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<li class="toctree-l2"><a class="reference internal" href="../nn.html#convolution-layers">Convolution layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv1d"><span class="hidden-section">Conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv2d"><span class="hidden-section">Conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv3d"><span class="hidden-section">Conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#convtranspose1d"><span class="hidden-section">ConvTranspose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#convtranspose2d"><span class="hidden-section">ConvTranspose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#convtranspose3d"><span class="hidden-section">ConvTranspose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#pooling-layers">Pooling layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxpool1d"><span class="hidden-section">MaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxpool2d"><span class="hidden-section">MaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxpool3d"><span class="hidden-section">MaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxunpool1d"><span class="hidden-section">MaxUnpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxunpool2d"><span class="hidden-section">MaxUnpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxunpool3d"><span class="hidden-section">MaxUnpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avgpool1d"><span class="hidden-section">AvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avgpool2d"><span class="hidden-section">AvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avgpool3d"><span class="hidden-section">AvgPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#fractionalmaxpool2d"><span class="hidden-section">FractionalMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lppool1d"><span class="hidden-section">LPPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lppool2d"><span class="hidden-section">LPPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptivemaxpool1d"><span class="hidden-section">AdaptiveMaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptivemaxpool2d"><span class="hidden-section">AdaptiveMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptivemaxpool3d"><span class="hidden-section">AdaptiveMaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptiveavgpool1d"><span class="hidden-section">AdaptiveAvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptiveavgpool2d"><span class="hidden-section">AdaptiveAvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptiveavgpool3d"><span class="hidden-section">AdaptiveAvgPool3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#padding-layers">Padding layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#reflectionpad1d"><span class="hidden-section">ReflectionPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#reflectionpad2d"><span class="hidden-section">ReflectionPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#replicationpad1d"><span class="hidden-section">ReplicationPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#replicationpad2d"><span class="hidden-section">ReplicationPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#replicationpad3d"><span class="hidden-section">ReplicationPad3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#zeropad2d"><span class="hidden-section">ZeroPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#constantpad1d"><span class="hidden-section">ConstantPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#constantpad2d"><span class="hidden-section">ConstantPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#constantpad3d"><span class="hidden-section">ConstantPad3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#non-linear-activations-weighted-sum-nonlinearity">Non-linear activations (weighted sum, nonlinearity)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#elu"><span class="hidden-section">ELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#hardshrink"><span class="hidden-section">Hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#hardtanh"><span class="hidden-section">Hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#leakyrelu"><span class="hidden-section">LeakyReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#logsigmoid"><span class="hidden-section">LogSigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#prelu"><span class="hidden-section">PReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#relu"><span class="hidden-section">ReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#relu6"><span class="hidden-section">ReLU6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#rrelu"><span class="hidden-section">RReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#selu"><span class="hidden-section">SELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#sigmoid"><span class="hidden-section">Sigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softplus"><span class="hidden-section">Softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softshrink"><span class="hidden-section">Softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softsign"><span class="hidden-section">Softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#tanh"><span class="hidden-section">Tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#tanhshrink"><span class="hidden-section">Tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#threshold"><span class="hidden-section">Threshold</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#non-linear-activations-other">Non-linear activations (other)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmin"><span class="hidden-section">Softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmax"><span class="hidden-section">Softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmax2d"><span class="hidden-section">Softmax2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#logsoftmax"><span class="hidden-section">LogSoftmax</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#normalization-layers">Normalization layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batchnorm1d"><span class="hidden-section">BatchNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batchnorm2d"><span class="hidden-section">BatchNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#instancenorm1d"><span class="hidden-section">InstanceNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#instancenorm2d"><span class="hidden-section">InstanceNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#instancenorm3d"><span class="hidden-section">InstanceNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#layernorm"><span class="hidden-section">LayerNorm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#localresponsenorm"><span class="hidden-section">LocalResponseNorm</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#recurrent-layers">Recurrent layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#rnn"><span class="hidden-section">RNN</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lstm"><span class="hidden-section">LSTM</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#gru"><span class="hidden-section">GRU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lstmcell"><span class="hidden-section">LSTMCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#grucell"><span class="hidden-section">GRUCell</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#linear-layers">Linear layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#linear"><span class="hidden-section">Linear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#bilinear"><span class="hidden-section">Bilinear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#dropout-layers">Dropout layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#alphadropout"><span class="hidden-section">AlphaDropout</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#sparse-layers">Sparse layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#embedding"><span class="hidden-section">Embedding</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#embeddingbag"><span class="hidden-section">EmbeddingBag</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#distance-functions">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cosinesimilarity"><span class="hidden-section">CosineSimilarity</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pairwisedistance"><span class="hidden-section">PairwiseDistance</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#poissonnllloss"><span class="hidden-section">PoissonNLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#bceloss"><span class="hidden-section">BCELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#bcewithlogitsloss"><span class="hidden-section">BCEWithLogitsLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#tripletmarginloss"><span class="hidden-section">TripletMarginLoss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#vision-layers">Vision layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pixelshuffle"><span class="hidden-section">PixelShuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsample"><span class="hidden-section">Upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#dataparallel-layers-multi-gpu-distributed">DataParallel layers (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#distributeddataparallel"><span class="hidden-section">DistributedDataParallel</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#utilities">Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#clip-grad-norm"><span class="hidden-section">clip_grad_norm_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#clip-grad-value"><span class="hidden-section">clip_grad_value_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#weight-norm"><span class="hidden-section">weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#remove-weight-norm"><span class="hidden-section">remove_weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#packedsequence"><span class="hidden-section">PackedSequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pad-sequence"><span class="hidden-section">pad_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pack-sequence"><span class="hidden-section">pack_sequence</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#convolution-functions">Convolution functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id20"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id21"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id22"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv-transpose3d"><span class="hidden-section">conv_transpose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#pooling-functions">Pooling functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#max-pool1d"><span class="hidden-section">max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#max-pool2d"><span class="hidden-section">max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#max-pool3d"><span class="hidden-section">max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#max-unpool1d"><span class="hidden-section">max_unpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#max-unpool3d"><span class="hidden-section">max_unpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lp-pool1d"><span class="hidden-section">lp_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptive-max-pool3d"><span class="hidden-section">adaptive_max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptive-avg-pool3d"><span class="hidden-section">adaptive_avg_pool3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id23"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id24"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id25"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id26"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id27"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id28"><span class="hidden-section">selu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id29"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id30"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#glu"><span class="hidden-section">glu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id31"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id32"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id33"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id34"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id35"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id36"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id37"><span class="hidden-section">softmax</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../distributions.html#laplace"><span class="hidden-section">Laplace</span></a></li>
<li class="toctree-l2"><a class="reference internal" href="../distributions.html#lognormal"><span class="hidden-section">LogNormal</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../distributions.html#onehotcategorical"><span class="hidden-section">OneHotCategorical</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../distributions.html#module-torch.distributions.kl"><cite>KL Divergence</cite></a></li>
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<h1>Extending PyTorch<a class="headerlink" href="#extending-pytorch" title="Permalink to this headline">¶</a></h1>
<p>In this note we’ll cover ways of extending <a class="reference internal" href="../nn.html#module-torch.nn" title="torch.nn"><code class="xref py py-mod docutils literal"><span class="pre">torch.nn</span></code></a>,
<a class="reference internal" href="../autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal"><span class="pre">torch.autograd</span></code></a>, and writing custom C extensions utilizing our C
libraries.</p>
<div class="section" id="extending-torch-autograd">
<h2>Extending <a class="reference internal" href="../autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal"><span class="pre">torch.autograd</span></code></a><a class="headerlink" href="#extending-torch-autograd" title="Permalink to this headline">¶</a></h2>
<p>Adding operations to <a class="reference internal" href="../autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal"><span class="pre">autograd</span></code></a> requires implementing a new
<a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> subclass for each operation. Recall that <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> s
are what <a class="reference internal" href="../autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal"><span class="pre">autograd</span></code></a> uses to compute the results and gradients, and
encode the operation history. Every new function requires you to implement 2
methods:</p>
<ul class="simple">
<li><a class="reference internal" href="../autograd.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal"><span class="pre">forward()</span></code></a> - the code that performs the operation. It can take
as many arguments as you want, with some of them being optional, if you
specify the default values. All kinds of Python objects are accepted here.
<code class="xref py py-class docutils literal"><span class="pre">Variable</span></code> arguments will be converted to <code class="xref py py-class docutils literal"><span class="pre">Tensor</span></code> s before the
call, and their use will be registered in the graph. Note that this logic won’t
traverse lists/dicts/any other data structures and will only consider Variables
that are direct arguments to the call. You can return either a single
<code class="xref py py-class docutils literal"><span class="pre">Tensor</span></code> output, or a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.6)"><code class="xref py py-class docutils literal"><span class="pre">tuple</span></code></a> of <code class="xref py py-class docutils literal"><span class="pre">Tensor</span></code> s if there are
multiple outputs. Also, please refer to the docs of <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> to find
descriptions of useful methods that can be called only from <a class="reference internal" href="../autograd.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal"><span class="pre">forward()</span></code></a>.</li>
<li><a class="reference internal" href="../autograd.html#torch.autograd.Function.backward" title="torch.autograd.Function.backward"><code class="xref py py-meth docutils literal"><span class="pre">backward()</span></code></a> - gradient formula. It will be given
as many <code class="xref py py-class docutils literal"><span class="pre">Variable</span></code> arguments as there were outputs, with each of them
representing gradient w.r.t. that output. It should return as many
<code class="xref py py-class docutils literal"><span class="pre">Variable</span></code> s as there were inputs, with each of them containing the
gradient w.r.t. its corresponding input. If your inputs didn’t require
gradient (see <code class="xref py py-attr docutils literal"><span class="pre">needs_input_grad</span></code>), or were non-<code class="xref py py-class docutils literal"><span class="pre">Variable</span></code>
objects, you can return <code class="xref py py-class docutils literal"><span class="pre">None</span></code>. Also, if you have optional
arguments to <code class="xref py py-meth docutils literal"><span class="pre">forward()</span></code> you can return more gradients than there
were inputs, as long as they’re all <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.6)"><code class="docutils literal"><span class="pre">None</span></code></a>.</li>
</ul>
<p>Below you can find code for a <code class="docutils literal"><span class="pre">Linear</span></code> function from <a class="reference internal" href="../nn.html#module-torch.nn" title="torch.nn"><code class="xref py py-mod docutils literal"><span class="pre">torch.nn</span></code></a>, with
additional comments:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Inherit from Function</span>
<span class="k">class</span> <span class="nc">LinearFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
<span class="c1"># Note that both forward and backward are @staticmethods</span>
<span class="nd">@staticmethod</span>
<span class="c1"># bias is an optional argument</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span>
<span class="n">output</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">weight</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
<span class="k">if</span> <span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">output</span> <span class="o">+=</span> <span class="n">bias</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
<span class="k">return</span> <span class="n">output</span>
<span class="c1"># This function has only a single output, so it gets only one gradient</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
<span class="c1"># This is a pattern that is very convenient - at the top of backward</span>
<span class="c1"># unpack saved_tensors and initialize all gradients w.r.t. inputs to</span>
<span class="c1"># None. Thanks to the fact that additional trailing Nones are</span>
<span class="c1"># ignored, the return statement is simple even when the function has</span>
<span class="c1"># optional inputs.</span>
<span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
<span class="n">grad_input</span> <span class="o">=</span> <span class="n">grad_weight</span> <span class="o">=</span> <span class="n">grad_bias</span> <span class="o">=</span> <span class="kc">None</span>
<span class="c1"># These needs_input_grad checks are optional and there only to</span>
<span class="c1"># improve efficiency. If you want to make your code simpler, you can</span>
<span class="c1"># skip them. Returning gradients for inputs that don't require it is</span>
<span class="c1"># not an error.</span>
<span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
<span class="n">grad_input</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span>
<span class="k">if</span> <span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
<span class="n">grad_weight</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="k">if</span> <span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">[</span><span class="mi">2</span><span class="p">]:</span>
<span class="n">grad_bias</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">grad_input</span><span class="p">,</span> <span class="n">grad_weight</span><span class="p">,</span> <span class="n">grad_bias</span>
</pre></div>
</div>
<p>Now, to make it easier to use these custom ops, we recommend aliasing their
<code class="docutils literal"><span class="pre">apply</span></code> method:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">linear</span> <span class="o">=</span> <span class="n">LinearFunction</span><span class="o">.</span><span class="n">apply</span>
</pre></div>
</div>
<p>Here, we give an additional example of a function that is parametrized by
non-Variable arguments:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MulConstant</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">constant</span><span class="p">):</span>
<span class="c1"># ctx is a context object that can be used to stash information</span>
<span class="c1"># for backward computation</span>
<span class="n">ctx</span><span class="o">.</span><span class="n">constant</span> <span class="o">=</span> <span class="n">constant</span>
<span class="k">return</span> <span class="n">tensor</span> <span class="o">*</span> <span class="n">constant</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
<span class="c1"># We return as many input gradients as there were arguments.</span>
<span class="c1"># Gradients of non-Tensor arguments to forward must be None.</span>
<span class="k">return</span> <span class="n">grad_output</span> <span class="o">*</span> <span class="n">ctx</span><span class="o">.</span><span class="n">constant</span><span class="p">,</span> <span class="kc">None</span>
</pre></div>
</div>
<p>You probably want to check if the backward method you implemented actually
computes the derivatives of your function. It is possible by comparing with
numerical approximations using small finite differences:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">gradcheck</span>
<span class="c1"># gradcheck takes a tuple of tensors as input, check if your gradient</span>
<span class="c1"># evaluated with these tensors are close enough to numerical</span>
<span class="c1"># approximations and returns True if they all verify this condition.</span>
<span class="nb">input</span> <span class="o">=</span> <span class="p">(</span><span class="n">Variable</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">double</span><span class="p">(),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">Variable</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">double</span><span class="p">(),</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">),)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">gradcheck</span><span class="p">(</span><span class="n">Linear</span><span class="o">.</span><span class="n">apply</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="extending-torch-nn">
<h2>Extending <a class="reference internal" href="../nn.html#module-torch.nn" title="torch.nn"><code class="xref py py-mod docutils literal"><span class="pre">torch.nn</span></code></a><a class="headerlink" href="#extending-torch-nn" title="Permalink to this headline">¶</a></h2>
<p><a class="reference internal" href="../nn.html#module-torch.nn" title="torch.nn"><code class="xref py py-mod docutils literal"><span class="pre">nn</span></code></a> exports two kinds of interfaces - modules and their functional
versions. You can extend it in both ways, but we recommend using modules for
all kinds of layers, that hold any parameters or buffers, and recommend using
a functional form parameter-less operations like activation functions, pooling,
etc.</p>
<p>Adding a functional version of an operation is already fully covered in the
section above.</p>
<div class="section" id="adding-a-module">
<h3>Adding a <a class="reference internal" href="../nn.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal"><span class="pre">Module</span></code></a><a class="headerlink" href="#adding-a-module" title="Permalink to this headline">¶</a></h3>
<p>Since <a class="reference internal" href="../nn.html#module-torch.nn" title="torch.nn"><code class="xref py py-mod docutils literal"><span class="pre">nn</span></code></a> heavily utilizes <a class="reference internal" href="../autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal"><span class="pre">autograd</span></code></a>, adding a new
<a class="reference internal" href="../nn.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal"><span class="pre">Module</span></code></a> requires implementing a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a>
that performs the operation and can compute the gradient. From now on let’s
assume that we want to implement a <code class="docutils literal"><span class="pre">Linear</span></code> module and we have the function
implemented as in the listing above. There’s very little code required to
add this. Now, there are two functions that need to be implemented:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">__init__</span></code> (<em>optional</em>) - takes in arguments such as kernel sizes, numbers
of features, etc. and initializes parameters and buffers.</li>
<li><a class="reference internal" href="../nn.html#torch.nn.Module.forward" title="torch.nn.Module.forward"><code class="xref py py-meth docutils literal"><span class="pre">forward()</span></code></a> - instantiates a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal"><span class="pre">Function</span></code></a> and
uses it to perform the operation. It’s very similar to a functional wrapper
shown above.</li>
</ul>
<p>This is how a <code class="docutils literal"><span class="pre">Linear</span></code> module can be implemented:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Linear</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">output_features</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Linear</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_features</span> <span class="o">=</span> <span class="n">input_features</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output_features</span> <span class="o">=</span> <span class="n">output_features</span>
<span class="c1"># nn.Parameter is a special kind of Variable, that will get</span>
<span class="c1"># automatically registered as Module's parameter once it's assigned</span>
<span class="c1"># as an attribute. Parameters and buffers need to be registered, or</span>
<span class="c1"># they won't appear in .parameters() (doesn't apply to buffers), and</span>
<span class="c1"># won't be converted when e.g. .cuda() is called. You can use</span>
<span class="c1"># .register_buffer() to register buffers.</span>
<span class="c1"># nn.Parameters require gradients by default.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">output_features</span><span class="p">,</span> <span class="n">input_features</span><span class="p">))</span>
<span class="k">if</span> <span class="n">bias</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">output_features</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># You should always register all possible parameters, but the</span>
<span class="c1"># optional ones can be None if you want.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="s1">'bias'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="c1"># Not a very smart way to initialize weights</span>
<span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="c1"># See the autograd section for explanation of what happens here.</span>
<span class="k">return</span> <span class="n">LinearFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># (Optional)Set the extra information about this module. You can test</span>
<span class="c1"># it by printing an object of this class.</span>
<span class="k">return</span> <span class="s1">'in_features=</span><span class="si">{}</span><span class="s1">, out_features=</span><span class="si">{}</span><span class="s1">, bias=</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">in_features</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_features</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="writing-custom-c-extensions">
<h2>Writing custom C extensions<a class="headerlink" href="#writing-custom-c-extensions" title="Permalink to this headline">¶</a></h2>
<p>Coming soon. For now you can find an example at
<a class="reference external" href="https://github.com/pytorch/extension-ffi">GitHub</a>.</p>
</div>
</div>
</div>
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