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https://github.com/huggingface/transformers.git
synced 2025-07-31 02:02:21 +06:00
Remove dead code in tests.
This commit is contained in:
Aymeric Augustin
parent
b0ee7c7df3
commit
40255ab002
@ -233,80 +233,6 @@ class TFCommonTestCases:
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self.model_tester.seq_length,
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self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
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def test_headmasking(self):
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pass
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# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# config.output_attentions = True
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# config.output_hidden_states = True
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# configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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# for model_class in self.all_model_classes:
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# model = model_class(config=configs_no_init)
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# model.eval()
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# # Prepare head_mask
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# # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
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# head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
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# head_mask[0, 0] = 0
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# head_mask[-1, :-1] = 0
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# head_mask.requires_grad_(requires_grad=True)
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# inputs = inputs_dict.copy()
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# inputs['head_mask'] = head_mask
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# outputs = model(**inputs)
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# # Test that we can get a gradient back for importance score computation
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# output = sum(t.sum() for t in outputs[0])
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# output = output.sum()
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# output.backward()
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# multihead_outputs = head_mask.grad
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# attentions = outputs[-1]
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# hidden_states = outputs[-2]
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# # Remove Nan
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# self.assertIsNotNone(multihead_outputs)
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# self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
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# self.assertAlmostEqual(
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# attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
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# self.assertNotEqual(
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# attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
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# self.assertNotEqual(
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# attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
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# self.assertAlmostEqual(
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# attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
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# self.assertNotEqual(
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# attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
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def test_head_pruning(self):
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pass
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# if not self.test_pruning:
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# return
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# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# for model_class in self.all_model_classes:
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# config.output_attentions = True
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# config.output_hidden_states = False
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# model = model_class(config=config)
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# model.eval()
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# heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
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# -1: [0]}
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# model.prune_heads(heads_to_prune)
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# outputs = model(**inputs_dict)
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# attentions = outputs[-1]
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# self.assertEqual(
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# attentions[0].shape[-3], 1)
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# self.assertEqual(
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# attentions[1].shape[-3], self.model_tester.num_attention_heads)
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# self.assertEqual(
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# attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
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def test_hidden_states_output(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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@ -323,43 +249,6 @@ class TFCommonTestCases:
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.seq_length, self.model_tester.hidden_size])
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def test_resize_tokens_embeddings(self):
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pass
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# original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# if not self.test_resize_embeddings:
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# return
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# for model_class in self.all_model_classes:
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# config = copy.deepcopy(original_config)
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# model = model_class(config)
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# model_vocab_size = config.vocab_size
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# # Retrieve the embeddings and clone theme
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# model_embed = model.resize_token_embeddings(model_vocab_size)
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# cloned_embeddings = model_embed.weight.clone()
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# # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
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# model_embed = model.resize_token_embeddings(model_vocab_size + 10)
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# self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
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# # Check that it actually resizes the embeddings matrix
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# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
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# # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
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# model_embed = model.resize_token_embeddings(model_vocab_size - 15)
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# self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
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# # Check that it actually resizes the embeddings matrix
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# self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
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# # Check that adding and removing tokens has not modified the first part of the embedding matrix.
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# models_equal = True
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# for p1, p2 in zip(cloned_embeddings, model_embed.weight):
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# if p1.data.ne(p2.data).sum() > 0:
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# models_equal = False
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# self.assertTrue(models_equal)
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def test_model_common_attributes(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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@ -369,40 +258,6 @@ class TFCommonTestCases:
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x = model.get_output_embeddings()
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assert x is None or isinstance(x, tf.keras.layers.Layer)
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def test_tie_model_weights(self):
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pass
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# config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# def check_same_values(layer_1, layer_2):
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# equal = True
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# for p1, p2 in zip(layer_1.weight, layer_2.weight):
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# if p1.data.ne(p2.data).sum() > 0:
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# equal = False
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# return equal
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# for model_class in self.all_model_classes:
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# if not hasattr(model_class, 'tie_weights'):
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# continue
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# config.torchscript = True
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# model_not_tied = model_class(config)
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# params_not_tied = list(model_not_tied.parameters())
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# config_tied = copy.deepcopy(config)
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# config_tied.torchscript = False
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# model_tied = model_class(config_tied)
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# params_tied = list(model_tied.parameters())
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# # Check that the embedding layer and decoding layer are the same in size and in value
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# self.assertGreater(len(params_not_tied), len(params_tied))
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# # Check that after resize they remain tied.
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# model_tied.resize_token_embeddings(config.vocab_size + 10)
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# params_tied_2 = list(model_tied.parameters())
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# self.assertGreater(len(params_not_tied), len(params_tied))
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# self.assertEqual(len(params_tied_2), len(params_tied))
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def test_determinism(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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@ -461,29 +316,5 @@ def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
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return output
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class TFModelUtilsTest(unittest.TestCase):
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@pytest.mark.skipif('tensorflow' not in sys.modules, reason="requires TensorFlow")
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def test_model_from_pretrained(self):
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pass
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# logging.basicConfig(level=logging.INFO)
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# for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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# config = BertConfig.from_pretrained(model_name)
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# self.assertIsNotNone(config)
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# self.assertIsInstance(config, PretrainedConfig)
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# model = BertModel.from_pretrained(model_name)
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# model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
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# self.assertIsNotNone(model)
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# self.assertIsInstance(model, PreTrainedModel)
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# for value in loading_info.values():
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# self.assertEqual(len(value), 0)
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# config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
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# model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
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# self.assertEqual(model.config.output_attentions, True)
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# self.assertEqual(model.config.output_hidden_states, True)
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# self.assertEqual(model.config, config)
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if __name__ == "__main__":
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unittest.main()
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