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 | class IntelEmbedding(BaseEmbedding):
folder_name: str = Field(description="Folder name to load from.")
max_length: int = Field(description="Maximum length of input.")
pooling: str = Field(description="Pooling strategy. One of ['cls', 'mean'].")
normalize: str = Field(default=True, description="Normalize embeddings or not.")
query_instruction: Optional[str] = Field(
description="Instruction to prepend to query text."
)
text_instruction: Optional[str] = Field(
description="Instruction to prepend to text."
)
cache_folder: Optional[str] = Field(
description="Cache folder for huggingface files."
)
_model: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_device: Any = PrivateAttr()
def __init__(
self,
folder_name: str,
pooling: str = "cls",
max_length: Optional[int] = None,
normalize: bool = True,
query_instruction: Optional[str] = None,
text_instruction: Optional[str] = None,
model: Optional[Any] = None,
tokenizer: Optional[Any] = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
callback_manager: Optional[CallbackManager] = None,
device: Optional[str] = None,
):
try:
from optimum.intel import IPEXModel
except ImportError:
raise ImportError(
"Optimum-Intel requires the following dependencies; please install with "
"`pip install optimum[exporters] "
"optimum-intel neural-compressor intel_extension_for_pytorch`"
)
model = model or IPEXModel.from_pretrained(folder_name)
tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name)
device = device or infer_torch_device()
if max_length is None:
try:
max_length = int(model.config.max_position_embeddings)
except Exception:
raise ValueError(
"Unable to find max_length from model config. "
"Please provide max_length."
)
try:
max_length = min(max_length, int(tokenizer.model_max_length))
except Exception as exc:
print(f"An error occurred while retrieving tokenizer max length: {exc}")
if pooling not in ["cls", "mean"]:
raise ValueError(f"Pooling {pooling} not supported.")
super().__init__(
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
folder_name=folder_name,
max_length=max_length,
pooling=pooling,
normalize=normalize,
query_instruction=query_instruction,
text_instruction=text_instruction,
)
self._model = model
self._tokenizer = tokenizer
self._device = device
@classmethod
def class_name(cls) -> str:
return "IntelEmbedding"
def _mean_pooling(self, model_output: Any, attention_mask: Any) -> Any:
"""Mean Pooling - Take attention mask into account for correct averaging."""
import torch
# First element of model_output contains all token embeddings
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
def _cls_pooling(self, model_output: list) -> Any:
"""Use the CLS token as the pooling token."""
if isinstance(model_output, dict):
token_embeddings = model_output["last_hidden_state"]
else:
token_embeddings = model_output[0]
return token_embeddings[:, 0]
def _embed(self, sentences: List[str]) -> List[List[float]]:
"""Embed sentences."""
encoded_input = self._tokenizer(
sentences,
padding=True,
max_length=self.max_length,
truncation=True,
return_tensors="pt",
)
import torch
with torch.inference_mode(), torch.cpu.amp.autocast():
model_output = self._model(**encoded_input)
if self.pooling == "cls":
embeddings = self._cls_pooling(model_output)
else:
embeddings = self._mean_pooling(
model_output, encoded_input["attention_mask"].to(self._device)
)
if self.normalize:
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
return embeddings.tolist()
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
query = format_query(query, self.model_name, self.query_instruction)
return self._embed([query])[0]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""Get query embedding async."""
return self._get_query_embedding(query)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Get text embedding async."""
return self._get_text_embedding(text)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
text = format_text(text, self.model_name, self.text_instruction)
return self._embed([text])[0]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
texts = [
format_text(text, self.model_name, self.text_instruction) for text in texts
]
return self._embed(texts)
|
