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106 lines
3.4 KiB
106 lines
3.4 KiB
from __future__ import annotations
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from collections.abc import Callable
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try:
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from typing import Self
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except ImportError:
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from typing_extensions import Self
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from torch import Tensor, nn
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from sentence_transformers.models.Module import Module
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from sentence_transformers.util import fullname, import_from_string
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class Dense(Module):
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"""
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Feed-forward function with activation function.
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This layer takes a fixed-sized sentence embedding and passes it through a feed-forward layer. Can be used to generate deep averaging networks (DAN).
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Args:
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in_features: Size of the input dimension
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out_features: Output size
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bias: Add a bias vector
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activation_function: Pytorch activation function applied on
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output
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init_weight: Initial value for the matrix of the linear layer
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init_bias: Initial value for the bias of the linear layer
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"""
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config_keys: list[str] = [
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"in_features",
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"out_features",
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"bias",
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"activation_function",
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]
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def __init__(
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self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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activation_function: Callable[[Tensor], Tensor] | None = nn.Tanh(),
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init_weight: Tensor | None = None,
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init_bias: Tensor | None = None,
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):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.bias = bias
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self.activation_function = nn.Identity() if activation_function is None else activation_function
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self.linear = nn.Linear(in_features, out_features, bias=bias)
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if init_weight is not None:
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self.linear.weight = nn.Parameter(init_weight)
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if init_bias is not None:
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self.linear.bias = nn.Parameter(init_bias)
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def forward(self, features: dict[str, Tensor]):
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features.update({"sentence_embedding": self.activation_function(self.linear(features["sentence_embedding"]))})
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return features
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def get_sentence_embedding_dimension(self) -> int:
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return self.out_features
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def get_config_dict(self):
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return {
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"in_features": self.in_features,
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"out_features": self.out_features,
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"bias": self.bias,
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"activation_function": fullname(self.activation_function),
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}
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def save(self, output_path: str, *args, safe_serialization: bool = True, **kwargs) -> None:
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self.save_config(output_path)
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self.save_torch_weights(output_path, safe_serialization=safe_serialization)
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def __repr__(self):
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return f"Dense({self.get_config_dict()})"
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@classmethod
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def load(
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cls,
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model_name_or_path: str,
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subfolder: str = "",
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token: bool | str | None = None,
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cache_folder: str | None = None,
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revision: str | None = None,
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local_files_only: bool = False,
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**kwargs,
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) -> Self:
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hub_kwargs = {
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"subfolder": subfolder,
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"token": token,
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"cache_folder": cache_folder,
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"revision": revision,
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"local_files_only": local_files_only,
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}
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config = cls.load_config(model_name_or_path=model_name_or_path, **hub_kwargs)
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config["activation_function"] = import_from_string(config["activation_function"])()
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model = cls(**config)
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model = cls.load_torch_weights(model_name_or_path=model_name_or_path, model=model, **hub_kwargs)
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return model
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