859 lines
34 KiB
Python
859 lines
34 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 Pix2Struct model."""
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import copy
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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 numpy as np
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import requests
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from transformers import Pix2StructConfig, Pix2StructTextConfig, Pix2StructVisionConfig
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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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Pix2StructForConditionalGeneration,
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Pix2StructProcessor,
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Pix2StructTextModel,
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Pix2StructVisionModel,
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)
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if is_vision_available():
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from PIL import Image
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class Pix2StructVisionModelTester:
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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=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=12,
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patch_embed_hidden_size=12,
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projection_dim=32,
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max_patches=64,
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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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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=1e-10,
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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.image_size = image_size
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self.patch_embed_hidden_size = patch_embed_hidden_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.max_patches = max_patches
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self.seq_length = self.max_patches
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self.patch_proj_dim = ((patch_size**2) * num_channels) + 2
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self.projection_dim = projection_dim
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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.dropout = dropout
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self.attention_dropout = attention_dropout
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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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flattened_patches = floats_tensor([self.batch_size, self.max_patches, self.patch_proj_dim])
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config = self.get_config()
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return config, flattened_patches
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def get_config(self):
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return Pix2StructVisionConfig(
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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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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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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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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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patch_embed_hidden_size=self.patch_embed_hidden_size,
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)
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def create_and_check_model(self, config, flattened_patches):
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model = Pix2StructVisionModel(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(flattened_patches)
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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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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, flattened_patches = config_and_inputs
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inputs_dict = {
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"flattened_patches": flattened_patches,
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"attention_mask": torch.randint(0, 2, (self.batch_size, self.max_patches)),
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}
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return config, inputs_dict
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@require_torch
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class Pix2StructVisionModelTest(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 Pix2Struct 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 = (Pix2StructVisionModel,) 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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def setUp(self):
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self.model_tester = Pix2StructVisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=Pix2StructVisionConfig, has_text_modality=False, hidden_size=37
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="Pix2StructVision does not use inputs_embeds")
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def test_inputs_embeds(self):
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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 = ["flattened_patches"]
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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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@unittest.skip(reason="Training is tested directly on `Pix2StructTextImageModelTest`")
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def test_training(self):
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pass
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@unittest.skip(reason="Training is tested directly on `Pix2StructTextImageModelTest`")
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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="Training is tested directly on `Pix2StructTextImageModelTest`")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="Pix2StructVisionModel 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="Pix2StructVisionModel 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 = "google/pix2struct-textcaps-base"
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model = Pix2StructVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class Pix2StructTextModelTester:
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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_labels=True,
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vocab_size=99,
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hidden_size=12,
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projection_dim=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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dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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bos_token_id=0,
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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_labels = use_labels
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self.d_kv = hidden_size // num_attention_heads
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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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.dropout = dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = scope
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self.bos_token_id = bos_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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config = self.get_config()
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return config, input_ids, input_mask
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def get_config(self):
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return Pix2StructTextConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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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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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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bos_token_id=self.bos_token_id,
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d_kv=self.d_kv,
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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = Pix2StructTextModel(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)
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result = model(input_ids)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_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, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class Pix2StructTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (Pix2StructTextModel,) 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 = Pix2StructTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Pix2StructTextConfig, 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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@unittest.skip(reason="Training is tested directly on `Pix2StructTextImageModelTest`")
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def test_training(self):
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pass
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@unittest.skip(reason="Training is tested directly on `Pix2StructTextImageModelTest`")
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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="Pix2Struct 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="Pix2StructTextModel 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="Pix2StructTextModel 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 = "google/pix2struct-textcaps-base"
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model = Pix2StructTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class Pix2StructModelTester:
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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 = Pix2StructTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = Pix2StructVisionModelTester(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.seq_length = self.text_model_tester.seq_length # need seq_length for common tests
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, flattened_patches = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config(text_config, vision_config)
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return config, input_ids, attention_mask, flattened_patches
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def get_config(self, text_config, vision_config):
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return Pix2StructConfig.from_text_vision_configs(text_config, vision_config, projection_dim=64)
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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, decoder_attention_mask, flattened_patches = config_and_inputs
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attention_mask = (flattened_patches.sum(dim=-1) != 0).float()
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inputs_dict = {
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"decoder_input_ids": input_ids,
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"labels": input_ids,
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"decoder_attention_mask": decoder_attention_mask,
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"flattened_patches": flattened_patches,
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"attention_mask": attention_mask,
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}
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return config, inputs_dict
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@require_torch
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class Pix2StructModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (Pix2StructForConditionalGeneration,) if is_torch_available() else ()
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pipeline_model_mapping = {"image-to-text": Pix2StructForConditionalGeneration} 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 = True
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test_attention_outputs = False
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test_torchscript = False
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def setUp(self):
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self.model_tester = Pix2StructModelTester(self)
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def test_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config).to(torch_device)
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output = model(**input_dict)
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self.assertEqual(
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output[1].shape,
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(
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self.model_tester.vision_model_tester.batch_size,
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self.model_tester.text_model_tester.seq_length,
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self.model_tester.text_model_tester.vocab_size,
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),
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)
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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="Retain_grad is tested in individual model tests")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="Pix2StructModel does not have input/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 = [
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"flattened_patches",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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"head_mask",
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"decoder_head_mask",
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"cross_attn_head_mask",
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"encoder_outputs",
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"past_key_values",
|
|
"labels",
|
|
"decoder_inputs_embeds",
|
|
"use_cache",
|
|
]
|
|
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
|
|
def test_training(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes[:-1]:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
# hardcode labels to be the same as input_ids
|
|
inputs["labels"] = inputs["input_ids"]
|
|
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
def test_training_gradient_checkpointing(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes[:-1]:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.use_cache = False
|
|
config.return_dict = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.gradient_checkpointing_enable()
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
# hardcode labels to be the same as input_ids
|
|
inputs["labels"] = inputs["input_ids"]
|
|
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
# override as the `logit_scale` parameter initilization is different for Pix2Struct
|
|
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":
|
|
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",
|
|
)
|
|
|
|
# overwrite because `vocab_size` is not an attribute of `Pix2StructConfig` but rather `Pix2StructTextConfig`
|
|
def test_resize_tokens_embeddings(self):
|
|
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
if self.model_tester.is_training is False:
|
|
model.eval()
|
|
|
|
model_vocab_size = config.text_config.vocab_size
|
|
# Retrieve the embeddings and clone theme
|
|
model_embed = model.resize_token_embeddings(model_vocab_size)
|
|
cloned_embeddings = model_embed.weight.clone()
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
|
|
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
# Decoder input ids should be clamped to the maximum size of the vocabulary
|
|
if "decoder_input_ids" in inputs_dict:
|
|
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
|
|
models_equal = True
|
|
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
# overwrite because `vocab_size` is not an attribute of `Pix2StructConfig` but rather `Pix2StructTextConfig`
|
|
def test_resize_embeddings_untied(self):
|
|
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
original_config.tie_word_embeddings = False
|
|
|
|
# if model cannot untied embeddings -> leave test
|
|
if original_config.tie_word_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config).to(torch_device)
|
|
|
|
# if no output embeddings -> leave test
|
|
if model.get_output_embeddings() is None:
|
|
continue
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_vocab_size = config.text_config.vocab_size
|
|
model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
# Decoder input ids should be clamped to the maximum size of the vocabulary
|
|
if "decoder_input_ids" in inputs_dict:
|
|
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
@unittest.skip(reason="Pix2Struct doesn't use tied weights")
|
|
def test_tied_model_weights_key_ignore(self):
|
|
pass
|
|
|
|
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"]
|
|
flattened_patches = inputs_dict["flattened_patches"] # Pix2Struct needs flattened_patches
|
|
traced_model = torch.jit.trace(model, (input_ids, flattened_patches))
|
|
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 Pix2StructConfig and check if we can load Pix2StructVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = Pix2StructVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save Pix2StructConfig and check if we can load Pix2StructTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = Pix2StructTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
|
|
# We will verify our results on an image of a stop sign
|
|
def prepare_img():
|
|
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
|
|
im = Image.open(requests.get(url, stream=True).raw)
|
|
return im
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
@slow
|
|
class Pix2StructIntegrationTest(unittest.TestCase):
|
|
def test_inference_image_captioning(self):
|
|
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base").to(torch_device)
|
|
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base")
|
|
image = prepare_img()
|
|
|
|
# image only
|
|
inputs = processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
self.assertEqual(
|
|
processor.decode(predictions[0], skip_special_tokens=True), "A stop sign is on a street corner."
|
|
)
|
|
|
|
def test_batched_inference_image_captioning(self):
|
|
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base").to(torch_device)
|
|
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base")
|
|
image_1 = prepare_img()
|
|
|
|
second_url = (
|
|
"https://www.connollycove.com/wp-content/uploads/2019/06/temple-bar-dublin-world-famous-irish-pub.jpg"
|
|
)
|
|
image_2 = Image.open(requests.get(second_url, stream=True).raw)
|
|
|
|
# image only
|
|
inputs = processor(images=[image_1, image_2], return_tensors="pt").to(torch_device)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
self.assertEqual(
|
|
processor.decode(predictions[0], skip_special_tokens=True), "A stop sign is on a street corner."
|
|
)
|
|
|
|
self.assertEqual(
|
|
processor.decode(predictions[1], skip_special_tokens=True),
|
|
"A row of books including The Temple Bar and Guiness.",
|
|
)
|
|
|
|
def test_batched_inference_image_captioning_conditioned(self):
|
|
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base").to(torch_device)
|
|
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base")
|
|
image_1 = prepare_img()
|
|
|
|
second_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/temple-bar-dublin-world-famous-irish-pub.jpg"
|
|
image_2 = Image.open(requests.get(second_url, stream=True).raw)
|
|
texts = ["A picture of", "An photography of"]
|
|
|
|
# image only
|
|
inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", add_special_tokens=False).to(
|
|
torch_device
|
|
)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
self.assertEqual(
|
|
processor.decode(predictions[0], skip_special_tokens=True),
|
|
"A picture of a stop sign with a red stop sign",
|
|
)
|
|
|
|
self.assertEqual(
|
|
processor.decode(predictions[1], skip_special_tokens=True),
|
|
"An photography of the Temple Bar and other places in the city.",
|
|
)
|
|
|
|
def test_vqa_model(self):
|
|
model_id = "google/pix2struct-ai2d-base"
|
|
|
|
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
|
|
image = Image.open(requests.get(image_url, stream=True).raw)
|
|
|
|
model = Pix2StructForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(
|
|
torch_device
|
|
)
|
|
processor = Pix2StructProcessor.from_pretrained(model_id)
|
|
|
|
# image only
|
|
text = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
|
|
|
|
inputs = processor(images=image, return_tensors="pt", text=text).to(torch_device, torch.bfloat16)
|
|
|
|
predictions = model.generate(**inputs)
|
|
self.assertEqual(processor.decode(predictions[0], skip_special_tokens=True), "ash cloud")
|
|
|
|
def test_vqa_model_batched(self):
|
|
model_id = "google/pix2struct-ai2d-base"
|
|
|
|
image_urls = [
|
|
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg",
|
|
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo-2.png",
|
|
]
|
|
|
|
images = [Image.open(requests.get(image_url, stream=True).raw) for image_url in image_urls]
|
|
|
|
texts = [
|
|
"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud",
|
|
"What is the producer in the diagram? (1) Phytoplankton (2) Zooplankton (3) Large fish (4) Small fish",
|
|
]
|
|
|
|
model = Pix2StructForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(
|
|
torch_device
|
|
)
|
|
processor = Pix2StructProcessor.from_pretrained(model_id)
|
|
|
|
inputs = processor(images=images, return_tensors="pt", text=texts).to(torch_device, torch.bfloat16)
|
|
|
|
predictions = model.generate(**inputs)
|
|
self.assertEqual(processor.decode(predictions[0], skip_special_tokens=True), "ash cloud")
|
|
self.assertEqual(processor.decode(predictions[1], skip_special_tokens=True), "Phytoplankton")
|