generation_utils.py

# coding=utf-8
import warnings
import copy
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union

import torch
import torch.distributions as dists
from torch.nn import functional as F
from transformers import __version__
from transformers.generation.configuration_utils import GenerationConfig
from transformers.utils import ModelOutput, is_torchdynamo_compiling, logging

logger = logging.get_logger(__name__)


def _apply_top_p_k_temp(logits, temperature=0.0, top_p=None, top_k=None):
    if temperature and temperature > 0:
        logits = logits / temperature
    if top_p is not None and top_p < 1:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
        sorted_indices_to_remove = cumulative_probs > top_p
        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
        sorted_indices_to_remove[..., 0] = 0
        mask = torch.zeros_like(logits, dtype=torch.bool, device=logits.device)
        mask = mask.scatter_(-1, sorted_indices, sorted_indices_to_remove)
        logits = logits.masked_fill(mask, torch.finfo(logits.dtype).min)
    if top_k is not None:
        top_k = int(min(top_k, logits.size(-1)))
        if top_k > 0:
            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
            logits = logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
    return logits


def _confidence_from_probs(
    probs: torch.Tensor,            # [..., V]
    chosen_ids: Optional[torch.Tensor],  # [...]
    mode: str                       # 'entropy' | 'maskgit_plus' | 'topk_margin'
) -> torch.Tensor:
    """返回“越大越自信”的标量分数，与解码一致。"""
    if mode == "entropy":
        eps = 1e-10
        logp = torch.log(probs + eps)
        return -(probs * logp).sum(dim=-1)          # -H(p)
    elif mode == "maskgit_plus":
        assert chosen_ids is not None, "maskgit_plus 需要 chosen_ids"
        return torch.gather(probs, -1, chosen_ids.unsqueeze(-1)).squeeze(-1)  # p(x0)
    elif mode == "topk_margin":
        sorted_probs, _ = torch.sort(probs, dim=-1, descending=True)
        return sorted_probs[..., 0] - sorted_probs[..., 1]                    # top1 - top2
    else:
        raise ValueError(f"Unknown conf mode: {mode}")


@dataclass
class DreamModelOutput(ModelOutput):
    sequences: torch.LongTensor = None
    history: Optional[Tuple[torch.FloatTensor]] = None


class DreamGenerationConfig(GenerationConfig):
    def __init__(self, **kwargs):
        # sampling
        self.temperature: float = kwargs.pop("temperature", 0.0)
        self.top_p: Optional[float] = kwargs.pop("top_p", None)
        self.top_k: Optional[int] = kwargs.pop("top_k", None)

        # length
        self.max_length = kwargs.pop("max_length", 20)
        self.max_new_tokens = kwargs.pop("max_new_tokens", None)

        # diffusion
        self.eps: float = kwargs.pop("eps", 1e-3)
        self.steps: int = kwargs.pop("steps", 512)

        # vanilla 的打分算法（rcr=False 时使用）
        self.alg: str = kwargs.pop("alg", 'maskgit_plus')  # 'origin' | 'maskgit_plus' | 'topk_margin' | 'entropy'
        self.alg_temp: Optional[float] = kwargs.pop("alg_temp", None)

        # === RCR ===
        self.rcr: bool = kwargs.pop("rcr", False)
        # rcr=True 时用于解码 & 历史分一致的置信度定义
        self.conf_alg: str = kwargs.pop("conf_alg", 'maskgit_plus')  # 'maskgit_plus' | 'topk_margin' | 'entropy'
        # 注意：下两项会被 _sample 内部“写死”为 1/4 到 3/4，总是覆盖
        self.rcr_start_step: int = kwargs.pop("rcr_start_step", 0)
        self.rcr_end_step: int = kwargs.pop("rcr_end_step", None) or self.steps
        # 是否保护“本步刚写”的 token 不被回遮
        self.rcr_protect_current_step: bool = kwargs.pop("rcr_protect_current_step", False)

        # outputs
        self.num_return_sequences: int = kwargs.pop("num_return_sequences", 1)
        self.return_dict_in_generate: bool = kwargs.pop("return_dict_in_generate", False)
        self.output_history: bool = kwargs.pop("output_history", False)

        # special tokens
        self.mask_token_id = kwargs.pop("mask_token_id", None)
        self.pad_token_id = kwargs.pop("pad_token_id", None)
        self.bos_token_id = kwargs.pop("bos_token_id", None)
        self.eos_token_id = kwargs.pop("eos_token_id", None)

        # misc
        self.generation_kwargs = kwargs.pop("generation_kwargs", {})

        # bookkeeping
        self._from_model_config = kwargs.pop("_from_model_config", False)
        self._commit_hash = kwargs.pop("_commit_hash", None)
        self.transformers_version = kwargs.pop("transformers_version", __version__)

        if not self._from_model_config:
            for key, value in kwargs.items():
                try:
                    setattr(self, key, value)
                except AttributeError as err:
                    logger.error(f"Can't set {key} with value {value} for {self}")
                    raise err

        self.validate(is_init=True)

    def validate(self, is_init=False):
        # 简单边界
        self.rcr_start_step = max(0, int(self.rcr_start_step))
        self.rcr_end_step = max(self.rcr_start_step, int(self.rcr_end_step))


class DreamGenerationMixin:
    @staticmethod
    def _expand_inputs_for_generation(
        expand_size: int = 1,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.LongTensor] = None
    ):
        if expand_size == 1:
            return input_ids, attention_mask
        if input_ids is not None:
            input_ids = input_ids.repeat_interleave(expand_size, dim=0)
        if attention_mask is not None:
            attention_mask = attention_mask.repeat_interleave(expand_size, dim=0)
        return input_ids, attention_mask

    def _validate_generated_length(self, generation_config, input_ids_length, has_default_max_length):
        if is_torchdynamo_compiling():
            return
        if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
            warnings.warn(
                f"Using default `max_length` (={generation_config.max_length}). Prefer `max_new_tokens`.",
                UserWarning,
            )
        if input_ids_length >= generation_config.max_length:
            raise ValueError(
                f"Input length is {input_ids_length}, but `max_length` is {generation_config.max_length}. "
                "Increase `max_length` or set `max_new_tokens`."
            )

    def _prepare_generated_length(self, generation_config, has_default_max_length, input_ids_length):
        if generation_config.max_new_tokens is not None:
            if not has_default_max_length and generation_config.max_length is not None:
                logger.warning("Both `max_new_tokens` and `max_length` are set. `max_new_tokens` takes precedence.")
            generation_config.max_length = generation_config.max_new_tokens + input_ids_length
        elif has_default_max_length:
            if generation_config.max_length == DreamGenerationConfig().max_length:
                generation_config.max_length = generation_config.max_length + input_ids_length
                mpe = getattr(self.config, "max_position_embeddings", None)
                if mpe is not None:
                    generation_config.max_length = min(generation_config.max_length, mpe)
        return generation_config

    def _prepare_generation_config(self, generation_config: Optional[DreamGenerationConfig], **kwargs: Dict) -> DreamGenerationConfig:
        using_model_generation_config = False
        if generation_config is None:
            generation_config = DreamGenerationConfig.from_model_config(self.config)
            using_model_generation_config = True

        if not is_torchdynamo_compiling():
            generation_config = copy.deepcopy(generation_config)
            _ = generation_config.update(**kwargs)
            if not using_model_generation_config:
                if generation_config.bos_token_id is None:
                    generation_config.bos_token_id = self.generation_config.bos_token_id
                if generation_config.eos_token_id is None:
                    generation_config.eos_token_id = self.generation_config.eos_token_id
                if generation_config.pad_token_id is None:
                    generation_config.pad_token_id = self.generation_config.pad_token_id
                if generation_config.mask_token_id is None:
                    generation_config.mask_token_id = self.generation_config.mask_token_id

        return generation_config

    def _prepare_special_tokens(self, generation_config: DreamGenerationConfig, device=None):
        def _tensor_or_none(token, device=None):
            if token is None:
                return token
            device = device if device is not None else self.device
            if isinstance(token, torch.Tensor):
                return token.to(device)
            return torch.tensor(token, device=device, dtype=torch.long)

        bos_token_tensor = _tensor_or_none(generation_config.bos_token_id, device=device)
        eos_token_tensor = _tensor_or_none(generation_config.eos_token_id, device=device)
        pad_token_tensor = _tensor_or_none(generation_config.pad_token_id, device=device)
        mask_token_tensor = _tensor_or_none(generation_config.mask_token_id, device=device)

        if eos_token_tensor is not None and eos_token_tensor.ndim == 0:
            eos_token_tensor = eos_token_tensor.unsqueeze(0)
        if pad_token_tensor is None and eos_token_tensor is not None:
            pad_token_tensor = eos_token_tensor[0]
            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{pad_token_tensor} for open-end generation.")

        generation_config._bos_token_tensor = bos_token_tensor
        generation_config._eos_token_tensor = eos_token_tensor
        generation_config._pad_token_tensor = pad_token_tensor
        generation_config._mask_token_tensor = mask_token_tensor

    @torch.no_grad()
    def diffusion_generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        generation_config: Optional[DreamGenerationConfig] = None,
        **kwargs,
    ):
        generation_config = self._prepare_generation_config(generation_config, **kwargs)
        generation_tokens_hook_func = kwargs.pop("generation_tokens_hook_func", lambda step, x, logits: x)
        generation_logits_hook_func = kwargs.pop("generation_logits_hook_func", lambda step, x, logits: logits)

        assert inputs is not None
        input_ids = inputs
        device = input_ids.device
        attention_mask = kwargs.pop("attention_mask", None)
        self._prepare_special_tokens(generation_config, device=device)

        input_ids_length = input_ids.shape[-1]
        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
        generation_config = self._prepare_generated_length(
            generation_config=generation_config,
            has_default_max_length=has_default_max_length,
            input_ids_length=input_ids_length,
        )

        self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)

        if not is_torchdynamo_compiling() and self.device.type != input_ids.device.type:
            warnings.warn(
                "You are calling .generate() with `input_ids` on a device different from the model.",
                UserWarning,
            )
        if (
            hasattr(generation_config, "pad_token_id")
            and torch.any(input_ids == generation_config.pad_token_id)
            and attention_mask is None
        ):
            warnings.warn(
                "Padding detected but no attention mask was passed. Set `attention_mask` for correct generation.",
                UserWarning,
            )

        input_ids, attention_mask = self._expand_inputs_for_generation(
            expand_size=generation_config.num_return_sequences,
            input_ids=input_ids,
            attention_mask=attention_mask,
        )

        return self._sample(
            input_ids,
            attention_mask=attention_mask,
            generation_config=generation_config,
            generation_tokens_hook_func=generation_tokens_hook_func,
            generation_logits_hook_func=generation_logits_hook_func,
        )

    def _sample(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.LongTensor],
        generation_config: DreamGenerationConfig,
        generation_tokens_hook_func,
        generation_logits_hook_func
    ):
        output_history = generation_config.output_history
        return_dict_in_generate = generation_config.return_dict_in_generate
        max_length = generation_config.max_length
        mask_token_id = generation_config.mask_token_id
        steps = generation_config.steps
        eps = generation_config.eps
        alg = generation_config.alg
        alg_temp = generation_config.alg_temp
        temperature = generation_config.temperature
        top_p = generation_config.top_p
        top_k = generation_config.top_k

        rcr = generation_config.rcr
        conf_alg = generation_config.conf_alg if rcr else generation_config.alg

        # === 写死 RCR 生效窗口：总步数的 1/4 到 3/4（左闭右开 [start, end)）===
        rcr_start = max(0, steps // 4)
        rcr_end = max(rcr_start, min(steps, (3 * steps) // 4))

        protect_cur = bool(generation_config.rcr_protect_current_step)

        histories = [] if (return_dict_in_generate and output_history) else None

        # pad input_ids to max_length
        x = F.pad(input_ids, (0, max_length - input_ids.shape[1]), value=mask_token_id)

        if attention_mask is not None and torch.any(attention_mask == 0.0):
            attention_mask = F.pad(attention_mask, (0, max_length - attention_mask.shape[1]), value=1.0)
            tok_idx = attention_mask.long().cumsum(-1) - 1
            tok_idx.masked_fill_(attention_mask == 0, 1)
            attention_mask = torch.logical_and(
                attention_mask.unsqueeze(1).unsqueeze(-2),
                attention_mask.unsqueeze(1).unsqueeze(-1),
            )
        else:
            tok_idx = None
            attention_mask = "full"

        timesteps = torch.linspace(1, eps, steps + 1, device=x.device)

        # ==== RCR 状态 ====
        if rcr:
            init_mask_bool = (x == mask_token_id)               # 初始生成区域
            init_mask_count = init_mask_bool.sum(dim=1)         # [B]
            hist_conf = torch.zeros_like(x, dtype=torch.float32, device=x.device)  # 历史最大置信度
            gen_mask = torch.zeros_like(x, dtype=torch.bool, device=x.device)      # 已确认位置
            written_step = torch.full_like(x, -1, dtype=torch.int32, device=x.device)

        x = generation_tokens_hook_func(None, x, None)

        for i in range(steps):
            mask_index = (x == mask_token_id)

            # 前向 + Dream 的右移对齐
            logits = self(x, attention_mask, tok_idx).logits
            logits = torch.cat([logits[:, :1], logits[:, :-1]], dim=1)
            logits = generation_logits_hook_func(i, x, logits)

            # 时间步
            t = timesteps[i]
            s = timesteps[i + 1]

            # —— 仅抽出 mask 位置的 logits 并做过滤 ——
            mask_logits = logits[mask_index]
            if mask_logits.numel() == 0:
                x = generation_tokens_hook_func(i, x, logits)
                if histories is not None:
                    histories.append(x.clone())
                continue

            mask_logits = _apply_top_p_k_temp(mask_logits, temperature, top_p, top_k)
            probs = torch.softmax(mask_logits, dim=-1)

            # 采样 / 贪心拿到 x0
            if temperature and temperature > 0:
                try:
                    x0 = dists.Categorical(probs=probs).sample()
                except Exception:
                    x0 = probs.argmax(dim=-1)
            else:
                x0 = probs.argmax(dim=-1)

            # 统一置信度（与解码一致）
            conf_now = _confidence_from_probs(
                probs=probs,
                chosen_ids=x0 if conf_alg == "maskgit_plus" else None,
                mode=conf_alg
            ).to(torch.float32)  # [M]

            # ====== 计算当步写入配额 k_t（与 vanilla 一致）======
            Mt = mask_index.sum().item()
            ratio = (1.0 - (s.item() / t.item())) if i < steps - 1 else 1.0
            k_t = int(Mt * ratio)

            # —— 写入：top-k_t ——（无论 RCR 窗口与否，先写）
            full_conf_now = torch.full((x.size(0), x.size(1)), -1e9, dtype=torch.float32, device=x.device)
            full_x0 = torch.full_like(x, mask_token_id, dtype=torch.long)
            full_conf_now[mask_index] = conf_now
            full_x0[mask_index] = x0

            for b in range(x.size(0)):
                masked_b = int(mask_index[b].sum().item())
                if masked_b == 0 or k_t <= 0:
                    continue
                k_b = min(k_t, masked_b)
                _, sel_idx = torch.topk(full_conf_now[b], k=k_b, largest=True)
                x[b, sel_idx] = full_x0[b, sel_idx]
                if rcr:
                    gen_mask[b, sel_idx] = True
                    written_step[b, sel_idx] = i
                    # 更新历史最大置信度（与解码同定义）
                    hist_conf[b, sel_idx] = torch.maximum(hist_conf[b, sel_idx], full_conf_now[b, sel_idx])

            # —— RCR 窗口外：不回遮，仅跟踪历史；窗口内：执行回遮到目标累计 ——
            if rcr and (rcr_start <= i < rcr_end):
                for b in range(x.size(0)):
                    M0 = int(init_mask_count[b].item())
                    target_cum = M0 if i >= steps - 1 else int(M0 * (1.0 - (s.item() / t.item())))
                    # 当前累计确认：初始生成区域内的已确认数
                    C_t = int((gen_mask[b] & init_mask_bool[b]).sum().item())
                    over = max(0, C_t - target_cum)
                    if over <= 0:
                        continue

                    # 候选：初始区域 ∧ 已确认（可选：排除本步刚写）
                    cand = torch.where(gen_mask[b] & init_mask_bool[b])[0]
                    if cand.numel() == 0:
                        continue
                    if protect_cur:
                        mask_old = (written_step[b, cand] < i)
                        cand = cand[mask_old]
                        if cand.numel() == 0:
                            # 全是本步写的，且要求保护，则跳过回遮
                            continue

                    over = min(over, int(cand.numel()))
                    scores = hist_conf[b, cand]  # 越大越自信
                    _, low_local = torch.topk(scores, k=over, largest=False)
                    low_global = cand[low_local]

                    # 回遮
                    x[b, low_global] = mask_token_id
                    gen_mask[b, low_global] = False
                    # 历史分数与 written_step 保留

            x = generation_tokens_hook_func(i, x, logits)
            if histories is not None:
                histories.append(x.clone())

        if return_dict_in_generate:
            return DreamModelOutput(sequences=x, history=histories)
        else:
            return x