fix flax examples tests (#14646)

* make tensorboard optional

* update test_fetcher for flax examples

* make the tests slow

examples/flax/question-answering/run_qa.py

@@ -40,7 +40,6 @@ import optax
 import transformers
 from flax import struct, traverse_util
 from flax.jax_utils import replicate, unreplicate
-from flax.metrics import tensorboard
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository
@@ -52,6 +51,7 @@ from transformers import (
     HfArgumentParser,
     PreTrainedTokenizerFast,
     TrainingArguments,
+    is_tensorboard_available,
 )
 from transformers.file_utils import get_full_repo_name
 from transformers.utils import check_min_version
@@ -716,8 +716,23 @@ def main():
         logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
 
     # Define a summary writer
-    summary_writer = tensorboard.SummaryWriter(training_args.output_dir)
-    summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard and jax.process_index() == 0:
+        try:
+            from flax.metrics.tensorboard import SummaryWriter
+
+            summary_writer = SummaryWriter(training_args.output_dir)
+            summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+        except ImportError as ie:
+            has_tensorboard = False
+            logger.warning(
+                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
+            )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not installed: "
+            "Please run pip install tensorboard to enable."
+        )
 
     def write_train_metric(summary_writer, train_metrics, train_time, step):
         summary_writer.scalar("train_time", train_time, step)
@@ -833,7 +848,7 @@ def main():
                 # Save metrics
                 train_metric = unreplicate(train_metric)
                 train_time += time.time() - train_start
-                if jax.process_index() == 0:
+                if has_tensorboard and jax.process_index() == 0:
                     write_train_metric(summary_writer, train_metrics, train_time, cur_step)
 
                 epochs.write(
@@ -898,7 +913,7 @@ def main():
 
                 logger.info(f"Step... ({cur_step}/{total_steps} | Evaluation metrics: {eval_metrics})")
 
-                if jax.process_index() == 0:
+                if has_tensorboard and jax.process_index() == 0:
                     write_eval_metric(summary_writer, eval_metrics, cur_step)
 
             if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):

examples/flax/test_examples.py

@@ -96,6 +96,7 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_accuracy"], 0.75)
 
+    @slow
     def test_run_clm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -155,6 +156,7 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["test_rougeL"], 7)
             self.assertGreaterEqual(result["test_rougeLsum"], 7)
 
+    @slow
     def test_run_mlm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -208,6 +210,7 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_accuracy"], 0.42)
 
+    @slow
     def test_run_ner(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -240,6 +243,7 @@ class ExamplesTests(TestCasePlus):
             self.assertGreaterEqual(result["eval_accuracy"], 0.75)
             self.assertGreaterEqual(result["eval_f1"], 0.3)
 
+    @slow
     def test_run_qa(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)

examples/flax/text-classification/run_flax_glue.py

@@ -33,11 +33,16 @@ import optax
 import transformers
 from flax import struct, traverse_util
 from flax.jax_utils import replicate, unreplicate
-from flax.metrics import tensorboard
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository
-from transformers import AutoConfig, AutoTokenizer, FlaxAutoModelForSequenceClassification, PretrainedConfig
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    FlaxAutoModelForSequenceClassification,
+    PretrainedConfig,
+    is_tensorboard_available,
+)
 from transformers.file_utils import get_full_repo_name
 
 
@@ -404,8 +409,23 @@ def main():
         logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
 
     # Define a summary writer
-    summary_writer = tensorboard.SummaryWriter(args.output_dir)
-    summary_writer.hparams(vars(args))
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard and jax.process_index() == 0:
+        try:
+            from flax.metrics.tensorboard import SummaryWriter
+
+            summary_writer = SummaryWriter(args.output_dir)
+            summary_writer.hparams(vars(args))
+        except ImportError as ie:
+            has_tensorboard = False
+            logger.warning(
+                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
+            )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not installed: "
+            "Please run pip install tensorboard to enable."
+        )
 
     def write_metric(train_metrics, eval_metrics, train_time, step):
         summary_writer.scalar("train_time", train_time, step)
@@ -513,7 +533,10 @@ def main():
         logger.info(f"    Done! Eval metrics: {eval_metric}")
 
         cur_step = epoch * (len(train_dataset) // train_batch_size)
-        write_metric(train_metrics, eval_metric, train_time, cur_step)
+
+        # Save metrics
+        if has_tensorboard and jax.process_index() == 0:
+            write_metric(train_metrics, eval_metric, train_time, cur_step)
 
         # save checkpoint after each epoch and push checkpoint to the hub
         if jax.process_index() == 0:

examples/flax/token-classification/run_flax_ner.py

@@ -36,7 +36,6 @@ import optax
 import transformers
 from flax import struct, traverse_util
 from flax.jax_utils import replicate, unreplicate
-from flax.metrics import tensorboard
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
 from huggingface_hub import Repository
@@ -46,6 +45,7 @@ from transformers import (
     FlaxAutoModelForTokenClassification,
     HfArgumentParser,
     TrainingArguments,
+    is_tensorboard_available,
 )
 from transformers.file_utils import get_full_repo_name
 from transformers.utils import check_min_version
@@ -472,8 +472,23 @@ def main():
         logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
 
     # Define a summary writer
-    summary_writer = tensorboard.SummaryWriter(training_args.output_dir)
-    summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+    has_tensorboard = is_tensorboard_available()
+    if has_tensorboard and jax.process_index() == 0:
+        try:
+            from flax.metrics.tensorboard import SummaryWriter
+
+            summary_writer = SummaryWriter(training_args.output_dir)
+            summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
+        except ImportError as ie:
+            has_tensorboard = False
+            logger.warning(
+                f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
+            )
+    else:
+        logger.warning(
+            "Unable to display metrics through TensorBoard because the package is not installed: "
+            "Please run pip install tensorboard to enable."
+        )
 
     def write_train_metric(summary_writer, train_metrics, train_time, step):
         summary_writer.scalar("train_time", train_time, step)
@@ -605,7 +620,7 @@ def main():
                 # Save metrics
                 train_metric = unreplicate(train_metric)
                 train_time += time.time() - train_start
-                if jax.process_index() == 0:
+                if has_tensorboard and jax.process_index() == 0:
                     write_train_metric(summary_writer, train_metrics, train_time, cur_step)
 
                 epochs.write(
@@ -663,7 +678,7 @@ def main():
                         f"Step... ({cur_step}/{total_steps} | Validation f1: {eval_metrics['f1']}, Validation Acc: {eval_metrics['accuracy']})"
                     )
 
-                if jax.process_index() == 0:
+                if has_tensorboard and jax.process_index() == 0:
                     write_eval_metric(summary_writer, eval_metrics, cur_step)
 
             if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):

utils/tests_fetcher.py

@@ -431,6 +431,8 @@ def infer_tests_to_run(output_file, diff_with_last_commit=False, filters=None):
             # Example files are tested separately
             elif f.startswith("examples/pytorch"):
                 test_files_to_run.append("examples/pytorch/test_examples.py")
+            elif f.startswith("examples/flax"):
+                test_files_to_run.append("examples/flax/test_examples.py")
             else:
                 new_tests = module_to_test_file(f)
                 if new_tests is not None: