[NVIDIA] Support Flashinfer TRTLLM FP8-q/kv/out Attention Kernel

[elvischenv]

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.

Purpose

• Refactor the AttentionStaticQuantPattern in fusion_attn pass, which will fuse the attn+fp8_quant pattern for using the TRTLLM FP8-in FP8-out kernel.
• Align with other backends, Flashinfer will insert FP8 quant op for query if kv cache dtype is set to FP8.

Test Plan && Test Result

Functional:

• FP8 TRTLLM Prefill/Decode kernel unit test: tests/kernels/attention/test_flashinfer_trtllm_attention.py

====== 192 passed, 10 warnings in 143.25s (0:02:23) ======

• AttentionStaticQuantPattern fusion_attn pass: tests/compile/test_fusion_attn.py::test_attention_quant_pattern

====== 6 passed, 4 warnings in 33.01s ========

E2E Performance: nvidia/Llama-4-Scout-17B-16E-Instruct-FP8

main

--kv-cache-dtype = auto                                                      --kv-cache-dtype = fp8
============ Serving Benchmark Result ============                           ============ Serving Benchmark Result ============
Successful requests:                     640                                 Successful requests:                     640
Maximum request concurrency:             128                                 Maximum request concurrency:             128
Benchmark duration (s):                  209.72                              Benchmark duration (s):                  229.07
Total input tokens:                      653975                              Total input tokens:                      653975
Total generated tokens:                  655360                              Total generated tokens:                  655360
Request throughput (req/s):              3.05                                Request throughput (req/s):              2.79
Output token throughput (tok/s):         3124.98                             Output token throughput (tok/s):         2860.98
Total Token throughput (tok/s):          6243.37                             Total Token throughput (tok/s):          5715.91
---------------Time to First Token----------------                           ---------------Time to First Token----------------
Mean TTFT (ms):                          1367.30                             Mean TTFT (ms):                          1413.01
Median TTFT (ms):                        1007.55                             Median TTFT (ms):                        1050.07
P99 TTFT (ms):                           5422.35                             P99 TTFT (ms):                           5486.11
-----Time per Output Token (excl. 1st token)------                           -----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          39.61                               Mean TPOT (ms):                          43.35
Median TPOT (ms):                        39.88                               Median TPOT (ms):                        43.60
P99 TPOT (ms):                           40.60                               P99 TPOT (ms):                           44.49
---------------Inter-token Latency----------------                           ---------------Inter-token Latency----------------
Mean ITL (ms):                           39.61                               Mean ITL (ms):                           43.35
Median ITL (ms):                         36.09                               Median ITL (ms):                         39.67
P99 ITL (ms):                            326.60                              P99 ITL (ms):                            317.85
==================================================                           ==================================================
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|    |Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|    |-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9181|±  |0.0076|    |gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9234|±  |0.0073|
|     |       |strict-match    |     5|exact_match|↑  |0.8999|±  |0.0083|    |     |       |strict-match    |     5|exact_match|↑  |0.8999|±  |0.0083|

PR

--kv-cache-dtype = auto                                                      --kv-cache-dtype = fp8
============ Serving Benchmark Result ============                           ============ Serving Benchmark Result ============
Successful requests:                     640                                 Successful requests:                     640
Maximum request concurrency:             128                                 Maximum request concurrency:             128
Benchmark duration (s):                  213.18                              Benchmark duration (s):                  231.03
Total input tokens:                      653975                              Total input tokens:                      653975
Total generated tokens:                  655360                              Total generated tokens:                  655360
Request throughput (req/s):              3.00                                Request throughput (req/s):              2.77
Output token throughput (tok/s):         3074.24                             Output token throughput (tok/s):         2836.64
Total Token throughput (tok/s):          6141.98                             Total Token throughput (tok/s):          5667.28
---------------Time to First Token----------------                           ---------------Time to First Token----------------
Mean TTFT (ms):                          1375.34                             Mean TTFT (ms):                          1439.79
Median TTFT (ms):                        1019.52                             Median TTFT (ms):                        1061.45
P99 TTFT (ms):                           5287.76                             P99 TTFT (ms):                           5711.34
-----Time per Output Token (excl. 1st token)------                           -----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          40.28                               Mean TPOT (ms):                          43.71
Median TPOT (ms):                        40.52                               Median TPOT (ms):                        43.82
P99 TPOT (ms):                           41.37                               P99 TPOT (ms):                           44.86
---------------Inter-token Latency----------------                           ---------------Inter-token Latency----------------
Mean ITL (ms):                           40.28                               Mean ITL (ms):                           43.71
Median ITL (ms):                         36.71                               Median ITL (ms):                         40.04
P99 ITL (ms):                            309.98                              P99 ITL (ms):                            342.09
==================================================                           ==================================================
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|    |Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|    |-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9181|±  |0.0076|    |gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9234|±  |0.0073|
|     |       |strict-match    |     5|exact_match|↑  |0.9007|±  |0.0082|    |     |       |strict-match    |     5|exact_match|↑  |0.9007|±  |0.0082|

--kv-cache-dtype = fp8, enable_attn_fusion
============ Serving Benchmark Result ============
Successful requests:                     640
Maximum request concurrency:             128
Benchmark duration (s):                  199.56
Total input tokens:                      653975
Total generated tokens:                  655360
Request throughput (req/s):              3.21
Output token throughput (tok/s):         3284.02
Total Token throughput (tok/s):          6561.10
---------------Time to First Token----------------
Mean TTFT (ms):                          1347.81
Median TTFT (ms):                        999.97
P99 TTFT (ms):                           5192.63
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          37.65
Median TPOT (ms):                        37.89
P99 TPOT (ms):                           38.71
---------------Inter-token Latency----------------
Mean ITL (ms):                           37.65
Median ITL (ms):                         34.13
P99 ITL (ms):                            302.65
==================================================
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9128|±  |0.0078|
|     |       |strict-match    |     5|exact_match|↑  |0.8923|±  |0.0085|

(Optional) Documentation Update

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[gemini-code-assist]

Code Review

This pull request adds support for the Flashinfer TRT-LLM FP8-query/output attention kernel. The changes span across benchmarks, tests, and core attention backend logic. The main changes involve updating the Flashinfer API calls to support an out parameter for in-place operations, and adding logic to handle FP8 quantization for queries and outputs. The PR also includes a significant refactoring of CUDA graph support for attention backends.

My review identifies two main issues. First, a critical bug in vllm/attention/layer.py where the query_scale for FP8 quantization is not being correctly propagated to the attention implementation. Second, a high-severity issue in vllm/v1/attention/backends/flashinfer.py where the usage of the TRT-LLM attention kernel is hardcoded, which limits flexibility.

[mergify]

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

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

[nvpohanh]

not related to this PR, but I think has_nvidia_artifactory() can be removed because FlashInfer now supports downloading all cubins at once.

[nvpohanh]

can do this in another PR

[elvischenv]

Got it.

[mgoin]

This PR is looking really good! I think this is essentially good to merge, so I've merged with main to see if we can get green

[ProExpertProg]

This PR is looking really good! Thanks for all your hard work

[mergify]

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

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