652 lines
24 KiB
Python
652 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2023 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 ALIGN model."""
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import inspect
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import os
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import tempfile
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import unittest
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import requests
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from transformers import AlignConfig, AlignProcessor, AlignTextConfig, AlignVisionConfig
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from transformers.testing_utils import (
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is_flax_available,
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require_torch,
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require_vision,
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slow,
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torch_device,
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)
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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 transformers import (
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AlignModel,
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AlignTextModel,
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AlignVisionModel,
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)
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if is_vision_available():
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from PIL import Image
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if is_flax_available():
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pass
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class AlignVisionModelTester:
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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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image_size=32,
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num_channels=3,
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kernel_sizes=[3, 3, 5],
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in_channels=[32, 16, 24],
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out_channels=[16, 24, 30],
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hidden_dim=64,
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strides=[1, 1, 2],
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num_block_repeats=[1, 1, 2],
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expand_ratios=[1, 6, 6],
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is_training=True,
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hidden_act="gelu",
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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.image_size = image_size
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self.num_channels = num_channels
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self.kernel_sizes = kernel_sizes
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_dim = hidden_dim
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self.strides = strides
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self.num_block_repeats = num_block_repeats
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self.expand_ratios = expand_ratios
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self.is_training = is_training
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self.hidden_act = hidden_act
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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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config = self.get_config()
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return config, pixel_values
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def get_config(self):
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return AlignVisionConfig(
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num_channels=self.num_channels,
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kernel_sizes=self.kernel_sizes,
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in_channels=self.in_channels,
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out_channels=self.out_channels,
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hidden_dim=self.hidden_dim,
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strides=self.strides,
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num_block_repeats=self.num_block_repeats,
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expand_ratios=self.expand_ratios,
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hidden_act=self.hidden_act,
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)
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def create_and_check_model(self, config, pixel_values):
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model = AlignVisionModel(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)
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patch_size = self.image_size // 4
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, config.hidden_dim, patch_size, patch_size)
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)
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, config.hidden_dim))
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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 = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class AlignVisionModelTest(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 ALIGN 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 = (AlignVisionModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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has_attentions = False
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def setUp(self):
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self.model_tester = AlignVisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=AlignVisionConfig, has_text_modality=False, hidden_size=37
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)
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def test_config(self):
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self.create_and_test_config_common_properties()
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self.config_tester.create_and_test_config_to_json_string()
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self.config_tester.create_and_test_config_to_json_file()
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self.config_tester.create_and_test_config_from_and_save_pretrained()
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self.config_tester.create_and_test_config_with_num_labels()
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self.config_tester.check_config_can_be_init_without_params()
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self.config_tester.check_config_arguments_init()
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def create_and_test_config_common_properties(self):
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return
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@unittest.skip(reason="AlignVisionModel does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="AlignVisionModel does not use inputs_embeds")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(reason="AlignVisionModel does not support input and output embeddings")
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def test_model_common_attributes(self):
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pass
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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_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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num_blocks = sum(config.num_block_repeats) * 4
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self.assertEqual(len(hidden_states), num_blocks)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[self.model_tester.image_size // 2, self.model_tester.image_size // 2],
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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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@slow
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def test_model_from_pretrained(self):
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model_name = "kakaobrain/align-base"
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model = AlignVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class AlignTextModelTester:
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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=99,
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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=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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scope=None,
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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.seq_length = seq_length
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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.vocab_size = vocab_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.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.scope = scope
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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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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 AlignTextConfig(
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vocab_size=self.vocab_size,
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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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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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is_decoder=False,
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initializer_range=self.initializer_range,
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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 = AlignTextModel(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, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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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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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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) = 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 AlignTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (AlignTextModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = AlignTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=AlignTextConfig, 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(
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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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@unittest.skip(reason="ALIGN does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Align does not use inputs_embeds")
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def test_inputs_embeds_matches_input_ids(self):
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pass
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@unittest.skip(reason="AlignTextModel has no base class and is 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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@unittest.skip(reason="AlignTextModel has no base class and is 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 = "kakaobrain/align-base"
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model = AlignTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class AlignModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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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 = AlignTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = AlignVisionModelTester(parent, **vision_kwargs)
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self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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test_config, input_ids, token_type_ids, input_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, pixel_values
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def get_config(self):
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return AlignConfig.from_text_vision_configs(
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self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
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)
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def create_and_check_model(self, config, input_ids, token_type_ids, attention_mask, pixel_values):
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model = AlignModel(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(input_ids, pixel_values, attention_mask, token_type_ids)
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self.parent.assertEqual(
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result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
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)
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self.parent.assertEqual(
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result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_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, token_type_ids, input_mask, pixel_values = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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"pixel_values": pixel_values,
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"return_loss": True,
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}
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return config, inputs_dict
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@require_torch
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class AlignModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (AlignModel,) if is_torch_available() else ()
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pipeline_model_mapping = {"feature-extraction": AlignModel} if is_torch_available() else {}
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fx_compatible = False
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = False
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def setUp(self):
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self.model_tester = AlignModelTester(self)
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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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@unittest.skip(reason="Start to fail after using torch `cu118`.")
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def test_multi_gpu_data_parallel_forward(self):
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super().test_multi_gpu_data_parallel_forward()
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@unittest.skip(reason="Hidden_states is tested in individual model tests")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
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def test_inputs_embeds(self):
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pass
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|
@unittest.skip(reason="Align does not use inputs_embeds")
|
|
def test_inputs_embeds_matches_input_ids(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="AlignModel does not have input/output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
# override as the `temperature` parameter initilization is different for ALIGN
|
|
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 `temperature` is initilized as per the original implementation
|
|
if name == "temperature":
|
|
self.assertAlmostEqual(
|
|
param.data.item(),
|
|
1.0,
|
|
delta=1e-3,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
elif name == "text_projection.weight":
|
|
self.assertTrue(
|
|
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
|
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
|
|
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"] # ALIGN needs pixel_values
|
|
traced_model = torch.jit.trace(model, (input_ids, pixel_values))
|
|
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 = {}
|
|
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_vision_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save AlignConfig and check if we can load AlignVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = AlignVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save AlignConfig and check if we can load AlignTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = AlignTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "kakaobrain/align-base"
|
|
model = AlignModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
# 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 AlignModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "kakaobrain/align-base"
|
|
model = AlignModel.from_pretrained(model_name).to(torch_device)
|
|
processor = AlignProcessor.from_pretrained(model_name)
|
|
|
|
image = prepare_img()
|
|
texts = ["a photo of a cat", "a photo of a dog"]
|
|
inputs = processor(text=texts, images=image, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
self.assertEqual(
|
|
outputs.logits_per_image.shape,
|
|
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
|
)
|
|
self.assertEqual(
|
|
outputs.logits_per_text.shape,
|
|
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
|
)
|
|
expected_logits = torch.tensor([[9.7093, 3.4679]], device=torch_device)
|
|
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|