fix the mxfp4 packed qk weight loading issue for llama4

[xuebwang]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

Error:

 File "/home/xuebwang/vllm-project/vllm/vllm/model_executor/models/llama4.py", line 617, in permute
    return w.view(n_heads, attn_in // n_heads // 2, 2,
RuntimeError: shape '[8, 64, 2, 5120]' is invalid for input of size 2621440

Model configuration:

• MXFP4 quantization on Llama4 scout model using amd-quark.
• This issue happens on layers language_model.model.layers.*.self_attn.q_proj and language_model.model.layers.*.self_attn.k_proj.

Root cause analysis:

• For MXFP4 quantization, the exported weights are packed into uint8 in column wise compression, i.e., from [M, N] to [M, N/2]. Therefore, the weight w is in shape of [attn_in, attn_out//2].

• On the other hand, the permutation for qk weight is doing something like swap adjacent rows. The data order of the columns given a row would not be broken.

• Therefore, one can safely and simply half the attn_out dimension as attn_out = attn_out // 2.

Test Plan

Test Result

(Optional) Documentation Update

[gemini-code-assist]

Code Review

This PR fixes a weight loading issue for llama4 with mxfp4 packed weights. The change correctly adjusts the output dimension for packed tensors. My review includes a suggestion to make the check for packed weights more robust by using the quantization configuration, which will prevent potential issues with other 8-bit quantization schemes.

[gemini-code-assist]

While this fix is likely correct for the intended mxfp4 case, relying solely on w.dtype is a bit fragile. It assumes that any uint8 or int8 weights encountered here are packed 4-bit tensors.

To make this more robust and prevent potential issues if other 8-bit quantization schemes are used in the future, I suggest explicitly checking for the mxfp4 quantization configuration. This makes the intention clearer and safer.

I'm assuming the quantization name is 'mxfp4' based on the PR title. Please verify and adjust if needed.

if (self.quant_config and self.quant_config.get_name() == "mxfp4"
                and w.dtype in [torch.uint8, torch.int8]):
    attn_out = attn_out // 2

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[nvpohanh]

this should be fixed in my #21499

[fxmarty-amd]

Agree with gemini, this is not very robust.

e.g. wouldn't

vllm/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

Lines 57 to 63 in 9ace2ea

	weight = ModelWeightParameter(data=torch.empty(
	sum(output_partition_sizes),
	input_size_per_partition,
	dtype=torch.int8),
	input_dim=1,
	output_dim=0,
	weight_loader=weight_loader)

or

vllm/vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_int8.py

Lines 54 to 57 in 9ace2ea

	weight = ModelWeightParameter(data=torch.empty(
	sum(output_partition_sizes),
	input_size_per_partition,
	dtype=torch.int8),

fail here?

shouldn't there be a pack factor defined for all quantization methods, being used here?

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @xuebwang.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[nvpohanh]

this should already by fixed by #21499