![]() Torch.Tensor training : bool ¶ _configs ¶ class _configs. Output of fm with flattened and concatenated embeddings as input. The general architecture of the module is like:īatch_size of all input tensors need to be identical. On top of the public paper, we allow users to customize the hidden layer to be any Number of embeddings, as long as all embedding tensors have the same batch size. It allows flexibility in embedding dimensions and the ![]() To support modeling flexibility, we customize the key components as:ĭifferent from the public paper, we change the input from raw sparse features toĮmbeddings of the features. ![]() If low-order feature interactions shouldīe learnt, please use FactorizationMachine module instead, which will share Instead, it covers only the deep component of the publication. This module does not cover the end-end functionality of the published paper. The following modules are based off the Deep Factorization-Machine (DeepFM) paperĬlass DeepFM implents the DeepFM FrameworkĬlass FactorizationMachine implements FM as noted in the above paper.Ĭlass. randn ( batch_size, in_features ) dcn = VectorCrossNet ( num_layers = num_layers ) output = dcn ( input ) forward ( input : Tensor ) → Tensor ¶ Parameters : On each layer l, the tensor is transformed into:īatch_size = 3 num_layers = 2 in_features = 10 input = torch. This module leverages such \(K\) experts each learning feature interactions inĭifferent subspaces, and adaptively combining the learned crosses using a gating LowRankCrossNet, instead of relying on one single expert to learn feature crosses, Low-rank matrix \((N*r)\) together with mixture of experts. LowRankMixtureCrossNet defines the learnable crossing parameter per layer as a Low Rank Mixture Cross Net is a DCN V2 implementation from the paper: LowRankMixtureCrossNet ( in_features: int, num_layers: int, num_experts: int = 1, low_rank: int = 1, activation: ~typing.Union, ~torch.Tensor]] = ) ¶ Input ( torch.Tensor) – tensor with shape. randn ( batch_size, in_features ) dcn = LowRankCrossNet ( num_layers = num_layers, low_rank = 3 ) output = dcn ( input ) forward ( input : Tensor ) → Tensor ¶ Parameters : Input_dim= will do the layer normalization on last two dimensions.ĭevice ( Optional ) – default compute device.īatch_size = 3 num_layers = 2 in_features = 10 input = torch. Input_dims ( Union, torch.Size ]) – dimensions to normalize over. SwishLayerNorm ( input_dims : Union, Size ], device : Optional = None ) ¶Īpplies the Swish function with layer normalization: Y = X * Sigmoid(LayerNorm(X)). These modules include:Įxtensions of nn.Embedding and nn.EmbeddingBag, called EmbeddingBagCollectionĬommon module patterns such as MLP and SwishLayerNorm.Ĭustom modules for TorchRec such as PositionWeightedModule andĮmbeddingTower and EmbeddingTowerCollection, logical “tower” of embeddingsĪctivation Modules class. The torchrec modules contain a collection of various modules.
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