# Copyright 2024 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 SigLIP model.""" import inspect import tempfile import unittest import numpy as np import requests from parameterized import parameterized from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig from transformers.testing_utils import ( 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, 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 SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel if is_vision_available(): from PIL import Image from transformers import SiglipProcessor class SiglipModelTesterMixin(ModelTesterMixin): 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", ) 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") class SiglipVisionModelTester: def __init__( self, parent, batch_size=12, image_size=4, patch_size=2, num_channels=3, is_training=True, hidden_size=64, num_hidden_layers=2, num_attention_heads=2, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, 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.is_training = is_training 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.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs 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 SiglipVisionConfig( 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, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = SiglipVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs_for_common 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 @require_torch class SiglipVisionModelTest(SiglipModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (SiglipVisionModel,) if is_torch_available() else () test_resize_embeddings = False # MP works but offload doesn't work when the MultiheadAttention is offloaded # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"] # in the dispatch_model function test_cpu_offload = False test_disk_offload_safetensors = False test_disk_offload_bin = False def setUp(self): self.model_tester = SiglipVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=SiglipVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="SIGLIP 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) @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @slow def test_model_from_pretrained(self): model_name = "google/siglip-base-patch16-224" model = SiglipVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) def test_eager_matches_sdpa_inference(self, *args): # adding only flaky decorator here and call the parent test method return getattr(ModelTesterMixin, self._testMethodName)(self) class SiglipTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=2, num_attention_heads=2, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, ): 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.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 self.initializer_range = initializer_range self.scope = scope # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs 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 SiglipTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, 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, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = SiglipTextModel(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)) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common 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 SiglipTextModelTest(SiglipModelTesterMixin, unittest.TestCase): all_model_classes = (SiglipTextModel,) if is_torch_available() else () model_split_percents = [0.5, 0.8, 0.9] # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.setUp with CLIP->Siglip def setUp(self): self.model_tester = SiglipTextModelTester(self) self.config_tester = ConfigTester(self, config_class=SiglipTextConfig, hidden_size=37) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config def test_config(self): self.config_tester.run_common_tests() # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model 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="SiglipTextModel does not support standalone training") def test_training(self): pass @unittest.skip(reason="SiglipTextModel does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipTextModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipTextModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Siglip does not use inputs_embeds") # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_inputs_embeds def test_inputs_embeds(self): pass @slow def test_model_from_pretrained(self): model_name = "google/siglip-base-patch16-224" model = SiglipTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class SiglipModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = SiglipTextModelTester(parent, **text_kwargs) self.vision_model_tester = SiglipVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training # Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs 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 SiglipConfig( text_config=self.text_model_tester.get_config().to_dict(), vision_config=self.vision_model_tester.get_config().to_dict(), ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = SiglipModel(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_loss": False, } return config, inputs_dict @require_torch class SiglipModelTest(SiglipModelTesterMixin, PipelineTesterMixin, unittest.TestCase): additional_model_inputs = ["pixel_values"] all_model_classes = (SiglipModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": SiglipModel} if is_torch_available() else {} test_resize_embeddings = False test_attention_outputs = False # MP works but offload doesn't work when the MultiheadAttention is offloaded # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"] # in the dispatch_model function test_cpu_offload = False test_disk_offload_safetensors = False test_disk_offload_bin = False _is_composite = True def setUp(self): self.model_tester = SiglipModelTester(self) self.config_tester = ConfigTester(self, config_class=SiglipConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model 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") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_inputs_embeds def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="SiglipModel does not have input/output embeddings") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_get_set_embeddings def test_model_get_set_embeddings(self): pass # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->Siglip def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save SiglipConfig and check if we can load SiglipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = SiglipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save SiglipConfig and check if we can load SiglipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = SiglipTextConfig.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 = "google/siglip-base-patch16-224" model = SiglipModel.from_pretrained(model_name) self.assertIsNotNone(model) class SiglipForImageClassificationModelTester(SiglipModelTester): def __init__(self, parent): super().__init__(parent) self.batch_size = self.vision_model_tester.batch_size self.num_hidden_layers = self.vision_model_tester.num_hidden_layers self.hidden_size = self.vision_model_tester.hidden_size self.seq_length = self.vision_model_tester.seq_length def prepare_config_and_inputs(self): _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, pixel_values 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 @require_torch class SiglipForImageClassificationModelTest(SiglipModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (SiglipForImageClassification,) if is_torch_available() else () pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {} test_resize_embeddings = False test_attention_outputs = False # MP works but offload doesn't work when the MultiheadAttention is offloaded # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"] # in the dispatch_model function test_cpu_offload = False test_disk_offload_safetensors = False test_disk_offload_bin = False _is_composite = True def setUp(self): self.model_tester = SiglipForImageClassificationModelTester(self) @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing_use_reentrant_false(self): pass # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image @require_vision @require_torch class SiglipModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "google/siglip-base-patch16-224" model = SiglipModel.from_pretrained(model_name).to(torch_device) processor = SiglipProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=["a photo of 2 cats", "a photo of 2 dogs"], images=image, padding="max_length", return_tensors="pt" ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits_per_image = outputs.logits_per_image logits_per_text = outputs.logits_per_text # verify the logits self.assertEqual( logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[-0.7567, -10.3354]], device=torch_device) torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3) # verify the probs probs = torch.sigmoid(logits_per_image) # these are the probabilities expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device) torch.testing.assert_close(probs, expected_probs, rtol=1e-3, atol=1e-3) @slow def test_inference_interpolate_pos_encoding(self): model_name = "google/siglip-base-patch16-224" model = SiglipModel.from_pretrained(model_name).to(torch_device) # 640 x 480 image image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") processor = SiglipProcessor.from_pretrained(model_name, do_resize=False, size={"height": 480, "width": 640}) inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs, interpolate_pos_encoding=True) # verify the shape # patch size = 16 # batch size 1, (640/16) * (480/16) = 1200 patches, 768 hidden size expected_shape = torch.Size((1, 1200, 768)) self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)