Update generation_utils.py

generation_utils.py

@@ -1,18 +1,5 @@
 # coding=utf-8
-# Copyright 2024 The Dream team, HKUNLP Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
+# Copyright 2024 ...
 import warnings
 import copy
 from dataclasses import dataclass
@@ -22,74 +9,106 @@ 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,
-)
+from transformers.generation.configuration_utils import GenerationConfig
+from transformers.utils import ModelOutput, is_torchdynamo_compiling, logging
 
 logger = logging.get_logger(__name__)
 
 
 def top_p_logits(logits, top_p=None):
+    if top_p is None or top_p >= 1:
+        return logits
     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
-    # Shift the indices to the right to keep the first token above the threshold
     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)
-    return logits
-
-def top_k_logits(logits, top_k=None):
-    top_k = min(top_k, logits.size(-1))  # Safety check
-    # Remove all tokens with a probability less than the last token of the top-k
-    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
-
+    return logits.masked_fill(mask, torch.finfo(logits.dtype).min)
 
-def sample_tokens(logits, temperature=0.0, top_p=None, top_k=None, margin_confidence=False, neg_entropy=False):
 
+def top_k_logits(logits, top_k=None):
+    if top_k is None:
+        return logits
+    top_k = min(int(top_k), logits.size(-1))
+    thresh = torch.topk(logits, top_k)[0][..., -1, None]
+    indices_to_remove = logits < thresh
+    return logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
+
+
+def sample_tokens(
+    logits,
+    temperature=0.0,
+    top_p=None,
+    top_k=None,
+    margin_confidence=False,
+    neg_entropy=False,
+):
+    # temperature
     if temperature > 0:
         logits = logits / temperature
-    if top_p is not None and top_p < 1:
-        logits = top_p_logits(logits, top_p)
-    if top_k is not None:
-        logits = top_k_logits(logits, top_k)
+
+    # filtering
+    logits = top_p_logits(logits, top_p)
+    logits = top_k_logits(logits, top_k)
+
+    # probs
     probs = torch.softmax(logits, dim=-1)
 
+    # sample or argmax
     if temperature > 0:
         try:
             x0 = dists.Categorical(probs=probs).sample()
             confidence = torch.gather(probs, -1, x0.unsqueeze(-1)).squeeze(-1)
-        except:
+        except Exception:
             confidence, x0 = probs.max(dim=-1)
     else:
         confidence, x0 = probs.max(dim=-1)
-    
+
+    # confidence variants
     if margin_confidence:
         sorted_probs, _ = torch.sort(probs, dim=-1, descending=True)
-        # Extract top1 and top2 probabilities
-        top1_probs = sorted_probs[:, 0] 
-        top2_probs = sorted_probs[:, 1] 
-        # Calculate confidence as top1 - top2
-        confidence = top1_probs - top2_probs 
-    
+        top1_probs = sorted_probs[..., 0]
+        top2_probs = sorted_probs[..., 1]
+        confidence = top1_probs - top2_probs
+
     if neg_entropy:
         epsilon = 1e-10
         log_probs = torch.log(probs + epsilon)
-        confidence = torch.sum(probs * log_probs, dim=-1)
-    
+        # 注意：neg_entropy 越大代表越“确定”
+        confidence = -(probs * log_probs).sum(dim=-1)
+
     return confidence, x0
 
 
+def get_num_transfer_tokens_maskgit(mask_index: torch.Tensor, steps: int, mode: str = "linear") -> torch.Tensor:
+    """
+    LLaDA 风格：预计算每一步要“转移（解码）”的 token 数（逐样本），保证总量等于总 mask 数。
+    mask_index: [B, L] bool
+    return: [B, steps] long
+    """
+    device = mask_index.device
+    num_masked_tokens = mask_index.sum(dim=-1, keepdim=True).float()  # [B,1]
+
+    t = torch.linspace(0, 1, steps + 1, device=device)[1:]  # (steps,)
+    if mode == "linear":
+        ratio = t
+    elif mode == "cosine":
+        ratio = 1 - torch.cos(t * torch.pi / 2)
+    elif mode == "pow2":
+        ratio = t ** 2
+    elif mode == "sqrt":
+        ratio = torch.sqrt(t)
+    else:
+        raise ValueError(f"Unknown mode: {mode}")
+
+    # 累积配额（四舍五入），再做差得到每步配额
+    cum = (ratio.unsqueeze(0) * num_masked_tokens).round().long()         # [B, steps]
+    per_step = torch.diff(cum, dim=-1, prepend=torch.zeros_like(cum[:, :1]))
+    return per_step  # [B, steps], 每行之和 ≈ num_masked_tokens（四舍五入引入±1 误差）
+
+
 @dataclass
 class DreamModelOutput(ModelOutput):
     sequences: torch.LongTensor = None
@@ -106,19 +125,20 @@ class DreamGenerationConfig(GenerationConfig):
         # 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')
+        self.alg: str = kwargs.pop("alg", "origin")
         self.alg_temp: Optional[float] = kwargs.pop("alg_temp", None)
 
         # RCR specific parameters
         self.rcr: bool = kwargs.pop("rcr", False)
-        self.conf_alg: str = kwargs.pop("conf_alg", 'maskgit_plus')
+        self.conf_alg: str = kwargs.pop("conf_alg", "maskgit_plus")
+        self.mode: str = kwargs.pop("mode", "linear")  # LLaDA 调度
 
-        # Parameters that define the output variables of `generate`
+        # Output control
         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 that can be used at generation time
+        # 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)
@@ -127,16 +147,12 @@ class DreamGenerationConfig(GenerationConfig):
         # Wild card
         self.generation_kwargs = kwargs.pop("generation_kwargs", {})
 
-        # The remaining attributes do not parametrize `.generate()`, but are informative and/or used by the hub
-        # interface.
+        # Hub info
         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__)
 
-        # Additional attributes without default values
         if not self._from_model_config:
-            # we don't want to copy values from the model config if we're initializing a `GenerationConfig` from a
-            # model's default configuration file
             for key, value in kwargs.items():
                 try:
                     setattr(self, key, value)
@@ -144,22 +160,19 @@ class DreamGenerationConfig(GenerationConfig):
                     logger.error(f"Can't set {key} with value {value} for {self}")
                     raise err
 
-        # Validate the values of the attributes
         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
+        attention_mask: Optional[torch.LongTensor] = None,
     ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
-        """Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
-        # Do not call torch.repeat_interleave if expand_size is 1 because it clones
-        # the input tensor and thus requires more memory although no change is applied
         if expand_size == 1:
             return input_ids, attention_mask
         if input_ids is not None:
@@ -168,132 +181,47 @@ class DreamGenerationMixin:
             attention_mask = attention_mask.repeat_interleave(expand_size, dim=0)
         return input_ids, attention_mask
 
-    def _apply_rcr_logic(self, x, x0, confidence, mask_index, overtime_confidence,
-                        mask_token_id, step, total_steps, s, t):
-        """
-        Apply Running Confidence Remasking (RCR) logic adapted for Dream model.
-        """
-        batch_size = x.shape[0]
-
-        # Calculate number of tokens to transfer using Dream's scheduling
-        num_mask_token = mask_index.sum() / mask_index.shape[0]
-        number_transfer_tokens = int(num_mask_token * (1 - s / t)) if step < total_steps - 1 else int(num_mask_token)
-
-        # Create full confidence tensor matching x dimensions
-        full_confidence = torch.full_like(x, -torch.inf, device=x.device, dtype=confidence.dtype)
-
-        # Create temporary tensor for x0 that matches x dimensions
-        x_temp = torch.zeros_like(x, device=x.device, dtype=torch.long) + mask_token_id
-
-        # Fill masked positions with x0 and confidence
-        x_temp[mask_index] = x0.clone()
-        full_confidence[mask_index] = confidence
-
-        # RCR: Select tokens based on cumulative confidence
-        for j in range(batch_size):
-            if number_transfer_tokens > 0:
-                batch_full_confidence = full_confidence[j]
-
-                # Select top confident tokens to transfer
-                _, select_indices = torch.topk(batch_full_confidence, k=number_transfer_tokens, largest=True)
-                x[j, select_indices] = x_temp[j, select_indices]
-                overtime_confidence[j, select_indices] = batch_full_confidence[select_indices].clone().float()
-
-                # RCR: Re-mask lowest confidence tokens for next steps
-                if step < total_steps - 1:
-                    # Find tokens that have been generated (non-zero confidence)
-                    generated_mask = overtime_confidence[j] > 0
-                    if generated_mask.any():
-                        # Calculate tokens to re-mask for next iteration
-                        next_num_mask_tokens = int(num_mask_token * (1 - torch.linspace(1, s, total_steps + 1, device=x.device)[step + 2] / t))
-
-                        if next_num_mask_tokens > 0:
-                            # Get confidence of generated tokens
-                            generated_confidence = overtime_confidence[j][generated_mask]
-                            generated_indices = torch.where(generated_mask)[0]
-
-                            if len(generated_confidence) >= next_num_mask_tokens:
-                                # Re-mask lowest confidence tokens
-                                _, local_mask_indices = torch.topk(
-                                    generated_confidence,
-                                    k=next_num_mask_tokens,
-                                    largest=False
-                                )
-                                global_mask_indices = generated_indices[local_mask_indices]
-                                x[j, global_mask_indices] = mask_token_id
-                                overtime_confidence[j, global_mask_indices] = 0.0
-
     def _validate_generated_length(self, generation_config, input_ids_length, has_default_max_length):
-        """Performs validation related to the resulting generated length"""
-
-        # Can't throw warnings/exceptions during compilation
         if is_torchdynamo_compiling():
             return
-
-        # 1. Max length warnings related to poor parameterization
         if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
-            # 20 is the default max_length of the generation config
             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.",
+                f"Using the model-agnostic default `max_length` (={generation_config.max_length}); "
+                f"prefer setting `max_new_tokens`.",
                 UserWarning,
             )
         if input_ids_length >= generation_config.max_length:
-            input_ids_string = "input_ids"
             raise ValueError(
-                f"Input length of {input_ids_string} is {input_ids_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_length` or, better yet, setting `max_new_tokens`."
+                f"Input length is {input_ids_length}, but `max_length` is set to {generation_config.max_length}. "
+                f"Increase `max_length` or set `max_new_tokens`."
             )
 
-    def _prepare_generated_length(
-        self,
-        generation_config,
-        has_default_max_length,
-        input_ids_length,
-    ):
-        """Prepared max and min length in generation configs to avoid clashes between similar attributes"""
-
+    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` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
+                    f"Both `max_new_tokens` and `max_length` 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
                 max_position_embeddings = getattr(self.config, "max_position_embeddings", None)
                 if max_position_embeddings is not None:
                     generation_config.max_length = min(generation_config.max_length, max_position_embeddings)
-
         return generation_config
 
     def _prepare_generation_config(
         self, generation_config: Optional[DreamGenerationConfig], **kwargs: Dict
     ) -> DreamGenerationConfig:
-        """
-        Prepares the base generation config, then applies any generation configuration options from kwargs. This
-        function handles retrocompatibility with respect to configuration files.
-        """
-        # priority: `generation_config` argument > `model.generation_config` (the default generation config)
         using_model_generation_config = False
         if generation_config is None:
             generation_config = DreamGenerationConfig.from_model_config(self.config)
             using_model_generation_config = True
 
-        # `torch.compile` can't compile `copy.deepcopy`, arguments in `kwargs` that are part of `generation_config`
-        # will mutate the object with `.update`. As such, passing these arguments through `kwargs` is disabled -- an
-        # exception will be raised in `_validate_model_kwargs`
         if not is_torchdynamo_compiling():
             generation_config = copy.deepcopy(generation_config)
             _kwargs = generation_config.update(**kwargs)
-            # If `generation_config` is provided, let's fallback ALL special tokens to the default values for the model
             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
@@ -311,20 +239,9 @@ class DreamGenerationMixin:
         generation_config: DreamGenerationConfig,
         device: Optional[Union[torch.device, str]] = None,
     ):
-        """
-        Prepares the special tokens for generation, overwriting the generation config with their processed versions
-        converted to tensor.
-
-        Note that `generation_config` is changed in place and stops being serializable after this method is called.
-        That is no problem if called within `generate` (`generation_config` is a local copy that doesn't leave the
-        function). However, if called outside `generate`, consider creating a copy of `generation_config` first.
-        """
-
-        # Convert special tokens to tensors
         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)
@@ -335,19 +252,13 @@ class DreamGenerationMixin:
         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)
 
-        # We can have more than one eos token. Always treat it as a 1D tensor (when it exists).
         if eos_token_tensor is not None and eos_token_tensor.ndim == 0:
             eos_token_tensor = eos_token_tensor.unsqueeze(0)
 
-        # Set pad token if unset (and there are conditions to do so)
         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.")
 
-        # Update generation config with the updated special tokens tensors
-        # NOTE: this must be written into a different attribute name than the one holding the original special tokens
-        # (in their non-tensor form), in order to enable end-to-end compilation. See
-        # https://pytorch.org/docs/stable/torch.compiler_cudagraph_trees.html#limitations
         generation_config._bos_token_tensor = bos_token_tensor
         generation_config._eos_token_tensor = eos_token_tensor
         generation_config._pad_token_tensor = pad_token_tensor
@@ -360,19 +271,16 @@ class DreamGenerationMixin:
         generation_config: Optional[DreamGenerationConfig] = None,
         **kwargs,
     ) -> Union[DreamModelOutput, torch.LongTensor]:
-        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
         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)
 
-        # 2. Define model inputs
         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)
 
-        # 3. Prepare `max_length`.
         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(
@@ -380,35 +288,23 @@ class DreamGenerationMixin:
             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)
-        
-        # 4. Check input_ids
+
         if not is_torchdynamo_compiling() and self.device.type != input_ids.device.type:
             warnings.warn(
-                "You are calling .generate() with the `input_ids` being on a device type different"
-                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
-                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
-                " Please make sure that you have put `input_ids` to the"
-                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
-                " running `.generate()`.",
+                "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
-        ):
+        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 was detected but no attention mask is passed here. For correct "
-                "generation results, please set `attention_mask` when batch-padding inputs.",
+                "Padding detected but no attention_mask is passed. For correct results, pass attention_mask.",
                 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 
+            attention_mask=attention_mask,
         )
 
         result = self._sample(
@@ -416,7 +312,7 @@ class DreamGenerationMixin:
             attention_mask=attention_mask,
             generation_config=generation_config,
             generation_tokens_hook_func=generation_tokens_hook_func,
-            generation_logits_hook_func=generation_logits_hook_func
+            generation_logits_hook_func=generation_logits_hook_func,
         )
         return result
 
@@ -426,9 +322,10 @@ class DreamGenerationMixin:
         attention_mask: Optional[torch.LongTensor],
         generation_config: DreamGenerationConfig,
         generation_tokens_hook_func,
-        generation_logits_hook_func
+        generation_logits_hook_func,
     ) -> Union[DreamModelOutput, torch.LongTensor]:
-        # init values
+
+        # --- init values ---
         output_history = generation_config.output_history
         return_dict_in_generate = generation_config.return_dict_in_generate
         max_length = generation_config.max_length
@@ -441,22 +338,20 @@ class DreamGenerationMixin:
         top_p = generation_config.top_p
         top_k = generation_config.top_k
 
-        # RCR specific values
+        # RCR specific
         rcr = generation_config.rcr
         conf_alg = generation_config.conf_alg
+        mode = generation_config.mode
 
         histories = [] if (return_dict_in_generate and output_history) else None
 
-        # pad input_ids to max_length
+        # pad to max_length with [MASK]
         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):
-            # we do not mask the [MASK] tokens so value = 1.0
             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 is of shape [B, N]
-            # broadcast to [B, 1, N, N]
             attention_mask = torch.logical_and(
                 attention_mask.unsqueeze(1).unsqueeze(-2),
                 attention_mask.unsqueeze(1).unsqueeze(-1),
@@ -465,74 +360,126 @@ class DreamGenerationMixin:
             tok_idx = None
             attention_mask = "full"
 
+        # global linear schedule 1 -> eps
         timesteps = torch.linspace(1, eps, steps + 1, device=x.device)
 
-        # RCR tracking - initialize overtime confidence tracking
+        # 初始 mask（用于预分配 per-step token 预算；与 LLaDA 类似）
+        initial_mask_index = (x == mask_token_id)  # [B, L]
+        per_step_tokens = get_num_transfer_tokens_maskgit(initial_mask_index, steps, mode=mode)  # [B, steps]
+
+        # RCR tracking
         overtime_confidence = torch.zeros_like(x, dtype=torch.float32) if rcr else None
 
-        # this allows user-defined token control of the intermediate steps
+        # user-defined token control
         x = generation_tokens_hook_func(None, x, None)
+
         for i in range(steps):
-            mask_index = (x == mask_token_id)
+            # 当前还未确定的 mask 位置
+            mask_index = (x == mask_token_id)  # [B, L]
+
+            # 模型 logits（单步预测 + 向右对齐）
             logits = self(x, attention_mask, tok_idx).logits
-            logits = torch.cat([logits[:,:1], logits[:, :-1]], dim=1)
+            logits = torch.cat([logits[:, :1], logits[:, :-1]], dim=1)
 
-            # this allows user-defined logits control of the intermediate steps
+            # user-defined logits control
             logits = generation_logits_hook_func(i, x, logits)
 
-            mask_logits = logits[mask_index]
+            # 只取 mask 位置对应的 logits 参与采样
+            mask_logits = logits[mask_index]  # [M, V] (M=mask 个数)
             t = timesteps[i]
             s = timesteps[i + 1]
-        
-            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
-                _, x0[transfer_index_t_s]= sample_tokens(mask_logits[transfer_index_t_s], temperature=temperature, top_p=top_p, top_k=top_k)
+
+            if alg == "origin":
+                # 原 Dream 迁移（保留）
+                p_transfer = 1 - (s / t).item() if i < steps - 1 else 1.0
+                x0 = torch.zeros_like(x[mask_index], device=x.device, dtype=torch.long) + mask_token_id
+                transfer_index_t_s = (torch.rand(*x0.shape, device=x.device) < p_transfer)
+                _, x0[transfer_index_t_s] = sample_tokens(
+                    mask_logits[transfer_index_t_s],
+                    temperature=temperature,
+                    top_p=top_p,
+                    top_k=top_k,
+                )
                 x[mask_index] = x0.clone()
             else:
-                if alg == 'maskgit_plus' or (rcr and conf_alg == 'maskgit_plus'):
+                # 选择置信度算法：RCR 时优先 conf_alg；非 RCR 时用 alg 的同名变体
+                choose = conf_alg if rcr else alg
+                if choose == "maskgit_plus":
                     confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k)
-                elif alg == 'topk_margin' or (rcr and conf_alg == 'topk_margin'):
+                elif choose == "topk_margin":
                     confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, margin_confidence=True)
-                elif alg == 'entropy' or (rcr and conf_alg == 'entropy'):
-                    confidence, x0 = sample_tokens(mask_logits, temperature, top_p=top_p, top_k=top_k, neg_entropy=True)
+                elif choose == "entropy":
+                    confidence, x0 = sample_tokens(mask_logits, temperature=temperature, top_p=top_p, top_k=top_k, neg_entropy=True)
                 else:
-                    raise RuntimeError(f"Unknown alg: {alg}")
+                    raise RuntimeError(f"Unknown alg/conf_alg: {choose}")
+
+                # 将预测/置信度写回到全长（非 mask 位置用原 token / -inf）
+                full_conf = torch.full_like(x, -torch.inf, device=x.device, dtype=logits.dtype)  # [B, L]
+                x_temp = torch.zeros_like(x, device=x.device, dtype=torch.long) + mask_token_id  # [B, L]
+                x_temp[mask_index] = x0.clone()
+                full_conf[mask_index] = confidence
+
+                if not rcr:
+                    # ---------- 非 RCR：逐样本的“当步配额” ----------
+                    k_per_row = per_step_tokens[:, i]  # [B]
+                    B = x.size(0)
+                    for j in range(B):
+                        k_j = int(k_per_row[j].item())
+                        if k_j <= 0:
+                            continue
+                        # clamp：不能超过当前样本剩余 mask 数
+                        masked_count_j = mask_index[j].sum().item()
+                        k_j = min(k_j, int(masked_count_j))
+                        if k_j <= 0:
+                            continue
+                        # 只在 mask 内选 topk（full_conf 的非 mask 处已是 -inf）
+                        _, select_idx = torch.topk(full_conf[j], k_j, largest=True)
+                        x[j, select_idx] = x_temp[j, select_idx]
 
-                # Apply RCR logic if enabled
-                if rcr:
-                    print(f"[RCR EXEC] Step {i}: RCR logic executed")
-                    self._apply_rcr_logic(x, x0, confidence, mask_index, overtime_confidence,
-                                        mask_token_id, i, steps, s, t)
                 else:
-                    # Original Dream sampling logic
-                    num_mask_token = mask_index.sum() / mask_index.shape[0]
-                    number_transfer_tokens = int(num_mask_token * (1 - s / t)) if i < steps - 1 else int(num_mask_token)
-                    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]
-
-            # this allows user-defined token control of the intermediate steps
+                    # ---------- RCR：LLaDA 风格的“累积选取 + 下一步反遮盖” ----------
+                    B = x.size(0)
+                    for j in range(B):
+                        # 当步+未来的总剩余配额（从第 i 步到最后一步）
+                        total_remaining_tokens = int(per_step_tokens[j, i:].sum().item())
+                        if total_remaining_tokens <= 0:
+                            continue
+
+                        masked_count_j = mask_index[j].sum().item()
+                        k_total = min(total_remaining_tokens, int(masked_count_j))
+                        if k_total <= 0:
+                            continue
+
+                        # 1) 累积选取：一次性选出“当步至结尾”应确定的 token 集合
+                        #    （在 mask 内的 topk）
+                        _, select_indices = torch.topk(full_conf[j], k_total, largest=True)
+                        x[j, select_indices] = x_temp[j, select_indices]
+                        overtime_confidence[j, select_indices] = full_conf[j, select_indices].clone().float()
+
+                        # 2) 下一步前：把“下一步之后还应保留给未来步数的那部分”按最低置信度反遮盖回去
+                        if i < (steps - 1):
+                            next_to_keep_for_future = int(per_step_tokens[j, i + 1 :].sum().item())
+                            if next_to_keep_for_future > 0:
+                                # 仅在“已选中的位置”（overtime_confidence>0）里，反遮盖最低置信度的那部分
+                                current_conf = overtime_confidence[j]
+                                # 把 0 置信度（未生成）位置临时设成 +inf，避免被误选为“最低”
+                                safe_conf = torch.where(current_conf == 0.0, torch.tensor(float("inf"), device=x.device), current_conf)
+                                # 需要反遮盖的数量不应超过当前已选中的数
+                                gen_count = (safe_conf != float("inf")).sum().item()
+                                k_remask = min(next_to_keep_for_future, int(gen_count))
+                                if k_remask > 0:
+                                    # 选“最不自信”的 k_remask 个
+                                    _, local_mask_indices = torch.topk(safe_conf, k_remask, largest=False)
+                                    x[j, local_mask_indices] = mask_token_id
+                                    overtime_confidence[j, local_mask_indices] = 0.0  # 清零表示撤回
+
+            # user-defined token control
             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,
-            )
+            return DreamModelOutput(sequences=x, history=histories)
         else:
-            return x
+            return x