[SpecDecode] Support FlashInfer in DraftModelRunner #6926

bong-furiosa · 2024-07-30T04:46:21Z

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This PR resolves the benchmark_serving.py error for Speculative Decoding when using FlashInfer Backend.

After fixing, we can see that the benchmark results are correctly displayed in both FlashAttn Backend and FlashInfer Backend.

FLASH_ATTN backend

============ Serving Benchmark Result ============
Successful requests:                     100       
Benchmark duration (s):                  22.43     
Total input tokens:                      26322     
Total generated tokens:                  11433     
Request throughput (req/s):              4.46      
Input token throughput (tok/s):          1173.60   
Output token throughput (tok/s):         509.76    
---------------Time to First Token----------------
Mean TTFT (ms):                          54.68     
Median TTFT (ms):                        37.58     
P99 TTFT (ms):                           221.78    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          44.23     
Median TPOT (ms):                        34.16     
P99 TPOT (ms):                           158.26    
---------------Inter-token Latency----------------
Mean ITL (ms):                           70.08     
Median ITL (ms):                         55.27     
P99 ITL (ms):                            363.48    
==================================================

FLASHINFER Backend

============ Serving Benchmark Result ============
Successful requests:                     100       
Benchmark duration (s):                  21.89     
Total input tokens:                      26322     
Total generated tokens:                  11425     
Request throughput (req/s):              4.57      
Input token throughput (tok/s):          1202.55   
Output token throughput (tok/s):         521.97    
---------------Time to First Token----------------
Mean TTFT (ms):                          68.02     
Median TTFT (ms):                        57.64     
P99 TTFT (ms):                           318.30    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          44.91     
Median TPOT (ms):                        32.33     
P99 TPOT (ms):                           177.95    
---------------Inter-token Latency----------------
Mean ITL (ms):                           66.46     
Median ITL (ms):                         50.02     
P99 ITL (ms):                            346.13    
==================================================

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github-actions · 2024-07-30T04:46:32Z

👋 Hi! Thank you for contributing to the vLLM project.
Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which consists a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of default ones by unblocking the steps in your fast-check build on Buildkite UI.

Once the PR is approved and ready to go, please make sure to run full CI as it is required to merge (or just use auto-merge).

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🚀

bong-furiosa · 2024-07-30T06:17:43Z

/ready

comaniac · 2024-07-30T21:12:25Z

Thanks for the PR! A question I have is we currently have a guard to make sure only FlashAttention can use draft model runner, so if you use FlashInfer, vLLM shouldn't use draft model runner. I'm curious how this PR could fix the issue you mentioned.

bong-furiosa · 2024-07-31T01:50:08Z

@comaniac Thank you for your interest on this PR!

Please let me explain the purpose of this PR first, and then I'll describe the issues it addresses. 🙇

Purpose of this PR

Indeed, the current Speculative Decoding in vLLM selects the FlashAttention as a default backend.
As far as I know, to enable the FlashInfer backend, we need to manually set the environment variable VLLM_ATTENTION_BACKEND=FLASHINFER as mentioned in #4353 (link)

However, I found that recently @LiuXiaoxuanPKU has been working on integrating FlashInfer as a backend in vLLM and extending support to CUDAGraph for general LLM inference (#4353, #4628).

Additionally, according to the Speculative Decoding (with Batch Expansion, I guess...) experiments conducted by @cadedaniel, it was confirmed that using FlashInfer resulted in lower latency compared to other techniques.

I anticipate that FlashInfer will be used as the vLLM Speculative Decoding backend in the future. Although, implementing FlashInfer with CUDA Graph seems really challenging.

Therefore, I did PR modifying the code to enable the use of FlashInfer as the backend for vLLM Speculative Decoding in the current v0.5.3.post1. I believe this addresses a bug (or oversight?) that has been present since draft_model_runner was first introduced in v0.5.1. 🤔

Addressed issue

The solution of the issue was simple. I copied the necessary "flashinfer" related code blocks from the ModelRunner class in model_runner.py directly into draft_model_runner.py.

As mentioned in [Bug]: Speculative Decoding + FlashInfer + benchmark_serving.py TransferEncodingError ISSUE #6885 , this PR addresses the issue where vLLM Speculative Decoding did not work when the FlashInfer backend was activated.
In spec_decode_worker.py, the acceptance rates for the FlashAttention backend and the FlashInfer backend were recorded as 0.42 and 0.41, respectively (manually calculated in SpecDecodeWorker._run_speculative_decoding_step).

Although it may take a considerable amount of time for FlashInfer to be integrated officially into the Speculative Decoding in vLLM, I believe this PR addresses bugs that could arise during that process.

If the answer is not what you were looking for, please feel free to ask additional questions or close this PR. 🙇

comaniac

Otherwise LGTM. Can you add a unit test?

comaniac · 2024-08-02T00:32:29Z

vllm/spec_decode/draft_model_runner.py

+# (bong-furiosa)
+# Resolve the issue of the wrapper variable
+# not being defined during the execution of
+# FlashInfer backend Speculative Decoding.


This comment is not necessary as the code itself is straightforward.

comaniac · 2024-08-02T00:32:47Z

vllm/spec_decode/draft_model_runner.py

+        # (bong-furiosa)
+        # Resolve the issue of the wrapper variable
+        # not being defined during the execution of
+        # FlashInfer backend Speculative Decoding.


Ditto. Unnecessary comments.

comaniac · 2024-08-02T00:33:02Z

vllm/spec_decode/draft_model_runner.py

+        # (bong-furiosa)
+        # Resolve the issue of the wrapper variable
+        # not being defined during the execution of
+        # FlashInfer backend Speculative Decoding.


Ditto. Unnecessary comments.

comaniac · 2024-08-02T00:33:51Z

vllm/spec_decode/draft_model_runner.py

+        if self.attn_backend.get_name() == "flashinfer":
+            assert model_input.attn_metadata is not None
+            assert model_input.input_tokens is not None
+            if self.flashinfer_decode_workspace_buffer is None:
+                self.flashinfer_decode_workspace_buffer = torch.empty(
+                    FLASHINFER_WORKSPACE_BUFFER_SIZE,
+                    dtype=torch.uint8,
+                    device=self.device)
+                self.flashinfer_decode_wrapper = \
+                    BatchDecodeWithPagedKVCacheWrapper(
+                    self.flashinfer_decode_workspace_buffer, "NHD")
+                self.flashinfer_prefill_workspace_buffer = torch.empty(
+                    FLASHINFER_WORKSPACE_BUFFER_SIZE,
+                    dtype=torch.uint8,
+                    device=self.device)
+                self.flashinfer_prefill_wrapper = \
+                    BatchPrefillWithPagedKVCacheWrapper(
+                    self.flashinfer_prefill_workspace_buffer, "NHD")
+
+            model_input.attn_metadata.prefill_wrapper = \
+                self.flashinfer_prefill_wrapper
+            if model_input.attn_metadata.use_cuda_graph:
+                batch_size = model_input.input_tokens.shape[0]
+                model_input.attn_metadata.decode_wrapper = self.graph_runners[
+                    model_input.
+                    virtual_engine][batch_size].flashinfer_decode_wrapper
+            else:
+                model_input.attn_metadata.decode_wrapper = \
+                    self.flashinfer_decode_wrapper
+            model_input.attn_metadata.begin_forward()


This should be in the "else" branch above, because the if branch will always use flash attention backend.

bong-furiosa · 2024-08-02T02:11:54Z

@comaniac Thank you for the advice on refining the code!
I would appreciate it if you could check the refined code.
Also, it looks like there have been many updates to vLLM since the current PR.
If any issues arise with the PR, I will sync and PR again.

Unfortunately, I do not have the time or ability to write unit test for the modified code...
I assume that using os.environ['VLLM_ATTENTION_BACKEND'] to test offline Spec Decoding in FLASH_ATTN or FLASHINFER backend would suffice. However, I am concerned about my ability to write the unit tests correctly. 😢

Therefore, would it be okay to expect that vLLM experts or other users write and confirm the unit test code? 🤔 🙇

bong-furiosa · 2024-08-04T04:26:52Z

/ready

bong-furiosa · 2024-08-05T07:34:20Z

@comaniac Sorry for being late. I kept getting a failure log while checking the entrypoints(?) in fastcheck.
I would appreciate it if you could review the pr content and any additional advice when you have time. 🙇 🙇

comaniac

Merge this PR first given that it doesn't fail existing cases. I'll try to find a time to add a unit test later.

Signed-off-by: Alvant <[email protected]>

bong-furiosa mentioned this pull request Jul 30, 2024

[Bug]: Speculative Decoding + FlashInfer + benchmark_serving.py TransferEncodingError ISSUE #6885

Closed

comaniac self-assigned this Jul 30, 2024

github-actions bot added the ready ONLY add when PR is ready to merge/full CI is needed label Jul 30, 2024

comaniac changed the title ~~[Bugifx] fixed draft_model_runner.py #6885~~ [SpecDecode] Support FlashInfer in DraftModelRunner Jul 30, 2024

comaniac removed the ready ONLY add when PR is ready to merge/full CI is needed label Jul 30, 2024

comaniac reviewed Aug 2, 2024

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bong-furiosa mentioned this pull request Aug 2, 2024

[BUILD/CI Bug]: Report for buildkite/fastcheck/pr/metrics-tracing-test and suggesting a solution. #7064

Closed

bong-furiosa force-pushed the pr2upstream branch from bf9f6ac to 06d30a4 Compare August 2, 2024 06:37

github-actions bot added the ready ONLY add when PR is ready to merge/full CI is needed label Aug 4, 2024

bong-furiosa added 11 commits August 5, 2024 00:26

fixed draft_model_runner.py

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bong-furiosa force-pushed the pr2upstream branch from 2b6b45f to df14b01 Compare August 5, 2024 00:28

bong-furiosa added 2 commits August 5, 2024 01:21

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comaniac approved these changes Aug 5, 2024

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comaniac merged commit e963045 into vllm-project:main Aug 5, 2024
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dtrifiro mentioned this pull request Aug 5, 2024

Sync with [email protected] opendatahub-io/vllm#120

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sfc-gh-mkeralapura pushed a commit to sfc-gh-mkeralapura/vllm that referenced this pull request Aug 12, 2024

[SpecDecode] Support FlashInfer in DraftModelRunner (vllm-project#6926)

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kylesayrs pushed a commit to neuralmagic/vllm that referenced this pull request Aug 17, 2024

[SpecDecode] Support FlashInfer in DraftModelRunner (vllm-project#6926)

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Alvant pushed a commit to compressa-ai/vllm that referenced this pull request Oct 26, 2024

[SpecDecode] Support FlashInfer in DraftModelRunner (vllm-project#6926)

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Signed-off-by: Alvant <[email protected]>

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[SpecDecode] Support FlashInfer in DraftModelRunner #6926

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[SpecDecode] Support FlashInfer in DraftModelRunner #6926

[SpecDecode] Support FlashInfer in DraftModelRunner #6926

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bong-furiosa commented Jul 30, 2024

FLASH_ATTN backend

FLASHINFER Backend

PR Title and Classification

Code Quality

Notes for Large Changes

What to Expect for the Reviews

Thank You

github-actions bot commented Jul 30, 2024

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bong-furiosa commented Jul 31, 2024

Purpose of this PR

Addressed issue

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