Revert "[s2s] test_distributed_eval (huggingface#8315)"

This reverts commit a21fe56.

docs/source/testing.rst

@@ -450,8 +450,7 @@ Inside tests:
 
 .. code-block:: bash
 
-   from transformers.testing_utils import get_gpu_count
-   n_gpu = get_gpu_count() # works with torch and tf
+   torch.cuda.device_count()
 
 
 

examples/seq2seq/test_finetune_trainer.py

@@ -2,9 +2,9 @@
 import sys
 from unittest.mock import patch
 
-from transformers import BertTokenizer, EncoderDecoderModel
+from transformers import BertTokenizer, EncoderDecoderModel, is_torch_available
 from transformers.file_utils import is_datasets_available
-from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, slow
+from transformers.testing_utils import TestCasePlus, execute_subprocess_async, slow
 from transformers.trainer_callback import TrainerState
 from transformers.trainer_utils import set_seed
 
@@ -13,6 +13,9 @@
 from .test_seq2seq_examples import MBART_TINY
 
 
+if is_torch_available():
+    import torch
+
 set_seed(42)
 MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
@@ -193,7 +196,7 @@ def run_trainer(self, eval_steps: int, max_len: str, model_name: str, num_train_
         """.split()
         # --eval_beams  2
 
-        n_gpu = get_gpu_count()
+        n_gpu = torch.cuda.device_count()
         if n_gpu > 1:
             distributed_args = f"""
                 -m torch.distributed.launch

examples/seq2seq/test_seq2seq_examples_multi_gpu.py

@@ -3,14 +3,7 @@
 import os
 import sys
 
-from transformers.testing_utils import (
-    TestCasePlus,
-    execute_subprocess_async,
-    get_gpu_count,
-    require_torch_gpu,
-    require_torch_multigpu,
-    slow,
-)
+from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multigpu
 
 from .test_seq2seq_examples import CHEAP_ARGS, make_test_data_dir
 from .utils import load_json
@@ -87,30 +80,3 @@ def convert(k, v):
         self.assertEqual(len(metrics["test"]), 1)
         desired_n_evals = int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1)
         self.assertEqual(len(metrics["val"]), desired_n_evals)
-
-    @slow
-    @require_torch_gpu
-    def test_distributed_eval(self):
-        output_dir = self.get_auto_remove_tmp_dir()
-        args = f"""
-            --model_name Helsinki-NLP/opus-mt-en-ro
-            --save_dir {output_dir}
-            --data_dir test_data/wmt_en_ro
-            --num_beams 2
-            --task translation
-        """.split()
-
-        # we want this test to run even if there is only one GPU, but if there are more we use them all
-        n_gpu = get_gpu_count()
-        distributed_args = f"""
-            -m torch.distributed.launch
-            --nproc_per_node={n_gpu}
-            {self.test_file_dir}/run_distributed_eval.py
-        """.split()
-        cmd = [sys.executable] + distributed_args + args
-        execute_subprocess_async(cmd, env=self.get_env())
-
-        metrics_save_path = os.path.join(output_dir, "test_bleu.json")
-        metrics = load_json(metrics_save_path)
-        # print(metrics)
-        self.assertGreaterEqual(metrics["bleu"], 25)

src/transformers/testing_utils.py

@@ -297,22 +297,6 @@ def require_ray(test_case):
         return test_case
 
 
-def get_gpu_count():
-    """
-    Return the number of available gpus (regardless of whether torch or tf is used)
-    """
-    if _torch_available:
-        import torch
-
-        return torch.cuda.device_count()
-    elif _tf_available:
-        import tensorflow as tf
-
-        return len(tf.config.list_physical_devices("GPU"))
-    else:
-        return 0
-
-
 def get_tests_dir(append_path=None):
     """
     Args: