# 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 VideoMAE model.""" import copy import tempfile import unittest from functools import cached_property import numpy as np from huggingface_hub import hf_hub_download from pytest import mark from transformers import VideoMAEConfig from transformers.models.auto import get_values from transformers.testing_utils import ( Expectations, is_flaky, require_flash_attn, require_torch, require_torch_accelerator, require_vision, slow, torch_device, ) from transformers.utils import check_torch_load_is_safe, 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 ( MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEForPreTraining, VideoMAEForVideoClassification, VideoMAEModel, ) if is_vision_available(): from transformers import VideoMAEImageProcessor class VideoMAEModelTester: def __init__( self, parent, batch_size=13, image_size=10, num_channels=3, patch_size=2, tubelet_size=2, num_frames=2, 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, mask_ratio=0.9, scope=None, attn_implementation="eager", ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.patch_size = patch_size self.tubelet_size = tubelet_size self.num_frames = num_frames 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 VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame self.num_patches_per_frame = (image_size // patch_size) ** 2 self.seq_length = (num_frames // tubelet_size) * self.num_patches_per_frame # use this variable to define bool_masked_pos self.num_masks = int(mask_ratio * self.seq_length) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [self.batch_size, self.num_frames, 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 VideoMAEConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_frames=self.num_frames, tubelet_size=self.tubelet_size, 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, 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 = VideoMAEModel(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 = VideoMAEForPreTraining(config) model.to(torch_device) model.eval() # important: each video needs to have the same number of masked patches # hence we define a single mask, which we then repeat for each example in the batch mask = torch.ones((self.num_masks,)) mask = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))]) bool_masked_pos = mask.expand(self.batch_size, -1).bool() result = model(pixel_values, bool_masked_pos) # model only returns predictions for masked patches num_masked_patches = mask.sum().item() decoder_num_labels = 3 * self.tubelet_size * self.patch_size**2 self.parent.assertEqual(result.logits.shape, (self.batch_size, num_masked_patches, decoder_num_labels)) 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 VideoMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as VideoMAE does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else () ) pipeline_model_mapping = ( {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification} if is_torch_available() else {} ) # Addition keys that are required for forward, used in tests where we manipulate and create new input dict from scratch additional_model_inputs = ["bool_masked_pos"] test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = VideoMAEModelTester(self) self.config_tester = ConfigTester(self, config_class=VideoMAEConfig, has_text_modality=False, hidden_size=37) def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class == VideoMAEForPreTraining: # important: each video needs to have the same number of masked patches # hence we define a single mask, which we then repeat for each example in the batch mask = torch.ones((self.model_tester.num_masks,)) mask = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))]) batch_size = inputs_dict["pixel_values"].shape[0] bool_masked_pos = mask.expand(batch_size, -1).bool() inputs_dict["bool_masked_pos"] = bool_masked_pos.to(torch_device) if return_labels: if model_class in [ *get_values(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="VideoMAE 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) @slow def test_model_from_pretrained(self): model_name = "MCG-NJU/videomae-base" model = VideoMAEModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_attention_outputs(self): if not self.has_attentions: self.skipTest(reason="Model does not have attentions") else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks seq_len = ( num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length ) inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_hidden_layers + 1 self.assertEqual(len(hidden_states), expected_num_layers) num_visible_patches = self.model_tester.seq_length - self.model_tester.num_masks seq_length = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @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) outputs = model(**inputs_dict, output_hidden_states=True) 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 a video of eating spaghetti # Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227] def prepare_video(): file = hf_hub_download( repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset" ) video = np.load(file) return list(video) @require_torch @require_vision class VideoMAEModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): # logits were tested with a different mean and std, so we use the same here return ( VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) if is_vision_available() else None ) @slow def test_inference_for_video_classification(self): model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics").to( torch_device ) image_processor = self.default_image_processor video = prepare_video() inputs = image_processor(video, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 400)) self.assertEqual(outputs.logits.shape, expected_shape) expectations = Expectations( { (None, None): [0.3669, -0.0688, -0.2421], ("cuda", 8): [0.3668, -0.0690, -0.2421], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4) @slow def test_inference_for_pretraining(self): model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(torch_device) image_processor = self.default_image_processor video = prepare_video() inputs = image_processor(video, return_tensors="pt").to(torch_device) # add boolean mask, indicating which patches to mask local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt") check_torch_load_is_safe() inputs["bool_masked_pos"] = torch.load(local_path, weights_only=True) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size([1, 1408, 1536]) expected_slice = torch.tensor( [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]], device=torch_device ) self.assertEqual(outputs.logits.shape, expected_shape) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4) # verify the loss (`config.norm_pix_loss` = `True`) expected_loss = torch.tensor([0.5142], device=torch_device) torch.testing.assert_close(outputs.loss, expected_loss, rtol=1e-4, atol=1e-4) # verify the loss (`config.norm_pix_loss` = `False`) model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short", norm_pix_loss=False).to( torch_device ) with torch.no_grad(): outputs = model(**inputs) expected_loss = torch.tensor(torch.tensor([0.6469]), device=torch_device) torch.testing.assert_close(outputs.loss, expected_loss, rtol=1e-4, atol=1e-4)