DeepSeekv4 ROCm Optimization

[bobofang11235]

Purpose

• [Fix][Rocm] Handle DeepSeek-V4 UE8M0 and FP8 dtype
  Decode UE8M0 scale tensors before using them in ROCm fallback paths so E8M0 scales are not multiplied directly as float8 tensors. Use the current platform FP8 dtype for DeepSeek-V4 indexer and inverse-RoPE quantization,
  and select the Triton FNUZ FP8 type when required by the ROCm platform.
• [Feat][Rocm] Add DeepSeek-V4 sparse FlashMLA fallback
  Route DeepSeek-V4 sparse FlashMLA prefill and decode calls through ROCm fallback implementations so ROCm can share the same FlashMLA API path as CUDA.
• [Fix][Rocm] Preserve FP4 parameter dtype for AITER MXFP4 MoE
  Wrap shuffled AITER MXFP4 weights as fresh Parameters so FP4 dtype metadata is preserved without changing non-ROCm MoE backend routing.
• [BugFix][Attention] Fix NaN in Triton merge_attn_states when both LSEs are -inf
  Fix NaN output in the Triton merge_attn_states kernel when both prefix_lse and suffix_lse are -inf.
  When both prefix and suffix have no tokens (e.g. chunked prefill with zero context length), both LSEs are -inf. Per IEEE 754, -inf - (-inf) = NaN, which propagates through exp and division into the final output.
• [Fix][Rocm] Add generic fp8_einsum fallback for DeepGEMM
  Provide a ROCm-only torch fallback for fp8_einsum when DeepGEMM is unavailable while preserving the existing CUDA and non-ROCm dispatch behavior.
• Support cuda graph full and piecewise mode

( this PR is based on 920bf3e of upstream )

Test Plan

Test Result

docker image: docker pull rocm/vllm-dev:deepseek-v4-mi35x
machine: mi355x
aiter version: d2454ad18a0d7c7795162ab0f550e8a0397840bd ( https://github.com/ROCm/aiter main branch )
vllm version: this PR

server command

export HF_HOME=/data/huggingface-cache
export VLLM_ROCM_USE_AITER=1
export VLLM_ROCM_USE_AITER_LINEAR=1
export HSA_TOOLS_DISABLE_REGISTER=1
export VLLM_DSV4_ROCM_GRAPH_SAFE=1

vllm serve DeepSeek-V4-Pro \
  --host localhost \
  --port 8001 \
  --dtype auto \
  --tensor-parallel-size 8 \
  --max-num-seqs 128 \
  --distributed-executor-backend mp \
  --trust-remote-code \
  --gpu-memory-utilization 0.6 \
  --moe-backend triton_unfused \
  --tokenizer-mode deepseek_v4 \
  --reasoning-parser deepseek_v4 \
  --kv-cache-dtype fp8_e4m3 \
  --compilation-config '{"mode":3,"cudagraph_mode": "FULL_AND_PIECEWISE"}'

client command

#!/usr/bin/env bash
set -e
export PYTHONPATH=/opt/aiter:/opt/aiter/aiter/jit/utils:${PYTHONPATH}
PORT=${PORT:-8001}
MODEL=${MODEL:-DeepSeek-V4-Pro}
NUM_PROMPTS=${NUM_PROMPTS:-20}
CONCURRENCY=${CONCURRENCY:-10}
INPUT_LEN=${INPUT_LEN:-1000}
OUTPUT_LEN=${OUTPUT_LEN:-100}
TS=$(date +%Y%m%d_%H%M%S)
LOG=/opt/scripts/vllm/logs/client_${TS}.log

mkdir -p /opt/scripts/vllm/logs

echo ""
echo "========== Sanity Check: Single Chat Completion =========="
curl -s "http://localhost:${PORT}/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d "{\"model\":\"${MODEL}\",\"messages\":[{\"role\":\"user\",\"content\":\"What is 15% of 240? Answer concisely.\"}],\"max_tokens\":64}" \
  | python3 -m json.tool \
  | tee -a "$LOG"

echo ""
echo ""
vllm bench serve \
  --base-url "http://localhost:${PORT}" \
  --model "${MODEL}" --tokenizer "${MODEL}" \
  --dataset-name random \
  --random-input-len "${INPUT_LEN}" \
  --random-output-len "${OUTPUT_LEN}" \
  --num-prompts "${NUM_PROMPTS}" --max-concurrency "${CONCURRENCY}" \
  --num-warmups 1 \
  --save-result \
  --result-filename "/opt/scripts/vllm/logs/bench_${TS}.json" \
  --seed 0
  2>&1 | tee -a "$LOG"

echo ""
echo "[$(date)] Benchmark complete. Log: $LOG"

Result

============ Serving Benchmark Result ============
Successful requests:                     20
Failed requests:                         0
Maximum request concurrency:             10
Benchmark duration (s):                  21.28
Total input tokens:                      20000
Total generated tokens:                  2000
Request throughput (req/s):              0.94
Output token throughput (tok/s):         93.99
Peak output token throughput (tok/s):    100.00
Peak concurrent requests:                20.00
Total token throughput (tok/s):          1033.86
---------------Time to First Token----------------
Mean TTFT (ms):                          594.83
Median TTFT (ms):                        621.92
P99 TTFT (ms):                           645.92
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          101.35
Median TPOT (ms):                        101.16
P99 TPOT (ms):                           103.94
---------------Inter-token Latency----------------
Mean ITL (ms):                           101.35
Median ITL (ms):                         101.07
P99 ITL (ms):                            103.88
==================================================

accuracy command

lm_eval --model local-completions --model_args model=$MODEL,base_url=http://0.0.0.0:8001/v1/completions,tokenizer_backend=None,tokenized_requests=False,num_concurrent=64,max_retries=10,max_gen_toks=2048,timeout=60000 --batch_size auto --tasks gsm8k --num_fewshot 8 --output_path . 2>&1 | tee -a eval.log

Result

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     8|exact_match|↑  |0.9105|±  |0.0079|
|     |       |strict-match    |     8|exact_match|↑  |0.8992|±  |0.0083|

---

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.

[claude]

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[gemini-code-assist]

Code Review

This pull request enables ROCm support for DeepSeek-V4 by introducing ROCm-specific kernels and PyTorch fallbacks for attention and MoE operations. Key changes include the integration of the AITER backend, specialized handling for the e8m0 FP8 format, and ROCm-compatible Triton kernels. The review feedback correctly identifies a broadcasting error in the MQA logits fallback, hardcoded device strings that should be generalized, and an incorrect type conversion for exponent-only scales in the blocked K-cache dequantization logic.

[gemini-code-assist]

Avoid hardcoding device="cuda". It is better to use the device of the input tensors to ensure compatibility across different execution environments.

Suggested change

	device="cuda",
	device=q_fp8.device,

[gemini-code-assist]

Avoid hardcoding device="cuda". It is better to use the device of the input tensors to ensure compatibility across different execution environments.

Suggested change

	device="cuda",
	device=q_fp8.device,

[gemini-code-assist]

The input_scale tensor contains float8_e8m0fnu values, which are exponent-only scales. Directly casting them to bfloat16 using .to() will not correctly decode the exponent bias. You should use the _decode_e8m0_scales helper defined in this file to perform the correct conversion.

Suggested change

	cur_scales = input_scale[:, :, tile_idx].to(torch.bfloat16).unsqueeze(-1)
	cur_scales = _decode_e8m0_scales(input_scale[:, :, tile_idx]).to(torch.bfloat16).unsqueeze(-1)

[tjtanaa]

@bobofang11235 I noticed there are also changes to support fnuz. Please also disclose the testing results (accuracy and performance) of fnuz.

Since this is an optimization PR, can you highlight what has been optimized? E2E performance before the PR and after the PR? (if the model is too slow to run to show e2e perf, please highlight what is optimized and how much gain are we getting from the highlighted optimization)

[wuhuikx]

Hi @bobofang11235 Since the base PR #40871 has been merged, can you help update the test results with the official docker vllm/vllm-open-rocm:nightly?

[bobofang11235]

Hi @tjtanaa，I tested the e2e accuracy, and the result is same as PR#40871

|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     8|exact_match|↑  |0.9530|±  |0.0058|
|     |       |strict-match    |     8|exact_match|↑  |0.9538|±  |0.0058|

And the optimized part:

• Support cuda graph ( full and piecewise mode ): Before this PR, we could only run eager mode
• Support longer input length ( this PR test 10K ): Before this PR, if we tried to run input-lenght 10K, the server would crash

Hi @wuhuikx
I rebased on the upstream vllm after PR-40871 merged. The results in the comment are tested after rebase.

[bobofang11235]

Hi @tjtanaa
Here is the new compare results before and after this PR

client command

vllm bench serve \
  --base-url "http://localhost:${PORT}" \
  --model "${MODEL}" --tokenizer "${MODEL}" \
  --dataset-name random \
  --random-input-len 512 \
  --random-output-len 512 \
  --num-prompts 4 --max-concurrency 1 \
  --num-warmups 1 \
  --save-result \
  --result-filename "/opt/scripts/vllm/logs/bench_${TS}.json" \
  2>&1 | tee -a "$LOG"

before this PR

============ Serving Benchmark Result ============
Successful requests:                     4
Failed requests:                         0
Maximum request concurrency:             1
Benchmark duration (s):                  666.62
Total input tokens:                      2048
Total generated tokens:                  2048
Request throughput (req/s):              0.01
Output token throughput (tok/s):         3.07
Peak output token throughput (tok/s):    4.00
Peak concurrent requests:                2.00
Total token throughput (tok/s):          6.14
---------------Time to First Token----------------
Mean TTFT (ms):                          551.90
Median TTFT (ms):                        557.97
P99 TTFT (ms):                           560.74
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          325.05
Median TPOT (ms):                        325.06
P99 TPOT (ms):                           325.28
---------------Inter-token Latency----------------
Mean ITL (ms):                           325.05
Median ITL (ms):                         325.93
P99 ITL (ms):                            334.21
==================================================

after this PR

============ Serving Benchmark Result ============
Successful requests:                     4
Failed requests:                         0
Maximum request concurrency:             1
Benchmark duration (s):                  490.08
Total input tokens:                      2048
Total generated tokens:                  2048
Request throughput (req/s):              0.01
Output token throughput (tok/s):         4.18
Peak output token throughput (tok/s):    5.00
Peak concurrent requests:                2.00
Total token throughput (tok/s):          8.36
---------------Time to First Token----------------
Mean TTFT (ms):                          430.08
Median TTFT (ms):                        476.51
P99 TTFT (ms):                           477.68
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          238.92
Median TPOT (ms):                        238.92
P99 TPOT (ms):                           238.95
---------------Inter-token Latency----------------
Mean ITL (ms):                           238.92
Median ITL (ms):                         238.86
P99 ITL (ms):                            239.23
==================================================

[tjtanaa]

@whx-sjtu PTAL

[tjtanaa]

We need to rethink this. This changes also reflects on CUDA code path

[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