5f8241c25c
### What this PR does / why we need it? Change as little existing code as possible to add v1 pooling task's support, notice that i move down the `vllm.v1.worker.gpu_input_batch` to vllm-ascend, Considering the frequent changes in upstream interfaces, in order to decouple, so i move it here ### How was this patch tested? CI passed with new added/existing test, and I have a simple test was first conducted locally which is adapted from https://www.modelscope.cn/models/Qwen/Qwen3-Embedding-0.6B, just like bellow： ```python import os import torch from vllm import LLM os.environ["VLLM_USE_MODELSCOPE"]="True" def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery:{query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'What is the capital of China?'), get_detailed_instruct(task, 'Explain gravity') ] # No need to add instruction for retrieval documents documents = [ "The capital of China is Beijing.", "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun." ] input_texts = queries + documents model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed") outputs = model.embed(input_texts) embeddings = torch.tensor([o.outputs.embedding for o in outputs]) scores = (embeddings[:2] @ embeddings[2:].T) print(scores.tolist()) # [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]] ``` --------- Signed-off-by: wangli <wangli858794774@gmail.com> Signed-off-by: wangli <858794774@qq.com> Co-authored-by: wangli <858794774@qq.com>
33 lines
1.0 KiB
Python
33 lines
1.0 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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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/vllm/v1/pool/metadata.py
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#
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from dataclasses import dataclass
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from typing import Optional
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import torch
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from vllm.pooling_params import PoolingParams
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@dataclass
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class PoolingMetadata:
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"""Tensors for pooling."""
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prompt_lens: torch.Tensor
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prompt_token_ids: Optional[torch.Tensor]
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pooling_params: list[PoolingParams]
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