File size: 6,664 Bytes
59f2128 5aa60f8 aad97b0 59f2128 a7b7255 7fc23a9 59f2128 7fc23a9 59f2128 a9b487c 7fc23a9 59f2128 7c4c108 db5b5ea 59f2128 81baed5 59f2128 7fc23a9 59f2128 1a57b91 01e9e45 59f2128 db5b5ea 2e66948 59f2128 66b9dcc 846c662 66b9dcc 01e9e45 7fc23a9 59f2128 01e9e45 59f2128 01e9e45 59f2128 01e9e45 59f2128 01e9e45 59f2128 a8f4322 59f2128 a9b487c a7b7255 59f2128 be242ca 59f2128 aad97b0 59f2128 be242ca 378ac54 be242ca 5aa60f8 59f2128 3f39241 be242ca 3f39241 be242ca 3f39241 378ac54 59f2128 378ac54 59f2128 378ac54 59f2128 378ac54 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import requests
import time
import os
import numpy as np
from tqdm import tqdm
from typing import Any, List, Optional, Dict
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
class OptimumEncoder(BaseModel, Embeddings):
_tokenizer: Any
_model: Any
_torch: Any
def __init__(
self,
name: str = "mixedbread-ai/mxbai-embed-large-v1",
device: Optional[str] = None,
cache_dir: Optional[str] = None,
**kwargs: Any
)-> None:
super().__init__(**kwargs)
self.name = name
self.device = device
self.cache_dir = cache_dir
self._tokenizer, self._model = self._initialize_hf_model()
def _initialize_hf_model(self):
try:
import onnxruntime as ort
from optimum.onnxruntime import ORTModelForFeatureExtraction
except ImportError:
raise ImportError(
"Please install optimum and onnxruntime to use OptimumEncoder. "
"You can install it with: "
"`pip install transformers optimum[onnxruntime-gpu]`"
)
try:
import torch
except ImportError:
raise ImportError(
"Please install Pytorch to use OptimumEncoder. "
"You can install it with: "
"`pip install semantic-router[local]`"
)
try:
from transformers import AutoTokenizer
except ImportError:
raise ImportError(
"Please install transformers to use OptimumEncoder. "
"You can install it with: "
"`pip install semantic-router[local]`"
)
self._torch = torch
tokenizer = AutoTokenizer.from_pretrained(
self.name
)
#provider_options = {
# "trt_engine_cache_enable": True,
# "trt_engine_cache_path": os.getenv('HF_HOME'),
# "trt_fp16_enable": True
#}
session_options = ort.SessionOptions()
session_options.log_severity_level = 0
ort_model = ORTModelForFeatureExtraction.from_pretrained(
model_id=self.name,
file_name='model_fp16.onnx',
subfolder='onnx',
provider='CUDAExecutionProvider',
use_io_binding=True,
#provider_options=provider_options,
session_options=session_options
)
# print("Building engine for a short sequence...")
# short_text = ["short"]
# short_encoded_input = tokenizer(
# short_text, padding=True, truncation=True, return_tensors="pt"
# ).to(self.device)
# short_output = ort_model(**short_encoded_input)
# print("Building engine for a long sequence...")
# long_text = ["a very long input just for demo purpose, this is very long" * 10]
# long_encoded_input = tokenizer(
# long_text, padding=True, truncation=True, return_tensors="pt"
# ).to(self.device)
# long_output = ort_model(**long_encoded_input)
# text = ["Replace me by any text you'd like."]
# encoded_input = tokenizer(
# text, padding=True, truncation=True, return_tensors="pt"
# ).to(self.device)
# for i in range(3):
# output = ort_model(**encoded_input)
return tokenizer, ort_model
class Config:
"""Configuration for this pydantic object."""
extra = Extra.allow
def embed_documents(
self,
docs: List[str],
batch_size: int = 32,
normalize_embeddings: bool = True,
pooling_strategy: str = "mean"
) -> List[List[float]]:
all_embeddings = []
for i in tqdm(range(0, len(docs), batch_size)):
batch_docs = docs[i : i + batch_size]
encoded_input = self._tokenizer(
batch_docs, padding=True, truncation=True, return_tensors="pt"
).to(self.device)
with self._torch.no_grad():
model_output = self._model(**encoded_input)
if pooling_strategy == "mean":
embeddings = self._mean_pooling(
model_output, encoded_input["attention_mask"]
)
elif pooling_strategy == "max":
embeddings = self._max_pooling(
model_output, encoded_input["attention_mask"]
)
else:
raise ValueError(
"Invalid pooling_strategy. Please use 'mean' or 'max'."
)
if normalize_embeddings:
embeddings = self._torch.nn.functional.normalize(embeddings, p=2, dim=1)
all_embeddings.extend(embeddings.tolist())
return all_embeddings
def embed_query(
self,
docs: str,
normalize_embeddings: bool = True,
pooling_strategy: str = "mean"
) -> List[float]:
encoded_input = self._tokenizer(
docs, padding=True, truncation=True, return_tensors="pt"
).to(self.device)
with self._torch.no_grad():
model_output = self._model(**encoded_input)
if pooling_strategy == "mean":
embeddings = self._mean_pooling(
model_output, encoded_input["attention_mask"]
)
elif pooling_strategy == "max":
embeddings = self._max_pooling(
model_output, encoded_input["attention_mask"]
)
else:
raise ValueError(
"Invalid pooling_strategy. Please use 'mean' or 'max'."
)
if normalize_embeddings:
embeddings = self._torch.nn.functional.normalize(embeddings, p=2, dim=1)
print(embeddings)
return embeddings.tolist()
def _mean_pooling(self, model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return self._torch.sum(
token_embeddings * input_mask_expanded, 1
) / self._torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def _max_pooling(self, model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
token_embeddings[input_mask_expanded == 0] = -1e9
return self._torch.max(token_embeddings, 1)[0] |