# coding=utf-8 # Copyright 2022 Meta Platforms authors and 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 FLAVA model. """ import inspect import os import random import tempfile import unittest import numpy as np import requests from transformers import ( FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) from transformers.testing_utils import 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 ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( FlavaForPreTraining, FlavaImageCodebook, FlavaImageModel, FlavaModel, FlavaMultimodalModel, FlavaTextModel, ) from transformers.models.flava.modeling_flava import ( FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST, FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, ) else: FlavaModel = None FlavaForPreTraining = None torch = {} if is_vision_available(): from PIL import Image from transformers import FlavaProcessor class FlavaImageModelTester: def __init__( self, parent, batch_size=12, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, image_size=30, patch_size=2, num_channels=3, qkv_bias=True, mask_token=True, vocab_size=99, ): self.parent = parent self.batch_size = batch_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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias self.mask_token = mask_token self.vocab_size = vocab_size def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) num_patches = self.image_size // self.patch_size bool_masked_pos = ( torch.rand((self.batch_size, num_patches, num_patches), device=pixel_values.device) < 0.9 ).long() config = self.get_config() return config, pixel_values, bool_masked_pos def get_config(self): return FlavaImageConfig( 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, initializer_range=self.initializer_range, layer_norm_eps=self.layer_norm_eps, image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, qkv_bias=self.qkv_bias, mask_token=self.mask_token, vocab_size=self.vocab_size, ) def create_and_check_model(self, config, pixel_values, bool_masked_pos): model = FlavaImageModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values, bool_masked_pos) # 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, bool_masked_pos = config_and_inputs inputs_dict = {"pixel_values": pixel_values, "bool_masked_pos": bool_masked_pos} return config, inputs_dict @require_torch class FlavaImageModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as FLAVA does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (FlavaImageModel,) if is_torch_available() else () test_pruning = False test_torchscript = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = FlavaImageModelTester(self) self.config_tester = ConfigTester(self, config_class=FlavaImageConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_inputs_embeds(self): # FLAVA does not use inputs_embeds pass def test_model_common_attributes(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) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # in FLAVA, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) image_size = (self.model_tester.image_size, self.model_tester.image_size) patch_size = (self.model_tester.patch_size, self.model_tester.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 1 for model_class in self.all_model_classes: 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) 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)) added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, 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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # FLAVA has a different seq_length image_size = (self.model_tester.image_size, self.model_tester.image_size) patch_size = (self.model_tester.patch_size, self.model_tester.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_length = num_patches + 1 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) def test_training(self): pass def test_training_gradient_checkpointing(self): pass # skip this test as FlavaImageModel has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_from_base(self): pass # skip this test as FlavaImageModel has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = FlavaImageModel.from_pretrained(model_name) self.assertIsNotNone(model) class FlavaTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, vocab_size=102, type_vocab_size=2, max_position_embeddings=512, position_embedding_type="absolute", hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, qkv_bias=True, ): self.parent = parent self.batch_size = batch_size self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.seq_length = seq_length self.vocab_size = vocab_size self.type_vocab_size = type_vocab_size self.max_position_embeddings = max_position_embeddings self.position_embedding_type = position_embedding_type 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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.pad_token_id = pad_token_id 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 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) config = self.get_config() return config, input_ids, token_type_ids, input_mask def get_config(self): return FlavaTextConfig( vocab_size=self.vocab_size, type_vocab_size=self.type_vocab_size, max_position_embeddings=self.max_position_embeddings, position_embedding_type=self.position_embedding_type, 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, initializer_range=self.initializer_range, layer_norm_eps=self.layer_norm_eps, pad_token_id=self.pad_token_id, qkv_bias=self.qkv_bias, ) def create_and_check_model(self, config, input_ids, token_type_ids, input_mask): model = FlavaTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, token_type_ids=token_type_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.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, token_type_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class FlavaTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (FlavaTextModel,) if is_torch_available() else () test_pruning = False test_head_masking = False test_torchscript = False def setUp(self): self.model_tester = FlavaTextModelTester(self) self.config_tester = ConfigTester(self, config_class=FlavaTextConfig, hidden_size=37) 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) def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_inputs_embeds(self): # FLAVA does not use inputs_embeds pass # skip this test as FlavaTextModel has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_from_base(self): pass # skip this test as FlavaTextModel has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = FlavaTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class FlavaMultimodalModelTester: def __init__( self, parent, batch_size=12, seq_length=44, use_input_mask=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, qkv_bias=True, ce_ignore_index=-100, use_cls_token=True, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.use_input_mask = use_input_mask 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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.ce_ignore_index = ce_ignore_index self.use_cls_token = use_cls_token def prepare_config_and_inputs(self): hidden_states = floats_tensor([self.batch_size, self.seq_length - 1, self.hidden_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, hidden_states, input_mask def get_config(self): return FlavaMultimodalConfig( 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, initializer_range=self.initializer_range, layer_norm_eps=self.layer_norm_eps, qkv_bias=self.qkv_bias, use_cls_token=self.use_cls_token, ce_ignore_index=self.ce_ignore_index, ) def create_and_check_model(self, config, hidden_states, input_mask): model = FlavaMultimodalModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(hidden_states, attention_mask=input_mask) result = model(hidden_states) 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.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, hidden_states, input_mask = config_and_inputs inputs_dict = {"hidden_states": hidden_states, "attention_mask": input_mask} return config, inputs_dict @require_torch class FlavaMultimodalModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (FlavaMultimodalModel,) if is_torch_available() else () test_pruning = False test_head_masking = False test_resize_embeddings = False test_torchscript = False def setUp(self): self.model_tester = FlavaMultimodalModelTester(self) self.config_tester = ConfigTester( self, config_class=FlavaMultimodalConfig, has_text_modality=False, hidden_size=37 ) 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) 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 = ["hidden_states"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model_common_attributes(self): # No embedding in multimodal model pass def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_inputs_embeds(self): # FLAVA does not use inputs_embeds pass # skip this test as FlavaMultimodalModel has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_from_base(self): pass # skip this test as FlavaMultimodalModel has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = FlavaMultimodalModel.from_pretrained(model_name) self.assertIsNotNone(model) class FlavaImageCodebookTester: def __init__( self, parent, batch_size=12, image_size=112, num_channels=3, hidden_size=32, num_groups=2, vocab_size=99, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.hidden_size = hidden_size self.num_groups = num_groups self.vocab_size = vocab_size 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 FlavaImageCodebookConfig( hidden_size=self.hidden_size, num_groups=self.num_groups, vocab_size=self.vocab_size ) def create_and_check_model(self, config, pixel_values): model = FlavaImageCodebook(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) self.parent.assertEqual( result.shape, (self.batch_size, config.vocab_size, self.image_size // 8, self.image_size // 8) ) 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 FlavaImageCodebookTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (FlavaImageCodebook,) if is_torch_available() else () test_pruning = False test_head_masking = False test_resize_embeddings = False test_torchscript = False has_attentions = False def setUp(self): self.model_tester = FlavaImageCodebookTester(self) self.config_tester = ConfigTester(self, config_class=FlavaImageCodebookConfig, has_text_modality=False) 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_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) @unittest.skip(reason="Flava does not output attentions") def test_attention_outputs(self): pass def test_model_common_attributes(self): # No embedding in multimodal model pass def test_training(self): pass def test_hidden_states_output(self): pass def test_retain_grad_hidden_states_attentions(self): # no attentions pass def test_training_gradient_checkpointing(self): pass def test_inputs_embeds(self): # FLAVA does not use inputs_embeds pass def test_model_outputs_equivalence(self): pass # skip this test as FlavaImageCodebook has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_from_base(self): pass # skip this test as FlavaImageCodebook has no base class and is # not available in MODEL_MAPPING def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = FlavaImageCodebook.from_pretrained(model_name) self.assertIsNotNone(model) class FlavaModelTester: model_class = FlavaModel def __init__( self, parent, text_kwargs=None, image_kwargs=None, multimodal_kwargs=None, image_codebook_kwargs=None, is_training=True, hidden_size=32, projection_dim=32, initializer_range=0.02, layer_norm_eps=1e-12, ): if text_kwargs is None: text_kwargs = {} if image_kwargs is None: image_kwargs = {} if multimodal_kwargs is None: multimodal_kwargs = {} if image_codebook_kwargs is None: image_codebook_kwargs = {} self.parent = parent self.image_model_tester = FlavaImageModelTester(parent, **image_kwargs) self.text_model_tester = FlavaTextModelTester(parent, **text_kwargs) self.multimodal_model_tester = FlavaMultimodalModelTester(parent, **multimodal_kwargs) self.image_codebook_tester = FlavaImageCodebookTester(parent, **image_codebook_kwargs) self.is_training = is_training self.config_tester = ConfigTester(self, config_class=FlavaConfig, hidden_size=37) self.hidden_size = hidden_size self.projection_dim = projection_dim self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps def test_config(self): self.config_tester.run_common_tests() def prepare_config_and_inputs_for_common(self): _, pixel_values, bool_masked_pos = self.image_model_tester.prepare_config_and_inputs() _, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() config = self.get_config() return config, { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "bool_masked_pos": bool_masked_pos, } def get_config(self): return FlavaConfig.from_configs( self.image_model_tester.get_config(), self.text_model_tester.get_config(), self.multimodal_model_tester.get_config(), self.image_codebook_tester.get_config(), hidden_size=self.hidden_size, projection_dim=self.projection_dim, initializer_range=self.initializer_range, layer_norm_eps=self.layer_norm_eps, ) def create_and_check_model(self, config, inputs): self._test_model(config, inputs, test_image=True) self._test_model(config, inputs, test_text=True) self._test_model(config, inputs, test_image=True, test_text=True) def _test_model(self, config, inputs, test_image=False, test_text=False): model = self.model_class(config).to(torch_device).eval() with torch.no_grad(): result = model( input_ids=inputs["input_ids"] if test_text else None, attention_mask=inputs["attention_mask"] if test_text else None, token_type_ids=inputs["token_type_ids"] if test_text else None, pixel_values=inputs["pixel_values"] if test_image else None, bool_masked_pos=inputs["bool_masked_pos"] if test_image else None, ) image_size = (self.image_model_tester.image_size, self.image_model_tester.image_size) patch_size = (self.image_model_tester.patch_size, self.image_model_tester.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) if test_image: self.parent.assertEqual( result.image_embeddings.shape, (self.image_model_tester.batch_size, num_patches + 1, self.image_model_tester.hidden_size), ) else: self.parent.assertIsNone(result.image_embeddings) if test_text: self.parent.assertEqual( result.text_embeddings.shape, ( self.text_model_tester.batch_size, self.text_model_tester.seq_length, self.text_model_tester.hidden_size, ), ) else: self.parent.assertIsNone(result.text_embeddings) if test_image and test_text: self.parent.assertEqual( result.multimodal_embeddings.shape, ( self.multimodal_model_tester.batch_size, self.text_model_tester.seq_length + num_patches + 2, self.multimodal_model_tester.hidden_size, ), ) else: self.parent.assertIsNone(result.multimodal_embeddings) @require_torch class FlavaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (FlavaModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": FlavaModel} if is_torch_available() else {} class_for_tester = FlavaModelTester test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = self.class_for_tester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.create_and_check_model(*config_and_inputs) # hidden_states are tested in individual model tests def test_hidden_states_output(self): pass # input_embeds are tested in individual model tests def test_inputs_embeds(self): pass # tested in individual model tests def test_retain_grad_hidden_states_attentions(self): pass # FlavaModel does not have input/output embeddings def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for FLAVA def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale" or name == "flava.logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False configs_no_init.return_loss = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # FLAVA needs pixel_values if "input_ids_masked" in inputs_dict: # For pretraining inputs = (input_ids, inputs_dict["input_ids_masked"], pixel_values) else: inputs = (input_ids, pixel_values) traced_model = torch.jit.trace(model, inputs) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() # Non persistent buffers won't be in original state dict loaded_model_state_dict.pop("text_model.embeddings.token_type_ids", None) non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_image_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save FlavaConfig and check if we can load FlavaImageConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) image_config = FlavaImageConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.image_config.to_dict(), image_config.to_dict()) # Save FlavaConfig and check if we can load FlavaTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = FlavaTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) # Save FlavaConfig and check if we can load FlavaMultimodalConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) multimodal_config = FlavaMultimodalConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.multimodal_config.to_dict(), multimodal_config.to_dict()) # overwrite from common since FlavaModel/TFFlavaModel return FLAVAOutput/TFFLAVAOutput @slow def test_model_from_pretrained(self): for model_name in FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = FlavaModel.from_pretrained(model_name) self.assertIsNotNone(model) class FlavaForPreTrainingTester(FlavaModelTester): model_class = FlavaForPreTraining def prepare_config_and_inputs_for_common(self): _, pixel_values, bool_masked_pos = self.image_model_tester.prepare_config_and_inputs() _, input_ids, token_type_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() config = self.get_config() input_ids_masked = input_ids.detach().clone() input_ids_masked[:, 1:3] = 100 mlm_labels = input_ids.detach().clone() mlm_labels[:, :] = config.ce_ignore_index mlm_labels[:, 1:3] = input_ids[:, 1:3] mim_labels = torch.randint( 0, self.image_model_tester.vocab_size, bool_masked_pos.size(), device=bool_masked_pos.device ).long() mim_labels[bool_masked_pos.ne(True)] = config.ce_ignore_index itm_labels = torch.ones(mlm_labels.size(0), device=bool_masked_pos.device).long() return config, { "input_ids": input_ids, "input_ids_masked": input_ids_masked, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "bool_masked_pos": bool_masked_pos, "mlm_labels": mlm_labels, "mim_labels": mim_labels, "itm_labels": itm_labels, "return_loss": True, } def _test_model(self, config, inputs, test_image=False, test_text=False): model = self.model_class(config).to(torch_device).eval() with torch.no_grad(): result = model( input_ids=inputs["input_ids"] if test_text else None, input_ids_masked=inputs["input_ids_masked"] if test_text else None, attention_mask=inputs["attention_mask"] if test_text else None, token_type_ids=inputs["token_type_ids"] if test_text else None, pixel_values=inputs["pixel_values"] if test_image else None, bool_masked_pos=inputs["bool_masked_pos"] if test_image else None, mlm_labels=inputs["mlm_labels"], mim_labels=inputs["mim_labels"], itm_labels=inputs["itm_labels"], return_loss=inputs["return_loss"], ) image_size = (self.image_model_tester.image_size, self.image_model_tester.image_size) patch_size = (self.image_model_tester.patch_size, self.image_model_tester.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) if test_image: self.parent.assertEqual( result.image_embeddings.shape, (self.image_model_tester.batch_size, num_patches + 1, self.image_model_tester.hidden_size), ) if not test_text: self.parent.assertEqual( result.loss_info.mim.dim(), 0, ) self.parent.assertEqual( result.mim_logits.shape, (inputs["bool_masked_pos"].sum().item(), self.image_model_tester.vocab_size), ) else: self.parent.assertIsNone(result.image_embeddings) if test_text: self.parent.assertEqual( result.text_embeddings.shape, ( self.text_model_tester.batch_size, self.text_model_tester.seq_length, self.text_model_tester.hidden_size, ), ) if not test_image: self.parent.assertEqual(result.loss_info.mlm.dim(), 0) self.parent.assertEqual( result.mlm_logits.shape, ( (inputs["mlm_labels"] != self.multimodal_model_tester.ce_ignore_index).sum().item(), self.text_model_tester.vocab_size, ), ) else: self.parent.assertIsNone(result.text_embeddings) if test_image and test_text: self.parent.assertEqual( result.multimodal_masked_embeddings.shape, ( self.multimodal_model_tester.batch_size, self.text_model_tester.seq_length + num_patches + 2, self.multimodal_model_tester.hidden_size, ), ) self.parent.assertEqual( result.itm_logits.shape, (self.text_model_tester.batch_size, 2), ) self.parent.assertEqual( result.mmm_text_logits.shape, ( (inputs["mlm_labels"] != self.multimodal_model_tester.ce_ignore_index).sum().item(), self.text_model_tester.vocab_size, ), ) self.parent.assertEqual( result.mmm_image_logits.shape, (inputs["bool_masked_pos"].sum().item(), self.image_model_tester.vocab_size), ) self.parent.assertEqual( result.contrastive_logits_per_image.shape, (self.image_model_tester.batch_size, self.text_model_tester.batch_size), ) self.parent.assertEqual( result.contrastive_logits_per_text.shape, (self.text_model_tester.batch_size, self.image_model_tester.batch_size), ) for item in [ result.loss_info.global_contrastive, result.loss_info.itm, result.loss_info.mmm_text, result.loss_info.mmm_image, ]: self.parent.assertEqual(item.dim(), 0) for item in [result.loss_info.mim, result.loss_info.mlm]: self.parent.assertIsNone(item) else: self.parent.assertIsNone(result.multimodal_masked_embeddings) for item in [ result.loss_info.global_contrastive, result.loss_info.itm, result.loss_info.mmm_text, result.loss_info.mmm_image, ]: self.parent.assertIsNone(item) self.parent.assertIsNone(result.multimodal_embeddings) @require_torch class FlavaForPreTrainingTest(FlavaModelTest): all_model_classes = (FlavaForPreTraining,) if is_torch_available() else () class_for_tester = FlavaForPreTrainingTester test_torchscript = False # 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_vision @require_torch class FlavaModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "facebook/flava-full" model = FlavaModel.from_pretrained(model_name).to(torch_device) processor = FlavaProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=["a photo of a cat", "a photo of a dog"], images=[image, image], padding="max_length", max_length=77, return_tensors="pt", ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs, return_dict=True) # verify the embeddings self.assertAlmostEqual(outputs.image_embeddings.sum().item(), -1352.53540, places=4) self.assertAlmostEqual(outputs.text_embeddings.sum().item(), -198.98225, places=4) self.assertAlmostEqual(outputs.multimodal_embeddings.sum().item(), -3988.51367, places=4) @require_vision @require_torch class FlavaForPreTrainingIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "facebook/flava-full" model = FlavaForPreTraining.from_pretrained(model_name).to(torch_device) processor = FlavaProcessor.from_pretrained(model_name) torch.manual_seed(1) random.seed(1) image = prepare_img() inputs = processor( text=["a photo of a cat", "a photo of a dog"], images=[image, image], padding="max_length", max_length=77, return_tensors="pt", return_codebook_pixels=True, return_image_mask=True, ) inputs["input_ids_masked"] = inputs["input_ids"].clone() inputs["input_ids_masked"][0, 4:6] = 103 inputs["mlm_labels"] = inputs["input_ids"].clone() inputs["mlm_labels"][:, :] = -100 inputs["mlm_labels"][0, 4:6] = inputs["input_ids"][0, 4:6] inputs = inputs.to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.contrastive_logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.contrastive_logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[16.1291, 8.4033], [16.1291, 8.4033]], device=torch_device) self.assertTrue(torch.allclose(outputs.contrastive_logits_per_image, expected_logits, atol=1e-3)) self.assertAlmostEqual(outputs.loss_info.mmm_text.item(), 1.75533199, places=4) self.assertAlmostEqual(outputs.loss_info.mmm_image.item(), 7.0290069, places=4) self.assertAlmostEqual(outputs.loss.item(), 11.0626, places=4)