Function embedding

python/oneflow/nn/modules/sparse.py:193–263

r"""A simple lookup table that looks up embeddings in a fixed dictionary and size. This module is often used to retrieve word embeddings using indices. The input to the module is a list of indices, and the embedding matrix, and the output is the corresponding word embeddings. See :

(
    input,
    weight,
    padding_idx=None,
    max_norm=None,
    norm_type=2.0,
    scale_grad_by_freq=False,
    sparse=False,
)

def embedding(
    input,
    weight,
    padding_idx=None,
    max_norm=None,
    norm_type=2.0,
    scale_grad_by_freq=False,
    sparse=False,
):
    r"""A simple lookup table that looks up embeddings in a fixed dictionary and size.

    This module is often used to retrieve word embeddings using indices.
    The input to the module is a list of indices, and the embedding matrix,
    and the output is the corresponding word embeddings.

    See :class:`oneflow.nn.Embedding` for more details.

    Args:
        input (oneflow.LongTensor): Tensor containing 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
        padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the gradient;
                                     therefore, the embedding vector at :attr:`padding_idx` is not updated during training,
                                     i.e. it remains as a fixed "pad".
        max_norm (float, optional): If given, each embedding vector with norm larger than max_norm is renormalized to have 
                                    norm max_norm
        norm_type (float, optional): The p of 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

    For example:

    .. code-block:: python

        >>> import oneflow as flow
        >>> import oneflow.nn.functional as F

        >>> # a batch of 2 samples of 4 indices each
        >>> input = flow.tensor([[1,2,4,5],[4,3,2,9]])
        >>> # an embedding matrix containing 10 tensors of size 3
        >>> embedding_matrix = flow.rand(10, 3)
        >>> output = F.embedding(input, embedding_matrix)
        >>> output.shape
        oneflow.Size([2, 4, 3])
        >>> # example with padding_idx
        >>> input = flow.tensor([[0,2,0,5]])
        >>> output = F.embedding(input, embedding_matrix, padding_idx=0)
        >>> output.shape
        oneflow.Size([1, 4, 3])
    """

    assert sparse is False, "Not support sparse=True yet!"
    if padding_idx is not None:
        if padding_idx > 0:
            assert padding_idx < weight.size(
                0
            ), "Padding_idx must be within num_embeddings"
        elif padding_idx < 0: