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118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from collections.abc import Sequence
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import mteb
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import numpy as np
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import pytest
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from tests.models.utils import EmbedModelInfo
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from vllm.model_executor.model_loader.utils import set_default_torch_dtype
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# Most models on the STS12 task (See #17175):
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# - Model implementation and minor changes in tensor dtype
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# results in differences less than 1e-4
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# - Different model results in differences more than 1e-3
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# 1e-4 is a good tolerance threshold
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MTEB_EMBED_TASKS = ["STS12"]
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MTEB_EMBED_TOL = 1e-4
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class VllmMtebEncoder(mteb.Encoder):
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def __init__(self, vllm_model):
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super().__init__()
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self.model = vllm_model
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self.rng = np.random.default_rng(seed=42)
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def encode(
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self,
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sentences: Sequence[str],
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*args,
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**kwargs,
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) -> np.ndarray:
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# Hoping to discover potential scheduling
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# issues by randomizing the order.
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r = self.rng.permutation(len(sentences))
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sentences = [sentences[i] for i in r]
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outputs = self.model.encode(sentences, use_tqdm=False)
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embeds = np.array(outputs)
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embeds = embeds[np.argsort(r)]
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return embeds
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class OpenAIClientMtebEncoder(mteb.Encoder):
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def __init__(self, model_name: str, client):
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super().__init__()
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self.model_name = model_name
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self.client = client
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self.rng = np.random.default_rng(seed=42)
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def encode(self, sentences: Sequence[str], *args, **kwargs) -> np.ndarray:
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# Hoping to discover potential scheduling
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# issues by randomizing the order.
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r = self.rng.permutation(len(sentences))
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sentences = [sentences[i] for i in r]
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embeddings = self.client.embeddings.create(model=self.model_name,
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input=sentences)
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outputs = [d.embedding for d in embeddings.data]
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embeds = np.array(outputs)
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embeds = embeds[np.argsort(r)]
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return embeds
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def run_mteb_embed_task(encoder, tasks):
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tasks = mteb.get_tasks(tasks=tasks)
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evaluation = mteb.MTEB(tasks=tasks)
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results = evaluation.run(encoder, verbosity=0, output_folder=None)
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main_score = results[0].scores["test"][0]["main_score"]
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return main_score
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def run_mteb_embed_task_st(model_name, tasks):
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(model_name)
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return run_mteb_embed_task(model, tasks)
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def mteb_test_embed_models(hf_runner,
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vllm_runner,
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model_info: EmbedModelInfo,
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vllm_extra_kwargs=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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vllm_extra_kwargs = vllm_extra_kwargs or {}
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with vllm_runner(model_info.name,
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task="embed",
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max_model_len=None,
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dtype=model_info.dtype,
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**vllm_extra_kwargs) as vllm_model:
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if model_info.architecture:
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assert (model_info.architecture
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in vllm_model.model.llm_engine.model_config.architectures)
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vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
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MTEB_EMBED_TASKS)
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vllm_dtype = vllm_model.model.llm_engine.model_config.dtype
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model_dtype = getattr(
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vllm_model.model.llm_engine.model_config.hf_config, "torch_dtype",
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vllm_dtype)
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with set_default_torch_dtype(model_dtype) and hf_runner(
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model_info.name, is_sentence_transformer=True,
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dtype=model_dtype) as hf_model:
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st_main_score = run_mteb_embed_task(hf_model, MTEB_EMBED_TASKS)
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print("VLLM:", vllm_dtype, vllm_main_score)
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print("SentenceTransformer:", model_dtype, st_main_score)
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print("Difference:", st_main_score - vllm_main_score)
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assert st_main_score == pytest.approx(vllm_main_score, rel=MTEB_EMBED_TOL)
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