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Embedding

class torch.nn.Embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, sparse: bool = False, _weight: Optional[torch.Tensor] = None)[source]

A simple lookup table that stores embeddings of a fixed dictionary and size.

This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.

Parameters
  • 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 (boolean, optional) – 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.

Variables

~Embedding.weight (Tensor) – the learnable weights of the module of shape (num_embeddings, embedding_dim) initialized from N(0,1)\mathcal{N}(0, 1)

Shape:
  • Input: ()(*) , LongTensor of arbitrary shape containing the indices to extract

  • Output: (,H)(*, H) , where * is the input shape and H=embedding_dimH=\text{embedding\_dim}

Note

Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)

Note

With padding_idx set, the embedding vector at padding_idx is initialized to all zeros. However, note that this vector can be modified afterwards, e.g., using a customized initialization method, and thus changing the vector used to pad the output. The gradient for this vector from Embedding is always zero.

Note

When max_norm is not None, Embedding’s forward method will modify the weight tensor in-place. Since tensors needed for gradient computations cannot be modified in-place, performing a differentiable operation on Embedding.weight before calling Embedding’s forward method requires cloning Embedding.weight when max_norm is not None. For example:

n, d, m = 3, 5, 7
embedding = nn.Embedding(n, d, max_norm=True)
W = torch.randn((m, d), requires_grad=True)
idx = torch.tensor([1, 2])
a = embedding.weight.clone() @ W.t()  # weight must be cloned for this to be differentiable
b = embedding(idx) @ W.t()  # modifies weight in-place
out = (a.unsqueeze(0) + b.unsqueeze(1))
loss = out.sigmoid().prod()
loss.backward()

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]]])


>>> # 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]]])
classmethod from_pretrained(embeddings, freeze=True, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False)[source]

Creates Embedding instance from given 2-dimensional FloatTensor.

Parameters
  • 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]])

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