LLamaRecipes/recipes/quickstart/finetuning/datasets/vqa_dataset_old.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 3 Community License Agreement.


import copy
from datasets import load_dataset
import itertools
import torch
# check system prompt token seq or user prompt token seq is in the current token list
def check_header(targets,seq):
    for i in range(len(seq)-3):
        if seq[i:i+3] in targets:
            return True
    return False
def replace_target(target,seq):
    for i in range(len(seq)-3):
        if seq[i:i+3] == target:
            seq[i],seq[i+1],seq[i+2] = -100,-100,-100
    return seq
def tokenize_dialog(dialog, images, processor):
    # If vocab size is above 128000, use the chat template to generate the tokens as it is from Llama 3 family models
    text_prompt = processor.apply_chat_template(dialog)
    #print("text_prompt",text_prompt)
    batch = processor(images=images, text=text_prompt,padding = True, return_tensors="pt")    
    labels = copy.copy(batch["input_ids"].tolist()[0])
    eot_indices = [i for i,n in enumerate(labels) if n == 128009]
    last_idx = 0
    # system prompt header "<|start_header_id|>system<|end_header_id|>" has been tokenized to [128006, 9125, 128007]
    # user prompt header "<|start_header_id|>user<|end_header_id|>" has been tokenized to [128006, 882, 128007]
    prompt_header_seqs = [[128006, 9125, 128007],[128006, 882, 128007]]
    for n, idx in enumerate(eot_indices):
        current_seq = labels[last_idx:idx+1]
        if check_header(prompt_header_seqs,current_seq):
            # found prompt header, indicating that this seq should be masked
            labels[last_idx:idx+1] = [-100] * (idx-last_idx+1)
        else:
            last_idx = idx+1
        # Lastly mask all the assistant header prompt <|start_header_id|>assistant<|end_header_id|>, which has been tokenized to [128006, 78191, 128007]
    assistant_header_seq = [128006, 78191, 128007]
    labels = replace_target(assistant_header_seq,labels)
    #print("labels",labels)
    # print("pixel_values .shape",batch["pixel_values"].shape)
    # print("batch_size, num_concurrent_media, num_tiles, num_channels, height, width = pixel_values.shape")

    batch["labels"] = torch.tensor(labels)
    #pixel_values .shape torch.Size([1, 1, 4, 3, 560, 560])
    batch["pixel_values"] = torch.squeeze(batch["pixel_values"], 1)
    # pixel_values .shape torch.Size([1, 4, 3, 560, 560])
    print("pixel_values .shape",batch["pixel_values"].shape)
    # exit()
    # combined_tokens = {
    #     # "input_ids": list(itertools.chain(*(t for t in dialog_tokens))),
    #     # "labels": list(itertools.chain(*(t for t in labels_tokens))),
    #     "input_ids": dialog_tokens,
    #     "labels": labels,
    #     "attention_mask": [1]*len(dialog_tokens),
    #     "pixel_values": batch["pixel_values"],
    #     "aspect_ratio_ids": batch["aspect_ratio_ids"],
    #     "aspect_ratio_mask": batch["aspect_ratio_mask"],
    #     "cross_attention_mask": batch["cross_attention_mask"]
    # }
    # input_ids =  list(itertools.chain(*(t for t in dialog_tokens))),
    # labels = list(itertools.chain(*(t for t in labels_tokens))),
    # attention_mask = [1]*len(list(itertools.chain(*(t for t in dialog_tokens)))),
    # pixel_values =  batch["pixel_values"],
    # image_sizes = batch["image_sizes"]
#    print("combined_tokens",combined_tokens[image_sizes])
    
    return batch
def image_tokenize(sample, processor):
    processor.tokenizer.padding_side = "right" # during training, one always uses padding on the right
    images,sample_text = sample["images"],sample["messages"]
    dialog = []
    for line in sample_text:
        content = []
        messages = line["content"]
        role = line["role"]
        for message in messages:
            if message["type"] == "image":
                content.append({"type": "image"})
            elif message["type"] == "text":
                content.append({"type": "text", "text": message["text"].strip()})
        dialog.append({"role": role,"content":content})
    return tokenize_dialog(dialog,images, processor)


def get_custom_dataset(dataset_config, processor, split, split_ratio=0.9):
    # load_dataset will return DatasetDict that contains all the data in the train set
    dataset_dict = load_dataset("remyxai/vqasynth_spacellava")
    dataset = dataset_dict[split]
    dataset = dataset.select(range(100))
    tokenized_datasets = dataset.map(lambda x: image_tokenize(x, processor))
    tokenized_datasets = tokenized_datasets.remove_columns(dataset.column_names)
    return tokenized_datasets