# 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 Fuyu model.""" import io import unittest import pytest import requests from parameterized import parameterized from transformers import FuyuConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_torch_accelerator, slow from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_vision_available(): from PIL import Image if is_torch_available() and is_vision_available(): from transformers import FuyuProcessor if is_torch_available(): from transformers import FuyuForCausalLM, FuyuModel class FuyuModelTester: def __init__( self, parent, batch_size=13, seq_length=7, image_size=30, patch_size=15, num_channels=3, is_training=True, use_input_mask=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, num_labels=3, num_choices=4, pad_token_id=0, scope=None, ): 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_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.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id 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]) sequence_labels = None token_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) config = self.get_config() return config, input_ids, input_mask, sequence_labels, token_labels def get_config(self): return FuyuConfig( 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, pad_token_id=self.pad_token_id, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class FuyuModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( FuyuModel, FuyuForCausalLM, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"text-generation": FuyuForCausalLM, "image-text-to-text": FuyuForCausalLM} if is_torch_available() else {} ) test_head_masking = False test_pruning = False test_cpu_offload = False test_disk_offload = False test_model_parallel = False def setUp(self): self.model_tester = FuyuModelTester(self) @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(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 @parameterized.expand([("random",), ("same",)]) @pytest.mark.generate @unittest.skip("Fuyu doesn't support assisted generation due to the need to crop/extend image patches indices") def test_assisted_decoding_matches_greedy_search(self): pass @pytest.mark.generate @unittest.skip("Fuyu doesn't support assisted generation due to the need to crop/extend image patches indices") def test_assisted_decoding_sample(self): pass # TODO: Fix me (once this model gets more usage) @unittest.skip(reason="Does not work on the tiny model.") def test_disk_offload_bin(self): super().test_disk_offload() # TODO: Fix me (once this model gets more usage) @unittest.skip(reason="Does not work on the tiny model.") def test_disk_offload_safetensors(self): super().test_disk_offload() # TODO: Fix me (once this model gets more usage) @unittest.skip(reason="Does not work on the tiny model.") def test_model_parallelism(self): super().test_model_parallelism() @unittest.skip(reason="Fuyu `prepare_inputs_for_generation` function doesn't have cache position.") def test_generate_continue_from_inputs_embeds(): pass @slow @require_torch_accelerator class FuyuModelIntegrationTest(unittest.TestCase): @cached_property def default_processor(self): return FuyuProcessor.from_pretrained("adept/fuyu-8b") @cached_property def default_model(self): return FuyuForCausalLM.from_pretrained("adept/fuyu-8b") def test_greedy_generation(self): processor = self.default_processor model = self.default_model url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bus.png" image = Image.open(io.BytesIO(requests.get(url).content)) text_prompt_coco_captioning = "Generate a coco-style caption.\n" inputs = processor(images=image, text=text_prompt_coco_captioning, return_tensors="pt") generated_ids = model.generate(**inputs, max_new_tokens=10) # take the last 8 tokens (in order to skip special \n\x04 characters) and decode them generated_text = processor.batch_decode(generated_ids[:, -8:], skip_special_tokens=True)[0] self.assertEqual(generated_text, "A blue bus parked on the side of a road.") """ @slow @require_torch_accelerator def test_model_8b_chat_greedy_generation_bus_color(self): EXPECTED_TEXT_COMPLETION = "The bus is blue.\n|ENDOFTEXT|" text_prompt_bus_color = "What color is the bus?\n" model_inputs_bus_color = self.processor(text=text_prompt_bus_color, images=self.bus_image_pil) generated_tokens = self.model.generate(**model_inputs_bus_color, max_new_tokens=10) text = self.processor.tokenizer.batch_decode(generated_tokens) end_sequence = text[0].split("\x04")[1] clean_sequence = ( end_sequence[: end_sequence.find("|ENDOFTEXT|") + len("|ENDOFTEXT|")] if "|ENDOFTEXT|" in end_sequence else end_sequence ) self.assertEqual(EXPECTED_TEXT_COMPLETION, clean_sequence) @slow @require_torch_accelerator def test_model_8b_chat_greedy_generation_chart_vqa(self): EXPECTED_TEXT_TOKENS = ["The","life expectancy","at","birth","of male","s in","","20","18","is","","80",".","7",".","\n","|ENDOFTEXT|",] # fmt: skip expected_text_completion = " ".join(EXPECTED_TEXT_TOKENS) # TODO make sure the end string matches text_prompt_chart_vqa = "What is the highest life expectancy at birth of male?\n" chart_image_url = ( "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/chart.png" ) chart_image_pil = Image.open(io.BytesIO(requests.get(chart_image_url).content)) model_inputs_chart_vqa = self.processor(text=text_prompt_chart_vqa, images=chart_image_pil) generated_tokens = self.model.generate(**model_inputs_chart_vqa, max_new_tokens=10) text = self.processor.tokenizer.batch_decode(generated_tokens) end_sequence = text[0].split("\x04")[1] clean_sequence = ( end_sequence[: end_sequence.find("|ENDOFTEXT|") + len("|ENDOFTEXT|")] if "|ENDOFTEXT|" in end_sequence else end_sequence ) self.assertEqual(expected_text_completion, clean_sequence) @slow @require_torch_accelerator def test_model_8b_chat_greedy_generation_bounding_box(self): EXPECTED_TEXT_COMPLETION = "\x00194213202244\x01|ENDOFTEXT|" text_prompt_bbox = "When presented with a box, perform OCR to extract text contained within it. If provided with text, generate the corresponding bounding box.\\nWilliams" # noqa: E231 bbox_image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/bbox_sample_image.png" bbox_image_pil = Image.open(io.BytesIO(requests.get(bbox_image_url).content)) model_inputs_bbox = self.processor(text=text_prompt_bbox, images=bbox_image_pil) generated_tokens = self.model.generate(**model_inputs_bbox, max_new_tokens=10) text = self.processor.tokenizer.batch_decode(generated_tokens) end_sequence = text[0].split("\x04")[1] clean_sequence = ( end_sequence[: end_sequence.find("|ENDOFTEXT|") + len("|ENDOFTEXT|")] if "|ENDOFTEXT|" in end_sequence else end_sequence ) self.assertEqual(EXPECTED_TEXT_COMPLETION, clean_sequence) """