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Unverified Commit bebaa140 authored by Thomas Wolf's avatar Thomas Wolf Committed by GitHub
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Merge pull request #2045 from aaugustin/remove-dead-code 

Remove dead code in tests.
parents 2d5d86e0 40255ab0
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transformers/tests/modeling_tf_common_test.py
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...@@ -233,80 +233,6 @@ class TFCommonTestCases: ...@@ -233,80 +233,6 @@ class TFCommonTestCases:
self.model_tester.seq_length, self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length]) self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
def test_headmasking(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# config.output_attentions = True
# config.output_hidden_states = True
# configs_no_init = _config_zero_init(config) # To be sure we have no Nan
# for model_class in self.all_model_classes:
# model = model_class(config=configs_no_init)
# model.eval()
# # Prepare head_mask
# # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
# head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
# head_mask[0, 0] = 0
# head_mask[-1, :-1] = 0
# head_mask.requires_grad_(requires_grad=True)
# inputs = inputs_dict.copy()
# inputs['head_mask'] = head_mask
# outputs = model(**inputs)
# # Test that we can get a gradient back for importance score computation
# output = sum(t.sum() for t in outputs[0])
# output = output.sum()
# output.backward()
# multihead_outputs = head_mask.grad
# attentions = outputs[-1]
# hidden_states = outputs[-2]
# # Remove Nan
# self.assertIsNotNone(multihead_outputs)
# self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
# self.assertAlmostEqual(
# attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
# self.assertAlmostEqual(
# attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
# self.assertNotEqual(
# attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
pass
# if not self.test_pruning:
# return
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# for model_class in self.all_model_classes:
# config.output_attentions = True
# config.output_hidden_states = False
# model = model_class(config=config)
# model.eval()
# heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
# -1: [0]}
# model.prune_heads(heads_to_prune)
# outputs = model(**inputs_dict)
# attentions = outputs[-1]
# self.assertEqual(
# attentions[0].shape[-3], 1)
# self.assertEqual(
# attentions[1].shape[-3], self.model_tester.num_attention_heads)
# self.assertEqual(
# attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_hidden_states_output(self): def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
...@@ -323,43 +249,6 @@ class TFCommonTestCases: ...@@ -323,43 +249,6 @@ class TFCommonTestCases:
list(hidden_states[0].shape[-2:]), list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size]) [self.model_tester.seq_length, self.model_tester.hidden_size])
def test_resize_tokens_embeddings(self):
pass
# original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# if not self.test_resize_embeddings:
# return
# for model_class in self.all_model_classes:
# config = copy.deepcopy(original_config)
# model = model_class(config)
# model_vocab_size = config.vocab_size
# # Retrieve the embeddings and clone theme
# model_embed = model.resize_token_embeddings(model_vocab_size)
# cloned_embeddings = model_embed.weight.clone()
# # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
# model_embed = model.resize_token_embeddings(model_vocab_size + 10)
# self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# # Check that it actually resizes the embeddings matrix
# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
# model_embed = model.resize_token_embeddings(model_vocab_size - 15)
# self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# # Check that it actually resizes the embeddings matrix
# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# # Check that adding and removing tokens has not modified the first part of the embedding matrix.
# models_equal = True
# for p1, p2 in zip(cloned_embeddings, model_embed.weight):
# if p1.data.ne(p2.data).sum() > 0:
# models_equal = False
# self.assertTrue(models_equal)
def test_model_common_attributes(self): def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
...@@ -369,40 +258,6 @@ class TFCommonTestCases: ...@@ -369,40 +258,6 @@ class TFCommonTestCases:
x = model.get_output_embeddings() x = model.get_output_embeddings()
assert x is None or isinstance(x, tf.keras.layers.Layer) assert x is None or isinstance(x, tf.keras.layers.Layer)
def test_tie_model_weights(self):
pass
# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# def check_same_values(layer_1, layer_2):
# equal = True
# for p1, p2 in zip(layer_1.weight, layer_2.weight):
# if p1.data.ne(p2.data).sum() > 0:
# equal = False
# return equal
# for model_class in self.all_model_classes:
# if not hasattr(model_class, 'tie_weights'):
# continue
# config.torchscript = True
# model_not_tied = model_class(config)
# params_not_tied = list(model_not_tied.parameters())
# config_tied = copy.deepcopy(config)
# config_tied.torchscript = False
# model_tied = model_class(config_tied)
# params_tied = list(model_tied.parameters())
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertGreater(len(params_not_tied), len(params_tied))
# # Check that after resize they remain tied.
# model_tied.resize_token_embeddings(config.vocab_size + 10)
# params_tied_2 = list(model_tied.parameters())
# self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertEqual(len(params_tied_2), len(params_tied))
def test_determinism(self): def test_determinism(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
...@@ -461,29 +316,5 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None): ...@@ -461,29 +316,5 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
return output return output
class TFModelUtilsTest(unittest.TestCase):
@pytest.mark.skipif('tensorflow' not in sys.modules, reason="requires TensorFlow")
def test_model_from_pretrained(self):
pass
# logging.basicConfig(level=logging.INFO)
# for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
# config = BertConfig.from_pretrained(model_name)
# self.assertIsNotNone(config)
# self.assertIsInstance(config, PretrainedConfig)
# model = BertModel.from_pretrained(model_name)
# model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
# self.assertIsNotNone(model)
# self.assertIsInstance(model, PreTrainedModel)
# for value in loading_info.values():
# self.assertEqual(len(value), 0)
# config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
# self.assertEqual(model.config.output_attentions, True)
# self.assertEqual(model.config.output_hidden_states, True)
# self.assertEqual(model.config, config)
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()
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