Source code for torch.nn.modules.sparse
# mypy: allow-untyped-defs
from typing import Optional
import torch
from torch import Tensor
from torch.nn.parameter import Parameter
from .module import Module
from .. import functional as F
from .. import init
__all__ = ['Embedding', 'EmbeddingBag']
[docs]class Embedding(Module):
r"""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.
Args:
num_embeddings (int): size of the dictionary of embeddings
embedding_dim (int): the size of each embedding vector
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". For a newly constructed Embedding,
the embedding vector at :attr:`padding_idx` will default to all zeros,
but can be updated to another value to be used as the padding vector.
max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
is renormalized to have norm :attr:`max_norm`.
norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
scale_grad_by_freq (bool, optional): If given, this will scale gradients by the inverse of frequency of
the words in the mini-batch. Default ``False``.
sparse (bool, optional): If ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor.
See Notes for more details regarding sparse gradients.
Attributes:
weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
initialized from :math:`\mathcal{N}(0, 1)`
Shape:
- Input: :math:`(*)`, IntTensor or LongTensor of arbitrary shape containing the indices to extract
- Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`
.. note::
Keep in mind that only a limited number of optimizers support
sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`),
:class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)
.. note::
When :attr:`max_norm` is not ``None``, :class:`Embedding`'s forward method will modify the
:attr:`weight` tensor in-place. Since tensors needed for gradient computations cannot be
modified in-place, performing a differentiable operation on ``Embedding.weight`` before
calling :class:`Embedding`'s forward method requires cloning ``Embedding.weight`` when
:attr:`max_norm` is not ``None``. For example::
n, d, m = 3, 5, 7
embedding = nn.Embedding(n, d, max_norm=True)
W = torch.randn((m, d), requires_grad=True)
idx = torch.tensor([1, 2])
a = embedding.weight.clone() @ W.t() # weight must be cloned for this to be differentiable
b = embedding(idx) @ W.t() # modifies weight in-place
out = (a.unsqueeze(0) + b.unsqueeze(1))
loss = out.sigmoid().prod()
loss.backward()
Examples::
>>> # an Embedding module containing 10 tensors of size 3
>>> embedding = nn.Embedding(10, 3)
>>> # a batch of 2 samples of 4 indices each
>>> input = torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]])
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> embedding(input)
tensor([[[-0.0251, -1.6902, 0.7172],
[-0.6431, 0.0748, 0.6969],
[ 1.4970, 1.3448, -0.9685],
[-0.3677, -2.7265, -0.1685]],
[[ 1.4970, 1.3448, -0.9685],
[ 0.4362, -0.4004, 0.9400],
[-0.6431, 0.0748, 0.6969],
[ 0.9124, -2.3616, 1.1151]]])
>>> # example with padding_idx
>>> embedding = nn.Embedding(10, 3, padding_idx=0)
>>> input = torch.LongTensor([[0, 2, 0, 5]])
>>> embedding(input)
tensor([[[ 0.0000, 0.0000, 0.0000],
[ 0.1535, -2.0309, 0.9315],
[ 0.0000, 0.0000, 0.0000],
[-0.1655, 0.9897, 0.0635]]])
>>> # example of changing `pad` vector
>>> padding_idx = 0
>>> embedding = nn.Embedding(3, 3, padding_idx=padding_idx)
>>> embedding.weight
Parameter containing:
tensor([[ 0.0000, 0.0000, 0.0000],
[-0.7895, -0.7089, -0.0364],
[ 0.6778, 0.5803, 0.2678]], requires_grad=True)
>>> with torch.no_grad():
... embedding.weight[padding_idx] = torch.ones(3)
>>> embedding.weight
Parameter containing:
tensor([[ 1.0000, 1.0000, 1.0000],
[-0.7895, -0.7089, -0.0364],
[ 0.6778, 0.5803, 0.2678]], requires_grad=True)
"""
__constants__ = ['num_embeddings', 'embedding_dim', 'padding_idx', 'max_norm',
'norm_type', 'scale_grad_by_freq', 'sparse']
num_embeddings: int
embedding_dim: int
padding_idx: Optional[int]
max_norm: Optional[float]
norm_type: float
scale_grad_by_freq: bool
weight: Tensor
freeze: bool
sparse: bool
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
sparse: bool = False, _weight: Optional[Tensor] = None, _freeze: bool = False,
device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
if padding_idx is not None:
if padding_idx > 0:
assert padding_idx < self.num_embeddings, 'Padding_idx must be within num_embeddings'
elif padding_idx < 0:
assert padding_idx >= -self.num_embeddings, 'Padding_idx must be within num_embeddings'
padding_idx = self.num_embeddings + padding_idx
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
if _weight is None:
self.weight = Parameter(torch.empty((num_embeddings, embedding_dim), **factory_kwargs),
requires_grad=not _freeze)
self.reset_parameters()
else:
assert list(_weight.shape) == [num_embeddings, embedding_dim], \
'Shape of weight does not match num_embeddings and embedding_dim'
self.weight = Parameter(_weight, requires_grad=not _freeze)
self.sparse = sparse
def reset_parameters(self) -> None:
init.normal_(self.weight)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
def forward(self, input: Tensor) -> Tensor:
return F.embedding(
input, self.weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
def extra_repr(self) -> str:
s = '{num_embeddings}, {embedding_dim}'
if self.padding_idx is not None:
s += ', padding_idx={padding_idx}'
if self.max_norm is not None:
s += ', max_norm={max_norm}'
if self.norm_type != 2:
s += ', norm_type={norm_type}'
if self.scale_grad_by_freq is not False:
s += ', scale_grad_by_freq={scale_grad_by_freq}'
if self.sparse is not False:
s += ', sparse=True'
return s.format(**self.__dict__)
[docs] @classmethod
def from_pretrained(cls, embeddings, freeze=True, padding_idx=None,
max_norm=None, norm_type=2., scale_grad_by_freq=False,
sparse=False):
r"""Create Embedding instance from given 2-dimensional FloatTensor.
Args:
embeddings (Tensor): FloatTensor containing weights for the Embedding.
First dimension is being passed to Embedding as ``num_embeddings``, second as ``embedding_dim``.
freeze (bool, optional): If ``True``, the tensor does not get updated in the learning process.
Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``
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): See module initialization documentation.
norm_type (float, optional): See module initialization documentation. Default ``2``.
scale_grad_by_freq (bool, optional): See module initialization documentation. Default ``False``.
sparse (bool, optional): See module initialization documentation.
Examples::
>>> # FloatTensor containing pretrained weights
>>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
>>> embedding = nn.Embedding.from_pretrained(weight)
>>> # Get embeddings for index 1
>>> input = torch.LongTensor([1])
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> embedding(input)
tensor([[ 4.0000, 5.1000, 6.3000]])
"""
assert embeddings.dim() == 2, \
'Embeddings parameter is expected to be 2-dimensional'
rows, cols = embeddings.shape
embedding = cls(
num_embeddings=rows,
embedding_dim=cols,
_weight=embeddings,
_freeze=freeze,
padding_idx=padding_idx,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse)
return embedding
[docs]class EmbeddingBag(Module):
r"""Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings.
For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`,
and with 2D inputs, this class
* with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``,
* with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``,
* with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``.
However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these
operations.
EmbeddingBag also supports per-sample weights as an argument to the forward
pass. This scales the output of the Embedding before performing a weighted
reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the
only supported ``mode`` is ``"sum"``, which computes a weighted sum according to
:attr:`per_sample_weights`.
Args:
num_embeddings (int): size of the dictionary of embeddings
embedding_dim (int): the size of each embedding vector
max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm`
is renormalized to have norm :attr:`max_norm`.
norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``.
scale_grad_by_freq (bool, 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 (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag.
``"sum"`` computes the weighted sum, taking :attr:`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"``
sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See
Notes for more details regarding sparse gradients. Note: this option is not
supported when ``mode="max"``.
include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element
is equivalent to the size of `indices`. This matches the CSR format.
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". For a newly constructed
EmbeddingBag, the embedding vector at :attr:`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 :attr:`padding_idx` is excluded from the
reduction.
Attributes:
weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)`
initialized from :math:`\mathcal{N}(0, 1)`.
Examples::
>>> # an EmbeddingBag module containing 10 tensors of size 3
>>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
>>> # a batch of 2 samples of 4 indices each
>>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
>>> offsets = torch.tensor([0, 4], dtype=torch.long)
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> embedding_sum(input, offsets)
tensor([[-0.8861, -5.4350, -0.0523],
[ 1.1306, -2.5798, -1.0044]])
>>> # Example with padding_idx
>>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2)
>>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long)
>>> offsets = torch.tensor([0, 4], dtype=torch.long)
>>> embedding_sum(input, offsets)
tensor([[ 0.0000, 0.0000, 0.0000],
[-0.7082, 3.2145, -2.6251]])
>>> # An EmbeddingBag can be loaded from an Embedding like so
>>> embedding = nn.Embedding(10, 3, padding_idx=2)
>>> embedding_sum = nn.EmbeddingBag.from_pretrained(
embedding.weight,
padding_idx=embedding.padding_idx,
mode='sum')
"""
__constants__ = ['num_embeddings', 'embedding_dim', 'max_norm', 'norm_type',
'scale_grad_by_freq', 'mode', 'sparse', 'include_last_offset',
'padding_idx']
num_embeddings: int
embedding_dim: int
max_norm: Optional[float]
norm_type: float
scale_grad_by_freq: bool
weight: Tensor
mode: str
sparse: bool
include_last_offset: bool
padding_idx: Optional[int]
def __init__(self, num_embeddings: int, embedding_dim: int,
max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
mode: str = 'mean', sparse: bool = False, _weight: Optional[Tensor] = None,
include_last_offset: bool = False, padding_idx: Optional[int] = None,
device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
if padding_idx is not None:
if padding_idx > 0:
assert padding_idx < self.num_embeddings, 'padding_idx must be within num_embeddings'
elif padding_idx < 0:
assert padding_idx >= -self.num_embeddings, 'padding_idx must be within num_embeddings'
padding_idx = self.num_embeddings + padding_idx
self.padding_idx = padding_idx
if _weight is None:
self.weight = Parameter(torch.empty((num_embeddings, embedding_dim), **factory_kwargs))
self.reset_parameters()
else:
assert list(_weight.shape) == [num_embeddings, embedding_dim], \
'Shape of weight does not match num_embeddings and embedding_dim'
self.weight = Parameter(_weight)
self.mode = mode
self.sparse = sparse
self.include_last_offset = include_last_offset
def reset_parameters(self) -> None:
init.normal_(self.weight)
self._fill_padding_idx_with_zero()
def _fill_padding_idx_with_zero(self) -> None:
if self.padding_idx is not None:
with torch.no_grad():
self.weight[self.padding_idx].fill_(0)
[docs] def forward(self, input: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None) -> Tensor:
"""Forward pass of EmbeddingBag.
Args:
input (Tensor): Tensor containing bags of indices into the embedding matrix.
offsets (Tensor, optional): Only used when :attr:`input` is 1D. :attr:`offsets` determines
the starting index position of each bag (sequence) in :attr:`input`.
per_sample_weights (Tensor, optional): a tensor of float / double weights, or None
to indicate all weights should be taken to be ``1``. If specified, :attr:`per_sample_weights`
must have exactly the same shape as input and is treated as having the same
:attr:`offsets`, if those are not ``None``. Only supported for ``mode='sum'``.
Returns:
Tensor output shape of `(B, embedding_dim)`.
.. note::
A few notes about ``input`` and ``offsets``:
- :attr:`input` and :attr:`offsets` have to be of the same type, either int or long
- If :attr:`input` is 2D of shape `(B, N)`, it will be treated as ``B`` bags (sequences)
each of fixed length ``N``, and this will return ``B`` values aggregated in a way
depending on the :attr:`mode`. :attr:`offsets` is ignored and required to be ``None`` in this case.
- If :attr:`input` is 1D of shape `(N)`, it will be treated as a concatenation of
multiple bags (sequences). :attr:`offsets` is required to be a 1D tensor containing the
starting index positions of each bag in :attr:`input`. Therefore, for :attr:`offsets` of shape `(B)`,
:attr:`input` will be viewed as having ``B`` bags. Empty bags (i.e., having 0-length) will have
returned vectors filled by zeros.
"""
return F.embedding_bag(input, self.weight, offsets,
self.max_norm, self.norm_type,
self.scale_grad_by_freq, self.mode, self.sparse,
per_sample_weights, self.include_last_offset,
self.padding_idx)
def extra_repr(self) -> str:
s = '{num_embeddings}, {embedding_dim}'
if self.max_norm is not None:
s += ', max_norm={max_norm}'
if self.norm_type != 2:
s += ', norm_type={norm_type}'
if self.scale_grad_by_freq is not False:
s += ', scale_grad_by_freq={scale_grad_by_freq}'
s += ', mode={mode}'
if self.padding_idx is not None:
s += ', padding_idx={padding_idx}'
return s.format(**{k: repr(v) for k, v in self.__dict__.items()})
[docs] @classmethod
def from_pretrained(cls, embeddings: Tensor, freeze: bool = True, max_norm: Optional[float] = None,
norm_type: float = 2., scale_grad_by_freq: bool = False,
mode: str = 'mean', sparse: bool = False, include_last_offset: bool = False,
padding_idx: Optional[int] = None) -> 'EmbeddingBag':
r"""Create EmbeddingBag instance from given 2-dimensional FloatTensor.
Args:
embeddings (Tensor): FloatTensor containing weights for the EmbeddingBag.
First dimension is being passed to EmbeddingBag as 'num_embeddings', second as 'embedding_dim'.
freeze (bool, optional): If ``True``, the tensor does not get updated in the learning process.
Equivalent to ``embeddingbag.weight.requires_grad = False``. Default: ``True``
max_norm (float, optional): See module initialization documentation. Default: ``None``
norm_type (float, optional): See module initialization documentation. Default ``2``.
scale_grad_by_freq (bool, optional): See module initialization documentation. Default ``False``.
mode (str, optional): See module initialization documentation. Default: ``"mean"``
sparse (bool, optional): See module initialization documentation. Default: ``False``.
include_last_offset (bool, optional): See module initialization documentation. Default: ``False``.
padding_idx (int, optional): See module initialization documentation. Default: ``None``.
Examples::
>>> # FloatTensor containing pretrained weights
>>> weight = torch.FloatTensor([[1, 2.3, 3], [4, 5.1, 6.3]])
>>> embeddingbag = nn.EmbeddingBag.from_pretrained(weight)
>>> # Get embeddings for index 1
>>> input = torch.LongTensor([[1, 0]])
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> embeddingbag(input)
tensor([[ 2.5000, 3.7000, 4.6500]])
"""
assert embeddings.dim() == 2, \
'Embeddings parameter is expected to be 2-dimensional'
rows, cols = embeddings.shape
embeddingbag = cls(
num_embeddings=rows,
embedding_dim=cols,
_weight=embeddings,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
mode=mode,
sparse=sparse,
include_last_offset=include_last_offset,
padding_idx=padding_idx)
embeddingbag.weight.requires_grad = not freeze
return embeddingbag
