# Copyright 2023 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 Idefics model.""" import unittest from functools import cached_property import pytest from parameterized import parameterized from transformers import BitsAndBytesConfig, IdeficsConfig, is_torch_available, is_vision_available from transformers.testing_utils import ( TestCasePlus, require_bitsandbytes, require_torch, require_vision, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin 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 transformers import IdeficsForVisionText2Text, IdeficsModel, IdeficsProcessor from transformers.models.idefics.configuration_idefics import IdeficsPerceiverConfig, IdeficsVisionConfig if is_vision_available(): from PIL import Image class IdeficsModelTester: def __init__( self, parent, batch_size=1, seq_length=7, image_size=30, patch_size=2, num_channels=3, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, alpha_initializer="ones", num_labels=3, scope=None, modality_type_vocab_size=2, vision_embed_dim=32, vision_patch_size=2, vision_image_size=30, vision_num_attention_heads=4, vision_num_hidden_layers=2, vision_intermediate_size=37, perceiver_qk_layer_norms_perceiver=False, perceiver_resampler_depth=2, perceiver_resampler_head_dim=8, perceiver_resampler_n_heads=2, perceiver_resampler_n_latents=16, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids 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.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.alpha_initializer = alpha_initializer self.num_labels = num_labels self.scope = scope self.modality_type_vocab_size = modality_type_vocab_size self.vision_embed_dim = vision_embed_dim self.vision_patch_size = vision_patch_size self.vision_image_size = vision_image_size self.vision_num_attention_heads = vision_num_attention_heads self.vision_num_hidden_layers = vision_num_hidden_layers self.vision_intermediate_size = vision_intermediate_size self.vision_config = IdeficsVisionConfig( embed_dim=self.vision_embed_dim, patch_size=self.vision_patch_size, image_size=self.vision_image_size, num_attention_heads=self.vision_num_attention_heads, num_hidden_layers=self.vision_num_hidden_layers, intermediate_size=self.vision_intermediate_size, ).to_dict() self.perceiver_qk_layer_norms_perceiver = perceiver_qk_layer_norms_perceiver self.perceiver_resampler_depth = perceiver_resampler_depth self.perceiver_resampler_head_dim = perceiver_resampler_head_dim self.perceiver_resampler_n_heads = perceiver_resampler_n_heads self.perceiver_resampler_n_latents = perceiver_resampler_n_latents self.perceiver_config = IdeficsPerceiverConfig( qk_layer_norms_perceiver=self.perceiver_qk_layer_norms_perceiver, resampler_depth=self.perceiver_resampler_depth, resampler_head_dim=self.perceiver_resampler_head_dim, resampler_n_heads=self.perceiver_resampler_n_heads, resampler_n_latents=self.perceiver_resampler_n_latents, ) # we set the expected sequence length (which is used in several tests) # this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1 def prepare_config_and_inputs(self, num_images=1, interpolate_pos_encoding=False, image_expansion=0): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) pixel_values = floats_tensor( [ self.batch_size, num_images, self.num_channels, self.image_size + image_expansion, self.image_size + image_expansion, ] ) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, num_images]) config = self.get_config() return (config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding) def prepare_config_and_inputs_gate_tests(self): # Create a list of configs and inputs, to test 2 things: # 1. For the same image, the output should be different when image_attention_mask is filled with 0s vs filled with 1s. # 2. For 2 different images, the output should be the same when image_attention_mask is filled with 0s. interpolate_pos_encoding = False input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) pixel_values = floats_tensor( [ self.batch_size, 1, self.num_channels, self.image_size, self.image_size, ] ) pixel_values_list = [ pixel_values.clone(), pixel_values.clone(), pixel_values.clone().fill_(0.6), pixel_values.clone().fill_(0.3), ] attention_mask = None if self.use_input_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) image_attention_mask = random_attention_mask([self.batch_size, self.seq_length, 1]) image_attention_mask_list = [ image_attention_mask.clone().fill_(0), image_attention_mask.clone().fill_(1), image_attention_mask.clone().fill_(0), image_attention_mask.clone().fill_(0), ] config = self.get_config() inputs_list = [] for pixel_values, image_attention_mask in zip(pixel_values_list, image_attention_mask_list): inputs_list.append( { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "image_attention_mask": image_attention_mask, "interpolate_pos_encoding": interpolate_pos_encoding, } ) inputs_w_same_img = inputs_list[:2] inputs_w_0_img_attn = inputs_list[2:] return config, inputs_w_same_img, inputs_w_0_img_attn def get_config(self): return IdeficsConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, 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, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, alpha_initializer=self.alpha_initializer, num_labels=self.num_labels, modality_type_vocab_size=self.modality_type_vocab_size, vision_config=self.vision_config, ) def create_and_check_model( self, config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding, ): model = IdeficsModel(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, pixel_values=pixel_values, image_attention_mask=image_attention_mask, interpolate_pos_encoding=interpolate_pos_encoding, ) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, input_ids.shape[1], self.hidden_size) ) def create_and_check_model_gen( self, config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding, ): model = IdeficsForVisionText2Text(config) model.to(torch_device) model.eval() model.generate( input_ids, attention_mask=input_mask, pixel_values=pixel_values, image_attention_mask=image_attention_mask, interpolate_pos_encoding=interpolate_pos_encoding, max_length=self.seq_length + 2, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, pixel_values, image_attention_mask, interpolate_pos_encoding, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": input_mask, "pixel_values": pixel_values, "image_attention_mask": image_attention_mask, "interpolate_pos_encoding": interpolate_pos_encoding, } return config, inputs_dict def prepare_pixel_values(self): return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) @require_torch class IdeficsModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (IdeficsModel, IdeficsForVisionText2Text) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": IdeficsModel, "image-text-to-text": IdeficsForVisionText2Text, "any-to-any": IdeficsForVisionText2Text, } if is_torch_available() else {} ) def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) # XXX: IdeficsForVisionText2TextTest has no MODEL_FOR group yet, but it should be the same # as MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, so for now manually changing to do the right thing # as super won't do it if return_labels: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) @unittest.skip("Idefics requires both text and image inputs which is currently not done in this test.") def test_eager_matches_sdpa_inference( self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels ): pass def test_model_outputs_equivalence(self): try: orig = self.all_model_classes # IdeficsModel.forward doesn't have labels input arg - only IdeficsForVisionText2Text does self.all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else () super().test_model_outputs_equivalence() finally: self.all_model_classes = orig def setUp(self): self.model_tester = IdeficsModelTester(self) self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model_single_image(self): config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=1, interpolate_pos_encoding=False, image_expansion=0 ) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_multiple_images(self): config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=2, interpolate_pos_encoding=False, image_expansion=0 ) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_image_pos_embeddings_interpolation_single_image(self): config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=1, interpolate_pos_encoding=True, image_expansion=2 ) self.model_tester.create_and_check_model(*config_and_inputs) config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=1, interpolate_pos_encoding=True, image_expansion=0 ) self.model_tester.create_and_check_model(*config_and_inputs) def test_model_with_image_pos_embeddings_interpolation_multiple_images(self): config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=2, interpolate_pos_encoding=True, image_expansion=2 ) self.model_tester.create_and_check_model(*config_and_inputs) config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=2, interpolate_pos_encoding=True, image_expansion=0 ) self.model_tester.create_and_check_model(*config_and_inputs) def test_generate_with_image_pos_embeddings_interpolation_single_image(self): config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=1, interpolate_pos_encoding=True, image_expansion=2 ) self.model_tester.create_and_check_model_gen(*config_and_inputs) def test_generate_with_image_pos_embeddings_interpolation_multiple_images(self): config_and_inputs = self.model_tester.prepare_config_and_inputs( num_images=2, interpolate_pos_encoding=True, image_expansion=2 ) self.model_tester.create_and_check_model_gen(*config_and_inputs) def test_cross_attention_gates(self): config, inputs_w_same_img, inputs_w_0_img_attn = self.model_tester.prepare_config_and_inputs_gate_tests() model = IdeficsModel(config=config).to(torch_device) model.eval() test_1_results = [] for inputs in inputs_w_same_img: with torch.no_grad(): last_hidden_states = model(**inputs).last_hidden_state last_hidden_states = model(**inputs).last_hidden_state test_1_results.append(last_hidden_states) self.assertNotEqual(test_1_results[0].sum().item(), test_1_results[1].sum().item()) test_2_results = [] for inputs in inputs_w_0_img_attn: with torch.no_grad(): last_hidden_states = model(**inputs).last_hidden_state test_2_results.append(last_hidden_states) self.assertEqual(test_2_results[0].sum().item(), test_2_results[1].sum().item()) def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") for model_class in self.all_model_classes: # IdeficsModel does not support training, users should use # IdeficsForVisionText2Text for this purpose if model_class == IdeficsModel: self.skipTest(reason="IdeficsModel does not support training") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") for model_class in self.all_model_classes: # IdeficsModel does not support training, users should use # IdeficsForVisionText2Text for this purpose if model_class == IdeficsModel: self.skipTest(reason="IdeficsModel does not support training") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""") def test_retain_grad_hidden_states_attentions(self): return @pytest.mark.generate @unittest.skip(reason="""IDEFICS cannot generate with no images provided!""") def test_generate_without_input_ids(self): pass @pytest.mark.generate @unittest.skip(reason="""IDEFICS cannot generate with no images provided!""") def test_generate_continue_from_inputs_embeds(self): pass def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertFalse(attentions[0] is None) self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertFalse(self_attentions[0] is None) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) @slow def test_model_from_pretrained(self): model_name = "HuggingFaceM4/idefics-9b" model = IdeficsModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip("Idefics has a hard requirement on SDPA") def test_sdpa_can_dispatch_non_composite_models(self): pass @unittest.skip(reason="Idefics can't do text-only inference") def test_generate_from_random_inputs_embeds( self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels ): pass @pytest.mark.generate def test_left_padding_compatibility(self): # Overwrite -- Idefics needs to prepare `image_attention_mask`, and it must be padded accordingly _, inputs_dict = self.prepare_config_and_inputs_for_generate() input_ids = inputs_dict["input_ids"] image_attention_mask = inputs_dict["image_attention_mask"] pad_size_img = (input_ids.shape[0], 32, image_attention_mask.shape[-1]) extra_img_mask = torch.zeros(pad_size_img, dtype=image_attention_mask.dtype, device=torch_device) padded_image_attention_mask = torch.cat([extra_img_mask, image_attention_mask], dim=1) # `image_attention_mask` is randomly generated in `prepare_config_and_inputs_for_generate`, and it must match # its padded version for the test to be valid -- we need to pass both unpadded_custom_inputs = {"image_attention_mask": image_attention_mask} padded_custom_inputs = {"image_attention_mask": padded_image_attention_mask} super().test_left_padding_compatibility( unpadded_custom_inputs=unpadded_custom_inputs, padded_custom_inputs=padded_custom_inputs ) @unittest.skip(reason="Idefics can't do text-only inference (test filters non-text inputs)") def test_eager_padding_matches_padding_free_with_position_ids(self): pass @unittest.skip(reason="Idefics can't do text-only inference (test filters non-text inputs)") def test_sdpa_padding_matches_padding_free_with_position_ids(self): pass @require_torch class IdeficsForVisionText2TextTest(IdeficsModelTest, GenerationTesterMixin, unittest.TestCase): all_model_classes = (IdeficsForVisionText2Text,) if is_torch_available() else () def setUp(self): self.model_tester = IdeficsModelTester( self, modality_type_vocab_size=3, ) self.config_tester = ConfigTester(self, config_class=IdeficsConfig, hidden_size=37) @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) @unittest.skip("Idefics requires both text and image inputs which is currently not done in this test.") def test_eager_matches_sdpa_inference( self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels ): pass @pytest.mark.generate def test_generate_continue_from_past_key_values(self): """Overwrite because IDEFICS needs image attention mask to be also processed""" # Tests that we can continue generating from past key values, returned from a previous `generate` call for model_class in self.all_generative_model_classes: config, inputs = self.model_tester.prepare_config_and_inputs_for_common() # Let's make it always: # 1. use cache (for obvious reasons) # 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which # would make the test flaky (e.g. EOS is generated on iteration 1 on both generations, but the # continuation would force it to generate beyond an EOS token) # 3. ignore `token_type_ids` for simplicity # 4. ignore `forced_eos_token_id`, which requires further manipulation of the continuation inputs and is # active by default on some models # 5. ignore `encoder_no_repeat_ngram_size`, which is set by default in some encoder-decoder models. When # we use their decoder as a stand-alone model, `encoder_no_repeat_ngram_size` actually prevents # repetition exclusively from the prompt. This test relies on comparing one call vs 2 calls # with cache, what is considered a prompt is different in the two cases. model = model_class(config).to(torch_device) model.eval() model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1 model.generation_config.forced_eos_token_id = None model.generation_config.encoder_no_repeat_ngram_size = 0 model.generation_config.use_cache = True # Traditional way of generating text, with `return_dict_in_generate` to return the past key values outputs = model.generate(**inputs, do_sample=False, max_new_tokens=4, return_dict_in_generate=True) # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the # inputs may need to be tweaked across `generate` calls (like the attention mask). outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=3, return_dict_in_generate=True) # Continue from the tokens generated above, preparing the inputs accordingly inputs["past_key_values"] = outputs_cached.past_key_values new_attention_len = outputs_cached.sequences.shape[-1] inputs["input_ids"] = outputs_cached.sequences if "attention_mask" in inputs: inputs["attention_mask"] = torch.nn.functional.pad( inputs["attention_mask"], (0, new_attention_len - inputs["attention_mask"].shape[1]), mode="constant", value=1, ) if "image_attention_mask" in inputs: inputs["image_attention_mask"] = inputs["image_attention_mask"][:, -1:, :] outputs_cached = model.generate(**inputs, do_sample=False, max_new_tokens=1, return_dict_in_generate=True) # The two sets of generated text and past kv should be equal to each other self.assertListEqual(outputs.sequences.tolist(), outputs_cached.sequences.tolist()) self._check_caches_are_equal(outputs.past_key_values, outputs_cached.past_key_values) @pytest.mark.generate def test_generate_without_input_ids(self): """Overwrite because IDEFICS needs image attention mask to be also processed and requires image at input always.""" config, input_dict = self.prepare_config_and_inputs_for_generate() pixel_values = input_dict["pixel_values"] image_attention_mask = input_dict["image_attention_mask"][:, -1:, :] # hack in case they are equal, otherwise the attn mask will be [0] if config.bos_token_id == config.pad_token_id: config.pad_token_id = None for model_class in self.all_generative_model_classes: model = model_class(config).to(torch_device) model.eval() output_ids_generate = model.generate( pixel_values=pixel_values, image_attention_mask=image_attention_mask, do_sample=False, max_new_tokens=self.max_new_tokens, remove_invalid_values=True, ) self.assertIsNotNone(output_ids_generate) @pytest.mark.generate def test_generate_continue_from_inputs_embeds(self): """Overwrite for IDEFICS: Ensure image attention mask is processed while continuing from `inputs_embeds`.""" for model_class in self.all_generative_model_classes: config, inputs = self.model_tester.prepare_config_and_inputs_for_common() print(inputs) model = model_class(config).to(torch_device).eval() model.generation_config.pad_token_id = model.generation_config.eos_token_id = -1 model.generation_config.forced_eos_token_id = None model.generation_config.use_cache = True input_ids = inputs.pop("input_ids") input_embeds = model.get_input_embeddings()(input_ids) generation_kwargs = { "return_dict_in_generate": True, "do_sample": False, } inputs["inputs_embeds"] = input_embeds # Traditional way of generating text, with `return_dict_in_generate` to return the past key values outputs = model.generate(**inputs, max_new_tokens=4, **generation_kwargs) # Let's generate again, but passing the past key values in between (3 + 1 = 4 tokens). Note that the # inputs may need to be tweaked across `generate` calls (like the attention mask). initial_output = model.generate(**inputs, max_new_tokens=3, **generation_kwargs) inputs["past_key_values"] = initial_output.past_key_values new_attention_len = input_ids.shape[1] + initial_output.sequences.shape[-1] continued_embeds = torch.cat([input_embeds, model.get_input_embeddings()(initial_output.sequences)], dim=1) inputs["inputs_embeds"] = continued_embeds if "attention_mask" in inputs: inputs["attention_mask"] = torch.nn.functional.pad( inputs["attention_mask"], (0, new_attention_len - inputs["attention_mask"].shape[1]), mode="constant", value=1, ) if "image_attention_mask" in inputs: inputs["image_attention_mask"] = inputs["image_attention_mask"][..., -1:, :] cached_output = model.generate(**inputs, max_new_tokens=1, **generation_kwargs) # Verify that the combined outputs match the full generation. combined_output_sequences = torch.concat([initial_output.sequences, cached_output.sequences], axis=1) self.assertListEqual(outputs.sequences.tolist(), combined_output_sequences.tolist()) self._check_caches_are_equal(outputs.past_key_values, cached_output.past_key_values) def _check_attentions_for_generate( self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values ): """ Overwrite from generation tests because Idefics has only SDPA layers. Do not skip because we still want generation tests to run. Rather we can remove checks for shape. """ pass @unittest.skip(reason="We only test the model that takes in multiple images") def test_custom_4d_attention_mask(self): pass @unittest.skip(reason="IDEFICS cannot compile due to dynamic control flow when checking inputs") def test_generate_with_static_cache(self): pass @unittest.skip(reason="We only test the model that takes in multiple images") def test_model(self): pass @unittest.skip(reason="We only test the model that takes in multiple images") def test_for_token_classification(self): pass @unittest.skip(reason="""IDEFICS does not support retaining the gradients of the hidden states and attention""") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip("Idefics has a hard requirement on SDPA") def test_sdpa_can_dispatch_non_composite_models(self): pass @unittest.skip( "Idefics has a separate test runner for generation tests with complex inheritance, causing this check to fail" ) def test_generation_tester_mixin_inheritance(self): pass @unittest.skip(reason="Idefics can't do text-only inference") def test_generate_from_random_inputs_embeds( self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels ): pass @require_torch @require_vision class IdeficsModelIntegrationTest(TestCasePlus): @cached_property def default_processor(self): return ( IdeficsProcessor.from_pretrained("HuggingFaceM4/idefics-9b", revision="refs/pr/11") if is_vision_available() else None ) @require_bitsandbytes @slow def test_inference_natural_language_visual_reasoning(self): cat_image_path = self.tests_dir / "fixtures/tests_samples/COCO/000000039769.png" cats_image_obj = Image.open(cat_image_path) # 2 cats dogs_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_nlvr2/raw/main/image1.jpeg" prompts = [ [ "User:", dogs_image_url, "Describe this image.\nAssistant: An image of two dogs.\n", "User:", cats_image_obj, "Describe this image.\nAssistant:", ], [ "User:", cats_image_obj, "Describe this image.\nAssistant: An image of two kittens.\n", "User:", dogs_image_url, "Describe this image.\nAssistant:", ], ] # the CI gpu is small so using quantization to fit quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype="float16", ) model = IdeficsForVisionText2Text.from_pretrained( "HuggingFaceM4/idefics-9b", quantization_config=quantization_config, device_map="auto" ) processor = self.default_processor inputs = processor(text=prompts, return_tensors="pt", padding="longest").to(torch_device) generated_ids = model.generate(**inputs, max_length=100) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) # keep for debugging for i, t in enumerate(generated_text): t = bytes(t, "utf-8").decode("unicode_escape") print(f"{i}:\n{t}\n") self.assertIn("image of two cats", generated_text[0]) self.assertIn("image of two dogs", generated_text[1])