generation_utils.py

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:
        # top-p
        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
        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


@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 specific params
        self.eps: float = kwargs.pop("eps", 1e-3)
        self.steps: int = kwargs.pop("steps", 512)
        self.alg: str = kwargs.pop("alg", 'origin')  # vanilla 使用
        self.alg_temp: Optional[float] = kwargs.pop("alg_temp", None)

        # RCR
        self.rcr: bool = kwargs.pop("rcr", False)
        # 注意：论文版 RCR 会忽略这里的 conf_alg，并统一用“选中 token 概率”做 running max
        self.conf_alg: str = kwargs.pop("conf_alg", 'maskgit_plus')

        # 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):
        pass


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

    # =============== 论文版 RCR：运行最大置信度 + 直接选 n_t 回遮 ===============
    def _apply_rcr_logic_paper(
        self,
        x: torch.Tensor,                 # [B, L]
        rmax_conf: torch.Tensor,         # [B, L], float32, running max of selected-token prob
        init_mask_bool: torch.Tensor,    # [B, L], 初始生成区域（最开始是 MASK 的位置）
        init_mask_count: torch.Tensor,   # [B], 初始 MASK 数 M0
        mask_token_id: int,
        step: int,
        total_steps: int,
        s: torch.Tensor,
        t: torch.Tensor,
    ):
        """
        目标：在“初始生成区域”(init_mask_bool) 内，让“已确认个数”符合 vanilla 的线性进度；
             但位置选择依据“历史最大置信度 rmax_conf”——每步保留 rmax_conf 高的，回遮 rmax_conf 低的。

        做法：
          target_cum = floor(M0 * (1 - s/t))    # 最后一步 = M0
          在 init_mask_bool[j] 内按 rmax_conf[j] 降序选 target_cum 个 => 保持已确认（不 mask）
          其余位置设为 mask_token_id
        """
        B, L = x.shape
        for j in range(B):
            M0 = int(init_mask_count[j].item())
            if step < total_steps - 1:
                target_cum = int(M0 * (1.0 - (s.item() / t.item())))
            else:
                target_cum = M0

            # 在初始生成区域内排序
            region_idx = torch.where(init_mask_bool[j])[0]
            if region_idx.numel() == 0:
                continue

            # rmax_conf 越大越稳，保留前 target_cum 个
            scores = rmax_conf[j, region_idx]  # float32
            # 防御：若还没更新过，rmax_conf 初始 0.0，会被优先回遮（符合“历史没自信过”的直觉）
            target_cum = min(target_cum, int(region_idx.numel()))
            if target_cum <= 0:
                # 全部保持 mask
                x[j, region_idx] = mask_token_id
                continue

            _, keep_local = torch.topk(scores, k=target_cum, largest=True)
            keep_global = region_idx[keep_local]

            # 其余回遮
            mask_global = torch.ones_like(region_idx, dtype=torch.bool, device=x.device)
            mask_global[keep_local] = False
            remask_idx = region_idx[mask_global]

            if remask_idx.numel() > 0:
                x[j, remask_idx] = mask_token_id
            # keep_global 上保持当前写入的 token，不动

    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 the model-agnostic default `max_length` (={generation_config.max_length}) to control the "
                "generation length. We recommend setting `max_new_tokens` to control the maximum length of the "
                "generation.",
                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(
                    f"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 different device than 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  # 打开则走论文版 RCR（历史最大 top-1 概率）
        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)

        if rcr:
            # 初始生成区域（prompt 之外扩展出来的那一段）
            init_mask_bool = (x == mask_token_id)         # [B, L]
            init_mask_count = init_mask_bool.sum(dim=1)   # [B]
            # 历史最大“被选 token 概率”（float32）
            rmax_conf = torch.zeros_like(x, dtype=torch.float32, device=x.device)
            logger.warning(
                "[RCR] Using PAPER version: running-max of SELECTED-TOKEN PROB; "
                "this overrides `conf_alg` (e.g., entropy) for remasking decisions."
            )

        x = generation_tokens_hook_func(None, x, None)

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

            # 前向
            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)

            t = timesteps[i]
            s = timesteps[i + 1]

            if not rcr:
                # ===== vanilla 路径（保持你原来的实现）=====
                mask_logits = logits[mask_index]
                if alg == 'origin':
                    p_transfer = 1 - s / t if i < steps - 1 else 1
                    x0 = torch.zeros_like(x[mask_index], device=self.device, dtype=torch.long) + mask_token_id
                    transfer_index_t_s = torch.rand(*x0.shape, device=self.device) < p_transfer
                    if transfer_index_t_s.any():
                        logits_sub = mask_logits[transfer_index_t_s]
                        logits_sub = _apply_top_p_k_temp(logits_sub, temperature, top_p, top_k)
                        probs_sub = torch.softmax(logits_sub, dim=-1)
                        try:
                            x0_sel = dists.Categorical(probs=probs_sub).sample()
                        except Exception:
                            x0_sel = probs_sub.argmax(dim=-1)
                        x0[transfer_index_t_s] = x0_sel
                    x[mask_index] = x0.clone()
                else:
                    # 按你 vanilla 的 top-k / alg_temp 逻辑
                    mask_logits = _apply_top_p_k_temp(logits[mask_index], temperature, top_p, top_k)
                    probs = torch.softmax(mask_logits, dim=-1)
                    if temperature and temperature > 0:
                        try:
                            x0 = dists.Categorical(probs=probs).sample()
                            confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)
                        except Exception:
                            confidence, x0 = probs.max(dim=-1)
                    else:
                        confidence, x0 = probs.max(dim=-1)

                    avg_mask_now = (mask_index.sum().item() / max(1, mask_index.shape[0]))
                    ratio = (1.0 - (s.item() / t.item())) if i < steps - 1 else 1.0
                    number_transfer_tokens = int(avg_mask_now * ratio)

                    full_confidence = torch.full_like(x, -torch.inf, device=self.device, dtype=logits.dtype)
                    full_confidence[mask_index] = confidence

                    if number_transfer_tokens > 0:
                        if alg_temp is None or alg_temp == 0:
                            _, transfer_index = torch.topk(full_confidence, number_transfer_tokens)
                        else:
                            full_confidence = full_confidence / alg_temp
                            full_confidence = F.softmax(full_confidence, dim=-1)
                            transfer_index = torch.multinomial(full_confidence, num_samples=number_transfer_tokens)
                        x_ = torch.zeros_like(x, device=self.device, dtype=torch.long) + mask_token_id
                        x_[mask_index] = x0.clone()
                        row_indices = torch.arange(x.size(0), device=self.device).unsqueeze(1).expand_as(transfer_index)
                        x[row_indices, transfer_index] = x_[row_indices, transfer_index]

            else:
                # ===== 论文版 RCR =====
                # 1) 仅对当前 mask 的位置，做 top_p/top_k/temperature 过滤后采样（或贪心）
                mask_logits = logits[mask_index]
                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
                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)

                # 被选 token 的概率 p_sel（论文要求用这个做“历史置信度”）
                p_sel = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)  # [M], float32

                # 写入选中的 token
                x_maskwrite = torch.full_like(x, mask_token_id, dtype=torch.long)
                x_maskwrite[mask_index] = x0
                x = torch.where(mask_index, x_maskwrite, x)

                # 更新 running-max 置信度（float32）
                # 先铺到全长
                full_p_sel = torch.zeros_like(x, dtype=torch.float32)
                full_p_sel[mask_index] = p_sel.to(torch.float32)
                rmax_conf = torch.maximum(rmax_conf, full_p_sel)

                # 2) 基于 rmax_conf 直接确定“下一步要保留的已确认个数”，其余全部回遮
                self._apply_rcr_logic_paper(
                    x=x,
                    rmax_conf=rmax_conf,
                    init_mask_bool=init_mask_bool,
                    init_mask_count=init_mask_count,
                    mask_token_id=mask_token_id,
                    step=i,
                    total_steps=steps,
                    s=s, t=t,
                )

            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