Spaces:
Runtime error
Runtime error
File size: 8,168 Bytes
ed4d993 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
import importlib.util
import os
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra
class QuantizedBgeEmbeddings(BaseModel, Embeddings):
"""Leverage Itrex runtime to unlock the performance of compressed NLP models.
Please ensure that you have installed intel-extension-for-transformers.
Input:
model_name: str = Model name.
max_seq_len: int = The maximum sequence length for tokenization. (default 512)
pooling_strategy: str =
"mean" or "cls", pooling strategy for the final layer. (default "mean")
query_instruction: Optional[str] =
An instruction to add to the query before embedding. (default None)
document_instruction: Optional[str] =
An instruction to add to each document before embedding. (default None)
padding: Optional[bool] =
Whether to add padding during tokenization or not. (default True)
model_kwargs: Optional[Dict] =
Parameters to add to the model during initialization. (default {})
encode_kwargs: Optional[Dict] =
Parameters to add during the embedding forward pass. (default {})
onnx_file_name: Optional[str] =
File name of onnx optimized model which is exported by itrex.
(default "int8-model.onnx")
Example:
.. code-block:: python
from langchain_community.embeddings import QuantizedBgeEmbeddings
model_name = "Intel/bge-small-en-v1.5-sts-int8-static-inc"
encode_kwargs = {'normalize_embeddings': True}
hf = QuantizedBgeEmbeddings(
model_name,
encode_kwargs=encode_kwargs,
query_instruction="Represent this sentence for searching relevant passages: "
)
""" # noqa: E501
def __init__(
self,
model_name: str,
*,
max_seq_len: int = 512,
pooling_strategy: str = "mean", # "mean" or "cls"
query_instruction: Optional[str] = None,
document_instruction: Optional[str] = None,
padding: bool = True,
model_kwargs: Optional[Dict] = None,
encode_kwargs: Optional[Dict] = None,
onnx_file_name: Optional[str] = "int8-model.onnx",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
# check sentence_transformers python package
if importlib.util.find_spec("intel_extension_for_transformers") is None:
raise ImportError(
"Could not import intel_extension_for_transformers python package. "
"Please install it with "
"`pip install -U intel-extension-for-transformers`."
)
# check torch python package
if importlib.util.find_spec("torch") is None:
raise ImportError(
"Could not import torch python package. "
"Please install it with `pip install -U torch`."
)
# check onnx python package
if importlib.util.find_spec("onnx") is None:
raise ImportError(
"Could not import onnx python package. "
"Please install it with `pip install -U onnx`."
)
self.model_name_or_path = model_name
self.max_seq_len = max_seq_len
self.pooling = pooling_strategy
self.padding = padding
self.encode_kwargs = encode_kwargs or {}
self.model_kwargs = model_kwargs or {}
self.normalize = self.encode_kwargs.get("normalize_embeddings", False)
self.batch_size = self.encode_kwargs.get("batch_size", 32)
self.query_instruction = query_instruction
self.document_instruction = document_instruction
self.onnx_file_name = onnx_file_name
self.load_model()
def load_model(self) -> None:
from huggingface_hub import hf_hub_download
from intel_extension_for_transformers.transformers import AutoModel
from transformers import AutoConfig, AutoTokenizer
self.hidden_size = AutoConfig.from_pretrained(
self.model_name_or_path
).hidden_size
self.transformer_tokenizer = AutoTokenizer.from_pretrained(
self.model_name_or_path,
)
onnx_model_path = os.path.join(self.model_name_or_path, self.onnx_file_name) # type: ignore[arg-type]
if not os.path.exists(onnx_model_path):
onnx_model_path = hf_hub_download(
self.model_name_or_path, filename=self.onnx_file_name
)
self.transformer_model = AutoModel.from_pretrained(
onnx_model_path, use_embedding_runtime=True
)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.allow
def _embed(self, inputs: Any) -> Any:
import torch
engine_input = [value for value in inputs.values()]
outputs = self.transformer_model.generate(engine_input)
if "last_hidden_state:0" in outputs:
last_hidden_state = outputs["last_hidden_state:0"]
else:
last_hidden_state = [out for out in outputs.values()][0]
last_hidden_state = torch.tensor(last_hidden_state).reshape(
inputs["input_ids"].shape[0], inputs["input_ids"].shape[1], self.hidden_size
)
if self.pooling == "mean":
emb = self._mean_pooling(last_hidden_state, inputs["attention_mask"])
elif self.pooling == "cls":
emb = self._cls_pooling(last_hidden_state)
else:
raise ValueError("pooling method no supported")
if self.normalize:
emb = torch.nn.functional.normalize(emb, p=2, dim=1)
return emb
@staticmethod
def _cls_pooling(last_hidden_state: Any) -> Any:
return last_hidden_state[:, 0]
@staticmethod
def _mean_pooling(last_hidden_state: Any, attention_mask: Any) -> Any:
try:
import torch
except ImportError as e:
raise ImportError(
"Unable to import torch, please install with `pip install -U torch`."
) from e
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
)
sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
def _embed_text(self, texts: List[str]) -> List[List[float]]:
inputs = self.transformer_tokenizer(
texts,
max_length=self.max_seq_len,
truncation=True,
padding=self.padding,
return_tensors="pt",
)
return self._embed(inputs).tolist()
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed a list of text documents using the Optimized Embedder model.
Input:
texts: List[str] = List of text documents to embed.
Output:
List[List[float]] = The embeddings of each text document.
"""
try:
import pandas as pd
except ImportError as e:
raise ImportError(
"Unable to import pandas, please install with `pip install -U pandas`."
) from e
docs = [
self.document_instruction + d if self.document_instruction else d
for d in texts
]
# group into batches
text_list_df = pd.DataFrame(docs, columns=["texts"]).reset_index()
# assign each example with its batch
text_list_df["batch_index"] = text_list_df["index"] // self.batch_size
# create groups
batches = list(text_list_df.groupby(["batch_index"])["texts"].apply(list))
vectors = []
for batch in batches:
vectors += self._embed_text(batch)
return vectors
def embed_query(self, text: str) -> List[float]:
if self.query_instruction:
text = self.query_instruction + text
return self._embed_text([text])[0]
|