# # 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. # # ruff: noqa: E501 import os import pytest from tests.e2e.singlecard.utils import PROMPTS_LONG, PROMPTS_SHORT, LLMTestCase, gen_and_valid CASE_QWEN_ACLGRAPH = LLMTestCase( model="Qwen/Qwen3-0.6B", prompts=PROMPTS_SHORT, golden_answers=[ " Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the", " the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president", " Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of", " not just a technological challenge but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and", ], ) CASE_DS_ACLGRAPH = LLMTestCase( model="vllm-ascend/DeepSeek-V2-Lite-W8A8", quantization="ascend", prompts=PROMPTS_SHORT, golden_answers=[ "\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2", " a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the", " Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art", " here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of", ], ) CASE_QWEN_FULL = LLMTestCase( model="Qwen/Qwen3-0.6B", prompts=PROMPTS_SHORT, golden_answers=[ " Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the", " the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president", " Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of", " not just a technological challenge but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and", ], ) CASE_DS_FULL = LLMTestCase( model="vllm-ascend/DeepSeek-V2-Lite-W8A8", quantization="ascend", prompts=PROMPTS_SHORT, golden_answers=[ "\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2", " a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the", " Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art", " here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of", ], ) CASE_QWEN_FULL_DECODE_ONLY = LLMTestCase( model="Qwen/Qwen3-0.6B", prompts=PROMPTS_LONG, golden_answers=[ " \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the", " \n\nTo solve this problem, we can use the following approach: Let $P$ be the perimeter of the square. Then, the expected value of the area", " \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can", ], ) CASE_DS_FULL_DECODE_ONLY = LLMTestCase( model="vllm-ascend/DeepSeek-V2-Lite-W8A8", quantization="ascend", prompts=PROMPTS_LONG, golden_answers=[ "\n\nSelect an assignment template", "\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use", "\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations", ], ) CASE_QWEN_EX = LLMTestCase( model="Qwen/Qwen3-0.6B", prompts=PROMPTS_LONG, golden_answers=[ " \n\nTo solve this problem, we need to use the Law of Sines and Law of Cosines. Let me start by drawing triangle $ABC$ with the", " \n\nTo solve this problem, we can use the following approach: Let $P$ be the perimeter of the square. Then, the expected value of the area", " \n\nTo solve this problem, we can use the following approach: Let $ \\alpha $ be the common real root of the two equations. Then, we can", ], ) CASE_DS_EX = LLMTestCase( model="vllm-ascend/DeepSeek-V2-Lite-W8A8", quantization="ascend", prompts=PROMPTS_LONG, golden_answers=[ "\n\nSelect an assignment template", "\n\nI'm not sure how to approach this problem. I'm not sure if I should use the law of total probability or if I should use", "\n\n## Answer\n\n$a + b + c = 0$\n\nSolution\n\nLet $x$ be the common root of the equations", ], ) @pytest.mark.parametrize("cur_case", [CASE_QWEN_ACLGRAPH, CASE_DS_ACLGRAPH]) def test_piecewise_res_consistency(cur_case: LLMTestCase): runner_kwargs = { "model_name": cur_case.model, "max_model_len": 1024, "cudagraph_capture_sizes": [1, 2, 4, 8], "quantization": cur_case.quantization, } gen_and_valid( runner_kwargs=runner_kwargs, prompts=cur_case.prompts, sampling_params=cur_case.sampling_params, golden_answers=cur_case.golden_answers, ) @pytest.mark.parametrize("cur_case", [CASE_QWEN_FULL, CASE_DS_FULL]) def test_full_res_consistency(cur_case: LLMTestCase, monkeypatch): monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False) runner_kwargs = { "model_name": cur_case.model, "max_model_len": 1024, "compilation_config": {"cudagraph_capture_sizes": [4, 8, 32, 64], "cudagraph_mode": "FULL_DECODE_ONLY"}, "quantization": cur_case.quantization, } gen_and_valid( runner_kwargs=runner_kwargs, prompts=cur_case.prompts, sampling_params=cur_case.sampling_params, golden_answers=cur_case.golden_answers, ) @pytest.mark.parametrize("cur_case", [CASE_QWEN_FULL_DECODE_ONLY, CASE_DS_FULL_DECODE_ONLY]) def test_full_decode_only_res_consistency(cur_case: LLMTestCase, monkeypatch): monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False) runner_kwargs = { "model_name": cur_case.model, "max_model_len": 1024, "compilation_config": {"cudagraph_capture_sizes": [4, 8, 32, 64], "cudagraph_mode": "FULL_DECODE_ONLY"}, "quantization": cur_case.quantization, "additional_config": {"ascend_compilation_config": {"enable_npugraph_ex": False}}, } gen_and_valid( runner_kwargs=runner_kwargs, prompts=cur_case.prompts, sampling_params=cur_case.sampling_params, golden_answers=cur_case.golden_answers, ) @pytest.mark.parametrize("cur_case", [CASE_QWEN_EX, CASE_DS_EX]) def test_npugraph_ex_res_consistency(cur_case: LLMTestCase, monkeypatch): monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False) runner_kwargs = { "model_name": cur_case.model, "quantization": cur_case.quantization, "max_model_len": 1024, "compilation_config": {"cudagraph_capture_sizes": [4, 8, 32, 64], "cudagraph_mode": "FULL_DECODE_ONLY"}, "additional_config": {"ascend_compilation_config": {"enable_npugraph_ex": True}}, } gen_and_valid( runner_kwargs=runner_kwargs, prompts=cur_case.prompts, sampling_params=cur_case.sampling_params, golden_answers=cur_case.golden_answers, ) # The accuracy has already been verified in the previous test case. # This test case is used to check whether the functionality works properly # after enabling the static kernel and whether it is uninstalled as expected. @pytest.mark.parametrize("cur_case", [CASE_QWEN_EX]) def test_npugraph_ex_with_static_kernel(cur_case: LLMTestCase, monkeypatch): monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False) runner_kwargs = { "model_name": cur_case.model, "quantization": cur_case.quantization, "max_model_len": 1024, "compilation_config": {"cudagraph_capture_sizes": [4, 8], "cudagraph_mode": "FULL_DECODE_ONLY"}, "additional_config": { "ascend_compilation_config": { "enable_npugraph_ex": True, "enable_static_kernel": True, } }, } gen_and_valid( runner_kwargs=runner_kwargs, prompts=cur_case.prompts, sampling_params=cur_case.sampling_params, golden_answers=cur_case.golden_answers, ) # Check whether the static kernel is properly uninstall ascend_home_path = os.environ["ASCEND_HOME_PATH"] static_kernel_install_path = os.path.join(ascend_home_path, "opp/static_kernel/ai_core") assert not os.path.exists(static_kernel_install_path)