# 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 Siglip2 model.""" import inspect import tempfile import unittest import numpy as np from parameterized import parameterized from pytest import mark from transformers import Siglip2Config, Siglip2TextConfig, Siglip2VisionConfig from transformers.testing_utils import ( Expectations, is_flaky, require_flash_attn, require_torch, require_torch_accelerator, 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 Siglip2ForImageClassification, Siglip2Model, Siglip2TextModel, Siglip2VisionModel if is_vision_available(): from PIL import Image, ImageDraw from transformers import Siglip2Processor class Siglip2ModelTesterMixin(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") @require_flash_attn @require_torch_accelerator @mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence(self): dtype = torch.float16 for model_class in self.all_model_classes: if not model_class._supports_flash_attn: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") # Prepare inputs config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if "pixel_values" in inputs_dict: inputs_dict["pixel_values"] = inputs_dict["pixel_values"].to(dtype) # Separate masks attention_masks = {} if "attention_mask" in inputs_dict: # attention_masks["attention_mask"] = inputs_dict.pop("attention_mask") inputs_dict["attention_mask"] = None if "pixel_attention_mask" in inputs_dict: attention_masks["pixel_attention_mask"] = inputs_dict.pop("pixel_attention_mask") inputs_dict["pixel_attention_mask"] = None # Save and load model with flash attention 2 and eager attentions with tempfile.TemporaryDirectory() as tmp_dir: model = model_class(config) model.save_pretrained(tmp_dir) model = model_class.from_pretrained(tmp_dir, dtype=dtype) model_fa = model_class.from_pretrained(tmp_dir, dtype=dtype, attn_implementation="flash_attention_2") model_fa.to(torch_device) model.to(torch_device) # Run forward pass without attention masks with torch.no_grad(): outputs = model(**inputs_dict, output_hidden_states=True) outputs_fa = model_fa(**inputs_dict, output_hidden_states=True) # Choose which key to compare key = [k for k in ["logits", "logits_per_image", "last_hidden_state"] if k in outputs][0] torch.testing.assert_close(outputs[key], outputs_fa[key], atol=4e-2, rtol=4e-2) # Run forward pass with attention masks inputs_dict.update(attention_masks) with torch.no_grad(): outputs = model(**inputs_dict, output_hidden_states=True) outputs_fa = model_fa(**inputs_dict, output_hidden_states=True) output_tensor = outputs[key] output_tensor_fa = outputs_fa[key] # Mask out padded tokens, they are different for SDPA and Flash Attention 2 if key == "last_hidden_state" and "pixel_attention_mask" in inputs_dict: output_tensor = output_tensor * inputs_dict["pixel_attention_mask"][..., None] output_tensor_fa = output_tensor_fa * inputs_dict["pixel_attention_mask"][..., None] elif key == "last_hidden_state" and inputs_dict.get("attention_mask", None) is not None: output_tensor = output_tensor * inputs_dict["attention_mask"][..., None] output_tensor_fa = output_tensor_fa * inputs_dict["attention_mask"][..., None] torch.testing.assert_close(output_tensor, output_tensor_fa, atol=4e-2, rtol=4e-2) # Check with inference + dropout model.train() _ = model_fa(**inputs_dict, output_hidden_states=True) @unittest.skip(reason="Siglip2 has default right padding (tested in test_flash_attn_2_inference_equivalence)") def test_flash_attn_2_inference_equivalence_right_padding(self): pass @unittest.skip(reason="SDPA can't dispatch on flash with not None `attention_mask`") def test_sdpa_can_dispatch_on_flash(self): pass class Siglip2VisionModelTester: def __init__( self, parent, batch_size=12, num_patches=16, image_num_patches=24, patch_size=2, num_channels=3, is_training=True, hidden_size=64, num_hidden_layers=2, num_attention_heads=4, 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.num_patches = num_patches 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 self.seq_length = image_num_patches def prepare_config_and_inputs(self): pixel_values = floats_tensor( [self.batch_size, self.seq_length, self.num_channels * self.patch_size * self.patch_size] ) pixel_attention_mask = torch.zeros(self.batch_size, self.seq_length, device=torch_device, dtype=torch.long) spatial_shapes = [ (height, width) for height in range(1, self.seq_length) for width in range(1, self.seq_length) if height * width <= self.seq_length ] * self.batch_size spatial_shapes = spatial_shapes[: self.batch_size] spatial_shapes = torch.tensor(spatial_shapes, device=torch_device, dtype=torch.long) for i, (height, width) in enumerate(spatial_shapes): pixel_attention_mask[i, : height * width] = 1 config = self.get_config() return config, pixel_values, pixel_attention_mask, spatial_shapes def get_config(self): return Siglip2VisionConfig( num_patches=self.num_patches, 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, pixel_attention_mask, spatial_shapes): model = Siglip2VisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values, pixel_attention_mask, spatial_shapes) 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, pixel_attention_mask, spatial_shapes = self.prepare_config_and_inputs() inputs_dict = { "pixel_values": pixel_values, "pixel_attention_mask": pixel_attention_mask, "spatial_shapes": spatial_shapes, } return config, inputs_dict @require_torch class Siglip2VisionModelTest(Siglip2ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (Siglip2VisionModel,) if is_torch_available() else () additional_model_inputs = ["pixel_attention_mask", "spatial_shapes"] 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 = ["Siglip2MultiheadAttentionPoolingHead"] # 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 = Siglip2VisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Siglip2VisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="SIGLIP2 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="Siglip2VisionModel does not support standalone training") def test_training(self): pass @unittest.skip(reason="Siglip2VisionModel does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Siglip2VisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="Siglip2VisionModel 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/siglip2-base-patch16-naflex" model = Siglip2VisionModel.from_pretrained(model_name) self.assertIsNotNone(model) @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) @is_flaky() 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 Siglip2TextModelTester: 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=4, 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 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 Siglip2TextConfig( 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 = Siglip2TextModel(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 Siglip2TextModelTest(Siglip2ModelTesterMixin, unittest.TestCase): all_model_classes = (Siglip2TextModel,) if is_torch_available() else () test_resize_embeddings = False model_split_percents = [0.5, 0.8, 0.9] def setUp(self): self.model_tester = Siglip2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Siglip2TextConfig, 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="Siglip2TextModel does not support standalone training") def test_training(self): pass @unittest.skip(reason="Siglip2TextModel does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Siglip2TextModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="Siglip2TextModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Siglip2 does not use inputs_embeds") def test_inputs_embeds(self): pass @slow def test_model_from_pretrained(self): model_name = "google/siglip2-base-patch16-naflex" model = Siglip2TextModel.from_pretrained(model_name) self.assertIsNotNone(model) class Siglip2ModelTester: 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 = Siglip2TextModelTester(parent, **text_kwargs) self.vision_model_tester = Siglip2VisionModelTester(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, pixel_attention_mask, spatial_shapes = ( self.vision_model_tester.prepare_config_and_inputs() ) config = self.get_config() return config, input_ids, attention_mask, pixel_values, pixel_attention_mask, spatial_shapes def get_config(self): return Siglip2Config( 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, pixel_attention_mask, spatial_shapes ): model = Siglip2Model(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, pixel_attention_mask, spatial_shapes, 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, pixel_attention_mask, spatial_shapes = config_and_inputs inputs_dict = { "input_ids": input_ids, "pixel_values": pixel_values, "pixel_attention_mask": pixel_attention_mask, "spatial_shapes": spatial_shapes, "attention_mask": attention_mask, "position_ids": None, "return_loss": False, } return config, inputs_dict @require_torch class Siglip2ModelTest(Siglip2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Siglip2Model,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": Siglip2Model} if is_torch_available() else {} additional_model_inputs = [ "pixel_values", "pixel_attention_mask", "spatial_shapes", ] 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 = ["Siglip2MultiheadAttentionPoolingHead"] # 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 = Siglip2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Siglip2Config, 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 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="Siglip2Model does not have input/output embeddings") def test_model_get_set_embeddings(self): pass def test_load_vision_text_config(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() # Save Siglip2Config and check if we can load Siglip2VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Siglip2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Siglip2Config and check if we can load Siglip2TextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = Siglip2TextConfig.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/siglip2-base-patch16-naflex" model = Siglip2Model.from_pretrained(model_name) self.assertIsNotNone(model) @require_flash_attn @require_torch_accelerator @mark.flash_attn_test def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest("Siglip2 does not support right padding") class Siglip2ForImageClassificationModelTester(Siglip2ModelTester): 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, pixel_attention_mask, spatial_shapes = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, pixel_values, pixel_attention_mask, spatial_shapes def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, pixel_attention_mask, spatial_shapes = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "pixel_attention_mask": pixel_attention_mask, "spatial_shapes": spatial_shapes, } return config, inputs_dict @require_torch class Siglip2ForImageClassificationModelTest(Siglip2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Siglip2ForImageClassification,) if is_torch_available() else () pipeline_model_mapping = {"image-classification": Siglip2ForImageClassification} if is_torch_available() else {} additional_model_inputs = ["pixel_values", "pixel_attention_mask", "spatial_shapes"] 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 = ["Siglip2MultiheadAttentionPoolingHead"] # 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 = Siglip2ForImageClassificationModelTester(self) @unittest.skip(reason="Siglip2ForImageClassification does not support inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Siglip2ForImageClassification does not support inputs_embeds") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing_use_reentrant_false(self): pass # Draw a circle on an images with different aspect ratios def prepare_images(): shapes = [(224, 224), (1024, 1024), (224, 1024)] images = [] for height, width in shapes: image = Image.new("RGB", (width, height), color="red") draw = ImageDraw.Draw(image) center_x = image.width // 2 center_y = image.height // 2 radius = min(center_x, center_y) // 8 * 7 draw.ellipse( (center_x - radius, center_y - radius, center_x + radius, center_y + radius), fill="blue", outline="green", width=image.width // 20, ) images.append(image) return images @require_vision @require_torch class Siglip2ModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "google/siglip2-base-patch16-naflex" model = Siglip2Model.from_pretrained(model_name).to(torch_device) processor = Siglip2Processor.from_pretrained(model_name) images = prepare_images() text = [ "circle", "ellipsoid", "blue circle on red background", "blue circle with green border on red background", "green circle on red background", "a dog", "a blue dog with a green border on a red background", ] inputs = processor(text=text, images=images, return_tensors="pt") inputs = inputs.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 shape 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])), ) # verify the logits values # fmt: off expected_logits_per_texts = Expectations({ ("cuda", None): [ [ 1.0195, -0.0280, -1.4468], [ -4.5395, -6.2269, -1.5667], [ 4.1757, 5.0358, 3.5159], [ 9.4264, 10.1879, 6.3353], [ 2.4409, 3.1058, 4.5491], [-12.3230, -13.7355, -13.4632], [ 1.1520, 1.1687, -1.9647], ], ("rocm", (9, 5)): [ [ 1.0236, -0.0376, -1.4464], [ -4.5358, -6.2235, -1.5628], [ 4.1708, 5.0334, 3.5187], [ 9.4241, 10.1828, 6.3366], [ 2.4371, 3.1062, 4.5530], [-12.3173, -13.7240, -13.4580], [ 1.1502, 1.1716, -1.9623] ], ("xpu", 3): [ [ 1.0195, -0.0280, -1.4468], [ -4.5395, -6.2269, -1.5667], [ 4.1757, 5.0358, 3.5159], [ 9.4264, 10.1879, 6.3353], [ 2.4409, 3.1058, 4.5491], [-12.3230, -13.7355, -13.4632], [ 1.1520, 1.1687, -1.9647] ], }) EXPECTED_LOGITS_PER_TEXT = torch.tensor(expected_logits_per_texts.get_expectation()).to(torch_device) # fmt: on torch.testing.assert_close(outputs.logits_per_text, EXPECTED_LOGITS_PER_TEXT, rtol=1e-3, atol=1e-3)