fef18b60bc
Refactor E2E CI to make it clear and faster
1. remove some uesless e2e test
2. remove some uesless function
3. Make sure all test runs with VLLMRunner to avoid oom error
4. Make sure all ops test end with torch.empty_cache to avoid oom error
5. run the test one by one to avoid resource limit error
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
105 lines
3.1 KiB
Python
105 lines
3.1 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 modelscope import snapshot_download # type: ignore
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from tests.e2e.conftest import VllmRunner
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def test_models_distributed_Qwen3_MOE_TP2():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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tensor_parallel_size=2,
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distributed_executor_backend="mp",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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tensor_parallel_size=2,
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enable_expert_parallel=True,
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distributed_executor_backend="mp",
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enforce_eager=False,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_models_distributed_Qwen3_MOE_W8A8():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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snapshot_download("vllm-ascend/Qwen3-30B-A3B-W8A8"),
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max_model_len=8192,
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tensor_parallel_size=2,
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quantization="ascend",
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enforce_eager=True,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH_AIV():
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os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
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example_prompts = [
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"Hello, my name is",
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]
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dtype = "auto"
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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dtype=dtype,
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tensor_parallel_size=2,
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enforce_eager=False,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH():
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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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example_prompts = [
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"Hello, my name is",
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]
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dtype = "auto"
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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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dtype=dtype,
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tensor_parallel_size=2,
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enforce_eager=False,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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