Switch from using sum for flattening lists of lists in group_texts (#14472)

* remove sum for list flattening

* change to chain(*)

* make chain object a list

* delete empty lines

per sgugger's suggestions

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Nicholas Broad <nicholas@nmbroad.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

examples/flax/language-modeling/run_clm_flax.py

@@ -27,6 +27,7 @@ import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain
 from pathlib import Path
 from typing import Callable, Optional
 
@@ -430,7 +431,7 @@ def main():
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
     def group_texts(examples):
         # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
         # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
         # customize this part to your needs.

examples/flax/language-modeling/run_mlm_flax.py

@@ -25,6 +25,7 @@ import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain
 
 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
 from pathlib import Path
@@ -453,7 +454,7 @@ if __name__ == "__main__":
         # max_seq_length.
         def group_texts(examples):
             # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
             total_length = len(concatenated_examples[list(examples.keys())[0]])
             # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
             # customize this part to your needs.

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -25,6 +25,7 @@ import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain
 from pathlib import Path
 from typing import Dict, List, Optional
 
@@ -563,7 +564,7 @@ if __name__ == "__main__":
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
     def group_texts(examples):
         # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
         # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
         # customize this part to your needs.

examples/pytorch/language-modeling/run_clm.py

@@ -26,6 +26,7 @@ import math
 import os
 import sys
 from dataclasses import dataclass, field
+from itertools import chain
 from typing import Optional
 
 import datasets
@@ -408,7 +409,7 @@ def main():
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
     def group_texts(examples):
         # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
         # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
         # customize this part to your needs.

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -27,6 +27,7 @@ import logging
 import math
 import os
 import random
+from itertools import chain
 from pathlib import Path
 
 import datasets
@@ -366,7 +367,7 @@ def main():
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
     def group_texts(examples):
         # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
         # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
         # customize this part to your needs.

examples/pytorch/language-modeling/run_mlm.py

@@ -26,6 +26,7 @@ import math
 import os
 import sys
 from dataclasses import dataclass, field
+from itertools import chain
 from typing import Optional
 
 import datasets
@@ -432,7 +433,7 @@ def main():
         # max_seq_length.
         def group_texts(examples):
             # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
             total_length = len(concatenated_examples[list(examples.keys())[0]])
             # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
             # customize this part to your needs.

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -27,6 +27,7 @@ import logging
 import math
 import os
 import random
+from itertools import chain
 from pathlib import Path
 
 import datasets
@@ -406,7 +407,7 @@ def main():
         # max_seq_length.
         def group_texts(examples):
             # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
             total_length = len(concatenated_examples[list(examples.keys())[0]])
             # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
             # customize this part to your needs.

examples/pytorch/language-modeling/run_plm.py

@@ -23,6 +23,7 @@ import math
 import os
 import sys
 from dataclasses import dataclass, field
+from itertools import chain
 from typing import Optional
 
 import datasets
@@ -403,7 +404,7 @@ def main():
         # max_seq_length.
         def group_texts(examples):
             # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
             total_length = len(concatenated_examples[list(examples.keys())[0]])
             # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
             # customize this part to your needs.

examples/pytorch/multiple-choice/run_swag.py

@@ -22,6 +22,7 @@ import logging
 import os
 import sys
 from dataclasses import dataclass, field
+from itertools import chain
 from typing import Optional, Union
 
 import datasets
@@ -185,7 +186,7 @@ class DataCollatorForMultipleChoice:
         flattened_features = [
             [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
         ]
-        flattened_features = sum(flattened_features, [])
+        flattened_features = list(chain(*flattened_features))
 
         batch = self.tokenizer.pad(
             flattened_features,
@@ -333,8 +334,8 @@ def main():
         ]
 
         # Flatten out
-        first_sentences = sum(first_sentences, [])
-        second_sentences = sum(second_sentences, [])
+        first_sentences = list(chain(*first_sentences))
+        second_sentences = list(chain(*second_sentences))
 
         # Tokenize
         tokenized_examples = tokenizer(

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -24,6 +24,7 @@ import math
 import os
 import random
 from dataclasses import dataclass
+from itertools import chain
 from pathlib import Path
 from typing import Optional, Union
 
@@ -224,7 +225,7 @@ class DataCollatorForMultipleChoice:
         flattened_features = [
             [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
         ]
-        flattened_features = sum(flattened_features, [])
+        flattened_features = list(chain(*flattened_features))
 
         batch = self.tokenizer.pad(
             flattened_features,
@@ -365,8 +366,8 @@ def main():
         labels = examples[label_column_name]
 
         # Flatten out
-        first_sentences = sum(first_sentences, [])
-        second_sentences = sum(second_sentences, [])
+        first_sentences = list(chain(*first_sentences))
+        second_sentences = list(chain(*second_sentences))
 
         # Tokenize
         tokenized_examples = tokenizer(

examples/research_projects/jax-projects/model_parallel/run_clm_mp.py

@@ -23,6 +23,7 @@ import os
 import sys
 import time
 from dataclasses import dataclass, field
+from itertools import chain
 from pathlib import Path
 from typing import Callable, Optional
 
@@ -364,7 +365,7 @@ def main():
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
     def group_texts(examples):
         # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
         # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
         # customize this part to your needs.

examples/tensorflow/language-modeling/run_clm.py

@@ -30,6 +30,7 @@ import random
 import sys
 from dataclasses import dataclass, field
 from functools import partial
+from itertools import chain
 from pathlib import Path
 from typing import Optional
 
@@ -406,7 +407,7 @@ def main():
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
     def group_texts(examples):
         # Concatenate all texts.
-        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
         # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
         # customize this part to your needs.

examples/tensorflow/language-modeling/run_mlm.py

@@ -32,6 +32,7 @@ import random
 import sys
 from dataclasses import dataclass, field
 from functools import partial
+from itertools import chain
 from pathlib import Path
 from typing import Optional
 
@@ -462,7 +463,7 @@ def main():
         # max_seq_length.
         def group_texts(examples):
             # Concatenate all texts.
-            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+            concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
             total_length = len(concatenated_examples[list(examples.keys())[0]])
             # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
             # customize this part to your needs.

examples/tensorflow/multiple-choice/run_swag.py

@@ -22,6 +22,7 @@ import logging
 import os
 import sys
 from dataclasses import dataclass, field
+from itertools import chain
 from pathlib import Path
 from typing import Optional
 
@@ -342,8 +343,8 @@ def main():
         ]
 
         # Flatten out
-        first_sentences = sum(first_sentences, [])
-        second_sentences = sum(second_sentences, [])
+        first_sentences = list(chain(*first_sentences))
+        second_sentences = list(chain(*second_sentences))
 
         # Tokenize
         tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True, max_length=max_seq_length)

src/transformers/file_utils.py

@@ -35,6 +35,7 @@ from dataclasses import fields
 from enum import Enum
 from functools import partial, wraps
 from hashlib import sha256
+from itertools import chain
 from pathlib import Path
 from types import ModuleType
 from typing import Any, BinaryIO, ContextManager, Dict, List, Optional, Tuple, Union
@@ -2129,7 +2130,7 @@ class _LazyModule(ModuleType):
             for value in values:
                 self._class_to_module[value] = key
         # Needed for autocompletion in an IDE
-        self.__all__ = list(import_structure.keys()) + sum(import_structure.values(), [])
+        self.__all__ = list(import_structure.keys()) + list(chain(*import_structure.values()))
         self.__file__ = module_file
         self.__spec__ = module_spec
         self.__path__ = [os.path.dirname(module_file)]