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Fix noaux_tc cuda Error 700 in CUDAGraph and Add wfp8apf8 moe quant method (#4115)
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Browse Source 
* improve per_token_quant_fp8 performance

* support moe wfp8apf8

* check glm test

* fix noaux_tc op in cudagraph, support noaux_tc return the correct

* check

* check inf and overwrite score in noaux_tc

---------

Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
This commit is contained in:
chen
2025-09-22 21:27:37 +08:00
committed by GitHub
parent 6b47773bd6
commit f38b174a75
17 changed files with 924 additions and 125 deletions
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8
fastdeploy/model_executor/layers/moe/triton_moe_kernels.py
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@@ -59,6 +59,7 @@ def fused_moe_kernel_paddle(
compute_type_enum: tl.constexpr,
use_fp8_w8a8: tl.constexpr,
use_int8_w8a16: tl.constexpr,
per_channel_quant: tl.constexpr,
even_Ks: tl.constexpr,
):
"""
@@ -121,6 +122,13 @@ def fused_moe_kernel_paddle(
a_scale_ptrs = a_scale_ptr + (offs_token // top_k) * stride_asm
offs_bsn = offs_bn // group_n
b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + offs_bsn * stride_bsn
# channel-wise
elif per_channel_quant:
b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + offs_bn[None, :] * stride_bsn
b_scale = tl.load(b_scale_ptrs)
# Load per-token scale for activations
a_scale_ptrs = a_scale_ptr + (offs_token // top_k) * stride_asm
a_scale = tl.load(a_scale_ptrs, mask=token_mask, other=0.0)[:, None]
else:
# (Zkk): every expert has one activation scale and weight scale.
a_scale = tl.load(a_scale_ptr + off_experts)