1b424fb7f1
### What this PR does / why we need it? This PR revise the test cases of various features on the warehouse which add the enablement of aclgraph to the test cases. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? ut - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: lilinsiman <lilinsiman@gmail.com>
114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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MODEL = "Qwen/Qwen3-0.6B"
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@pytest.mark.parametrize("enforce_eager", [True, False])
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def test_concurrent_partial_prefill(enforce_eager):
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with VllmRunner(MODEL,
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additional_config={
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'ascend_scheduler_config': {
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'enabled': True,
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},
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},
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max_num_seqs=3,
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max_num_batched_tokens=2048,
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enforce_eager=enforce_eager,
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max_model_len=2048,
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gpu_memory_utilization=0.7) as vllm_model:
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outputs = vllm_model.model.generate(["Hello my name is Robert and I"] *
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3)
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assert len(outputs) == 3
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for output in outputs:
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assert len(output.outputs) == 1
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@pytest.mark.parametrize("enforce_eager", [True, False])
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def test_prefix_cache_stats_is_recorded(enforce_eager):
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with VllmRunner(MODEL,
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additional_config={
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'ascend_scheduler_config': {
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'enabled': True,
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},
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},
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max_num_seqs=3,
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max_num_batched_tokens=2048,
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enforce_eager=enforce_eager,
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max_model_len=2048,
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gpu_memory_utilization=0.7) as vllm_model:
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# 17 tokens will make sure first 16 tokens are cached in a block
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input_tokens = {"prompt_token_ids": [101] * 129}
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_ = vllm_model.model.generate([input_tokens])
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outputs = vllm_model.model.generate([input_tokens])
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assert outputs[0].num_cached_tokens == 128
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@pytest.mark.parametrize("max_tokens",
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[4]) # cannot align results when max_tokens > 4
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@pytest.mark.parametrize("chunked_prefill_token_size", [16])
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def test_chunked_prefill_with_ascend_scheduler(
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max_tokens: int, chunked_prefill_token_size: int) -> None:
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example_prompts = [
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"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
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]
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max_num_seqs = chunked_prefill_token_size
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max_num_batched_tokens = chunked_prefill_token_size
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with VllmRunner(MODEL,
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additional_config={
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'ascend_scheduler_config': {
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'enabled': True,
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'enable_chunked_prefill': True,
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},
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},
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max_num_seqs=max_num_seqs,
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max_num_batched_tokens=max_num_batched_tokens,
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max_model_len=2048,
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gpu_memory_utilization=0.7) as vllm_model:
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chunked_prefill_output = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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with VllmRunner(MODEL,
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additional_config={
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'ascend_scheduler_config': {
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'enabled': True,
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},
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},
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max_model_len=2048,
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gpu_memory_utilization=0.7) as vllm_model:
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vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_output,
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outputs_1_lst=chunked_prefill_output,
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name_0="vllm_output",
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name_1="chunked_prefill_output",
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)
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def test_async_scheduling() -> None:
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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] * 10
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sampling_params = SamplingParams(temperature=0.2,
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max_tokens=10,
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stop_token_ids=None)
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with VllmRunner(
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"Qwen/Qwen2.5-0.5B-Instruct",
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max_model_len=4096,
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max_num_seqs=50,
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dtype="bfloat16",
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gpu_memory_utilization=0.9,
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async_scheduling=True,
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) as vllm_model:
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vllm_model.generate(prompts, sampling_params=sampling_params)
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