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---
license: apache-2.0
pipeline_tag: text-generation
tags:
- dllm
- diffusion
- llm
- text_generation
library_name: transformers
---
# ReFusion
[](http://arxiv.org/abs/2512.13586)
[](https://github.com/ML-GSAI/ReFusion)
**ReFusion** is a masked diffusion model that achieves superior performance and efficiency, featuring full KV cache reuse while simultaneously supporting any-order generation.
# Quickstart
The following code snippet shows how to load the tokenizer and model and how to generate content.
```python
import torch
import numpy as np
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
import pandas as pd
import os
import random
import copy
import math
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, AutoConfig
from typing import Optional, Dict, Any, Tuple, List
def add_gumbel_noise(logits, temperature):
'''
The Gumbel max is a method for sampling categorical distributions.
According to arXiv:2409.02908, for MDM, low-precision Gumbel Max improves perplexity score but reduces generation quality.
Thus, we use float64.
'''
if temperature == 0:
return logits
logits = logits.to(torch.float64)
noise = torch.rand_like(logits, dtype=torch.float64)
gumbel_noise = (- torch.log(noise)) ** temperature
return logits.exp() / gumbel_noise
import torch
import torch.nn.functional as F
@ torch.no_grad()
def generate_refusion(model, tokenizer, prompt, gen_length=128, temperature=0., mask_id=151670, slot_size=8,
model_path='', serial_num_blocks=2, slot_threshold=0.9, token_threshold=0.9):
slot_threshold = slot_threshold
token_threshold = token_threshold
sum_TPF = 0.0
forward_count = 0
eos_token_id = tokenizer.eos_token_id
batch_size = 1
prompt_len = prompt.shape[1]
device = model.device
gen_pad_len = (slot_size - (gen_length % slot_size)) % slot_size
gen_length = gen_length + gen_pad_len
gen_x = torch.full((batch_size, gen_length), mask_id, dtype=torch.long, device=device)
prompt_pos_ids = torch.arange(prompt_len, dtype=torch.long, device=device).unsqueeze(0)
gen_pos_ids = torch.arange(prompt_len, prompt_len + gen_length, dtype=torch.long, device=device).unsqueeze(0)
cur_x = prompt.clone()
cur_pos = prompt_pos_ids.clone()
cur_slot_size = slot_size
eos_flag = False
block_length = gen_length // serial_num_blocks
past_key_values = None
for serial_num_block in range(serial_num_blocks):
# block level
cur_gen_x = gen_x[:, serial_num_block*block_length:(serial_num_block+1)*block_length] # (batch_size, block_length)
cur_gen_pos_ids = gen_pos_ids[:, serial_num_block*block_length:(serial_num_block+1)*block_length] # (batch_size, block_length)
cur_gen_blocks_x = cur_gen_x.reshape(batch_size, -1, cur_slot_size)
cur_gen_blocks_pos_ids = cur_gen_pos_ids.reshape(batch_size, -1, cur_slot_size)
# slot level generation
while cur_gen_blocks_x.numel() > 0:
cur_gen_blocks_x = cur_gen_blocks_x.reshape(batch_size, -1, cur_slot_size)
cur_gen_blocks_pos_ids = cur_gen_blocks_pos_ids.reshape(batch_size, -1, cur_slot_size)
flat_gen_blocks_x = cur_gen_blocks_x.view(batch_size, -1)
flat_gen_blocks_pos_ids = cur_gen_blocks_pos_ids.view(batch_size, -1)
prefix_block_tag = False
# MDM
if past_key_values is None:
input_x = torch.cat((cur_x, flat_gen_blocks_x), dim=1)
input_pos_ids = torch.cat((cur_pos, flat_gen_blocks_pos_ids), dim=1)
outputs = model(
input_ids=input_x,
position_ids=input_pos_ids,
past_key_values=past_key_values,
use_cache=True
)
else:
outputs = model(
input_ids=flat_gen_blocks_x,
position_ids=flat_gen_blocks_pos_ids,
past_key_values=past_key_values,
use_cache=True
)
logits = outputs.logits
gen_logits = logits[:, -flat_gen_blocks_x.shape[1]:, :]
past_key_values = outputs.past_key_values
past_key_values.crop(cur_x.shape[1])
assert cur_x.shape[-1] == past_key_values[0][0].shape[-2]
logits_with_noise = add_gumbel_noise(gen_logits, temperature=temperature)
x0_gen = torch.argmax(logits_with_noise, dim=-1)
x0_gen_blocks = x0_gen.view(batch_size, -1, cur_slot_size)
p_softmax = F.softmax(gen_logits, dim=-1)
x0_p_softmax = torch.gather(p_softmax, dim=-1, index=torch.unsqueeze(x0_gen, -1)).squeeze(-1)
x0_p_softmax_blocks = x0_p_softmax.view(batch_size, -1, cur_slot_size)
block_confidence_softmax = x0_p_softmax_blocks[:,:,0] # (bsz, num_slots)
is_confident_block = block_confidence_softmax > slot_threshold
counts_block = torch.sum(is_confident_block, dim=1).item()
topk_indices_relative = is_confident_block[0].nonzero(as_tuple=True)[0]
if counts_block <= 0:
counts_block = 1
_, topk_indices_relative = torch.topk(block_confidence_softmax.squeeze(0), k=1)
# choose slot
topk_indices_relative, _ = torch.sort(topk_indices_relative)
chosen_gen_blocks = x0_gen_blocks[0, topk_indices_relative, :]
chosen_position_ids = cur_gen_blocks_pos_ids[0, topk_indices_relative, :]
chosen_p_softmax_blocks = x0_p_softmax_blocks[0, topk_indices_relative, :]
# Global Verification
outputs = model(
input_ids=chosen_gen_blocks.reshape(1, -1),
position_ids=chosen_position_ids.reshape(1, -1),
past_key_values=past_key_values,
use_cache=True,
)
AR_logits = outputs.logits #[1, len, vocab_len]
AR_logits = torch.cat([AR_logits[:,:1], AR_logits[:, :-1]], dim=1)
AR_p_softmax = F.softmax(AR_logits, dim=-1) #[1, len, 1]
AR_x0_p_softmax = torch.gather(AR_p_softmax, dim=-1, index=torch.unsqueeze(chosen_gen_blocks.reshape(1, -1), -1)).squeeze(-1) #[1, len]
AR_x0_p_softmax_blocks = AR_x0_p_softmax.reshape(-1, cur_slot_size)
chosen_p_softmax_blocks[:,1:] = AR_x0_p_softmax_blocks[:,1:]
prob_mask = chosen_p_softmax_blocks > token_threshold
prob_mask[:, 0] = 1
tag_blocks = torch.cumprod(prob_mask.int(), dim=-1)
tag_tokens = torch.cumprod(prob_mask.int().reshape(1, -1), dim=-1)
prefix_len = torch.sum(tag_tokens, dim=-1)
flat_chosen_gen_blocks = chosen_gen_blocks.reshape(1, -1)
confident_prefix_tokens = flat_chosen_gen_blocks[:, :prefix_len]
if prefix_len > 0:
is_eos_in_prefix = (confident_prefix_tokens.squeeze(0) == eos_token_id)
eos_found_flag = torch.any(is_eos_in_prefix)
remain_indices = []
indices_to_remove = set()
if eos_found_flag:
first_eos_pos_tensor = torch.argmax(is_eos_in_prefix.int())
eos_block_pos = first_eos_pos_tensor // cur_slot_size + 1
eos_token_pos = first_eos_pos_tensor - (first_eos_pos_tensor // cur_slot_size) * cur_slot_size
eos_block = topk_indices_relative[eos_block_pos-1].item()
remain_indices.extend(topk_indices_relative[:eos_block_pos].tolist())
topk_indices_relative = torch.tensor([], device=device)
eos_flag = True
indices_after_eos = list(range(eos_block, cur_gen_blocks_x.shape[1]))
indices_to_remove.update(indices_after_eos)
elif (prefix_len // cur_slot_size) > 0:
num_prefix_blocks = prefix_len // cur_slot_size
remain_indices.extend(topk_indices_relative[:num_prefix_blocks].tolist())
topk_indices_relative = topk_indices_relative[num_prefix_blocks:]
tag_blocks = tag_blocks[num_prefix_blocks:]
if len(remain_indices) > 0:
indices_to_remove.update(remain_indices)
token_indices = []
for i_idx, b_idx in enumerate(remain_indices):
start_index = b_idx * cur_slot_size
current_block_len = cur_slot_size
# If EOS exists and this is the last slot, then adjust the length.
if eos_found_flag and i_idx == len(remain_indices) - 1:
current_block_len = eos_token_pos + 1
end_index = start_index + current_block_len
block_range = torch.arange(start_index, end_index, dtype=torch.long, device=device)
token_indices.append(block_range)
full_token_indices = torch.cat(token_indices)
cur_x = torch.cat((cur_x, x0_gen[:, full_token_indices]), dim=1)
cur_pos = torch.cat((cur_pos, flat_gen_blocks_pos_ids[:, full_token_indices]), dim=1)
past_key_values = outputs.past_key_values
past_key_values.crop(cur_x.shape[1])
assert cur_x.shape[-1] == past_key_values[0][0].shape[-2]
prefix_block_tag = True
sum_TPF += cur_slot_size * len(remain_indices) / 2
forward_count += 1
if prefix_block_tag == True:
keep_mask = torch.ones(cur_gen_blocks_x.shape[1], dtype=torch.bool, device=device)
keep_mask[list(indices_to_remove)] = False
cur_gen_blocks_x = cur_gen_blocks_x[:, keep_mask, :]
cur_gen_blocks_pos_ids = cur_gen_blocks_pos_ids[:, keep_mask, :]
continue
elif prefix_block_tag == False:
past_key_values = outputs.past_key_values
past_key_values.crop(cur_x.shape[1])
assert cur_x.shape[-1] == past_key_values[0][0].shape[-2]
indices_to_remove = set(topk_indices_relative.tolist())
current_speculative_blocks = chosen_gen_blocks.clone()
accepted_prefix_len = 0
eos_found_in_loop = False
if past_key_values is not None and counts_block > 1:
past_key_values.batch_repeat_interleave(counts_block)
for loop_iter in range(cur_slot_size):
if not torch.any(tag_blocks == 0):
break
input_tokens = current_speculative_blocks[:, accepted_prefix_len:]
input_pos = chosen_position_ids[:, accepted_prefix_len:]
current_tags = tag_blocks[:, accepted_prefix_len:]
masked_input_tokens = torch.where(current_tags.bool(), input_tokens, mask_id)
# Prediction
draft_len = past_key_values[0][0].shape[2]
draft_outputs = model(
input_ids=masked_input_tokens,
position_ids=input_pos,
past_key_values=past_key_values,
use_cache=False,
)
past_key_values.crop(draft_len)
draft_logits = draft_outputs.logits
proposed_tokens = torch.argmax(draft_logits, dim=-1)
input_tokens = torch.where(current_tags.bool(), input_tokens, proposed_tokens)
current_speculative_blocks[:, accepted_prefix_len:] = input_tokens
# Verification
verify_outputs = model(
input_ids=input_tokens,
position_ids=input_pos,
past_key_values=past_key_values,
use_cache=True,
)
verify_logits = verify_outputs.logits
verify_logits = torch.cat([verify_logits[:,:1], verify_logits[:, :-1]], dim=1)
verify_probs = F.softmax(verify_logits, dim=-1)
gathered_probs = torch.gather(verify_probs, -1, input_tokens.unsqueeze(-1)).squeeze(-1)
prob_mask = gathered_probs > token_threshold
# Keep at least one token
update_tag_blocks = F.pad(tag_blocks[:, accepted_prefix_len:], (1, 0), value=1)[:, :-1]
prob_mask[update_tag_blocks == 1] = True
new_tags = torch.cumprod(prob_mask.int(), dim=-1)
tag_blocks[:, accepted_prefix_len:] = new_tags
newly_verified_mask = (tag_blocks[:, accepted_prefix_len:] == 1)
is_eos_in_new = (current_speculative_blocks[:, accepted_prefix_len:] == eos_token_id) & newly_verified_mask
if torch.any(is_eos_in_new):
eos_found_in_loop = True
first_eos_block_idx = torch.where(torch.any(is_eos_in_new, dim=1))[0][0].item()
current_speculative_blocks = current_speculative_blocks[:first_eos_block_idx+1]
tag_blocks = tag_blocks[:first_eos_block_idx+1]
tag_blocks[first_eos_block_idx] = 1
chosen_position_ids = chosen_position_ids[:first_eos_block_idx+1]
topk_indices_relative = topk_indices_relative[:first_eos_block_idx+1]
if verify_outputs.past_key_values is not None:
verify_outputs.past_key_values.batch_select_minibatch(first_eos_block_idx + 1)
current_tags = tag_blocks[:, accepted_prefix_len:]
len_per_block = torch.sum(current_tags, dim=1)
newly_accepted_len = torch.min(len_per_block).item()
if newly_accepted_len > 0:
if torch.any(tag_blocks == 0):
accepted_prefix_len = accepted_prefix_len + newly_accepted_len - 1
else:
accepted_prefix_len = accepted_prefix_len + newly_accepted_len
past_key_values = verify_outputs.past_key_values
if past_key_values is not None:
past_key_values.crop(cur_x.shape[1] + accepted_prefix_len)
sum_TPF += (cur_slot_size * counts_block) / (loop_iter * 2 + 2)
forward_count += 1
ar_kv_cache = tuple(
(
layer_past[0][:, :, -cur_slot_size:, :], # key
layer_past[1][:, :, -cur_slot_size:, :] # value
)
for layer_past in past_key_values
)
past_key_values.crop(cur_x.shape[1])
past_key_values.batch_select_indices(torch.tensor([0]).to(device))
eos_mask = (current_speculative_blocks == eos_token_id) # (k*cur_slot_size)
keep_mask = (torch.cumsum(eos_mask.flatten().int(), dim=-1) - eos_mask.flatten().int()) == 0
kept_tokens = current_speculative_blocks.flatten()[keep_mask].reshape(batch_size, -1)
kept_pos_ids = chosen_position_ids.flatten()[keep_mask].reshape(batch_size, -1)
# update KV cache
if kept_tokens.numel() > 0 and ar_kv_cache is not None:
new_past = []
for i, (key, val) in enumerate(ar_kv_cache):
num_heads, _, head_dim = key.shape[1], key.shape[2], key.shape[3]
flat_key = key.permute(1, 0, 2, 3).reshape(1, num_heads, -1, head_dim)
flat_val = val.permute(1, 0, 2, 3).reshape(1, num_heads, -1, head_dim)
kept_key = flat_key[:, :, keep_mask, :]
kept_val = flat_val[:, :, keep_mask, :]
new_past.append((kept_key, kept_val))
kept_kv = tuple(new_past)
past_key_values.full_update(kept_kv)
cur_x = torch.cat((cur_x, kept_tokens), dim=1)
cur_pos = torch.cat((cur_pos, kept_pos_ids), dim=1)
assert cur_x.shape[-1] == past_key_values[0][0].shape[-2]
if eos_found_in_loop:
indices_after_eos = list(range(first_eos_block_idx, cur_gen_blocks_x.shape[1]))
indices_to_remove.update(indices_after_eos)
eos_flag = True
keep_mask = torch.ones(cur_gen_blocks_x.shape[1], dtype=torch.bool, device=device)
keep_mask[list(indices_to_remove)] = False
cur_gen_blocks_x = cur_gen_blocks_x[:, keep_mask, :]
cur_gen_blocks_pos_ids = cur_gen_blocks_pos_ids[:, keep_mask, :]
if eos_flag:
break
_, re_mask_indices = torch.sort(cur_pos, dim=-1)
x = torch.gather(cur_x, dim=-1, index=re_mask_indices)
TPF = sum_TPF / forward_count
return x, TPF
def main():
device = 'cuda'
model_path = "ReFusion"
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
prompt = "You are an expert Python programmer. Your task is to write a single Python function to solve the problem described below, and here is your task: Write a function to sum all amicable numbers from 1 to a specified number.\n\nDirectly after the '[BEGIN]' marker, you must write only the Python code for the function. Do not provide any explanations, comments, or introductory text. The function must include the 'def' line, its arguments, the function body, and a 'return' statement. Your code should pass these tests:\n\nassert amicable_numbers_sum(999)==504\nassert amicable_numbers_sum(9999)==31626\nassert amicable_numbers_sum(99)==0\n[BEGIN]\n"
m = [{"role": "user", "content": prompt}, ]
prompt = tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=False, enable_thinking=True)
print(prompt)
input_ids = tokenizer(prompt)['input_ids']
input_ids = torch.tensor(input_ids).to(device).unsqueeze(0)
out, TPF = generate_refusion(model, tokenizer, input_ids, gen_length=512, temperature=0., mask_id=151670, slot_size=4, model_path=model_path, serial_num_blocks=32, slot_threshold=0.6, token_threshold=0.3)
print(tokenizer.batch_decode(out[:, input_ids.shape[1]:], skip_special_tokens=True)[0])
print("---------TPF:", TPF)
if __name__ == '__main__':
main()
```
# Citation
If you find our work helpful, please consider citing our paper.
```bibtex
@misc{li2025refusiondiffusionlargelanguage,
title={ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding},
author={Jia-Nan Li and Jian Guan and Wei Wu and Chongxuan Li},
year={2025},
eprint={2512.13586},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.13586},
}
``` |