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<!DOCTYPE html>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<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#threshold"><span class="hidden-section">Threshold</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#sigmoid"><span class="hidden-section">Sigmoid</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#logsigmoid"><span class="hidden-section">LogSigmoid</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#tanhshrink"><span class="hidden-section">Tanhshrink</span></a></li>
<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#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>
</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>
</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#nllloss2d"><span class="hidden-section">NLLLoss2d</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#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>
</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#id16"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id17"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id18"><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-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-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>
</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#id19"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id20"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id21"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id22"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id23"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id24"><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#id25"><span class="hidden-section">prelu</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#id26"><span class="hidden-section">logsigmoid</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#id27"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id28"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id29"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id30"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id31"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id32"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id33"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id34"><span class="hidden-section">sigmoid</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../nn.html#normalization-functions">Normalization functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#batch-norm"><span class="hidden-section">batch_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#normalize"><span class="hidden-section">normalize</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../nn.html#linear-functions">Linear functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id35"><span class="hidden-section">linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../nn.html#dropout-functions">Dropout functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id36"><span class="hidden-section">dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#alpha-dropout"><span class="hidden-section">alpha_dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id37"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id38"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../nn.html#id39">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#pairwise-distance"><span class="hidden-section">pairwise_distance</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#cosine-similarity"><span class="hidden-section">cosine_similarity</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../nn.html#id40">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#binary-cross-entropy"><span class="hidden-section">binary_cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#poisson-nll-loss"><span class="hidden-section">poisson_nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#cosine-embedding-loss"><span class="hidden-section">cosine_embedding_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#cross-entropy"><span class="hidden-section">cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#hinge-embedding-loss"><span class="hidden-section">hinge_embedding_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#kl-div"><span class="hidden-section">kl_div</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#l1-loss"><span class="hidden-section">l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#mse-loss"><span class="hidden-section">mse_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#margin-ranking-loss"><span class="hidden-section">margin_ranking_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#multilabel-margin-loss"><span class="hidden-section">multilabel_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#multilabel-soft-margin-loss"><span class="hidden-section">multilabel_soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#multi-margin-loss"><span class="hidden-section">multi_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#nll-loss"><span class="hidden-section">nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#binary-cross-entropy-with-logits"><span class="hidden-section">binary_cross_entropy_with_logits</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#smooth-l1-loss"><span class="hidden-section">smooth_l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#soft-margin-loss"><span class="hidden-section">soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#triplet-margin-loss"><span class="hidden-section">triplet_margin_loss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../nn.html#vision-functions">Vision functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#pixel-shuffle"><span class="hidden-section">pixel_shuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#pad"><span class="hidden-section">pad</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#id42"><span class="hidden-section">upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#upsample-nearest"><span class="hidden-section">upsample_nearest</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#upsample-bilinear"><span class="hidden-section">upsample_bilinear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#grid-sample"><span class="hidden-section">grid_sample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../../nn.html#affine-grid"><span class="hidden-section">affine_grid</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../nn.html#torch-nn-init">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../optim.html">torch.optim</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../optim.html#how-to-use-an-optimizer">How to use an optimizer</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../optim.html#constructing-it">Constructing it</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../optim.html#per-parameter-options">Per-parameter options</a></li>
<li class="toctree-l3"><a class="reference internal" href="../../optim.html#taking-an-optimization-step">Taking an optimization step</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../../optim.html#optimizer-step"><code class="docutils literal"><span class="pre">optimizer.step()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../../optim.html#optimizer-step-closure"><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../../optim.html#algorithms">Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../optim.html#how-to-adjust-learning-rate">How to adjust Learning Rate</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../../autograd.html">torch.autograd</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../../autograd.html#variable">Variable</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../../autograd.html#api-compatibility">API compatibility</a></li>
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<h1>Source code for torch.distributed</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">torch.distributed provides an MPI-like interface for exchanging tensor</span>
<span class="sd">data accross multi-machine networks. It supports a few different backends</span>
<span class="sd">and initialization methods.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">_INITIALIZED_PG</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">_INITIALIZED_MW</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">_initialized</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">_scope</span> <span class="o">=</span> <span class="nb">locals</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_extend_scope</span><span class="p">(</span><span class="n">module</span><span class="p">):</span>
<span class="n">_scope</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">k</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">module</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'_'</span><span class="p">)})</span>
<span class="k">def</span> <span class="nf">is_available</span><span class="p">():</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_has_distributed</span><span class="p">()</span>
<div class="viewcode-block" id="init_process_group"><a class="viewcode-back" href="../../distributed.html#torch.distributed.init_process_group">[docs]</a><span class="k">def</span> <span class="nf">init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">'env://'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Initializes the distributed package.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> backend (str): Name of the backend to use. Depending on build-time configuration</span>
<span class="sd"> valid values include: ``tcp``, ``mpi`` and ``gloo``.</span>
<span class="sd"> init_method (str, optional): URL specifying how to initialize the package.</span>
<span class="sd"> world_size (int, optional): Number of processes participating in the job.</span>
<span class="sd"> rank (int, optional): Rank of the current process.</span>
<span class="sd"> group_name (str, optional): Group name. See description of init methods.</span>
<span class="sd"> """</span>
<span class="n">world_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'world_size'</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">group_name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'group_name'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span>
<span class="n">rank</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'rank'</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"got unexpected keyword arguments: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="s2">","</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">is_available</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"PyTorch built without distributed support"</span><span class="p">)</span>
<span class="k">global</span> <span class="n">_initialized</span>
<span class="k">if</span> <span class="n">_initialized</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"trying to initialize torch.distributed twice!"</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_init_process_group</span><span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">init_method</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span>
<span class="n">group_name</span><span class="p">,</span> <span class="n">rank</span><span class="p">)</span>
<span class="n">_initialized</span> <span class="o">=</span> <span class="n">_INITIALIZED_PG</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_init_extension</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="n">reduce_op</span><span class="p">,</span> <span class="n">group</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"distributed module initialization failed"</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">init_master_worker</span><span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">init_method</span><span class="o">=</span><span class="s1">'env://'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"""</span>
<span class="s2"> ================================================================================</span>
<span class="s2"> WARNING</span>
<span class="s2"> ================================================================================</span>
<span class="s2"> Master-worker mode is still experimental. The API will change without</span>
<span class="s2"> notice and we're can't guarantee full correctness and expected performance yet.</span>
<span class="s2"> We'll announce it once it's ready.</span>
<span class="s2"> """</span><span class="p">)</span>
<span class="n">world_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'world_size'</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">group_name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'group_name'</span><span class="p">,</span> <span class="s1">''</span><span class="p">)</span>
<span class="n">rank</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'rank'</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"got unexpected keyword arguments: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="s2">","</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">is_available</span><span class="p">():</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"PyTorch built without distributed support"</span><span class="p">)</span>
<span class="k">global</span> <span class="n">_initialized</span>
<span class="k">if</span> <span class="n">_initialized</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"trying to initialize torch.distributed twice!"</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_init_master_worker</span><span class="p">(</span><span class="n">backend</span><span class="p">,</span> <span class="n">init_method</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span>
<span class="n">group_name</span><span class="p">,</span> <span class="n">rank</span><span class="p">)</span>
<span class="n">_initialized</span> <span class="o">=</span> <span class="n">_INITIALIZED_MW</span>
<span class="kn">import</span> <span class="nn">torch.distributed.collectives</span> <span class="k">as</span> <span class="nn">collectives</span>
<span class="kn">import</span> <span class="nn">torch.distributed.remote_types</span> <span class="k">as</span> <span class="nn">remote_types</span>
<span class="n">_extend_scope</span><span class="p">(</span><span class="n">collectives</span><span class="p">)</span>
<span class="n">_extend_scope</span><span class="p">(</span><span class="n">remote_types</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_init_extension</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">reduce_op</span><span class="p">,</span> <span class="n">group</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"distributed module initialization failed"</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">reduce_op</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="n">SUM</span> <span class="o">=</span> <span class="nb">object</span><span class="p">()</span>
<span class="n">PRODUCT</span> <span class="o">=</span> <span class="nb">object</span><span class="p">()</span>
<span class="n">MAX</span> <span class="o">=</span> <span class="nb">object</span><span class="p">()</span>
<span class="n">MIN</span> <span class="o">=</span> <span class="nb">object</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">group</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="n">WORLD</span> <span class="o">=</span> <span class="nb">object</span><span class="p">()</span>
<span class="k">class</span> <span class="nc">_DistributedRequest</span><span class="p">(</span><span class="nb">object</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">request</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">request</span> <span class="o">=</span> <span class="n">request</span>
<span class="k">def</span> <span class="nf">is_completed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_request_is_completed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">request</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">wait</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_request_wait</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">request</span><span class="p">)</span>
<div class="viewcode-block" id="get_rank"><a class="viewcode-back" href="../../distributed.html#torch.distributed.get_rank">[docs]</a><span class="k">def</span> <span class="nf">get_rank</span><span class="p">():</span>
<span class="sd">"""Returns the rank of current process.</span>
<span class="sd"> Rank is a unique identifier assigned to each process withing a distributed</span>
<span class="sd"> group. They are always consecutive integers ranging from 0 to ``world_size``.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_get_rank</span><span class="p">()</span></div>
<div class="viewcode-block" id="get_world_size"><a class="viewcode-back" href="../../distributed.html#torch.distributed.get_world_size">[docs]</a><span class="k">def</span> <span class="nf">get_world_size</span><span class="p">():</span>
<span class="sd">"""Returns the number of processes in the distributed group."""</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_get_num_processes</span><span class="p">()</span></div>
<div class="viewcode-block" id="isend"><a class="viewcode-back" href="../../distributed.html#torch.distributed.isend">[docs]</a><span class="k">def</span> <span class="nf">isend</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">):</span>
<span class="sd">"""Sends a tensor asynchronously.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Tensor to send.</span>
<span class="sd"> dst (int): Destination rank.</span>
<span class="sd"> Returns:</span>
<span class="sd"> A distributed request object.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">_DistributedRequest</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_isend</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">))</span></div>
<div class="viewcode-block" id="irecv"><a class="viewcode-back" href="../../distributed.html#torch.distributed.irecv">[docs]</a><span class="k">def</span> <span class="nf">irecv</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="p">):</span>
<span class="sd">"""Receives a tensor asynchronously.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Tensor to fill with received data.</span>
<span class="sd"> src (int): Source rank.</span>
<span class="sd"> Returns:</span>
<span class="sd"> A distributed request object.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">_DistributedRequest</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_irecv</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="p">))</span></div>
<div class="viewcode-block" id="send"><a class="viewcode-back" href="../../distributed.html#torch.distributed.send">[docs]</a><span class="k">def</span> <span class="nf">send</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">):</span>
<span class="sd">"""Sends a tensor synchronously.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Tensor to send.</span>
<span class="sd"> dst (int): Destination rank.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_send</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">)</span></div>
<div class="viewcode-block" id="recv"><a class="viewcode-back" href="../../distributed.html#torch.distributed.recv">[docs]</a><span class="k">def</span> <span class="nf">recv</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Receives a tensor synchronously.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Tensor to fill with received data.</span>
<span class="sd"> src (int): Source rank.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">if</span> <span class="n">src</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_recv_any_source</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_recv</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="p">)</span></div>
<div class="viewcode-block" id="broadcast"><a class="viewcode-back" href="../../distributed.html#torch.distributed.broadcast">[docs]</a><span class="k">def</span> <span class="nf">broadcast</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">):</span>
<span class="sd">"""Broadcasts the tensor to the whole group.</span>
<span class="sd"> ``tensor`` must have the same number of elements in all processes</span>
<span class="sd"> participating in the collective.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Data to be sent if ``src`` is the rank of current</span>
<span class="sd"> process, and tensor to be used to save received data otherwise.</span>
<span class="sd"> src (int): Source rank.</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_broadcast</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span></div>
<div class="viewcode-block" id="all_reduce"><a class="viewcode-back" href="../../distributed.html#torch.distributed.all_reduce">[docs]</a><span class="k">def</span> <span class="nf">all_reduce</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">op</span><span class="o">=</span><span class="n">reduce_op</span><span class="o">.</span><span class="n">SUM</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">):</span>
<span class="sd">"""Reduces the tensor data accross all machines in such a way that all get</span>
<span class="sd"> the final result.</span>
<span class="sd"> After the call ``tensor`` is going to be bitwise identical in all processes.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Input and output of the collective. The function</span>
<span class="sd"> operates in-place.</span>
<span class="sd"> op (optional): One of the values from ``torch.distributed.reduce_op``</span>
<span class="sd"> enum. Specifies an operation used for element-wise reductions.</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_all_reduce</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span></div>
<div class="viewcode-block" id="reduce"><a class="viewcode-back" href="../../distributed.html#torch.distributed.reduce">[docs]</a><span class="k">def</span> <span class="nf">reduce</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">,</span> <span class="n">op</span><span class="o">=</span><span class="n">reduce_op</span><span class="o">.</span><span class="n">SUM</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">):</span>
<span class="sd">"""Reduces the tensor data accross all machines.</span>
<span class="sd"> Only the process with rank ``dst`` is going to receive the final result.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Input and output of the collective. The function</span>
<span class="sd"> operates in-place.</span>
<span class="sd"> op (optional): One of the values from ``torch.distributed.reduce_op``</span>
<span class="sd"> enum. Specifies an operation used for element-wise reductions.</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_reduce</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">,</span> <span class="n">op</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span></div>
<div class="viewcode-block" id="all_gather"><a class="viewcode-back" href="../../distributed.html#torch.distributed.all_gather">[docs]</a><span class="k">def</span> <span class="nf">all_gather</span><span class="p">(</span><span class="n">tensor_list</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">):</span>
<span class="sd">"""Gathers tensors from the whole group in a list.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor_list (list[Tensor]): Output list. It should contain</span>
<span class="sd"> correctly-sized tensors to be used for output of the collective.</span>
<span class="sd"> tensor (Tensor): Tensor to be broadcast from current process.</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_all_gather</span><span class="p">(</span><span class="n">tensor_list</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">group</span><span class="p">)</span></div>
<div class="viewcode-block" id="gather"><a class="viewcode-back" href="../../distributed.html#torch.distributed.gather">[docs]</a><span class="k">def</span> <span class="nf">gather</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Gathers a list of tensors in a single process.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Input tensor.</span>
<span class="sd"> dst (int): Destination rank. Required in all processes except the one that</span>
<span class="sd"> is receiveing the data.</span>
<span class="sd"> gather_list (list[Tensor]): List of appropriately-sized tensors to</span>
<span class="sd"> use for received data. Required only in the receiving process.</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="n">my_rank</span> <span class="o">=</span> <span class="n">get_rank</span><span class="p">()</span>
<span class="n">dst</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'dst'</span><span class="p">,</span> <span class="n">my_rank</span><span class="p">)</span>
<span class="n">gather_list</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'gather_list'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">_group</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'group'</span><span class="p">,</span> <span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"got unexpected kwargs"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">dst</span> <span class="o">==</span> <span class="n">my_rank</span><span class="p">:</span>
<span class="k">if</span> <span class="n">gather_list</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"gather_list is a required argument in gather destination"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_gather_recv</span><span class="p">(</span><span class="n">gather_list</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">_group</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">gather_list</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"non-empty gather_list can be given only to gather destination"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_gather_send</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="p">,</span> <span class="n">_group</span><span class="p">)</span></div>
<div class="viewcode-block" id="scatter"><a class="viewcode-back" href="../../distributed.html#torch.distributed.scatter">[docs]</a><span class="k">def</span> <span class="nf">scatter</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">"""Scatters a list of tensors to all processes in a group.</span>
<span class="sd"> Each process will receive exactly one tensor and store its data in the</span>
<span class="sd"> ``tensor`` argument.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> tensor (Tensor): Output tensor.</span>
<span class="sd"> src (int): Source rank. Required in all processes except the one that</span>
<span class="sd"> is sending the data.</span>
<span class="sd"> scatter_list (list[Tensor]): List of tensors to scatter. Required only</span>
<span class="sd"> in the process that is sending the data.</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="n">my_rank</span> <span class="o">=</span> <span class="n">get_rank</span><span class="p">()</span>
<span class="n">src</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'src'</span><span class="p">,</span> <span class="n">my_rank</span><span class="p">)</span>
<span class="n">scatter_list</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'scatter_list'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
<span class="n">_group</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'group'</span><span class="p">,</span> <span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kwargs</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"got unexpected kwargs"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">src</span> <span class="o">==</span> <span class="n">my_rank</span><span class="p">:</span>
<span class="k">if</span> <span class="n">scatter_list</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"scatter_list is a required argument in scatter source"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_scatter_send</span><span class="p">(</span><span class="n">scatter_list</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">_group</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">scatter_list</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"non-empty can be given only to scatter source"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_scatter_recv</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="p">,</span> <span class="n">_group</span><span class="p">)</span></div>
<div class="viewcode-block" id="barrier"><a class="viewcode-back" href="../../distributed.html#torch.distributed.barrier">[docs]</a><span class="k">def</span> <span class="nf">barrier</span><span class="p">(</span><span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="o">.</span><span class="n">WORLD</span><span class="p">):</span>
<span class="sd">"""Synchronizes all processes.</span>
<span class="sd"> This collective blocks processes until the whole group enters this function.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> group (optional): Group of the collective.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_barrier</span><span class="p">(</span><span class="n">group</span><span class="p">)</span></div>
<div class="viewcode-block" id="new_group"><a class="viewcode-back" href="../../distributed.html#torch.distributed.new_group">[docs]</a><span class="k">def</span> <span class="nf">new_group</span><span class="p">(</span><span class="n">ranks</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""Creates a new distributed group.</span>
<span class="sd"> This function requires that all processes in the main group (i.e. all</span>
<span class="sd"> processes that are part of the distributed job) enter this function, even</span>
<span class="sd"> if they are not going to be members of the group. Additionally, groups</span>
<span class="sd"> should be created in the same order in all processes.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> ranks (list[int]): List of ranks of group members.</span>
<span class="sd"> Returns:</span>
<span class="sd"> A handle of distributed group that can be given to collective calls.</span>
<span class="sd"> """</span>
<span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">_initialized</span> <span class="o">==</span> <span class="n">_INITIALIZED_PG</span><span class="p">,</span> \
<span class="s2">"collective only supported in process-group mode"</span>
<span class="k">if</span> <span class="n">ranks</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">ranks</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">get_world_size</span><span class="p">()))</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_new_group</span><span class="p">(</span><span class="n">ranks</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_register_stream</span><span class="p">(</span><span class="n">stream</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">_initialized</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">"torch.distributed needs to be initialized first"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_dist_register_stream</span><span class="p">(</span><span class="n">stream</span><span class="p">)</span>
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