Add tests for no_trainer and fix existing examples

.circleci/config.yml

@@ -587,6 +587,7 @@ jobs:
             - run: pip install --upgrade pip
             - run: pip install .[sklearn,torch,sentencepiece,testing,torch-speech]
             - run: pip install -r examples/pytorch/_tests_requirements.txt
+            - run: pip install git+https://github.com/huggingface/accelerate
             - save_cache:
                   key: v0.4-torch_examples-{{ checksum "setup.py" }}
                   paths:

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -23,6 +23,7 @@
 # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -537,7 +538,10 @@ def group_texts(examples):
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    accelerator.save_state(f"step_{completed_steps}")
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
 
             if completed_steps >= args.max_train_steps:
                 break
@@ -581,7 +585,10 @@ def group_texts(examples):
                 )
 
         if args.checkpointing_steps == "epoch":
-            accelerator.save_state(f"epoch_{epoch}")
+            output_dir = f"epoch_{epoch}"
+            if args.output_dir is not None:
+                output_dir = os.path.join(args.output_dir, output_dir)
+            accelerator.save_state(output_dir)
 
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
@@ -592,6 +599,9 @@ def group_texts(examples):
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
 
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump({"perplexity": perplexity}, f)
+
 
 if __name__ == "__main__":
     main()

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -23,6 +23,7 @@
 # You can also adapt this script on your own mlm task. Pointers for this are left as comments.
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -457,9 +458,11 @@ def group_texts(examples):
     train_dataset = tokenized_datasets["train"]
     eval_dataset = tokenized_datasets["validation"]
 
-    # Log a few random samples from the training set:
-    for index in random.sample(range(len(train_dataset)), 3):
-        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+    # Conditional for small test subsets
+    if len(train_dataset) > 3:
+        # Log a few random samples from the training set:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
 
     # Data collator
     # This one will take care of randomly masking the tokens.
@@ -581,7 +584,10 @@ def group_texts(examples):
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    accelerator.save_state(f"step_{completed_steps}")
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
 
             if completed_steps >= args.max_train_steps:
                 break
@@ -625,7 +631,10 @@ def group_texts(examples):
                 )
 
         if args.checkpointing_steps == "epoch":
-            accelerator.save_state(f"epoch_{epoch}")
+            output_dir = f"epoch_{epoch}"
+            if args.output_dir is not None:
+                output_dir = os.path.join(args.output_dir, output_dir)
+            accelerator.save_state(output_dir)
 
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
@@ -636,6 +645,9 @@ def group_texts(examples):
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
 
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump({"perplexity": perplexity}, f)
+
 
 if __name__ == "__main__":
     main()

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -19,6 +19,7 @@
 # You can also adapt this script on your own multiple choice task. Pointers for this are left as comments.
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -540,7 +541,10 @@ def preprocess_function(examples):
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    accelerator.save_state(f"step_{completed_steps}")
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
 
             if completed_steps >= args.max_train_steps:
                 break
@@ -578,6 +582,12 @@ def preprocess_function(examples):
                     commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
                 )
 
+        if args.checkpointing_steps == "epoch":
+            output_dir = f"epoch_{epoch}"
+            if args.output_dir is not None:
+                output_dir = os.path.join(args.output_dir, output_dir)
+            accelerator.save_state(output_dir)
+
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
         unwrapped_model = accelerator.unwrap_model(model)
@@ -586,6 +596,8 @@ def preprocess_function(examples):
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump({"eval_accuracy": eval_metric["accuracy"]}, f)
 
 
 if __name__ == "__main__":

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -19,6 +19,7 @@
 # You can also adapt this script on your own question answering task. Pointers for this are left as comments.
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -783,11 +784,20 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    accelerator.save_state(f"step_{completed_steps}")
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
 
             if completed_steps >= args.max_train_steps:
                 break
 
+        if args.checkpointing_steps == "epoch":
+            output_dir = f"epoch_{epoch}"
+            if args.output_dir is not None:
+                output_dir = os.path.join(args.output_dir, output_dir)
+            accelerator.save_state(output_dir)
+
         if args.push_to_hub and epoch < args.num_train_epochs - 1:
             accelerator.wait_for_everyone()
             unwrapped_model = accelerator.unwrap_model(model)
@@ -879,9 +889,6 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
 
         accelerator.log(log, step=completed_steps)
 
-    if args.checkpointing_steps == "epoch":
-        accelerator.save_state(f"epoch_{epoch}")
-
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
         unwrapped_model = accelerator.unwrap_model(model)
@@ -890,6 +897,8 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump({"eval_f1": eval_metric["f1"], "eval_exact": eval_metric["exact"]}, f)
 
 
 if __name__ == "__main__":

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -19,6 +19,7 @@
 # You can also adapt this script on your own summarization task. Pointers for this are left as comments.
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -602,7 +603,10 @@ def postprocess_text(preds, labels):
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    accelerator.save_state(f"step_{completed_steps}")
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
 
             if completed_steps >= args.max_train_steps:
                 break
@@ -669,7 +673,10 @@ def postprocess_text(preds, labels):
                 )
 
         if args.checkpointing_steps == "epoch":
-            accelerator.save_state(f"epoch_{epoch}")
+            output_dir = f"epoch_{epoch}"
+            if args.output_dir is not None:
+                output_dir = os.path.join(args.output_dir, output_dir)
+            accelerator.save_state(output_dir)
 
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
@@ -679,6 +686,16 @@ def postprocess_text(preds, labels):
             tokenizer.save_pretrained(args.output_dir)
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump(
+                {
+                    "eval_rouge1": result["rouge1"],
+                    "eval_rouge2": result["rouge2"],
+                    "eval_rougeL": result["rougeL"],
+                    "eval_rougeLsum": result["rougeLsum"],
+                },
+                f,
+            )
 
 
 if __name__ == "__main__":