afe00505de
### What this PR does / why we need it?
Due to the differences in operators used and execution order between
xlite and eager modes, there will be slight precision discrepancies.
This patch skip the xlite e2e tests.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
vLLM version: v0.12.0
vLLM main:
ad32e3e19c
Signed-off-by: lulina <lina.lulina@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
133 lines
4.0 KiB
Python
133 lines
4.0 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.skip
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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.skip
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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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