# 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 Idefics2 model.""" import copy import tempfile import unittest from io import BytesIO import pytest import requests from transformers import ( AutoProcessor, BitsAndBytesConfig, Idefics2Config, Idefics2ForConditionalGeneration, Idefics2Model, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( Expectations, cleanup, require_bitsandbytes, require_flash_attn, require_torch, require_torch_accelerator, require_torch_multi_accelerator, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch if is_vision_available(): from PIL import Image class Idefics2VisionText2TextModelTester: def __init__( self, parent, is_training=True, batch_size=2, num_images=2, seq_length=10, vision_config={ "image_size": 12, "patch_size": 12, "num_channels": 3, "hidden_size": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 32, "dropout": 0.1, "attention_dropout": 0.1, "initializer_range": 0.02, }, perceiver_config={ "hidden_act": "silu", "resampler_n_latents": 2, "resampler_depth": 2, "resampler_n_heads": 2, "num_key_value_heads": 1, "resampler_head_dim": 12, "attention_dropout": 0.0, }, text_config={ "vocab_size": 100, "hidden_size": 64, "intermediate_size": 56, "num_hidden_layers": 2, "num_attention_heads": 2, "num_key_value_heads": 2, "hidden_act": "silu", "max_position_embeddings": 256, "initializer_range": 0.02, "rms_norm_eps": 1e-6, "pad_token_id": 0, # None in the original configuration_mistral, we set it to the unk_token_id "bos_token_id": 1, "eos_token_id": 2, "image_token_id": 99, "tie_word_embeddings": False, "rope_theta": 10000.0, "sliding_window": 32, "attention_dropout": 0.0, }, use_cache=False, tie_word_embeddings=False, image_token_id=99, ): self.parent = parent self.pad_token_id = text_config["pad_token_id"] self.is_training = is_training self.batch_size = batch_size self.num_images = num_images self.num_channels = 3 self.seq_length = seq_length self.use_cache = use_cache self.image_token_id = image_token_id self.tie_word_embeddings = tie_word_embeddings # Hack - add properties here so use common tests self.vocab_size = text_config["vocab_size"] self.num_hidden_layers = text_config["num_hidden_layers"] self.num_attention_heads = text_config["num_attention_heads"] self.hidden_size = text_config["hidden_size"] self.vision_config = vision_config self.perceiver_config = perceiver_config self.text_config = text_config def get_config(self): return Idefics2Config( use_cache=self.use_cache, image_token_id=self.image_token_id, tie_word_embeddings=self.tie_word_embeddings, vision_config=self.vision_config, perceiver_config=self.perceiver_config, text_config=self.text_config, vocab_size=self.vocab_size, ) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [ self.batch_size, self.num_images, self.vision_config["num_channels"], self.vision_config["image_size"], self.vision_config["image_size"], ] ) 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 input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 1 # For simplicity just set the last n tokens to the image token n_image_tokens_per_batch = self.num_images * self.perceiver_config["resampler_n_latents"] input_ids[input_ids == self.image_token_id] = self.pad_token_id input_ids[:, -n_image_tokens_per_batch:] = self.image_token_id attention_mask = input_ids.ne(1).to(torch_device) inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class Idefics2ModelTest(ModelTesterMixin, unittest.TestCase): """ Model tester for `Idefics2`. """ all_model_classes = (Idefics2Model,) if is_torch_available() else () test_resize_embeddings = True _is_composite = True def setUp(self): self.model_tester = Idefics2VisionText2TextModelTester(self) self.config_tester = ConfigTester( self, config_class=Idefics2Config, has_text_modality=False, common_properties=["image_token_id"] ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="input_embeds cannot be passed in without input_ids") def test_inputs_embeds(): pass @unittest.skip(reason="input_embeds cannot be passed in without input_ids") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="Model does not support padding right") def test_flash_attn_2_generate_padding_right(self): pass @unittest.skip(reason="Model does not support padding right") def test_flash_attn_2_inference_padding_right(self): pass # We need to override as we need to prepare such that the image token is the last token def test_resize_tokens_embeddings(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.text_config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Ignore copy # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"] model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.image_token_id # make sure that decoder_input_ids are resized as well if "decoder_input_ids" in inputs_dict: inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1) self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size) self.assertTrue(model.config.text_config.vocab_size, model.vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) # Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size target_dimension = 128 model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0], target_dimension) with self.assertRaisesRegex( ValueError, "Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer", ): model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3) # We need to override as we need to prepare such that the image token is the last token def test_resize_embeddings_untied(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"] model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.image_token_id # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) 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) model_sdpa = model_class.from_pretrained(tmpdirname) model_sdpa = model_sdpa.eval().to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") self.assertTrue(model_sdpa.vision_model.config._attn_implementation == "sdpa") self.assertTrue(model_sdpa.connector.perceiver_resampler.config._attn_implementation == "sdpa") model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager") model_eager = model_eager.eval().to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager") self.assertTrue(model_eager.connector.perceiver_resampler.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): class_name = submodule.__class__.__name__ if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name: raise ValueError("The eager model should not have SDPA attention layers") @require_torch class Idefics2ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTesterMixin, unittest.TestCase): """ Model tester for `Idefics2ForConditionalGeneration`. """ all_model_classes = (Idefics2ForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = {"image-text-to-text": Idefics2ForConditionalGeneration} if is_torch_available() else () test_resize_embeddings = True def setUp(self): self.model_tester = Idefics2VisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Idefics2Config, has_text_modality=False) @unittest.skip(reason="input_embeds cannot be passed in without input_ids") def test_inputs_embeds(): pass @unittest.skip(reason="Model does not support padding right") def test_flash_attn_2_generate_padding_right(self): pass @unittest.skip(reason="Model does not support padding right") def test_flash_attn_2_inference_padding_right(self): pass @pytest.mark.generate @slow @unittest.skip( reason="Idefics2 doesn't support SDPA for all backbones, vision backbones has only eager/FA2 attention" ) def test_eager_matches_sdpa_generate(self): pass # We need to override as we need to prepare such that the image token is the last token def test_resize_tokens_embeddings(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.text_config.vocab_size # Retrieve the embeddings and clone theme model_embed = model.resize_token_embeddings(model_vocab_size) cloned_embeddings = model_embed.weight.clone() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"] model.model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. models_equal = True for p1, p2 in zip(cloned_embeddings, model_embed.weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1) self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size) self.assertTrue(model.config.text_config.vocab_size, model.vocab_size) model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0] // 64, 0) # Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size target_dimension = 128 model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64) self.assertTrue(model_embed.weight.shape[0], target_dimension) with self.assertRaisesRegex( ValueError, "Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer", ): model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3) # We need to override as we need to prepare such that the image token is the last token def test_resize_embeddings_untied(self): (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common() original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) model.eval() # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.text_config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10) output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds = model.get_output_embeddings() self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2) n_images = self.model_tester.num_images * self.model_tester.perceiver_config["resampler_n_latents"] model.model.image_token_id = model_vocab_size - 15 - 1 inputs_dict["input_ids"][:, -n_images:] = model.model.image_token_id # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) def test_inputs_embeds_matches_input_ids_with_generate(self): # overwrite because IDEFICS needs ids and embeds at the input to be not None config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) pad_token_id = config.pad_token_id if config.pad_token_id is not None else 1 wte = model.get_input_embeddings() input_ids = inputs["input_ids"] # some models infer position ids/attn mask differently when input ids # by check if pad_token let's make sure no padding is in input ids not_pad_token_id = pad_token_id + 1 if max(0, pad_token_id - 1) == 0 else pad_token_id - 1 input_ids[input_ids == pad_token_id] = not_pad_token_id del inputs["input_ids"] inputs_embeds = wte(input_ids) out_ids = model.generate(input_ids=input_ids, **inputs, max_new_tokens=2) out_embeds = model.generate(input_ids=input_ids, inputs_embeds=inputs_embeds, **inputs, max_new_tokens=2) torch.testing.assert_close(out_embeds, out_ids) @require_torch class Idefics2ForConditionalGenerationIntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b-base") self.image1 = Image.open( BytesIO( requests.get( "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" ).content ) ) self.image2 = Image.open( BytesIO(requests.get("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg").content) ) self.image3 = Image.open( BytesIO( requests.get( "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg" ).content ) ) def tearDown(self): cleanup(torch_device, gc_collect=True) @slow @require_torch_multi_accelerator def test_integration_test(self): model = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b-base", dtype=torch.bfloat16, device_map="auto", ) # Create inputs text = "In this image, we see" images = self.image1 inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True) inputs.to(torch_device) generated_ids = model.generate(**inputs, max_new_tokens=10) generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True) # Batch affects generated text. Single batch output: ['In this image, we see the Statue of Liberty in the foreground and'] expected_generated_text = "In this image, we see the Statue of Liberty, the New York City" self.assertEqual(generated_texts[0], expected_generated_text) @slow @require_bitsandbytes def test_integration_test_4bit(self): # Let' s make sure we test the preprocessing to replace what is used model = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b-base", quantization_config=BitsAndBytesConfig(load_in_4bit=True) ) # Create pixel inputs text = ["In this image, we see", "bla, bla "] images = [[self.image1], [self.image2, self.image3]] inputs = self.processor(text=text, images=images, padding=True, return_tensors="pt").to(torch_device) generated_ids = model.generate(**inputs, max_new_tokens=10) generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True) expected_generated_texts = Expectations( { ("xpu", 3): "In this image, we see the Statue of Liberty, the Hudson River,", ("cuda", None): "In this image, we see the Statue of Liberty, the Hudson River,", ("rocm", (9, 5)): "In this image, we see the Statue of Liberty, the New York City", } ) EXPECTED_GENERATED_TEXT = expected_generated_texts.get_expectation() self.assertEqual(generated_texts[0], EXPECTED_GENERATED_TEXT) @slow @require_bitsandbytes def test_integration_test_4bit_batch2(self): # Let' s make sure we test the preprocessing to replace what is used model = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b-base", quantization_config=BitsAndBytesConfig(load_in_4bit=True) ) from datasets import load_dataset dataset = load_dataset("nielsr/docvqa_1200_examples", split="test") text = [f"{dataset[40]['query']['en']}", f"{dataset[41]['query']['en']}"] images = [[dataset[40]["image"]], [dataset[41]["image"]]] inputs = self.processor(text=text, images=images, padding=True, return_tensors="pt").to(torch_device) generated_ids = model.generate(**inputs, max_new_tokens=64) batched_generated_texts = self.processor.batch_decode(generated_ids, skip_special_tokens=True) text = f"{dataset[40]['query']['en']}" images = dataset[40]["image"] inputs = self.processor(text=text, images=images, padding=True, return_tensors="pt").to(torch_device) generated_ids = model.generate(**inputs, max_new_tokens=64) generated_text_0 = self.processor.batch_decode(generated_ids, skip_special_tokens=True) text = f"{dataset[41]['query']['en']}" images = dataset[41]["image"] inputs = self.processor(text=text, images=images, padding=True, return_tensors="pt").to(torch_device) generated_ids = model.generate(**inputs, max_new_tokens=64) generated_text_1 = self.processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(batched_generated_texts[0], generated_text_0[0]) self.assertEqual(batched_generated_texts[1], generated_text_1[0]) @pytest.mark.flash_attn_test @require_flash_attn @require_torch_accelerator @require_bitsandbytes def test_flash_attn_2_eager_equivalence(self): # Create inputs text = "In this image, we see" images = self.image1 inputs = self.processor(text=text, images=images, return_tensors="pt", padding=True) inputs.to(torch_device) # Eager model model_eager = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b-base", attn_implementation="eager", quantization_config=BitsAndBytesConfig(load_in_4bit=True), ) generated_ids_eager = model_eager.generate(**inputs, max_new_tokens=10) generated_texts_eager = self.processor.batch_decode(generated_ids_eager, skip_special_tokens=True) del model_eager # Flash Attention 2 model model_flash_attention_2 = Idefics2ForConditionalGeneration.from_pretrained( "HuggingFaceM4/idefics2-8b-base", attn_implementation="flash_attention_2", quantization_config=BitsAndBytesConfig(load_in_4bit=True), ) generated_ids_flash_attention_2 = model_flash_attention_2.generate(**inputs, max_new_tokens=10) generated_texts_flash_attention_2 = self.processor.batch_decode( generated_ids_flash_attention_2, skip_special_tokens=True ) self.assertEqual(generated_texts_eager[0], generated_texts_flash_attention_2[0])