# 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 chameleon model.""" import copy import unittest import requests from transformers import BitsAndBytesConfig, ChameleonConfig, is_torch_available, is_vision_available from transformers.testing_utils import ( Expectations, require_bitsandbytes, require_read_token, require_torch, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_vision_available(): from PIL import Image if is_torch_available(): import torch from transformers import ( ChameleonForConditionalGeneration, ChameleonModel, ChameleonProcessor, ) class ChameleonModelTester: def __init__( self, parent, batch_size=13, seq_length=35, is_training=False, use_input_mask=True, use_labels=True, vocab_size=99, image_token_id=4, hidden_size=32, num_hidden_layers=2, num_attention_heads=2, num_key_value_heads=2, 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, vq_num_embeds=5, vq_embed_dim=5, vq_channel_multiplier=[1, 2], vq_img_token_start_id=10, # has to be less than vocab size when added with vq_num_embeds 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.image_token_id = image_token_id self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_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 self.vq_num_embeds = vq_num_embeds self.vq_embed_dim = vq_embed_dim self.vq_channel_multiplier = vq_channel_multiplier self.vq_img_token_start_id = vq_img_token_start_id 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 = torch.tril(torch.ones_like(input_ids).to(torch_device)) sequence_labels = None token_labels = None choice_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) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): # create dummy vocab map for image2bpe mapping if it needs remapping # we assume that vocab size is big enough to account for image tokens somewhere in the beginning # same way as in real ckpt, when img tokens are in first half of embeds # we will need "vq_num_embeds" amount of tokens vocab_map = {i: chr(i) for i in range(self.vocab_size)} vocab_map[self.image_token_id] = "" start = self.vq_img_token_start_id end = self.vq_img_token_start_id + self.vq_num_embeds for i in range(start, end): image_token_infix = "".join(chr(ord("A") + int(c)) for c in str(i)) # dummy str for each image token, anything starting with IMGIMG vocab_map[i] = f"IMGIMG{image_token_infix}Z" return ChameleonConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, vocabulary_map={v: k for k, v in vocab_map.items()}, vq_config=self.get_vq_config(), ) def get_vq_config(self): return { "embed_dim": self.vq_embed_dim, "num_embeddings": self.vq_num_embeds, "latent_channels": self.vq_embed_dim, "in_channels": 3, "base_channels": 32, # we have a GroupNorm of 32 groups, so can't do less "channel_multiplier": self.vq_channel_multiplier, } def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = ChameleonModel(config=config) model.to(torch_device) model.eval() 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)) 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, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class ChameleonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (ChameleonModel, ChameleonForConditionalGeneration) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": ChameleonModel, "text-generation": ChameleonForConditionalGeneration, } if is_torch_available() else {} ) def setUp(self): self.model_tester = ChameleonModelTester(self) self.config_tester = ConfigTester(self, config_class=ChameleonConfig, 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("Chameleon forces some token ids to be -inf!") def test_batching_equivalence(self): pass class ChameleonVision2SeqModelTester(ChameleonModelTester): def __init__(self, parent, image_size=10, **kwargs): super().__init__(parent, **kwargs) self.image_size = image_size self.image_seq_length = 25 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids[input_ids == self.image_token_id] = self.pad_token_id input_ids[:, : self.image_seq_length] = self.image_token_id attention_mask = input_ids.ne(self.pad_token_id).to(torch_device) pixel_values = floats_tensor([self.batch_size, 3, self.image_size, self.image_size]) config = self.get_config() return config, input_ids, attention_mask, pixel_values def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values} return config, inputs_dict @require_torch class ChameleonVision2SeqModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (ChameleonModel, ChameleonForConditionalGeneration) if is_torch_available() else () pipeline_model_mapping = ( { "image-text-to-text": ChameleonForConditionalGeneration, "any-to-any": ChameleonForConditionalGeneration, } if is_torch_available() else {} ) def setUp(self): self.model_tester = ChameleonVision2SeqModelTester(self) self.config_tester = ConfigTester(self, config_class=ChameleonConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip("Chameleon forces some token ids to be -inf!") def test_batching_equivalence(self): pass @unittest.skip("Chameleon cannot do offload because it uses `self.linear.weight` in forward") def test_cpu_offload(self): pass @unittest.skip("Chameleon cannot do offload because it uses `self.linear.weight` in forward") def test_disk_offload_bin(self): pass @unittest.skip("Chameleon cannot do offload because it uses `self.linear.weight` in forward") def test_disk_offload_safetensors(self): pass @unittest.skip("Chameleon applies key/query norm which doesn't work with packing") def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self): pass @unittest.skip("Chameleon applies key/query norm which doesn't work with packing") def test_eager_padding_matches_padding_free_with_position_ids(self): pass @unittest.skip("Chameleon applies key/query norm which doesn't work with packing") def test_sdpa_padding_matches_padding_free_with_position_ids(self): pass def test_mismatching_num_image_tokens(self): """ Tests that VLMs through an error with explicit message saying what is wrong when number of images don't match number of image tokens in the text. Also we need to test multi-image cases when one prompr has multiple image tokens. """ config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config).to(torch_device) model.eval() curr_input_dict = copy.deepcopy(input_dict) # the below tests modify dict in-place _ = model(**curr_input_dict) # successful forward with no modifications # remove one image but leave the image token in text curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-1:, ...] with self.assertRaises(ValueError): _ = model(**curr_input_dict) # simulate multi-image case by concatenating inputs where each has exactly one image/image-token input_ids = curr_input_dict["input_ids"][:1] pixel_values = curr_input_dict["pixel_values"][:1] input_ids = torch.cat([input_ids, input_ids], dim=0) # one image and two image tokens raise an error with self.assertRaises(ValueError): _ = model(input_ids=input_ids, pixel_values=pixel_values) # two images and two image tokens don't raise an error pixel_values = torch.cat([pixel_values, pixel_values], dim=0) _ = model(input_ids=input_ids, pixel_values=pixel_values) @require_torch class ChameleonIntegrationTest(unittest.TestCase): @slow @require_bitsandbytes @require_read_token def test_model_7b(self): model = ChameleonForConditionalGeneration.from_pretrained( "facebook/chameleon-7b", quantization_config=BitsAndBytesConfig(load_in_4bit=True), device_map="auto" ) processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b") image = Image.open( requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw ) prompt = "Describe what do you see here and tell me about the history behind it?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device, torch.float16) # greedy generation outputs EXPECTED_TEXT_COMPLETIONS = Expectations( { ("xpu", 3): ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Altair. The star map is set against a black background, with the constellations visible in the night'], ("cuda", 7): ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Alpha Centauri. The star map is a representation of the night sky, showing the positions of stars in'], ("cuda", 8): ['Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot representing the position of the star Alpha Centauri. Alpha Centauri is the brightest star in the constellation Centaurus and is located'], } ) # fmt: skip EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation() generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False) text = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @slow @require_bitsandbytes @require_read_token def test_model_7b_batched(self): model = ChameleonForConditionalGeneration.from_pretrained( "facebook/chameleon-7b", quantization_config=BitsAndBytesConfig(load_in_4bit=True), device_map="auto" ) processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b") image = Image.open( requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw ) image_2 = Image.open( requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw ) prompts = [ "Describe what do you see here and tell me about the history behind it?", "What constellation is this image showing?", ] inputs = processor(images=[image, image_2], text=prompts, padding=True, return_tensors="pt").to( model.device, torch.float16 ) # greedy generation outputs EXPECTED_TEXT_COMPLETIONS = Expectations( { ("xpu", 3): [ 'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot in the center representing the star Alpha Centauri. The star map is a representation of the night sky, showing the positions of stars in', 'What constellation is this image showing?The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.', ], ("cuda", 7): [ 'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot representing the position of the star Alpha Centauri. Alpha Centauri is the brightest star in the constellation Centaurus and is located', 'What constellation is this image showing?The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.', ], ("cuda", 8): [ 'Describe what do you see here and tell me about the history behind it?The image depicts a star map, with a bright blue dot representing the position of the star Alpha Centauri. Alpha Centauri is the brightest star in the constellation Centaurus and is located', 'What constellation is this image showing?The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.The image shows the constellation of Orion.', ], } ) # fmt: skip EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation() generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False) text = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @slow @require_bitsandbytes @require_read_token def test_model_7b_multi_image(self): model = ChameleonForConditionalGeneration.from_pretrained( "facebook/chameleon-7b", quantization_config=BitsAndBytesConfig(load_in_4bit=True), device_map="auto" ) processor = ChameleonProcessor.from_pretrained("facebook/chameleon-7b") image = Image.open( requests.get("https://nineplanets.org/wp-content/uploads/2020/12/the-big-dipper-1.jpg", stream=True).raw ) image_2 = Image.open( requests.get("https://www.kxan.com/wp-content/uploads/sites/40/2020/10/ORION.jpg", stream=True).raw ) prompt = "What do these two images have in common?" inputs = processor(images=[image, image_2], text=prompt, return_tensors="pt").to(model.device, torch.float16) # greedy generation outputs EXPECTED_TEXT_COMPLETION = ['What do these two images have in common?The two images show a connection between the night sky and the internet. The first image shows a starry night sky, with the stars arranged in a pattern that resembles the structure of the internet. The'] # fmt: skip generated_ids = model.generate(**inputs, max_new_tokens=40, do_sample=False) text = processor.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text)