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[Feat]Qwen3 Moe supports npu_add_rms_norm_quant op by default, update op with bias, resolve conflict with weight prefetch (#3465)

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### What this PR does / why we need it?
1.qwen3 moe uses add_rms_norm_quant op instead of 'add_rms_norm op and
quant op' during quantization scene.
2.torch_npu.add_rms_norm_quant op fixed accuracy while model weights is
quantized by anti_method m4, m4 quantization is asymmetric outlier
suppression method, it will generate none-zero norm bias,
add_rms_norm_quant op updated to add this parameter to calculate.
3. add torch-npu check

### Does this PR introduce _any_ user-facing change?
new feature works if torch_npu version >= torch_npu-2.7.1.dev20250919

### How was this patch tested?
1.no special parameters to set, no new envs to set. new feature works if
torch_npu version >= torch_npu-2.7.1.dev20250919
2.use qwen3 moe quantization model to test ,such as
Qwen3-235B-A22B-W8A8, Qwen3-30B-A3B-W8A8,
Qwen3-235B-A22B-Instruct-2507-m4 (anti_method m4)

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

---------

Signed-off-by: h30027576 <huangdong51@huawei.com>
This commit is contained in:
huangdong2022
2025-10-17 09:30:51 +08:00
committed by GitHub
parent 4c4a8458a5
commit 3a53bbc508
9 changed files with 121 additions and 38 deletions
Download Patch File Download Diff File Expand all files Collapse all files

61
vllm_ascend/ops/layernorm.py
View File

@@ -18,28 +18,43 @@
from typing import Optional, Tuple, Union, cast
import torch
from vllm.config import get_current_vllm_config
from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.layernorm import GemmaRMSNorm, RMSNorm
from vllm_ascend.utils import version_check
def _addrmsnorm_forward_oot(
self,
x: torch.Tensor,
residual: torch.Tensor,
layer: Optional[torch.nn.Module] = None,
bias: Optional[torch.nn.Parameter] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
import torch_npu
from vllm_ascend.utils import is_310p
torch_npu_check = version_check()
if layer is not None and not is_310p():
x, _, residual = torch_npu.npu_add_rms_norm_quant(
x,
residual,
self.weight,
layer.aclnn_input_scale,
layer.aclnn_input_offset,
epsilon=self.variance_epsilon)
if torch_npu_check:
x, _, residual = torch_npu.npu_add_rms_norm_quant(
x,
residual,
self.weight,
layer.aclnn_input_scale,
layer.aclnn_input_offset,
beta=bias,
epsilon=self.variance_epsilon)
else:
x, _, residual = torch_npu.npu_add_rms_norm_quant(
x,
residual,
self.weight,
layer.aclnn_input_scale,
layer.aclnn_input_offset,
epsilon=self.variance_epsilon)
else:
if is_310p():
orig_dtype = residual.dtype
@@ -50,12 +65,32 @@ def _addrmsnorm_forward_oot(
else:
x, _, residual = torch_npu.npu_add_rms_norm(
x, residual, self.weight, self.variance_epsilon)
if torch_npu_check and bias is not None:
x.add_(bias)
torch.ops.vllm.maybe_wait_prefetch_done(x)
return x, residual
class AscendRMSNorm(RMSNorm):
def __init__(
self,
hidden_size: int,
eps: float = 1e-6,
var_hidden_size: Optional[int] = None,
has_weight: bool = True,
dtype: Optional[torch.dtype] = None,
) -> None:
super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
vllm_config = get_current_vllm_config()
self.bias = None
self.torch_npu_check = version_check()
# quantization with anti_method m4 will generate none-zero norm bias
if self.torch_npu_check and vllm_config.quant_config is not None and \
any("norm.bias" in name for name in vllm_config.quant_config.quant_description.keys()):
self.bias = torch.nn.Parameter(torch.zeros(hidden_size),
requires_grad=False)
def forward_oot(
self,
x: torch.Tensor,
@@ -66,10 +101,13 @@ class AscendRMSNorm(RMSNorm):
if residual is not None:
assert x.size(0) == residual.size(0)
x, residual = _addrmsnorm_forward_oot(
self, x, residual, self.next_need_quant_fusion_linear)
self, x, residual, self.next_need_quant_fusion_linear,
self.bias)
return x, residual
x, residual = torch_npu.npu_rms_norm(x, self.weight,
self.variance_epsilon)
if self.torch_npu_check and self.bias is not None:
x.add_(self.bias)
return x
@property
@@ -99,6 +137,13 @@ class AscendRMSNorm(RMSNorm):
# does not need to be repeated
if not forward_context.prefetch_mlp_enabled:
forward_context.layer_idx += 1
elif fusion_linear == "qkv_moe":
next_linear = model_instance.model.layers[
layer_idx].self_attn.qkv_proj
forward_context.fusion_linear = "gate_moe"
elif fusion_linear == "gate_moe":
forward_context.fusion_linear = "qkv_moe"
forward_context.layer_idx += 1
from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
if next_linear is not None and \
not isinstance(next_linear.quant_method.quant_method, AscendW8A8LinearMethod):

1
vllm_ascend/ops/moe/moe_mlp.py
View File

@@ -177,7 +177,6 @@ def quant_apply_mlp(hidden_states: torch.Tensor,
group_type=0,
group_list=group_list,
output_dtype=_output_dtype)[0]
return hidden_states

6
vllm_ascend/ops/weight_prefetch.py
View File

@@ -7,6 +7,7 @@ from vllm.forward_context import get_forward_context
from vllm_ascend.ascend_config import WeightPrefetchConfig
from vllm_ascend.ops.linear import (AscendQKVParallelLinear,
AscendRowParallelLinear)
from vllm_ascend.utils import version_check
SUPPORTED_MODULES = ["attn", "mlp", "moe"]
MOE_PREFETCH_TOKEN_THRESHOLD = 96
@@ -82,14 +83,15 @@ class WeightPrefetchMethod:
if not self.moe.is_active_this_forward:
return
forward_context = get_forward_context()
if not version_check():
forward_context.layer_idx += 1
weight = forward_context.model_instance.model.layers[
forward_context.layer_idx].mlp.experts.w13_weight
forward_context.layer_idx - 1].mlp.experts.w13_weight
weight_size = weight.data.element_size() * weight.data.numel(
) * self.moe.prefetch_ratio.get(prefix, 0)
torch.ops.vllm.prefetch_preprocess(weight=weight,
start_flag=None,
max_weight_size=int(weight_size))
forward_context.layer_idx += 1
def maybe_prefetch_moe_weight_postprocess(self, stop_flag: torch.Tensor):
if not self.moe.is_active_this_forward: