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import requests
import time
import os
import numpy as np
from typing import Any, List, Optional, Dict
from pydantic.v1 import PrivateAttr
from semantic_router.encoders import BaseEncoder
from semantic_router.utils.logger import logger
class OptimumEncoder(BaseEncoder):
name: str = "mixedbread-ai/mxbai-embed-large-v1"
type: str = "huggingface"
score_threshold: float = 0.5
tokenizer_kwargs: Dict = {}
model_kwargs: Dict = {}
device: Optional[str] = None
_tokenizer: Any = PrivateAttr()
_model: Any = PrivateAttr()
_torch: Any = PrivateAttr()
def __init__(self, **data):
super().__init__(**data)
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,
**self.tokenizer_kwargs,
)
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,
model_type='model_fp16',
subfolder='onnx',
provider=['TensorrtExecutionProvider'],
provider_options=provider_options,
session_options=session_options,
**self.model_kwargs
)
print("Building engine for a short sequence...")
short_text = ["short"]
short_encoded_input = tokenizer(
short_text, padding=True, truncation=True, return_tensors="pt"
).to("cuda")
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.generate(**encoded_input)
return tokenizer, model
def __call__(
self,
docs: List[str],
batch_size: int = 32,
normalize_embeddings: bool = True,
pooling_strategy: str = "mean",
matryoshka_dim: int = 1024,
convert_to_numpy: bool = False
) -> List[List[float]] | List[np.ndarray]:
all_embeddings = []
for i in 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:
if convert_to_numpy:
embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
else:
embeddings = self._torch.nn.functional.normalize(embeddings, p=2, dim=1).detach().cpu().tolist()
original_dimensions = embeddings.size(1)
if original_dimensions > matryoshka_dim:
embeddings = embeddings[:, :matryoshka_dim]
all_embeddings.extend(embeddings)
return all_embeddings
def _mean_pooling(self, model_output, attention_mask, convert_to_numpy):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
sum = self._torch.sum(
token_embeddings * input_mask_expanded, 1
) / self._torch.clamp(input_mask_expanded.sum(1), min=1e-9)
if convert_to_numpy:
return sum.detach().cpu().numpy()
else:
return max
def _max_pooling(self, model_output, attention_mask, convert_to_numpy):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
token_embeddings[input_mask_expanded == 0] = -1e9
max = self._torch.max(token_embeddings, 1)[0]
if convert_to_numpy:
return max.detach().cpu().numpy()
else:
return max |