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# Copyright (c) 2025 NVIDIA CORPORATION.
# Licensed under the MIT license.
# Adapted from https://github.com/NVlabs/VILA/tree/main under the Apache 2.0 license.
# LICENSE is in incl_licenses directory.
import torch
# 4 block
import triton
import triton.language as tl
from triton.language.extra.cuda import libdevice
from .common import FP8_MAX_VALUE, SCALE_MIN_THRES
"""Calculate the gradient of bias Operator"""
"""Input uses per-tensor quantization, and should be transposed"""
"""Output uses similar to the bias shape"""
"""The input can be 2D or 3D, but the calculation is performed in 2D"""
# The kernel with 1 load operation and 4 store operation
def get_configs_io_block():
configs = []
for nstages in [3, 4, 5]:
for block_m in [32, 64, 128]:
for block_n in [32, 64, 128]:
for nwarps in [4, 8, 16]:
configs.append(
triton.Config(
{"BLOCK_M": block_m, "BLOCK_N": block_n},
num_stages=nstages,
num_warps=nwarps,
)
)
return configs
convert_str_to_fp8 = {"E4M3": torch.float8_e4m3fn, "E5M2": torch.float8_e5m2}
@triton.autotune(
configs=[] + get_configs_io_block(),
key=[
"N",
],
)
@triton.heuristics(
{
"BLOCK_SN": lambda args: args["BLOCK_N"] // args["QB"],
}
)
@triton.jit
def _fp8_trans_grad_bias_kernel(
output_scale_ptr, # output
input_t_ptr, # input
M,
N,
SN,
QB: tl.constexpr,
fp8_max, # shape
input_stride_0,
input_stride_1, # input stride
s_output_stride_0,
s_output_stride_1, # scale of output stride
SCALE_MIN_THRES: tl.constexpr,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_SN: tl.constexpr,
): # CUDA block size
# Block PID
pid = tl.program_id(0)
NUM_BLOCK_N = tl.cdiv(N, BLOCK_N)
pid_dim0 = pid // NUM_BLOCK_N
pid_dim1 = pid % NUM_BLOCK_N
# pointers
input_block_ptr = tl.make_block_ptr(
base=input_ptr,
shape=(M, N),
strides=(input_stride_0, input_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_N),
block_shape=(BLOCK_M, BLOCK_N),
order=(1, 0),
)
input = tl.load(input_block_ptr, boundary_check=(0, 1))
input = input.to(tl.float32)
output = tl.reshape(input, (BLOCK_M, BLOCK_SN, QB))
# Quantize Scale calculation
abs_output = tl.abs(output)
max_val = tl.max(abs_output, axis=2) + SCALE_MIN_THRES
scale_output = max_val / fp8_max
scale_output = tl.reshape(scale_output, (BLOCK_M, BLOCK_SN, 1))
scale_output = scale_output.to(output_scale_ptr.type.element_ty)
scale_output = tl.reshape(scale_output, (BLOCK_M, BLOCK_SN))
scale_output_ptr = tl.make_block_ptr(
base=output_scale_ptr,
shape=(M, SN),
strides=(s_output_stride_0, s_output_stride_1),
offsets=(pid_dim0 * BLOCK_M, pid_dim1 * BLOCK_SN),
block_shape=(BLOCK_M, BLOCK_SN),
order=(1, 0),
)
tl.store(scale_output_ptr, scale_output, boundary_check=(0, 1))
def fp8_quantize_and_transpose(x, QB, fp8type, transpose_output_2d=False, stochastic=False):
# Change batched 3D input to 2D
batched = False
if len(x.shape) == 3:
batched = True
BS = x.shape[0]
x = x.reshape(-1, x.shape[-1])
# defining the input and output tensor
M, N = x.shape
SN = N // QB
fp8type = convert_str_to_fp8[fp8type]
s_y = torch.empty((M, SN), dtype=torch.float32, device=x.device)
fp8MaxValue = FP8_MAX_VALUE[fp8type] # E4M3 and E5M2 have different max value
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]),)
_fp8_quantize_and_transpose_kernel[grid](
s_y,
x,
M,
N,
SN,
QB,
fp8MaxValue,
x.stride(0),
x.stride(1),
s_y.stride(0),
s_y.stride(1),
SCALE_MIN_THRES=SCALE_MIN_THRES,
)
s_y_max = s_y.max()
qy, s_y_max, qy_t = fp8_division_transpose(
x, QB, fp8type, s_y_max, stochastic=stochastic
) # Stochastic Rounding happens here
# Recover 2D to 3D
if batched:
qy = qy.reshape(BS, -1, qy.shape[-1])
if not transpose_output_2d:
qy_t = qy_t.reshape(BS, -1, qy_t.shape[-1])
return qy, s_y_max, qy_t # y_t is expected to be 2D tensor
# I change the dtype of both the input tensor and the output tensor. I use torch.float32, torch.float16, and torch.fp8
configs = []
for SL in [8192]:
configs.append(
triton.testing.Benchmark( # test different matrix size influence
x_names=["CDIM"],
x_vals=[1024, 2048, 4096, 8192],
line_arg="provider",
line_vals=["triton", "torch"],
line_names=["triton", "torch"],
styles=[("blue", "-"), ("green", "-")],
ylabel="time-cost",
plot_name=f"FP8gelu<SL={SL}>",
args={"BS": 4, "SL": SL, "QB": 16, "fp8type": torch.float8_e4m3fn, "mode": "time-consuming"},
)
)
@triton.testing.perf_report(configs)
def bench_load_store(
BS, SL, CDIM, QB, fp8type, provider, mode="forward"
): # I only use triton as the provider, and mode when benchmarking
# create data
x = torch.randn(BS, SL, CDIM).cuda()
_qx = x.reshape(BS, SL, CDIM // QB, QB)
sx = _qx.abs().amax(dim=(3)) / FP8_MAX_VALUE[fp8type]
sx = sx.to(torch.bfloat16)
_qx = (_qx / sx.unsqueeze(3)).to(fp8type)
qx = _qx.reshape(BS, SL, CDIM)
quantiles = [0.5, 0.2, 0.8]
# utility functions
if provider == "triton":
def y_fwd():
fp8_quantize_and_transpose(qx, sx, QB)
if provider == "torch":
torch_gelu = torch.nn.SiLU()
def y_fwd():
return torch_gelu(x)
# forward pass
if mode == "time-consuming":
convert_func = lambda ms: ms
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, quantiles=quantiles, rep=100)
# backward pass
if mode == "gbps":
convert_func = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, quantiles=quantiles, rep=100)
return convert_func(ms), convert_func(max_ms), convert_func(min_ms)
def validity_check(BS, SL, CDIM, QB, fp8type=torch.float8_e4m3fn):
# create data
x = torch.randn(BS * SL, CDIM).cuda()
# torch result
# triton result
x_triton, s_triton, x_triton_t = fp8_quantize_and_transpose(x, QB, "E4M3")
_x_triton = x_triton.reshape(BS * SL, CDIM // QB, QB)
_x_triton = _x_triton.to(torch.float32)
s_triton = s_triton.unsqueeze(2)
output_triton = (_x_triton * s_triton).reshape(BS * SL, CDIM)
import IPython
IPython.embed()
if __name__ == "__main__":
torch.manual_seed(0)
torch.set_printoptions(precision=8, linewidth=1600, sci_mode=False, edgeitems=3)
validity_check(BS=4, SL=256, CDIM=512, QB=16, fp8type=torch.float8_e4m3fn)
bench_load_store.run(save_path=f"result/time/multi_quantize_block_quantize/BLSZ=64", print_data=True)
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