Feat: Support DeepseekV4-Pro on MI355 Platform ( Draft only )

[bobofang11235]

Purpose

Main changes include:

• Add ROCm sparse MLA fallback coverage for DeepSeek-V4 prefill/decode.
• Add ROCm sparse attention indexer fallback registration.
• Preserve the corrected quantize_and_insert_k_cache UE8M0 block-layout writer for SWA KV cache insertion.
• Add ROCm-safe fallback handling for DeepSeek-V4 wo_a FP8 einsum.
• Keep unstable optimization paths env-gated, including Sparse MLA BMM and wo_a BMM prototypes.
• Validate the no-eager piecewise CUDAGraph serving path on ROCm.

ENV

aiter version: dcb0639d870783c2bc0c530e465f301032e756dc
flydsl version: f85bd3f7de80295370deae891e27fc9a34782806

Test Plan

Server command used for the best validated accuracy run:

export PYTHONPATH=/opt/aiter:/opt/aiter/aiter/jit/utils
export HIP_FORCE_DEV_KERNARG=1
export HSA_NO_SCRATCH_RECLAIM=1
export NCCL_MIN_NCHANNELS=112
export TORCH_BLAS_PREFER_HIPBLASLT=1
export AITER_LOG_LEVEL=WARNING
export VLLM_NO_USAGE_STATS=1
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_LINEAR=0
export VLLM_ROCM_USE_AITER_RMSNORM=1
export VLLM_ROCM_USE_AITER_TRITON_ROPE=1
export VLLM_ROCM_USE_AITER_MOE=1
export VLLM_ROCM_USE_AITER_MLA=1
export VLLM_DSV4_ROCM_WOA_BMM=1
vllm serve /dev/shm/models/DeepSeek-V4-Pro \
  --trust-remote-code \
  --tokenizer-mode deepseek_v4 \
  --reasoning-parser deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --tensor-parallel-size 8 \
  --quantization fp8 \
  --max-model-len 32768 \
  --max-num-seqs 32 \
  --max-num-batched-tokens 8192 \
  --gpu-memory-utilization 0.85 \
  --disable-custom-all-reduce \
  --host 0.0.0.0 \
  --port 8000 \
  --kv-cache-dtype fp8_e4m3 \
  --compilation-config '{"mode":3,"cudagraph_mode":1,"cudagraph_capture_sizes":[1,2,4,8]}'

Accuracy test:

lm_eval run \
  --model local-completions \
  --model_args model=/dev/shm/models/DeepSeek-V4-Pro,base_url=http://127.0.0.1:8000/v1/completions,num_concurrent=2,tokenizer_backend=none,tokenized_requests=False,max_retries=10,max_gen_toks=2048,timeout=1800 \
  --batch_size auto \
  --tasks gsm8k \
  --num_fewshot 8 \
  --limit 16 \
  --log_samples \
  --gen_kwargs temperature=0

Performance test:

vllm bench serve \
  --base-url http://127.0.0.1:8000 \
  --model /dev/shm/models/DeepSeek-V4-Pro \
  --tokenizer /dev/shm/models/DeepSeek-V4-Pro \
  --dataset-name random \
  --random-input-len 10240 \
  --random-output-len 512 \
  --num-prompts 4 \
  --max-concurrency 2 \
  --num-warmups 1

Test Result

Validated results:

GSM8K --limit 16: 0.9375
Mean TTFT: 4134.77 ms
Mean TPOT / ITL: 454.22 ms

---

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• 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.
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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @bobofang11235.

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

[gemini-code-assist]

Code Review

This pull request implements ROCm support for DeepSeek-V4 by introducing PyTorch and Triton fallbacks for NVIDIA-specific kernels, including FlashMLA, sparse attention indexing, and MoE operations. It also adds a tensor-dumping debug utility and updates quantization logic to support UE8M0 scales on ROCm. The review feedback correctly identifies the use of NVIDIA-specific FP8 types in ROCm fallback paths, recommending the use of FNUZ variants to maintain numerical accuracy.

[gemini-code-assist]

The use of tl.float8e4nv (NVIDIA's E4M3 format) in a ROCm-specific fallback kernel is likely incorrect. ROCm platforms typically use the E4M3FNUZ format (tl.float8e4b8 in Triton). Using the wrong FP8 interpretation will lead to significant numerical errors due to different bias and NaN/Inf representations.

Suggested change

	x_fp8 = x_uint8.to(tl.float8e4nv, bitcast=True)
	x_fp8 = x_uint8.to(tl.float8e4b8, bitcast=True)

[gemini-code-assist]

In the pure-PyTorch fallback for ROCm, torch.float8_e4m3fn is an NVIDIA-specific type that may not be available or correctly interpreted on ROCm. It should be replaced with torch.float8_e4m3fnuz to match the platform's native FP8 format.

Suggested change

	fp8_vals = fp8_bytes.view(torch.float8_e4m3fn).to(torch.float32)
	fp8_vals = fp8_bytes.view(torch.float8_e4m3fnuz).to(torch.float32)

[bobofang11235]

here is a new PR #41601 ( based on #40871 )