xc-llm-ascend/tests/e2e/multicard/test_qwen3_next.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
#
"""Compare the short outputs of HF and vLLM when using greedy sampling.

Run `pytest tests/e2e/multicard/test_qwen3_next.py`.
"""
from tests.e2e.conftest import VllmRunner


def test_models_distributed_Qwen3_NEXT_TP4():
    example_prompts = [
        "Hello, my name is",
    ] * 4
    max_tokens = 5
    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
                    tensor_parallel_size=4,
                    max_model_len=4096,
                    gpu_memory_utilization=0.8,
                    distributed_executor_backend="mp",
                    enforce_eager=True) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)
        del vllm_model


def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
    example_prompts = [
        "Hello, my name is",
    ] * 4
    max_tokens = 5
    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
                    tensor_parallel_size=4,
                    max_model_len=4096,
                    gpu_memory_utilization=0.8,
                    distributed_executor_backend="mp",
                    enforce_eager=False,
                    compilation_config={
                        "cudagraph_mode": "FULL_DECODE_ONLY",
                        "cudagraph_capture_sizes": [1, 8, 24, 48, 60]
                    }) as vllm_model:
        vllm_model.generate_greedy(example_prompts, max_tokens)
        del vllm_model


def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
    example_prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    max_tokens = 20

    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
                    tensor_parallel_size=4,
                    max_model_len=4096,
                    gpu_memory_utilization=0.8,
                    distributed_executor_backend="mp") as vllm_model:
        ref_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
    del vllm_model

    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
                    tensor_parallel_size=4,
                    max_model_len=4096,
                    gpu_memory_utilization=0.8,
                    distributed_executor_backend="mp",
                    additional_config={
                        "ascend_scheduler_config": {
                            "enabled": True,
                            "enable_chunked_prefill": False
                        }
                    },
                    speculative_config={
                        "method": "qwen3_next_mtp",
                        "num_speculative_tokens": 1
                    }) as spec_vllm_model:
        spec_outputs = spec_vllm_model.generate_greedy(example_prompts,
                                                       max_tokens)
    del spec_vllm_model

    matches = 0
    misses = 0
    for ref_output, spec_output in zip(ref_outputs, spec_outputs):
        ref_token_ids = ref_output[0]
        spec_token_ids = spec_output[0]
        if ref_token_ids == spec_token_ids[:len(ref_token_ids)]:
            matches += 1
        else:
            misses += 1
            print(f"ref_output: {ref_output[1]}")
            print(f"spec_output: {spec_output[1]}")

    assert matches > int(0.66 * len(ref_outputs))