import unittest import base64 from io import BytesIO from PIL import Image from transformers import AutoProcessor from pdelfin.train.dataloader import ( build_batch_query_response_vision_dataset, ) from pdelfin.train.dataprep import ( prepare_data_for_qwen2_training, build_finetuning_prompt ) class TestDataprep(unittest.TestCase): def testTokenizationMatches(self): ds = build_batch_query_response_vision_dataset( query_glob_path="s3://ai2-oe-data/jakep/openai_batch_data_v2_mini/*.jsonl", response_glob_path="s3://ai2-oe-data/jakep/openai_batch_done_v2_mini/*.json", ) example = ds[0] processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct") full_messages = [ { "role": "user", "content": [ { "type": "image", "image": example["input_prompt_image_base64"] # Placeholder }, {"type": "text", "text": build_finetuning_prompt(example["raw_page_text"])}, ], }, { "role": "assistant", "content": example["response"] } ] text = processor.apply_chat_template(full_messages, tokenize=False, add_generation_prompt=False) # Decode image from base64 main_image = Image.open(BytesIO(base64.b64decode(example["input_prompt_image_base64"]))) width, height = main_image.size assert 1800 <= max(width, height) <= 2200, f"Image size {width}x{height} invalid" main_image = main_image.resize((width // 2, height // 2), Image.LANCZOS) # Process inputs using processor inference_inputs = processor( text=[text], images=[main_image], padding=True, return_tensors="np", ) print(inference_inputs) print(inference_inputs["input_ids"].shape) training_inputs = prepare_data_for_qwen2_training(example, processor=processor) print(training_inputs) print(training_inputs["input_ids"].shape) print("Original tokenization") print(processor.tokenizer.decode(inference_inputs["input_ids"][0])) print("\n\n") print("Assembled tokenization") print(processor.tokenizer.decode(training_inputs["input_ids"])) print("\n\n") # Make sure that the token streams are the same self.assertEqual(processor.tokenizer.decode(inference_inputs["input_ids"][0]), processor.tokenizer.decode(training_inputs["input_ids"])) # Make sure that the labels are masked with -100s properly # You only want the last assistant generation itself to be not -100, and thus contributing to the loss # Find the positions where labels are not -100 non_masked_positions = training_inputs['labels'] != -100 # Extract the tokens at those positions label_tokens = training_inputs['input_ids'][non_masked_positions] # Decode those tokens decoded_labels = processor.tokenizer.decode(label_tokens) assistant_response_with_end = example["response"] + "<|im_end|>\n" # Assert that the decoded labels match the assistant's response with <|im_end|>\n self.assertEqual(decoded_labels, assistant_response_with_end)