[ROCm] Don't re-quantize bf16 MLA kv_b_proj to fp4/fp8 for GLM MoE DSA

[amd-sriram]

Purpose

On ROCm, the AITER MLA path assumes a bf16 kv_b_proj is unquantized and
quantizes it to fp4/fp8. This is wrong for GlmMoeDsaForCausalLM (e.g.
amd/GLM-5.2-MXFP4), where the MLA weights are intentionally bf16 and only the
MoE experts are MXFP4.

Quantizing these MLA weights to fp4 adds ~11% error per attention layer. It
accumulates across layers and pushes greedy decoding into repetition loops.

This PR skips fp4/fp8 MLA quantization for GLM MoE DSA (matching SGLang). The MoE
experts stay MXFP4, so the model remains int4.

Test Plan

Serve amd/GLM-5.2-MXFP4 on gfx950 with the AITER sparse MLA backend, greedy
(temperature=0), reasoning_effort="high":

curl -s localhost:8990/v1/chat/completions -H 'Content-Type: application/json' -d '{
  "model": "amd/GLM-5.2-MXFP4",
  "messages": [{"role":"user","content":"What is the capital of France?"}],
  "max_tokens": 200, "temperature": 0.0, "reasoning_effort": "high"
}'

Test Result

Before — repeats indefinitely:

... No, the user asked "What is the capital of France?" No, the user asked ...

After:

...</think>The capital of France is Paris.   (finish_reason: stop)

Confirmed MLA weights stay bf16 and MoE experts remain MXFP4.

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[fxmarty-amd]

In the first place, I am not sure why VLLM_ROCM_USE_AITER_FP8BMM (and VLLM_ROCM_USE_AITER_FP4BMM) are enabled by default at:

vllm/vllm/envs.py

Lines 1252 to 1256 in dc9f845

	# Whether to use aiter triton fp8 bmm kernel
	# By default is enabled.
	"VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
	os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
	),

These are potentially destructive optimizations (as you found out), so I am skeptical why they are enabled by default. If anything, if quantizing kv_b_proj was safe, models should be shipped as such?

AFAIK there is no such silent kv_b_proj quantization on non-AMD devices? Seems like in this case it is ALWAYS kept in bf16, no matter the original precision:

vllm/vllm/model_executor/layers/attention/mla_attention.py

Lines 907 to 913 in b8168e3

	def process_weights_after_loading(self, act_dtype: torch.dtype):
	# we currently do not have quantized bmm's which are needed for
	# `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform
	# the bmm's in 16-bit, the extra memory overhead of this is fairly low
	kv_b_proj_weight = get_and_maybe_dequant_weights(
	self.kv_b_proj, out_dtype=act_dtype
	).T

and

vllm/vllm/model_executor/layers/attention/mla_attention.py

Lines 990 to 994 in b8168e3

	else:
	# Convert from (L, N, V) to (N, L, V)
	self.W_UV = W_UV.transpose(0, 1)
	# Convert from (L, N, P) to (N, P, L)
	self.W_UK_T = W_UK.permute(1, 2, 0)

This default originally comes from:

• #25896 (fp8, vllm/v1/attention/backends/mla/common.py)
• #32238 (fp4)

cc @zyongye @maleksan85 for comment

[fxmarty-amd]

Okay on sglang side, on nvidia, by default:

• With SGL_USE_DEEPGEMM_BMM=1 and guarded by weight_block_size=[128, 128] / fp8 checkpoint: https://github.com/sgl-project/sglang/blob/4ad418d2c3d43cb3c699bc9419d32673b1fca7d8/python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mla.py#L457 & https://github.com/sgl-project/sglang/blob/4ad418d2c3d43cb3c699bc9419d32673b1fca7d8/python/sglang/srt/models/deepseek_common/deepseek_weight_loader.py#L672-L685 => Block FP8 is used.
• Without DeepGEMM: https://github.com/sgl-project/sglang/blob/4ad418d2c3d43cb3c699bc9419d32673b1fca7d8/python/sglang/srt/models/deepseek_common/attention_forward_methods/forward_mla.py#L525 & https://github.com/sgl-project/sglang/blob/4ad418d2c3d43cb3c699bc9419d32673b1fca7d8/python/sglang/srt/models/deepseek_common/deepseek_weight_loader.py#L644-L660 ==> FP8 quantization is used ONLY if the checkpoint holds kv_b_proj as FP8.

On sglang side, on AMD:

• Only DeepseekV3ForCausalLM arch quantizes kv_b_proj to MXFP4, only in case it is BF16 in the checkpoint (?) https://github.com/sgl-project/sglang/blob/4ad418d2c3d43cb3c699bc9419d32673b1fca7d8/python/sglang/srt/models/deepseek_common/deepseek_weight_loader.py#L632-L642. In case block-FP8 in the checkpoint (as for huggingface.co/deepseek-ai/DeepSeek-V3.2), it is requantized to per-tensor FP8.

So not as aggressive as here (esp. on nvidia side).

[mergify]

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

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