659 lines
25 KiB
Python
659 lines
25 KiB
Python
# coding=utf-8
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# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch BridgeTower model."""
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import tempfile
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import unittest
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import numpy as np
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from transformers import (
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BridgeTowerConfig,
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BridgeTowerTextConfig,
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BridgeTowerVisionConfig,
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is_torch_available,
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is_vision_available,
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)
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import cached_property
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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BridgeTowerForContrastiveLearning,
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BridgeTowerForImageAndTextRetrieval,
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BridgeTowerForMaskedLM,
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BridgeTowerModel,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import BridgeTowerProcessor
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class BridgeTowerTextModelTester:
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def __init__(
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self,
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parent,
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hidden_act="gelu",
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hidden_size=64,
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initializer_factor=1,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=2,
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intermediate_size=128,
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tie_word_embeddings=False,
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output_hidden_states=False,
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):
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self.parent = parent
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self.hidden_act = hidden_act
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self.hidden_size = hidden_size
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self.initializer_factor = initializer_factor
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self.layer_norm_eps = layer_norm_eps
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self.num_attention_heads = num_attention_heads
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self.num_hidden_layers = num_hidden_layers
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self.intermediate_size = intermediate_size
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self.tie_word_embeddings = tie_word_embeddings
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self.vocab_size = 99
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self.seq_length = 4
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self.batch_size = 1
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self.is_training = False
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self.output_hidden_states = output_hidden_states
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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config = self.get_config()
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return config, input_ids, attention_mask
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def get_config(self):
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return BridgeTowerTextConfig(
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hidden_act=self.hidden_act,
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hidden_size=self.hidden_size,
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initializer_factor=self.initializer_factor,
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layer_norm_eps=self.layer_norm_eps,
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num_attention_heads=self.num_attention_heads,
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num_hidden_layers=self.num_hidden_layers,
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intermediate_size=self.intermediate_size,
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tie_word_embeddings=self.tie_word_embeddings,
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output_hidden_states=self.output_hidden_states,
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vocab_size=self.vocab_size,
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)
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class BridgeTowerImageModelTester:
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def __init__(
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self,
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parent,
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hidden_size=64,
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initializer_factor=1,
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layer_norm_eps=1e-05,
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num_hidden_layers=2,
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init_layernorm_from_vision_encoder=False,
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output_hidden_states=False,
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image_size=64,
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):
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self.parent = parent
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self.hidden_size = hidden_size
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self.initializer_factor = initializer_factor
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self.layer_norm_eps = layer_norm_eps
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self.num_hidden_layers = num_hidden_layers
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self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder
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self.num_channels = 3
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self.num_image_features = 17
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self.batch_size = 1
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self.image_size = image_size
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self.is_training = False
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self.output_hidden_states = output_hidden_states
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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pixel_mask = random_attention_mask([self.batch_size, self.image_size, self.image_size])
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config = self.get_config()
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return config, pixel_values, pixel_mask
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def get_config(self):
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return BridgeTowerVisionConfig(
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hidden_size=self.hidden_size,
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initializer_factor=self.initializer_factor,
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layer_norm_eps=self.layer_norm_eps,
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num_hidden_layers=self.num_hidden_layers,
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init_layernorm_from_vision_encoder=self.init_layernorm_from_vision_encoder,
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num_channels=self.num_channels,
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num_image_features=self.num_image_features,
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batch_size=self.batch_size,
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image_size=self.image_size,
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is_training=self.is_training,
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output_hidden_states=self.output_hidden_states,
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)
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class BridgeTowerModelTester:
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def __init__(
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self,
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parent,
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text_kwargs=None,
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vision_kwargs=None,
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share_cross_modal_transformer_layers=True,
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share_link_tower_layers=False,
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link_tower_type="add",
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init_layernorm_from_vision_encoder=False,
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contrastive_hidden_size=512,
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logit_scale_init_value=2.6592,
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hidden_size=64,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=128,
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):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.text_model_tester = BridgeTowerTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = BridgeTowerImageModelTester(parent, **vision_kwargs)
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self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers
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self.share_link_tower_layers = share_link_tower_layers
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self.link_tower_type = link_tower_type
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self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder
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self.contrastive_hidden_size = contrastive_hidden_size
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self.logit_scale_init_value = logit_scale_init_value
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self.batch_size = 1
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self.expected_num_hidden_layers = 8
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self.is_training = False
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values, pixel_mask = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return (config, input_ids, attention_mask, pixel_values, pixel_mask)
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def get_config(self):
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return BridgeTowerConfig.from_text_vision_configs(
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text_config=self.text_model_tester.get_config(),
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vision_config=self.vision_model_tester.get_config(),
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share_cross_modal_transformer_layers=self.share_cross_modal_transformer_layers,
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share_link_tower_layers=self.share_link_tower_layers,
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link_tower_type=self.link_tower_type,
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init_layernorm_from_vision_encoder=self.init_layernorm_from_vision_encoder,
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contrastive_hidden_size=self.contrastive_hidden_size,
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logit_scale_init_value=self.logit_scale_init_value,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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)
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def create_and_check_model(
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self,
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config,
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input_ids,
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attention_mask,
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pixel_values,
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pixel_mask,
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):
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model = BridgeTowerModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask)
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result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values)
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self.parent.assertEqual(
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result["text_features"].shape,
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(self.batch_size, self.text_model_tester.seq_length, self.text_model_tester.hidden_size),
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)
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self.parent.assertEqual(
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result["image_features"].shape,
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(self.batch_size, self.vision_model_tester.num_image_features, self.vision_model_tester.hidden_size),
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)
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self.parent.assertEqual(
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result["pooler_output"].shape,
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(self.batch_size, self.text_model_tester.hidden_size + self.vision_model_tester.hidden_size),
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)
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def create_and_check_for_image_and_text_retrieval(
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self,
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config,
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input_ids,
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attention_mask,
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pixel_values,
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pixel_mask,
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):
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bridgetower_itm_output_last_dimension = 2
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model = BridgeTowerForImageAndTextRetrieval(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask)
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result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, bridgetower_itm_output_last_dimension))
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def create_and_check_for_masked_language_modeling(
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self,
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config,
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input_ids,
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attention_mask,
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pixel_values,
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pixel_mask,
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):
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model = BridgeTowerForMaskedLM(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask)
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result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values)
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self.parent.assertEqual(
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result.logits.shape,
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(self.batch_size, self.text_model_tester.seq_length, self.text_model_tester.vocab_size),
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(config, input_ids, attention_mask, pixel_values, pixel_mask) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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"pixel_mask": pixel_mask,
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}
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return config, inputs_dict
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@require_torch
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class BridgeTowerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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BridgeTowerModel,
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BridgeTowerForImageAndTextRetrieval,
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BridgeTowerForMaskedLM,
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BridgeTowerForContrastiveLearning,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = {"feature-extraction": BridgeTowerModel} if is_torch_available() else {}
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is_training = False
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test_headmasking = False
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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has_attentions = False
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@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
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def test_cpu_offload(self):
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pass
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@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
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def test_disk_offload(self):
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pass
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@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
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def test_model_parallelism(self):
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pass
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# function to extract meaningful tensor from output per different model_class
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def extract_output(self, outputs, model_class):
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return outputs["pooler_output"] if model_class == "BridgeTowerModel" else outputs["logits"]
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def setUp(self):
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self.model_tester = BridgeTowerModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BridgeTowerConfig, hidden_size=37, vocab_size=99)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_image_and_text_retrieval(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_image_and_text_retrieval(*config_and_inputs)
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def test_for_masked_language_modeling(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_language_modeling(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "BridgeTower/bridgetower-base"
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model = BridgeTowerModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@slow
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def test_save_load_fast_init_from_base(self):
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# Override as it is a slow test on this model
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super().test_save_load_fast_init_from_base()
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# Override as extracting meaningful tensor from output is different for BridgeTower
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def test_save_load(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**input_dict)
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out_2 = self.extract_output(outputs, model_class.__name__)
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out_2 = out_2.cpu().numpy()
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out_2[np.isnan(out_2)] = 0
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model = model_class.from_pretrained(tmpdirname)
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model.to(torch_device)
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with torch.no_grad():
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after_outputs = model(**input_dict)
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# Make sure we don't have nans
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out_1 = self.extract_output(after_outputs, model_class.__name__)
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out_1 = out_1.cpu().numpy()
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out_1[np.isnan(out_1)] = 0
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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# Override this as `hidden states output` is different for BridgeTower
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states_text, hidden_states_vision, hidden_states_cross = (
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outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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)
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expected_num_layers = self.model_tester.expected_num_hidden_layers
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self.assertEqual(
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sum((len(hidden_states_text), len(hidden_states_vision), len(hidden_states_cross))),
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expected_num_layers,
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)
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seq_length = self.model_tester.text_model_tester.seq_length
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num_image_features = self.model_tester.vision_model_tester.num_image_features
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self.assertListEqual(
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list(hidden_states_text[0].shape[-2:]),
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[seq_length, self.model_tester.text_model_tester.hidden_size],
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)
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self.assertListEqual(
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list(hidden_states_vision[0].shape),
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[num_image_features, 1, self.model_tester.vision_model_tester.hidden_size],
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)
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self.assertListEqual(
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list(hidden_states_cross[0][0].shape[-2:]),
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[seq_length, self.model_tester.text_model_tester.hidden_size],
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)
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self.assertListEqual(
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list(hidden_states_cross[0][1].shape[-2:]),
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[num_image_features, self.model_tester.vision_model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# Override as `hidden states output` is different for BridgeTower
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def test_retain_grad_hidden_states_attentions(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.output_hidden_states = True
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config.output_attentions = self.has_attentions
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# no need to test all models as different heads yield the same functionality
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model_class = self.all_model_classes[0]
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model = model_class(config)
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model.to(torch_device)
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inputs = self._prepare_for_class(inputs_dict, model_class)
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outputs = model(**inputs)
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output = outputs[0]
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# Encoder-/Decoder-only models
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hidden_states = outputs.hidden_states[0][0]
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hidden_states.retain_grad()
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if self.has_attentions:
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attentions = outputs.attentions[0][0]
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attentions.retain_grad()
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output.flatten()[0].backward(retain_graph=True)
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self.assertIsNotNone(hidden_states.grad)
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if self.has_attentions:
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self.assertIsNotNone(attentions.grad)
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# override as the `logit_scale` parameter initilization is different for BRIDGE TOWER
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|
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:
|
|
if name == "logit_scale":
|
|
self.assertAlmostEqual(
|
|
param.data.item(),
|
|
config.logit_scale_init_value,
|
|
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",
|
|
)
|
|
|
|
@unittest.skip(reason="""Bridge Tower does not have input/output embeddings. So this test is not applicable.""")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="""Bridge Tower does not have input/output embeddings. Thus this test is not applicable.""")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Bridge Tower does not use inputs_embeds")
|
|
def test_inputs_embeds_matches_input_ids(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 BridgeTowerModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_processor(self):
|
|
return (
|
|
BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
|
|
if is_vision_available()
|
|
else None
|
|
)
|
|
|
|
@slow
|
|
def test_image_and_text_retrieval(self):
|
|
model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to(
|
|
torch_device
|
|
)
|
|
model.eval()
|
|
processor = self.default_processor
|
|
image = prepare_img()
|
|
text = "a bunch of cats laying on a tower."
|
|
inputs = processor(image, text, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size([1, 2])
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
self.assertTrue(outputs.logits[0, 1].item() > outputs.logits[0, 0].item())
|
|
|
|
# verify loss
|
|
inputs["labels"] = torch.ones(1, dtype=torch.long, device=torch_device)
|
|
inputs = inputs.to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
self.assertAlmostEqual(outputs.loss.item(), 0.5108, places=4)
|
|
|
|
@slow
|
|
def test_masked_language_modeling(self):
|
|
model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to(torch_device)
|
|
model.eval()
|
|
processor = self.default_processor
|
|
image = prepare_img()
|
|
text = "a bunch of <mask> laying on a tower."
|
|
inputs = processor(image, text, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size([1, 11, 50265])
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
# verify predicted word
|
|
predicted_id = outputs.logits.argmax(dim=-1).squeeze(0).tolist()[4]
|
|
self.assertTrue(processor.decode([predicted_id]) == " cats")
|
|
|
|
# verify loss
|
|
inputs["labels"] = inputs["input_ids"].clone()
|
|
inputs = inputs.to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
self.assertAlmostEqual(outputs.loss.item(), 5.7373, places=4)
|
|
|
|
@slow
|
|
def test_constrastive_learning(self):
|
|
model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc").to(
|
|
torch_device
|
|
)
|
|
model.eval()
|
|
processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc")
|
|
image = prepare_img()
|
|
text = "a bunch of cats laying on a tower."
|
|
inputs = processor(image, text, padding=True, return_tensors="pt").to(torch_device)
|
|
with torch.no_grad():
|
|
outputs = model(**inputs, output_hidden_states=True, return_loss=True)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size([1, 3, 512])
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
|
|
@slow
|
|
@require_torch
|
|
class BridgeTowerModelTrainingTest(unittest.TestCase):
|
|
all_training_supported_model_classes = (
|
|
(BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerForContrastiveLearning)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
|
|
def setUp(self):
|
|
self.model_tester = BridgeTowerModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=BridgeTowerConfig, hidden_size=37, vocab_size=99)
|
|
|
|
def _prepare_inputs_for_training(self, model_class):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if model_class == BridgeTowerForMaskedLM:
|
|
inputs_dict["labels"] = inputs_dict["input_ids"]
|
|
elif model_class == BridgeTowerForImageAndTextRetrieval:
|
|
inputs_dict["labels"] = ids_tensor([1], 2)
|
|
elif model_class == BridgeTowerForContrastiveLearning:
|
|
inputs_dict["return_loss"] = True
|
|
return config, inputs_dict
|
|
|
|
def _get_non_used_layer_names(self, model_class):
|
|
non_used_layer_names = ["text_model.pooler"]
|
|
if model_class == BridgeTowerForMaskedLM:
|
|
non_used_layer_names = non_used_layer_names + [
|
|
# This number `1` actually depends on the number of layers in `cross_modal_image_layers` (by minus 1)
|
|
"cross_modal_image_layers.1",
|
|
"cross_modal_image_pooler",
|
|
"cross_modal_text_pooler",
|
|
]
|
|
return non_used_layer_names
|
|
|
|
def _is_layer_used(self, model_class, layer_name):
|
|
non_used_layer_names = self._get_non_used_layer_names(model_class)
|
|
for non_used_layer_name in non_used_layer_names:
|
|
if non_used_layer_name in layer_name:
|
|
return False
|
|
return True
|
|
|
|
def test_training(self):
|
|
for model_class in self.all_training_supported_model_classes:
|
|
config, inputs_dict = self._prepare_inputs_for_training(model_class)
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
|
|
loss = model(**inputs_dict).loss
|
|
loss.backward()
|
|
|
|
# verify the gradients of used layers' weight are not None
|
|
for name, param in model.named_parameters():
|
|
if self._is_layer_used(model_class, name):
|
|
self.assertIsNotNone(param.grad, f"Gradients should not be None - got {param.grad} for {name}")
|