9eb62935b8
### What this PR does / why we need it? Calculate in advance the workspace memory size needed for the PagedAttention operator to avoid deadlocks during resource cleanup. This PR requires torch_npu version 0920 or newer. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com> Co-authored-by: wangxiaoxin-sherie <wangxiaoxin7@huawei.com>
73 lines
2.6 KiB
Python
73 lines
2.6 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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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/e2e/multicard/test_qwen3_moe.py`.
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"""
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import os
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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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def test_models_distributed_Qwen3_MOE_TP2_WITH_FULLGRAPH():
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if 'HCCL_OP_EXPANSION_MODE' in os.environ:
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del os.environ['HCCL_OP_EXPANSION_MODE']
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prompts = [
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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]
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model = "Qwen/Qwen3-30B-A3B"
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sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
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with VllmRunner(model,
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max_model_len=1024,
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tensor_parallel_size=2,
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enforce_eager=False,
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compilation_config={"cudagraph_mode":
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"FULL_DECODE_ONLY"}) as runner:
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vllm_fullgraph_outputs = runner.model.generate(prompts,
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sampling_params)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=True,
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
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vllm_fullgraph_outputs_list = []
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for output in vllm_fullgraph_outputs:
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vllm_fullgraph_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=vllm_fullgraph_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="vllm_fullgraph_outputs",
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)
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