118 lines
3.7 KiB
Python
118 lines
3.7 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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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/e2e/pull_request/two_card/test_flashcomm_distributed.py`.
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"""
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import os
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from unittest.mock import patch
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import pytest
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from vllm import SamplingParams
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from vllm.config import KVTransferConfig
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from tests.e2e.conftest import VllmRunner
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QWEN_DENSE_MODELS = [
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"vllm-ascend/Qwen3-0.6B-W8A8",
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]
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@pytest.mark.skip(reason="test is broken, fix me")
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
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@patch.dict(os.environ, {"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE": "1"})
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def test_qwen3_moe_fc2_oshard_tp2() -> None:
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example_prompts = [
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"Hello, my name is",
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]
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sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
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with VllmRunner(
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"Qwen/Qwen3-30B-A3B",
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dtype="auto",
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tensor_parallel_size=2,
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distributed_executor_backend="mp",
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enable_expert_parallel=True,
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enforce_eager=True,
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additional_config={"layer_sharding": ["o_proj"]},
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kv_transfer_config=KVTransferConfig(kv_role="kv_producer"),
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) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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@pytest.mark.skip(reason="test is broken, fix me")
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
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def test_deepseek_v2_lite_fc1_tp2() -> None:
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example_prompts = [
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"test" * 1001,
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]
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sampling_params = SamplingParams(max_tokens=5, temperature=0.0, top_k=50, top_p=0.9)
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with VllmRunner(
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"vllm-ascend/DeepSeek-V2-Lite-W8A8",
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dtype="auto",
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tensor_parallel_size=2,
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distributed_executor_backend="mp",
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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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) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
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@pytest.mark.skip(reason="test is broken, fix me")
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
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def test_qwen3_dense_fc1_tp2(model):
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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model,
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max_model_len=8192,
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dtype="auto",
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tensor_parallel_size=2,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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quantization="ascend",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
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@pytest.mark.skip(reason="test is broken, fix me")
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
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def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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model,
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max_model_len=8192,
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dtype="auto",
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tensor_parallel_size=2,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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quantization="ascend",
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additional_config={"weight_prefetch_config": {"enabled": True}},
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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