Generation tests: update imagegpt input name, remove unused functions (#33663)
This commit is contained in:
Joao Gante
@@ -14,10 +14,7 @@
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# limitations under the License.
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import copy
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import inspect
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import os
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import tempfile
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import unittest
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from transformers import ImageGPTConfig
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@@ -26,13 +23,7 @@ from transformers.utils import cached_property, is_torch_available, is_vision_av
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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@@ -108,7 +99,7 @@ class ImageGPTModelTester:
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def prepare_config_and_inputs(
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self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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pixel_values = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
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input_mask = None
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if self.use_input_mask:
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@@ -140,7 +131,7 @@ class ImageGPTModelTester:
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return (
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config,
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pixel_values,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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@@ -177,66 +168,37 @@ class ImageGPTModelTester:
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config.max_position_embeddings = 1024
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return config
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def prepare_config_and_inputs_for_decoder(self):
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(
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config,
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pixel_values,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs()
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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pixel_values,
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input_mask,
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head_mask,
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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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def create_and_check_imagegpt_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
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def create_and_check_imagegpt_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = ImageGPTModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(pixel_values, token_type_ids=token_type_ids)
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result = model(pixel_values)
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result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(len(result.past_key_values), config.n_layer)
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def create_and_check_lm_head_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
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def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
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model = ImageGPTForCausalImageModeling(config)
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model.to(torch_device)
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model.eval()
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labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
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result = model(pixel_values, token_type_ids=token_type_ids, labels=labels)
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result = model(input_ids, token_type_ids=token_type_ids, labels=labels)
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self.parent.assertEqual(result.loss.shape, ())
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# ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
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def create_and_check_imagegpt_for_image_classification(
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self, config, pixel_values, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
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self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
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):
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config.num_labels = self.num_labels
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model = ImageGPTForImageClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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@@ -244,7 +206,7 @@ class ImageGPTModelTester:
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(
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config,
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pixel_values,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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@@ -255,7 +217,7 @@ class ImageGPTModelTester:
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) = config_and_inputs
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inputs_dict = {
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"pixel_values": pixel_values,
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"head_mask": head_mask,
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}
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@@ -275,7 +237,6 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
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else {}
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)
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test_missing_keys = False
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input_name = "pixel_values"
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# as ImageGPTForImageClassification isn't included in any auto mapping, we add labels here
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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@@ -351,228 +312,6 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
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expected_arg_names = ["input_ids"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_resize_tokens_embeddings(self):
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(
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original_config,
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inputs_dict,
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) = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.test_resize_embeddings:
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self.skipTest(reason="test_resize_embeddings is set to False")
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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.to(torch_device)
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if self.model_tester.is_training is False:
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model.eval()
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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 the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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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 the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary
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inputs_dict["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
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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_resize_embeddings_untied(self):
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(
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original_config,
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inputs_dict,
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) = self.model_tester.prepare_config_and_inputs_for_common()
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if not self.test_resize_embeddings:
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self.skipTest(reason="test_resize_embeddings is set to False")
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original_config.tie_word_embeddings = False
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# if model cannot untied embeddings -> leave test
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if original_config.tie_word_embeddings:
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self.skipTest(reason="tie_word_embeddings is set to False")
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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).to(torch_device)
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# if no output embeddings -> leave test
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if model.get_output_embeddings() is None:
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continue
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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_vocab_size = config.vocab_size
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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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output_embeds = model.get_output_embeddings()
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self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
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# Check bias if present
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if output_embeds.bias is not None:
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self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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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.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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output_embeds = model.get_output_embeddings()
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self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
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# Check bias if present
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if output_embeds.bias is not None:
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self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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# Input ids should be clamped to the maximum size of the vocabulary
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inputs_dict["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
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# Check that the model can still do a forward pass successfully (every parameter should be resized)
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model(**self._prepare_for_class(inputs_dict, model_class))
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def test_inputs_embeds(self):
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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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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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pixel_values = inputs["pixel_values"]
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del inputs["pixel_values"]
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wte = model.get_input_embeddings()
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inputs["inputs_embeds"] = wte(pixel_values)
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with torch.no_grad():
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model(**inputs)[0]
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# override because ImageGPT main input name is `pixel_values`
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# NOTE: in latest transformers this is deprecated, `input_ids` should be used. TODO
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def test_inputs_embeds_matches_input_ids(self):
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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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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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with torch.no_grad():
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out_ids = model(**inputs)[0]
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pixel_values = inputs["pixel_values"]
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del inputs["pixel_values"]
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wte = model.get_input_embeddings()
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inputs["inputs_embeds"] = wte(pixel_values)
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with torch.no_grad():
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out_embeds = model(**inputs)[0]
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self.assertTrue(torch.allclose(out_embeds, out_ids))
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def _create_and_check_torchscript(self, config, inputs_dict):
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if not self.test_torchscript:
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self.skipTest(reason="test_torchscript is set to False")
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configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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configs_no_init.torchscript = True
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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.to(torch_device)
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model.eval()
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inputs = self._prepare_for_class(inputs_dict, model_class)
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try:
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pixel_values = inputs["pixel_values"]
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traced_model = torch.jit.trace(model, pixel_values)
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except RuntimeError:
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self.fail("Couldn't trace module.")
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with tempfile.TemporaryDirectory() as tmp_dir_name:
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pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
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try:
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torch.jit.save(traced_model, pt_file_name)
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except Exception:
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self.fail("Couldn't save module.")
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try:
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loaded_model = torch.jit.load(pt_file_name)
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except Exception:
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self.fail("Couldn't load module.")
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model.to(torch_device)
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model.eval()
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loaded_model.to(torch_device)
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loaded_model.eval()
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model_state_dict = model.state_dict()
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loaded_model_state_dict = loaded_model.state_dict()
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non_persistent_buffers = {}
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for key in loaded_model_state_dict.keys():
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if key not in model_state_dict.keys():
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non_persistent_buffers[key] = loaded_model_state_dict[key]
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loaded_model_state_dict = {
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key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
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}
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self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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model_buffers = list(model.buffers())
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for non_persistent_buffer in non_persistent_buffers.values():
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found_buffer = False
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for i, model_buffer in enumerate(model_buffers):
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if torch.equal(non_persistent_buffer, model_buffer):
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found_buffer = True
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break
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self.assertTrue(found_buffer)
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model_buffers.pop(i)
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models_equal = True
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for layer_name, p1 in model_state_dict.items():
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if layer_name in loaded_model_state_dict:
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p2 = loaded_model_state_dict[layer_name]
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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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@unittest.skip(reason="The model doesn't support left padding") # and it's not used enough to be worth fixing :)
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def test_left_padding_compatibility(self):
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pass
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