U dY@stddlmZddlZddlmZddlmZddlmZddlm Z dd lm Z Gd d d eZ Gd d d eZ dS))OptionalN)Tensor) Parameter)Module) functional)initc seZdZUdZdddddddgZeed<eed<eeed<eeed<eed<e ed<e ed <e ed<deeeeeeee e ee d d fdd Z d dddZ d dddZ e e dddZedddZedddZZS) EmbeddingaeA 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 (boolean, 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]]) >>> 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) num_embeddings embedding_dim padding_idxmax_norm norm_typescale_grad_by_freqsparseweightN@F) r r r rrrr_weightreturnc s| | d} tt|||_||_|dk rn|dkrH||jksntdn&|dkrn||j ksdtd|j|}||_||_||_||_ |dkrt t j ||ff| |_ |n$t|j||gkstdt ||_ ||_dS)Ndevicedtyperz)Padding_idx must be within num_embeddings?Shape of weight does not match num_embeddings and embedding_dim)superr __init__r r AssertionErrorr rrrrtorchemptyrreset_parameterslistshaper) selfr r r rrrrrrrfactory_kwargs __class__;/tmp/pip-unpacked-wheel-ua33x9lu/torch/nn/modules/sparse.pyrxs,    zEmbedding.__init__rcCst|j|dSNr Znormal_r_fill_padding_idx_with_zeror"r&r&r'rs zEmbedding.reset_parametersc Cs4|jdk r0t|j|jdW5QRXdSNrr rZno_gradrZfill_r,r&r&r'r+s  z%Embedding._fill_padding_idx_with_zero)inputrc Cs"t||j|j|j|j|j|jSr))F embeddingrr rrrr)r"r/r&r&r'forwardszEmbedding.forwardcCsld}|jdk r|d7}|jdk r(|d7}|jdkr:|d7}|jdk rL|d7}|jdk r^|d7}|jf|jS) N!{num_embeddings}, {embedding_dim}, padding_idx={padding_idx}, max_norm={max_norm}r, norm_type={norm_type}F), scale_grad_by_freq={scale_grad_by_freq}z , sparse=True)r rrrrformat__dict__r"sr&r&r' extra_reprs     zEmbedding.extra_reprTc CsD|dkstd|j\}} ||| ||||||d} | | j_| S)a'Creates 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 (boolean, 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 (boolean, 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]) >>> embedding(input) tensor([[ 4.0000, 5.1000, 6.3000]]) r4Embeddings parameter is expected to be 2-dimensional)r r rr rrrrZdimrr!rZ requires_grad) cls embeddingsfreezer rrrrrowscolsr1r&r&r'from_pretraineds   zEmbedding.from_pretrained)NNrFFNNN)TNNrFF)__name__ __module__ __qualname____doc__ __constants__int__annotations__rfloatboolrrrr+r2strr< classmethodrD __classcell__r&r&r$r'r sR _   r c s.eZdZUdZdddddddd d g Zeed<eed<eeed<eed<e ed<e ed <e ed<e ed<e ed <eeed <d!eeeeee e e ee e eed d fdd Z d dddZ d dddZd"e ee ee e dddZe dddZed#e e eeee e e e eedd dd ZZS)$ EmbeddingBagaComputes 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 (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. ``"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) >>> 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') r r rrrmoderinclude_last_offsetr rNrFmean) r r rrrrRrrrSr rc s| | d} tt|||_||_||_||_||_| dk r| dkrZ| |jkstdn&| dkr| |j ksvtd|j| } | |_ |dkrt t j ||ff| |_ |n$t|j||gkstdt ||_ ||_||_| |_dS)Nrrz)padding_idx must be within num_embeddingsr)rrQrr r rrrrr rrrrrr r!rRrrS)r"r r rrrrRrrrSr rrr#r$r&r'r9s0    zEmbeddingBag.__init__r(cCst|j|dSr)r*r,r&r&r'rWs zEmbeddingBag.reset_parametersc Cs4|jdk r0t|j|jdW5QRXdSr-r.r,r&r&r'r+[s  z(EmbeddingBag._fill_padding_idx_with_zero)r/offsetsper_sample_weightsrc Cs.t||j||j|j|j|j|j||j|j S)aForward 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. ) r0Z embedding_bagrrrrrRrrSr )r"r/rUrVr&r&r'r2`s zEmbeddingBag.forwardcCsbd}|jdk r|d7}|jdkr(|d7}|jdk r:|d7}|d7}|jdk rT|d7}|jf|jS) Nr3r5rr6Fr7z , mode={mode}r4)rrrr r8r9r:r&r&r'r<s    zEmbeddingBag.extra_reprT) r@rArrrrRrrSr rc CsH|dkstd|j\} } || | |||||||| d } | | j_| S)aQCreates 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 (boolean, 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 (boolean, optional): See module initialization documentation. Default ``False``. mode (string, 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]]) >>> embeddingbag(input) tensor([[ 2.5000, 3.7000, 4.6500]]) rr=) r r rrrrrRrrSr r>) r?r@rArrrrRrrSr rBrCZ embeddingbagr&r&r'rDs$  zEmbeddingBag.from_pretrained) NrFrTFNFNNN)NN)TNrFrTFFN)rErFrGrHrIrJrKrrLrMrrNrrr+r2r<rOrDrPr&r&r$r'rQsx L  %  rQ)typingrrrZtorch.nn.parameterrmodulerrr0r r rQr&r&r&r's      R