74aa968a9f
### What this PR does / why we need it?
add pcp accuracy e2e test case
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
103 lines
3.2 KiB
Python
103 lines
3.2 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 context parallel.
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Run `pytest tests/e2e/multicard/long_sequence/test_accuracy.py`.
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"""
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import pytest
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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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from vllm_ascend.utils import vllm_version_is
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MODELS = [
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"Qwen/Qwen3-8B",
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"vllm-ascend/DeepSeek-V2-Lite-W8A8",
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]
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@pytest.mark.skipif(vllm_version_is('0.12.0'),
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reason="0.12.0 is not supported for context sequence.")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [10])
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def test_output_between_tp_and_cp(
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model: str,
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max_tokens: int,
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) -> None:
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prompts = [
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"The president of the United States is", "The capital of France is"
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]
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common_kwargs = {
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"max_model_len": 1024,
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}
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if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
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cp_kwargs = {
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"tensor_parallel_size": 2,
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"decode_context_parallel_size": 2,
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"prefill_context_parallel_size": 2,
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"enable_expert_parallel": True,
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"enforce_eager": True,
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"quantization": "ascend",
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}
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tp_kwargs = {
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"tensor_parallel_size": 4,
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"enable_expert_parallel": True,
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"enforce_eager": True,
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"quantization": "ascend",
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}
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else:
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cp_kwargs = {
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"tensor_parallel_size": 1,
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"decode_context_parallel_size": 1,
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"prefill_context_parallel_size": 2,
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"enforce_eager": False,
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"compilation_config": {
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"cudagraph_capture_sizes": [4, 8, 24, 48, 60]
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},
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}
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tp_kwargs = {
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"tensor_parallel_size": 2,
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"enforce_eager": True,
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}
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cp_full_kwargs = {}
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cp_full_kwargs.update(common_kwargs)
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cp_full_kwargs.update(cp_kwargs)
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tp_full_kwargs = {}
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tp_full_kwargs.update(common_kwargs)
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tp_full_kwargs.update(tp_kwargs)
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with VllmRunner(model, **cp_full_kwargs) as runner:
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vllm_context_parallel_outputs = runner.generate_greedy(
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prompts, max_tokens)
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with VllmRunner(model, **tp_full_kwargs) as runner:
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vllm_eager_outputs = runner.generate_greedy(prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs,
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outputs_1_lst=vllm_context_parallel_outputs,
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name_0="vllm_eager_outputs",
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name_1="vllm_context_parallel_outputs",
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)
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