truncation function is fully tested

examples/run_seq2seq_finetuning.py

@@ -41,7 +41,7 @@ import numpy as np
 import torch
 from torch.utils.data import Dataset
 
-from transformers import BertConfig, Bert2Rnd, BertTokenizer
+from transformers import BertTokenizer
 
 logger = logging.getLogger(__name__)
 
@@ -57,19 +57,23 @@ class TextDataset(Dataset):
 
     CNN/Daily News:
 
-    The CNN/Daily News raw datasets are downloaded from [1]. The stories are stored in different files; the summary appears at the end of the story as
-    sentences that are prefixed by the special `@highlight` line. To process the
-    data, untar both datasets in the same folder, and pass the path to this
+    The CNN/Daily News raw datasets are downloaded from [1]. The stories are
+    stored in different files; the summary appears at the end of the story as
+    sentences that are prefixed by the special `@highlight` line. To process
+    the data, untar both datasets in the same folder, and pass the path to this
     folder as the "data_dir argument. The formatting code was inspired by [2].
 
     [1] https://cs.nyu.edu/~kcho/
     [2] https://github.com/abisee/cnn-dailymail/
     """
-    def __init_(self, tokenizer, data_dir='', block_size=512):
+
+    def __init_(self, tokenizer, data_dir="", block_size=512):
         assert os.path.isdir(data_dir)
 
         # Load features that have already been computed if present
-        cached_features_file = os.path.join(directory, "cached_lm_{}_{}".format(block_size, data_dir))
+        cached_features_file = os.path.join(
+            data_dir, "cached_lm_{}_{}".format(block_size, data_dir)
+        )
         if os.path.exists(cached_features_file):
             logger.info("Loading features from cached file %s", cached_features_file)
             with open(cached_features_file, "rb") as source:
@@ -78,7 +82,7 @@ class TextDataset(Dataset):
 
         logger.info("Creating features from dataset at %s", data_dir)
 
-        datasets = ['cnn', 'dailymail']
+        datasets = ["cnn", "dailymail"]
         for dataset in datasets:
             path_to_stories = os.path.join(data_dir, dataset, "stories")
             assert os.path.isdir(path_to_stories)
@@ -99,7 +103,9 @@ class TextDataset(Dataset):
                 story = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(story))
                 summary = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(summary))
                 story_seq, summary_seq = _fit_to_block_size(story, summary, block_size)
-                example = tokenizer.add_special_token_sequence_pair(story_seq, summary_seq)
+                example = tokenizer.add_special_token_sequence_pair(
+                    story_seq, summary_seq
+                )
                 self.examples.append(example)
 
         logger.info("Saving features into cache file %s", cached_features_file)
@@ -117,7 +123,9 @@ def process_story(raw_story):
     """ Process the text contained in a story file.
     Returns the story and the summary
     """
-    file_lines = list(filter(lambda x: len(x) != 0, [line.strip() for line in raw_story.split("\n")]))
+    file_lines = list(
+        filter(lambda x: len(x) != 0, [line.strip() for line in raw_story.split("\n")])
+    )
 
     # for some unknown reason some lines miss a period, add it
     file_lines = [_add_missing_period(line) for line in file_lines]
@@ -145,7 +153,7 @@ def process_story(raw_story):
 
 
 def _add_missing_period(line):
-    END_TOKENS = ['.', '!', '?', '...', "'", "`", '"', u'\u2019', u'\u2019', ")"]
+    END_TOKENS = [".", "!", "?", "...", "'", "`", '"', u"\u2019", u"\u2019", ")"]
     if line.startswith("@highlight"):
         return line
     if line[-1] in END_TOKENS:
@@ -154,34 +162,35 @@ def _add_missing_period(line):
 
 
 def _fit_to_block_size(src_sequence, tgt_sequence, block_size):
-    """ Concatenate the sequences and adapt their lengths to the block size.
+    """ Adapt the source and target sequences' lengths to the block size.
 
-    Following [1] we truncate the source and target + tokens sequences so they fit
-    in the block size. If the concatenated sequence is longer than 512 we follow
-    the 75%/25% rule in [1]: limit the source sequence's length to 384 and the
-    target sequence's length to 128.
+    If the concatenated sequence (source + target + 3 special tokens) would be
+    longer than the block size we use the 75% / 25% rule followed in [1]. For a
+    block size of 512 this means limiting the source sequence's length to 384
+    and the target sequence's length to 128.
 
     [1] Dong, Li, et al. "Unified Language Model Pre-training for Natural
     Language Understanding and Generation." arXiv preprint arXiv:1905.03197 (2019).
     """
     SRC_MAX_LENGTH = int(0.75 * block_size) - 2  # CLS and EOS token
-    TGT_MAX_LENGTH = block_size - SRC_MAX_LENGTH - 1  # EOS token
+    TGT_MAX_LENGTH = block_size - (SRC_MAX_LENGTH + 2) - 1  # EOS token
 
-    # we dump the examples that are too small to fit in the block size for the
+    # We dump the examples that are too small to fit in the block size for the
     # sake of simplicity. You can modify this by adding model-specific padding.
-    if len(src_sequence) + len(src_sequence) + 3 < block_size:
+    if len(src_sequence) + len(tgt_sequence) + 3 < block_size:
         return None
 
-    # the source sequence has `[SEP_i]` special tokens with i \in [0,9]. We keep them for now.
     if len(src_sequence) > SRC_MAX_LENGTH:
         if len(tgt_sequence) > TGT_MAX_LENGTH:
             src_sequence = src_sequence[:SRC_MAX_LENGTH]
             tgt_sequence = tgt_sequence[:TGT_MAX_LENGTH]
         else:
-            src_sequence = src_sequence[block_size - len(tgt_sequence) - 3]
+            remain_size = block_size - len(tgt_sequence) - 3
+            src_sequence = src_sequence[:remain_size]
     else:
         if len(tgt_sequence) > TGT_MAX_LENGTH:
-            tgt_sequence = tgt_sequence[block_size - len(src_sequence) - 3]
+            remain_size = block_size - len(src_sequence) - 3
+            tgt_sequence = tgt_sequence[:remain_size]
 
     return src_sequence, tgt_sequence
 
@@ -200,44 +209,50 @@ def main():
     parser = argparse.ArgumentParser()
 
     # Required parameters
-    parser.add_argument("--data_dir",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="The input training data file (a text file).")
-    parser.add_argument("--output_dir",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="The output directory where the model predictions and checkpoints will be written.")
+    parser.add_argument(
+        "--data_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The input training data file (a text file).",
+    )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
 
     # Optional parameters
-    parser.add_argument("--model_name_or_path",
-                        default="bert-base-cased",
-                        type=str,
-                        help="The model checkpoint for weights initialization.")
+    parser.add_argument(
+        "--model_name_or_path",
+        default="bert-base-cased",
+        type=str,
+        help="The model checkpoint for weights initialization.",
+    )
     parser.add_argument("--seed", default=42, type=int)
     args = parser.parse_args()
 
     # Set up training device
-    device = torch.device("cpu")
+    # device = torch.device("cpu")
 
     # Set seed
     set_seed(args)
 
     # Load pretrained model and tokenizer
-    config_class, model_class, tokenizer_class = BertConfig, Bert2Rnd, BertTokenizer
-    config = config_class.from_pretrained(args.model_name_or_path)
+    tokenizer_class = BertTokenizer
+    # config = config_class.from_pretrained(args.model_name_or_path)
     tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
-    model = model_class.from_pretrained(args.model_name_or_path, config=config)
-    model.to(device)
+    # model = model_class.from_pretrained(args.model_name_or_path, config=config)
+    # model.to(device)
 
     logger.info("Training/evaluation parameters %s", args)
 
     # Training
-    train_dataset = load_and_cache_examples(args, tokenizer)
-    global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-    logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
+    _ = load_and_cache_examples(args, tokenizer)
+    # global_step, tr_loss = train(args, train_dataset, model, tokenizer)
+    # logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
 
 if __name__ == "__main__":

examples/run_seq2seq_finetuning_test.py

@@ -14,50 +14,50 @@
 # limitations under the License.
 import unittest
 
-from .run_seq2seq_finetuning import process_story, _fit_to_block_size
+from run_seq2seq_finetuning import _fit_to_block_size
 
 
 class DataLoaderTest(unittest.TestCase):
-    def __init__(self, block_size=10):
-        self.block_size = block_size
+    def setUp(self):
+        self.block_size = 10
 
-    def source_and_target_too_small(self):
+    def test_source_and_target_too_small(self):
         """ When the sum of the lengths of the source and target sequences is
         smaller than the block size (minus the number of special tokens), skip the example. """
         src_seq = [1, 2, 3, 4]
         tgt_seq = [5, 6]
         self.assertEqual(_fit_to_block_size(src_seq, tgt_seq, self.block_size), None)
 
-    def source_and_target_fit_exactly(self):
+    def test_source_and_target_fit_exactly(self):
         """ When the sum of the lengths of the source and target sequences is
         equal to the block size (minus the number of special tokens), return the
         sequences unchanged. """
         src_seq = [1, 2, 3, 4]
         tgt_seq = [5, 6, 7]
         fitted_src, fitted_tgt = _fit_to_block_size(src_seq, tgt_seq, self.block_size)
-        self.assertListEqual(src_seq == fitted_src)
-        self.assertListEqual(tgt_seq == fitted_tgt)
+        self.assertListEqual(src_seq, fitted_src)
+        self.assertListEqual(tgt_seq, fitted_tgt)
 
-    def source_too_big_target_ok(self):
+    def test_source_too_big_target_ok(self):
         src_seq = [1, 2, 3, 4, 5, 6]
         tgt_seq = [1, 2]
         fitted_src, fitted_tgt = _fit_to_block_size(src_seq, tgt_seq, self.block_size)
-        self.assertListEqual(src_seq == [1, 2, 3, 4, 5])
-        self.assertListEqual(tgt_seq == fitted_tgt)
+        self.assertListEqual(fitted_src, [1, 2, 3, 4, 5])
+        self.assertListEqual(fitted_tgt, fitted_tgt)
 
-    def target_too_big_source_ok(self):
+    def test_target_too_big_source_ok(self):
         src_seq = [1, 2, 3, 4]
         tgt_seq = [1, 2, 3, 4]
         fitted_src, fitted_tgt = _fit_to_block_size(src_seq, tgt_seq, self.block_size)
-        self.assertListEqual(src_seq == src_seq)
-        self.assertListEqual(tgt_seq == [1, 2, 3])
+        self.assertListEqual(fitted_src, src_seq)
+        self.assertListEqual(fitted_tgt, [1, 2, 3])
 
-    def source_and_target_too_big(self):
+    def test_source_and_target_too_big(self):
         src_seq = [1, 2, 3, 4, 5, 6, 7]
         tgt_seq = [1, 2, 3, 4, 5, 6, 7]
         fitted_src, fitted_tgt = _fit_to_block_size(src_seq, tgt_seq, self.block_size)
-        self.assertListEqual(src_seq == [1, 2, 3, 4, 5])
-        self.assertListEqual(tgt_seq == [1, 2])
+        self.assertListEqual(fitted_src, [1, 2, 3, 4, 5])
+        self.assertListEqual(fitted_tgt, [1, 2])
 
 
 if __name__ == "__main__":