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[Feat]Qwen3 Moe supports npu_add_rms_norm_quant op by default, update op with norm bias (#3205)

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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.

### Does this PR introduce _any_ user-facing change?
please use a 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.
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: huangdong2022 <huangdong51@huawei.com>
Signed-off-by: h30027576 <huangdong51@huawei.com>
This commit is contained in:
huangdong2022
2025-10-09 20:18:10 +08:00
committed by GitHub
parent 81aff9c555
commit 23db56a340
4 changed files with 57 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
tests/ut/ops/test_layernorm.py
View File

@@ -24,7 +24,7 @@ def mock_add_rms_norm(x, residual, weight, eps):
def mock_add_rms_norm_quant(x, residual, weight, quant_scale, quant_offset,
epsilon):
beta, epsilon):
x_out = 2 * x
residual_out = 2 * residual
x_out_quant = x_out.to(torch.int8)
@@ -94,7 +94,7 @@ class TestAscendRMSNorm(PytestBase):
mock_model_instance = mocker.MagicMock()
mock_forward_context.model_instance = mock_model_instance
mock_model_instance.model.layers = [
mocker.MagicMock() for _ in range(2)
mocker.MagicMock() for _ in range(3)
]
mock_layer_0 = mock_model_instance.model.layers[0]
@@ -124,7 +124,7 @@ class TestAscendRMSNorm(PytestBase):
mock_forward_context.addrmsnorm_quant_fusion_enabled = True
mock_forward_context.prefetch_mlp_enabled = False
mock_forward_context.layer_idx = 0
mock_forward_context.num_hidden_layers = 2
mock_forward_context.num_hidden_layers = 3
mock_forward_context.fusion_linear = "gate_up_dense"
# Ensure fusion and layer_idx increment are handled correctly
@@ -144,18 +144,32 @@ class TestAscendRMSNorm(PytestBase):
assert mock_forward_context.fusion_linear == "gate_up_dense"
assert mock_forward_context.layer_idx == 1
mock_forward_context.fusion_linear = "gate_moe"
x_out, residual_out = layer.forward_oot(x, residual)
assert mock_get_forward_context.call_count == 3
assert mock_forward_context.fusion_linear == "qkv_dense"
assert mock_forward_context.fusion_linear == "qkv_moe"
assert mock_forward_context.layer_idx == 2
x_out, residual_out = layer.forward_oot(x, residual)
assert mock_get_forward_context.call_count == 4
assert mock_forward_context.fusion_linear == "qkv_dense"
assert mock_forward_context.fusion_linear == "gate_moe"
assert mock_forward_context.layer_idx == 2
# last layer returned directly
x_out, residual_out = layer.forward_oot(x, residual)
assert mock_get_forward_context.call_count == 5
assert mock_forward_context.fusion_linear == "qkv_moe"
assert mock_forward_context.layer_idx == 3
x_out, residual_out = layer.forward_oot(x, residual)
assert mock_get_forward_context.call_count == 6
assert mock_forward_context.fusion_linear == "qkv_moe"
assert mock_forward_context.layer_idx == 3
if __name__ == '__main__':
unittest.main()

4
vllm_ascend/ascend_forward_context.py
View File

@@ -147,12 +147,14 @@ def set_ascend_forward_context(
# Once the necessary conditions are met, support for MOE models will also be added.
from vllm_ascend.quantization.quant_config import AscendQuantConfig
addrmsnorm_quant_fusion_enabled = isinstance(vllm_config.quant_config, AscendQuantConfig) and \
vllm_config.model_config.hf_config.model_type in ["llama", "qwen2", "qwen3"] and \
vllm_config.model_config.hf_config.model_type in ["llama", "qwen2", "qwen3", "qwen3_moe"] and \
forward_context.layer_idx is not None
if addrmsnorm_quant_fusion_enabled:
forward_context.model_instance = model_instance
forward_context.num_hidden_layers = vllm_config.model_config.hf_config.num_hidden_layers
forward_context.fusion_linear = "gate_up_dense" if forward_context.layer_idx == 0 else "qkv_dense"
if vllm_config.model_config.hf_config.model_type == "qwen3_moe":
forward_context.fusion_linear = "gate_moe" if forward_context.layer_idx == 0 else "qkv_moe"
forward_context.addrmsnorm_quant_fusion_enabled = addrmsnorm_quant_fusion_enabled
if num_tokens is None and attn_metadata is not None:

61
vllm_ascend/ops/layernorm.py
View File

@@ -15,9 +15,10 @@
# This file is a part of the vllm-ascend project.
#
from typing import Optional, Tuple, Union, cast
from typing import Optional, Tuple, Union
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
@@ -27,6 +28,7 @@ def _addrmsnorm_forward_oot(
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
@@ -39,6 +41,7 @@ def _addrmsnorm_forward_oot(
self.weight,
layer.aclnn_input_scale,
layer.aclnn_input_offset,
beta=bias,
epsilon=self.variance_epsilon)
else:
if is_310p():
@@ -50,12 +53,31 @@ def _addrmsnorm_forward_oot(
else:
x, _, residual = torch_npu.npu_add_rms_norm(
x, residual, self.weight, self.variance_epsilon)
if 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
# quantization with anti_method m4 will generate none-zero norm bias
if vllm_config is not None 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,
@@ -67,10 +89,13 @@ class AscendRMSNorm(RMSNorm):
residual = torch.ops.vllm.maybe_chunk_residual(x, residual)
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.bias is not None:
x.add_(self.bias)
return x
@property
@@ -100,6 +125,13 @@ 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):
@@ -107,31 +139,6 @@ class AscendRMSNorm(RMSNorm):
return next_linear
class AscendQuantRMSNorm(AscendRMSNorm):
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)
self.bias = torch.nn.Parameter(torch.zeros(hidden_size),
requires_grad=False)
def forward_oot(
self,
x: torch.Tensor,
residual: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
if residual is not None:
x, residual = super().forward_oot(x, residual)
return x.add_(self.bias), residual
return cast(torch.Tensor, super().forward_oot(x)).add_(self.bias)
class AscendGemmaRMSNorm(GemmaRMSNorm):
def forward_oot(

8
vllm_ascend/utils.py
View File

@@ -505,8 +505,7 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
from vllm_ascend.ops.activation import AscendQuickGELU, AscendSiluAndMul
from vllm_ascend.ops.common_fused_moe import (AscendFusedMoE,
AscendSharedFusedMoE)
from vllm_ascend.ops.layernorm import (AscendGemmaRMSNorm,
AscendQuantRMSNorm, AscendRMSNorm)
from vllm_ascend.ops.layernorm import AscendGemmaRMSNorm, AscendRMSNorm
from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
AscendMergedColumnParallelLinear,
AscendQKVParallelLinear,
@@ -537,11 +536,6 @@ def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
"MultiHeadLatentAttention": AscendMultiHeadLatentAttention,
}
if vllm_config is not None and \
vllm_config.quant_config is not None and \
any("norm.bias" in name for name in vllm_config.quant_config.quant_description.keys()):
REGISTERED_ASCEND_OPS["RMSNorm"] = AscendQuantRMSNorm
for name, op_cls in REGISTERED_ASCEND_OPS.items():
CustomOp.register_oot(_decorated_op_cls=op_cls, name=name)