Embedding (original) (raw)

class torch.nn.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, _freeze=False, device=None, dtype=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 specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed Embedding, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector.
• max_norm (float, optional) – If given, each embedding vector with norm larger than max_normis 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 (bool, 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:

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: (∗)(*), IntTensor or 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

When max_norm is not None, Embedding’s forward method will modify theweight 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 whenmax_norm is not None. For example:

n, d, m = 3, 5, 7 embedding = nn.Embedding(n, d, max_norm=1.0) 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]]])

example of changing pad vector

padding_idx = 0 embedding = nn.Embedding(3, 3, padding_idx=padding_idx) embedding.weight Parameter containing: tensor([[ 0.0000, 0.0000, 0.0000], [-0.7895, -0.7089, -0.0364], [ 0.6778, 0.5803, 0.2678]], requires_grad=True) with torch.no_grad(): ... embedding.weight[padding_idx] = torch.ones(3) embedding.weight Parameter containing: tensor([[ 1.0000, 1.0000, 1.0000], [-0.7895, -0.7089, -0.0364], [ 0.6778, 0.5803, 0.2678]], requires_grad=True)

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

Create 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 (bool, 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) – If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”.
• max_norm (float, optional) – See module initialization documentation.
• norm_type (float, optional) – See module initialization documentation. Default 2.
• scale_grad_by_freq (bool, 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]])