[Bugfix][ROCm][Kimi-K3] Feed the DFlash drafter the AttnRes mixture instead of prefix sum

[tarik-sarac]

Purpose

#50487 made the Kimi-K3 DFlash drafter read the pre-norm AttnRes mixture instead of
the running prefix sum, and added VLLM_KIMI_K3_AUX_ATTN_RES_STREAM to gate it. That
fix landed only in vllm/models/kimi_k3/nvidia/model.py. The ROCm implementation in
vllm/models/kimi_k3/amd/linear.py still taps the prefix sum and never reads the
flag, so setting it on AMD is a silent no-op.

Under AttnRes a layer only forwards the current block's running prefix; the committed
blocks live in the bank carried as residual. What the next consumer reads -- and what
the DFlash drafters were trained against -- is the pre-norm mixture over
bank[:num_blocks] + prefix. Tapping the prefix hands the drafter a tensor of the
right shape and dtype with the wrong value: nothing raises, acceptance simply drops.

The dispatch in vllm/models/kimi_k3/__init__.py is current_platform.is_rocm() with
no architecture gate, so gfx950 (CDNA4) is affected identically to gfx942.

This mirrors #50487's structure so the two paths stay comparable: the same config-time
rejection of an aux layer ending a non-final PP stage, the same one-shot log of which
layers are tapped and which mode is in force, the same fallback to the prefix when the
feature is off or the model is not AttnRes, and the same consumer-weight selection. It
reuses the existing AMD _apply_attn_res helper, which expands to the identical
attn_res(...) call the NVIDIA path makes. The capture is read-only beside the existing
tap -- it never writes back into hidden_states or residual, so the target's logits
are unchanged.

Test Plan

pytest tests/models/kimi_k3/test_amd_aux_attn_res_stream.py

New file, the AMD twin of tests/models/kimi_k3/test_aux_attn_res_stream.py. It asserts
the selection -- which weights and block count each tap uses -- rather than the mixture
itself, which is covered by test_amd_attn_res.py. Five cases:

1. feature off via the flag -> returns the plain prefix, kernel not called
2. feature on but the model is not AttnRes -> same fallback (the block size is what
   constructs the weights the lookups read)
3. a tap with a consumer layer following -> uses layers[idx+1]'s norm/proj and its
   prev_valid_blocks
4. the last layer on the final PP rank -> uses the model's output-side aggregation,
   counted the way forward() counts it
5. an aux layer ending a non-final PP stage -> rejected at configuration time

End-to-end: Kimi-K3 at TP8 on 8xMI325X (gfx942), DSPARK gamma=3, fp8 KV,
--max-model-len 163840, prefix caching off, measured with a same-prompt differential
over identical corpus slices.

Test Result

	before	after
accepted tokens per step	2.35	2.86
decode throughput	baseline	+23%

[claude]

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