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[Bugfix] Fix moe weight losing all extra attrs after process_weights_after_loading. (#16854)

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Signed-off-by: charlifu <charlifu@amd.com>
This commit is contained in:
Charlie Fu
2025-04-28 16:05:07 -05:00
committed by GitHub
parent cc5befbced
commit ed2462030f
2 changed files with 6 additions and 11 deletions
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15
vllm/model_executor/layers/fused_moe/layer.py
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@@ -113,12 +113,9 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
super().process_weights_after_loading(layer)
layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight(
layer.w13_weight.data),
requires_grad=False)
layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight(
layer.w2_weight.data),
requires_grad=False)
# Padding the weight for better performance on ROCm
layer.w13_weight.data = self._maybe_pad_weight(layer.w13_weight.data)
layer.w2_weight.data = self._maybe_pad_weight(layer.w2_weight.data)
# Lazy import to avoid importing triton.
from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
is_rocm_aiter_moe_enabled, shuffle_weights)
@@ -127,10 +124,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
shuffled_w13, shuffled_w2 = shuffle_weights(
layer.w13_weight.data, layer.w2_weight.data)
layer.w13_weight = torch.nn.Parameter(shuffled_w13,
requires_grad=False)
layer.w2_weight = torch.nn.Parameter(shuffled_w2,
requires_grad=False)
layer.w13_weight.data = shuffled_w13
layer.w2_weight.data = shuffled_w2
if current_platform.is_cpu():
if current_platform.get_cpu_architecture() == CpuArchEnum.X86: