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fbc1304 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | 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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