U d@/@s~ddlmZddlZddlmZddlmZmZddlmZm Z m Z ddl m Z dd l mZed d d gZGd dde ZdS)) namedtupleN)Tensor)ListSequence) Sequential ModuleListLinear)Module) log_softmax _ASMoutputoutputlosscseZdZUdZeed<eed<eeed<eed<eed<e ed<e ed<deee eeed d fd d Z d dddZ eeedddZddZeedddZeedddZZS)AdaptiveLogSoftmaxWithLossuEfficient softmax approximation as described in `Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou `__. Adaptive softmax is an approximate strategy for training models with large output spaces. It is most effective when the label distribution is highly imbalanced, for example in natural language modelling, where the word frequency distribution approximately follows the `Zipf's law`_. Adaptive softmax partitions the labels into several clusters, according to their frequency. These clusters may contain different number of targets each. Additionally, clusters containing less frequent labels assign lower dimensional embeddings to those labels, which speeds up the computation. For each minibatch, only clusters for which at least one target is present are evaluated. The idea is that the clusters which are accessed frequently (like the first one, containing most frequent labels), should also be cheap to compute -- that is, contain a small number of assigned labels. We highly recommend taking a look at the original paper for more details. * :attr:`cutoffs` should be an ordered Sequence of integers sorted in the increasing order. It controls number of clusters and the partitioning of targets into clusters. For example setting ``cutoffs = [10, 100, 1000]`` means that first `10` targets will be assigned to the 'head' of the adaptive softmax, targets `11, 12, ..., 100` will be assigned to the first cluster, and targets `101, 102, ..., 1000` will be assigned to the second cluster, while targets `1001, 1002, ..., n_classes - 1` will be assigned to the last, third cluster. * :attr:`div_value` is used to compute the size of each additional cluster, which is given as :math:`\left\lfloor\frac{\texttt{in\_features}}{\texttt{div\_value}^{idx}}\right\rfloor`, where :math:`idx` is the cluster index (with clusters for less frequent words having larger indices, and indices starting from :math:`1`). * :attr:`head_bias` if set to True, adds a bias term to the 'head' of the adaptive softmax. See paper for details. Set to False in the official implementation. .. warning:: Labels passed as inputs to this module should be sorted according to their frequency. This means that the most frequent label should be represented by the index `0`, and the least frequent label should be represented by the index `n_classes - 1`. .. note:: This module returns a ``NamedTuple`` with ``output`` and ``loss`` fields. See further documentation for details. .. note:: To compute log-probabilities for all classes, the ``log_prob`` method can be used. Args: in_features (int): Number of features in the input tensor n_classes (int): Number of classes in the dataset cutoffs (Sequence): Cutoffs used to assign targets to their buckets div_value (float, optional): value used as an exponent to compute sizes of the clusters. Default: 4.0 head_bias (bool, optional): If ``True``, adds a bias term to the 'head' of the adaptive softmax. Default: ``False`` Returns: ``NamedTuple`` with ``output`` and ``loss`` fields: * **output** is a Tensor of size ``N`` containing computed target log probabilities for each example * **loss** is a Scalar representing the computed negative log likelihood loss Shape: - input: :math:`(N, \texttt{in\_features})` or :math:`(\texttt{in\_features})` - target: :math:`(N)` or :math:`()` where each value satisfies :math:`0 <= \texttt{target[i]} <= \texttt{n\_classes}` - output1: :math:`(N)` or :math:`()` - output2: ``Scalar`` .. _Zipf's law: https://en.wikipedia.org/wiki/Zipf%27s_law in_features n_classescutoffs div_value head_biasheadtail@FN)rrrrrreturnc sj||d}tt|t|}|t|ksnt|dksnt||dksntt|t|ksnt dd|Drvt d||_ ||_ ||g|_ ||_||_|j d|_t|j d|_|j|j|_t|j |jfd|ji||_t|_t|jD]p} t|j |j| d} |j | d|j | } tt|j | fddi|t| | fddi|} |j| qdS) N)devicedtyperrcSsg|]}t||kqS)int).0crr=/tmp/pip-unpacked-wheel-ua33x9lu/torch/nn/modules/adaptive.py sz7AdaptiveLogSoftmaxWithLoss.__init__..zcutoffs should be a sequence of unique, positive integers sorted in an increasing order, where each value is between 1 and n_classes-1ZbiasF)superr__init__listsortedminmaxlensetany ValueErrorrrrrrshortlist_sizeZ n_clustersZ head_sizer rrrrangerrappend) selfrrrrrrrZfactory_kwargsiZhszZoszZ projection __class__rr r#psB     z#AdaptiveLogSoftmaxWithLoss.__init__)rcCs.|j|jD]\}}||qdS)N)rreset_parametersr)r/Zi2hZh2orrr r3s z+AdaptiveLogSoftmaxWithLoss.reset_parameters)input_target_rcCsL|}|dkrH|d|dkr,td|dkrttd|n,|dkrl|dkrttd|ntd|dk}|r|n|d}|r|n|d}d}|d}||} ||} dg|j} tt| dD]} | | } | | d}|| k||k@}| }| dkr"q| dkr@| d|||nx||| }| d|}|j| d|}|j| d}| d||t|dd}|d|d}| d|| d|| 7}q||krtd |jd||||}t|dd}| |d| d 7} | }|sB| d} t| |S) NrrzBInput and target should have the same size in the batch dimension.r zE1D target tensor expects 2D input tensors, but found inputs with sizezE0D target tensor expects 1D input tensors, but found inputs with sizez;0D or 1D target tensor expected, multi-target not supporteddimzMTarget values should be in [0, {}], but values in range [{}, {}] were found. )r7size RuntimeError unsqueezeZ new_zeros new_emptyrr-r(ZnonzeroZsqueezeZnumelZ index_copy_Z index_selectrr,Z index_fill_r Zgatherformatrr&itemr'rZmeanr )r/r4r5Ztarg_dimZ is_batchedinputtargetZ used_rowsZ batch_sizerZ gather_indsZ cutoff_valuesr0Zlow_idxZhigh_idxZ target_maskZ row_indicesZrelative_targetZ input_subsetcluster_outputZ cluster_indexcluster_logprobZ local_logprob head_output head_logprobrrrr forwardsj                   z"AdaptiveLogSoftmaxWithLoss.forwardc Cs||d|jf}t|dd}|ddd|jf|ddd|jf<tt|j|jddD]Z\}\}}|j||}t|dd} | |dd|j|f d} | |dd||f<qd|S)za Given input tensor, and output of `self.head`, compute the log of the full distribution rrr6N) r;r8rr r, enumerateziprrr:) r/r>rBoutrCr0Z start_idxZstop_idxr@rAZoutput_logprobrrr _get_full_log_probs (&  z-AdaptiveLogSoftmaxWithLoss._get_full_log_prob)r>rcCs||}|||S)a Computes log probabilities for all :math:`\texttt{n\_classes}` Args: input (Tensor): a minibatch of examples Returns: log-probabilities of for each class :math:`c` in range :math:`0 <= c <= \texttt{n\_classes}`, where :math:`\texttt{n\_classes}` is a parameter passed to ``AdaptiveLogSoftmaxWithLoss`` constructor. Shape: - Input: :math:`(N, \texttt{in\_features})` - Output: :math:`(N, \texttt{n\_classes})` )rrH)r/r>rBrrr log_probs z#AdaptiveLogSoftmaxWithLoss.log_probcCs||}tj|dd}||jk}| }|r4|S|rV|||}tj|ddS|||||}tj|dd||<|SdS)a This is equivalent to `self.log_prob(input).argmax(dim=1)`, but is more efficient in some cases. Args: input (Tensor): a minibatch of examples Returns: output (Tensor): a class with the highest probability for each example Shape: - Input: :math:`(N, \texttt{in\_features})` - Output: :math:`(N)` rr6N)rtorchZargmaxr,r*allrH)r/r>rBrZnot_in_shortlistZall_in_shortlistrIrrr predicts     z"AdaptiveLogSoftmaxWithLoss.predict)rFNN)__name__ __module__ __qualname____doc__r__annotations__rfloatboolr rrr#r3rr rDrHrIrL __classcell__rrr1r rs2 U 3Jr) collectionsrrJrtypingrrrrr moduler Z functionalr r rrrrr s