### What this PR does / why we need it?
Add test_qwen3_5.py for base scenarios tp4 on Qwen3.5-27B and
Qwen3.5-35B-A3B.
- vLLM version: main
- vLLM main:
4034c3d32e
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Signed-off-by: pppeng <zepengliu912@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
75 lines
2.7 KiB
Python
75 lines
2.7 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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from tests.e2e.conftest import VllmRunner
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def test_qwen3_5_27b_distributed_mp_tp4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3.5-27B",
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tensor_parallel_size=4,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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max_model_len=4096,
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gpu_memory_utilization=0.90,
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distributed_executor_backend="mp") as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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def test_qwen3_5_35b_distributed_mp_tp4():
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example_prompts = [
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"Hello, my name is",
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] * 4
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max_tokens = 5
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with VllmRunner("Qwen/Qwen3.5-35B-A3B",
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tensor_parallel_size=4,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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max_model_len=4096,
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gpu_memory_utilization=0.90,
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distributed_executor_backend="mp") as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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def test_qwen3_5_35b_distributed_mp_tp4_full_decode_only_mtp3():
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example_prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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max_tokens = 20
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with VllmRunner("Qwen/Qwen3.5-35B-A3B",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.90,
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distributed_executor_backend="mp",
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compilation_config={
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"cudagraph_mode": "FULL_DECODE_ONLY",
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"cudagraph_capture_sizes": [4, 8, 12, 16],
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},
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speculative_config={
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"method": "qwen3_5_mtp",
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"num_speculative_tokens": 3,
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}) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model |