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tests/models/imagegpt/test_modeling_imagegpt.py

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+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import inspect
+import unittest
+from functools import cached_property
+
+from transformers import ImageGPTConfig
+from transformers.testing_utils import require_torch, require_vision, run_test_using_subprocess, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        ImageGPTForCausalImageModeling,
+        ImageGPTForImageClassification,
+        ImageGPTModel,
+    )
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ImageGPTImageProcessorPil
+
+
+class ImageGPTModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=14,
+        seq_length=7,
+        is_training=True,
+        use_token_type_ids=True,
+        use_input_mask=True,
+        use_labels=True,
+        use_mc_token_ids=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_token_type_ids = use_token_type_ids
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.use_mc_token_ids = use_mc_token_ids
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = None
+
+    def prepare_config_and_inputs(
+        self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
+    ):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        mc_token_ids = None
+        if self.use_mc_token_ids:
+            mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config(
+            gradient_checkpointing=gradient_checkpointing,
+            scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
+            reorder_and_upcast_attn=reorder_and_upcast_attn,
+        )
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        )
+
+    def get_config(
+        self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
+    ):
+        return ImageGPTConfig(
+            vocab_size=self.vocab_size,
+            n_embd=self.hidden_size,
+            n_layer=self.num_hidden_layers,
+            n_head=self.num_attention_heads,
+            n_inner=self.intermediate_size,
+            activation_function=self.hidden_act,
+            resid_pdrop=self.hidden_dropout_prob,
+            attn_pdrop=self.attention_probs_dropout_prob,
+            n_positions=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+            use_cache=True,
+            gradient_checkpointing=gradient_checkpointing,
+            scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
+            reorder_and_upcast_attn=reorder_and_upcast_attn,
+        )
+
+    def get_pipeline_config(self):
+        config = self.get_config()
+        config.vocab_size = 513
+        config.max_position_embeddings = 1024
+        return config
+
+    def create_and_check_imagegpt_model(self, config, input_ids, input_mask, token_type_ids, *args):
+        model = ImageGPTModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        result = model(input_ids, token_type_ids=token_type_ids)
+        result = model(input_ids, token_type_ids=token_type_ids)
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(len(result.past_key_values), config.n_layer)
+
+    def create_and_check_lm_head_model(self, config, input_ids, input_mask, token_type_ids, *args):
+        model = ImageGPTForCausalImageModeling(config)
+        model.to(torch_device)
+        model.eval()
+
+        labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
+        result = model(input_ids, token_type_ids=token_type_ids, labels=labels)
+        self.parent.assertEqual(result.loss.shape, ())
+        # ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
+
+    def create_and_check_imagegpt_for_image_classification(
+        self, config, input_ids, input_mask, token_type_ids, mc_token_ids, sequence_labels, *args
+    ):
+        config.num_labels = self.num_labels
+        model = ImageGPTForImageClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+
+        (
+            config,
+            input_ids,
+            input_mask,
+            token_type_ids,
+            mc_token_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+
+        inputs_dict = {
+            "input_ids": input_ids,
+            "token_type_ids": token_type_ids,
+        }
+
+        return config, inputs_dict
+
+
+@require_torch
+class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
+    )
+    pipeline_model_mapping = (
+        {"image-feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
+        if is_torch_available()
+        else {}
+    )
+    test_missing_keys = False
+
+    # as ImageGPTForImageClassification isn't included in any auto mapping, we add labels here
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class.__name__ == "ImageGPTForImageClassification":
+                inputs_dict["labels"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+
+        return inputs_dict
+
+    # we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
+    def _check_scores(self, batch_size, scores, generated_length, config):
+        expected_shape = (batch_size, config.vocab_size - 1)
+        self.assertIsInstance(scores, tuple)
+        self.assertEqual(len(scores), generated_length)
+        self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
+
+    # After #33632, this test still passes, but many subsequential tests fail with `device-side assert triggered`.
+    # We need to put `@slow` whenever `run_test_using_subprocess` is used.
+    @slow
+    @run_test_using_subprocess
+    def test_beam_search_generate_dict_outputs_use_cache(self):
+        super().test_beam_search_generate_dict_outputs_use_cache()
+
+    def setUp(self):
+        self.model_tester = ImageGPTModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ImageGPTConfig, n_embd=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_imagegpt_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_imagegpt_model(*config_and_inputs)
+
+    def test_imagegpt_causal_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
+
+    def test_imagegpt_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_imagegpt_for_image_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "openai/imagegpt-small"
+        model = ImageGPTModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["input_ids"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    @unittest.skip(reason="Model inputs don't fit test pattern")  # and it's not used enough to be worth fixing :)
+    def test_past_key_values_format(self):
+        pass
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_torch
+@require_vision
+class ImageGPTModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return ImageGPTImageProcessorPil.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
+
+    @slow
+    def test_inference_causal_lm_head(self):
+        model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1024, 512))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
+        ).to(torch_device)
+
+        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)