first commit

tests/models/altclip/test_modeling_altclip.py

@@ -0,0 +1,545 @@
+# Copyright 2022 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch AltCLIP model."""
+
+import inspect
+import unittest
+
+import numpy as np
+import requests
+from parameterized import parameterized
+
+from transformers import AltCLIPConfig, AltCLIPProcessor, AltCLIPTextConfig, AltCLIPVisionConfig
+from transformers.testing_utils import is_flaky, require_torch, require_vision, slow, torch_device
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    import torch.nn as nn
+
+    from transformers import AltCLIPModel, AltCLIPTextModel, AltCLIPVisionModel
+
+if is_vision_available():
+    from PIL import Image
+
+
+class AltCLIPVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        projection_dim=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches + 1
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return AltCLIPVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = AltCLIPVisionModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class AltCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (AltCLIPVisionModel,) if is_torch_available() else ()
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = AltCLIPVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=AltCLIPVisionConfig, has_text_modality=False, hidden_size=36
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="CLIP does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    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 = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    @unittest.skip(reason="AltCLIPVisionModel use the same cv backbone with CLIP model.")
+    def test_model_from_pretrained(self):
+        pass
+
+
+class AltCLIPTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        projection_dim=32,
+        project_dim=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.projection_dim = projection_dim
+        self.project_dim = project_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        if input_mask is not None:
+            batch_size, seq_length = input_mask.shape
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
+            for batch_idx, start_index in enumerate(rnd_start_indices):
+                input_mask[batch_idx, :start_index] = 1
+                input_mask[batch_idx, start_index:] = 0
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return AltCLIPTextConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            projection_dim=self.projection_dim,
+            project_dim=self.project_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+            pad_token_id=1,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = AltCLIPTextModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_ids, attention_mask=input_mask)
+            result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class AltCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (AltCLIPTextModel,) if is_torch_available() else ()
+    # AltCLIPTextModel has large embeddings relative to model size, so we need higher split percentages
+    model_split_percents = [0.5, 0.8, 0.9]
+
+    # TODO (@SunMarc): Fix me
+    @unittest.skip(reason="It's broken.")
+    def test_resize_tokens_embeddings(self):
+        super().test_resize_tokens_embeddings()
+
+    def setUp(self):
+        self.model_tester = AltCLIPTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=AltCLIPTextConfig, hidden_size=32)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="This module does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+    def test_model_outputs_equivalence(self):
+        pass
+
+    @unittest.skip(reason="Result of the model is a dict")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="AltCLIP does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "BAAI/AltCLIP"
+        model = AltCLIPTextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+class AltCLIPModelTester:
+    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
+        if text_kwargs is None:
+            text_kwargs = {}
+        if vision_kwargs is None:
+            vision_kwargs = {}
+
+        self.parent = parent
+        self.text_model_tester = AltCLIPTextModelTester(parent, **text_kwargs)
+        self.vision_model_tester = AltCLIPVisionModelTester(parent, **vision_kwargs)
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+        self.is_training = is_training
+
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+        return config, input_ids, attention_mask, pixel_values
+
+    def get_config(self):
+        return AltCLIPConfig(
+            text_config=self.text_model_tester.get_config().to_dict(),
+            vision_config=self.vision_model_tester.get_config().to_dict(),
+            projection_dim=64,
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
+        model = AltCLIPModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            model(input_ids, pixel_values, attention_mask)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "return_loss": True,
+        }
+        return config, inputs_dict
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@require_torch
+class AltCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (AltCLIPModel,) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": AltCLIPModel} if is_torch_available() else {}
+
+    test_resize_embeddings = False
+    test_attention_outputs = False
+    additional_model_inputs = ["pixel_values"]
+
+    # TODO: Fix the failed tests when this model gets more usage
+    def is_pipeline_test_to_skip(
+        self,
+        pipeline_test_case_name,
+        config_class,
+        model_architecture,
+        tokenizer_name,
+        image_processor_name,
+        feature_extractor_name,
+        processor_name,
+    ):
+        if pipeline_test_case_name == "FeatureExtractionPipelineTests":
+            return True
+
+        return False
+
+    def setUp(self):
+        self.model_tester = AltCLIPModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=AltCLIPConfig,
+            has_text_modality=False,
+            common_properties=["projection_dim", "logit_scale_init_value"],
+        )
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    @slow
+    @is_flaky()
+    def test_eager_matches_sdpa_inference(self, *args):
+        # adding only flaky decorator here and call the parent test method
+        return getattr(ModelTesterMixin, self._testMethodName)(self)
+
+    @unittest.skip(reason="Hidden_states is tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="CLIPModel does not have input/output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "BAAI/AltCLIP"
+        model = AltCLIPModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@require_vision
+@require_torch
+class AltCLIPModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "BAAI/AltCLIP"
+        model = AltCLIPModel.from_pretrained(model_name).to(torch_device)
+        processor = AltCLIPProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(text=["一张猫的照片", "一张狗的照片"], images=image, padding=True, return_tensors="pt").to(torch_device)  # fmt: skip
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # verify the logits
+        self.assertEqual(
+            outputs.logits_per_image.shape,
+            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+
+        probs = outputs.logits_per_image.softmax(dim=1)
+        expected_probs = torch.tensor([[9.9942e-01, 5.7805e-04]], device=torch_device)
+
+        torch.testing.assert_close(probs, expected_probs, rtol=5e-3, atol=5e-3)
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        # ViT models have an `interpolate_pos_encoding` argument in their forward method,
+        # allowing to interpolate the pre-trained position embeddings in order to use
+        # the model on higher resolutions. The DINO model by Facebook AI leverages this
+        # to visualize self-attention on higher resolution images.
+        model_name = "BAAI/AltCLIP"
+        model = AltCLIPModel.from_pretrained(model_name).to(torch_device)
+
+        image_processor = AltCLIPProcessor.from_pretrained(
+            model_name, size={"shortest_edge": 180}, crop_size={"height": 180, "width": 180}
+        )
+
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        inputs = image_processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)
+
+        # interpolate_pos_encodiung false should return value error
+        with self.assertRaises(ValueError, msg="doesn't match model"):
+            with torch.no_grad():
+                model(**inputs, interpolate_pos_encoding=False)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 145, 1024))
+        print("nilesh ")
+        print(outputs.vision_model_output.last_hidden_state.shape)
+        print(outputs.vision_model_output.last_hidden_state[0, :3, :3])
+
+        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [
+                [-0.3577, -0.5977, 0.3555],
+                [0.4544, 0.1660, 0.6583],
+                [1.1715, -0.4870, 0.1645],
+            ]
+        ).to(torch_device)
+
+        torch.testing.assert_close(
+            outputs.vision_model_output.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4
+        )