/// <summary>
/// A simple lookup table that stores embeddings of a fixed dictionary and size.
/// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.
/// </summary>
/// <param name="x">An input tensor of arbitrary shape.</param>
/// <param name="num_embeddings">Size of the dictionary of embeddings, the vocabulary size.</param>
/// <param name="embedding_dims">The size of each embedding vector</param>
/// <param name="max_norm">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>
/// <param name="norm_type">The p of the p-norm to compute for the max_norm option. Default 2.</param>
/// <param name="scale_grad_by_freq">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>
/// <param name="mode">"sum", "mean" or "max". Specifies the way to reduce the bag.
/// "sum" computes the weighted sum, taking per_sample_weights into consideration.
/// "mean" computes the average of the values in the bag, "max" computes the max value over each bag. Default: "mean"</param>
/// <param name="sparse">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>
/// <param name="include_last_offset">if true, offsets has one additional element, where the last element is equivalent to the size of indices.</param>
/// <returns></returns>
/// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>
static public TorchTensor EmbeddingBag(TorchTensor x, long num_embeddings, long embedding_dims, double?max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false)
{
using (var d = Modules.EmbeddingBag(num_embeddings, embedding_dims, max_norm, norm_type, scale_grad_by_freq, mode, sparse, include_last_offset)) {
return(d.forward(x));
}
}
/// <summary>
/// A simple lookup table that stores embeddings of a fixed dictionary and size.
/// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.
/// </summary>
/// <param name="embeddings">FloatTensor containing weights for the EmbeddingBag in two dimensions. First dimension is being passed to EmbeddingBag as num_embeddings, second as embedding_dim.</param>
/// <param name="freeze">If true (the default), the tensor does not get updated in the learning</param>
/// <param name="max_norm">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>
/// <param name="norm_type">The p of the p-norm to compute for the max_norm option. Default 2.</param>
/// <param name="scale_grad_by_freq">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>
/// <param name="mode"></param>
/// <param name="sparse">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>
/// <param name="include_last_offset">If true, offsets has one additional element, where the last element is equivalent to the size of indices. This matches the CSR format.</param>
/// <param name="padding_index"> 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”. For a newly constructed EmbeddingBag, the embedding vector at padding_idx will default to all zeros, but can be updated to another value to be used as the padding vector. Note that the embedding vector at padding_idx is excluded from the reduction.</param>
/// <returns></returns>
/// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>
public static EmbeddingBag EmbeddingBag_from_pretrained(Tensor embeddings, bool freeze = true, double?max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false, long padding_index = -1)
{
var res = THSNN_EmbeddingBag_from_pretrained(embeddings.Handle, freeze,
max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,
norm_type, scale_grad_by_freq, (long)mode, sparse, include_last_offset, padding_index, out var boxedHandle);
if (res == IntPtr.Zero)
{
torch.CheckForErrors();
}
return(new EmbeddingBag(res, boxedHandle));
}
/// <summary>
/// A simple lookup table that stores embeddings of a fixed dictionary and size.
/// This module is often used to store word embeddings and retrieve them using indices. The input to the module is a list of indices, and the output is the corresponding word embeddings.
/// </summary>
/// <param name="num_embeddings">Size of the dictionary of embeddings, the vocabulary size.</param>
/// <param name="embedding_dims">The size of each embedding vector</param>
/// <param name="max_norm">If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm.</param>
/// <param name="norm_type">The p of the p-norm to compute for the max_norm option. Default 2.</param>
/// <param name="scale_grad_by_freq">If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default: false.</param>
/// <param name="mode">"sum", "mean" or "max". Specifies the way to reduce the bag.
/// "sum" computes the weighted sum, taking per_sample_weights into consideration.
/// "mean" computes the average of the values in the bag, "max" computes the max value over each bag. Default: "mean"</param>
/// <param name="sparse">If true, gradient w.r.t. weight matrix will be a sparse tensor. Default: false</param>
/// <param name="include_last_offset">if true, offsets has one additional element, where the last element is equivalent to the size of indices.</param>
/// <returns></returns>
/// <remarks>Keep in mind that only a limited number of optimizers support sparse gradients: currently it’s optim.SGD (CUDA and CPU), optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)</remarks>
static public EmbeddingBag EmbeddingBag(long num_embeddings, long embedding_dims, double?max_norm = null, double norm_type = 2.0, bool scale_grad_by_freq = false, EmbeddingBagMode mode = EmbeddingBagMode.Mean, bool sparse = false, bool include_last_offset = false)
{
var res = THSNN_EmbeddingBag_ctor(num_embeddings, embedding_dims,
max_norm.HasValue ? max_norm.Value : 0.0, max_norm.HasValue,
norm_type, scale_grad_by_freq, (long)mode, sparse, include_last_offset, out var boxedHandle);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new EmbeddingBag(res, boxedHandle));
}
