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import os
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
os.environ["OPENBLAS_NUM_THREADS"] = "32"
import numpy as np
import torch
import mteb
from mteb.encoder_interface import PromptType
from sentence_transformers import SentenceTransformer
from mteb.models.wrapper import Wrapper
from typing import Sequence
from typing import Any
from transformers import AutoTokenizer, AutoModel
class DeweySingleVectorWrapper:
def __init__(self, model_dir, batch_size: int = 8):
self.model = SentenceTransformer(
model_dir,
trust_remote_code=True,
model_kwargs={
"torch_dtype": torch.bfloat16, # fp16 容易计算出nan
"attn_implementation": "flash_attention_2"
},
config_kwargs={"single_vector_type": "mean"}
).cuda().bfloat16().eval()
self.model.max_seq_length = max_seq_length
self.pool = self.model.start_multi_process_pool()
self.batch_size = batch_size
def encode(
self,
sentences: list[str],
task_name: str,
prompt_type: PromptType | None = None,
**kwargs,
) -> np.ndarray:
if prompt_type.value == "query":
prompt = RETRIEVE_Q_PROMPT
else:
prompt = RETRIEVE_P_PROMPT
vectors = self.model.encode_multi_process(
sentences=sentences,
pool=self.pool,
show_progress_bar=True,
batch_size=self.batch_size,
normalize_embeddings=True,
prompt=prompt,
precision="float32"
)
return vectors
class DeweyMultiVectorWrapper(Wrapper):
def __init__(
self,
model_dir: str,
batch_size: int = 8,
*args,
**kwargs,
) -> None:
self.model = AutoModel.from_pretrained(
model_dir,
trust_remote_code=True,
attn_implementation="flash_attention_2"
).cuda().bfloat16()
self.batch_size = batch_size
self.model.tokenizer = AutoTokenizer.from_pretrained(model_dir)
def encode(
self,
sentences: Sequence[str],
*,
task_name: str,
prompt_type: PromptType | None = None,
**kwargs: Any,
) -> np.ndarray:
if prompt_type.value == "query":
prompt = RETRIEVE_Q_PROMPT
else:
prompt = RETRIEVE_P_PROMPT
if prompt_type.value == "query":
pred = self.model.encode(
sentences=list(sentences),
use_cuda=True,
show_progress_bar=True,
chunk_size=-1,
chunk_overlap=32,
convert_to_tensor=True,
max_seq_length=max_seq_length,
batch_size=self.batch_size,
normalize_embeddings=True,
prompt=prompt,
fast_chunk=False
)[0]
# query vector do not need multi vector, we only use mean as final one vector
pred = [vecs[1:2, :] for vecs in pred]
else:
pred = self.model.encode(
sentences=list(sentences),
use_cuda=True,
show_progress_bar=True,
chunk_size=256,
chunk_overlap=32,
convert_to_tensor=True,
max_seq_length=max_seq_length,
batch_size=self.batch_size,
normalize_embeddings=True,
prompt=prompt,
fast_chunk=True,
)[0]
pred = torch.nn.utils.rnn.pad_sequence(pred, batch_first=True, padding_value=0)
return pred.cpu().numpy()
def similarity(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
if not isinstance(a, torch.Tensor):
a = torch.tensor(a, dtype=torch.float32)
if not isinstance(b, torch.Tensor):
b = torch.tensor(b, dtype=torch.float32)
if len(a.shape) == 2:
a = a.unsqueeze(0)
if len(b.shape) == 2:
b = b.unsqueeze(0)
scores = torch.einsum(
"ash,bth->abst",
a,
b,
)
return scores.max(axis=-1).values.sum(axis=-1)
RETRIEVE_Q_PROMPT = "<|START_INSTRUCTION|>Answer the question<|END_INSTRUCTION|>"
RETRIEVE_P_PROMPT = "<|START_INSTRUCTION|>Candidate document<|END_INSTRUCTION|>"
if __name__ == "__main__":
################# evaluate single vector #################
# batch_size = 4
# max_seq_length = 128 * 1024
# model = DeweySingleVectorWrapper("infgrad/dewey_en_beta", batch_size=batch_size)
# output_folder = f"./long_embed_benchmark/dewey_en_beta_single_vector_128k"
# tasks = list(mteb.get_benchmark("LongEmbed"))
# evaluation = mteb.MTEB(tasks=tasks)
# evaluation.run(model, output_folder=output_folder, verbosity=2, overwrite_results=False)
################# evaluate multi vectors #################
batch_size = 4
max_seq_length = 128 * 1024
model = DeweyMultiVectorWrapper("infgrad/dewey_en_beta", batch_size=batch_size)
output_folder = f"./long_embed_benchmark/dewey_en_beta_multi_vectors"
tasks = list(mteb.get_benchmark("LongEmbed"))
evaluation = mteb.MTEB(tasks=tasks)
evaluation.run(model, output_folder=output_folder, verbosity=2, overwrite_results=False)
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