[examples] SummarizationDataset cleanup (#3451)

examples/summarization/bart/utils.py

@@ -1,35 +1,35 @@
 import os
 
+import torch
 from torch.utils.data import Dataset
 
+from transformers.tokenization_utils import trim_batch
+
+
+def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
+    examples = []
+    with open(data_path, "r") as f:
+        for text in f.readlines():
+            tokenized = tokenizer.batch_encode_plus(
+                [text], max_length=max_length, pad_to_max_length=pad_to_max_length, return_tensors=return_tensors,
+            )
+            examples.append(tokenized)
+    return examples
+
 
 class SummarizationDataset(Dataset):
-    def __init__(self, tokenizer, data_dir="./cnn-dailymail/cnn_dm/", type_path="train", block_size=1024):
-        super(SummarizationDataset,).__init__()
+    def __init__(
+        self,
+        tokenizer,
+        data_dir="./cnn-dailymail/cnn_dm/",
+        type_path="train",
+        max_source_length=1024,
+        max_target_length=56,
+    ):
+        super().__init__()
         self.tokenizer = tokenizer
-
-        self.source = []
-        self.target = []
-
-        print("loading " + type_path + " source.")
-
-        with open(os.path.join(data_dir, type_path + ".source"), "r") as f:
-            for text in f.readlines():  # each text is a line and a full story
-                tokenized = tokenizer.batch_encode_plus(
-                    [text], max_length=block_size, pad_to_max_length=True, return_tensors="pt"
-                )
-                self.source.append(tokenized)
-            f.close()
-
-        print("loading " + type_path + " target.")
-
-        with open(os.path.join(data_dir, type_path + ".target"), "r") as f:
-            for text in f.readlines():  # each text is a line and a summary
-                tokenized = tokenizer.batch_encode_plus(
-                    [text], max_length=56, pad_to_max_length=True, return_tensors="pt"
-                )
-                self.target.append(tokenized)
-            f.close()
+        self.source = encode_file(tokenizer, os.path.join(data_dir, type_path + ".source"), max_source_length)
+        self.target = encode_file(tokenizer, os.path.join(data_dir, type_path + ".target"), max_target_length)
 
     def __len__(self):
         return len(self.source)
@@ -37,7 +37,20 @@ class SummarizationDataset(Dataset):
     def __getitem__(self, index):
         source_ids = self.source[index]["input_ids"].squeeze()
         target_ids = self.target[index]["input_ids"].squeeze()
-
-        src_mask = self.source[index]["attention_mask"].squeeze()  # might need to squeeze
-
+        src_mask = self.source[index]["attention_mask"].squeeze()
         return {"source_ids": source_ids, "source_mask": src_mask, "target_ids": target_ids}
+
+    @staticmethod
+    def trim_seq2seq_batch(batch, pad_token_id):
+        y = trim_batch(batch["target_ids"], pad_token_id)
+        source_ids, source_mask = trim_batch(batch["source_ids"], pad_token_id, attention_mask=batch["source_mask"])
+        return source_ids, source_mask, y
+
+    def collate_fn(self, batch):
+        input_ids = torch.stack([x["source_ids"] for x in batch])
+        masks = torch.stack([x["source_mask"] for x in batch])
+        target_ids = torch.stack([x["target_ids"] for x in batch])
+        pad_token_id = self.tokenizer.pad_token_id
+        y = trim_batch(target_ids, pad_token_id)
+        source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
+        return {"source_ids": source_ids, "source_mask": source_mask, "target_ids": y}