File size: 7,437 Bytes
9b970dd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | """AAM Diffusion LLM — DAPO Training
Decoupled Clip & Dynamic Sampling Policy Optimization (Yu et al., 2025).
Four improvements over GRPO:
1. Decoupled Clip (asymmetric epsilon)
2. Dynamic Sampling (filter zero-variance groups)
3. Token-Level Policy Gradient Loss
4. Overlong Filtering
"""
from __future__ import annotations
import copy
import logging
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
@dataclass
class DAPOConfig:
clip_ratio_low: float = 0.2
clip_ratio_high: float = 0.28
dynamic_sampling: bool = True
token_level_loss: bool = True
overlong_filter: bool = True
max_response_length: int = 2048
num_responses_per_prompt: int = 8
kl_coefficient: float = 0.1
discount_factor: float = 1.0
use_reward_normalization: bool = True
use_advantage_normalization: bool = True
learning_rate: float = 1e-6
reference_model_freeze: bool = True
entropy_coefficient: float = 0.01
max_grad_norm: float = 1.0
temperature: float = 0.7
reward_shaping: str = "centered"
def __post_init__(self) -> None:
if self.clip_ratio_low <= 0:
raise ValueError(f"clip_ratio_low must be positive, got {self.clip_ratio_low}")
if self.clip_ratio_high <= 0:
raise ValueError(f"clip_ratio_high must be positive, got {self.clip_ratio_high}")
if self.num_responses_per_prompt < 2:
raise ValueError(f"num_responses_per_prompt must be >= 2, got {self.num_responses_per_prompt}")
class DAPOTrainer:
"""DAPO Trainer for AAM Diffusion LLM."""
def __init__(
self,
config: DAPOConfig,
policy_model: nn.Module,
reference_model: Optional[nn.Module] = None,
reward_fn: Optional[Callable] = None,
optimizer: Optional[torch.optim.Optimizer] = None,
) -> None:
self.config = config
self.policy_model = policy_model
self.reward_fn = reward_fn
if reference_model is not None:
self.reference_model = reference_model
elif config.kl_coefficient > 0:
self.reference_model = copy.deepcopy(policy_model)
else:
self.reference_model = None
if self.reference_model is not None and config.reference_model_freeze:
for param in self.reference_model.parameters():
param.requires_grad = False
trainable_params = [p for p in policy_model.parameters() if p.requires_grad]
self.optimizer = optimizer or torch.optim.AdamW(
trainable_params, lr=config.learning_rate, betas=(0.9, 0.95), weight_decay=0.01,
)
self.device = next(policy_model.parameters()).device
def compute_dapo_loss(
self,
log_probs: torch.Tensor,
old_log_probs: torch.Tensor,
ref_log_probs: torch.Tensor,
rewards: torch.Tensor,
attention_mask: torch.Tensor,
) -> Tuple[torch.Tensor, Dict[str, float]]:
cfg = self.config
log_ratio = log_probs - old_log_probs
ratio = torch.exp(log_ratio)
advantages = self._compute_advantages(rewards)
advantages_expanded = advantages.unsqueeze(-1).expand_as(log_probs) if advantages.dim() == 1 else advantages
clipped_ratio = torch.clamp(ratio, 1.0 - cfg.clip_ratio_low, 1.0 + cfg.clip_ratio_high)
surr1 = ratio * advantages_expanded
surr2 = clipped_ratio * advantages_expanded
if cfg.token_level_loss:
per_token_loss = -torch.min(surr1, surr2) * attention_mask
num_valid_tokens = attention_mask.sum(dim=-1, keepdim=True).clamp(min=1)
policy_loss = (per_token_loss.sum(dim=-1) / num_valid_tokens.squeeze(-1)).mean()
else:
per_token_loss = -torch.min(surr1, surr2) * attention_mask
seq_loss = per_token_loss.sum(dim=-1) / attention_mask.sum(dim=-1).clamp(min=1)
policy_loss = seq_loss.mean()
kl_penalty = torch.tensor(0.0, device=log_probs.device)
if ref_log_probs is not None and cfg.kl_coefficient > 0:
kl_per_token = torch.exp(log_probs) * (log_probs - ref_log_probs) * attention_mask
kl_penalty = cfg.kl_coefficient * (kl_per_token.sum(dim=-1) / attention_mask.sum(dim=-1).clamp(min=1)).mean()
entropy = torch.tensor(0.0, device=log_probs.device)
if cfg.entropy_coefficient > 0:
per_token_entropy = -torch.exp(log_probs) * log_probs * attention_mask
entropy = (per_token_entropy.sum(dim=-1) / attention_mask.sum(dim=-1).clamp(min=1)).mean()
loss = policy_loss + kl_penalty - cfg.entropy_coefficient * entropy
with torch.no_grad():
metrics = {
"dapo/policy_loss": policy_loss.item(),
"dapo/kl_penalty": kl_penalty.item() if isinstance(kl_penalty, torch.Tensor) else kl_penalty,
"dapo/entropy": entropy.item() if isinstance(entropy, torch.Tensor) else entropy,
"dapo/loss": loss.item(),
"dapo/mean_reward": rewards.mean().item(),
}
return loss, metrics
def _compute_advantages(self, rewards: torch.Tensor) -> torch.Tensor:
cfg = self.config
if cfg.use_reward_normalization and rewards.numel() > 1:
rewards = self._shape_rewards(rewards, cfg.reward_shaping)
advantages = rewards.clone()
if cfg.use_advantage_normalization and advantages.numel() > 1:
adv_std = advantages.std()
if adv_std > 1e-8:
advantages = (advantages - advantages.mean()) / (adv_std + 1e-8)
return advantages
def _shape_rewards(self, rewards: torch.Tensor, strategy: str) -> torch.Tensor:
if strategy == "raw":
return rewards
if strategy == "centered":
return rewards - rewards.mean()
if strategy == "rank_based":
sorted_indices = rewards.argsort()
ranks = torch.zeros_like(rewards, dtype=torch.float32)
ranks[sorted_indices] = torch.arange(len(rewards), dtype=torch.float32, device=rewards.device) / max(len(rewards) - 1, 1)
return 2.0 * ranks - 1.0
return rewards
def filter_prompts(
self,
prompts: List[str],
responses: List[List[str]],
rewards: torch.Tensor,
) -> Tuple[List[str], List[List[str]], torch.Tensor, Dict[str, int]]:
if not self.config.dynamic_sampling:
return prompts, responses, rewards, {"filtered": 0, "total": len(prompts)}
if rewards.dim() == 1:
has_variance = rewards > 1e-6
else:
reward_std_per_prompt = rewards.std(dim=-1)
has_variance = reward_std_per_prompt > 1e-6
valid_indices = has_variance.nonzero(as_tuple=True)[0]
if len(valid_indices) == 0:
return prompts, responses, rewards, {"filtered": len(prompts), "total": len(prompts)}
filtered_prompts = [prompts[i] for i in valid_indices]
filtered_responses = [responses[i] for i in valid_indices]
filtered_rewards = rewards[valid_indices]
num_filtered = len(prompts) - len(valid_indices)
return filtered_prompts, filtered_responses, filtered_rewards, {"filtered": num_filtered, "total": len(prompts)}
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