data/data_utils.py

# Copyright 2025 Bytedance Ltd. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0


import math
import random
from PIL import Image

import torch
from torch.nn.attention.flex_attention import or_masks, and_masks


def create_sparse_mask(document_lens, split_lens, attn_modes, device):
    def causal_mask(b, h, q_idx, kv_idx):
        return q_idx >= kv_idx

    def full_and_noise_mask(b, h, q_idx, kv_idx):
        return (full_and_noise_seq_id[q_idx] == full_and_noise_seq_id[kv_idx]) & (full_and_noise_seq_id[q_idx] >= 0)

    def remove_noise_mask(b, h, q_idx, kv_idx):
        return (~((noise_seq_id[kv_idx] >= 0) & (noise_seq_id[q_idx] != noise_seq_id[kv_idx])))

    def sample_mask(b, h, q_idx, kv_idx):
        return document_id[q_idx] == document_id[kv_idx]

    full_and_noise_tmp = []
    noise_tmp = []

    for i, (length, model) in enumerate(zip(split_lens, attn_modes)):
        value = i if model in ['full', 'noise'] else -1
        full_and_noise_tmp.extend([value] * length)
        value_noise = i if model == 'noise' else -1
        noise_tmp.extend([value_noise] * length)

    full_and_noise_seq_id = torch.Tensor(full_and_noise_tmp).to(device)
    noise_seq_id = torch.Tensor(noise_tmp).to(device)

    document_id = torch.cat([torch.full((l,), i) for i, l in enumerate(document_lens, start=1)]).to(device)

    return and_masks(or_masks(causal_mask, full_and_noise_mask), remove_noise_mask, sample_mask)


def patchify(image, patch_size):
    p = patch_size
    c, h, w = image.shape
    assert h % p == 0 and w % p == 0
    image = image.reshape(c, h // p, p, w // p, p)
    image = torch.einsum("chpwq->hwpqc", image)
    image = image.reshape(-1, p**2 * c)
    return image


def get_flattened_position_ids_extrapolate(img_h, img_w, patch_size, max_num_patches_per_side):
    num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size
    coords_h = torch.arange(0, num_patches_h)
    coords_w = torch.arange(0, num_patches_w)
    pos_ids = (coords_h[:, None] * max_num_patches_per_side + coords_w).flatten()
    return pos_ids


def get_flattened_position_ids_interpolate(img_h, img_w, patch_size, max_num_patches_per_side):
    num_patches_h, num_patches_w = img_h // patch_size, img_w // patch_size
    boundaries = torch.arange(1 / max_num_patches_per_side, 1.0, 1 / max_num_patches_per_side)
    fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / num_patches_h)
    fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / num_patches_w)
    bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True)
    bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True)
    pos_ids = (bucket_coords_h[:, None] * max_num_patches_per_side + bucket_coords_w).flatten()
    return pos_ids


def prepare_attention_mask_per_sample(split_lens, attn_modes, device="cpu"):
    """
    nested_split_lens: A list of N lists of ints. Each int indicates the length of a split within 
        a sample, where each sample contains multiple splits with different attn modes.
    nested_attn_modes: whether to use full attn in each split.
    """
    sample_len = sum(split_lens)
    attention_mask = torch.zeros((sample_len, sample_len), dtype=torch.bool, device=device)

    csum = 0
    for s, attn_mode in zip(split_lens, attn_modes):
        assert attn_mode in ['causal', 'full', 'noise']
        if attn_mode == "causal":
            attention_mask[csum:csum + s, csum:csum + s] = torch.ones((s, s), device=device).tril()
            attention_mask[csum:csum + s, :csum] = 1
        else:
            attention_mask[csum:csum + s, csum:csum + s] = torch.ones((s, s))
            attention_mask[csum:csum + s, :csum] = 1
        csum += s

    csum = 0
    for s, attn_mode in zip(split_lens, attn_modes):
        if attn_mode == "noise":
            attention_mask[:, csum : csum + s] = torch.zeros((sample_len, s))
            attention_mask[csum : csum + s, csum : csum + s] = torch.ones((s, s))
        csum += s

    attention_mask = torch.zeros_like(attention_mask, dtype=torch.float).masked_fill_(
        ~attention_mask, float("-inf")
    )

    return attention_mask


def split_integer_exp_decay(S, ng_sample_decay=1.0):
    if ng_sample_decay == 1.0:
        N = random.randint(1, S)
    else:
        base = (1 - ng_sample_decay) / (1 - math.pow(ng_sample_decay, S))
        p = [base * math.pow(ng_sample_decay, i) for i in range(S)]
        N = random.choices(list(range(1, S + 1)), p, k=1)[0]
    cumsum = [0] + sorted(random.sample(range(1, S), N - 1)) + [S]
    result = [cumsum[i+1] - cumsum[i] for i in range(len(cumsum) - 1)]
    return result, cumsum


def pil_img2rgb(image):
    if image.mode == "RGBA" or image.info.get("transparency", None) is not None:
        image = image.convert("RGBA")
        white = Image.new(mode="RGB", size=image.size, color=(255, 255, 255))
        white.paste(image, mask=image.split()[3])
        image = white
    else:
        image = image.convert("RGB")

    return image


def add_special_tokens(tokenizer):
    from .constant import (
        ADD_REASONING_TOKENS,
        REASONING_SPECIAL_TOKENS,
        TOOL_CALL_START, TOOL_CALL_END,
        TOOL_RESULT_START, TOOL_RESULT_END,
        OBSERVATION_START, OBSERVATION_END,
        ANSWER_START, ANSWER_END,
        GENERATE_DEPTH_START, GENERATE_DEPTH_END,
        GENERATE_IMAGE_START, GENERATE_IMAGE_END,
    )

    all_special_tokens = []
    for k, v in tokenizer.special_tokens_map.items():
        if isinstance(v, str):
            all_special_tokens.append(v)
        elif isinstance(v, list):
            all_special_tokens += v

    # Existing tokens are picked up from vocab directly; new ones are
    # appended in this order so embedding-row layout is reproducible.
    base_tokens = [
        '<|im_start|>',
        '<|im_end|>',
        '<|vision_start|>',
        '<|vision_end|>',
    ]
    # BAGEL_REASONING_TOKENS=0 falls back to the pre-v5 behaviour; see
    # data/constant.py for why the switch is an env var.
    candidate_tokens = base_tokens + (
        list(REASONING_SPECIAL_TOKENS) if ADD_REASONING_TOKENS else []
    )

    new_tokens = []
    seen = set(all_special_tokens)
    for tok in candidate_tokens:
        if tok in seen:
            continue
        # Skip tokens that already have a dedicated id in the tokenizer
        # vocab (added on a previous call or shipped with the checkpoint).
        existing_id = tokenizer.convert_tokens_to_ids(tok)
        unk_id = getattr(tokenizer, 'unk_token_id', None)
        if existing_id is not None and existing_id != unk_id:
            seen.add(tok)
            continue
        new_tokens.append(tok)
        seen.add(tok)

    num_new_tokens = tokenizer.add_tokens(new_tokens)

    def _id(tok):
        return tokenizer.convert_tokens_to_ids(tok)

    new_token_ids = dict(
        bos_token_id=_id('<|im_start|>'),
        eos_token_id=_id('<|im_end|>'),
        start_of_image=_id('<|vision_start|>'),
        end_of_image=_id('<|vision_end|>'),
        # # reasoning / tool-use tags
        # tool_call_start=_id(TOOL_CALL_START),
        # tool_call_end=_id(TOOL_CALL_END),
        # tool_result_start=_id(TOOL_RESULT_START),
        # tool_result_end=_id(TOOL_RESULT_END),
        # observation_start=_id(OBSERVATION_START),
        # observation_end=_id(OBSERVATION_END),
        # answer_start=_id(ANSWER_START),
        # answer_end=_id(ANSWER_END),
        # generate_depth_start=_id(GENERATE_DEPTH_START),
        # generate_depth_end=_id(GENERATE_DEPTH_END),
        # generate_image_start=_id(GENERATE_IMAGE_START),
        # generate_image_end=_id(GENERATE_IMAGE_END),
    )

    return tokenizer, new_token_ids, num_new_tokens


def len2weight(x, loss_reduction='square'):
    if x == 0:
        return x
    if loss_reduction == 'token':
        return 1
    if loss_reduction == 'sample':
        return 1 / x
    if loss_reduction == 'square':
        return 1 / (x ** 0.5)
    raise NotImplementedError(loss_reduction)