o h@sTdZddlmZddlZddlmZddlmZedgdZGdddej Z dS) a5 Keeps an LSTM in TreeStack form. The TreeStack nodes keep the hx and cx for the LSTM, along with a "value" which represents whatever the user needs to store. The TreeStacks can be ppped to get back to the previous LSTM state. The module itself implements three methods: initial_state, push_states, output ) namedtupleN) TreeStackNode)valuelstm_hxlstm_cxcs6eZdZfddZd ddZddZdd ZZS) LSTMTreeStackc st||_|r|dtjdtj|ddn|dt |d||dt |d|tj ||||d|_ ||_ d S) a Prepare LSTM and parameters input_size: dimension of the inputs to the LSTM hidden_size: LSTM internal & output dimension num_lstm_layers: how many layers of LSTM to use dropout: value of the LSTM dropout uses_boundary_vector: if set, learn a start_embedding parameter. otherwise, use zeros input_dropout: an nn.Module to dropout inputs. TODO: allow a float parameter as well start_embeddingg?T) requires_grad input_zeros hidden_zeros) input_size hidden_size num_layersdropoutN) super__init__uses_boundary_vectorregister_parametertorchnn Parameterrandnregister_bufferzerosLSTMlstm input_dropout)selfrrnum_lstm_layersrrr __class__e/var/www/html/env_mimamsha/lib/python3.10/site-packages/stanza/models/constituency/lstm_tree_stack.pyrs $ zLSTMTreeStack.__init__NcCsf|jr|jdd}||\}\}}|ddddf}n |j}|j}|j}tt|||dddS)aw Return an initial state, either based on zeros or based on the initial embedding and LSTM Note that LSTM start operation is already batched, in a sense The subsequent batch built this way will be used for batch_size trees Returns a stack with None value, hx & cx either based on the start_embedding or zeros, and no parent. rNr )rparentlength)rr unsqueezerr r rr)r initial_valuestartoutputhxcxr#r#r$ initial_state0s zLSTMTreeStack.initial_statecst||}tjdd|Dddtjdd|Ddd||f\}\fddtt||D}|S)z Starting from a list of current stacks, put the inputs through the LSTM and build new stack nodes. B = stacks.len() = values.len() inputs must be of shape 1 x B x input_size cSg|]}|jjqSr#rr.0tr#r#r$ Nz-LSTMTreeStack.push_states..r )axiscSr.r#)rrr0r#r#r$r3Or4c sZg|])\}\}}|t|dd||dddfdd||dddfqS)Nr )pushr)r1istack transitionr,r+r#r$r3Qs J)rrcatr enumeratezip)rstacksvaluesinputsr* new_stacksr#r:r$ push_statesDs   zLSTMTreeStack.push_statescCs|jjddddfS)z Return the last layer of the lstm_hx as the output from a stack Refactored so that alternate structures have an easy way of getting the output rNr/)rr8r#r#r$r*UszLSTMTreeStack.output)N)__name__ __module__ __qualname__rr-rBr* __classcell__r#r#r!r$rs   r) __doc__ collectionsrrtorch.nnr%stanza.models.constituency.tree_stackrrModulerr#r#r#r$s