EmbeddingBag(num_embeddings: int, embedding_dim: int, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, mode: str = 'mean', sparse: bool = False, _weight: Optional[torch.Tensor] = None, include_last_offset: bool = False)¶
Computes sums or means of ‘bags’ of embeddings, without instantiating the intermediate embeddings.
For bags of constant length and no
per_sample_weights, this class
EmbeddingBagis much more time and memory efficient than using a chain of these operations.
EmbeddingBag also supports per-sample weights as an argument to the forward pass. This scales the output of the Embedding before performing a weighted reduction as specified by
per_sample_weights`is passed, the only supported
"sum", which computes a weighted sum according to
num_embeddings (int) – size of the dictionary of embeddings
embedding_dim (int) – the size of each embedding vector
max_norm (float, optional) – If given, each embedding vector with norm larger than
max_normis renormalized to have norm
norm_type (float, optional) – The p of the p-norm to compute for the
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 (string, optional) –
"max". Specifies the way to reduce the bag.
"sum"computes the weighted sum, taking
"mean"computes the average of the values in the bag,
"max"computes the max value over each bag. Default:
sparse (bool, optional) – if
True, gradient w.r.t.
weightmatrix will be a sparse tensor. See Notes for more details regarding sparse gradients. Note: this option is not supported when
include_last_offset (bool, optional) – if
offsetshas one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.
~EmbeddingBag.weight (Tensor) – the learnable weights of the module of shape (num_embeddings, embedding_dim) initialized from .
offsets(LongTensor, optional), and
inputis 2D of shape (B, N),
it will be treated as
Bbags (sequences) each of fixed length
N, and this will return
Bvalues aggregated in a way depending on the
offsetsis ignored and required to be
Nonein this case.
inputis 1D of shape (N),
it will be treated as a concatenation of multiple bags (sequences).
offsetsis required to be a 1D tensor containing the starting index positions of each bag in
input. Therefore, for
offsetsof shape (B),
inputwill be viewed as having
Bbags. Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.
- 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_weightsmust have exactly the same shape as input and is treated as having the same
offsets, if those are not
None. Only supported for
Output shape: (B, embedding_dim)
>>> # an Embedding module containing 10 tensors of size 3 >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') >>> # a batch of 2 samples of 4 indices each >>> input = torch.LongTensor([1,2,4,5,4,3,2,9]) >>> offsets = torch.LongTensor([0,4]) >>> embedding_sum(input, offsets) tensor([[-0.8861, -5.4350, -0.0523], [ 1.1306, -2.5798, -1.0044]])
from_pretrained(embeddings: torch.Tensor, freeze: bool = True, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, mode: str = 'mean', sparse: bool = False, include_last_offset: bool = False) → torch.nn.modules.sparse.EmbeddingBag¶
Creates EmbeddingBag instance from given 2-dimensional FloatTensor.
embeddings (Tensor) – FloatTensor containing weights for the EmbeddingBag. First dimension is being passed to EmbeddingBag as ‘num_embeddings’, second as ‘embedding_dim’.
freeze (boolean, optional) – If
True, the tensor does not get updated in the learning process. Equivalent to
embeddingbag.weight.requires_grad = False. Default:
max_norm (float, optional) – See module initialization documentation. Default:
norm_type (float, optional) – See module initialization documentation. Default
scale_grad_by_freq (boolean, optional) – See module initialization documentation. Default
mode (string, optional) – See module initialization documentation. Default:
sparse (bool, optional) – See module initialization documentation. Default:
include_last_offset (bool, optional) – See module initialization documentation. Default:
>>> # FloatTensor containing pretrained weights >>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]]) >>> embeddingbag = nn.EmbeddingBag.from_pretrained(weight) >>> # Get embeddings for index 1 >>> input = torch.LongTensor([[1, 0]]) >>> embeddingbag(input) tensor([[ 2.5000, 3.7000, 4.6500]])