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[bugfix] fix w8a8dynamic fused_moe trans nz (#5199)

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### What this PR does / why we need it?
Currently, `torch_npu.npu_grouped_matmul_swiglu_quant` can only support
weight nz, so we need to trans w13_weight, w2_weight to nz forcely.

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

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: zzzzwwjj <1183291235@qq.com>
This commit is contained in:
zzzzwwjj
2025-12-22 17:45:34 +08:00
committed by GitHub
parent 55beac9c91
commit 052e472453
2 changed files with 52 additions and 3 deletions
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45
tests/ut/quantization/test_w8a8_dynamic.py
View File

@@ -59,3 +59,48 @@ class TestAscendW8A8FusedMoEMethod(TestBase):
self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
self.assertEqual(param_dict["w13_weight_scale"].shape,
(self.num_experts, 2 * self.intermediate_size, 1))
def build_layer(self):
layer = torch.nn.Module()
layer.w13_weight = torch.nn.Parameter(torch.empty(
self.num_experts,
2 * self.intermediate_size,
self.hidden_size,
dtype=torch.int8),
requires_grad=False)
layer.w2_weight = torch.nn.Parameter(torch.empty(
self.num_experts,
self.hidden_size,
self.intermediate_size,
dtype=torch.int8),
requires_grad=False)
w13_weight_scale = torch.zeros(
(self.num_experts, 2 * self.intermediate_size, 1),
dtype=torch.float32)
layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale,
requires_grad=False)
w13_weight_offset = torch.zeros(
(self.num_experts, 2 * self.intermediate_size, 1),
dtype=torch.float32)
layer.w13_weight_offset = torch.nn.Parameter(w13_weight_offset,
requires_grad=False)
w2_weight_scale = torch.zeros((self.num_experts, self.hidden_size, 1),
dtype=torch.float32)
layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
requires_grad=False)
w2_weight_offset = torch.zeros((self.num_experts, self.hidden_size, 1),
dtype=torch.float32)
layer.w2_weight_offset = torch.nn.Parameter(w2_weight_offset,
requires_grad=False)
return layer
@patch('torch_npu.npu_format_cast')
def test_process_weights_after_loading(self, mock_npu_format_cast):
def func_by_args(weight, num_format):
return weight
mock_npu_format_cast.side_effect = func_by_args
new_layer = self.build_layer()
self.quant_method.process_weights_after_loading(new_layer)
mock_npu_format_cast.assert_called()

10
vllm_ascend/quantization/w8a8_dynamic.py
View File

@@ -29,7 +29,7 @@ from vllm_ascend.ascend_forward_context import MoECommType
from vllm_ascend.distributed.parallel_state import get_mc2_group
from vllm_ascend.flash_common3_context import get_flash_common3_context
from vllm_ascend.ops.fused_moe.experts_selector import select_experts
from vllm_ascend.utils import maybe_trans_nz
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, maybe_trans_nz
class AscendW8A8DynamicLinearMethod:
@@ -276,8 +276,12 @@ class AscendW8A8DynamicFusedMoEMethod:
1, 2).contiguous()
layer.w2_weight.data = layer.w2_weight.data.transpose(1,
2).contiguous()
layer.w13_weight.data = maybe_trans_nz(layer.w13_weight.data)
layer.w2_weight.data = maybe_trans_nz(layer.w2_weight.data)
# TODO(zzzzwwjj): Currently, `torch_npu.npu_grouped_matmul_swiglu_quant`
# can only support weight nz.
layer.w13_weight.data = torch_npu.npu_format_cast(
layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
layer.w2_weight.data = torch_npu.npu_format_cast(
layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
layer.w13_weight_scale.data.shape[0], -1)
layer.w13_weight_scale_fp32 = layer.w13_weight_scale.data.to(