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DeepSeek fix: awq x mergedreplicatedlinear #23764

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DeepSeek fix: awq x mergedreplicatedlinear #23764

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ffec0da
fix: awq x mergedreplicatedlinear
mickaelseznec Aug 27, 2025
0a4568e
nice catch
mickaelseznec Aug 27, 2025
f6bded1
nit: no need for specific PerTensorScale path
mickaelseznec Aug 29, 2025
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12 changes: 8 additions & 4 deletions vllm/model_executor/layers/linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -431,14 +431,18 @@ def weight_loader(self,
block_n)
shard_size = ((self.output_sizes[loaded_shard_id] + block_n - 1) //
block_n)
elif isinstance(param, PerTensorScaleParameter):
shard_offset = loaded_shard_id
shard_size = 1
Comment on lines -434 to -436
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Why remove this case?

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PerTensorScaleParameter is a subclass of BasevLLMParameter, and PerTensorScaleParameter.load_merged_column_weight is doing the right weight loading, so I reckon it a code quality improvement.

TLDR: We have previously set shard_offset and shard_size to something not what the name represents just to make the following weight overloading part(param.data[shard_offset:shard_offset + shard_size] = loaded_weight) working, but PerTensorScaleParameter.load_merged_column_weight which is just BasevLLMParameter._assert_and_load is doing exactly the same.

else:
shard_offset = sum(self.output_sizes[:loaded_shard_id])
shard_size = self.output_sizes[loaded_shard_id]

param.data[shard_offset:shard_offset + shard_size] = loaded_weight
if isinstance(param, BasevLLMParameter):
param.load_merged_column_weight(loaded_weight=loaded_weight,
shard_id=loaded_shard_id,
shard_offset=shard_offset,
shard_size=shard_size,
tp_rank=0)
Comment on lines -441 to +443
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While I do think calling the kwarg tp_rank is misleading, it does the trick and I can't come up with a better naming.

else:
param.data[shard_offset:shard_offset + shard_size] = loaded_weight


@CustomOp.register("column_parallel_linear")
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4 changes: 2 additions & 2 deletions vllm/model_executor/parameter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,7 +122,7 @@ def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):

param_data = self.data

tp_rank = get_tensor_model_parallel_rank()
tp_rank = kwargs.get("tp_rank", get_tensor_model_parallel_rank())
param_data = param_data.narrow(self.output_dim, shard_offset,
shard_size)
loaded_weight = loaded_weight.narrow(self.output_dim,
Expand Down Expand Up @@ -456,4 +456,4 @@ def _adjust_shard_indexes_for_packing(shard_size, shard_offset, packed_factor,
shard_offset=shard_offset,
bitblas_tile_size=bitblas_tile_size)

return shard_size, shard_offset
return shard_size, shard_offset