449f8f65a7
### What this PR does / why we need it?
Support KV-Sharing feature in CLA (cross layer attention) models, which
sharing kv cache in some layers.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
70 lines
2.0 KiB
Python
70 lines
2.0 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 cross layer attention model with and without aclgraph.
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Run `pytest tests/e2e/singlecard/test_cross_layer_attn_model.py`.
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"""
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import os
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import pytest
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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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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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MODELS = [
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"google/gemma-3n-E2B-it",
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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_aclgraph(
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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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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=False,
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cudagraph_capture_sizes=[4],
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) as vllm_model:
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vllm_aclgraph_outputs = vllm_model.generate_greedy(prompts, max_tokens)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=True,
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) as vllm_model:
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vllm_eager_outputs = vllm_model.generate_greedy(prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs,
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outputs_1_lst=vllm_aclgraph_outputs,
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name_0="vllm_eager_outputs",
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name_1="vllm_aclgraph_outputs",
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)
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