Revert "Tests"

This reverts commit b35e7b68ca.

src/transformers/trainer.py

@@ -80,7 +80,6 @@ from .trainer_pt_utils import (
     SequentialDistributedSampler,
     distributed_broadcast_scalars,
     distributed_concat,
-    get_parameter_names,
     nested_concat,
     nested_detach,
     nested_numpify,
@@ -614,15 +613,14 @@ class Trainer:
         Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
         """
         if self.optimizer is None:
-            decay_parameters = get_parameter_names(self.model, [torch.nn.LayerNorm])
+            no_decay = ["bias", "LayerNorm.weight"]
-            decay_parameters = [name for name in decay_parameters if "bias" not in name]
             optimizer_grouped_parameters = [
                 {
-                    "params": [p for n, p in self.model.named_parameters() if n in decay_parameters],
+                    "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
                     "weight_decay": self.args.weight_decay,
                 },
                 {
-                    "params": [p for n, p in self.model.named_parameters() if n not in decay_parameters],
+                    "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
                     "weight_decay": 0.0,
                 },
             ]

src/transformers/trainer_pt_utils.py

@@ -672,19 +672,3 @@ def save_state(self):
 
     path = os.path.join(self.args.output_dir, "trainer_state.json")
     self.state.save_to_json(path)
-
-
-def get_parameter_names(model, forbidden_layer_types):
-    """
-    Returns the names of the model parameters that are not inside a forbidden layer.
-    """
-    result = []
-    for name, child in model.named_children():
-        result += [
-            f"{name}.{n}"
-            for n in get_parameter_names(child, forbidden_layer_types)
-            if not isinstance(child, tuple(forbidden_layer_types))
-        ]
-    # Add model specific parameters (defined with nn.Parameter) since they are not in any child.
-    result += list(model._parameters.keys())
-    return result

tests/test_trainer.py

@@ -59,8 +59,6 @@ if is_torch_available():
     )
     from transformers.modeling_utils import unwrap_model
 
-    from .test_trainer_utils import TstLayer
-
 
 PATH_SAMPLE_TEXT = f"{get_tests_dir()}/fixtures/sample_text.txt"
 
@@ -992,18 +990,6 @@ class TrainerIntegrationTest(unittest.TestCase):
         # should be about half of fp16_init
         # perfect world: fp32_init/2 == fp16_eval
         self.assertAlmostEqual(fp16_eval, fp32_init / 2, delta=5_000)
-    
-    def test_no_wd_param_group(self):
-        model = torch.nn.Sequential(TstLayer(128), torch.nn.ModuleList([TstLayer(128), TstLayer(128)]))
-        trainer = Trainer(model=model)
-        trainer.create_optimizer_and_scheduler(10)
-        # fmt: off
-        wd_names = ['0.linear1.weight', '0.linear2.weight', '1.0.linear1.weight', '1.0.linear2.weight', '1.1.linear1.weight', '1.1.linear2.weight']
-        # fmt: on
-        wd_params = [p for n, p in model.named_parameters() if n in wd_names]
-        no_wd_params = [p for n, p in model.named_parameters() if n not in wd_names]
-        self.assertListEqual(trainer.optimizer.param_groups[0]["params"], wd_params)
-        self.assertListEqual(trainer.optimizer.param_groups[1]["params"], no_wd_params)
 
 
 @require_torch

tests/test_trainer_utils.py

@@ -30,23 +30,8 @@ if is_torch_available():
         DistributedTensorGatherer,
         LabelSmoother,
         LengthGroupedSampler,
-        get_parameter_names
     )
 
-    class TstLayer(torch.nn.Module):
-        def __init__(self, hidden_size):
-            super().__init__()
-            self.linear1 = torch.nn.Linear(hidden_size, hidden_size)
-            self.ln1 = torch.nn.LayerNorm(hidden_size)
-            self.linear2 = torch.nn.Linear(hidden_size, hidden_size)
-            self.ln2 = torch.nn.LayerNorm(hidden_size)
-            self.bias = torch.nn.Parameter(torch.zeros(hidden_size))
-
-        def forward(self, x):
-            h = self.ln1(torch.nn.functional.relu(self.linear1(x)))
-            h = torch.nn.functional.relu(self.linear2(x))
-            return self.ln2(x + h + self.bias)
-
 
 @require_torch
 class TrainerUtilsTest(unittest.TestCase):
@@ -132,12 +117,3 @@ class TrainerUtilsTest(unittest.TestCase):
         self.assertEqual(lengths[indices_process_0[0]], 50)
         # The indices should be a permutation of range(100)
         self.assertEqual(list(sorted(indices_process_0 + indices_process_1)), list(range(100)))
-
-    def test_get_parameter_names(self):
-        model = torch.nn.Sequential(TstLayer(128), torch.nn.ModuleList([TstLayer(128), TstLayer(128)]))
-        # fmt: off
-        self.assertEqual(
-            get_parameter_names(model, [torch.nn.LayerNorm]),
-            ['0.linear1.weight', '0.linear1.bias', '0.linear2.weight', '0.linear2.bias', '0.bias', '1.0.linear1.weight', '1.0.linear1.bias', '1.0.linear2.weight', '1.0.linear2.bias', '1.0.bias', '1.1.linear1.weight', '1.1.linear1.bias', '1.1.linear2.weight', '1.1.linear2.bias', '1.1.bias']
-        )
-        # fmt: on