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tests/models/vit_mae/test_modeling_vit_mae.py

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+# 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 ViTMAE model."""
+
+import copy
+import math
+import tempfile
+import unittest
+from functools import cached_property
+
+import numpy as np
+from pytest import mark
+
+from transformers import ViTMAEConfig
+from transformers.testing_utils import (
+    is_flaky,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    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 ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import PreTrainedModel, ViTMAEForPreTraining, ViTMAEModel, set_seed
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ViTImageProcessorPil
+
+
+class ViTMAEModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=30,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        use_labels=True,
+        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,
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+        num_labels=3,
+        scope=None,
+        mask_ratio=0.5,
+        attn_implementation="eager",
+    ):
+        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.use_labels = use_labels
+        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.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.mask_ratio = mask_ratio
+        self.scope = scope
+        self.attn_implementation = attn_implementation
+
+        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
+        # (we add 1 for the [CLS] token)
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
+        self.mask_ratio = mask_ratio
+        self.num_masks = int(mask_ratio * self.seq_length)
+        self.mask_length = num_patches
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return ViTMAEConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            mask_ratio=self.mask_ratio,
+            decoder_hidden_size=self.hidden_size,
+            decoder_intermediate_size=self.intermediate_size,
+            decoder_num_attention_heads=self.num_attention_heads,
+            decoder_num_hidden_layers=self.num_hidden_layers,
+            attn_implementation=self.attn_implementation,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = ViTMAEModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_pretraining(self, config, pixel_values, labels):
+        model = ViTMAEForPreTraining(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(pixel_values)
+        num_patches = (self.image_size // self.patch_size) ** 2
+        expected_num_channels = self.patch_size**2 * self.num_channels
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels))
+
+        # test greyscale images
+        config.num_channels = 1
+        model = ViTMAEForPreTraining(config)
+        model.to(torch_device)
+        model.eval()
+        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
+        result = model(pixel_values)
+        expected_num_channels = self.patch_size**2
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class ViTMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-feature-extraction": ViTMAEModel} if is_torch_available() else {}
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = ViTMAEModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=32)
+
+    def flash_attn_inference_equivalence(
+        self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2
+    ) -> None:
+        r"""
+        Same as `ModelTesterMixin.flash_attn_inference_equivalence`, but resets RNG before each loaded
+        model's eager/kernel forward pair so random masking in ViTMAE matches between backends.
+        """
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        _has_run_at_least_one_model = False
+
+        for model_class in self.all_model_classes:
+            if not model_class._supports_attention_backend and not attn_implementation.startswith("flash_attention"):
+                continue
+
+            set_seed(42)
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+            config = self._prepare_config_headdim(config, 16)
+
+            if getattr(config, "sliding_window", None):
+                config.sliding_window = 2
+
+            model = model_class(config)
+            if not all(
+                submodel._supports_flash_attn for submodel in model.modules() if isinstance(submodel, PreTrainedModel)
+            ):
+                continue
+
+            _has_run_at_least_one_model = True
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                main_input = inputs_dict[model.main_input_name]
+                if isinstance(main_input, torch.Tensor):
+                    main_input = main_input[:1]
+                    if torch.is_floating_point(main_input):
+                        main_input = main_input.to(torch.bfloat16)
+                first_inputs = {model.main_input_name: main_input, "output_hidden_states": True}
+                if model.main_input_name != "input_ids" and "input_ids" in inputs_dict:
+                    first_inputs["input_ids"] = inputs_dict["input_ids"][:1]
+                if model.main_input_name != "pixel_values" and "pixel_values" in inputs_dict:
+                    if "image_grid_thw" in inputs_dict:
+                        continue
+                    first_inputs["pixel_values"] = inputs_dict["pixel_values"][:1].to(torch.bfloat16)
+                if "image_sizes" in inputs_dict:
+                    first_inputs["image_sizes"] = inputs_dict["image_sizes"][:1]
+                if model.config.is_encoder_decoder:
+                    decoder_input_ids = inputs_dict.get("decoder_input_ids", first_inputs.get("input_ids"))
+                    if decoder_input_ids is not None:
+                        first_inputs["decoder_input_ids"] = decoder_input_ids[:1]
+
+                dummy_attention_mask = inputs_dict.get("attention_mask", None)
+                if dummy_attention_mask is not None:
+                    dummy_attention_mask = dummy_attention_mask[:1]
+                    if padding_side == "left":
+                        dummy_attention_mask[:, 1:] = 1
+                        dummy_attention_mask[:, 0] = 0
+                    else:
+                        dummy_attention_mask[:, :-1] = 1
+                        dummy_attention_mask[:, -1] = 0
+
+                second_inputs = copy.deepcopy(first_inputs)
+                if dummy_attention_mask is not None:
+                    second_inputs["attention_mask"] = dummy_attention_mask
+                    if model.config.is_encoder_decoder:
+                        second_inputs["decoder_attention_mask"] = dummy_attention_mask
+
+                first_inputs = self._prepare_for_class(first_inputs, model_class)
+                first_inputs = {
+                    k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in first_inputs.items()
+                }
+                second_inputs = self._prepare_for_class(second_inputs, model_class)
+                second_inputs = {
+                    k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in second_inputs.items()
+                }
+
+                model = model_class.from_pretrained(
+                    tmpdirname, dtype=torch.bfloat16, attn_implementation="eager", device_map=torch_device
+                )
+
+                set_seed(12345)
+
+                outputs = model(**first_inputs)
+                logits_1_eager = (
+                    outputs.hidden_states[-1]
+                    if "hidden_states" in outputs
+                    else outputs.logits_per_image
+                    if not model.config.is_encoder_decoder
+                    else outputs.decoder_hidden_states[-1]
+                )
+                outputs = model(**second_inputs)
+                logits_2_eager = (
+                    outputs.hidden_states[-1]
+                    if "hidden_states" in outputs
+                    else outputs.logits_per_image
+                    if not model.config.is_encoder_decoder
+                    else outputs.decoder_hidden_states[-1]
+                )
+
+                del model
+                model = model_class.from_pretrained(
+                    tmpdirname, dtype=torch.bfloat16, attn_implementation=attn_implementation, device_map=torch_device
+                )
+
+                set_seed(12345)
+
+                outputs = model(**first_inputs)
+                logits_1_fa = (
+                    outputs.hidden_states[-1]
+                    if "hidden_states" in outputs
+                    else outputs.logits_per_image
+                    if not model.config.is_encoder_decoder
+                    else outputs.decoder_hidden_states[-1]
+                )
+                outputs = model(**second_inputs)
+                logits_2_fa = (
+                    outputs.hidden_states[-1]
+                    if "hidden_states" in outputs
+                    else outputs.logits_per_image
+                    if not model.config.is_encoder_decoder
+                    else outputs.decoder_hidden_states[-1]
+                )
+
+                torch.testing.assert_close(logits_1_eager, logits_1_fa, atol=atol, rtol=rtol)
+                if padding_side == "left":
+                    torch.testing.assert_close(logits_2_eager[1:], logits_2_fa[1:], atol=atol, rtol=rtol)
+                else:
+                    torch.testing.assert_close(logits_2_eager[:-1], logits_2_fa[:-1], atol=atol, rtol=rtol)
+
+        if not _has_run_at_least_one_model:
+            self.skipTest(
+                f"Model architecture does not support {attn_implementation}, or setting its attention dynamically"
+            )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="ViTMAE 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_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_pretraining(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
+
+    def test_save_load(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            # make random mask reproducible
+            torch.manual_seed(2)
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            out_2 = outputs[0].cpu().numpy()
+            out_2[np.isnan(out_2)] = 0
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model = model_class.from_pretrained(tmpdirname)
+                model.to(torch_device)
+                # make random mask reproducible
+                torch.manual_seed(2)
+                with torch.no_grad():
+                    after_outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+                # Make sure we don't have nans
+                out_1 = after_outputs[0].cpu().numpy()
+                out_1[np.isnan(out_1)] = 0
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)
+
+    @unittest.skip(
+        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
+    to get deterministic results."""
+    )
+    def test_determinism(self):
+        pass
+
+    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""")
+    def test_model_outputs_equivalence(self):
+        pass
+
+    @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass")
+    def test_batching_equivalence(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/vit-base-patch16-224"
+        model = ViTMAEModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @mark.flash_attn_test
+    @slow
+    @is_flaky()
+    def test_flash_attn_2_inference_equivalence(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        for model_class in self.all_model_classes:
+            if not model_class._supports_flash_attn:
+                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            inputs_dict = self._prepare_for_class(inputs_dict, model_class)
+            inputs_dict["pixel_values"] = inputs_dict["pixel_values"].to(torch.bfloat16)
+
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model_fa = model_class.from_pretrained(
+                    tmpdirname, dtype=torch.bfloat16, attn_implementation="flash_attention_2"
+                )
+                model_fa.to(torch_device)
+
+                model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16)
+                model.to(torch_device)
+
+                # ForPretraining model has random `noise` -> need to set seed
+                # to make the test deterministic
+                torch.manual_seed(12345)
+                outputs = model(**inputs_dict, output_hidden_states=True)
+                torch.manual_seed(12345)
+                outputs_fa = model_fa(**inputs_dict, output_hidden_states=True)
+
+                logits = (
+                    outputs.hidden_states[-1]
+                    if not model.config.is_encoder_decoder
+                    else outputs.decoder_hidden_states[-1]
+                )
+                logits_fa = (
+                    outputs_fa.hidden_states[-1]
+                    if not model.config.is_encoder_decoder
+                    else outputs_fa.decoder_hidden_states[-1]
+                )
+
+                assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2)
+
+                # check with inference + dropout
+                model.train()
+                _ = model_fa(**inputs_dict)
+
+    @unittest.skip("Not applicable for VideoMAE")
+    def test_flash_attn_2_inference_equivalence_right_padding(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 ViTMAEModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return ViTImageProcessorPil.from_pretrained("facebook/vit-mae-base")
+
+    @cached_property
+    def default_model(self):
+        return ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(torch_device)
+
+    @slow
+    def test_inference_for_pretraining(self):
+        np.random.seed(2)
+
+        model = self.default_model
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
+
+        vit_mae_config = ViTMAEConfig()
+        num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
+        noise = torch.from_numpy(np.random.uniform(size=(1, num_patches))).to(device=torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, noise=noise)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 196, 768))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = torch.tensor(
+            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]
+        )
+
+        torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice.to(torch_device), rtol=1e-4, atol=1e-4)
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        # ViTMAE 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.
+
+        np.random.seed(2)
+
+        model = self.default_model
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt", do_resize=False).to(torch_device)
+
+        vit_mae_config = ViTMAEConfig()
+        num_patches = (image.height // vit_mae_config.patch_size) * (image.width // vit_mae_config.patch_size)
+        noise = torch.from_numpy(np.random.uniform(size=(1, num_patches))).to(device=torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, noise=noise, interpolate_pos_encoding=True)
+
+        # verify the logits
+        expected_shape = torch.Size((1, 1200, 768))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+    @slow
+    def test_inference_interpolate_pos_encoding_custom_sizes(self):
+        # Ensure custom sizes are correctly handled when interpolating the position embeddings
+
+        np.random.seed(2)
+
+        model = self.default_model
+        image_processor = self.default_image_processor
+
+        image = prepare_img()
+        inputs = image_processor(images=image, return_tensors="pt", size={"height": 256, "width": 256}).to(
+            torch_device
+        )
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(
+                **inputs,
+                interpolate_pos_encoding=True,
+            )
+
+        # verify the logits
+        expected_shape = torch.Size((1, 256, 768))
+        self.assertEqual(outputs.logits.shape, expected_shape)