((ry_2*1344) + (rx_2*192)) + (rc_7*64)) + ff_3)], "int32")))
for ax3_inner_7 in T.serial(0, 64):
T_cast_21[((ax0_ax1_fused_ax2_fused_7*64) + ax3_inner_7)] = T.cast(T.max(T.min(T.q_multiply_shift((Conv2dOutput_7[ax3_inner_7] + placeholder_67[ax3_inner_7]), 1939887962, 31, -9, dtype="int32"), 255), 0), "uint8")
@T.prim_func
def tvmgen_default_fused_nn_max_pool2d_cast(placeholder_28: T.handle, T_cast_6: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_fused_nn_max_pool2d_cast", "tir.noalias": True})
placeholder_29 = T.match_buffer(placeholder_28, [802816], dtype="uint8", elem_offset=0, align=64, offset_factor=1)
T_cast_7 = T.match_buffer(T_cast_6, [177], dtype="int16", elem_offset=0, align=64, offset_factor=1)
tensor_2 = T.decl_buffer([200704], "uint8")
for ax0_ax1_fused_4 in T.serial(0, 56):
for ax2_4 in T.serial(0, 56):
for ax3_init in T.serial(0, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_init)] = T.uint8(0)
for rv0_rv1_fused_1, ax3_2 in T.grid(9, 64):
tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)] = T.max(tensor_2[(((ax0_ax1_fused_4*3584) + (ax2_4*64)) + ax3_2)], T.if_then_else(((((ax0_ax1_fused_4*2 |
) + T.floordiv(rv0_rv1_fused_1, 3)) < 112) and (((ax2_4*2) + T.floormod(rv0_rv1_fused_1, 3)) < 112)), placeholder_29[(((((ax0_ax1_fused_4*14336) + (T.floordiv(rv0_rv1_fused_1, 3)*7168)) + (ax2_4*128)) + (T.floormod(rv0_rv1_fused_1, 3)*64)) + ax3_2)], T.uint8(0), dtype="uint8"))
for ax0_ax1_fused_5 in T.serial(0, 56):
for ax2_5, ax3_3 in T.grid(56, 64):
T_cast_7[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)] = T.cast(tensor_2[(((ax0_ax1_fused_5*3584) + (ax2_5*64)) + ax3_3)], "int16")
@T.prim_func
def tvmgen_default_run_model(input: T.handle, output: T.handle) -> None:
T.func_attr({"global_symbol": "tvmgen_default_run_model", "runner_function": True})
T.attr("default", "device_id", 0)
T.attr("default", "device_type", 1)
sid_9 = T.allocate([301056], "int8", "global")
sid_8 = T.allocate([802816], "int8", "global")
T.evaluate(T.call_extern("tvmgen_default_fused_cast_subtract", input, T.lookup_param("p0", dtype="handle"), sid_9, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_conv2d_add_fixed_point_multiply_clip_cast", sid_9, T.lookup_param("p1", dtype="handle"), T.lookup_param("p2", dtype="handle"), sid_8, dtype="int32"))
T.evaluate(T.call_extern("tvmgen_default_fused_nn_max_pool2d_cast", sid_8, output, dtype="int32"))
__tvm_meta__ = None
def test_create_pool_info():
target = Target("c")
pool_info = WorkspacePoolInfo(
"foo_workspace",
[target],
)
assert pool_info.pool_name == "foo_workspace"
assert pool_info.size_hint_bytes == -1
pool_info = WorkspacePoolInfo(
"bar_workspace",
[target],
PoolInfoProperties(size_hint_bytes=1425),
)
assert pool_info.pool_name == "bar_workspace"
assert pool_info.size_hint_bytes == 1425
def test_create_buffer_info():
global_ws_pool = WorkspacePoolInfo(
"global_workspace",
[Target("c")],
)
buffer_info_obj = tvm.tir.usmp.BufferInf |
o(
name_hint="buf1", size_bytes=256, pool_candidates=[global_ws_pool]
)
assert buffer_info_obj.name_hint == "buf1"
assert buffer_info_obj.size_bytes == 256
assert list(buffer_info_obj.pool_candidates) == [global_ws_pool]
assert buffer_info_obj.alignment == 1
buffer_info_obj = tvm.tir.usmp.BufferInfo("buf2", 512, [global_ws_pool], 8)
assert buffer_info_obj.name_hint == "buf2"
assert buffer_info_obj.size_bytes == 512
assert list(buffer_info_obj.pool_candidates) == [global_ws_pool]
assert buffer_info_obj.alignment == 8
def test_create_pool_allocation():
pool_info = WorkspacePoolInfo(
"foo_workspace",
[Target("c")],
)
pool_allocation = usmp_utils.PoolAllocation(pool_info=pool_info, byte_offset=64)
assert pool_allocation.pool_info == pool_info
assert pool_allocation.byte_offset == 64
def _assign_poolinfos_to_allocates_in_primfunc(primfunc, pool_infos):
"""helper to assing poolinfos to allocate nodes in a tir.PrimFunc"""
def set_poolinfos(stmt):
if isinstance(stmt, tvm.tir.Allocate):
return tvm.tir.Allocate(
buffer_var=stmt.buffer_var,
dtype=stmt.dtype,
extents=stmt.extents,
condition=stmt.condition,
body=stmt.body,
annotations={tvm.tir.usmp.utils.CANDIDATE_MEMORY_POOL_ATTR: pool_infos},
)
return primfunc.with_body(stmt_functor.ir_transform(primfunc.body, None, set_poolinfos))
def _assign_poolinfos_to_allocates_in_irmodule(mod, pool_infos):
"""helper to assing poolinfos to allocate nodes in a IRModule"""
ret = tvm.IRModule()
for global_var, basefunc in mod.functions.items():
if isinstance(basefunc, tvm.tir.PrimFunc):
ret[global_var] = _assign_poolinfos_to_allocates_in_primfunc(basefunc, pool_infos)
return ret
def _assign_targets_to_primfuncs_irmodule(mod, target):
"""helper to assign target for PrimFunc in a IRModule"""
ret = tvm.IRModule() |
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