# Copyright 2026 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch TIPSv2 model.""" import inspect import tempfile import unittest from functools import cached_property from unittest.mock import patch from parameterized import parameterized from transformers import Tipsv2Config, Tipsv2TextConfig, Tipsv2VisionConfig from transformers.conversion_mapping import get_model_conversion_mapping from transformers.testing_utils import ( Expectations, is_flaky, require_torch, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available from ... import test_modeling_common from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin from ...test_processing_common import url_to_local_path if is_torch_available(): import torch from torch import nn from transformers import ( Tipsv2Model, Tipsv2Processor, Tipsv2TextModel, Tipsv2VisionBackbone, Tipsv2VisionModel, ) from transformers.image_utils import load_image_as_tensor class Tipsv2VisionModelTester: def __init__( self, parent, batch_size=4, image_size=28, patch_size=14, num_channels=3, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, mlp_ratio=2, num_register_tokens=1, initializer_range=0.02, is_training=True, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio self.num_register_tokens = num_register_tokens self.initializer_range = initializer_range self.is_training = is_training self.scope = scope self.num_patches = (image_size // patch_size) ** 2 self.seq_length = self.num_patches + 1 + num_register_tokens self.mask_length = self.num_patches self.num_masks = max(1, self.num_patches // 2) def get_config(self): return Tipsv2VisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, mlp_ratio=self.mlp_ratio, num_register_tokens=self.num_register_tokens, initializer_range=self.initializer_range, use_swiglu_ffn=False, ) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def create_and_check_model(self, config, pixel_values): model = Tipsv2VisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config, pixel_values = self.prepare_config_and_inputs() return config, {"pixel_values": pixel_values} @require_torch class Tipsv2VisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ TIPSv2VisionModel is a DINOv2-with-registers vision tower. It does not use token input ids, token embeddings, or text attention masks. """ all_model_classes = (Tipsv2VisionModel,) if is_torch_available() else () pipeline_model_mapping = {"image-feature-extraction": Tipsv2VisionModel} if is_torch_available() else {} model_split_percents = [0.5, 0.9] test_resize_embeddings = False has_attentions = False def setUp(self): self.model_tester = Tipsv2VisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Tipsv2VisionConfig, has_text_modality=False, hidden_size=16, num_attention_heads=4, ) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), nn.Module) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) arg_names = [*signature.parameters.keys()] self.assertListEqual(arg_names[:1], ["pixel_values"]) @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) @is_flaky() def test_eager_matches_sdpa_inference(self, *args): return getattr(ModelTesterMixin, self._testMethodName)(self) def test_reverse_loading_mapping(self, check_keys_were_modified=True, skip_base_model=False): # The MLP is mlp.fc1/mlp.fc2 or mlp.gate_proj/mlp.up_proj/mlp.down_proj depending on config.use_swiglu_ffn, # so only one of the two conversion sets can match, while the test expects every converter to match. # The tests run with use_swiglu_ffn=False, so the swiglu-only conversions are dropped from the mapping. def get_model_conversion_mapping_without_swiglu_ffn(*args, **kwargs): dropped_targets = {".mlp.gate_proj.weight", ".mlp.gate_proj.bias", ".mlp.down_proj."} return [ conversion for conversion in get_model_conversion_mapping(*args, **kwargs) if not dropped_targets.intersection(conversion._original_target_patterns) ] with patch.object( test_modeling_common, "get_model_conversion_mapping", get_model_conversion_mapping_without_swiglu_ffn, ): super().test_reverse_loading_mapping( check_keys_were_modified=check_keys_were_modified, skip_base_model=skip_base_model ) @require_torch class Tipsv2VisionBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (Tipsv2VisionBackbone,) if is_torch_available() else () config_class = Tipsv2VisionConfig has_attentions = False def setUp(self): self.model_tester = Tipsv2VisionModelTester(self) class Tipsv2TextModelTester: def __init__( self, parent, batch_size=4, seq_length=7, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=32, max_position_embeddings=16, initializer_range=0.02, is_training=True, use_input_mask=True, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.is_training = is_training self.use_input_mask = use_input_mask self.scope = scope def get_config(self): return Tipsv2TextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, intermediate_size=self.intermediate_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 3) + 3 attention_mask = None if self.use_input_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) attention_mask[:, 0] = 1 attention_mask[:, -1] = 0 input_ids = input_ids.masked_fill(attention_mask == 0, 0) config = self.get_config() return config, input_ids, attention_mask def create_and_check_model(self, config, input_ids, attention_mask): model = Tipsv2TextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=attention_mask) result_without_mask = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) self.parent.assertEqual( result_without_mask.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) ) def prepare_config_and_inputs_for_common(self): config, input_ids, attention_mask = self.prepare_config_and_inputs() return config, {"input_ids": input_ids, "attention_mask": attention_mask} @require_torch class Tipsv2TextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Tipsv2TextModel,) if is_torch_available() else () test_resize_embeddings = False model_split_percents = [0.5, 0.8, 0.9] def setUp(self): self.model_tester = Tipsv2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Tipsv2TextConfig, hidden_size=16, num_attention_heads=4) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) class Tipsv2ModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): text_kwargs = {} if text_kwargs is None else text_kwargs vision_kwargs = {} if vision_kwargs is None else vision_kwargs self.parent = parent self.text_model_tester = Tipsv2TextModelTester(parent, **text_kwargs) self.vision_model_tester = Tipsv2VisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size self.hidden_size = self.text_model_tester.hidden_size self.num_hidden_layers = self.text_model_tester.num_hidden_layers self.num_attention_heads = self.text_model_tester.num_attention_heads self.seq_length = self.text_model_tester.seq_length self.is_training = is_training def get_config(self): return Tipsv2Config( text_config=self.text_model_tester.get_config().to_dict(), vision_config=self.vision_model_tester.get_config().to_dict(), temperature_init_value=0.01, ) def prepare_config_and_inputs(self): _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = Tipsv2Model(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.parent.assertEqual( result.logits_per_image.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), ) self.parent.assertEqual(result.image_embeds.shape, (self.vision_model_tester.batch_size, self.hidden_size)) self.parent.assertEqual(result.text_embeds.shape, (self.text_model_tester.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config, input_ids, attention_mask, pixel_values = self.prepare_config_and_inputs() inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_torch class Tipsv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Tipsv2Model,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": Tipsv2Model} if is_torch_available() else {} additional_model_inputs = ["pixel_values", "attention_mask", "return_loss"] test_resize_embeddings = False has_attentions = False _is_composite = True def setUp(self): self.model_tester = Tipsv2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Tipsv2Config, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Hidden states are tested in the individual TIPSv2 text and vision tower tests.") def test_hidden_states_output(self): pass @unittest.skip(reason="Retained gradients for hidden states are tested in individual tower tests.") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip( reason="Inputs_embeds behavior is covered by the text tower; the composite model has image inputs too." ) def test_inputs_embeds(self): pass @unittest.skip( reason="Inputs_embeds behavior is covered by the text tower; the composite model has image inputs too." ) def test_inputs_embeds_matches_input_ids(self): pass def test_load_vision_text_config(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Tipsv2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = Tipsv2TextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) def prepare_img(): image = load_image_as_tensor( url_to_local_path( "https://huggingface.co/datasets/hf-internal-testing/fixtures-coco/resolve/main/val2017/000000039769.jpg" ) ) return image @require_torch @require_vision class Tipsv2ModelIntegrationTest(unittest.TestCase): model_id = "google/tipsv2-b14" @cached_property def default_processor(self): return Tipsv2Processor.from_pretrained(self.model_id) @slow def test_inference(self): model = Tipsv2Model.from_pretrained(self.model_id, device_map=torch_device).eval() text_queries = ["two cats on a sofa", "a cat lying down", "a dog on a couch", "an empty room"] processor = self.default_processor inputs = processor(images=prepare_img(), text=text_queries, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) vision_cfg = model.config.vision_config num_register_tokens = vision_cfg.num_register_tokens patch_tokens = outputs.vision_model_output.last_hidden_state[:, 1 + num_register_tokens :] # Tolerance of 1e-3 for vision outputs because of difference in PIL vs. torch preprocessing. EXPECTED_IMAGE_EMBEDS = Expectations( { ("cuda", None): [0.03267, 0.02216, 0.00546, 0.01890, -0.05426], ("cpu", None): [0.03267, 0.02216, 0.00546, 0.01890, -0.05426], ("xpu", None): [0.03267, 0.02216, 0.00546, 0.01890, -0.05426], } ) expected_image_embeds = torch.tensor(EXPECTED_IMAGE_EMBEDS.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.image_embeds[0, :5], expected_image_embeds, rtol=1e-3, atol=1e-3) EXPECTED_PATCH_TOKENS = Expectations( { ("cuda", None): [0.25287, -0.01092, -0.57542, 0.09660, -0.04010], ("cpu", None): [0.25287, -0.01092, -0.57542, 0.09660, -0.04010], ("xpu", None): [0.25287, -0.01092, -0.57542, 0.09660, -0.04010], } ) expected_patch_tokens = torch.tensor(EXPECTED_PATCH_TOKENS.get_expectation(), device=torch_device) torch.testing.assert_close(patch_tokens[0, 0, :5], expected_patch_tokens, rtol=1e-3, atol=1e-3) EXPECTED_TEXT_EMBEDS = Expectations( { ("cuda", None): [0.69319, 0.03710, 0.01194, 0.02136, -0.04281], ("cpu", None): [0.69319, 0.03710, 0.01194, 0.02136, -0.04281], ("xpu", None): [0.69319, 0.03710, 0.01194, 0.02136, -0.04281], } ) expected_text_embeds = torch.tensor(EXPECTED_TEXT_EMBEDS.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.text_embeds[0, :5], expected_text_embeds, rtol=1e-4, atol=1e-4) EXPECTED_LOGITS_PER_IMAGE = Expectations( { ("cuda", None): [31.12190, 26.99341, 20.26748, 17.55544], ("cpu", None): [31.12190, 26.99341, 20.26748, 17.55544], ("xpu", None): [31.12190, 26.99341, 20.26748, 17.55544], } ) expected_logits_per_image = torch.tensor(EXPECTED_LOGITS_PER_IMAGE.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.logits_per_image[0], expected_logits_per_image, rtol=1e-3, atol=1e-3) EXPECTED_LOGITS_PER_TEXT = Expectations( { ("cuda", None): [31.12190, 26.99341, 20.26748, 17.55544], ("cpu", None): [31.12190, 26.99341, 20.26748, 17.55544], ("xpu", None): [31.12190, 26.99341, 20.26748, 17.55544], } ) expected_logits_per_text = torch.tensor(EXPECTED_LOGITS_PER_TEXT.get_expectation(), device=torch_device) torch.testing.assert_close(outputs.logits_per_text[..., 0], expected_logits_per_text, rtol=1e-3, atol=1e-3) EXPECTED_VISION_POOLER_OUTPUT = Expectations( { ("cuda", None): [0.22055, 0.14960, 0.03689, 0.12756, -0.36632], ("cpu", None): [0.22055, 0.14960, 0.03689, 0.12756, -0.36632], ("xpu", None): [0.22055, 0.14960, 0.03689, 0.12756, -0.36632], } ) expected_vision_pooler_output = torch.tensor( EXPECTED_VISION_POOLER_OUTPUT.get_expectation(), device=torch_device ) torch.testing.assert_close( outputs.vision_model_output.pooler_output[0, :5], expected_vision_pooler_output, rtol=1e-3, atol=1e-3 ) EXPECTED_TEXT_POOLER_OUTPUT = Expectations( { ("cuda", None): [12.07207, 0.64612, 0.20788, 0.37204, -0.74551], ("cpu", None): [12.07207, 0.64612, 0.20788, 0.37204, -0.74551], ("xpu", None): [12.07207, 0.64612, 0.20788, 0.37204, -0.74551], } ) expected_text_pooler_output = torch.tensor(EXPECTED_TEXT_POOLER_OUTPUT.get_expectation(), device=torch_device) torch.testing.assert_close( outputs.text_model_output.pooler_output[0, :5], expected_text_pooler_output, rtol=1e-4, atol=1e-4 )