Support FlashMLA backend cuda graph by sleepcoo · Pull Request #4514 · sgl-project/sglang

sleepcoo · 2025-03-17T12:23:12Z

Motivation

Support FlashMLA backend cuda graph. Optimize index calculation, complete the calculation in init_forward

Modifications

Optimize the FlashInfer block table calculation logic to compute only once during the forward pass.
Support FlashMLA backend CUDA Graph.
Automatically set page=64 when launching FlashMLA.

Test

deepseekV3 accuracy test
GSM8K Accuracy: 0.980
MMLU Average accuracy: 0.878

todo

performance test
add unit test
enable speculative sampling in FlashMLA in the next PR @ispobock

…metadata.

Co-authored-by: yinfan98 <1106310035@qq.com> Co-authored-by: Hongbosherlock <hongbosherlock@gmail.com>

merrymercy · 2025-03-19T15:41:21Z

+
+        if forward_mode.is_decode_or_idle():
+            seq_lens = seq_lens[:bs]
+            max_seqlen_pad = triton.cdiv(seq_lens.max().item(), PAGE_SIZE)


We should avoid CPU-GPU synchronization by avoiding the use of seq_lens.max().item().
Can you derive this value from seq_lens_cpu?

I found this problem. I was too busy at work today and didn't have time to modify it. I will fix it tomorrow.

Nekofish-L · 2025-03-21T06:40:16Z

I attempted to test FlashMLA + CUDA Graph on your commit, but I was not successful. The following error occurred:

Assertion failed: /root/miniconda3/lib/python3.12/site-packages/deep_gemm/jit/../include/deep_gemm/tma_utils.cuh:80, condition: result == CUDA_SUCCESS
Fatal Python error: PyThreadState_Get: the function must be called with the GIL held, but the GIL is released (the current Python thread state is NULL)
Python runtime state: initialized

Thread 0x00007ff6337c6700 (most recent call first):
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/threading.py", line 331 in wait
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/threading.py", line 629 in wait
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/tqdm/_monitor.py", line 60 in run
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/threading.py", line 1045 in _bootstrap_inner
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/threading.py", line 1002 in _bootstrap

Current thread 0x00007ffb40851400 (most recent call first):
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/deep_gemm/jit/runtime.py", line 45 in __call__
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/deep_gemm/jit_kernels/gemm.py", line 178 in gemm_fp8_fp8_bf16_nt
  File "/data/deepseek/sglang/python/sglang/srt/layers/quantization/fp8_kernel.py", line 61 in deep_gemm_fp8_fp8_bf16_nt
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/_ops.py", line 1116 in __call__
  File "/data/deepseek/sglang/python/sglang/srt/layers/quantization/fp8_kernel.py", line 782 in w8a8_block_fp8_matmul
  File "/data/deepseek/sglang/python/sglang/srt/layers/quantization/fp8_utils.py", line 144 in apply_w8a8_block_fp8_linear
  File "/data/deepseek/sglang/python/sglang/srt/layers/quantization/fp8.py", line 422 in apply
  File "/data/deepseek/sglang/python/sglang/srt/layers/linear.py", line 1277 in forward
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747 in _call_impl
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736 in _wrapped_call_impl
  File "/data/deepseek/sglang/python/sglang/srt/models/deepseek_v2.py", line 813 in forward_absorb
  File "/data/deepseek/sglang/python/sglang/srt/models/deepseek_v2.py", line 698 in forward
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747 in _call_impl
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736 in _wrapped_call_impl
  File "/data/deepseek/sglang/python/sglang/srt/models/deepseek_v2.py", line 1061 in forward
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747 in _call_impl
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736 in _wrapped_call_impl
  File "/data/deepseek/sglang/python/sglang/srt/models/deepseek_v2.py", line 1166 in forward
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1747 in _call_impl
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1736 in _wrapped_call_impl
  File "/data/deepseek/sglang/python/sglang/srt/models/deepseek_v2.py", line 1206 in forward
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 116 in decorate_context
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 433 in run_once
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 440 in capture_one_batch_size
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 348 in capture
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/cuda_graph_runner.py", line 264 in __init__
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/model_runner.py", line 909 in init_cuda_graphs
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/model_runner.py", line 206 in initialize
  File "/data/deepseek/sglang/python/sglang/srt/model_executor/model_runner.py", line 168 in __init__
  File "/data/deepseek/sglang/python/sglang/srt/managers/tp_worker.py", line 74 in __init__
  File "/data/deepseek/sglang/python/sglang/srt/managers/tp_worker_overlap_thread.py", line 63 in __init__
  File "/data/deepseek/sglang/python/sglang/srt/managers/scheduler.py", line 227 in __init__
  File "/data/deepseek/sglang/python/sglang/srt/managers/scheduler.py", line 1809 in run_scheduler_process
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/multiprocessing/process.py", line 108 in run
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/multiprocessing/process.py", line 314 in _bootstrap
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/multiprocessing/spawn.py", line 135 in _main
  File "/root/miniconda3/envs/sgl_main/lib/python3.11/multiprocessing/spawn.py", line 122 in spawn_main
  File "<string>", line 1 in <module>

Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, charset_normalizer.md, requests.packages.charset_normalizer.md, requests.packages.chardet.md, psutil._psutil_linux, psutil._psutil_posix, torch._C, torch._C._dynamo.autograd_compiler, torch._C._dynamo.eval_frame, torch._C._dynamo.guards, torch._C._dynamo.utils, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, zmq.backend.cython._zmq, multidict._multidict, yarl._quoting_c, propcache._helpers_c, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket.mask, aiohttp._websocket.reader_c, frozenlist._frozenlist, uvloop.loop, setproctitle, yaml._yaml, markupsafe._speedups, PIL._imaging, PIL._imagingft, sentencepiece._sentencepiece, msgspec._core, msgpack._cmsgpack, google._upb._message, ray._raylet, cuda_utils, regex._regex, __triton_launcher (total: 52)

The test command I used is:

python3 -m sglang.launch_server --model /data/deepseek/DeepSeek-R1 \
    --dist-init-addr $master_ip:5000 --nnodes 2 --node-rank 0 \
    --host 0.0.0.0 --port 8124 --trust-remote-code --tp 16 --enable-flashmla --disable-cuda-graph


python3 -m sglang.launch_server --model /data/deepseek/DeepSeek-R1 \
    --dist-init-addr $master_ip:5000 --nnodes 2 --node-rank 1 \
    --host 0.0.0.0 --port 8124 --trust-remote-code --tp 16 --enable-flashmla --disable-cuda-graph

However, everything works fine as long as I don't add --enable-flashmha.

Environment:

CentOS
2 * H20 * 8

sgl-kernel                        0.0.5.post3
sglang                            0.4.4.post1         /data/deepseek/sglang/python
flash_mla                         1.0.0+b31bfe7

tianchongchong · 2025-03-21T07:51:21Z

hi @sleepcoo Great pr! but I did some simple tests, and it seems that the performance of flashmla is not as good as of triton_backend. What could be the reason?

command:

python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3 --max-concurrency 1 --random-input 128 --random-output 1024 --dataset-path /models/dataset/ShareGPT_V3_unfiltered_cleaned_split.json

flashmla:

python3 -m sglang.launch_server --model-path /models/DeepSeek-R1
--tensor-parallel-size 8 --disable-custom-all-reduce --host 0.0.0.0
--port 30000 --trust-remote-code --mem-fraction-static 0.9 --enable-flashmla

triton_backend:

python3 -m sglang.launch_server --model-path /models/DeepSeek-R1
--tensor-parallel-size 8 --disable-custom-all-reduce --host 0.0.0.0
--port 30000 --trust-remote-code --mem-fraction-static 0.9

result:

flashmla:

============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max reqeuest concurrency: 1
Successful requests: 3
Benchmark duration (s): 112.67
Total input tokens: 159
Total generated tokens: 2467
Total generated tokens (retokenized): 2463
Request throughput (req/s): 0.03
Input token throughput (tok/s): 1.41
Output token throughput (tok/s): 21.90
Total token throughput (tok/s): 23.31
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 37537.68
Median E2E Latency (ms): 38623.17
---------------Time to First Token----------------
Mean TTFT (ms): 320.54
Median TTFT (ms): 324.43
P99 TTFT (ms): 332.49
---------------Inter-Token Latency----------------
Mean ITL (ms): 45.31
Median ITL (ms): 45.33
P95 ITL (ms): 45.92
P99 ITL (ms): 46.17
Max ITL (ms): 50.67

triton_backend:

============ Serving Benchmark Result ============
Backend: sglang
Traffic request rate: inf
Max reqeuest concurrency: 1
Successful requests: 3
Benchmark duration (s): 110.48
Total input tokens: 159
Total generated tokens: 2467
Total generated tokens (retokenized): 2466
Request throughput (req/s): 0.03
Input token throughput (tok/s): 1.44
Output token throughput (tok/s): 22.33
Total token throughput (tok/s): 23.77
Concurrency: 1.00
----------------End-to-End Latency----------------
Mean E2E Latency (ms): 36809.89
Median E2E Latency (ms): 37824.62
---------------Time to First Token----------------
Mean TTFT (ms): 399.44
Median TTFT (ms): 350.24
P99 TTFT (ms): 523.33
---------------Inter-Token Latency----------------
Mean ITL (ms): 44.33
Median ITL (ms): 44.33
P95 ITL (ms): 44.83
P99 ITL (ms): 44.95
Max ITL (ms): 47.54

Environment:

Ubuntu 22.04
H20 * 8

sleepcoo · 2025-03-21T08:45:02Z

hi @sleepcoo Great pr! but I did some simple tests, and it seems that the performance of flashmla is not as good as of triton_backend. What could be the reason?

command:

python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3 --max-concurrency 1 --random-input 128 --random-output 1024 --dataset-path /models/dataset/ShareGPT_V3_unfiltered_cleaned_split.json

flashmla:

python3 -m sglang.launch_server --model-path /models/DeepSeek-R1 --tensor-parallel-size 8 --disable-custom-all-reduce --host 0.0.0.0 --port 30000 --trust-remote-code --mem-fraction-static 0.9 --enable-flashmla

triton_backend:

python3 -m sglang.launch_server --model-path /models/DeepSeek-R1 --tensor-parallel-size 8 --disable-custom-all-reduce --host 0.0.0.0 --port 30000 --trust-remote-code --mem-fraction-static 0.9

result:

flashmla:

============ Serving Benchmark Result ============ Backend: sglang Traffic request rate: inf Max reqeuest concurrency: 1 Successful requests: 3 Benchmark duration (s): 112.67 Total input tokens: 159 Total generated tokens: 2467 Total generated tokens (retokenized): 2463 Request throughput (req/s): 0.03 Input token throughput (tok/s): 1.41 Output token throughput (tok/s): 21.90 Total token throughput (tok/s): 23.31 Concurrency: 1.00 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 37537.68 Median E2E Latency (ms): 38623.17 ---------------Time to First Token---------------- Mean TTFT (ms): 320.54 Median TTFT (ms): 324.43 P99 TTFT (ms): 332.49 ---------------Inter-Token Latency---------------- Mean ITL (ms): 45.31 Median ITL (ms): 45.33 P95 ITL (ms): 45.92 P99 ITL (ms): 46.17 Max ITL (ms): 50.67

triton_backend:

============ Serving Benchmark Result ============ Backend: sglang Traffic request rate: inf Max reqeuest concurrency: 1 Successful requests: 3 Benchmark duration (s): 110.48 Total input tokens: 159 Total generated tokens: 2467 Total generated tokens (retokenized): 2466 Request throughput (req/s): 0.03 Input token throughput (tok/s): 1.44 Output token throughput (tok/s): 22.33 Total token throughput (tok/s): 23.77 Concurrency: 1.00 ----------------End-to-End Latency---------------- Mean E2E Latency (ms): 36809.89 Median E2E Latency (ms): 37824.62 ---------------Time to First Token---------------- Mean TTFT (ms): 399.44 Median TTFT (ms): 350.24 P99 TTFT (ms): 523.33 ---------------Inter-Token Latency---------------- Mean ITL (ms): 44.33 Median ITL (ms): 44.33 P95 ITL (ms): 44.83 P99 ITL (ms): 44.95 Max ITL (ms): 47.54

Environment:

Ubuntu 22.04

H20 * 8

In this PR, we have fixed the performance issues and tested it. In certain cases, flashmla has advantages.

sleepcoo and others added 3 commits March 17, 2025 08:21

Optimize index calculation, complete the calculation in init_forward_…

f5a0b3d

…metadata.

init flashmla backend cuda_graph

9e09a06

Merge branch 'main' into opt-flashmla-backend

4649c5e

sleepcoo changed the title ~~Optimize index calculation, complete the calculation in init_forward~~ Support FlashMLA backend cuda graph Mar 17, 2025

This was referenced Mar 13, 2025

Development Roadmap (2025 H1) #4042

Closed

[Feature] DeepSeek V3 optimization #2591

Closed

zhyncs added the high priority label Mar 17, 2025

zhyncs self-assigned this Mar 17, 2025

Support FlashMLA backend cuda graph capture

c28f9bc

Co-authored-by: yinfan98 <1106310035@qq.com> Co-authored-by: Hongbosherlock <hongbosherlock@gmail.com>

sleepcoo force-pushed the opt-flashmla-backend branch from 38f575a to c28f9bc Compare March 18, 2025 04:28

sleepcoo and others added 2 commits March 18, 2025 12:28

Merge branch 'main' into opt-flashmla-backend

a78abfa

Support FlashMLA backend cuda graph replay.

4b790da

Co-authored-by: yinfan98 <1106310035@qq.com> Co-authored-by: Hongbosherlock <hongbosherlock@gmail.com>

sleepcoo force-pushed the opt-flashmla-backend branch from 4a47be2 to 4b790da Compare March 18, 2025 05:10

sleepcoo and others added 3 commits March 18, 2025 05:42

fix todo

d40ef82

Merge branch 'main' into opt-flashmla-backend

069b8a4

fix cuda graph accuracy

0cdabe6

sleepcoo marked this pull request as ready for review March 18, 2025 09:28

sleepcoo requested review from ByronHsu, HaiShaw, Ying1123, hnyls2002, ispobock, merrymercy and zhyncs as code owners March 18, 2025 09:28

sleepcoo and others added 2 commits March 19, 2025 16:14

Merge branch 'main' into opt-flashmla-backend

b06301f

Merge branch 'main' into opt-flashmla-backend

f67ab56

zhyncs merged commit b6944f9 into sgl-project:main Mar 19, 2025

merrymercy reviewed Mar 19, 2025

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FlamingoPg mentioned this pull request Mar 19, 2025

[Fix] fix FlashMLA cudagraph config #4591

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Support FlashMLA backend cuda graph#4514

Support FlashMLA backend cuda graph#4514
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sleepcoo:opt-flashmla-backend

sleepcoo commented Mar 17, 2025 •

edited

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merrymercy Mar 19, 2025

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sleepcoo Mar 19, 2025

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Nekofish-L commented Mar 21, 2025

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tianchongchong commented Mar 21, 2025

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sleepcoo commented Mar 21, 2025

command:

flashmla:

triton_backend:

result:

flashmla:

triton_backend:

Environment:

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sleepcoo commented Mar 17, 2025 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Motivation

Modifications

Test

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merrymercy Mar 19, 2025

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sleepcoo Mar 19, 2025

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Nekofish-L commented Mar 21, 2025

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tianchongchong commented Mar 21, 2025

command:

flashmla:

triton_backend:

result:

flashmla:

triton_backend:

Environment:

Uh oh!

sleepcoo commented Mar 21, 2025

command:

flashmla:

triton_backend:

result:

flashmla:

triton_backend:

Environment:

Uh oh!

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sleepcoo commented Mar 17, 2025 •

edited

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