2be0fe2691
### What this PR does / why we need it? This patch adds support for the xlite graph wrapper to vllm_ascend. Xlite provides operator implementations of the transformer network on Ascend hardware. For details about xlite, please refer to the following link: https://gitee.com/openeuler/GVirt/blob/master/xlite/README.md The latest performance comparison data between xlite and the default aclgraph mode is as follows: ## Qwen3 32B TPS 910B3(A2) Online Inference Performance Comparison - aclgraph: main(c4a71fc6) - xlite-full: main(c4a71fc6) + xlite-full - xlite-decode-only: main(c4a71fc6) + xlite-decode-only - diff1: Performance comparison between xlite-full and aclgraph - diff2: Performance comparison between xlite-decode-only and aclgraph ### Does this PR introduce _any_ user-facing change? Enable the xlite graph mode by setting xlite_graph_config: --additional-config='{"xlite_graph_config": {"enabled": true}}' # Enabled for decode only --additional-config='{"xlite_graph_config": {"enabled": true, "full_mode": true}}' # Enabled for prefill and decode - vLLM version: v0.12.0 - vLLM main:ad32e3e19c--------- Signed-off-by: lulina <lina.lulina@huawei.com> Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
131 lines
3.9 KiB
Python
131 lines
3.9 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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#
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"""
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Compare the outputs of vLLM with and without xlite.
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Run `pytest tests/e2e/singlecard/test_xlite.py`.
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"""
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import pytest
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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MODELS = [
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"Qwen/Qwen3-0.6B",
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]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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def test_models_with_xlite_decode_only(
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model: str,
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max_tokens: int,
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) -> None:
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prompts = [
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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]
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sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
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with VllmRunner(
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model,
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block_size=128,
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max_model_len=1024,
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enforce_eager=False,
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additional_config={"xlite_graph_config": {
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"enabled": True
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}},
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) as runner:
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vllm_xlite_outputs = runner.model.generate(prompts, sampling_params)
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with VllmRunner(
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model,
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block_size=128,
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max_model_len=1024,
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enforce_eager=True,
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
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vllm_xlite_outputs_list = []
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for output in vllm_xlite_outputs:
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vllm_xlite_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=vllm_xlite_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="vllm_xlite_outputs",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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def test_models_with_xlite_full_mode(
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model: str,
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max_tokens: int,
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) -> None:
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prompts = [
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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]
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sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
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with VllmRunner(
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model,
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block_size=128,
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max_model_len=1024,
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enforce_eager=False,
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additional_config={
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"xlite_graph_config": {
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"enabled": True,
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"full_mode": True
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}
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},
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) as runner:
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vllm_xlite_outputs = runner.model.generate(prompts, sampling_params)
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with VllmRunner(
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model,
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block_size=128,
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max_model_len=1024,
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enforce_eager=True,
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
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vllm_xlite_outputs_list = []
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for output in vllm_xlite_outputs:
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vllm_xlite_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=vllm_xlite_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="vllm_xlite_outputs",
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)
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