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xc-llm-ascend/tests/ut/test_ascend_config.py

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#
# 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.
#
from vllm.config import VllmConfig
from tests.ut.base import TestBase
from vllm_ascend.ascend_config import (clear_ascend_config, get_ascend_config,
init_ascend_config)
class TestAscendConfig(TestBase):
@staticmethod
def _clean_up_ascend_config(func):
def wrapper(*args, **kwargs):
clear_ascend_config()
func(*args, **kwargs)
clear_ascend_config()
return wrapper
@_clean_up_ascend_config
def test_init_ascend_config_without_additional_config(self):
test_vllm_config = VllmConfig()
# No additional config given, check the default value here.
ascend_config = init_ascend_config(test_vllm_config)
self.assertIsNone(ascend_config.expert_map_path)
self.assertFalse(ascend_config.multistream_overlap_shared_expert)
Adopt inductor fusion and define quantization fusion pass (#4168) ### What this PR does / why we need it? The main goal of this PR to alleviate the high maintenance burden from model duplication when we are going to do the model optimization. Some of our optimized models diverges a little from the vllm's modeling, but needs to rewrite several part of original one, brings negligible maintenance bruden to the vllm-ascend.In order to solve that, we propose to leverage `torch.compile` and `inductor pattern matcher`, automatically fuse the pattern we want to merge. For more details can refer to the RFC https://github.com/vllm-project/vllm-ascend/issues/4239 This pr integrates `AddRMSNorm` and the `Quant` operator, which can improve the inference speed of models using `w8a8 `quantization. ### Does this PR introduce _any_ user-facing change? Yes, add new additional_config ### How was this patch tested? ```python def main(): prompts = [ "The president of the United States is Mr.", ] # Create a sampling params object. sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95) # Create an LLM. llm = LLM( model="/root/.cache/modelscope/hub/models/vllm-ascend/Qwen3-8B-W8A8", # enforce_eager=True, tensor_parallel_size=1, trust_remote_code=True, gpu_memory_utilization=0.7, quantization="ascend", ) # Generate texts from the prompts. outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` ```text Prompt: 'The president of the United States is Mr.', Generated text: ' Trump. The president of the United States is Mr. Biden. Which of the following statements is correct? \n\nA. Mr. Trump is Mr. Biden. \nB. Mr. Trump is not Mr. Biden. \nC. The president of the United States is not Mr. Trump. \nD. The president of the United States is not Mr. Biden.\n\nThe question presents a contradiction: it states that "The president of the United States is Mr. Trump" and "The president of' ``` - vLLM version: 86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24 - vLLM main: https://github.com/vllm-project/vllm/commit/86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24 --------- Signed-off-by: Icey <1790571317@qq.com> Signed-off-by: wxsIcey <1790571317@qq.com>
2025-12-04 10:29:48 +08:00
ascend_compilation_config = ascend_config.ascend_compilation_config
self.assertTrue(ascend_compilation_config.enable_quantization_fusion)
@_clean_up_ascend_config
def test_init_ascend_config_with_additional_config(self):
test_vllm_config = VllmConfig()
test_vllm_config.additional_config = {
Adopt inductor fusion and define quantization fusion pass (#4168) ### What this PR does / why we need it? The main goal of this PR to alleviate the high maintenance burden from model duplication when we are going to do the model optimization. Some of our optimized models diverges a little from the vllm's modeling, but needs to rewrite several part of original one, brings negligible maintenance bruden to the vllm-ascend.In order to solve that, we propose to leverage `torch.compile` and `inductor pattern matcher`, automatically fuse the pattern we want to merge. For more details can refer to the RFC https://github.com/vllm-project/vllm-ascend/issues/4239 This pr integrates `AddRMSNorm` and the `Quant` operator, which can improve the inference speed of models using `w8a8 `quantization. ### Does this PR introduce _any_ user-facing change? Yes, add new additional_config ### How was this patch tested? ```python def main(): prompts = [ "The president of the United States is Mr.", ] # Create a sampling params object. sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95) # Create an LLM. llm = LLM( model="/root/.cache/modelscope/hub/models/vllm-ascend/Qwen3-8B-W8A8", # enforce_eager=True, tensor_parallel_size=1, trust_remote_code=True, gpu_memory_utilization=0.7, quantization="ascend", ) # Generate texts from the prompts. outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` ```text Prompt: 'The president of the United States is Mr.', Generated text: ' Trump. The president of the United States is Mr. Biden. Which of the following statements is correct? \n\nA. Mr. Trump is Mr. Biden. \nB. Mr. Trump is not Mr. Biden. \nC. The president of the United States is not Mr. Trump. \nD. The president of the United States is not Mr. Biden.\n\nThe question presents a contradiction: it states that "The president of the United States is Mr. Trump" and "The president of' ``` - vLLM version: 86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24 - vLLM main: https://github.com/vllm-project/vllm/commit/86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24 --------- Signed-off-by: Icey <1790571317@qq.com> Signed-off-by: wxsIcey <1790571317@qq.com>
2025-12-04 10:29:48 +08:00
"ascend_compilation_config": {
"enable_quantization_fusion": False,
},
"multistream_overlap_shared_expert": True,
"expert_map_path": "test_expert_map_path",
"refresh": True,
}
ascend_config = init_ascend_config(test_vllm_config)
self.assertEqual(ascend_config.expert_map_path, "test_expert_map_path")
self.assertTrue(ascend_config.multistream_overlap_shared_expert)
Adopt inductor fusion and define quantization fusion pass (#4168) ### What this PR does / why we need it? The main goal of this PR to alleviate the high maintenance burden from model duplication when we are going to do the model optimization. Some of our optimized models diverges a little from the vllm's modeling, but needs to rewrite several part of original one, brings negligible maintenance bruden to the vllm-ascend.In order to solve that, we propose to leverage `torch.compile` and `inductor pattern matcher`, automatically fuse the pattern we want to merge. For more details can refer to the RFC https://github.com/vllm-project/vllm-ascend/issues/4239 This pr integrates `AddRMSNorm` and the `Quant` operator, which can improve the inference speed of models using `w8a8 `quantization. ### Does this PR introduce _any_ user-facing change? Yes, add new additional_config ### How was this patch tested? ```python def main(): prompts = [ "The president of the United States is Mr.", ] # Create a sampling params object. sampling_params = SamplingParams(max_tokens=100, temperature=0.6, top_k=40, top_p=0.95) # Create an LLM. llm = LLM( model="/root/.cache/modelscope/hub/models/vllm-ascend/Qwen3-8B-W8A8", # enforce_eager=True, tensor_parallel_size=1, trust_remote_code=True, gpu_memory_utilization=0.7, quantization="ascend", ) # Generate texts from the prompts. outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` ```text Prompt: 'The president of the United States is Mr.', Generated text: ' Trump. The president of the United States is Mr. Biden. Which of the following statements is correct? \n\nA. Mr. Trump is Mr. Biden. \nB. Mr. Trump is not Mr. Biden. \nC. The president of the United States is not Mr. Trump. \nD. The president of the United States is not Mr. Biden.\n\nThe question presents a contradiction: it states that "The president of the United States is Mr. Trump" and "The president of' ``` - vLLM version: 86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24 - vLLM main: https://github.com/vllm-project/vllm/commit/86e178f7c4d8c3b0eaf3c8e3f810a83f63b90e24 --------- Signed-off-by: Icey <1790571317@qq.com> Signed-off-by: wxsIcey <1790571317@qq.com>
2025-12-04 10:29:48 +08:00
ascend_compilation_config = ascend_config.ascend_compilation_config
self.assertFalse(ascend_compilation_config.enable_quantization_fusion)
@_clean_up_ascend_config
def test_get_ascend_config(self):
test_vllm_config = VllmConfig()
ascend_config = init_ascend_config(test_vllm_config)
self.assertEqual(get_ascend_config(), ascend_config)
@_clean_up_ascend_config
def test_get_ascend_config_without_init(self):
with self.assertRaises(RuntimeError):
get_ascend_config()
@_clean_up_ascend_config
def test_clear_ascend_config(self):
test_vllm_config = VllmConfig()
ascend_config = init_ascend_config(test_vllm_config)
self.assertEqual(get_ascend_config(), ascend_config)
clear_ascend_config()
with self.assertRaises(RuntimeError):
get_ascend_config()