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# Copyright 2024 Bytedance Ltd. and/or its affiliates
# Copyright 2022 The HuggingFace 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.
"""
Core functions to implement PPO algorithms.
The function implemented in this file should be used by trainer with different distributed strategies to
implement PPO
"""
from collections import defaultdict
import numpy as np
import torch
import verl.utils.torch_functional as verl_F
class AdaptiveKLController:
"""
Adaptive KL controller described in the paper:
https://arxiv.org/pdf/1909.08593.pdf
"""
def __init__(self, init_kl_coef, target_kl, horizon):
self.value = init_kl_coef
self.target = target_kl
self.horizon = horizon
def update(self, current_kl, n_steps):
target = self.target
proportional_error = np.clip(current_kl / target - 1, -0.2, 0.2)
mult = 1 + proportional_error * n_steps / self.horizon
self.value *= mult
class FixedKLController:
"""Fixed KL controller."""
def __init__(self, kl_coef):
self.value = kl_coef
def update(self, current_kl, n_steps):
pass
def get_kl_controller(kl_ctrl):
if kl_ctrl.type == "fixed":
return FixedKLController(kl_coef=kl_ctrl.kl_coef)
elif kl_ctrl.type == "adaptive":
assert kl_ctrl.horizon > 0, f"horizon must be larger than 0. Got {kl_ctrl.horizon}"
return AdaptiveKLController(init_kl_coef=kl_ctrl.kl_coef, target_kl=kl_ctrl.target_kl, horizon=kl_ctrl.horizon)
else:
raise NotImplementedError
def compute_gae_advantage_return(
token_level_rewards: torch.Tensor,
values: torch.Tensor,
response_mask: torch.Tensor,
gamma: torch.Tensor,
lam: torch.Tensor,
):
"""Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py
Args:
token_level_rewards: `(torch.Tensor)`
shape: (bs, response_length)
values: `(torch.Tensor)`
shape: (bs, response_length)
response_mask: `(torch.Tensor)`
shape: (bs, response_length). [EOS] mask. The token after [EOS] have mask zero.
gamma: `(float)`
discounted factor used in RL
lam: `(float)`
lambda value when computing Generalized Advantage Estimation (https://arxiv.org/abs/1506.02438)
Returns:
advantages: `(torch.Tensor)`
shape: (bs, response_length)
Returns: `(torch.Tensor)`
shape: (bs, response_length)
"""
with torch.no_grad():
lastgaelam = 0
advantages_reversed = []
gen_len = token_level_rewards.shape[-1]
for t in reversed(range(gen_len)):
nextvalues = values[:, t + 1] if t < gen_len - 1 else 0.0
delta = token_level_rewards[:, t] + gamma * nextvalues - values[:, t]
lastgaelam = delta + gamma * lam * lastgaelam
advantages_reversed.append(lastgaelam)
advantages = torch.stack(advantages_reversed[::-1], dim=1)
returns = advantages + values
advantages = verl_F.masked_whiten(advantages, response_mask)
return advantages, returns
# NOTE(sgm): this implementation only consider outcome supervision, where the reward is a scalar.
def compute_grpo_outcome_advantage(
token_level_rewards: torch.Tensor,
response_mask: torch.Tensor,
index: np.ndarray,
epsilon: float = 1e-6,
norm_adv_by_std_in_grpo: str = True,
):
"""
Compute advantage for GRPO, operating only on Outcome reward
(with only one scalar reward for each response).
Args:
token_level_rewards: `(torch.Tensor)`
shape: (bs, response_length)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
norm_adv_by_std_in_grpo: (bool)
whether to scale the GRPO advantage.
If True, the advantage is scaled by the std, as in the original GRPO.
If False, the advantage is not scaled, as in Dr.GRPO (https://arxiv.org/abs/2503.20783).
Returns:
advantages: `(torch.Tensor)`
shape: (bs, response_length)
Returns: `(torch.Tensor)`
shape: (bs, response_length)
"""
scores = token_level_rewards.sum(dim=-1)
id2score = defaultdict(list)
id2mean = {}
id2std = {}
with torch.no_grad():
bsz = scores.shape[0]
for i in range(bsz):
id2score[index[i]].append(scores[i])
for idx in id2score:
if len(id2score[idx]) == 1:
id2mean[idx] = torch.tensor(0.0)
id2std[idx] = torch.tensor(1.0)
elif len(id2score[idx]) > 1:
id2mean[idx] = torch.mean(torch.tensor(id2score[idx]))
id2std[idx] = torch.std(torch.tensor([id2score[idx]]))
else:
raise ValueError(f"no score in prompt index: {idx}")
for i in range(bsz):
if norm_adv_by_std_in_grpo:
scores[i] = (scores[i] - id2mean[index[i]]) / (id2std[index[i]] + epsilon)
else:
scores[i] = scores[i] - id2mean[index[i]]
scores = scores.unsqueeze(-1) * response_mask
return scores, scores
def compute_reinforce_plus_plus_baseline_outcome_advantage(token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: torch.Tensor, epsilon: float = 1e-6):
"""
Compute advantage for RF++-baseline (https://arxiv.org/abs/2501.03262), operating only on Outcome reward
(with only one scalar reward for each response).
Args:
token_level_rewards: `(torch.Tensor)`
shape: (bs, response_length)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
Returns:
advantages: `(torch.Tensor)`
shape: (bs, response_length)
Returns: `(torch.Tensor)`
shape: (bs, response_length)
"""
response_length = token_level_rewards.shape[-1]
scores = token_level_rewards.sum(dim=-1)
id2score = defaultdict(list)
id2mean = {}
with torch.no_grad():
bsz = scores.shape[0]
for i in range(bsz):
id2score[index[i]].append(scores[i])
for idx in id2score:
if len(id2score[idx]) == 1:
id2mean[idx] = torch.tensor(0.0)
elif len(id2score[idx]) > 1:
id2mean[idx] = torch.mean(torch.tensor(id2score[idx]))
else:
raise ValueError(f"no score in prompt index: {idx}")
for i in range(bsz):
scores[i] = scores[i] - id2mean[index[i]]
scores = scores.unsqueeze(-1).tile([1, response_length]) * response_mask
scores = verl_F.masked_whiten(scores, response_mask)
return scores, scores
def compute_rloo_outcome_advantage(token_level_rewards: torch.Tensor, response_mask: torch.Tensor, index: np.ndarray, epsilon: float = 1e-6):
"""
Compute advantage for RLOO based on https://arxiv.org/abs/2402.14740
Args:
token_level_rewards: `(torch.Tensor)`
shape: (bs, response_length)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
Returns:
advantages: `(torch.Tensor)`
shape: (bs, response_length)
Returns: `(torch.Tensor)`
shape: (bs, response_length)
"""
scores = token_level_rewards.sum(dim=-1)
id2score = defaultdict(list)
id2mean = {}
with torch.no_grad():
bsz = scores.shape[0]
for i in range(bsz):
id2score[index[i]].append(scores[i])
for idx in id2score:
if len(id2score[idx]) == 1:
id2mean[idx] = torch.tensor(0.0)
elif len(id2score[idx]) > 1:
id2mean[idx] = torch.mean(torch.tensor(id2score[idx]))
else:
raise ValueError(f"no score in prompt index: {idx}")
for i in range(bsz):
response_num = len(id2score[index[i]])
if response_num > 1:
scores[i] = scores[i] * response_num / (response_num - 1) - id2mean[index[i]] * response_num / (response_num - 1)
scores = scores.unsqueeze(-1) * response_mask
return scores, scores
def compute_reinforce_plus_plus_outcome_advantage(token_level_rewards: torch.Tensor, response_mask: torch.Tensor, gamma: torch.Tensor):
"""
Compute advantage for REINFORCE++.
This implementation is based on the paper: https://arxiv.org/abs/2501.03262
Args:
token_level_rewards: `(torch.Tensor)`
shape: (bs, response_length)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
Returns:
advantages: `(torch.Tensor)`
shape: (bs, response_length)
Returns: `(torch.Tensor)`
shape: (bs, response_length)
"""
with torch.no_grad():
returns = torch.zeros_like(token_level_rewards)
running_return = 0
for t in reversed(range(token_level_rewards.shape[1])):
running_return = token_level_rewards[:, t] + gamma * running_return
returns[:, t] = running_return
# Reset after EOS
running_return = running_return * response_mask[:, t]
advantages = verl_F.masked_whiten(returns, response_mask)
advantages = advantages * response_mask
return advantages, returns
def compute_remax_outcome_advantage(token_level_rewards: torch.Tensor, reward_baselines: torch.Tensor, response_mask: torch.Tensor):
"""
Compute advantage for ReMax, operating only on Outcome reward
This implementation is based on the paper: https://arxiv.org/abs/2310.10505
(with only one scalar reward for each response).
Args:
token_level_rewards: `(torch.Tensor)`
shape: (bs, response_length)
reward_baselines: `(torch.Tensor)`
shape: (bs,)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
Returns:
advantages: `(torch.Tensor)`
shape: (bs, response_length)
Returns: `(torch.Tensor)`
shape: (bs, response_length)
"""
with torch.no_grad():
returns = (token_level_rewards * response_mask).flip(dims=[-1]).cumsum(dim=-1).flip(dims=[-1])
advantages = returns - reward_baselines.unsqueeze(-1) * response_mask
return advantages, returns
def compute_rewards(token_level_scores, old_log_prob, ref_log_prob, kl_ratio):
kl = old_log_prob - ref_log_prob
return token_level_scores - kl * kl_ratio
def agg_loss(loss_mat: torch.Tensor, loss_mask: torch.Tensor, loss_agg_mode: str):
"""
Aggregate the loss matrix into a scalar.
Args:
loss_mat: `(torch.Tensor)`
shape: (bs, response_length)
loss_mask: `(torch.Tensor)`
shape: (bs, response_length)
loss_agg_mode: (str) choices: "token-mean" /
"seq-mean-token-sum" /
"seq-mean-token-mean" /
"seq-mean-token-sum-norm" /
"token-mean" is the default behavior
Returns:
loss: `a scalar torch.Tensor`
aggregated loss
"""
if loss_agg_mode == "token-mean":
loss = verl_F.masked_mean(loss_mat, loss_mask)
elif loss_agg_mode == "seq-mean-token-sum":
seq_losses = torch.sum(loss_mat * loss_mask, dim=-1) # token-sum
loss = torch.mean(seq_losses) # seq-mean
elif loss_agg_mode == "seq-mean-token-mean":
seq_losses = torch.sum(loss_mat * loss_mask, dim=-1) / torch.sum(loss_mask, dim=-1) # token-mean
loss = torch.mean(seq_losses) # seq-mean
elif loss_agg_mode == "seq-mean-token-sum-norm":
seq_losses = torch.sum(loss_mat * loss_mask, dim=-1)
loss = torch.sum(seq_losses) / loss_mask.shape[-1] # The divisor
# (loss_mask.shape[-1]) should ideally be constant
# throughout training to well-replicate the DrGRPO paper.
# TODO: Perhaps add user-defined normalizer argument to
# agg_loss to ensure divisor stays constant throughout.
else:
raise ValueError(f"Invalid loss_agg_mode: {loss_agg_mode}")
return loss
def compute_policy_loss(
old_log_prob,
log_prob,
advantages,
response_mask,
cliprange=None,
cliprange_low=None,
cliprange_high=None,
clip_ratio_c=3.0,
loss_agg_mode="token-mean",
):
"""Adapted from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1122
Args:
old_log_prob: `(torch.Tensor)`
shape: (bs, response_length)
log_prob: `(torch.Tensor)`
shape: (bs, response_length)
advantages: `(torch.Tensor)`
shape: (bs, response_length)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
cliprange: (float)
The clip range used in PPO. See https://arxiv.org/abs/1707.06347
cliprange_low: (float)
The lower clip range used in PPO.
cliprange_high: (float)
The higher clip range used in PPO.
clip_ratio_c: (float) default: 3.0
The lower bound of the ratio for dual-clip PPO, See https://arxiv.org/pdf/1912.09729
loss_agg_mode: (str) choices: "token-mean" /
"seq-mean-token-sum" /
"seq-mean-token-mean" /
"seq-mean-token-sum-norm" /
"token-mean" is the default behavior
Returns:
pg_loss: `a scalar torch.Tensor`
policy gradient loss computed via PPO
pg_clipfrac: (float)
the fraction of policy gradient loss being clipped
ppo_kl: (float)
the estimated KL divergence between the latest updating policy and the old sampling policy
pg_clipfrac_lower: (float)
the fraction of policy gradient loss being clipped when the advantage is negative
"""
assert clip_ratio_c > 1.0, "The lower bound of the clip_ratio_c for dual-clip PPO should be greater than 1.0," + f" but get the value: {clip_ratio_c}."
negative_approx_kl = log_prob - old_log_prob
ratio = torch.exp(negative_approx_kl)
ppo_kl = verl_F.masked_mean(-negative_approx_kl, response_mask)
pg_losses1 = -advantages * ratio
if cliprange_low is None:
cliprange_low = cliprange
if cliprange_high is None:
cliprange_high = cliprange
pg_losses2 = -advantages * torch.clamp(ratio, 1 - cliprange_low, 1 + cliprange_high) # - clip(ratio, 1-cliprange, 1+cliprange) * A
clip_pg_losses1 = torch.maximum(pg_losses1, pg_losses2) # max(-ratio * A, -clip(ratio, 1-cliprange, 1+cliprange) * A)
pg_clipfrac = verl_F.masked_mean(torch.gt(pg_losses2, pg_losses1).float(), response_mask)
pg_losses3 = -advantages * clip_ratio_c
clip_pg_losses2 = torch.min(pg_losses3, clip_pg_losses1)
pg_clipfrac_lower = verl_F.masked_mean(torch.gt(clip_pg_losses1, pg_losses3) * (advantages < 0).float(), response_mask)
pg_losses = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1)
pg_loss = agg_loss(loss_mat=pg_losses, loss_mask=response_mask, loss_agg_mode=loss_agg_mode)
return pg_loss, pg_clipfrac, ppo_kl, pg_clipfrac_lower
def compute_entropy_loss(logits, response_mask):
"""Compute Categorical entropy loss
Args:
logits: `(torch.Tensor)`
shape: (bs, response_length, vocab_size)
response_mask: `(torch.Tensor)`
shape: (bs, response_length)
Returns:
entropy: a scalar torch.Tensor
"""
# compute entropy
entropy = verl_F.entropy_from_logits(logits) # (bs, response_len)
entropy_loss = verl_F.masked_mean(entropy, mask=response_mask)
return entropy_loss
def compute_value_loss(vpreds, returns, values, response_mask, cliprange_value):
"""Compute the value loss. Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1151
Args:
vpreds (`torch.FloatTensor`):
Predicted values of the value head, shape (`batch_size`, `response_length`)
values (`torch.FloatTensor`):
Old values of value head, shape (`batch_size`, `response_length`)
returns: (`torch.FloatTensor`):
Ground truth returns, shape (`batch_size`, `response_length`)
Returns:
vf_loss: a scalar (`torch.FloatTensor`):
value function loss
vf_clipfrac: a float
The ratio of vf being clipped
"""
vpredclipped = verl_F.clip_by_value(vpreds, values - cliprange_value, values + cliprange_value)
vf_losses1 = (vpreds - returns) ** 2
vf_losses2 = (vpredclipped - returns) ** 2
vf_loss = 0.5 * verl_F.masked_mean(torch.max(vf_losses1, vf_losses2), response_mask)
vf_clipfrac = verl_F.masked_mean(torch.gt(vf_losses2, vf_losses1).float(), response_mask)
return vf_loss, vf_clipfrac
def kl_penalty(logprob: torch.FloatTensor, ref_logprob: torch.FloatTensor, kl_penalty) -> torch.FloatTensor:
"""Compute KL divergence given logprob and ref_logprob.
Copied from https://github.com/huggingface/trl/blob/main/trl/trainer/ppo_trainer.py#L1104
Args:
logprob:
ref_logprob:
Returns:
"""
if kl_penalty == "kl":
return logprob - ref_logprob
if kl_penalty == "abs":
return (logprob - ref_logprob).abs()
if kl_penalty == "mse":
return 0.5 * (logprob - ref_logprob).square()
# J. Schulman. Approximating kl divergence, 2020.
# # URL http://joschu.net/blog/kl-approx.html.
if kl_penalty == "low_var_kl":
kl = ref_logprob - logprob
ratio = torch.exp(kl)
kld = (ratio - kl - 1).contiguous()
return torch.clamp(kld, min=-10, max=10)
if kl_penalty == "full":
# so, here logprob and ref_logprob should contain the logits for every token in vocabulary
raise NotImplementedError
raise NotImplementedError
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