flambe.nn
¶
Subpackages¶
Submodules¶
Package Contents¶
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class
flambe.nn.
Module
[source]¶ Bases:
flambe.compile.Component
,torch.nn.Module
Base Flambé Module inteface.
Provides the exact same interface as Pytorch’s nn.Module, but extends it with a useful set of methods to access and clip parameters, as well as gradients.
This abstraction allows users to convert their modules with a single line change, by importing from Flambé instead. Just like every Pytorch module, a forward method should be implemented.
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named_trainable_params
:Iterator[Tuple[str, nn.Parameter]]¶ Get all the named parameters with requires_grad=True.
Returns: Iterator over the parameters and their name. Return type: Iterator[Tuple[str, nn.Parameter]]
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trainable_params
:Iterator[nn.Parameter]¶ Get all the parameters with requires_grad=True.
Returns: Iterator over the parameters Return type: Iterator[nn.Parameter]
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gradient_norm
:float¶ Compute the average gradient norm.
Returns: The current average gradient norm Return type: float
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parameter_norm
:float¶ Compute the average parameter norm.
Returns: The current average parameter norm Return type: float
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num_parameters
(self, trainable=False)¶ Gets the number of parameters in the model.
Returns: number of model params Return type: int
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clip_params
(self, threshold: float)¶ Clip the parameters to the given range.
Parameters: float – Values are clipped between -threshold, threshold
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clip_gradient_norm
(self, threshold: float)¶ Clip the norm of the gradient by the given value.
Parameters: float – Threshold to clip at
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class
flambe.nn.
SoftmaxLayer
(input_size: int, output_size: int, mlp_layers: int = 1, mlp_dropout: float = 0.0, mlp_hidden_activation: Optional[nn.Module] = None, take_log: bool = True)[source]¶ Bases:
flambe.nn.module.Module
Implement an SoftmaxLayer module.
Can be used to form a classifier out of any encoder. Note: by default takes the log_softmax so that it can be fed to the NLLLoss module. You can disable this behavior through the take_log argument.
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class
flambe.nn.
MixtureOfSoftmax
(input_size: int, output_size: int, k: int = 1, take_log: bool = True)[source]¶ Bases:
flambe.nn.module.Module
Implement the MixtureOfSoftmax output layer.
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pi
¶ softmax layer over the different softmax
Type: FullyConnected
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layers
¶ list of the k softmax layers
Type: [FullyConnected]
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forward
(self, data: Tensor)¶ Implement mixture of softmax for language modeling.
Parameters: data (torch.Tensor) – seq_len x batch_size x hidden_size Returns: out – output matrix of shape seq_len x batch_size x out_size Return type: Variable
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class
flambe.nn.
Embeddings
(num_embeddings: int, embedding_dim: int, padding_idx: int = 0, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, sparse: bool = False, positional_encoding: bool = False, positional_learned: bool = False, positonal_max_length: int = 5000)[source]¶ Bases:
flambe.nn.module.Module
Implement an Embeddings module.
This object replicates the usage of nn.Embedding but registers the from_pretrained classmethod to be used inside a Flambé configuration, as this does not happen automatically during the registration of PyTorch objects.
The module also adds optional positional encoding, which can either be sinusoidal or learned during training. For the non-learned positional embeddings, we use sine and cosine functions of different frequencies.
\[ext{PosEncoder}(pos, 2i) = sin(pos/10000^(2i/d_model)) ext{PosEncoder}(pos, 2i+1) = cos(pos/10000^(2i/d_model)) ext{where pos is the word position and i is the embed idx)\]-
classmethod
from_pretrained
(cls, embeddings: Tensor, freeze: bool = True, padding_idx: int = 0, max_norm: Optional[float] = None, norm_type: float = 2.0, scale_grad_by_freq: bool = False, sparse: bool = False, positional_encoding: bool = False, positional_learned: bool = False, positonal_max_length: int = 5000, positonal_embeddings: Optional[Tensor] = None, positonal_freeze: bool = True)¶ Create an Embeddings instance from pretrained embeddings.
Parameters: - embeddings (torch.Tensor) – FloatTensor containing weights for the Embedding. First dimension is being passed to Embedding as num_embeddings, second as embedding_dim.
- freeze (bool) – If True, the tensor does not get updated in the learning process. Default: True
- padding_idx (int, optional) – Pads the output with the embedding vector at
padding_idx
(initialized to zeros) whenever it encounters the index, by default 0 - max_norm (Optional[float], optional) – If given, each embedding vector with norm larger than
max_norm
is normalized to have normmax_norm
- norm_type (float, optional) – The p of the p-norm to compute for the
max_norm
option. Default2
. - scale_grad_by_freq (bool, optional) – If given, this will scale gradients by the inverse of
frequency of the words in the mini-batch. Default
False
. - sparse (bool, optional) – If
True
, gradient w.r.t.weight
matrix will be a sparse tensor. See Notes for more details. - positional_encoding (bool, optional) – If True, adds positonal encoding to the token embeddings.
By default, the embeddings are frozen sinusodial embeddings.
To learn these during training, set positional_learned.
Default
False
. - positional_learned (bool, optional) – Learns the positional embeddings during training instead
of using frozen sinusodial ones. Default
False
. - positonal_embeddings (torch.Tensor, optional) – If given, also replaces the positonal embeddings with this matrix. The max length will be ignored and replaced by the dimension of this matrix.
- positonal_freeze (bool, optional) – Whether the positonal embeddings should be frozen
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forward
(self, data: Tensor)¶ Perform a forward pass.
Parameters: data (Tensor) – The input tensor of shape [S x B] Returns: The output tensor of shape [S x B x E] Return type: Tensor
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classmethod
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class
flambe.nn.
Embedder
(embedding: Module, encoder: Module, pooling: Optional[Module] = None, embedding_dropout: float = 0, padding_idx: Optional[int] = 0, return_mask: bool = False)[source]¶ Bases:
flambe.nn.module.Module
Implements an Embedder module.
An Embedder takes as input a sequence of index tokens, and computes the corresponding embedded representations, and padding mask. The encoder may be initialized using a pretrained embedding matrix.
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drop
¶ The dropout layer
Type: nn.Dropout
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forward
(self, data: Tensor)¶ Performs a forward pass through the network.
Parameters: data (torch.Tensor) – The input data, as a float tensor of shape [S x B] Returns: Tuple[Tuple[Tensor, Tensor], Tensor] The encoded output, as a float tensor. May return a state if the encoder is an RNN and no pooling is provided. May also return a tuple if return_mask was passed in as a constructor argument. Return type: Union[Tensor, Tuple[Tensor, Tensor],
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class
flambe.nn.
MLPEncoder
(input_size: int, output_size: int, n_layers: int = 1, dropout: float = 0.0, output_activation: Optional[nn.Module] = None, hidden_size: Optional[int] = None, hidden_activation: Optional[nn.Module] = None)[source]¶ Bases:
flambe.nn.module.Module
Implements a multi layer feed forward network.
This module can be used to create output layers, or more complex multi-layer feed forward networks.
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seq
¶ the sequence of layers and activations
Type: nn.Sequential
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forward
(self, data: torch.Tensor)¶ Performs a forward pass through the network.
Parameters: data (torch.Tensor) – input to the model of shape (batch_size, input_size) Returns: output – output of the model of shape (batch_size, output_size) Return type: torch.Tensor
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class
flambe.nn.
RNNEncoder
(input_size: int, hidden_size: int, n_layers: int = 1, rnn_type: str = 'lstm', dropout: float = 0, bidirectional: bool = False, layer_norm: bool = False, highway_bias: float = 0, rescale: bool = True, enforce_sorted: bool = False, **kwargs)[source]¶ Bases:
flambe.nn.module.Module
Implements a multi-layer RNN.
This module can be used to create multi-layer RNN models, and provides a way to reduce to output of the RNN to a single hidden state by pooling the encoder states either by taking the maximum, average, or by taking the last hidden state before padding.
Padding is dealt with by using torch’s PackedSequence.
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forward
(self, data: Tensor, state: Optional[Tensor] = None, padding_mask: Optional[Tensor] = None)¶ Performs a forward pass through the network.
Parameters: - data (Tensor) – The input data, as a float tensor of shape [B x S x E]
- state (Tensor) – An optional previous state of shape [L x B x H]
- padding_mask (Tensor, optional) – The padding mask of shape [B x S], dtype should be bool
Returns: - Tensor – The encoded output, as a float tensor of shape [B x S x H]
- Tensor – The encoded state, as a float tensor of shape [L x B x H]
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class
flambe.nn.
PooledRNNEncoder
(input_size: int, hidden_size: int, n_layers: int = 1, rnn_type: str = 'lstm', dropout: float = 0, bidirectional: bool = False, layer_norm: bool = False, highway_bias: float = 0, rescale: bool = True, pooling: str = 'last')[source]¶ Bases:
flambe.nn.module.Module
Implement an RNNEncoder with additional pooling.
This class can be used to obtan a single encoded output for an input sequence. It also ignores the state of the RNN.
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forward
(self, data: Tensor, state: Optional[Tensor] = None, padding_mask: Optional[Tensor] = None)¶ Perform a forward pass through the network.
Parameters: - data (torch.Tensor) – The input data, as a float tensor of shape [B x S x E]
- state (Tensor) – An optional previous state of shape [L x B x H]
- padding_mask (Tensor, optional) – The padding mask of shape [B x S]
Returns: The encoded output, as a float tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
CNNEncoder
(input_channels: int, channels: List[int], conv_dim: int = 2, kernel_size: Union[int, List[Union[Tuple[int, ...], int]]] = 3, activation: nn.Module = None, pooling: nn.Module = None, dropout: float = 0, batch_norm: bool = True, stride: int = 1, padding: int = 0)[source]¶ Bases:
flambe.nn.module.Module
Implements a multi-layer n-dimensional CNN.
This module can be used to create multi-layer CNN models.
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forward
(self, data: Tensor)¶ Performs a forward pass through the network.
Parameters: data (torch.Tensor) – The input data, as a float tensor Returns: The encoded output, as a float tensor Return type: Union[Tensor, Tuple[Tensor, ..]]
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class
flambe.nn.
Sequential
(**kwargs: Dict[str, Union[Module, torch.nn.Module]])[source]¶ Bases:
flambe.nn.Module
Implement a Sequential module.
This class can be used in the same way as torch’s nn.Sequential, with the difference that it accepts kwargs arguments.
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forward
(self, data: torch.Tensor)¶ Performs a forward pass through the network.
Parameters: data (torch.Tensor) – input to the model Returns: output – output of the model Return type: torch.Tensor
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class
flambe.nn.
FirstPooling
[source]¶ Bases:
flambe.nn.Module
Get the last hidden state of a sequence.
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forward
(self, data: torch.Tensor, padding_mask: Optional[torch.Tensor] = None)¶ Performs a forward pass.
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- padding_mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The output data, as a tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
LastPooling
[source]¶ Bases:
flambe.nn.Module
Get the last hidden state of a sequence.
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forward
(self, data: torch.Tensor, padding_mask: Optional[torch.Tensor] = None)¶ Performs a forward pass.
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- padding_mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The output data, as a tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
SumPooling
[source]¶ Bases:
flambe.nn.Module
Get the sum of the hidden state of a sequence.
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forward
(self, data: torch.Tensor, padding_mask: Optional[torch.Tensor] = None)¶ Performs a forward pass.
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- padding_mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The output data, as a tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
AvgPooling
[source]¶ Bases:
flambe.nn.Module
Get the average of the hidden state of a sequence.
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forward
(self, data: torch.Tensor, padding_mask: Optional[torch.Tensor] = None)¶ Performs a forward pass.
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- padding_mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The output data, as a tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
StructuredSelfAttentivePooling
(input_size: int, attention_heads: int = 16, attention_units: Sequence[int] = (300, ), output_activation: Optional[torch.nn.Module] = None, hidden_activation: Optional[torch.nn.Module] = None, is_biased: bool = False, input_dropout: float = 0.0, attention_dropout: float = 0.0)[source]¶ Bases:
flambe.nn.Module
Structured Self Attentive Pooling.
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_compute_attention
(self, data: torch.Tensor, mask: torch.Tensor)¶ Computes the attention
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The attention, as a tensor of shape [B x S x HEADS]
Return type: torch.Tensor
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forward
(self, data: torch.Tensor, mask: Optional[torch.Tensor] = None)¶ Performs a forward pass.
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The output data, as a tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
GeneralizedPooling
(input_size: int, attention_units: Sequence[int] = (300, ), output_activation: Optional[torch.nn.Module] = None, hidden_activation: Optional[torch.nn.Module] = None, is_biased: bool = True, input_dropout: float = 0.0, attention_dropout: float = 0.0)[source]¶ Bases:
flambe.nn.pooling.StructuredSelfAttentivePooling
Self attention pooling.
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forward
(self, data: torch.Tensor, mask: Optional[torch.Tensor] = None)¶ Performs a forward pass.
Parameters: - data (torch.Tensor) – The input data, as a tensor of shape [B x S x H]
- mask (torch.Tensor) – The input mask, as a tensor of shape [B X S]
Returns: The output data, as a tensor of shape [B x H]
Return type: torch.Tensor
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class
flambe.nn.
Transformer
(input_size, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6, num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1)[source]¶ Bases:
flambe.nn.Module
A Transformer model
User is able to modify the attributes as needed. The architechture is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010.
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forward
(self, src: torch.Tensor, tgt: torch.Tensor, src_mask: Optional[torch.Tensor] = None, tgt_mask: Optional[torch.Tensor] = None, memory_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, tgt_key_padding_mask: Optional[torch.Tensor] = None, memory_key_padding_mask: Optional[torch.Tensor] = None)¶ Take in and process masked source/target sequences.
Parameters: - src (torch.Tensor) – the sequence to the encoder (required). shape: \((N, S, E)\).
- tgt (torch.Tensor) – the sequence to the decoder (required). shape: \((N, T, E)\).
- src_mask (torch.Tensor, optional) – the additive mask for the src sequence (optional). shape: \((S, S)\).
- tgt_mask (torch.Tensor, optional) – the additive mask for the tgt sequence (optional). shape: \((T, T)\).
- memory_mask (torch.Tensor, optional) – the additive mask for the encoder output (optional). shape: \((T, S)\).
- src_key_padding_mask (torch.Tensor, optional) – the ByteTensor mask for src keys per batch (optional). shape: \((N, S)\)
- tgt_key_padding_mask (torch.Tensor, optional) – the ByteTensor mask for tgt keys per batch (optional). shape: \((N, T)\).
- memory_key_padding_mask (torch.Tensor, optional) – the ByteTensor mask for memory keys per batch (optional). shape” \((N, S)\).
Returns: output (torch.Tensor) – The output sequence, shape: \((N, T, E)\).
Note ([src/tgt/memory]_mask should be filled with) – float(‘-inf’) for the masked positions and float(0.0) else. These masks ensure that predictions for position i depend only on the unmasked positions j and are applied identically for each sequence in a batch. [src/tgt/memory]_key_padding_mask should be a ByteTensor where False values are positions that should be masked with float(‘-inf’) and True values will be unchanged. This mask ensures that no information will be taken from position i if it is masked, and has a separate mask for each sequence in a batch.
Note (Due to the multi-head attention architecture in the) – transformer model, the output sequence length of a transformer is same as the input sequence (i.e. target) length of the decode.
where S is the source sequence length, T is the target sequence length, N is the batchsize, E is the feature number
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class
flambe.nn.
TransformerEncoder
(input_size: int = 512, d_model: int = 512, nhead: int = 8, num_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1)[source]¶ Bases:
flambe.nn.Module
TransformerEncoder is a stack of N encoder layers.
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forward
(self, src: torch.Tensor, memory: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None)¶ Pass the input through the endocder layers in turn.
Parameters: - src (torch.Tensor) – The sequence to the encoder (required).
- memory (torch.Tensor, optional) – Optional memory, unused by default.
- mask (torch.Tensor, optional) – The mask for the src sequence (optional).
- padding_mask (torch.Tensor, optional) – The mask for the src keys per batch (optional). Should be True for tokens to leave untouched, and False for padding tokens.
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_reset_parameters
(self)¶ Initiate parameters in the transformer model.
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class
flambe.nn.
TransformerDecoder
(input_size: int, d_model: int, nhead: int, num_layers: int, dim_feedforward: int = 2048, dropout: float = 0.1)[source]¶ Bases:
flambe.nn.Module
TransformerDecoder is a stack of N decoder layers
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forward
(self, tgt: torch.Tensor, memory: torch.Tensor, tgt_mask: Optional[torch.Tensor] = None, memory_mask: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, memory_key_padding_mask: Optional[torch.Tensor] = None)¶ Pass the inputs (and mask) through the decoder layer in turn.
Parameters: - tgt (torch.Tensor) – The sequence to the decoder (required).
- memory (torch.Tensor) – The sequence from the last layer of the encoder (required).
- tgt_mask (torch.Tensor, optional) – The mask for the tgt sequence (optional).
- memory_mask (torch.Tensor, optional) – The mask for the memory sequence (optional).
- padding_mask (torch.Tensor, optional) – The mask for the tgt keys per batch (optional). Should be True for tokens to leave untouched, and False for padding tokens.
- memory_key_padding_mask (torch.Tensor, optional) – The mask for the memory keys per batch (optional).
Returns: Return type: torch.Tensor
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_reset_parameters
(self)¶ Initiate parameters in the transformer model.
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class
flambe.nn.
TransformerSRU
(input_size: int = 512, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6, num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1, sru_dropout: Optional[float] = None, bidrectional: bool = False, **kwargs: Dict[str, Any])[source]¶ Bases:
flambe.nn.Module
A Transformer with an SRU replacing the FFN.
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forward
(self, src: torch.Tensor, tgt: torch.Tensor, src_mask: Optional[torch.Tensor] = None, tgt_mask: Optional[torch.Tensor] = None, memory_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None, tgt_key_padding_mask: Optional[torch.Tensor] = None, memory_key_padding_mask: Optional[torch.Tensor] = None)¶ Take in and process masked source/target sequences.
Parameters: - src (torch.Tensor) – the sequence to the encoder (required). shape: \((N, S, E)\).
- tgt (torch.Tensor) – the sequence to the decoder (required). shape: \((N, T, E)\).
- src_mask (torch.Tensor, optional) – the additive mask for the src sequence (optional). shape: \((S, S)\).
- tgt_mask (torch.Tensor, optional) – the additive mask for the tgt sequence (optional). shape: \((T, T)\).
- memory_mask (torch.Tensor, optional) – the additive mask for the encoder output (optional). shape: \((T, S)\).
- src_key_padding_mask (torch.Tensor, optional) – the ByteTensor mask for src keys per batch (optional). shape: \((N, S)\).
- tgt_key_padding_mask (torch.Tensor, optional) – the ByteTensor mask for tgt keys per batch (optional). shape: \((N, T)\).
- memory_key_padding_mask (torch.Tensor, optional) – the ByteTensor mask for memory keys per batch (optional). shape” \((N, S)\).
Returns: output (torch.Tensor) – The output sequence, shape: \((T, N, E)\).
Note ([src/tgt/memory]_mask should be filled with) – float(‘-inf’) for the masked positions and float(0.0) else. These masks ensure that predictions for position i depend only on the unmasked positions j and are applied identically for each sequence in a batch. [src/tgt/memory]_key_padding_mask should be a ByteTensor where False values are positions that should be masked with float(‘-inf’) and True values will be unchanged. This mask ensures that no information will be taken from position i if it is masked, and has a separate mask for each sequence in a batch.
Note (Due to the multi-head attention architecture in the) – transformer model, the output sequence length of a transformer is same as the input sequence (i.e. target) length of the decode.
where S is the source sequence length, T is the target sequence length, N is the batchsize, E is the feature number
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class
flambe.nn.
TransformerSRUEncoder
(input_size: int = 512, d_model: int = 512, nhead: int = 8, num_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1, sru_dropout: Optional[float] = None, bidirectional: bool = False, **kwargs: Dict[str, Any])[source]¶ Bases:
flambe.nn.Module
A TransformerSRUEncoder with an SRU replacing the FFN.
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forward
(self, src: torch.Tensor, state: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None)¶ Pass the input through the endocder layers in turn.
Parameters: - src (torch.Tensor) – The sequnce to the encoder (required).
- state (Optional[torch.Tensor]) – Optional state from previous sequence encoding. Only passed to the SRU (not used to perform multihead attention).
- mask (torch.Tensor, optional) – The mask for the src sequence (optional).
- padding_mask (torch.Tensor, optional) – The mask for the src keys per batch (optional). Should be True for tokens to leave untouched, and False for padding tokens.
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_reset_parameters
(self)¶ Initiate parameters in the transformer model.
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class
flambe.nn.
TransformerSRUDecoder
(input_size: int = 512, d_model: int = 512, nhead: int = 8, num_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1, sru_dropout: Optional[float] = None, **kwargs: Dict[str, Any])[source]¶ Bases:
flambe.nn.Module
A TransformerSRUDecoderwith an SRU replacing the FFN.
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forward
(self, tgt: torch.Tensor, memory: torch.Tensor, state: Optional[torch.Tensor] = None, tgt_mask: Optional[torch.Tensor] = None, memory_mask: Optional[torch.Tensor] = None, padding_mask: Optional[torch.Tensor] = None, memory_key_padding_mask: Optional[torch.Tensor] = None)¶ Pass the inputs (and mask) through the decoder layer in turn.
Parameters: - tgt (torch.Tensor) – The sequence to the decoder (required).
- memory (torch.Tensor) – The sequence from the last layer of the encoder (required).
- state (Optional[torch.Tensor]) – Optional state from previous sequence encoding. Only passed to the SRU (not used to perform multihead attention).
- tgt_mask (torch.Tensor, optional) – The mask for the tgt sequence (optional).
- memory_mask (torch.Tensor, optional) – The mask for the memory sequence (optional).
- padding_mask (torch.Tensor, optional) – The mask for the tgt keys per batch (optional). Should be True for tokens to leave untouched, and False for padding tokens.
- memory_key_padding_mask (torch.Tensor, optional) – The mask for the memory keys per batch (optional).
Returns: Return type: torch.Tensor
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_reset_parameters
(self)¶ Initiate parameters in the transformer model.
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