mwptoolkit.module.Decoder.rnn_decoder

class mwptoolkit.module.Decoder.rnn_decoder.AttentionalRNNDecoder(embedding_size, hidden_size, context_size, num_dec_layers, rnn_cell_type, dropout_ratio=0.0)

Bases: Module

Attention-based Recurrent Neural Network (RNN) decoder.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input_embeddings, hidden_states=None, encoder_outputs=None, encoder_masks=None)

Implement the attention-based decoding process.

Parameters

• input_embeddings (torch.Tensor) – source sequence embedding, shape: [batch_size, sequence_length, embedding_size].

• hidden_states (torch.Tensor) – initial hidden states, default: None.

• encoder_outputs (torch.Tensor) – encoder output features, shape: [batch_size, sequence_length, hidden_size], default: None.

• encoder_masks (torch.Tensor) – encoder state masks, shape: [batch_size, sequence_length], default: None.

Returns

output features, shape: [batch_size, sequence_length, num_directions * hidden_size]. hidden states, shape: [batch_size, num_layers * num_directions, hidden_size].

Return type

tuple(torch.Tensor, torch.Tensor)

init_hidden(input_embeddings)

Initialize initial hidden states of RNN.

Parameters

input_embeddings (torch.Tensor) – input sequence embedding, shape: [batch_size, sequence_length, embedding_size].

Returns

the initial hidden states.

Return type

torch.Tensor

training: bool

class mwptoolkit.module.Decoder.rnn_decoder.BasicRNNDecoder(embedding_size, hidden_size, num_layers, rnn_cell_type, dropout_ratio=0.0)

Bases: Module

Basic Recurrent Neural Network (RNN) decoder.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(input_embeddings, hidden_states=None)

Implement the decoding process.

Parameters

• input_embeddings (torch.Tensor) – target sequence embedding, shape: [batch_size, sequence_length, embedding_size].

• hidden_states (torch.Tensor) – initial hidden states, default: None.

Returns

output features, shape: [batch_size, sequence_length, num_directions * hidden_size]. hidden states, shape: [batch_size, num_layers * num_directions, hidden_size].

Return type

tuple(torch.Tensor, torch.Tensor)

init_hidden(input_embeddings)

Initialize initial hidden states of RNN.

Parameters

input_embeddings (torch.Tensor) – input sequence embedding, shape: [batch_size, sequence_length, embedding_size].

Returns

the initial hidden states.

Return type

torch.Tensor

training: bool

class mwptoolkit.module.Decoder.rnn_decoder.SalignedDecoder(operations, dim_hidden=300, dropout_rate=0.5, device=None)

Bases: Module

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(context, text_len, operands, stacks, prev_op, prev_output, prev_state, number_emb, N_OPS)

Parameters

• context (torch.Tensor) – Encoded context, with size [batch_size, text_len, dim_hidden].

• text_len (torch.Tensor) – Text length for each problem in the batch.

• operands (list of torch.Tensor) – List of operands embeddings for each problem in the batch. Each element in the list is of size [n_operands, dim_hidden].

• stacks (list of StackMachine) – List of stack machines used for each problem.

• prev_op (torch.LongTensor) – Previous operation, with size [batch, 1].

• prev_arg (torch.LongTensor) – Previous argument indices, with size [batch, 1]. Can be None for the first step.

• prev_output (torch.Tensor) – Previous decoder RNN outputs, with size [batch, dim_hidden]. Can be None for the first step.

• prev_state (torch.Tensor) – Previous decoder RNN state, with size [batch, dim_hidden]. Can be None for the first step.

Returns

op_logits: Logits of operation selection. arg_logits: Logits of argument choosing. outputs: Outputs of decoder RNN. state: Hidden state of decoder RNN.

Return type

tuple(torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor)

pad_and_cat(tensors, padding)

Pad lists to have same number of elements, and concatenate those elements to a 3d tensor.

Parameters

• tensors (list of list of Tensors) – Each list contains list of operand embeddings. Each operand embedding is of size (dim_element,).

• padding (Tensor) – Element used to pad lists, with size (dim_element,).

Returns

Length of lists in tensors. tensors (Tensor): Concatenated tensor after padding the list.

Return type

n_tensors (list of int)

training: bool