# coding=utf-8 # Copyright 2025 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 AIMv2 model.""" import inspect import tempfile import unittest import numpy as np import requests from parameterized import parameterized from transformers import Aimv2Config, Aimv2TextConfig, Aimv2VisionConfig from transformers.testing_utils import ( is_flaky, require_torch, require_vision, slow, torch_device, ) from transformers.utils import ( is_torch_available, is_vision_available, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin, _test_eager_matches_sdpa_inference, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( Aimv2Model, Aimv2TextModel, Aimv2VisionModel, ) if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor, AutoProcessor class Aimv2VisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=False, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return Aimv2VisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, ) def create_and_check_model(self, config, pixel_values): model = Aimv2VisionModel(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)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict class Aimv2ModelTesterMixin(ModelTesterMixin): """ Subclass of ModelTesterMixin with methods specific to testing Aimv2 models. The SDPA equivalence test is overridden here because Aimv2 models may have test/vision/text+vision inputs, different output logits, and are not supposed to be used or tested with padding_side="left". """ def test_sdpa_can_dispatch_composite_models(self): for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # Load the model with SDPA model_sdpa = model_class.from_pretrained(tmpdirname) # Load model with eager attention model_eager = model_class.from_pretrained( tmpdirname, attn_implementation="eager", ) model_eager = model_eager.eval().to(torch_device) if hasattr(model_sdpa, "vision_model"): self.assertTrue(model_sdpa.vision_model.config._attn_implementation == "sdpa") self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager") if hasattr(model_sdpa, "text_model"): self.assertTrue(model_sdpa.text_model.config._attn_implementation == "sdpa") self.assertTrue(model_eager.text_model.config._attn_implementation == "eager") self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") self.assertTrue(model_eager.config._attn_implementation == "eager") @require_torch class Aimv2VisionModelTest(Aimv2ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Aimv2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (Aimv2VisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = Aimv2VisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Aimv2VisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Aimv2 does not use inputs_embeds") def test_inputs_embeds(self): pass 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) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) 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 Aimv2TextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=False, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=25, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return Aimv2TextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, ) def create_and_check_model(self, config, input_ids, input_mask): model = Aimv2TextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = 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)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class Aimv2TextModelTest(Aimv2ModelTesterMixin, unittest.TestCase): all_model_classes = (Aimv2TextModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = Aimv2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Aimv2TextConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="Aimv2 does not use inputs_embeds") def test_inputs_embeds(self): pass class Aimv2ModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=False): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = Aimv2TextModelTester(parent, **text_kwargs) self.vision_model_tester = Aimv2VisionModelTester(parent, **vision_kwargs) 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, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return Aimv2Config( text_config=self.text_model_tester.get_config(), vision_config=self.vision_model_tester.get_config(), projection_dim=64, ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = Aimv2Model(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, 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) ) 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 config, inputs_dict @require_torch class Aimv2ModelTest(Aimv2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): additional_model_inputs = ["pixel_values"] all_model_classes = (Aimv2Model,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": Aimv2Model, "image-feature-extraction": Aimv2VisionModel} if is_torch_available() else {} ) test_resize_embeddings = False test_attention_outputs = False _is_composite = True def setUp(self): self.model_tester = Aimv2ModelTester(self) common_properties = ["projection_dim", "logit_scale_init_value"] self.config_tester = ConfigTester( self, config_class=Aimv2Config, has_text_modality=False, common_properties=common_properties ) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() print(config_and_inputs) self.model_tester.create_and_check_model(*config_and_inputs) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(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="Aimv2Model does not have input/output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip("Size mismatch on CUDA") def test_multi_gpu_data_parallel_forward(self): pass def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save Aimv2Config and check if we can load Aimv2VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Aimv2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Aimv2Config and check if we can load Aimv2TextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = Aimv2TextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) @is_flaky( max_attempts=2, description="sdpa gets nan values in some places while eager is fine. Except those places, the values are close", ) def test_eager_matches_sdpa_inference( self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels, ): "We need to relax a bit the `atols` for fp32 here due to the altup projections" atols = { ("cpu", False, torch.float32): 1e-6, ("cpu", False, torch.float16): 5e-3, ("cpu", False, torch.bfloat16): 3e-2, # this was relaxed ("cpu", True, torch.float32): 1e-6, ("cpu", True, torch.float16): 5e-3, ("cpu", True, torch.bfloat16): 3e-2, # this was relaxed ("cuda", False, torch.float32): 1e-6, ("cuda", False, torch.bfloat16): 3e-2, # this was relaxed ("cuda", False, torch.float16): 5e-3, ("cuda", True, torch.float32): 1e-6, ("cuda", True, torch.bfloat16): 3e-2, # this was relaxed ("cuda", True, torch.float16): 5e-3, } _test_eager_matches_sdpa_inference( self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels, atols=atols ) @require_vision @require_torch class Aimv2ModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "apple/aimv2-large-patch14-224-lit" model = Aimv2Model.from_pretrained(model_name, device_map=torch_device) processor = AutoProcessor.from_pretrained(model_name) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor( text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pt" ).to(model.device) # Forward pass with torch.no_grad(): outputs = model(**inputs) # Verify the logits self.assertEqual( outputs.logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) # handle device expected_logits = torch.tensor([[33.3550, 26.4255]]).to(model.device) torch.testing.assert_close(outputs.logits_per_image, expected_logits, atol=1e-3, rtol=1e-3) @require_vision @require_torch class Aimv2VisionModelIntegrationTests(unittest.TestCase): @slow def test_inference(self): model_name = "apple/aimv2-large-patch14-224" model = Aimv2VisionModel.from_pretrained(model_name, device_map=torch_device) processor = AutoImageProcessor.from_pretrained(model_name) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor(image, return_tensors="pt").to(model.device) with torch.no_grad(): output = model(**inputs) # Verify logits shape self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 256, 1024])) # Verify logits slice # fmt: off expected_logits = torch.tensor( [[ 0.0510, 0.0806, -0.0990, -0.0154], [ 2.7850, -2.5143, -0.3320, 2.4196], [ 2.8179, -2.4089, -0.2770, 2.3218], [ 2.7641, -2.4114, -0.3684, 2.2998], [ 2.7972, -2.3180, -0.4490, 2.2302], [ 2.8584, -2.5322, -0.2302, 2.4936], [-2.7849, 2.4121, 1.3670, -1.5514]]).to(model.device) # fmt: on output_slice = output.last_hidden_state.squeeze(0)[0:7, 0:4] self.assertTrue(torch.allclose(output_slice, expected_logits, atol=1e-3)) @slow def test_inference_for_native_resolution(self): model_name = "apple/aimv2-large-patch14-native" model = Aimv2VisionModel.from_pretrained(model_name, device_map="auto") processor = AutoImageProcessor.from_pretrained(model_name) image = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw) inputs = processor(image, return_tensors="pt").to(model.device) with torch.no_grad(): output = model(**inputs) # Verify logits shape self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 1530, 1024])) # Verify logits slice # fmt: off expected_logits = torch.tensor( [[-1.3342, 0.3720, 0.0963, 0.4159], [-1.5328, 0.4677, 0.0936, 0.4321], [-0.3775, -0.2758, -0.0803, -0.5367], [-1.3877, 0.5561, -1.9064, -1.1766], [-0.5148, 0.0108, -0.4515, -0.6402], [-0.3400, -0.1711, -0.1855, -0.4219], [-1.2877, -0.0585, -0.1646, 0.7420]]).to(model.device) # fmt: on output_slice = output.last_hidden_state.squeeze(0)[0:7, 0:4] self.assertTrue(torch.allclose(output_slice, expected_logits, atol=1e-3))