EmbeddingBag¶

class
torch.nn.
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)[source]¶ Computes sums or means of ‘bags’ of embeddings, without instantiating the intermediate embeddings.
For bags of constant length and no
per_sample_weights
and 2D inputs, this classHowever,
EmbeddingBag
is much more time and memory efficient than using a chain of these operations.EmbeddingBag also supports persample weights as an argument to the forward pass. This scales the output of the Embedding before performing a weighted reduction as specified by
mode
. Ifper_sample_weights`
is passed, the only supportedmode
is"sum"
, which computes a weighted sum according toper_sample_weights
. Parameters
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_norm
is renormalized to have normmax_norm
.norm_type (float, optional) – The p of the pnorm to compute for the
max_norm
option. Default2
.scale_grad_by_freq (boolean, optional) – if given, this will scale gradients by the inverse of frequency of the words in the minibatch. Default
False
. Note: this option is not supported whenmode="max"
.mode (string, optional) –
"sum"
,"mean"
or"max"
. Specifies the way to reduce the bag."sum"
computes the weighted sum, takingper_sample_weights
into consideration."mean"
computes the average of the values in the bag,"max"
computes the max value over each bag. Default:"mean"
sparse (bool, optional) – if
True
, gradient w.r.t.weight
matrix will be a sparse tensor. See Notes for more details regarding sparse gradients. Note: this option is not supported whenmode="max"
.include_last_offset (bool, optional) – if
True
,offsets
has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.
 Variables
~EmbeddingBag.weight (Tensor) – the learnable weights of the module of shape (num_embeddings, embedding_dim) initialized from $\mathcal{N}(0, 1)$ .
 Inputs:
input
(LongTensor),offsets
(LongTensor, optional), and per_index_weights
(Tensor, optional)If
input
is 2D of shape (B, N),it will be treated as
B
bags (sequences) each of fixed lengthN
, and this will returnB
values aggregated in a way depending on themode
.offsets
is ignored and required to beNone
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 ininput
. Therefore, foroffsets
of shape (B),input
will be viewed as havingB
bags. Empty bags (i.e., having 0length) 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_weights
must have exactly the same shape as input and is treated as having the sameoffsets
, if those are notNone
. Only supported formode='sum'
.
Output shape: (B, embedding_dim)
Examples:
>>> # 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]])

classmethod
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[source]¶ Creates EmbeddingBag instance from given 2dimensional FloatTensor.
 Parameters
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 toembeddingbag.weight.requires_grad = False
. Default:True
max_norm (float, optional) – See module initialization documentation. Default:
None
norm_type (float, optional) – See module initialization documentation. Default
2
.scale_grad_by_freq (boolean, optional) – See module initialization documentation. Default
False
.mode (string, optional) – See module initialization documentation. Default:
"mean"
sparse (bool, optional) – See module initialization documentation. Default:
False
.include_last_offset (bool, optional) – See module initialization documentation. Default:
False
.
Examples:
>>> # 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]])