730 lines
27 KiB
Python
730 lines
27 KiB
Python
# coding=utf-8
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# Copyright 2022 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 XCLIP 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 numpy as np
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from huggingface_hub import hf_hub_download
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from transformers import XCLIPConfig, XCLIPTextConfig, XCLIPVisionConfig
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from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 XCLIPModel, XCLIPTextModel, XCLIPVisionModel
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if is_vision_available():
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from transformers import XCLIPProcessor
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class XCLIPVisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=8,
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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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num_frames=8, # important; the batch size * time must be divisible by the number of frames
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is_training=True,
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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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mit_hidden_size=64,
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dropout=0.1,
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attention_dropout=0.1,
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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.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_frames = num_frames
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self.is_training = is_training
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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.mit_hidden_size = mit_hidden_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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# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor(
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[self.batch_size * self.num_frames, self.num_channels, self.image_size, self.image_size]
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)
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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 XCLIPVisionConfig(
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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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num_frames=self.num_frames,
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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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mit_hidden_size=self.mit_hidden_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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)
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def create_and_check_model(self, config, pixel_values):
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model = XCLIPVisionModel(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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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size * self.num_frames, num_patches + 1, self.hidden_size)
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)
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_frames, self.hidden_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = 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 XCLIPVisionModelTest(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 X-CLIP 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 = (XCLIPVisionModel,) 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 = XCLIPVisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=XCLIPVisionConfig, 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="X-CLIP 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 = ["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_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="XCLIPVisionModel 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="XCLIPVisionModel 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 = "microsoft/xclip-base-patch32"
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model = XCLIPVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_gradient_checkpointing_backward_compatibility(self):
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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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if not model_class.supports_gradient_checkpointing:
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continue
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print("Model class:", model_class)
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config.gradient_checkpointing = True
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model = model_class(config)
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self.assertTrue(model.is_gradient_checkpointing)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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# we add 1 here due to the special message token in X-CLIP's vision encoder
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seq_len = getattr(self.model_tester, "seq_length", None) + 1
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(outputs.attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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self.assertEqual(out_len + 1, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length],
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)
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@require_torch_multi_gpu
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def test_multi_gpu_data_parallel_forward(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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# some params shouldn't be scattered by nn.DataParallel
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# so just remove them if they are present.
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blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"]
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for k in blacklist_non_batched_params:
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inputs_dict.pop(k, None)
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# move input tensors to cuda:O
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for k, v in inputs_dict.items():
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if torch.is_tensor(v):
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inputs_dict[k] = v.to(0)
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for model_class in self.all_model_classes:
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model = model_class(config=config)
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model.to(0)
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model.eval()
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# Wrap model in nn.DataParallel
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model = nn.DataParallel(model)
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with torch.no_grad():
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test = self._prepare_for_class(inputs_dict, model_class)
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for k, v in test.items():
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if isinstance(v, torch.Tensor):
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print(k, v.shape)
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else:
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print(k, v)
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_ = model(**self._prepare_for_class(inputs_dict, model_class))
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class XCLIPTextModelTester:
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def __init__(
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self,
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parent,
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batch_size=8,
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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=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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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.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.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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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 XCLIPTextConfig(
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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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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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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = XCLIPTextModel(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.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, 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 XCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (XCLIPTextModel,) 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 = XCLIPTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=XCLIPTextConfig, 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="X-CLIP 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="XCLIPTextModel 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="XCLIPTextModel 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 = "microsoft/xclip-base-patch32"
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model = XCLIPTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class XCLIPModelTester:
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def __init__(
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self,
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parent,
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text_kwargs=None,
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vision_kwargs=None,
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projection_dim=64,
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mit_hidden_size=64,
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is_training=True,
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):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.projection_dim = projection_dim
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self.mit_hidden_size = mit_hidden_size
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self.text_model_tester = XCLIPTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = XCLIPVisionModelTester(parent, **vision_kwargs)
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self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
|
|
self.is_training = is_training
|
|
|
|
def prepare_config_and_inputs(self):
|
|
text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
|
|
vision_config, _ = self.vision_model_tester.prepare_config_and_inputs()
|
|
pixel_values = floats_tensor(
|
|
[
|
|
self.vision_model_tester.batch_size,
|
|
self.vision_model_tester.num_frames,
|
|
self.vision_model_tester.num_channels,
|
|
self.vision_model_tester.image_size,
|
|
self.vision_model_tester.image_size,
|
|
]
|
|
)
|
|
|
|
config = self.get_config()
|
|
|
|
return config, input_ids, attention_mask, pixel_values
|
|
|
|
def get_config(self):
|
|
return XCLIPConfig.from_text_vision_configs(
|
|
self.text_model_tester.get_config(),
|
|
self.vision_model_tester.get_config(),
|
|
projection_dim=self.projection_dim,
|
|
)
|
|
|
|
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
|
|
model = XCLIPModel(config).to(torch_device).eval()
|
|
with torch.no_grad():
|
|
result = model(input_ids, pixel_values, attention_mask)
|
|
self.parent.assertEqual(
|
|
result.logits_per_video.shape,
|
|
(self.vision_model_tester.batch_size, self.text_model_tester.batch_size),
|
|
)
|
|
self.parent.assertEqual(
|
|
result.logits_per_text.shape,
|
|
(self.text_model_tester.batch_size, self.vision_model_tester.batch_size),
|
|
)
|
|
|
|
def prepare_config_and_inputs_for_common(self):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
config, input_ids, attention_mask, pixel_values = config_and_inputs
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"pixel_values": pixel_values,
|
|
"return_loss": True,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class XCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (XCLIPModel,) if is_torch_available() else ()
|
|
pipeline_model_mapping = {"feature-extraction": XCLIPModel} if is_torch_available() else {}
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = False
|
|
maxdiff = None
|
|
|
|
def setUp(self):
|
|
self.model_tester = XCLIPModelTester(self)
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_model(*config_and_inputs)
|
|
|
|
@unittest.skip(reason="Hidden_states is tested in individual model tests")
|
|
def test_hidden_states_output(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
|
|
def test_inputs_embeds(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="XCLIPModel does not have input/output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="XCLIPModel does not support feedforward chunking")
|
|
def test_feed_forward_chunking(self):
|
|
pass
|
|
|
|
# override as the `logit_scale`, `prompts_generator.alpha` parameters require special treatment
|
|
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",
|
|
)
|
|
elif name == "prompts_generator.alpha":
|
|
self.assertAlmostEqual(param.data.mean().item(), model.config.prompt_alpha)
|
|
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"] # X-CLIP 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 XCLIPConfig and check if we can load XCLIPVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = XCLIPVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save XCLIPConfig and check if we can load XCLIPTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = XCLIPTextConfig.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 = "microsoft/xclip-base-patch32"
|
|
model = XCLIPModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
# We will verify our results on a spaghetti video
|
|
def prepare_video():
|
|
file = hf_hub_download(
|
|
repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_8_frames.npy", repo_type="dataset"
|
|
)
|
|
video = np.load(file)
|
|
return list(video)
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
class XCLIPModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "microsoft/xclip-base-patch32"
|
|
model = XCLIPModel.from_pretrained(model_name).to(torch_device)
|
|
processor = XCLIPProcessor.from_pretrained(model_name)
|
|
|
|
video = prepare_video()
|
|
inputs = processor(
|
|
text=["playing sports", "eating spaghetti", "go shopping"], videos=video, return_tensors="pt", padding=True
|
|
).to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
self.assertEqual(
|
|
outputs.logits_per_video.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([[14.0181, 20.2771, 14.4776]], device=torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits_per_video, expected_logits, atol=1e-3))
|