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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>
<li class="toctree-l3"><a class="reference internal" href="../../notes/autograd.html#requires-grad"><code class="docutils literal"><span class="pre">requires_grad</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../notes/autograd.html#how-autograd-encodes-the-history">How autograd encodes the history</a></li>
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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#prelu"><span class="hidden-section">PReLU</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#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>
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<h1>Source code for torch.cuda</h1><div class="highlight"><pre>
<span></span><span class="sd">"""</span>
<span class="sd">This package adds support for CUDA tensor types, that implement the same</span>
<span class="sd">function as CPU tensors, but they utilize GPUs for computation.</span>
<span class="sd">It is lazily initialized, so you can always import it, and use</span>
<span class="sd">:func:`is_available()` to determine if your system supports CUDA.</span>
<span class="sd">:ref:`cuda-semantics` has more details about working with CUDA.</span>
<span class="sd">"""</span>
<span class="kn">import</span> <span class="nn">contextlib</span>
<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">ctypes</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">multiprocessing.util</span> <span class="k">import</span> <span class="n">register_after_fork</span> <span class="k">as</span> <span class="n">_register_after_fork</span>
<span class="n">_initialized</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">_in_bad_fork</span> <span class="o">=</span> <span class="kc">False</span> <span class="c1"># this global is also used in torch.manual_seed</span>
<span class="n">_original_pid</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">_cudart</span> <span class="o">=</span> <span class="kc">None</span>
<div class="viewcode-block" id="is_available"><a class="viewcode-back" href="../../cuda.html#torch.cuda.is_available">[docs]</a><span class="k">def</span> <span class="nf">is_available</span><span class="p">():</span>
<span class="sd">"""Returns a bool indicating if CUDA is currently available."""</span>
<span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="p">,</span> <span class="s1">'_cuda_isDriverSufficient'</span><span class="p">)</span> <span class="ow">or</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">_cuda_isDriverSufficient</span><span class="p">()):</span>
<span class="k">return</span> <span class="kc">False</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">_cuda_getDeviceCount</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span></div>
<span class="k">def</span> <span class="nf">_sleep</span><span class="p">(</span><span class="n">cycles</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">_cuda_sleep</span><span class="p">(</span><span class="n">cycles</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_load_cudart</span><span class="p">():</span>
<span class="c1"># First check the main program for CUDA symbols</span>
<span class="n">lib</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">cdll</span><span class="o">.</span><span class="n">LoadLibrary</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">lib</span><span class="p">,</span> <span class="s1">'cudaGetErrorName'</span><span class="p">):</span>
<span class="k">return</span> <span class="n">lib</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"couldn't find libcudart. Make sure CUDA libraries are installed in a"</span>
<span class="s2">"default location, or that they're in </span><span class="si">{}</span><span class="s2">."</span>
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">'DYLD_LIBRARY_PATH'</span> <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">'Darwin'</span> <span class="k">else</span>
<span class="s1">'LD_LIBRARY_PATH'</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_check_driver</span><span class="p">():</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="p">,</span> <span class="s1">'_cuda_isDriverSufficient'</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"Torch not compiled with CUDA enabled"</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">_cuda_isDriverSufficient</span><span class="p">():</span>
<span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_getDriverVersion</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># found no NVIDIA driver on the system</span>
<span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"""</span>
<span class="s2">Found no NVIDIA driver on your system. Please check that you</span>
<span class="s2">have an NVIDIA GPU and installed a driver from</span>
<span class="s2">http://www.nvidia.com/Download/index.aspx"""</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># TODO: directly link to the alternative bin that needs install</span>
<span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="s2">"""</span>
<span class="s2">The NVIDIA driver on your system is too old (found version </span><span class="si">{}</span><span class="s2">).</span>
<span class="s2">Please update your GPU driver by downloading and installing a new</span>
<span class="s2">version from the URL: http://www.nvidia.com/Download/index.aspx</span>
<span class="s2">Alternatively, go to: https://pytorch.org/binaries to install</span>
<span class="s2">a PyTorch version that has been compiled with your version</span>
<span class="s2">of the CUDA driver."""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</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">_cuda_getDriverVersion</span><span class="p">())))</span>
<span class="k">def</span> <span class="nf">_lazy_init</span><span class="p">():</span>
<span class="k">global</span> <span class="n">_initialized</span><span class="p">,</span> <span class="n">_cudart</span><span class="p">,</span> <span class="n">_original_pid</span>
<span class="k">if</span> <span class="n">_initialized</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">if</span> <span class="n">_in_bad_fork</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">sys</span> <span class="k">import</span> <span class="n">version_info</span>
<span class="k">if</span> <span class="n">version_info</span> <span class="o"><</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">):</span>
<span class="n">msg</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"To use CUDA with multiprocessing, you must use Python "</span>
<span class="s2">"3.4+ and the 'spawn' start method"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">msg</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"To use CUDA with multiprocessing, you must use the "</span>
<span class="s2">"'spawn' start method"</span><span class="p">)</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"Cannot re-initialize CUDA in forked subprocess. "</span> <span class="o">+</span> <span class="n">msg</span><span class="p">)</span>
<span class="n">_check_driver</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">_cuda_init</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">_cuda_sparse_init</span><span class="p">()</span>
<span class="n">_cudart</span> <span class="o">=</span> <span class="n">_load_cudart</span><span class="p">()</span>
<span class="n">_cudart</span><span class="o">.</span><span class="n">cudaGetErrorName</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span>
<span class="n">_cudart</span><span class="o">.</span><span class="n">cudaGetErrorString</span><span class="o">.</span><span class="n">restype</span> <span class="o">=</span> <span class="n">ctypes</span><span class="o">.</span><span class="n">c_char_p</span>
<span class="n">_original_pid</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">getpid</span><span class="p">()</span>
<span class="n">_initialized</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">def</span> <span class="nf">_after_fork</span><span class="p">(</span><span class="n">arg</span><span class="p">):</span>
<span class="k">global</span> <span class="n">_initialized</span><span class="p">,</span> <span class="n">_in_bad_fork</span>
<span class="k">if</span> <span class="n">_initialized</span> <span class="ow">and</span> <span class="n">_original_pid</span> <span class="o">!=</span> <span class="n">os</span><span class="o">.</span><span class="n">getpid</span><span class="p">():</span>
<span class="n">_initialized</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">_in_bad_fork</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">_CudaBase</span><span class="o">.</span><span class="fm">__new__</span> <span class="o">=</span> <span class="n">_lazy_new</span>
<span class="n">_register_after_fork</span><span class="p">(</span><span class="n">_after_fork</span><span class="p">,</span> <span class="n">_after_fork</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">cudart</span><span class="p">():</span>
<span class="n">_lazy_init</span><span class="p">()</span>
<span class="k">return</span> <span class="n">_cudart</span>
<div class="viewcode-block" id="device"><a class="viewcode-back" href="../../cuda.html#torch.cuda.device">[docs]</a><span class="k">class</span> <span class="nc">device</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""Context-manager that changes the selected device.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> idx (int): device index to select. It's a no-op if this argument</span>
<span class="sd"> is negative.</span>
<span class="sd"> """</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">idx</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span>
<span class="bp">self</span><span class="o">.</span><span class="n">prev_idx</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
<span class="k">def</span> <span class="nf">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx</span> <span class="ow">is</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="k">return</span>
<span class="n">_lazy_init</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">prev_idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_getDevice</span><span class="p">()</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prev_idx</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx</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">_cuda_setDevice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prev_idx</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">idx</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">_cuda_setDevice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prev_idx</span><span class="p">)</span>
<span class="k">return</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="device_of"><a class="viewcode-back" href="../../cuda.html#torch.cuda.device_of">[docs]</a><span class="k">class</span> <span class="nc">device_of</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
<span class="sd">"""Context-manager that changes the current device to that of given object.</span>
<span class="sd"> You can use both tensors and storages as arguments. If a given object is</span>
<span class="sd"> not allocated on a GPU, this is a no-op.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> obj (Tensor or Storage): object allocated on the selected device.</span>
<span class="sd"> """</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">obj</span><span class="p">):</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">get_device</span><span class="p">()</span> <span class="k">if</span> <span class="n">obj</span><span class="o">.</span><span class="n">is_cuda</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span>
<span class="nb">super</span><span class="p">(</span><span class="n">device_of</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="n">idx</span><span class="p">)</span></div>
<div class="viewcode-block" id="set_device"><a class="viewcode-back" href="../../cuda.html#torch.cuda.set_device">[docs]</a><span class="k">def</span> <span class="nf">set_device</span><span class="p">(</span><span class="n">device</span><span class="p">):</span>
<span class="sd">"""Sets the current device.</span>
<span class="sd"> Usage of this function is discouraged in favor of :any:`device`. In most</span>
<span class="sd"> cases it's better to use ``CUDA_VISIBLE_DEVICES`` environmental variable.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> device (int): selected device. This function is a no-op if this</span>
<span class="sd"> argument is negative.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">device</span> <span class="o">>=</span> <span class="mi">0</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">_cuda_setDevice</span><span class="p">(</span><span class="n">device</span><span class="p">)</span></div>
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<div class="viewcode-block" id="stream"><a class="viewcode-back" href="../../cuda.html#torch.cuda.stream">[docs]</a><span class="k">def</span> <span class="nf">stream</span><span class="p">(</span><span class="n">stream</span><span class="p">):</span>
<span class="sd">"""Context-manager that selects a given stream.</span>
<span class="sd"> All CUDA kernels queued within its context will be enqueued on a selected</span>
<span class="sd"> stream.</span>
<span class="sd"> Arguments:</span>
<span class="sd"> stream (Stream): selected stream. This manager is a no-op if it's</span>
<span class="sd"> ``None``.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">stream</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">return</span>
<span class="n">prev_stream</span> <span class="o">=</span> <span class="n">current_stream</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">_cuda_setStream</span><span class="p">(</span><span class="n">stream</span><span class="o">.</span><span class="n">_cdata</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">finally</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">_cuda_setStream</span><span class="p">(</span><span class="n">prev_stream</span><span class="o">.</span><span class="n">_cdata</span><span class="p">)</span></div>
<div class="viewcode-block" id="device_count"><a class="viewcode-back" href="../../cuda.html#torch.cuda.device_count">[docs]</a><span class="k">def</span> <span class="nf">device_count</span><span class="p">():</span>
<span class="sd">"""Returns the number of GPUs available."""</span>
<span class="k">if</span> <span class="n">is_available</span><span class="p">():</span>
<span class="n">_lazy_init</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">_cuda_getDeviceCount</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span></div>
<div class="viewcode-block" id="current_device"><a class="viewcode-back" href="../../cuda.html#torch.cuda.current_device">[docs]</a><span class="k">def</span> <span class="nf">current_device</span><span class="p">():</span>
<span class="sd">"""Returns the index of a currently selected device."""</span>
<span class="n">_lazy_init</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">_cuda_getDevice</span><span class="p">()</span></div>
<div class="viewcode-block" id="synchronize"><a class="viewcode-back" href="../../cuda.html#torch.cuda.synchronize">[docs]</a><span class="k">def</span> <span class="nf">synchronize</span><span class="p">():</span>
<span class="sd">"""Waits for all kernels in all streams on current device to complete."""</span>
<span class="n">_lazy_init</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">_cuda_synchronize</span><span class="p">()</span></div>
<div class="viewcode-block" id="current_stream"><a class="viewcode-back" href="../../cuda.html#torch.cuda.current_stream">[docs]</a><span class="k">def</span> <span class="nf">current_stream</span><span class="p">():</span>
<span class="sd">"""Returns a currently selected :class:`Stream`."""</span>
<span class="n">_lazy_init</span><span class="p">()</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">Stream</span><span class="p">(</span><span class="n">_cdata</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_cuda_getCurrentStream</span><span class="p">())</span></div>
<div class="viewcode-block" id="current_blas_handle"><a class="viewcode-back" href="../../cuda.html#torch.cuda.current_blas_handle">[docs]</a><span class="k">def</span> <span class="nf">current_blas_handle</span><span class="p">():</span>
<span class="sd">"""Returns cublasHandle_t pointer to current cuBLAS handle"""</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">_cuda_getCurrentBlasHandle</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_host_allocator</span><span class="p">():</span>
<span class="n">_lazy_init</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">_cuda_cudaHostAllocator</span><span class="p">()</span>
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span> <span class="nf">_free_mutex</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">_cuda_lock_mutex</span><span class="p">()</span>
<span class="k">try</span><span class="p">:</span>
<span class="k">yield</span>
<span class="k">finally</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">_cuda_unlock_mutex</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">.random</span> <span class="k">import</span> <span class="o">*</span>
<span class="c1">################################################################################</span>
<span class="c1"># Define Storage and Tensor classes</span>
<span class="c1">################################################################################</span>
<span class="kn">from</span> <span class="nn">..tensor</span> <span class="k">import</span> <span class="n">_TensorBase</span>
<span class="kn">from</span> <span class="nn">..storage</span> <span class="k">import</span> <span class="n">_StorageBase</span>
<span class="k">def</span> <span class="nf">_dummy_type</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">init_err</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">class_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
<span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
<span class="s2">"Tried to instantiate dummy base class </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">class_name</span><span class="p">))</span>
<span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">storage_name</span><span class="p">,</span> <span class="p">(</span><span class="nb">object</span><span class="p">,),</span> <span class="p">{</span><span class="s2">"__init__"</span><span class="p">:</span> <span class="n">init_err</span><span class="p">})</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="p">,</span> <span class="s1">'CudaDoubleStorageBase'</span><span class="p">):</span>
<span class="c1"># Define dummy base classes</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'Double'</span><span class="p">,</span> <span class="s1">'Float'</span><span class="p">,</span> <span class="s1">'Long'</span><span class="p">,</span> <span class="s1">'Int'</span><span class="p">,</span> <span class="s1">'Short'</span><span class="p">,</span> <span class="s1">'Char'</span><span class="p">,</span> <span class="s1">'Byte'</span><span class="p">,</span> <span class="s1">'Half'</span><span class="p">]:</span>
<span class="n">storage_name</span> <span class="o">=</span> <span class="s1">'Cuda</span><span class="si">{0}</span><span class="s1">StorageBase'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="n">tensor_name</span> <span class="o">=</span> <span class="s1">'Cuda</span><span class="si">{0}</span><span class="s1">TensorBase'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</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="vm">__dict__</span><span class="p">[</span><span class="n">storage_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">_dummy_type</span><span class="p">(</span><span class="n">storage_name</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="vm">__dict__</span><span class="p">[</span><span class="n">tensor_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">_dummy_type</span><span class="p">(</span><span class="n">tensor_name</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="vm">__dict__</span><span class="p">[</span><span class="s1">'_CudaStreamBase'</span><span class="p">]</span> <span class="o">=</span> <span class="n">_dummy_type</span><span class="p">(</span><span class="s1">'CudaStreamBase'</span><span class="p">)</span>
<span class="nd">@staticmethod</span>
<span class="k">def</span> <span class="nf">_lazy_new</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="n">_lazy_init</span><span class="p">()</span>
<span class="c1"># We need this method only for lazy init, so we can remove it</span>
<span class="k">del</span> <span class="n">_CudaBase</span><span class="o">.</span><span class="fm">__new__</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">_CudaBase</span><span class="p">,</span> <span class="bp">cls</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_CudaBase</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="n">is_cuda</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">is_sparse</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">def</span> <span class="nf">type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">with</span> <span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_device</span><span class="p">()):</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">_CudaBase</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="fm">__new__</span> <span class="o">=</span> <span class="n">_lazy_new</span>
<span class="k">class</span> <span class="nc">DoubleStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaDoubleStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">FloatStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaFloatStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">LongStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaLongStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">IntStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaIntStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">ShortStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaShortStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">CharStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaCharStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">ByteStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaByteStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">HalfStorage</span><span class="p">(</span><span class="n">_CudaBase</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">CudaHalfStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">class</span> <span class="nc">DoubleTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaDoubleTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">DoubleStorage</span>
<span class="k">class</span> <span class="nc">FloatTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaFloatTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">FloatStorage</span>
<span class="k">class</span> <span class="nc">LongTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaLongTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LongStorage</span>
<span class="k">class</span> <span class="nc">IntTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaIntTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">IntStorage</span>
<span class="k">class</span> <span class="nc">ShortTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaShortTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ShortStorage</span>
<span class="k">class</span> <span class="nc">CharTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaCharTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># TODO</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">CharStorage</span>
<span class="k">class</span> <span class="nc">ByteTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaByteTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">False</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ByteStorage</span>
<span class="k">class</span> <span class="nc">HalfTensor</span><span class="p">(</span><span class="n">_CudaBase</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">CudaHalfTensorBase</span><span class="p">,</span> <span class="n">_TensorBase</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">is_signed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="kc">True</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">storage_type</span><span class="p">():</span>
<span class="k">return</span> <span class="n">HalfStorage</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">DoubleStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">FloatStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LongStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">IntStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">ShortStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">CharStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">ByteStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">HalfStorage</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">DoubleTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">FloatTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">LongTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">IntTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">ShortTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">CharTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">ByteTensor</span><span class="p">)</span>
<span class="n">torch</span><span class="o">.</span><span class="n">_tensor_classes</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">HalfTensor</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">sparse</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="k">import</span> <span class="n">nvtx</span>
<span class="kn">from</span> <span class="nn">.streams</span> <span class="k">import</span> <span class="n">Stream</span><span class="p">,</span> <span class="n">Event</span>
</pre></div>
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