torch.nn.functional.embedding_bag

torch.nn.functional.embedding_bag(input, weight, offsets=None, max_norm=None, norm_type=2, scale_grad_by_freq=False, mode='mean', sparse=False, per_sample_weights=None, include_last_offset=False, padding_idx=None)

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

See torch.nn.EmbeddingBag for more details.

Note

This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.

Parameters

• input (LongTensor) – Tensor containing bags of indices into the embedding matrix

• weight (Tensor) – The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size

• offsets (LongTensor, optional) – Only used when input is 1D. offsets determines the starting index position of each bag (sequence) in input.

• max_norm (float, optional) – If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm. Note: this will modify weight in-place.

• norm_type (float, optional) – The p in 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. Note: this option is not supported when mode="max".

• mode (string, optional) – "sum", "mean" or "max". Specifies the way to reduce the bag. Default: "mean"

• sparse (bool, optional) – if True, gradient w.r.t. weight will be a sparse tensor. See Notes under torch.nn.Embedding for more details regarding sparse gradients. Note: this option is not supported when mode="max".

• per_sample_weights (Tensor, optional) – a tensor of float / double weights, or None to indicate all weights should be taken to be 1. If specified, per_sample_weights must have exactly the same shape as input and is treated as having the same offsets, if those are not None.

• include_last_offset (bool, optional) – if True, the size of offsets is equal to the number of bags + 1. The last element is the size of the input, or the ending index position of the last bag (sequence).

• 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”. Note that the embedding vector at padding_idx is excluded from the reduction.

Shape:

• input (LongTensor) and offsets (LongTensor, optional)

  • If input is 2D of shape (B, N), it will be treated as B bags (sequences) each of fixed length N, and this will return B values aggregated in a way depending on the mode. offsets is ignored and required to be None in this case.

  • If input is 1D of shape (N), it will be treated as a concatenation of multiple bags (sequences). offsets is required to be a 1D tensor containing the starting index positions of each bag in input. Therefore, for offsets of shape (B), input will be viewed as having B bags. Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.

• weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)

• per_sample_weights (Tensor, optional). Has the same shape as input.

• output: aggregated embedding values of shape (B, embedding_dim)

Examples:

>>> # an Embedding module containing 10 tensors of size 3
>>> embedding_matrix = torch.rand(10, 3)
>>> # a batch of 2 samples of 4 indices each
>>> input = torch.tensor([1,2,4,5,4,3,2,9])
>>> offsets = torch.tensor([0,4])
>>> F.embedding_bag(input, embedding_matrix, offsets)
tensor([[ 0.3397,  0.3552,  0.5545],
        [ 0.5893,  0.4386,  0.5882]])

>>> # example with padding_idx
>>> embedding_matrix = torch.rand(10, 3)
>>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9])
>>> offsets = torch.tensor([0,4])
>>> F.embedding_bag(input, embedding_matrix, offsets, padding_idx=2, mode='sum')
tensor([[ 0.0000,  0.0000,  0.0000],
        [-0.7082,  3.2145, -2.6251]])