2a763b8326
### What this PR does / why we need it?
1. Remove the return statement, it will always skip following logic.
2. Update `deepseek` to `Qwen2.5-Instruct` for OOM in github e2e test
env.
3. Fix the comparison logic
### Does this PR introduce _any_ user-facing change?
NO.
### How was this patch tested?
Local Test.
- vLLM version: v0.10.0
- vLLM main:
0933f9d518
Signed-off-by: xleoken <xleoken@163.com>
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Compare the outputs of vLLM with and without aclgraph.
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Run `pytest tests/compile/test_aclgraph.py`.
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"""
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import pytest
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import torch
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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MODELS = ["Qwen/Qwen2.5-0.5B-Instruct"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [1])
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def test_models(
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model: str,
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max_tokens: int,
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) -> None:
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prompts = ["The president of the United States is"]
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sampling_params = SamplingParams(
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max_tokens=max_tokens,
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temperature=0.0,
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)
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with VllmRunner(model, long_prefill_token_threshold=20,
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enforce_eager=True) as vllm_model:
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output1 = vllm_model.generate(prompts, sampling_params)
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with VllmRunner(model,
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enforce_eager=True,
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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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}) as vllm_model:
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output2 = vllm_model.generate(prompts, sampling_params)
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# Extract the generated token IDs for comparison
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token_ids1 = output1[0][0][0]
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token_ids2 = output2[0][0][0]
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print(f"Token IDs 1: {token_ids1}")
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print(f"Token IDs 2: {token_ids2}")
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# Convert token IDs to tensors and calculate cosine similarity
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# Take the length of a shorter sequence to ensure consistent dimensions
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min_len = min(len(token_ids1), len(token_ids2))
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tensor1 = torch.tensor(token_ids1[:min_len], dtype=torch.float32)
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tensor2 = torch.tensor(token_ids2[:min_len], dtype=torch.float32)
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# Calculate similarity using torch.cosine_similarity
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similarity = torch.cosine_similarity(tensor1, tensor2, dim=0)
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print(f"Token IDs cosine similarity: {similarity.item()}")
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assert similarity > 0.95
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