apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2026-04-23 08:21:53 +08:00
Code Issues Actions 19 Packages Projects Releases Wiki Activity

[Optimization][OP]support per_token_group_fp8_quant cuda kernel (#6865)

Browse Source 
* support per_token_group_fp8_quant cuda kernel

* Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* update code

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
AIbin
2026-03-17 19:17:51 +08:00
committed by GitHub
parent b61731bb96
commit cb6819d086
3 changed files with 733 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files
fastdeploy/model_executor/layers/quantization/fp8_utils.py
+29 -25
View File
@@ -21,6 +21,9 @@ from paddleformers.utils.log import logger
from fastdeploy.model_executor.ops.triton_ops import _per_token_group_quant_fp8
from fastdeploy.platforms import current_platform
if current_platform.is_cuda():
from fastdeploy.model_executor.ops.gpu import per_token_group_fp8_quant
from ..utils import get_sm_version
@@ -162,6 +165,7 @@ def per_token_group_quant_fp8(
"""
dtype = paddle.float8_e4m3fn # current_platform.fp8_dtype() if dtype is None else dtype
assert x.ndim == 2, f"per_token_group_fp8_quant only supports ndim == 2, but got shape {tuple(x.shape)}"
assert x.shape[-1] % group_size == 0, (
f"the last dimension of `x` {x.shape[-1]} must be divisible " f"by `group_size` {group_size}"
)
@@ -177,30 +181,30 @@ def per_token_group_quant_fp8(
shape = x.shape[:-1] + (x.shape[-1] // group_size,)
x_s = paddle.empty(shape, dtype=paddle.float32)
# torch.ops._C.per_token_group_fp8_quant(
# x.contiguous(), x_q, x_s, group_size, eps, fp8_min, fp8_max, use_ue8m0
# )
# return x_q, x_s
M = x.numel() // group_size
N = group_size
BLOCK = triton.next_power_of_2(N)
# heuristics for number of warps
num_warps = min(max(BLOCK // 256, 1), 8)
num_stages = 1
_per_token_group_quant_fp8[(M,)](
x,
x_q,
x_s,
group_size,
x.shape[1],
x.stride(0),
eps,
fp8_min=fp8_min,
fp8_max=fp8_max,
use_ue8m0=use_ue8m0,
BLOCK=BLOCK,
num_warps=num_warps,
num_stages=num_stages,
)
if current_platform.is_cuda():
per_token_group_fp8_quant(x.contiguous(), x_q, x_s, group_size, eps, fp8_min, fp8_max, use_ue8m0)
else:
M = x.numel() // group_size
# N: int = group_size
BLOCK = triton.next_power_of_2
# heuristics for number of warps
num_warps = min(max(BLOCK // 256, 1), 8)
num_stages = 1
_per_token_group_quant_fp8[(M,)](
x.contiguous(),
x_q,
x_s,
group_size,
x.shape[1],
x.stride(0),
eps,
fp8_min=fp8_min,
fp8_max=fp8_max,
use_ue8m0=use_ue8m0,
BLOCK=BLOCK,
num_warps=num_warps,
num_stages=num_stages,
)
return x_q, x_s