[Core] Asynchronous Output Processor #7049

megha95 · 2024-08-01T22:33:43Z

This PR is an implementation for RFC #6913 .

Following changes were made:

Scheduler states' updates is decoupled from output processor. Earlier both _process_model_outputs and scheduler.schedule() were updating deque and free-ing memory of finished sequences.
Adds _advance_to_next_step in LLMEngine that does small but necessary steps in critical path: eg: appending new token id and updating prefill sequences' statuses to decode.
Adds a callback function to execute_model so that process_model_outputs is triggered right before sampler inside ModelRunner. Since GPU always runs ahead of CPU, this allows for complete overlap with cuda graph forward pass in each decoding step

Results
Throughput improves by 11% for Llama3.1-8B FP8 on 1xH100. Results are even better at high batch sizes. With my own benchmarking setup, I see 1.3x increase in throughput at high RPS.

There's a follow-up PR # that combines async output processor with multi-step and shows promising results. 30% improvement in TPOT.
Without async output processor

CUDA_VISIBLE_DEVICES=0 python3 benchmark_throughput.py --model /mnt/workdisk/chengli/models/meta-llama/Meta-Llama-3.1-8B-Instruct --backend vllm --input-len 128 --output-len 256 --num-prompts 1000 --tensor-parallel 1 --quantization fp8 --disable-output-proc-callback

Throughput: 31.98 requests/s, 12279.04 tokens/s

With async output processor

CUDA_VISIBLE_DEVICES=0 python3 benchmark_throughput.py --model /mnt/workdisk/chengli/models/meta-llama/Meta-Llama-3.1-8B-Instruct --backend vllm --input-len 128 --output-len 256 --num-prompts 1000 --tensor-parallel 1 --quantization fp8 

Throughput: 35.64 requests/s, 13687.30 tokens/s

To do:

Update stopping criteria and scheduling of sequences for when stop token id is generated in previous step
add support for AsyncLLM engine
No callback codepath for spec dec, pipeline parallelism and beam search
Run tests
Add tests
Add benchmark numbers

github-actions · 2024-08-01T22:33:55Z

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alexm-neuralmagic

@megha95 This is a good progress! Left some comments.

alexm-neuralmagic · 2024-08-09T18:40:18Z

vllm/config.py

@@ -129,6 +129,7 @@ def __init__(
        skip_tokenizer_init: bool = False,
        served_model_name: Optional[Union[str, List[str]]] = None,
        multimodal_config: Optional["MultiModalConfig"] = None,
+        use_output_proc_callback: Optional[bool] = False,


I think @WoosukKwon proposal to make the async callback default is a good direction, so we can see the benefits already from this PR. You can enable the callback by default and disable it only for the cases where it does not work currently (beam search, etc...). Follow up PRs can add the missing features.

vllm/engine/async_llm_engine.py

alexm-neuralmagic · 2024-08-09T18:50:49Z

vllm/engine/llm_engine.py

+        required if the worker is to perform async forward pass to next step.
+        """
+        for seq_group_metadata, sequence_group_outputs in zip(
+                seq_group_metadata_list, output):


As we discussed, most likely you need to do seq.is_finished() check here before doing append_token(..)

Updated now. Thanks for pointing this out.

vllm/engine/llm_engine.py

vllm/engine/output_processor/single_step.py

alexm-neuralmagic

Did a second pass, left comments

vllm/config.py

vllm/engine/arg_utils.py

vllm/engine/llm_engine.py

vllm/core/scheduler.py

vllm/config.py

WoosukKwon · 2024-08-13T17:37:48Z

vllm/config.py

+        # TO DO: assert no pipeline parallelism
+        # TO DO: assert no spec decoding


What are the issues in supporting these two features?

Supporting spec decoding will need more thought. Mostly because, execute_model inside spec dec's step is quite different from non-spec dec codepath and we need to figure out the right place to call the callback. Pipeline parallelism should be easier, and can be done in follow-up PR.

Supporting spec decoding will need more thought. Mostly because, execute_model inside spec dec's step is quite different from non-spec dec codepath and we need to figure out the right place to call the callback. Pipeline parallelism should be easier, and can be done in follow-up PR.

Hi, we have better performance with Asynchronous Output Processor, do we have any schedule to support async in spec dec's path? thanks

vllm/config.py

vllm/worker/model_runner.py

benchmarks/benchmark_throughput.py

vllm/executor/executor_base.py

vllm/engine/output_processor/multi_step.py

vllm/engine/arg_utils.py

alexm-neuralmagic · 2024-08-21T23:53:22Z

@comaniac would be good to get your quick feedback on this PR

comaniac · 2024-08-22T15:44:46Z

vllm/config.py

+            self.use_output_proc_callback = False
+
+        if speculative_config:
+            self.use_output_proc_callback = False


Also need a warning?

vllm/config.py

comaniac · 2024-08-23T00:15:01Z

vllm/core/scheduler.py

+        # For async output processing, we need to swap cache buffers between
+        # iterations. I.e. since the output processing is lagged one step,
+        # we cannot reuse the cached objects immediately when the schedule()
+        # is called again, but only when schedule() is called the second time.


Why the second time is guaranteed to be available?

comaniac · 2024-08-23T00:16:39Z

vllm/core/scheduler.py

        scheduler_outputs = self._schedule()
        now = time.time()

        if not self.cache_config.enable_prefix_caching:
            common_computed_block_nums = []

+        # TODO: Combine multi-step and async postprocessor
+        allow_output_proc_callback: bool = (


Why not just combining this logic into the _allow_output_proc_callback function?

It may be possible, will see if I can do it.

comaniac · 2024-08-23T00:18:45Z

vllm/core/scheduler.py

+        # Avoid deque alloc
+        self.tmp_queue: Deque[SequenceGroup] = deque()


Can you say more about this? Or use a better name for this queue. It's hard to catch its functionality in the rest of its use cases.

Its a deque acting as a temporary buffer to store the new seqs in running queue. It's only used inside free_finished_seq_groups, we save some time by avoiding re-allocation during each step.

vllm/engine/llm_engine.py

comaniac · 2024-08-23T00:28:05Z

vllm/engine/llm_engine.py

+                # Skip double logging when using async output proc
+                if finished_before and idx in finished_before:
+                    actual_num_batched_tokens -= 1
+                    continue


I can't connect the comment and the code. Can you elaborate it?

actual_num_batched_tokens -= 1 is required to avoid double-counting. With async output proc, number of batched tokens is already counted since its lagged by a step. If we have a token that was finished before, we won't count it again. This does not happen with non-async logging.

vllm/engine/output_processor/single_step.py

vllm/worker/model_runner.py

alexm-neuralmagic · 2024-08-23T17:54:37Z

@comaniac @WoosukKwon thanks for the detailed reviews, I think we addressed all of the comments, going over the final CI tests with @megha95 and some issue Woosuk found with beam search.

throughput_results.json

vllm/core/scheduler.py

vllm/config.py

vllm/engine/llm_engine.py

tests/distributed/test_pipeline_parallel.py

tests/distributed/test_pp_cudagraph.py

WoosukKwon

@megha95 @alexm-neuralmagic Huge thanks for the PR! Also, thanks a lot for the kind intro and explanation of this PR.

I'm happy with merging this PR once the remaining super-minor issues are fixed and the PR passes the CI again.

njhill

Thanks @megha95 @alexm-neuralmagic for the great work!

Do you have a sense for how much of the speedup comes from removal of the scheduling from the critical path vs removal of detokenization?

vllm/engine/llm_engine.py

vllm/executor/multiproc_gpu_executor.py

vllm/engine/async_llm_engine.py

vllm/engine/llm_engine.py

vllm/core/scheduler.py

megha95 · 2024-08-27T02:13:11Z

@njhill Thank you for the review. To answer your question:

Do you have a sense for how much of the speedup comes from removal of the scheduling from the critical path vs removal of detokenization?

We haven't removed scheduling from critical path. Most of the speedup in this PR comes from removing detokenization, free_finished_seq_groups and creation of RequestOutputs that is streamed back to user.

WoosukKwon · 2024-08-27T03:53:00Z

@megha95 @alexm-neuralmagic Thanks again for your great work!
@njhill Thanks for taking a look at the PR!

DarkLight1337 · 2024-09-02T03:41:47Z

It seems that this PR is causing the LoRA tests to fail on main.

Before this PR on main: https://buildkite.com/vllm/ci-aws/builds/7622
In this PR: https://buildkite.com/vllm/ci-aws/builds/7645

Can those who worked on this PR look into the issue further?

youkaichao · 2024-09-02T23:50:22Z

cc @megha95 @WoosukKwon

megha95 · 2024-09-03T14:59:17Z

@youkaichao @DarkLight1337 Having a look.

Co-authored-by: Alexander Matveev <alexm@neuralmagic.com>

rkooo567 · 2024-09-16T06:19:07Z

@megha95 do you know what it takes to support this feature for spmd architecture? (referring to

vllm/vllm/config.py

Line 378 in fc990f9

"Async output processing can not be enabled with ray spmd")

)

Co-authored-by: Alexander Matveev <alexm@neuralmagic.com>

FILL IN THE PR DESCRIPTION HERE This PR refer to [vllm-project#7049](vllm-project#7049) to implement Asynchronous Output Processor on HPU. It is open by default, to disable it, please pass the `--disable_async_output_proc` flag. From my local test on latest habana_main branch(commit 29fb5ed), the throughput improves from 3847 TPS to 4011 TPS. **BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE** --- <details>  <summary><b> PR Checklist (Click to Expand) </b></summary> <p>Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.</p> <h3>PR Title and Classification</h3> <p>Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:</p> <ul> <li><code>[Bugfix]</code> for bug fixes.</li> <li><code>[CI/Build]</code> for build or continuous integration improvements.</li> <li><code>[Doc]</code> for documentation fixes and improvements.</li> <li><code>[Model]</code> for adding a new model or improving an existing model. Model name should appear in the title.</li> <li><code>[Frontend]</code> For changes on the vLLM frontend (e.g., OpenAI API server, <code>LLM</code> class, etc.) </li> <li><code>[Kernel]</code> for changes affecting CUDA kernels or other compute kernels.</li> <li><code>[Core]</code> for changes in the core vLLM logic (e.g., <code>LLMEngine</code>, <code>AsyncLLMEngine</code>, <code>Scheduler</code>, etc.)</li> <li><code>[Hardware][Vendor]</code> for hardware-specific changes. Vendor name should appear in the prefix (e.g., <code>[Hardware][AMD]</code>).</li> <li><code>[Misc]</code> for PRs that do not fit the above categories. 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Co-authored-by: Alexander Matveev <alexm@neuralmagic.com> Signed-off-by: Alvant <alvasian@yandex.ru>

Co-authored-by: Alexander Matveev <alexm@neuralmagic.com>

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Combine async postprocessor and multi-step - first WIP version #7743

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[Core] Asynchronous Output Processor (vllm-project#7049)

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Correct type hint ROCm/vllm#173

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Async output process for HPU HabanaAI/vllm-fork#341

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[BugFix] Prevent exporting duplicate OpenTelemetry spans #9017

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[Bug]: vllm crashes when preemption of priority scheduling is triggered on vllm-0.6.3.dev173+g36ea7907.d20241011 #9342

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		# TO DO: assert no pipeline parallelism
		# TO DO: assert no spec decoding

		# Avoid deque alloc
		self.tmp_queue: Deque[SequenceGroup] = deque()

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