feat: MLA prefill enable FA4 fp8 output by carlyou · Pull Request #43050 · vllm-project/vllm

carlyou · 2026-05-19T04:41:12Z

Completes FlashAttn x Static FP8 in #35792

Purpose

Enables fused static FP8 output in FA4 backend
currently points at feat: FA4 flash att supports fused fp8 output flash-attention#135

Test Plan

unit tests
eval FP8 on H100 and B200
benchmark FP8 on H100 and B200

Test Result

Eval

============================================
  EVAL SUMMARY
============================================

Config:      mla_fa4_fp8_output/b200_dscoder_v2_lite_fp8_eval
Model:       RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8
Repo:        https://github.com/carlyou/vllm.git

Hardware/Runtime:
  GPU:       NVIDIA B200
  GPU mem:   183359 MiB
  CUDA:      13.0
  PyTorch:   2.11.0+cu130
  Python:    3.12.3
  Platform:  x86_64

Runs:
  - main_gsm8k:
      branch:     main
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 4096 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      eval:       $ python tests/evals/gsm8k/gsm8k_eval.py --num-shots 5 --num-questions 1319 --max-tokens 256 --temperature 0.0 --seed 42
  - feat_gsm8k:
      branch:     test--mla-fa4-native-fp8-output
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 4096 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      eval:       $ python tests/evals/gsm8k/gsm8k_eval.py --num-shots 5 --num-questions 1319 --max-tokens 256 --temperature 0.0 --seed 42

| Run        | Accuracy | Invalid | Questions | Latency (s) | Q/s  | Tokens | Tok/s |
| ---------- | -------- | ------- | --------- | ----------- | ---- | ------ | ----- |
| main_gsm8k |    0.785 |   0.000 |      1319 |      587.03 | 2.25 | 168785 | 287.5 |
| feat_gsm8k |    0.769 |   0.011 |      1319 |      600.43 | 2.20 | 167201 | 278.5 |

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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.

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Code Review

This pull request integrates support for fused FP8 output in Multi-Head Latent Attention (MLA) using FlashAttention-4 (FA4) on Blackwell GPUs. It updates the vllm-flash-attn dependency to a specific commit, modifies the attention backend interfaces to accept an output_scale parameter, and implements logic in mla_attention.py to bypass post-quantization when the attention kernel writes directly to the quantized output. I have no feedback to provide.

carlyou · 2026-05-19T16:57:29Z

Benchmark, numbers seem unstable, will rerun later.

============================================
  BENCHMARK SUMMARY
============================================

Config:      mla_fa4_fp8_output/b200_dscoder_v2_lite_fp8_bench
Model:       RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8
Repo:        https://github.com/carlyou/vllm.git
Work dir:    /tmp/vllm-bench

Hardware/Runtime:
  GPU:       NVIDIA B200
  GPU mem:   183359 MiB
  CUDA:      13.0
  PyTorch:   2.11.0+cu130
  Python:    3.12.3
  Platform:  x86_64

Runs:
  - feat_warmup:
      branch:     test--mla-fa4-native-fp8-output
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 16384 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      bench:      $ vllm bench serve --model RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --num-prompts 20 --request-rate inf --random-input-len 4096 --random-output-len 128 --num-warmups 5 --ignore-eos
  - feat_baseline:
      branch:     test--mla-fa4-native-fp8-output
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 16384 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      bench:      $ vllm bench serve --model RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --num-prompts 1000 --request-rate inf --random-input-len 4096 --random-output-len 128 --num-warmups 50 --ignore-eos
  - main_warmup:
      branch:     main
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 16384 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      bench:      $ vllm bench serve --model RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --num-prompts 20 --request-rate inf --random-input-len 4096 --random-output-len 128 --num-warmups 5 --ignore-eos
  - main_baseline:
      branch:     main
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 16384 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      bench:      $ vllm bench serve --model RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --num-prompts 1000 --request-rate inf --random-input-len 4096 --random-output-len 128 --num-warmups 50 --ignore-eos

| Metric                     | feat_warmup | feat_baseline | main_warmup | main_baseline |
| -------------------------- | -----------: | -------------: | -----------: | -------------: |
| Successful requests        |          20 |          1000 |          20 |          1000 |
| Failed requests            |           0 |             0 |           0 |             0 |
| Request rate (RPS)         |         N/A |           N/A |         N/A |           N/A |
| Benchmark duration (s)     |       18.11 |        180.72 |       48.15 |        234.52 |
| Total input tokens         |       81920 |       4096000 |       81920 |       4096000 |
| Total generated tokens     |        2560 |        128000 |        2560 |        128000 |
| Request throughput (req/s) |        1.10 |          5.53 |        0.42 |          4.26 |
| Output token tput (tok/s)  |      141.39 |        708.27 |       53.17 |        545.79 |
| Peak output tput (tok/s)   |      260.00 |       2542.00 |      260.00 |       2553.00 |
| Total token tput (tok/s)   |     4665.88 |      23372.88 |     1754.66 |      18011.17 |
| Peak concurrent requests   |       20.00 |       1000.00 |       20.00 |       1000.00 |
| **Time to First Token** |             |               |             |               |
| Mean TTFT (ms)             |     1030.18 |      40027.97 |    11673.50 |      38276.86 |
| Median TTFT (ms)           |      818.93 |      40016.72 |    11191.26 |      38265.54 |
| P99 TTFT (ms)              |     3087.98 |      67248.10 |    29057.46 |      64787.02 |
| **Time per Output Token** |             |               |             |               |
| Mean TPOT (ms)             |      123.87 |        214.17 |      260.21 |        296.99 |
| Median TPOT (ms)           |      127.86 |        109.88 |      273.99 |        106.34 |
| P99 TPOT (ms)              |      134.13 |        871.12 |      315.00 |       1314.57 |
| **Inter-token Latency** |             |               |             |               |
| Mean ITL (ms)              |      123.87 |        214.17 |      260.21 |        296.99 |
| Median ITL (ms)            |       78.29 |        108.16 |       80.01 |        104.80 |
| P99 ITL (ms)               |     1582.71 |       1046.98 |     1588.46 |       1989.05 |

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MatthewBonanni

LGTM, thanks for the contribution!

MatthewBonanni

Actually, are these numbers up to date? The accuracy regression / 0.011 invalid is concerning. Also these perf numbers contradict your later comment (which shows a speedup that does seem unrealistic?)

| Run        | Accuracy | Invalid | Questions | Latency (s) | Q/s  | Tokens | Tok/s |
| ---------- | -------- | ------- | --------- | ----------- | ---- | ------ | ----- |
| main_gsm8k |    0.785 |   0.000 |      1319 |      587.03 | 2.25 | 168785 | 287.5 |
| feat_gsm8k |    0.769 |   0.011 |      1319 |      600.43 | 2.20 | 167201 | 278.5 |

carlyou · 2026-05-28T17:59:23Z

Actually, are these numbers up to date? The accuracy regression / 0.011 invalid is concerning. Also these perf numbers contradict your later comment (which shows a speedup that does seem unrealistic?)
| Run        | Accuracy | Invalid | Questions | Latency (s) | Q/s  | Tokens | Tok/s |
| ---------- | -------- | ------- | --------- | ----------- | ---- | ------ | ----- |
| main_gsm8k |    0.785 |   0.000 |      1319 |      587.03 | 2.25 | 168785 | 287.5 |
| feat_gsm8k |    0.769 |   0.011 |      1319 |      600.43 | 2.20 | 167201 | 278.5 |

let me rerun both to confirm.

carlyou · 2026-05-29T15:32:27Z

@MatthewBonanni here's the new result:
Eval:

| Run        | Accuracy | Invalid | Questions | Latency (s) | Q/s     | Tokens | Tok/s |
| ---------- | -------- | ------- | --------- | ----------- | ------- | ------ | ----- |
| main_gsm8k |    0.787 |   0.000 |      1319 |      528.66 |    2.49 | 168370 | 318.5 |
| feat_gsm8k |    0.778 |   0.000 |      1319 |      525.19 |    2.51 | 168267 | 320.4 |

Benchmark (using vllm bench) has large variance in this case. I did more runs, and each showed different result:

  ┌────────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┐
  │ Metric (baseline)  │  A feat / main  │  B feat / main  │  C feat / main  │  D feat / main  │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ feat base          │ old c6c6f4f3a   │ 0173ea046       │ 0173ea046       │ 0173ea046       │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ Duration (s)       │ 184.3 / 205.5   │ 228.6 / 186.1   │ 223.9 / 228.8   │ 202.1 / 180.8   │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ Throughput (req/s) │ 5.43 / 4.87     │ 4.38 / 5.37     │ 4.47 / 4.37     │ 4.95 / 5.53     │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ Output tok/s       │ 694.5 / 622.8   │ 560.1 / 687.8   │ 571.6 / 559.5   │ 633.3 / 707.8   │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ Mean TTFT (ms)     │ 37,846 / 35,882 │ 39,297 / 36,724 │ 37,389 / 36,829 │ 28,498 / 28,147 │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ Mean TPOT (ms)     │ 219.2 / 228.5   │ 280.6 / 214.8   │ 276.2 / 276.9   │ 250.7 / 232.2   │
  ├────────────────────┼─────────────────┼─────────────────┼─────────────────┼─────────────────┤
  │ within-run verdict │ feat +11%       │ main +23%       │ feat +2%        │ main +12%       │
  └────────────────────┴─────────────────┴─────────────────┴─────────────────┴─────────────────┘

MatthewBonanni · 2026-05-29T15:35:15Z

@carlyou thanks! What are A, B, C, and D here? Different runs of the same benchmark?

carlyou · 2026-05-29T15:47:09Z

@carlyou thanks! What are A, B, C, and D here? Different runs of the same benchmark?

@MatthewBonanni sorry, they are different runs of the same bench setup.
Each run compares feat vs main branch

Runs:
  - feat_baseline:
      branch:     test--mla-fa4-native-fp8-output
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 16384 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      bench:      $ vllm bench serve --model RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --num-prompts 1000 --request-rate inf --random-input-len 4096 --random-output-len 128 --num-warmups 50 --ignore-eos
  - main_baseline:
      branch:     main
      server:     $ vllm serve RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --tensor-parallel-size 1 --max-model-len 16384 --trust-remote-code --port 8000 --gpu-memory-utilization 0.85 -cc {"cudagraph_mode": "NONE", "custom_ops": ["+quant_fp8"], "pass_config": {"fuse_attn_quant": true}}
      bench:      $ vllm bench serve --model RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 --num-prompts 1000 --request-rate inf --random-input-len 4096 --random-output-len 128 --num-warmups 50 --ignore-eos

MatthewBonanni · 2026-05-29T16:21:40Z

@carlyou thanks for providing the details. Maybe the results would be clearer with a batch size 1 / concurrency 1 benchmark where we focus on TTFT?

Alternatively, an even better option would be the microbenchmark in benchmarks/attention_benchmarks/benchmark.py (which you'll need to update to pass output_scale, and add a post-quant op to mla_runner.py for the baseline run)

MatthewBonanni · 2026-06-02T21:13:50Z

          vllm-flash-attn
          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG bce29425653ec0fbc579d329883030e832d15ada
+          GIT_TAG d0a0e2bf2113fcfd0336e5dd201a5fd89b297a8f


Now that we've landed vllm-project/flash-attention#141 and #44065, this change is no longer necessary.

Signed-off-by: Carl You <4531192+carlyou@users.noreply.github.com>

Co-authored-by: Matthew Bonanni <mbonanni@redhat.com> Signed-off-by: Carl Y <4531192+carlyou@users.noreply.github.com>

Signed-off-by: Carl You <4531192+carlyou@users.noreply.github.com>

carlyou · 2026-06-02T21:41:57Z

@MatthewBonanni added mla benchmark per suggestion. and rebased from latest main.

FP8 Output Results:
                       Attention Benchmark Results                        
┏━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┓
┃ Batch ┃         ┃ Batch ┃ post_quant ┃ post_quant ┃    fused ┃   fused ┃
┃ Spec  ┃ Type    ┃  Size ┃   Time (s) ┃    vs Best ┃ Time (s) ┃ vs Best ┃
┡━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━┩
│ q512  │ prefill │     1 │   0.000037 │     111.2% │ 0.000033 │  100.0% │
│ q1k   │ prefill │     1 │   0.000052 │     108.6% │ 0.000047 │  100.0% │
│ q2k   │ prefill │     1 │   0.000080 │     110.6% │ 0.000073 │  100.0% │
│ q4k   │ prefill │     1 │   0.000150 │     113.3% │ 0.000133 │  100.0% │
│ q8k   │ prefill │     1 │   0.000396 │     107.7% │ 0.000368 │  100.0% │
│ 2q4k  │ prefill │     2 │   0.000283 │     112.4% │ 0.000252 │  100.0% │
│ 4q4k  │ prefill │     4 │   0.000569 │     112.5% │ 0.000506 │  100.0% │
│ 8q4k  │ prefill │     8 │   0.001132 │     113.7% │ 0.000996 │  100.0% │
└───────┴─────────┴───────┴────────────┴────────────┴──────────┴─────────┘

please check the benchmark result above^
ps. the vllm bench run still has variance with bs=1 and output_len=1 (whichever runs last is faster...).

MatthewBonanni

Great! just a few more tweaks

MatthewBonanni · 2026-06-03T14:23:18Z

+        elif output_scale is not None:
+            # FA4 already wrote results in-place into `output`.
+            assert isinstance(output_prefill, torch.Tensor)
        else:
            assert isinstance(output_prefill, torch.Tensor)


These are identical branches

MatthewBonanni · 2026-06-03T14:25:24Z

+        # FA4 can write native fused FP8 (e4m3fn) output on Blackwell
+        # SM100/SM110 only (see flash-attention#135); FA4 natively handles
+        # MLA's mismatched qk/v head dims so no V padding is involved.
+        # Only static per-tensor FP8 is wired today; per-group FP8 / NVFP4
+        # still go through the post-quant path. get_device_capability() is
+        # @cache'd, so this stays cheap on the hot path.


nit: remove unnecessary comment

Suggested change

# FA4 can write native fused FP8 (e4m3fn) output on Blackwell

# SM100/SM110 only (see flash-attention#135); FA4 natively handles

# MLA's mismatched qk/v head dims so no V padding is involved.

# Only static per-tensor FP8 is wired today; per-group FP8 / NVFP4

# still go through the post-quant path. get_device_capability() is

# @cache'd, so this stays cheap on the hot path.

MatthewBonanni · 2026-06-03T14:30:33Z

+        if self._is_vllm_fa:
+            return self._flash_attn_varlen_diff_headdims(
+                q=q,
+                k=k,
+                v=v,
+                cu_seqlens_q=self._prefill_metadata.query_start_loc,
+                cu_seqlens_k=self._prefill_metadata.query_start_loc,
+                max_seqlen_q=self._prefill_metadata.max_query_len,
+                max_seqlen_k=self._prefill_metadata.max_query_len,
+                softmax_scale=self.scale,
+                causal=True,
+                return_softmax_lse=return_softmax_lse,
+                out=out,
+                output_scale=output_scale,
+            )
+        assert out is None and output_scale is None


Instead of adding this conditional, please modify self._flash_attn_varlen_diff_headdims to add out and output_scale to kwargs (see line 106)

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carlyou marked this pull request as ready for review May 19, 2026 16:57

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[Feature]: Support MLA attention + quant fusions #35792

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carlyou force-pushed the feat--mla-fa4-native-fp8-output branch from 5a3122b to 722d066 Compare May 27, 2026 21:02

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carlyou force-pushed the feat--mla-fa4-native-fp8-output branch from 722d066 to 50fd06a Compare June 2, 2026 07:44

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carlyou and others added 9 commits June 2, 2026 14:31

feat: MLA prefill enable FA4 fp8 output

915cc76

Signed-off-by: Carl You <4531192+carlyou@users.noreply.github.com>

add test & bump sha

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Update cmake/external_projects/vllm_flash_attn.cmake

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Co-authored-by: Matthew Bonanni <mbonanni@redhat.com> Signed-off-by: Carl Y <4531192+carlyou@users.noreply.github.com>

chore: update FlashAttention in cmake

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fix: address comment

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chore: benchmark & test

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chore: more benchmark

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fix: benchmark

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rebase

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carlyou force-pushed the feat--mla-fa4-native-fp8-output branch from 1477e2f to 1548033 Compare June 2, 2026 21:34

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carlyou commented May 19, 2026 •

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MatthewBonanni commented May 29, 2026 •

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carlyou commented May 29, 2026 •

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