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https://git.datalinker.icu/vllm-project/vllm.git
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142 lines
5.5 KiB
Python
142 lines
5.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any
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import pytest
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import torch._dynamo.config as dynamo_config
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from vllm import SamplingParams
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from vllm.logprobs import Logprob
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from ...conftest import VllmRunner
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from ...models.utils import check_outputs_equal
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MODEL = "Qwen/Qwen3-0.6B"
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@dynamo_config.patch(cache_size_limit=16)
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def test_preempt_and_async_scheduling_e2e(monkeypatch: pytest.MonkeyPatch):
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"""Test consistency of combos of async scheduling, preemption,
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uni/multiproc executor, and various sampling parameters."""
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first_prompt = (
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"The following numbers of the sequence "
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+ ", ".join(str(i) for i in range(10))
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+ " are:"
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)
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example_prompts = [first_prompt, "In one word, the capital of France is "] + [
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f"Tell me about the number {i}: " for i in range(32)
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]
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sampling_param_tests: list[dict[str, Any]] = [
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dict(),
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# dict(min_tokens=20),
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dict(presence_penalty=-1.0),
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dict(bad_words=["the", " the"]),
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dict(logprobs=2),
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dict(logprobs=2, presence_penalty=-1.0),
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]
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default_params = dict(
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temperature=0.0, # greedy
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max_tokens=20,
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)
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with monkeypatch.context() as m:
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m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
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# m.setenv("VLLM_BATCH_INVARIANT", "1")
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outputs: list[tuple[str, list]] = []
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for test_preemption in [False, True]:
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for executor in ["mp", "uni"]:
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for async_scheduling in [False, True]:
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cache_arg: dict[str, Any] = (
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dict(num_gpu_blocks_override=32)
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if test_preemption
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else dict(gpu_memory_utilization=0.7)
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)
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test_config = (
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f"executor={executor}, preemption={test_preemption},"
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f" async_sched={async_scheduling}"
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)
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print("-" * 80)
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print(f"---- TESTING: {test_config}")
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print("-" * 80)
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with VllmRunner(
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MODEL,
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max_model_len=512,
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enforce_eager=True,
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async_scheduling=async_scheduling,
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distributed_executor_backend=executor,
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dtype="float32", # avoid precision errors
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**cache_arg,
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) as vllm_model:
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results = []
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for override_params in sampling_param_tests:
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print(f"----------- RUNNING PARAMS: {override_params}")
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results.append(
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vllm_model.generate(
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example_prompts,
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sampling_params=SamplingParams(
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**default_params, **override_params
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),
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return_logprobs=True,
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)
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)
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if not outputs:
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# First check that the different parameter configs
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# actually result in different output.
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for (other_test_outs, other_test_logprobs), params in zip(
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results[1:], sampling_param_tests[1:]
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):
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with pytest.raises(AssertionError):
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check_outputs_equal(
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outputs_0_lst=results[0][0],
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outputs_1_lst=other_test_outs,
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name_0=f"baseline params={params}",
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name_1=f"other params={params}",
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)
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assert _all_logprobs_match(
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results[0][1], other_test_logprobs
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)
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outputs.append((test_config, results))
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baseline_config, baseline_tests = outputs[0]
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for test_config, test_outputs in outputs[1:]:
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for (base_outs, base_logprobs), (test_outs, test_logprobs), params in zip(
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baseline_tests, test_outputs, sampling_param_tests
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):
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check_outputs_equal(
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outputs_0_lst=base_outs,
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outputs_1_lst=test_outs,
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name_0=f"baseline=[{baseline_config}], params={params}",
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name_1=f"config=[{test_config}], params={params}",
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)
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assert _all_logprobs_match(base_logprobs, test_logprobs)
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print(f"PASSED: config=[{test_config}], params={params}")
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def _all_logprobs_match(req_a, req_b) -> bool:
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return (
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req_a == req_b
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or len(req_a) == len(req_b)
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and all(
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len(seq_a) == len(seq_b)
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and all(_logprobs_match(a, b) for a, b in zip(seq_a, seq_b))
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for seq_a, seq_b in zip(req_a, req_b)
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)
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)
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def _logprobs_match(lps_a: dict[int, Logprob], lps_b: dict[int, Logprob]) -> bool:
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return len(lps_a) == len(lps_b) and all(
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a.decoded_token == b.decoded_token
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and a.rank == b.rank
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and a.logprob == pytest.approx(b.logprob, rel=1e-3, abs=1e-6)
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for a, b in ((lps_a[x], lps_b[x]) for x in lps_a)
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)
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