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86ae693f20
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
70 lines
2.4 KiB
Python
70 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Sequence
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from typing import Optional
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import pytest
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from tests.conftest import HfRunner
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from tests.models.utils import (EmbedModelInfo, check_embeddings_close,
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matryoshka_fy)
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def run_embedding_correctness_test(
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hf_model: "HfRunner",
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inputs: list[str],
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vllm_outputs: Sequence[list[float]],
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dimensions: Optional[int] = None,
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):
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hf_outputs = hf_model.encode(inputs)
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if dimensions:
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hf_outputs = matryoshka_fy(hf_outputs, dimensions)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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def correctness_test_embed_models(hf_runner,
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vllm_runner,
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model_info: EmbedModelInfo,
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example_prompts,
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vllm_extra_kwargs=None,
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hf_model_callback=None):
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if not model_info.enable_test:
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# A model family has many models with the same architecture,
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# and we don't need to test each one.
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pytest.skip("Skipping test.")
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# The example_prompts has ending "\n", for example:
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# "Write a short story about a robot that dreams for the first time.\n"
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# sentence_transformers will strip the input texts, see:
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# https://github.com/UKPLab/sentence-transformers/blob/v3.1.1/sentence_transformers/models/Transformer.py#L159
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# This makes the input_ids different between hf_model and vllm_model.
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# So we need to strip the input texts to avoid test failing.
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example_prompts = [str(s).strip() for s in example_prompts]
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vllm_extra_kwargs = vllm_extra_kwargs or {}
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vllm_extra_kwargs["dtype"] = model_info.dtype
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with vllm_runner(model_info.name,
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runner="pooling",
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max_model_len=None,
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**vllm_extra_kwargs) as vllm_model:
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vllm_outputs = vllm_model.embed(example_prompts)
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with hf_runner(
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model_info.name,
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dtype="float32",
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is_sentence_transformer=True,
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) as hf_model:
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if hf_model_callback is not None:
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hf_model_callback(hf_model)
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run_embedding_correctness_test(hf_model, example_prompts, vllm_outputs)
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