1393 lines
53 KiB
Python
1393 lines
53 KiB
Python
# coding=utf-8
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# Copyright 2022 Meta Platforms authors and The 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 FLAVA model. """
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import inspect
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import os
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import random
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import tempfile
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import unittest
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import numpy as np
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import requests
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from transformers import (
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FlavaConfig,
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FlavaImageCodebookConfig,
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FlavaImageConfig,
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FlavaMultimodalConfig,
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FlavaTextConfig,
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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 is_torch_available, is_vision_available
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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 torch import nn
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from transformers import (
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FlavaForPreTraining,
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FlavaImageCodebook,
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FlavaImageModel,
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FlavaModel,
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FlavaMultimodalModel,
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FlavaTextModel,
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)
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else:
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FlavaModel = None
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FlavaForPreTraining = None
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torch = {}
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if is_vision_available():
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from PIL import Image
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from transformers import FlavaProcessor
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class FlavaImageModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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qkv_bias=True,
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mask_token=True,
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vocab_size=99,
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):
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self.parent = parent
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self.batch_size = batch_size
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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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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.qkv_bias = qkv_bias
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self.mask_token = mask_token
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self.vocab_size = vocab_size
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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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num_patches = self.image_size // self.patch_size
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bool_masked_pos = (
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torch.rand((self.batch_size, num_patches, num_patches), device=pixel_values.device) < 0.9
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).long()
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config = self.get_config()
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return config, pixel_values, bool_masked_pos
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def get_config(self):
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return FlavaImageConfig(
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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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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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initializer_range=self.initializer_range,
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layer_norm_eps=self.layer_norm_eps,
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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qkv_bias=self.qkv_bias,
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mask_token=self.mask_token,
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vocab_size=self.vocab_size,
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)
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def create_and_check_model(self, config, pixel_values, bool_masked_pos):
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model = FlavaImageModel(config=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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result = model(pixel_values, bool_masked_pos)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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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, pixel_values, bool_masked_pos = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values, "bool_masked_pos": bool_masked_pos}
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return config, inputs_dict
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@require_torch
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class FlavaImageModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as FLAVA does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (FlavaImageModel,) if is_torch_available() else ()
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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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test_head_masking = False
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def setUp(self):
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self.model_tester = FlavaImageModelTester(self)
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self.config_tester = ConfigTester(self, config_class=FlavaImageConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_inputs_embeds(self):
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# FLAVA does not use inputs_embeds
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pass
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def test_model_common_attributes(self):
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config, _ = 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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self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_forward_signature(self):
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config, _ = 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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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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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_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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# in FLAVA, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_len = num_patches + 1
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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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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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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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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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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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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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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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 = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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# FLAVA has a different seq_length
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_length = num_patches + 1
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.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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def test_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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# skip this test as FlavaImageModel has no base class and is
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# not available in MODEL_MAPPING
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def test_save_load_fast_init_from_base(self):
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pass
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# skip this test as FlavaImageModel has no base class and is
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# not available in MODEL_MAPPING
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "facebook/flava-full"
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model = FlavaImageModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class FlavaTextModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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vocab_size=102,
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type_vocab_size=2,
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max_position_embeddings=512,
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position_embedding_type="absolute",
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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qkv_bias=True,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.seq_length = seq_length
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self.vocab_size = vocab_size
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self.type_vocab_size = type_vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.position_embedding_type = position_embedding_type
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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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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.qkv_bias = qkv_bias
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self.pad_token_id = pad_token_id
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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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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask
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def get_config(self):
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return FlavaTextConfig(
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vocab_size=self.vocab_size,
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type_vocab_size=self.type_vocab_size,
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max_position_embeddings=self.max_position_embeddings,
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position_embedding_type=self.position_embedding_type,
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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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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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initializer_range=self.initializer_range,
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layer_norm_eps=self.layer_norm_eps,
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pad_token_id=self.pad_token_id,
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qkv_bias=self.qkv_bias,
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)
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def create_and_check_model(self, config, input_ids, token_type_ids, input_mask):
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model = FlavaTextModel(config=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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result = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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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, token_type_ids, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class FlavaTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (FlavaTextModel,) if is_torch_available() else ()
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test_pruning = False
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test_head_masking = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = FlavaTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=FlavaTextConfig, hidden_size=37)
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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_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(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):
|
|
model_name = "facebook/flava-full"
|
|
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
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(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):
|
|
model_name = "facebook/flava-full"
|
|
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
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(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):
|
|
model_name = "facebook/flava-full"
|
|
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
|
|
self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
|
|
|
|
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):
|
|
model_name = "facebook/flava-full"
|
|
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
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
|
|
# 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(), -4030.4602050, 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,
|
|
)
|
|
# Create a clone of the input_ids tensor that will be its masked version
|
|
inputs["input_ids_masked"] = inputs["input_ids"].clone()
|
|
# Mask the tokens "a" & "cat" from the "a photo of a cat" text using the special 103 value
|
|
inputs["input_ids_masked"][0, 4:6] = 103
|
|
# MLM labels. It is a cloned version of input_ids where all values are -100 (i.e., ignored)
|
|
# except those that are masked, whose original values are stored
|
|
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(), 2.0727925, places=4)
|
|
self.assertAlmostEqual(outputs.loss_info.mmm_image.item(), 7.0282096, places=4)
|
|
self.assertAlmostEqual(outputs.loss.item(), 11.3792324, places=4)
|
|
|
|
@slow
|
|
def test_inference_with_itm_labels(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,
|
|
)
|
|
# Create a clone of the input_ids tensor that will be its masked version
|
|
inputs["input_ids_masked"] = inputs["input_ids"].clone()
|
|
# Mask the tokens "a" & "cat" from the "a photo of a cat" text using the special 103 value
|
|
inputs["input_ids_masked"][0, 4:6] = 103
|
|
# MLM labels. It is a cloned version of input_ids where all values are -100 (i.e., ignored)
|
|
# except those that are masked, whose original values are stored
|
|
inputs["mlm_labels"] = inputs["input_ids"].clone()
|
|
inputs["mlm_labels"][:, :] = -100
|
|
inputs["mlm_labels"][0, 4:6] = inputs["input_ids"][0, 4:6]
|
|
# Manually create the itm_labels tensor that indicates if the image-text match.
|
|
# In this case, the firs pair matches and the second does not
|
|
inputs["itm_labels"] = torch.tensor([1, 0])
|
|
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((torch.count_nonzero(inputs["itm_labels"]).item(), inputs.input_ids.shape[0])),
|
|
)
|
|
self.assertEqual(
|
|
outputs.contrastive_logits_per_text.shape,
|
|
torch.Size((torch.count_nonzero(inputs["itm_labels"]).item(), 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(), 2.0727925, places=4)
|
|
self.assertAlmostEqual(outputs.loss_info.mmm_image.item(), 6.8965902, places=4)
|
|
self.assertAlmostEqual(outputs.loss.item(), 9.6084213, places=4)
|