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84 lines
3.2 KiB
Python
84 lines
3.2 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 typing import Any
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import pytest
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import torch
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from tests.conftest import HfRunner
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from tests.models.utils import LASTPoolingRerankModelInfo, RerankModelInfo
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from .mteb_utils import mteb_test_rerank_models
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mxbai_rerank_hf_overrides = {
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"architectures": ["Qwen2ForSequenceClassification"],
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"classifier_from_token": ["0", "1"],
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"method": "from_2_way_softmax",
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}
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RERANK_MODELS = [
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LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-base-v2",
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architecture="Qwen2ForSequenceClassification",
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hf_overrides=mxbai_rerank_hf_overrides,
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mteb_score=0.273,
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enable_test=True),
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LASTPoolingRerankModelInfo("mixedbread-ai/mxbai-rerank-large-v2",
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architecture="Qwen2ForSequenceClassification",
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hf_overrides=mxbai_rerank_hf_overrides,
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enable_test=False)
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]
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class MxbaiRerankerHfRunner(HfRunner):
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def __init__(self,
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model_name: str,
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dtype: str = "auto",
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*args: Any,
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**kwargs: Any) -> None:
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from transformers import AutoModelForCausalLM, AutoTokenizer
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super().__init__(model_name, dtype, auto_cls=AutoModelForCausalLM)
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self.tokenizer = AutoTokenizer.from_pretrained(model_name,
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padding_side='left')
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self.yes_loc = self.tokenizer.convert_tokens_to_ids("1")
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self.no_loc = self.tokenizer.convert_tokens_to_ids("0")
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def predict(self, prompts: list[list[str]], *args,
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**kwargs) -> torch.Tensor:
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def process_inputs(pairs):
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inputs = self.tokenizer(pairs,
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padding=False,
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truncation='longest_first',
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return_attention_mask=False)
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for i, ele in enumerate(inputs['input_ids']):
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inputs['input_ids'][i] = ele
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inputs = self.tokenizer.pad(inputs,
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padding=True,
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return_tensors="pt")
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for key in inputs:
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inputs[key] = inputs[key].to(self.model.device)
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return inputs
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@torch.no_grad()
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def compute_logits(inputs):
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logits = self.model(**inputs).logits[:, -1, :]
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yes_logits = logits[:, self.yes_loc]
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no_logits = logits[:, self.no_loc]
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logits = yes_logits - no_logits
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scores = logits.float().sigmoid()
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return scores
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scores = []
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for prompt in prompts:
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inputs = process_inputs([prompt])
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score = compute_logits(inputs)
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scores.append(score[0].item())
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return torch.Tensor(scores)
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@pytest.mark.parametrize("model_info", RERANK_MODELS)
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def test_rerank_models_mteb(vllm_runner, model_info: RerankModelInfo) -> None:
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mteb_test_rerank_models(MxbaiRerankerHfRunner, vllm_runner, model_info)
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