[trainer] apex fixes and tests (#9180)

examples/seq2seq/test_finetune_trainer.py

@@ -18,7 +18,7 @@ import unittest
 from unittest.mock import patch
 
 from transformers import BertTokenizer, EncoderDecoderModel
-from transformers.file_utils import is_datasets_available
+from transformers.file_utils import is_apex_available, is_datasets_available
 from transformers.integrations import is_fairscale_available
 from transformers.testing_utils import (
     TestCasePlus,
@@ -51,6 +51,17 @@ def require_fairscale(test_case):
         return test_case
 
 
+# a candidate for testing_utils
+def require_apex(test_case):
+    """
+    Decorator marking a test that requires apex
+    """
+    if not is_apex_available():
+        return unittest.skip("test requires apex")(test_case)
+    else:
+        return test_case
+
+
 class TestFinetuneTrainer(TestCasePlus):
     def finetune_trainer_quick(self, distributed=None, extra_args_str=None):
         output_dir = self.run_trainer(1, "12", MBART_TINY, 1, distributed, extra_args_str)
@@ -72,6 +83,7 @@ class TestFinetuneTrainer(TestCasePlus):
     def test_finetune_trainer_ddp(self):
         self.finetune_trainer_quick(distributed=True)
 
+    # it's crucial to test --sharded_ddp w/ and w/o --fp16
     @require_torch_multi_gpu
     @require_fairscale
     def test_finetune_trainer_ddp_sharded_ddp(self):
@@ -82,6 +94,10 @@ class TestFinetuneTrainer(TestCasePlus):
     def test_finetune_trainer_ddp_sharded_ddp_fp16(self):
         self.finetune_trainer_quick(distributed=True, extra_args_str="--sharded_ddp --fp16")
 
+    @require_apex
+    def test_finetune_trainer_apex(self):
+        self.finetune_trainer_quick(extra_args_str="--fp16 --fp16_backend=apex")
+
     @slow
     def test_finetune_trainer_slow(self):
         # There is a missing call to __init__process_group somewhere

src/transformers/trainer.py

@@ -53,7 +53,7 @@ from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import RandomSampler, SequentialSampler
 
 from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
-from .file_utils import WEIGHTS_NAME, is_datasets_available, is_in_notebook, is_torch_tpu_available
+from .file_utils import WEIGHTS_NAME, is_apex_available, is_datasets_available, is_in_notebook, is_torch_tpu_available
 from .modeling_utils import PreTrainedModel
 from .models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
 from .optimization import AdamW, get_linear_schedule_with_warmup
@@ -104,13 +104,10 @@ if is_in_notebook():
 
     DEFAULT_PROGRESS_CALLBACK = NotebookProgressCallback
 
-# Check if Pytorch version >= 1.6 to switch between Native AMP and Apex
+if is_apex_available():
-if version.parse(torch.__version__) < version.parse("1.6"):
-    from .file_utils import is_apex_available
-
-    if is_apex_available():
     from apex import amp
-else:
+
+if version.parse(torch.__version__) >= version.parse("1.6"):
     _is_native_amp_available = True
     from torch.cuda.amp import autocast
 
@@ -309,6 +306,7 @@ class Trainer:
                 backend = "amp" if _is_native_amp_available else "apex"
             else:
                 backend = args.fp16_backend
+            logger.info(f"Using {backend} fp16 backend")
 
             if backend == "amp":
                 self.use_amp = True