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Update unquantized_fused_moe_method.py #849

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Update unquantized_fused_moe_method.py #849

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bc36e6c
Update unquantized_fused_moe_method.py
aaab8b Dec 24, 2025
e1a762a
Update unquantized_fused_moe_method.py
aaab8b Dec 24, 2025
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8 changes: 4 additions & 4 deletions vllm/model_executor/layers/fused_moe/unquantized_fused_moe_method.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,16 +207,16 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
super().process_weights_after_loading(layer)

# 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)
layer.w13_weight.data.copy_(self._maybe_pad_weight(layer.w13_weight.data))
layer.w2_weight.data.copy_(self._maybe_pad_weight(layer.w2_weight.data))

if self.rocm_aiter_moe_enabled:
shuffled_w13, shuffled_w2 = rocm_aiter_ops.shuffle_weights(
layer.w13_weight.data, layer.w2_weight.data
)

layer.w13_weight.data = shuffled_w13
layer.w2_weight.data = shuffled_w2
layer.w13_weight.data.copy_(shuffled_w13)
layer.w2_weight.data.copy_(shuffled_w2)

if self.flashinfer_cutlass_moe_enabled:
# Swap halves to arrange as [w3; w1] (kernel expectation)
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