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import numpy as np |
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import torch |
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import torch.nn as nn |
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from torch.nn.parameter import Parameter |
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from torch.nn.functional import embedding |
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from ..init import assign_tensor |
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class Embedding(nn.Module): |
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r"""A simple lookup table that stores embeddings of a fixed dictionary and size. |
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This module is often used to store word embeddings and retrieve them using indices. |
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The input to the module is a list of indices, and the output is the corresponding |
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word embeddings. |
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Args: |
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num_embeddings (int): size of the dictionary of embeddings |
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embedding_dim (int): the size of each embedding vector |
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init_embedding (Tensor): If given, the embedding will be initialized with the given tensor. |
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freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process. |
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padding_idx (int, optional): If given, pads the output with zeros whenever it encounters the index. |
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max_norm (float, optional): If given, will renormalize the embeddings to always have a norm lesser than this |
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norm_type (float, optional): The p of the p-norm to compute for the max_norm option |
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scale_grad_by_freq (boolean, optional): if given, this will scale gradients by the frequency of |
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the words in the mini-batch. |
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sparse (boolean, optional): if True, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for |
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more details regarding sparse gradients. |
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Attributes: |
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weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) |
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Shape: |
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- Input: LongTensor `(N1, N2, ...,Nm, W)`, N = mini-batch, W = number of indices to extract per mini-batch |
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- Output: `(N1, N2, ..., Nm, W, embedding_dim)` |
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Notes: |
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Keep in mind that only a limited number of optimizers support |
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sparse gradients: currently it's `optim.SGD` (`cuda` and `cpu`), |
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and `optim.Adagrad` (`cpu`) |
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""" |
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def __init__(self, num_embeddings, embedding_dim, init_embedding=None, freeze=False, padding_idx=None, |
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max_norm=None, norm_type=2, scale_grad_by_freq=False, sparse=False): |
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super(Embedding, self).__init__() |
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self.num_embeddings = num_embeddings |
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self.embedding_dim = embedding_dim |
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self.padding_idx = padding_idx |
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self.max_norm = max_norm |
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self.norm_type = norm_type |
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self.scale_grad_by_freq = scale_grad_by_freq |
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self.weight = Parameter(torch.Tensor(num_embeddings, embedding_dim)) |
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self.frozen = freeze |
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self.sparse = sparse |
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self.reset_parameters(init_embedding) |
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def reset_parameters(self, init_embedding): |
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if init_embedding is None: |
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scale = np.sqrt(3.0 / self.embedding_dim) |
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self.weight.data.uniform_(-scale, scale) |
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else: |
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assign_tensor(self.weight, init_embedding) |
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if self.padding_idx is not None: |
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self.weight.data[self.padding_idx].fill_(0) |
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if self.frozen: |
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if init_embedding is None: |
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raise Warning('Freeze embeddings which are randomly initialized.') |
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self.weight.requires_grad = False |
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def freeze(self): |
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self.weight.requires_grad = False |
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self.frozen = True |
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def forward(self, input): |
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padding_idx = self.padding_idx |
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if padding_idx is None: |
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padding_idx = -1 |
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input_size = input.size() |
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if input.dim() > 2: |
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num_inputs = int(np.prod(input_size[:-1])) |
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input = input.view(num_inputs, input_size[-1]) |
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output_size = input_size + (self.embedding_dim,) |
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return embedding(input,self.weight,padding_idx,self.max_norm, |
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self.norm_type,self.scale_grad_by_freq, |
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self.sparse).view(output_size) |
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def __repr__(self): |
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s = '{name}({num_embeddings}, {embedding_dim}' |
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if self.padding_idx is not None: |
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s += ', padding_idx={padding_idx}' |
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if self.max_norm is not None: |
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s += ', max_norm={max_norm}' |
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if self.norm_type != 2: |
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s += ', norm_type={norm_type}' |
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if self.scale_grad_by_freq is not False: |
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s += ', scale_grad_by_freq={scale_grad_by_freq}' |
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if self.sparse is not False: |
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s += ', sparse=True' |
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s += ')' |
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return s.format(name=self.__class__.__name__, **self.__dict__) |
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