e04a87f4be
### What this PR does / why we need it?
- Fixes Qwen3-Next enable nz accuracy problem
### Does this PR introduce _any_ user-facing change?
N/A
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: Icey <1790571317@qq.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
103 lines
3.8 KiB
Python
103 lines
3.8 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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# 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_next.py`.
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"""
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from tests.e2e.conftest import VllmRunner
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def test_models_distributed_Qwen3_NEXT_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-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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enforce_eager=True) 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_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
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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-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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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": [1, 8, 24, 48, 60]
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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
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def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
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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-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp") as vllm_model:
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ref_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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del vllm_model
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with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
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tensor_parallel_size=4,
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max_model_len=4096,
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gpu_memory_utilization=0.8,
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distributed_executor_backend="mp",
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speculative_config={
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"method": "qwen3_next_mtp",
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"num_speculative_tokens": 1
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}) as spec_vllm_model:
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spec_outputs = spec_vllm_model.generate_greedy(example_prompts,
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max_tokens)
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del spec_vllm_model
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matches = 0
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misses = 0
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for ref_output, spec_output in zip(ref_outputs, spec_outputs):
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ref_token_ids = ref_output[0]
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spec_token_ids = spec_output[0]
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if ref_token_ids == spec_token_ids[:len(ref_token_ids)]:
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matches += 1
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else:
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misses += 1
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print(f"ref_output: {ref_output[1]}")
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print(f"spec_output: {spec_output[1]}")
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assert matches > int(0.66 * len(ref_outputs))
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