[Core] Reduce TTFT with concurrent partial prefills by joerunde · Pull Request #10235 · vllm-project/vllm

joerunde · 2024-11-11T21:59:38Z

Replaces #10061, as inspired by @njhill and @comaniac's comments. Co-authored by @prashantgupta24

Context: our customers running large multi-tenanted SaaS deployments of vLLM have a problem where high volumes of small-prompt requests are usually processed smoothly, but quickly pile up in a giant queue when a small number of large-prompt requests are submitted. We see the decoding throughput drop to zero on multiple replicas when this happens.

The current chunked prefill implementation only allows a single sequence to be partially prefilled at a time. This has a few limitations:

Multiple medium-sized prompts must wait to be prefilled serially, increasing TTFT for those in the back of the queue
A single very large prompt will block all other prompts from prefilling for many iterations. This can eventually starve decoding- for example a 130k token prompt with —max-num-batched-tokens=512 will take about 250 iterations to prefill, in which time the currently decoding sequences may all finish. Send a few of these requests at once and very quickly nothing will be decoding.

This PR implements both

An explicit setting for the number of sequences that can be partially prefilled concurrently. This can be configured with --max-num-partial-prefills=N
A limit on the number of “very long prompt” sequences that can be prefilled concurrently. This can be configured with
- --max-long-partial-prefills=N to set the limit on the number of long sequences that can be concurrently prefilled. This defaults to 1 sequence.
- --long-prefill-threashold=x% to set a percentage of the context length that determines which sequences are considered "long". This defaults to 4%

This is implemented in the v0 scheduler. We’re aware that the v1 implementation is underway and will later become the default, but we need a fix for our customers soon and we hope that what we discover here may help inform a different, better solution in the v1 scheduler.

To test this we created three scenarios, a “medium request” case, a “large request” case, and a “mixed” case.

For the medium request case, we created a subset of the sharegpt dataset with 900 small requests (<50 prompt characters) and 100 of the largest requests (typically between 10k and 20k prompt characters, which we call “medium” sized). We modified the benchmark_serving.py test to not filter out any of the small or large requests, and ran it with this dataset. What we expect to find is similar throughput compared to the main branch, but much lower TTFT on the small requests. Since 10% of the requests are larger than the rest, we should see better TTFT at p90 and below, with comparable TTFT above p90.

For the large request case, we took 990 of the smallest requests from the sharegpt dataset, and then took 10 of the largest requests and duplicated the prompts until they were around 100k characters in length. We ran this in the same way as the medium request case, and here we expect to see smaller TTFT across the board since the small requests will no longer be blocked from prefilling by the few very large requests.

For the mixed case, we used 850 “small”, and 140 “medium” requests, as well as 10 "large" requests where we duplicated the prompts up to 200k characters.

All tests were run on a single 80GB A100, with the command:

python benchmarks/benchmark_serving.py --model meta-llama/Meta-Llama-3.1-8B-Instruct --dataset-path ${test_case} --metric-percentiles 80,85,90,95,99 --request-rate 12

We ran the tests against the main branch (commit 874f551b3626321f6bf9a902b8fd9fc1fa7c7f2e), as well as this PR with the new optimization both disabled (--max-num-partial-prefills=1), and enabled (--max-num-partial-prefills=4)

The results are shown here:

The TTFT improvements are very easy to see- in the medium case we cut the p90 TTFT in half, and in the large case we cut it nearly 30x. In both cases we did not measure a throughput drop when run with --max-num-partial-prefills=1, and the throughput drop with --max-num-partial-prefills=4 is minimal.

Surprisingly, along with the massive TTFT improvements in the "mixed" test case, we also see a 4% throughput improvement (3506 tokens/s up from 3368 tokens/s). Based on the fact that ITL still looks a little slower, it seems that the throughput is higher simply because more requests were able to be successfully scheduled at the same time.

cc @rickyyx

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github-actions · 2024-11-11T21:59:49Z

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vllm/model_executor/layers/sampler.py

vllm/core/scheduler.py

comaniac · 2024-11-12T23:47:55Z

vllm/config.py

                 max_num_batched_tokens: Optional[int],
                 max_num_seqs: int,
                 max_model_len: int,
+                 num_prefill_slots: int = 1,


Is this actually "maximum number of prefill sequence in a batch"? If so could we name it something more informative, like max_num_batched_prefill_seqs ?

It's technically only the number of partial prefills allowed in a batch. You could still have like 100 sequence groups with 5 prompt tokens each all schedule in a single step here.

max_num_partial_prefills?

comaniac · 2024-11-12T23:51:26Z

vllm/core/scheduler.py

+        # Requests with more than (4% max context length) tokens to prefill
+        # are "big".


Why this definition and threshold?

The entire goal here is to not allow decode to be starved by the prefill phase blocking on long requests- this part of the PR description:

A single very large prompt will block all other prompts from prefilling for many iterations. This can eventually starve decoding- for example a 130k token prompt with —max-num-batched-tokens=512 will take about 250 iterations to prefill, in which time the currently decoding sequences may all finish. Send a few of these requests at once and very quickly nothing will be decoding.

Just allowing concurrent partial prefills doesn't solve the problem by itself, because multiple long requests could still block up the prefill. So what we do is only allow a single long request to prefill, and allow smaller requests to be pulled from the waiting queue instead of more long ones

vllm/core/scheduler.py

joerunde · 2024-11-14T20:37:58Z

tests/core/test_chunked_prefill_scheduler.py

+
+@pytest.mark.parametrize("model", ["facebook/opt-125m"])
+@pytest.mark.parametrize("max_num_partial_prefills", [2, 4, 8])
+def test_chunked_prefill_with_actual_engine(model: str,


cc @rickyyx here's what we tried to do to test that the sampler doesn't throw any assertions- we put multiple prompts into an engine and manually step it forward with them all partially prefilled

tests/core/test_chunked_prefill_scheduler.py

mergify · 2024-11-20T10:59:43Z

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

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