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remove redundant params in mla_preprocess kernel (#3530)

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### What this PR does / why we need it?

This pull request removes the redundant parameters `gamma1` and `beta1`
(also named `gamma0`/`beta0` in some places) from the `mla_preprocess`
kernel and its calling hierarchy. The changes are consistent across C++
kernel code, bindings, and Python call sites. The parameters were unused
in the lower-level functions, so their removal is a good cleanup.

### Does this PR introduce _any_ user-facing change?

The python interface of the kernel is affected, and the params of
`gamma0` and `beta0` are not needed.

### How was this patch tested?

The unit-test of the kernel is adapted accordingly.


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: mojave2 <chenchen145@huawei.com>
This commit is contained in:
Chen Chen
2025-10-21 19:20:13 +08:00
committed by GitHub
parent 80b8df881f
commit 6b290acfe1
9 changed files with 34 additions and 50 deletions
Download Patch File Download Diff File Expand all files Collapse all files

16
csrc/mla_preprocess/op_kernel/mla_preprocess_kernel.cpp
View File

@@ -19,7 +19,7 @@
#include "../op_host/tiling/mla_preprocess_tiling.h"
extern "C" __global__ __aicore__ void mla_preprocess(
GM_ADDR hiddenState, GM_ADDR gamma1, GM_ADDR beta1, GM_ADDR quantScale1, GM_ADDR quantOffset1, GM_ADDR wdqkv,
GM_ADDR hiddenState, GM_ADDR quantScale1, GM_ADDR quantOffset1, GM_ADDR wdqkv,
GM_ADDR bias1, GM_ADDR gamma2, GM_ADDR beta2, GM_ADDR quantScale2, GM_ADDR quantOffset2, GM_ADDR gamma3,
GM_ADDR sin1, GM_ADDR cos1, GM_ADDR sin2, GM_ADDR cos2, GM_ADDR keycache, GM_ADDR slotMapping, GM_ADDR wuq,
GM_ADDR bias2, GM_ADDR wuk, GM_ADDR descale1, GM_ADDR descale2, GM_ADDR ctkvScale, GM_ADDR qnopeScale, GM_ADDR q,
@@ -143,7 +143,7 @@ extern "C" __global__ __aicore__ void mla_preprocess(
case KEY_FP16_CACHEMODE_0_QUANTMODE_0: {
MLAPO_FP16::MLAOperation<CACHE_MODE_KVCACHE, DataFormat::NZ, DataFormat::NZ, DataFormat::ND> opFp16Cm0Qm0(
mlaTilingData, tiling);
opFp16Cm0Qm0.Init(hiddenState, gamma1, beta1, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
opFp16Cm0Qm0.Init(hiddenState, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
quantScale2, quantOffset2, gamma3, sin1, cos1, sin2, cos2, keycache, slotMapping, wuq,
bias2, wuk, descale1, descale2, ctkvScale, qnopeScale, q, keycacheOut, q2, keycacheOut2,
s1, s2, s3);
@@ -158,7 +158,7 @@ extern "C" __global__ __aicore__ void mla_preprocess(
case KEY_FP16_CACHEMODE_1_QUANTMODE_0: {
MLAPO_FP16::MLAOperation<CACHE_MODE_KROPE_CTKV, DataFormat::NZ, DataFormat::NZ, DataFormat::ND>
opFp16Cm1Qm0(mlaTilingData, tiling);
opFp16Cm1Qm0.Init(hiddenState, gamma1, beta1, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
opFp16Cm1Qm0.Init(hiddenState, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
quantScale2, quantOffset2, gamma3, sin1, cos1, sin2, cos2, keycache, slotMapping, wuq,
bias2, wuk, descale1, descale2, ctkvScale, qnopeScale, q, keycacheOut, q2, keycacheOut2,
s1, s2, s3);
@@ -174,7 +174,7 @@ extern "C" __global__ __aicore__ void mla_preprocess(
MLAPO_BF16::MLAOperation<__bf16, 0, DataFormat::NZ, DataFormat::NZ, DataFormat::ND,
QuantMode::PER_TENSOR_ASYMM_QUANT>
opBf16Cm0Qm0(mlaTilingData, tiling);
opBf16Cm0Qm0.Init(hiddenState, gamma1, beta1, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
opBf16Cm0Qm0.Init(hiddenState, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
quantScale2, quantOffset2, gamma3, sin1, cos1, sin2, cos2, keycache, slotMapping, wuq,
bias2, wuk, descale1, descale2, ctkvScale, qnopeScale, q, keycacheOut, q2, keycacheOut2,
s1, s2, s3, s4, s5);
@@ -190,7 +190,7 @@ extern "C" __global__ __aicore__ void mla_preprocess(
MLAPO_BF16::MLAOperation<__bf16, 1, DataFormat::NZ, DataFormat::NZ, DataFormat::ND,
QuantMode::PER_TENSOR_ASYMM_QUANT>
opBf16Cm1Qm0(mlaTilingData, tiling);
opBf16Cm1Qm0.Init(hiddenState, gamma1, beta1, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
opBf16Cm1Qm0.Init(hiddenState, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
quantScale2, quantOffset2, gamma3, sin1, cos1, sin2, cos2, keycache, slotMapping, wuq,
bias2, wuk, descale1, descale2, ctkvScale, qnopeScale, q, keycacheOut, q2, keycacheOut2,
s1, s2, s3, s4, s5);
@@ -206,7 +206,7 @@ extern "C" __global__ __aicore__ void mla_preprocess(
MLAPO_BF16::MLAOperation<__bf16, 3, DataFormat::NZ, DataFormat::NZ, DataFormat::ND,
QuantMode::PER_TENSOR_ASYMM_QUANT>
opBf16Cm3Qm0(mlaTilingData, tiling);
opBf16Cm3Qm0.Init(hiddenState, gamma1, beta1, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
opBf16Cm3Qm0.Init(hiddenState, quantScale1, quantOffset1, wdqkv, bias1, gamma2, beta2,
quantScale2, quantOffset2, gamma3, sin1, cos1, sin2, cos2, keycache, slotMapping, wuq,
bias2, wuk, descale1, descale2, ctkvScale, qnopeScale, q, keycacheOut, q2, keycacheOut2,
s1, s2, s3, s4, s5);
@@ -230,8 +230,6 @@ namespace vllm_ascend {
extern void mla_preprocess_impl(
void* stream,
void* hidden_state,
void* gamma1,
void* beta1,
void* quant_scale1,
void* quant_offset1,
void* wdqkv,
@@ -264,8 +262,6 @@ extern void mla_preprocess_impl(
{
mla_preprocess<<<block_dim, nullptr, stream>>>(
hidden_state,
gamma1,
beta1,
quant_scale1,
quant_offset1,
wdqkv,

6
csrc/mla_preprocess/op_kernel/mla_preprocess_mix_bf16.hpp
View File

@@ -2388,7 +2388,7 @@ public:
this->mlaParams = mlaParams_;
}
__aicore__ inline void Init(GM_ADDR hiddenStateGm, GM_ADDR gamma1Gm, GM_ADDR beta1Gm, GM_ADDR quantScale1Gm,
__aicore__ inline void Init(GM_ADDR hiddenStateGm, GM_ADDR quantScale1Gm,
GM_ADDR quantOffset1Gm, GM_ADDR wdqkvGm, GM_ADDR bias1Gm, GM_ADDR gamma2Gm,
GM_ADDR beta2Gm, GM_ADDR quantScale2Gm, GM_ADDR quantOffset2Gm, GM_ADDR gamma3Gm,
GM_ADDR sin1Gm, GM_ADDR cos1Gm, GM_ADDR sin2Gm, GM_ADDR cos2Gm, GM_ADDR keycacheGm,
@@ -2426,7 +2426,6 @@ public:
#endif
hiddenStateGmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ InDtype *>(hiddenStateGm));
gamma1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ InDtype *>(gamma1Gm));
quantScale1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ InDtype *>(quantScale1Gm));
quantOffset1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ int8_t *>(quantOffset1Gm));
wdqkvGmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ int8_t *>(wdqkvGm));
@@ -2444,7 +2443,6 @@ public:
qGmTensor2.SetGlobalBuffer(reinterpret_cast<__gm__ InDtype *>(qGm2));
bias1gmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ int32_t *>(bias1Gm));
bias2gmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ int32_t *>(bias2Gm));
beta1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ InDtype *>(beta1Gm));
beta2GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ InDtype *>(beta2Gm));
#ifdef __DAV_C220_VEC__
@@ -2711,7 +2709,6 @@ private:
AscendC::GlobalTensor<InDtype> hiddenStateGmTensor;
AscendC::GlobalTensor<InDtype> gamma1GmTensor;
AscendC::GlobalTensor<InDtype> quantScale1GmTensor;
AscendC::GlobalTensor<int8_t> quantOffset1GmTensor;
@@ -2741,7 +2738,6 @@ private:
AscendC::GlobalTensor<float> s5GmTensor;
AscendC::GlobalTensor<float> descale1gmTensor;
AscendC::GlobalTensor<float> descale2gmTensor;
AscendC::GlobalTensor<InDtype> beta1GmTensor;
AscendC::GlobalTensor<InDtype> beta2GmTensor;
AscendC::GlobalTensor<int32_t> bias1gmTensor;

11
csrc/mla_preprocess/op_kernel/mla_preprocess_mix_fp16.hpp
View File

@@ -294,8 +294,7 @@ class Quant
public:
__aicore__ inline Quant() {}
__aicore__ inline void Init(AscendC::GlobalTensor<T> gammaGmTensor, AscendC::GlobalTensor<T> betaGmTensor,
AscendC::GlobalTensor<T> quantScaleGmTensor,
__aicore__ inline void Init(AscendC::GlobalTensor<T> quantScaleGmTensor,
AscendC::GlobalTensor<int8_t> quantOffsetGmTensor,
AscendC::GlobalTensor<T> inputGmTensor, AscendC::GlobalTensor<int8_t> outputGmTensor,
uint32_t stride, uint32_t num_col, float avg_factor, uint64_t gm_offset,
@@ -2037,7 +2036,7 @@ public:
this->mlaParams = mlaParams_;
}
__aicore__ inline void Init(GM_ADDR hiddenStateGm, GM_ADDR gamma1Gm, GM_ADDR beta1Gm, GM_ADDR quantScale1Gm,
__aicore__ inline void Init(GM_ADDR hiddenStateGm, GM_ADDR quantScale1Gm,
GM_ADDR quantOffset1Gm, GM_ADDR wdqkvGm, GM_ADDR bias1Gm, GM_ADDR gamma2Gm,
GM_ADDR beta2Gm, GM_ADDR quantScale2Gm, GM_ADDR quantOffset2Gm, GM_ADDR gamma3Gm,
GM_ADDR sin1Gm, GM_ADDR cos1Gm, GM_ADDR sin2Gm, GM_ADDR cos2Gm, GM_ADDR keycacheGm,
@@ -2057,7 +2056,6 @@ public:
mm_w8a8_1.PreloadDoubleWeight();
#endif
hiddenStateGmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ half *>(hiddenStateGm));
gamma1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ half *>(gamma1Gm));
quantScale1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ half *>(quantScale1Gm));
quantOffset1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ int8_t *>(quantOffset1Gm));
@@ -2081,7 +2079,6 @@ public:
qGmTensor2.SetGlobalBuffer(reinterpret_cast<__gm__ half *>(qGm2));
bias2gmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ int32_t *>(bias2Gm));
beta1GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ half *>(beta1Gm));
beta2GmTensor.SetGlobalBuffer(reinterpret_cast<__gm__ half *>(beta2Gm));
#ifdef __DAV_C220_CUBE__
mm_w8a8_2.Init(s1GmTensor, wuqGmTensor, bias2gmTensor, descale2gmTensor, s2GmTensor, mlaParams, 1);
@@ -2105,7 +2102,7 @@ public:
row_work_ = 0;
}
this->splitN = mlaParams.perTaskNum;
Quant1.Init(gamma1GmTensor, beta1GmTensor, quantScale1GmTensor, quantOffset1GmTensor, hiddenStateGmTensor,
Quant1.Init(quantScale1GmTensor, quantOffset1GmTensor, hiddenStateGmTensor,
s1GmTensor, 0, num_col_1, 0.0001395089285,
vectorBlockIdx * static_cast<uint64_t>(row_work) * num_col_1,
vectorBlockIdx * static_cast<uint64_t>(row_work) * num_col_1, row_work_, mlaParams);
@@ -2316,7 +2313,6 @@ private:
AscendC::GlobalTensor<half> hiddenStateGmTensor;
AscendC::GlobalTensor<half> gamma1GmTensor;
AscendC::GlobalTensor<half> quantScale1GmTensor;
AscendC::GlobalTensor<int8_t> quantOffset1GmTensor;
@@ -2343,7 +2339,6 @@ private:
AscendC::GlobalTensor<half> s3GmTensor;
AscendC::GlobalTensor<uint64_t> descale1gmTensor;
AscendC::GlobalTensor<uint64_t> descale2gmTensor;
AscendC::GlobalTensor<half> beta1GmTensor;
AscendC::GlobalTensor<half> beta2GmTensor;
AscendC::GlobalTensor<int32_t> bias1gmTensor;

2
csrc/ops.h
View File

@@ -128,8 +128,6 @@ namespace vllm_ascend {
extern void mla_preprocess_impl(
void* stream,
void* hidden_state,
void* gamma1,
void* beta1,
void* quant_scale1,
void* quant_offset1,
void* wdqkv,

10
csrc/torch_binding.cpp
View File

@@ -108,7 +108,7 @@ std::tuple<at::Tensor, at::Tensor> rotary_embedding(at::Tensor &positions, at::T
}
std::tuple<at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &> mla_preprocess(
const at::Tensor &hiddenState, const at::Tensor &gamma0, const at::Tensor &beta0, const at::Tensor &wdqkv,
const at::Tensor &hiddenState, const at::Tensor &wdqkv,
const at::Tensor &descale0, const at::Tensor &gamma1, const at::Tensor &beta1, const at::Tensor &wuq,
const at::Tensor &descale1, const at::Tensor &gamma2, const at::Tensor &cos, const at::Tensor &sin,
const at::Tensor &wuk, const at::Tensor &kv_cache, const at::Tensor &kv_cache_rope, const at::Tensor &slotmapping,
@@ -135,8 +135,6 @@ std::tuple<at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &> mla_preproces
);
void *hidden_state_ptr = hiddenState.data_ptr();
void *gamma0_ptr = gamma0.data_ptr();
void *beta0_ptr = beta0.data_ptr();
void *quant_scale0_ptr = quant_scale0.data_ptr();
void *quant_offset0_ptr = quant_offset0.data_ptr();
void *wdqkv_ptr = wdqkv.data_ptr();
@@ -168,12 +166,12 @@ std::tuple<at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &> mla_preproces
at_npu::native::OpCommand cmd;
cmd.Name("mla_preprocess");
cmd.SetCustomHandler([stream, hidden_state_ptr, gamma0_ptr, beta0_ptr, quant_scale0_ptr, quant_offset0_ptr, wdqkv_ptr, bias0_ptr,
cmd.SetCustomHandler([stream, hidden_state_ptr, quant_scale0_ptr, quant_offset0_ptr, wdqkv_ptr, bias0_ptr,
gamma1_ptr, beta1_ptr, quant_scale1_ptr, quant_offset1_ptr, gamma2_ptr, sin_ptr, cos_ptr,
kv_cache_ptr, slotmapping_ptr, wuq_ptr, bias1_ptr, wuk_ptr, descale0_ptr, descale1_ptr, ctkv_scale_ptr,
qnope_scale_ptr, q_out0_ptr, kv_cache_out0_ptr, q_out1_ptr, kv_cache_out1_ptr, workspace_ptr,
tiling_ptr, block_dim]() -> int {
mla_preprocess_impl(stream, hidden_state_ptr, gamma0_ptr, beta0_ptr, quant_scale0_ptr, quant_offset0_ptr, wdqkv_ptr, bias0_ptr,
mla_preprocess_impl(stream, hidden_state_ptr, quant_scale0_ptr, quant_offset0_ptr, wdqkv_ptr, bias0_ptr,
gamma1_ptr, beta1_ptr, quant_scale1_ptr, quant_offset1_ptr, gamma2_ptr, sin_ptr, cos_ptr, sin_ptr, cos_ptr,
kv_cache_ptr, slotmapping_ptr, wuq_ptr, bias1_ptr, wuk_ptr, descale0_ptr, descale1_ptr, ctkv_scale_ptr,
qnope_scale_ptr, q_out0_ptr, kv_cache_out0_ptr, q_out1_ptr, kv_cache_out1_ptr, workspace_ptr,
@@ -502,7 +500,7 @@ TORCH_LIBRARY_EXPAND(CONCAT(_C, _ascend), ops)
ops.impl("sgmv_expand", torch::kPrivateUse1, &vllm_ascend::sgmv_expand);
ops.def(
"mla_preprocess(Tensor hiddenState, Tensor gamma0, Tensor beta0, Tensor wdqkv,"
"mla_preprocess(Tensor hiddenState, Tensor wdqkv,"
" Tensor descale0, Tensor gamma1, Tensor beta1, Tensor wuq, Tensor descale1,"
" Tensor gamma2, Tensor cos, Tensor sin, Tensor wuk, Tensor kv_cache,"
" Tensor kv_cache_rope, Tensor slotmapping, Tensor quant_scale0,"

2
csrc/torch_binding_meta.cpp
View File

@@ -83,8 +83,6 @@ at::Tensor sgmv_expand_meta(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_
std::tuple<at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &> mla_preprocess(
const at::Tensor &hiddenState,
const at::Tensor &gamma0,
const at::Tensor &beta0,
const at::Tensor &wdqkv,
const at::Tensor &descale0,
const at::Tensor &gamma1,

4
tests/e2e/singlecard/ops/test_mla_preprocess.py
View File

@@ -18,8 +18,6 @@ def test_mla_preprocess_kernel():
dtype = torch.bfloat16
hidden_states = torch.randn((token_num, N_7168), dtype=dtype).npu()
gamma0 = torch.randn((N_7168), dtype=dtype).npu()
beta0 = torch.randn((N_7168), dtype=dtype).npu()
quant_scale0 = torch.randn((1, ), dtype=dtype).npu()
quant_offset0 = torch.randint(0, 7, (1, ), dtype=torch.int8).npu()
@@ -74,8 +72,6 @@ def test_mla_preprocess_kernel():
torch.ops._C_ascend.mla_preprocess(
hidden_states,
gamma0,
beta0,
wdqkv,
de_scale0,
gamma1,

19
tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py
View File

@@ -1,5 +1,8 @@
from __future__ import annotations
import os
from unittest.mock import patch
import pytest
from vllm import SamplingParams
from vllm.config import CompilationConfig, CUDAGraphMode
@@ -108,3 +111,19 @@ def test_mtp2_correctness_full_graph(
model_name: str,
):
mtp_correctness(sampling_config, model_name, 2, CUDAGraphMode.FULL)
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_MLAPO": "1"})
def test_mtp_correctness_piecewise_graph_with_mlapo_kernel(
sampling_config: SamplingParams,
model_name: str,
):
mtp_correctness(sampling_config, model_name, 1)
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_MLAPO": "1"})
def test_mtp_correctness_full_graph_with_mlapo_kernel(
sampling_config: SamplingParams,
model_name: str,
):
mtp_correctness(sampling_config, model_name, 1, CUDAGraphMode.FULL)

14
vllm_ascend/attention/mla_v1.py
View File

@@ -716,17 +716,7 @@ class AscendMLAImpl(MLAAttentionImpl):
self.qb_qt_bias = qb_qt_bias.reshape(
self.num_heads * (self.qk_nope_head_dim + self.qk_rope_head_dim))
device = self.q_proj.weight.device
self.gamma0 = torch.ones(
[self.fused_qkv_a_proj.weight.shape[-1]],
dtype=act_dtype,
device=device,
)
self.beta0 = torch.zeros(
[self.fused_qkv_a_proj.weight.shape[-1]],
dtype=act_dtype,
device=device,
)
device = self.q_a_proj.weight.device
self.gamma1 = self.q_a_layernorm.weight.data
self.beta1 = self.q_a_layernorm.bias.data
self.gamma2 = self.kv_a_layernorm.weight.data
@@ -1085,8 +1075,6 @@ class AscendMLAImpl(MLAAttentionImpl):
torch.ops._C_ascend.mla_preprocess(
hidden_states,
self.gamma0,
self.beta0,
self.wd_qkv,
self.deq_scale_qkv,
self.gamma1,