66b0781840
### What this PR does / why we need it?
Refactor the e2e testcases.
- tests/e2e/multicard/test_weight_loader.py: Remove the unused code.
- tests/e2e/singlecard/multi-modal/test_internvl.py: Move to accuracy
test.
- tests/e2e/singlecard/test_aclgraph.py: Rename the file.
- tests/e2e/singlecard/test_embedding_aclgraph.py : Combine with
tests/e2e/singlecard/test_bge_model.py
- tests/e2e/singlecard/test_completion_with_prompt_embeds.py: Delete
eager mode and modify model to Qwen3-0.6B
- tests/e2e/singlecard/test_quantization.py: Modify model to
Qwen3-0.6B-W8A8
- tests/e2e/singlecard/test_vlm.py: Modify model to Qwen3-VL-8B
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: menogrey <1299267905@qq.com>
76 lines
2.7 KiB
Python
76 lines
2.7 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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# This file is a part of the vllm-ascend project.
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#
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import os
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import pytest
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from tests.e2e.conftest import VllmRunner
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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 = ["Qwen/Qwen3-0.6B"]
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def get_prompt_embeds(chat, tokenizer, embedding_layer):
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"""Convert chat messages to prompt embeddings."""
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token_ids = tokenizer.apply_chat_template(chat,
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add_generation_prompt=True,
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return_tensors='pt')
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prompt_embeds = embedding_layer(token_ids).squeeze(0)
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return prompt_embeds
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@pytest.mark.parametrize("model_name", MODELS)
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def test_mixed_prompt_embeds_and_text(model_name):
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"""Test mixed inputs with both prompt embeddings and text prompts."""
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# Prepare prompt embeddings for first request
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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transformers_model = AutoModelForCausalLM.from_pretrained(model_name)
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embedding_layer = transformers_model.get_input_embeddings()
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chat = [{"role": "user", "content": "What is AI?"}]
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prompt_embeds = get_prompt_embeds(chat, tokenizer, embedding_layer)
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# Prepare text prompt for second request
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text_prompt = "What is machine learning?"
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# Run inference with mixed inputs
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with VllmRunner(
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model_name,
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enable_prompt_embeds=True,
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) as vllm_runner:
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# Test prompt embeddings
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embeds_output = vllm_runner.model.generate({
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"prompt_embeds":
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prompt_embeds,
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})
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# Test text prompt
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text_output = vllm_runner.model.generate(text_prompt)
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# Verify both types of inputs work
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assert len(embeds_output) == 1
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assert len(text_output) == 1
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assert len(embeds_output[0].outputs[0].text) > 0
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assert len(text_output[0].outputs[0].text) > 0
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print("\n[Prompt Embeds Output]:", embeds_output[0].outputs[0].text)
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print("[Text Prompt Output]:", text_output[0].outputs[0].text)
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