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docs: fix formatting for embedding_bag (#54666)
Summary: fixes https://github.com/pytorch/pytorch/issues/43499 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54666 Reviewed By: H-Huang Differential Revision: D27411027 Pulled By: jbschlosser fbshipit-source-id: a84cc174155bd725e108d8f953a21bb8de8d9d23
This commit is contained in:
Jeff Yang
Facebook GitHub Bot
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2 changed files with 43 additions and 49 deletions
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@ -1976,7 +1976,7 @@ def embedding(
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Shape:
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- Input: LongTensor of arbitrary shape containing the indices to extract
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- Weight: Embedding matrix of floating point type with shape `(V, embedding_dim)`,
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where V = maximum index + 1 and embedding_dim = the embedding size
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where V = maximum index + 1 and embedding_dim = the embedding size
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- Output: `(*, embedding_dim)`, where `*` is the input shape
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Examples::
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@ -2082,33 +2082,25 @@ def embedding_bag(
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:attr:`offsets`, if those are not None.
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include_last_offset (bool, optional): if ``True``, the size of offsets is equal to the number of bags + 1.
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The last element is the size of the input, or the ending index position of the last bag (sequence).
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The last element is the size of the input, or the ending index position of the last bag (sequence).
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Shape:
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- :attr:`input` (LongTensor) and :attr:`offsets` (LongTensor, optional)
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- If :attr:`input` is 2D of shape `(B, N)`,
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- If :attr:`input` is 2D of shape `(B, N)`, it will be treated as ``B`` bags (sequences)
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each of fixed length ``N``, and this will return ``B`` values aggregated in a way
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depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case.
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it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and
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this will return ``B`` values aggregated in a way depending on the :attr:`mode`.
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:attr:`offsets` is ignored and required to be ``None`` in this case.
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- If :attr:`input` is 1D of shape `(N)`, it will be treated as a concatenation of
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multiple bags (sequences). :attr:`offsets` is required to be a 1D tensor containing
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the starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets`
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of shape `(B)`, :attr:`input` will be viewed as having ``B`` bags.
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Empty bags (i.e., having 0-length) will have returned vectors filled by zeros.
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- If :attr:`input` is 1D of shape `(N)`,
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- :attr:`weight` (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
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it will be treated as a concatenation of multiple bags (sequences).
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:attr:`offsets` is required to be a 1D tensor containing the
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starting index positions of each bag in :attr:`input`. Therefore,
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for :attr:`offsets` of shape `(B)`, :attr:`input` will be viewed as
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having ``B`` bags. Empty bags (i.e., having 0-length) will have
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returned vectors filled by zeros.
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- :attr:`weight` (Tensor): the learnable weights of the module of
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shape `(num_embeddings, embedding_dim)`
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- :attr:`per_sample_weights` (Tensor, optional). Has the same shape as
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:attr:`input`.
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- :attr:`per_sample_weights` (Tensor, optional). Has the same shape as :attr:`input`.
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- :attr:`output`: aggregated embedding values of shape `(B, embedding_dim)`
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@ -231,7 +231,7 @@ class EmbeddingBag(Module):
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EmbeddingBag also supports per-sample weights as an argument to the forward
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pass. This scales the output of the Embedding before performing a weighted
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reduction as specified by ``mode``. If :attr:`per_sample_weights`` is passed, the
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reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
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only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
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:attr:`per_sample_weights`.
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@ -259,34 +259,6 @@ class EmbeddingBag(Module):
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weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
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initialized from :math:`\mathcal{N}(0, 1)`.
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Inputs: :attr:`input` (IntTensor or LongTensor), :attr:`offsets` (IntTensor or LongTensor, optional), and
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:attr:`per_index_weights` (Tensor, optional)
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- :attr:`input` and :attr:`offsets` have to be of the same type, either int or long
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- If :attr:`input` is 2D of shape `(B, N)`,
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it will be treated as ``B`` bags (sequences) each of fixed length ``N``, and
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this will return ``B`` values aggregated in a way depending on the :attr:`mode`.
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:attr:`offsets` is ignored and required to be ``None`` in this case.
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- If :attr:`input` is 1D of shape `(N)`,
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it will be treated as a concatenation of multiple bags (sequences).
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:attr:`offsets` is required to be a 1D tensor containing the
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starting index positions of each bag in :attr:`input`. Therefore,
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for :attr:`offsets` of shape `(B)`, :attr:`input` will be viewed as
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having ``B`` bags. Empty bags (i.e., having 0-length) will have
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returned vectors filled by zeros.
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per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
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to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`
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must have exactly the same shape as input and is treated as having the same
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:attr:`offsets`, if those are not ``None``. Only supported for ``mode='sum'``.
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Output shape: `(B, embedding_dim)`
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Examples::
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>>> # an Embedding module containing 10 tensors of size 3
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@ -336,6 +308,36 @@ class EmbeddingBag(Module):
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init.normal_(self.weight)
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def forward(self, input: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None) -> Tensor:
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"""Forward pass of EmbeddingBag.
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Args:
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input (Tensor): Tensor containing bags of indices into the embedding matrix.
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offsets (Tensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines
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the starting index position of each bag (sequence) in :attr:`input`.
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per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
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to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`
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must have exactly the same shape as input and is treated as having the same
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:attr:`offsets`, if those are not ``None``. Only supported for ``mode='sum'``.
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Returns:
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Tensor output shape of `(B, embedding_dim)`.
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.. note::
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A few notes about ``input`` and ``offsets``:
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- :attr:`input` and :attr:`offsets` have to be of the same type, either int or long
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- If :attr:`input` is 2D of shape `(B, N)`, it will be treated as ``B`` bags (sequences)
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each of fixed length ``N``, and this will return ``B`` values aggregated in a way
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depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case.
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- If :attr:`input` is 1D of shape `(N)`, it will be treated as a concatenation of
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multiple bags (sequences). :attr:`offsets` is required to be a 1D tensor containing the
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starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets` of shape `(B)`,
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:attr:`input` will be viewed as having ``B`` bags. Empty bags (i.e., having 0-length) will have
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returned vectors filled by zeros.
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"""
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return F.embedding_bag(input, self.weight, offsets,
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self.max_norm, self.norm_type,
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self.scale_grad_by_freq, self.mode, self.sparse,
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