Add push_to_hub to no_trainer examples (#13659)

* Add push_to_hub to no_trainer examples

* Quality

* Document integration

* Roll out to other examples

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -27,6 +27,7 @@ import logging
 import math
 import os
 import random
+from pathlib import Path
 
 import datasets
 import torch
@@ -36,6 +37,7 @@ from tqdm.auto import tqdm
 
 import transformers
 from accelerate import Accelerator, DistributedType
+from huggingface_hub import Repository
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -48,6 +50,7 @@ from transformers import (
     get_scheduler,
     set_seed,
 )
+from transformers.file_utils import get_full_repo_name
 from transformers.utils.versions import require_version
 
 
@@ -176,7 +179,11 @@ def parse_args():
     parser.add_argument(
         "--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files."
     )
-
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument(
+        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
+    )
+    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
     args = parser.parse_args()
 
     # Sanity checks
@@ -190,8 +197,8 @@ def parse_args():
             extension = args.validation_file.split(".")[-1]
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
 
-    if args.output_dir is not None:
-        os.makedirs(args.output_dir, exist_ok=True)
+    if args.push_to_hub:
+        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
 
     return args
 
@@ -223,6 +230,18 @@ def main():
     if args.seed is not None:
         set_seed(args.seed)
 
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+            repo = Repository(args.output_dir, clone_from=repo_name)
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+    accelerator.wait_for_everyone()
+
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
@@ -480,10 +499,22 @@ def main():
 
         logger.info(f"epoch {epoch}: perplexity: {perplexity}")
 
+        if args.push_to_hub and epoch < args.num_train_epochs - 1:
+            accelerator.wait_for_everyone()
+            unwrapped_model = accelerator.unwrap_model(model)
+            unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+            if accelerator.is_main_process:
+                tokenizer.save_pretrained(args.output_dir)
+                repo.push_to_hub(commit_message=f"Training in progress epoch {epoch}", blocking=False)
+
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
         unwrapped_model = accelerator.unwrap_model(model)
         unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+        if accelerator.is_main_process:
+            tokenizer.save_pretrained(args.output_dir)
+            if args.push_to_hub:
+                repo.push_to_hub(commit_message="End of training")
 
 
 if __name__ == "__main__":

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -27,6 +27,7 @@ import logging
 import math
 import os
 import random
+from pathlib import Path
 
 import datasets
 import torch
@@ -36,6 +37,7 @@ from tqdm.auto import tqdm
 
 import transformers
 from accelerate import Accelerator, DistributedType
+from huggingface_hub import Repository
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -48,6 +50,7 @@ from transformers import (
     get_scheduler,
     set_seed,
 )
+from transformers.file_utils import get_full_repo_name
 from transformers.utils.versions import require_version
 
 
@@ -185,7 +188,11 @@ def parse_args():
     parser.add_argument(
         "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
     )
-
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument(
+        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
+    )
+    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
     args = parser.parse_args()
 
     # Sanity checks
@@ -199,8 +206,8 @@ def parse_args():
             extension = args.validation_file.split(".")[-1]
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
 
-    if args.output_dir is not None:
-        os.makedirs(args.output_dir, exist_ok=True)
+    if args.push_to_hub:
+        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
 
     return args
 
@@ -232,6 +239,18 @@ def main():
     if args.seed is not None:
         set_seed(args.seed)
 
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+            repo = Repository(args.output_dir, clone_from=repo_name)
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+    accelerator.wait_for_everyone()
+
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
@@ -518,10 +537,22 @@ def main():
 
         logger.info(f"epoch {epoch}: perplexity: {perplexity}")
 
+        if args.push_to_hub and epoch < args.num_train_epochs - 1:
+            accelerator.wait_for_everyone()
+            unwrapped_model = accelerator.unwrap_model(model)
+            unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+            if accelerator.is_main_process:
+                tokenizer.save_pretrained(args.output_dir)
+                repo.push_to_hub(commit_message=f"Training in progress epoch {epoch}", blocking=False)
+
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
         unwrapped_model = accelerator.unwrap_model(model)
         unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
+        if accelerator.is_main_process:
+            tokenizer.save_pretrained(args.output_dir)
+            if args.push_to_hub:
+                repo.push_to_hub(commit_message="End of training")
 
 
 if __name__ == "__main__":