[ROCm] DeepSeekV4-Flash-Base model enablement on ROCm with triton & torchfallback

[lcskrishna]

Purpose

This PR enables to run DeepSeekV4-Flash-Base model (FP8) on ROCm with triton & torch fallbacks. The following major changes have been performed:

• Quantization whitelist of deepseek_v4_fp8 (registration)
• Fp8 MoE Experts (Supports only experts_dtype=FP8 for now)
• MHC - The current implementation uses TileLang Kernels. This PR enables a fallback to torch naive implementation, the TileLang / equivalent will be enabled in further PRs.
• FP8 blockscale Einsum - created a fallback of torch dequant & torch.einsum fallback instead of using in deep_gemm
• TopK Softplus SQRT (CUDA) function - this fallsback to a naive torch softplus + topk + renorm.
• Router GEMM BF16 FP32 - currently fallsback to torch.linear
• Sparse Attention Indexer - (Skip Insert) - Custom Op rocm_sparse_attn_indexer_no_insert
• Flash MLA sparse fwd/decode - Created a temporary fallback rocm_flash_mla_sparse.py with Triton kernels.

Test Plan

Test Result

Server command

MODEL_DIR=/models/DSV4-Flash-Base
## clone from https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base

export VLLM_ENGINE_READY_TIMEOUT_S=3600
export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0

vllm serve ${MODEL_DIR} \
  --trust-remote-code \
  --kv-cache-dtype fp8 \
  --max-model-len 800000 \
  --gpu-memory-utilization 0.95 \
  --tensor-parallel-size 8 \
  --max-num-seqs 512 \
  --max-num-batched-tokens 512 \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 \
  --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --enforce-eager \
  --kernel-config '{"moe_backend":"triton"}' \
  "${EXTRA_ARGS[@]}"

Curl commands & results

curl -sS -X POST http://localhost:8000/v1/completions   -H 'Content-Type: application/json'   -d "{
    \"model\": \"$MODEL\",
    \"prompt\": \"The capital of France is\",
    \"max_tokens\": 8,
    \"temperature\": 0
  }" | python3 -m json.tool

curl -s http://0.0.0.0:8000/v1/completions   -H 'Content-Type: application/json'   -d '{"model":"/shared_inference/models_blog/DeepSeek-V4-Flash-
       "prompt":"Q: 17 * 23 = \nA:", "max_tokens":12, "temperature":0}'   | jq -r '.choices[0].text'

GSM8K Results

lm_eval --model local-completions \
    --tasks gsm8k \
    --model_args model=/models/DeepSeek-V4-Flash-FP8/,base_url=http://localhost:8000/v1/completions,num_concurrent=64,max_retries=3,tokenized_requests=False 

Result

2026-05-06:11:36:32 INFO [loggers.evaluation_tracker:119] Saving per-task samples to eval_results/gsm8k_20260506_105215/datasets__DeepSeek-V4-Flash-Base/*.jsonl
local-completions ({'model': '/datasets/DeepSeek-V4-Flash-Base/', 'base_url': 'http://0.0.0.0:8000/v1/completions', 'num_concurrent': 64, 'max_retries': 3, 'tokenized_requests': False, 'tokenizer_backend': None, 'max_gen_toks': 1024}), gen_kwargs: ({}), limit: None, num_fewshot: 5, batch_size: auto

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.9242	±	0.0073
		strict-match	5	exact_match	↑	0.9249	±	0.0073

---

SUCCESS. Results in ./eval_results/gsm8k_20260506_105215

---

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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.
• 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.

[claude]

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[zihaomu]

Hi @lcskrishna, thanks for the contribution!

We tried validating this PR on our ROCm setup (MI350X8) with DeepSeek-V4-Flash-Base FP8, and would like to share our observations.

Environment

• GPU: MI350X8
• Model: DeepSeek-V4-Flash-Base (FP8)

Observations (TP=1)

• The PR builds successfully and the server can start
• /health endpoint works as expected
• However, /v1/completions does not complete under normal execution

From our debugging, the issue seems to occur in the ROCm attention fallback path (around rocm_flash_mla_sparse.py), specifically near q.to(torch.float32), where we hit hipErrorIllegalState.

Additional debugging

• If we bypass the compressed aux/indexer path and force SWA-only prefill:
  • max_tokens=1 can return (e.g., " Paris")
  • max_tokens > 1 still triggers a GPU memory access fault during decode

This suggests the issue might be in the multi-token decode path rather than prefill.

TP=8

• In our setup, TP=8 is currently blocked earlier during initialization due to RCCL/PyNccl all-reduce issues, so we have not yet been able to validate generation in TP=8.

---

Could you help clarify the following so we can better align environments?

1. GPU model and ROCm version used for validation
2. PyTorch / Triton / vLLM versions
3. Whether validation was done with TP=1 or TP=8
4. The exact server launch command and required env variables
5. Whether rocm_flash_mla_sparse.py has been tested for multi-token decode

This will help us determine whether this is an environment mismatch or a missing ROCm fallback path.

Thanks!

[lcskrishna]

@tjtanaa the PR is rebased to main branch and removed unnecessary fallbacks. The code has been re-evaluated and tested as per the description for DeepSeekV4-Flash-Base on MI300 & MI350 with all the smoke tests (various CURL commands) & GSM8K matching. Below are the results.

GSM8K Results:

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
gsm8k	3	flexible-extract	5	exact_match	↑	0.9234	±	0.0073
		strict-match	5	exact_match	↑	0.9242	±	0.0073

One thing I would like to highlight is - the C++ extension - fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert produces garbage results at the moment and also doesn't work on MI300 due to some issues and requires some re-work. For now, the fallback for this kernel is still added and kept under the env variable scope.

cc: @wuhuikx @amathews-amd @sunway513

[lcskrishna]

@HAIAI

[tjtanaa]

We have landed triton sparse mla backend for dsv4 #41812 , and will land a aiter mhc PR #41946 that bugfix the dsv4 code path, remove the tilelang dependencies.

Can you help to check by then what we need to do to enable for DeepSeekV4-Flash-Base?

Moreover, we do not want to introduce more flags e.g. VLLM_ROCM_USE_V4_TRITON_FALLBACK

[lcskrishna]

Thanks @tjtanaa for providing an update on #41812 and #41946 - I believe I can drop the env variable once the PRs are merged. The following might be still needed though:

• _resolve_deepseek-v4_expert_dtype in deepseek_v4.py which the lazy resolver defaults to fp4 and then the MoE routes through MXFP4 which throws an error.
• fused_deepseek_v4_qnorm_rope_kv_rope_quant_insert -> The current codebase gives inconsistent results on various platforms (For ex: on MI300 - gives garbage output). So a fallback has been still added. I can go ahead and fix it inside C++ extension and create a seperate PR.
• MoRI IO connector/ PD Disaggregation changes - I can split it into a newer PR.

Could you please share rough timeline of merging the #41946 which I can re-base and re-evaluate. Please let me know your thoughts.

cc: @wuhuikx @HAIAI

[lcskrishna]

FP8 Check point fixes - More robust way of using expert_dtype: #42970

[mergify]

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

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