Embedding_bag — nnf_embedding

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

Usage

nnf_embedding_bag(
  input,
  weight,
  offsets = NULL,
  max_norm = NULL,
  norm_type = 2,
  scale_grad_by_freq = FALSE,
  mode = "mean",
  sparse = FALSE,
  per_sample_weights = NULL,
  include_last_offset = FALSE,
  padding_idx = NULL
)

Arguments

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 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 NULL 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 NULL.
include_last_offset: (bool, optional) if TRUE, the size of offsets is equal to the number of bags + 1.
padding_idx: (int, optional) If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.