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AdaptiveLogSoftmaxWithLoss
class torch.nn.AdaptiveLogSoftmaxWithLoss(in_features, n_classes, cutoffs, div_value=4.0, head_bias=False, device=None, dtype=None)
[source]-
Efficient softmax approximation as described in Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou.
Adaptive softmax is an approximate strategy for training models with large output spaces. It is most effective when the label distribution is highly imbalanced, for example in natural language modelling, where the word frequency distribution approximately follows the Zipf’s law.
Adaptive softmax partitions the labels into several clusters, according to their frequency. These clusters may contain different number of targets each. Additionally, clusters containing less frequent labels assign lower dimensional embeddings to those labels, which speeds up the computation. For each minibatch, only clusters for which at least one target is present are evaluated.
The idea is that the clusters which are accessed frequently (like the first one, containing most frequent labels), should also be cheap to compute – that is, contain a small number of assigned labels.
We highly recommend taking a look at the original paper for more details.
cutoffs
should be an ordered Sequence of integers sorted in the increasing order. It controls number of clusters and the partitioning of targets into clusters. For example settingcutoffs = [10, 100, 1000]
means that first10
targets will be assigned to the ‘head’ of the adaptive softmax, targets11, 12, …, 100
will be assigned to the first cluster, and targets101, 102, …, 1000
will be assigned to the second cluster, while targets1001, 1002, …, n_classes - 1
will be assigned to the last, third cluster.div_value
is used to compute the size of each additional cluster, which is given as , where is the cluster index (with clusters for less frequent words having larger indices, and indices starting from ).head_bias
if set to True, adds a bias term to the ‘head’ of the adaptive softmax. See paper for details. Set to False in the official implementation.
Warning
Labels passed as inputs to this module should be sorted according to their frequency. This means that the most frequent label should be represented by the index
0
, and the least frequent label should be represented by the indexn_classes - 1
.Note
This module returns a
NamedTuple
withoutput
andloss
fields. See further documentation for details.Note
To compute log-probabilities for all classes, the
log_prob
method can be used.- Parameters
-
- in_features (int) – Number of features in the input tensor
- n_classes (int) – Number of classes in the dataset
- cutoffs (Sequence) – Cutoffs used to assign targets to their buckets
- div_value (float, optional) – value used as an exponent to compute sizes of the clusters. Default: 4.0
- head_bias (bool, optional) – If
True
, adds a bias term to the ‘head’ of the adaptive softmax. Default:False
- Returns
-
- output is a Tensor of size
N
containing computed target log probabilities for each example - loss is a Scalar representing the computed negative log likelihood loss
- output is a Tensor of size
- Return type
-
NamedTuple
withoutput
andloss
fields
- Shape:
-
- input: or
- target: or where each value satisfies
- output1: or
- output2:
Scalar
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https://pytorch.org/docs/2.1/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html