Add Flax example tests (#14599)

* add test for glue

* add tests for clm

* fix clm test

* add summrization tests

* more tests

* fix few tests

* add test for t5 mlm

* fix t5 mlm test

* fix tests for multi device

* cleanup

* ci job

* fix metric file name

* make t5 more robust

examples/flax/language-modeling/run_clm_flax.py

@@ -21,6 +21,7 @@ https://huggingface.co/models?filter=causal-lm
 """
 # You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
 
+import json
 import logging
 import math
 import os
@@ -672,6 +673,32 @@ def main():
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
 
+    # Eval after training
+    if training_args.do_eval:
+        eval_metrics = []
+        eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
+        eval_steps = len(eval_dataset) // eval_batch_size
+        for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
+            # Model forward
+            batch = shard(next(eval_loader))
+            metrics = p_eval_step(state.params, batch)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda x: jnp.mean(x).item(), eval_metrics)
+
+        try:
+            eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            eval_metrics["perplexity"] = float("inf")
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
 
 if __name__ == "__main__":
     main()

examples/flax/language-modeling/run_mlm_flax.py

@@ -20,7 +20,9 @@ text file or a dataset.
 Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
 https://huggingface.co/models?filter=masked-lm
 """
+import json
 import logging
+import math
 import os
 import sys
 import time
@@ -271,7 +273,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
         summary_writer.scalar(f"eval_{metric_name}", value, step)
 
 
-if __name__ == "__main__":
+def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.
@@ -700,3 +702,41 @@ if __name__ == "__main__":
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+    # Eval after training
+    if training_args.do_eval:
+        num_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(num_eval_samples)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+
+        eval_metrics = []
+        for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
+            samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples, pad_to_multiple_of=16)
+
+            # Model forward
+            model_inputs = shard(model_inputs.data)
+            metrics = p_eval_step(state.params, model_inputs)
+            eval_metrics.append(metrics)
+
+        # normalize eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
+        eval_normalizer = eval_metrics.pop("normalizer")
+        eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
+
+        try:
+            perplexity = math.exp(eval_metrics["loss"])
+        except OverflowError:
+            perplexity = float("inf")
+        eval_metrics["perplexity"] = perplexity
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+    main()

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -20,6 +20,7 @@ Here is the full list of checkpoints on the hub that can be pretrained by this s
 https://huggingface.co/models?filter=t5
 """
 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
+import json
 import logging
 import os
 import sys
@@ -401,7 +402,7 @@ def write_eval_metric(summary_writer, eval_metrics, step):
         summary_writer.scalar(f"eval_{metric_name}", value, step)
 
 
-if __name__ == "__main__":
+def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.
@@ -522,9 +523,7 @@ if __name__ == "__main__":
             model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
         )
     elif model_args.model_name_or_path:
-        config = T5Config.from_pretrained(
-            model_args.model_name_or_path, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer)
-        )
+        config = T5Config.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
     else:
         config = CONFIG_MAPPING[model_args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
@@ -617,6 +616,7 @@ if __name__ == "__main__":
             model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
         )
     else:
+        config.vocab_size = len(tokenizer)
         model = FlaxT5ForConditionalGeneration(config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype))
 
     # Data collator
@@ -808,3 +808,33 @@ if __name__ == "__main__":
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
                         repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
+
+    # Eval after training
+    if training_args.do_eval:
+        num_eval_samples = len(tokenized_datasets["validation"])
+        eval_samples_idx = jnp.arange(num_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["validation"][int(idx)] for idx in batch_idx]
+            model_inputs = data_collator(samples)
+
+            # Model forward
+            model_inputs = shard(model_inputs.data)
+            metrics = p_eval_step(state.params, model_inputs)
+            eval_metrics.append(metrics)
+
+        # get eval metrics
+        eval_metrics = get_metrics(eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(), eval_metrics)
+
+        if jax.process_index() == 0:
+            eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
+            path = os.path.join(training_args.output_dir, "eval_results.json")
+            with open(path, "w") as f:
+                json.dump(eval_metrics, f, indent=4, sort_keys=True)
+
+
+if __name__ == "__main__":
+    main()