Embedding¶
- 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][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 atpadding_idx
is not updated during training, i.e. it remains as a fixed “pad”. For a newly constructed Embedding, the embedding vector atpadding_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_norm
is renormalized to have normmax_norm
.norm_type (float, optional) – The p of the p-norm to compute for the
max_norm
option. Default2
.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
- Shape:
Input: , IntTensor or LongTensor of arbitrary shape containing the indices to extract
Output: , where * is the input shape and
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) andoptim.Adagrad
(CPU)Note
When
max_norm
is notNone
,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 onEmbedding.weight
before callingEmbedding
’s forward method requires cloningEmbedding.weight
whenmax_norm
is notNone
. 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][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 asembedding_dim
.freeze (bool, optional) – If
True
, the tensor does not get updated in the learning process. Equivalent toembedding.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 atpadding_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]])