Merge pull request #18 from aasseman/feat/fixed-sentence-padding

Add fixed padding option to sentence_embeddings, sentence_indexer

Author: tkornuta-ibm

configs/default/components/models/sentence_embeddings.yml

@@ -25,6 +25,12 @@ import_word_mappings_from_globals: False
 # Flag informing whether word mappings will be exported to globals (LOADED)
 export_word_mappings_to_globals: False
 
+# Fixed padding length
+# -1  -> For each batch, automatically pad to the length of the longest sequence of the batch
+#        (variable from batch to batch)
+# > 0 -> Pad each pad to the chosen length (fixed for all batches)
+fixed_padding: -1
+
 # File containing pretrained embeddings (LOADED)
 # Empty means that no embeddings will be loaded.
 pretrained_embeddings_file: ''

configs/default/components/text/sentence_indexer.yml

@@ -25,6 +25,12 @@ import_word_mappings_from_globals: False
 # Flag informing whether word mappings will be exported to globals (LOADED)
 export_word_mappings_to_globals: False
 
+# Fixed padding length
+# -1  -> For each batch, automatically pad to the length of the longest sequence of the batch
+#        (variable from batch to batch)
+# > 0 -> Pad each pad to the chosen length (fixed for all batches)
+fixed_padding: -1
+
 # Operation mode. If 'reverse' is True, then it will change indices into words (LOADED)
 reverse: False
 

ptp/components/models/sentence_embeddings.py

@@ -25,6 +25,7 @@
 from ptp.data_types.data_definition import DataDefinition
 
 import ptp.components.utils.embeddings as emb
+from ptp.components.utils.word_mappings import pad_trunc_list
 
 
 class SentenceEmbeddings(Model, WordMappings):
@@ -56,6 +57,9 @@ def __init__(self, name, config):
         self.key_inputs = self.stream_keys["inputs"]
         self.key_outputs = self.stream_keys["outputs"]
 
+        # Force padding to a fixed length
+        self.fixed_padding = self.config['fixed_padding']
+
         # Retrieve embeddings size from configuration and export it to globals.
         self.embeddings_size = self.config['embeddings_size']
         self.globals["embeddings_size"] = self.embeddings_size
@@ -120,6 +124,11 @@ def forward(self, data_dict):
                 # Add index to outputs.
                 output_sample.append( output_index )
 
+            # Apply fixed padding to all sequences if requested
+            # Otherwise let torch.nn.utils.rnn.pad_sequence handle it and choose a dynamic padding
+            if self.fixed_padding > 0:
+                pad_trunc_list(output_sample, self.fixed_padding)
+
             #indices_list.append(self.app_state.FloatTensor(output_sample))
             indices_list.append(self.app_state.LongTensor(output_sample))
 

ptp/components/text/sentence_indexer.py

@@ -19,6 +19,7 @@
 from ptp.components.component import Component
 from ptp.components.mixins.word_mappings import WordMappings
 from ptp.data_types.data_definition import DataDefinition
+from ptp.components.utils.word_mappings import pad_trunc_list
 
 
 class SentenceIndexer(Component, WordMappings):
@@ -50,6 +51,9 @@ def __init__(self, name, config):
         # Read mode from the configuration.
         self.mode_reverse = self.config['reverse']
 
+        # Force padding to a fixed length
+        self.fixed_padding = self.config['fixed_padding']
+
         if self.mode_reverse:
             # We will need reverse (index:word) mapping.
             self.ix_to_word = dict((v,k) for k,v in self.word_to_ix.items())
@@ -140,10 +144,16 @@ def sentences_to_tensor(self, data_dict):
                 # Add index to outputs.
                 output_sample.append( output_index )
 
-            outputs_list.append(output_sample)
+            # Apply fixed padding to all sequences if requested
+            # Otherwise let torch.nn.utils.rnn.pad_sequence handle it and choose a dynamic padding
+            if self.fixed_padding > 0:
+                pad_trunc_list(output_sample, self.fixed_padding)
+
+            outputs_list.append(self.app_state.LongTensor(output_sample))
 
         # Transform the list of lists to tensor.
-        output = self.app_state.LongTensor(outputs_list)
+        # output = self.app_state.LongTensor(outputs_list)
+        output = torch.nn.utils.rnn.pad_sequence(outputs_list, batch_first=True)
         # Create the returned dict.
         data_dict.extend({self.key_outputs: output})
 
@@ -172,6 +182,12 @@ def tensor_indices_to_sentences(self, data_dict):
                 output_word = self.ix_to_word[token]
                 # Add index to outputs.
                 output_sample.append( output_word )
+
+            # Apply fixed padding to all sequences if requested
+            # Otherwise let torch.nn.utils.rnn.pad_sequence handle it and choose a dynamic padding
+            if self.fixed_padding > 0:
+                pad_trunc_list(output_sample, self.fixed_padding)
+
             # Add sentence to batch.
             outputs_list.append(output_sample)
 
@@ -204,6 +220,12 @@ def tensor_distributions_to_sentences(self, data_dict):
                 output_word = self.ix_to_word[token]
                 # Add index to outputs.
                 output_sample.append( output_word )
+
+            # Apply fixed padding to all sequences if requested
+            # Otherwise let torch.nn.utils.rnn.pad_sequence handle it and choose a dynamic padding
+            if self.fixed_padding > 0:
+                pad_trunc_list(output_sample, self.fixed_padding)
+
             # Add sentence to batch.
             outputs_list.append(output_sample)
 

ptp/components/utils/word_mappings.py

@@ -135,3 +135,21 @@ def save_word_mappings_to_csv_file(logger, folder, filename, word_to_ix, fieldna
             writer.writerow({fieldnames[0]:k, fieldnames[1]: v})
 
     logger.info("Saved mappings of size {} to file '{}'".format(len(word_to_ix), file_path))
+
+def pad_trunc_list(l: list, length: int, value = 0):
+    """
+    Will apply padding / clipping to list to meet requested length.
+    Works on the list in-place.
+
+    :param l: List to manipulate
+
+    :param length: Target length
+
+    :param value: Value to fill when padding. Default is int(0).
+
+    :return: None
+    """
+    if len(l) < length:
+        l.extend([value]*(length-len(l)))
+    elif len(l) > length:
+        del l[length:]