71df17f4e6
### What this PR does / why we need it?
This PR refactors the communication group of MC2 to keep it consistent
with vllm's EP group, making it compatible with PP.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
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Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
89 lines
3.5 KiB
Python
89 lines
3.5 KiB
Python
# 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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#
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import pytest
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from tests.e2e.conftest import DPVllmRunner, VllmRunner, wait_until_npu_memory_free
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from tests.e2e.model_utils import check_outputs_equal
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DS3 = "deepseek-ai/DeepSeek-V2-Lite-Chat"
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MODELS = [
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DS3,
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]
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MOE_MODELS = [
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DS3,
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]
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DATA_PARALLELS = [2]
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TENSOR_PARALLELS = [1,2]
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PIPELINE_PARALLELS = [2]
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DIST_EXECUTOR_BACKEND = ["mp", "ray"]
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prompts = [
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"Hello, my name is",
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"The future of AI is",
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]
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GOLDEN = [([100000, 17464, 11, 601, 1210, 317, 46462, 608, 245, 4541, 7712, 13, 2682, 6207, 317, 276, 2774, 340, 366, 254, 1608, 2784], 'Hello, my name is***** am a computer expert. My goal is to provide you with the best experience'), ([100000, 549, 3680, 280, 20838, 317, 6464, 11, 548, 359, 487, 82, 441, 1673, 895, 10694, 13, 1733, 20838, 5495, 11106, 276], 'The future of AI is bright, but it’s not without its challenges. As AI technology continues to')]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
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@pytest.mark.parametrize("pp_size", PIPELINE_PARALLELS)
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@pytest.mark.parametrize("distributed_executor_backend", DIST_EXECUTOR_BACKEND)
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@wait_until_npu_memory_free(target_free_percentage=0.6)
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def test_models_pp2_tp2(model: str, tp_size: int, pp_size: int, distributed_executor_backend: str) -> None:
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with VllmRunner(
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model,
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tensor_parallel_size=tp_size,
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pipeline_parallel_size=pp_size,
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cudagraph_capture_sizes=[1, 2, 4],
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distributed_executor_backend=distributed_executor_backend,
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gpu_memory_utilization=0.7,
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enable_expert_parallel=model in MOE_MODELS,
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) as vllm_model:
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outputs = vllm_model.generate_greedy(prompts, 16)
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check_outputs_equal(
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outputs_0_lst=outputs,
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outputs_1_lst=GOLDEN,
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name_0=f"{model}-tp{tp_size}pp{pp_size}",
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name_1="GOLDEN",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dp_size", DATA_PARALLELS)
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@pytest.mark.parametrize("pp_size", PIPELINE_PARALLELS)
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@pytest.mark.parametrize("distributed_executor_backend", DIST_EXECUTOR_BACKEND)
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@wait_until_npu_memory_free(target_free_percentage=0.6)
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def test_models_pp2_dp2(model: str, dp_size: int, pp_size: int, distributed_executor_backend: str) -> None:
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with DPVllmRunner(
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model,
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data_parallel_size=dp_size,
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pipeline_parallel_size=pp_size,
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cudagraph_capture_sizes=[1, 2, 4],
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distributed_executor_backend=distributed_executor_backend,
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gpu_memory_utilization=0.7,
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enable_expert_parallel=model in MOE_MODELS,
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) as vllm_model:
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outputs = vllm_model.generate_greedy(prompts, 16)
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check_outputs_equal(
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outputs_0_lst=outputs,
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outputs_1_lst=GOLDEN,
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name_0=f"{model}-dp{dp_size}pp{pp_size}",
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name_1="GOLDEN",
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)
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