[Examples] Add automatic dataset splitting in language-modeling examples (#9133)

* replaced jnp.split + removing textual model inputs + ensuring warmup_steps > 0

* Add automatic dataset splitting in language-modeling examples

examples/language-modeling/run_clm.py

@@ -113,6 +113,12 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
     preprocessing_num_workers: Optional[int] = field(
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."},
@@ -188,6 +194,17 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
     else:
         data_files = {}
         if data_args.train_file is not None:

examples/language-modeling/run_mlm.py

@@ -103,6 +103,12 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
     max_seq_length: Optional[int] = field(
         default=None,
         metadata={
@@ -199,6 +205,17 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
     else:
         data_files = {}
         if data_args.train_file is not None:

examples/language-modeling/run_mlm_flax.py

@@ -134,6 +134,12 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
     max_seq_length: Optional[int] = field(
         default=None,
         metadata={
@@ -413,7 +419,7 @@ def generate_batch_splits(samples_idx: jnp.ndarray, batch_size: int) -> jnp.ndar
     if samples_to_remove != 0:
         samples_idx = samples_idx[:-samples_to_remove]
     sections_split = nb_samples // batch_size
-    batch_idx = jnp.split(samples_idx, sections_split)
+    batch_idx = np.split(samples_idx, sections_split)
     return batch_idx
 
 
@@ -473,6 +479,17 @@ if __name__ == "__main__":
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
     else:
         data_files = {}
         if data_args.train_file is not None:
@@ -525,9 +542,9 @@ if __name__ == "__main__":
 
     def tokenize_function(examples):
         # Remove empty lines
-        examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
+        examples = [line for line in examples if len(line) > 0 and not line.isspace()]
         return tokenizer(
-            examples["text"],
+            examples,
             return_special_tokens_mask=True,
             padding=padding,
             truncation=True,
@@ -536,9 +553,10 @@ if __name__ == "__main__":
 
     tokenized_datasets = datasets.map(
         tokenize_function,
+        input_columns=[text_column_name],
         batched=True,
         num_proc=data_args.preprocessing_num_workers,
-        remove_columns=[text_column_name],
+        remove_columns=column_names,
         load_from_cache_file=not data_args.overwrite_cache,
     )
 
@@ -566,8 +584,9 @@ if __name__ == "__main__":
     ).create(model.params)
 
     # Create learning rate scheduler
+    # warmup_steps = 0 causes the Flax optimizer to return NaNs; warmup_steps = 1 is functionally equivalent.
     lr_scheduler_fn = create_learning_rate_scheduler(
-        base_learning_rate=training_args.learning_rate, warmup_steps=training_args.warmup_steps
+        base_learning_rate=training_args.learning_rate, warmup_steps=min(training_args.warmup_steps, 1)
     )
 
     # Create parallel version of the training and evaluation steps
@@ -606,13 +625,13 @@ if __name__ == "__main__":
         epochs.write(f"Loss: {loss}")
 
         # ======================== Evaluating ==============================
-        nb_eval_samples = len(tokenized_datasets["test"])
+        nb_eval_samples = len(tokenized_datasets["validation"])
         eval_samples_idx = jnp.arange(nb_eval_samples)
         eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
 
         eval_metrics = []
         for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
-            samples = [tokenized_datasets["test"][int(idx)] for idx in batch_idx]
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
             model_inputs = data_collator(samples, pad_to_multiple_of=16)
 
             # Model forward

examples/language-modeling/run_mlm_wwm.py

@@ -91,6 +91,12 @@ class DataTrainingArguments:
     Arguments pertaining to what data we are going to input our model for training and eval.
     """
 
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
     train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
     validation_file: Optional[str] = field(
         default=None,
@@ -107,6 +113,12 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
     max_seq_length: Optional[int] = field(
         default=None,
         metadata={
@@ -203,6 +215,21 @@ def main():
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
+    else:
         data_files = {}
         if data_args.train_file is not None:
             data_files["train"] = data_args.train_file

examples/language-modeling/run_plm.py

@@ -93,6 +93,12 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    validation_split_percentage: Optional[int] = field(
+        default=5,
+        metadata={
+            "help": "The percentage of the train set used as validation set in case there's no validation split"
+        },
+    )
     max_seq_length: int = field(
         default=512,
         metadata={
@@ -196,6 +202,17 @@ def main():
     if data_args.dataset_name is not None:
         # Downloading and loading a dataset from the hub.
         datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+        if "validation" not in datasets.keys():
+            datasets["validation"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[:{data_args.validation_split_percentage}%]",
+            )
+            datasets["train"] = load_dataset(
+                data_args.dataset_name,
+                data_args.dataset_config_name,
+                split=f"train[{data_args.validation_split_percentage}%:]",
+            )
     else:
         data_files = {}
         if data_args.train_file is not None: