Embedding

torch.embedding 实际上是一个查找表，一般用来存储词嵌入并通过indices从embedding中恢复词嵌入。

位置：

torch.nn.Embedding

参数及官方解释为：

• num_embeddings (int): size of the dictionary of embeddings
• embedding_dim (int): the size of each embedding vector
• padding_idx (int, optional) :If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.
• max_norm ((float, optional)):If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.
• norm_type (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
• scale_grad_by_freq: If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default False.
• sparse (bool, optional) :If True, gradient w.r.t. weight matrix will be a sparse tensor. See Notes for more details regarding sparse gradients.

Attributes:

• weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim) initialized from :math:\mathcal{N}(0, 1)

Shape:

• Input: :math:(*), LongTensor of arbitrary shape containing the indices to extract
• Output: :math:(*, H), where * is the input shape and :math:H=\text{embedding\_dim}

Examples::

>>> # an Embedding module containing 10 tensors of size 3
>>> embedding = nn.Embedding(10, 3)
>>> # a batch of 2 samples of 4 indices each
>>> input = torch.LongTensor([[1,2,4,5],[4,3,2,9]])
>>> embedding(input)
tensor([[[-0.0251, -1.6902,  0.7172],
         [-0.6431,  0.0748,  0.6969],
         [ 1.4970,  1.3448, -0.9685],
         [-0.3677, -2.7265, -0.1685]],

         [[ 1.4970,  1.3448, -0.9685],
         [ 0.4362, -0.4004,  0.9400],
         [-0.6431,  0.0748,  0.6969],
         [ 0.9124, -2.3616,  1.1151]]])

可以看到当index 相同的时候输出的 embedding是相同的。例如第一个sample的index=2 和第二个sample的index=2,
也就是说对于同一个embedding, 输入的index相同，对应的tensor相同。

with padding

>>> # example with padding_idx
>>> embedding = nn.Embedding(10, 3, padding_idx=0)
>>> input = torch.LongTensor([[0,2,0,5]])
>>> embedding(input)
tensor([[[ 0.0000,  0.0000,  0.0000],
         [ 0.1535, -2.0309,  0.9315],
         [ 0.0000,  0.0000,  0.0000],
         [-0.1655,  0.9897,  0.0635]]])

当有padding的时候，例如设置padding_idx = 0,也就是当第一个index 和第三个index = 0时，输出的tensor 自动padding为0，而index=2和index=5没有设置padding，所以输出没有被0 padding。

其中的一个classmethod

@classmethod
   def from_pretrained(cls, embeddings, freeze=True, padding_idx=None,
                       max_norm=None, norm_type=2., scale_grad_by_freq=False, sparse=False):
	r"""Creates Embedding instance from given 2-dimensional FloatTensor.

                Args:
                    embeddings (Tensor): FloatTensor containing weights for the Embedding.
                        First dimension is being passed to Embedding as ``num_embeddings``, second as ``embedding_dim``.
                    freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.
                        Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``
                    padding_idx (int, optional): See module initialization documentation.
                    max_norm (float, optional): See module initialization documentation.
                    norm_type (float, optional): See module initialization documentation. Default ``2``.
                    scale_grad_by_freq (boolean, optional): See module initialization documentation. Default ``False``.
                    sparse (bool, optional): See module initialization documentation.

                Examples::

                    >>> # FloatTensor containing pretrained weights
                    >>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
                    >>> embedding = nn.Embedding.from_pretrained(weight)
                    >>> # Get embeddings for index 1
                    >>> input = torch.LongTensor([1])
                    >>> embedding(input)
                    tensor([[ 4.0000,  5.1000,  6.3000]])
                """

EmbeddingBag

Computes sums or means of ‘bags’ of embeddings, without instantiating the intermediate embeddings.

支持的三种mode

• sum：is equivalent to ~torch.nn.Embedding followed by torch.sum(dim=0)
• mean：is equivalent to ~torch.nn.Embedding followed by torch.mean(dim=0)
• max：is equivalent to ~torch.nn.Embedding followed by torch.max(dim=0)
  但是用embeddingbag 的效率会更高。
  pytorch支持在forward pass 中增加 per-sample weights，但只在 mode == sum时支持。如果这个参数为0，在计算 weighted sum的时候所有的weight = 1，如果不为0，则按照设置的weight来计算weighted sum。
  其他参数和 embedding差不多