# 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)