787010a637
V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests
- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
80 lines
2.8 KiB
Python
80 lines
2.8 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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Execute the inference of fused_moe_allgather_ep and fused_moe_alltoall_ep.
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Run 'pytest tests/multicard/test_fused_moe_allgather_ep.py'.
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"""
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import os
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from unittest.mock import patch
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from modelscope import snapshot_download # type: ignore
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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@patch.dict(
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os.environ, {
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"VLLM_WORKER_MULTIPROC_METHOD": "spawn",
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"TASK_QUEUE_ENABLE": "1",
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"VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP": "1"
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})
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def test_generate_with_allgather():
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example_prompts = ["Hello, my name is"]
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sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
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with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V3-Pruning"),
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tensor_parallel_size=4,
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enforce_eager=True,
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max_model_len=1024,
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dtype="auto",
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enable_expert_parallel=True,
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additional_config={
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"ascend_scheduler_config": {
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"enabled": True,
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"chunked_prefill_enabled": False,
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},
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"expert_tensor_parallel_size": 1
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}) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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@patch.dict(os.environ, {
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"VLLM_WORKER_MULTIPROC_METHOD": "spawn",
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"TASK_QUEUE_ENABLE": "1"
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})
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def test_generate_with_alltoall():
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example_prompts = ["Hello, my name is"]
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sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
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with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V3-Pruning"),
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tensor_parallel_size=4,
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enforce_eager=True,
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max_model_len=1024,
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dtype="auto",
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enable_expert_parallel=True,
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additional_config={
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"ascend_scheduler_config": {
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"enabled": True,
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"chunked_prefill_enabled": False,
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},
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"expert_tensor_parallel_size": 1
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}) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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