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KL Estimator #423

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KL Estimator #423

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1f0e9c9
KL Estimator
vwxyzjn Jun 9, 2023
f67eb24
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vwxyzjn Jun 9, 2023
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4 changes: 3 additions & 1 deletion tests/test_no_peft.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,9 @@ def __getitem__(self, idx):
"ppo/loss/total",
"ppo/policy/entropy",
"ppo/policy/approxkl",
"ppo/policy/policykl",
"ppo/policy/approxkl_k1",
"ppo/policy/approxkl_k2",
"ppo/policy/approxkl_k3",
"ppo/policy/clipfrac",
"ppo/policy/advantages",
"ppo/policy/advantages_mean",
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4 changes: 3 additions & 1 deletion tests/test_ppo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,9 @@
"ppo/loss/total",
"ppo/policy/entropy",
"ppo/policy/approxkl",
"ppo/policy/policykl",
"ppo/policy/approxkl_k1",
"ppo/policy/approxkl_k2",
"ppo/policy/approxkl_k3",
"ppo/policy/clipfrac",
"ppo/policy/advantages",
"ppo/policy/advantages_mean",
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67 changes: 38 additions & 29 deletions trl/trainer/ppo_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -685,8 +685,8 @@ def collator(data):
all_stats.append(train_stats)

if self.config.early_stopping:
policykl = train_stats["policy/policykl"]
early_stop = self._early_stop(policykl)
approxkl = train_stats["policy/approxkl"]
early_stop = self._early_stop(approxkl)
if early_stop:
break

Expand Down Expand Up @@ -734,7 +734,7 @@ def collator(data):

return stats

def _early_stop(self, policykl):
def _early_stop(self, approxkl):
r"""
Handles the early stopping logic. If the policy KL is greater than the target KL, then the gradient is zeroed and
the optimization step is skipped.
Expand All @@ -751,7 +751,7 @@ def _early_stop(self, policykl):
if not self.config.early_stopping:
return early_stop

if not self.is_distributed and policykl > 1.5 * self.config.target_kl:
if not self.is_distributed and approxkl > 1.5 * self.config.target_kl:
self.optimizer.zero_grad()
early_stop = True
elif self.is_distributed:
Expand All @@ -760,11 +760,11 @@ def _early_stop(self, policykl):
# Wait for all processes to finish
dist.barrier()

# all gather the policykl
dist.all_reduce(policykl, dist.ReduceOp.SUM)
policykl /= self.accelerator.num_processes
# all gather the approxkl
dist.all_reduce(approxkl, dist.ReduceOp.SUM)
approxkl /= self.accelerator.num_processes

if policykl > 1.5 * self.config.target_kl:
if approxkl > 1.5 * self.config.target_kl:
self.optimizer.zero_grad()
early_stop = True
return early_stop
Expand Down Expand Up @@ -1011,16 +1011,17 @@ def loss(
values = values * mask
rewards = rewards * mask

for t in reversed(range(gen_len)):
nextvalues = values[:, t + 1] if t < gen_len - 1 else 0.0
delta = rewards[:, t] + self.config.gamma * nextvalues - values[:, t]
lastgaelam = delta + self.config.gamma * self.config.lam * lastgaelam
advantages_reversed.append(lastgaelam)
advantages = torch.stack(advantages_reversed[::-1]).transpose(0, 1)
with torch.no_grad():
for t in reversed(range(gen_len)):
nextvalues = values[:, t + 1] if t < gen_len - 1 else 0.0
delta = rewards[:, t] + self.config.gamma * nextvalues - values[:, t]
lastgaelam = delta + self.config.gamma * self.config.lam * lastgaelam
advantages_reversed.append(lastgaelam)
advantages = torch.stack(advantages_reversed[::-1]).transpose(0, 1)

returns = advantages + values
advantages = masked_whiten(advantages, mask)
advantages = advantages.detach()
returns = advantages + values
advantages = masked_whiten(advantages, mask)
advantages = advantages.detach()

vpredclipped = clip_by_value(
vpreds, values - self.config.cliprange_value, values + self.config.cliprange_value
Expand All @@ -1031,30 +1032,38 @@ def loss(
vf_loss = 0.5 * masked_mean(torch.max(vf_losses1, vf_losses2), mask)
vf_clipfrac = masked_mean(torch.gt(vf_losses2, vf_losses1).double(), mask)

ratio = torch.exp(logprobs - old_logprobs)
logratio = logprobs - old_logprobs
ratio = torch.exp(logratio)
pg_losses = -advantages * ratio
pg_losses2 = -advantages * torch.clamp(ratio, 1.0 - self.config.cliprange, 1.0 + self.config.cliprange)

pg_loss = masked_mean(torch.max(pg_losses, pg_losses2), mask)
pg_clipfrac = masked_mean(torch.gt(pg_losses2, pg_losses).double(), mask)

loss = pg_loss + self.config.vf_coef * vf_loss

entropy = masked_mean(entropy_from_logits(logits), mask)
approxkl = 0.5 * masked_mean((logprobs - old_logprobs) ** 2, mask)
policykl = masked_mean(old_logprobs - logprobs, mask)
with torch.no_grad():
pg_clipfrac = masked_mean(torch.gt(pg_losses2, pg_losses).double(), mask)
entropy = masked_mean(entropy_from_logits(logits), mask)
# calculate approx_kl http://joschu.net/blog/kl-approx.html
approxkl_k1 = masked_mean(-logratio, mask)
approxkl_k2 = 0.5 * masked_mean(logratio**2, mask)
approxkl_k3 = masked_mean((ratio - 1) - logratio, mask)
# by default use `k3`, the estimator which has lowest variance and is unbiased
approxkl = approxkl_k3

return_mean, return_var = masked_mean(returns, mask), masked_var(returns, mask)
value_mean, value_var = masked_mean(values, mask), masked_var(values, mask)

stats = dict(
loss=dict(policy=pg_loss.detach(), value=vf_loss.detach(), total=loss.detach()),
policy=dict(
entropy=entropy.detach(),
approxkl=approxkl.detach(),
policykl=policykl.detach(),
clipfrac=pg_clipfrac.detach(),
advantages=advantages.detach(),
advantages_mean=masked_mean(advantages, mask).detach(),
entropy=entropy,
approxkl=approxkl,
approxkl_k1=approxkl_k1,
approxkl_k2=approxkl_k2,
approxkl_k3=approxkl_k3,
clipfrac=pg_clipfrac,
advantages=advantages,
advantages_mean=masked_mean(advantages, mask),
ratio=ratio.detach(),
),
returns=dict(mean=return_mean.detach(), var=return_var.detach()),
Expand Down