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[feat] add entropy metric #6363

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Merged
TongLi3701 merged 1 commit into from
Jul 17, 2025
+48 −1
Merged

[feat] add entropy metric #6363

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39 changes: 38 additions & 1 deletion applications/ColossalChat/coati/distributed/grpo_consumer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
import wandb
from coati.distributed.consumer import BaseConsumer
from coati.distributed.loss import PolicyLoss
from coati.distributed.utils import memory_efficient_logprob
from coati.distributed.utils import entropy_from_logits, memory_efficient_logprob
from coati.trainer.utils import all_reduce_mean, all_reduce_sum
from transformers import AutoModelForCausalLM, AutoTokenizer

Expand Down Expand Up @@ -75,6 +75,7 @@ def __init__(
self.optimizer = HybridAdam(self.policy_model.parameters(), lr=grpo_config.get("lr", 1e-6))
self.accum_loss = torch.zeros(1, device=self.device)
self.accum_kl = torch.zeros(1, device=self.device)
self.accum_entropy = torch.zeros(1, device=self.device)
self.accum_advantages = torch.zeros(1, device=self.device)
self.raw_train_batch_reward = []
self.raw_train_batch_format_acc = []
Expand Down Expand Up @@ -244,6 +245,7 @@ def step(self, step_idx: int, pbar: Any, **kwargs) -> Optional[float]:
else self.booster.no_sync(self.policy_model, self.optimizer)
)
with ctx:
mini_batch_entropies = []
for forward_micro_batch_start in range(0, data["input_ids"].size(0), train_microbatch_size):
input_ids_forward_micro_batch = data["input_ids"][
forward_micro_batch_start : forward_micro_batch_start + train_microbatch_size
Expand Down Expand Up @@ -310,9 +312,11 @@ def step(self, step_idx: int, pbar: Any, **kwargs) -> Optional[float]:
data_policy_forward["reference_action_log_probs"] = reference_action_log_probs

kl = []
policy_model_logits = torch.empty_like(input_ids_forward_micro_batch, device=self.device)

def _criterion(outputs, inputs):
action_logits = outputs.logits
policy_model_logits.copy_(action_logits)
action_log_probs = memory_efficient_logprob(
action_logits / self.generate_config["temperature"],
inputs["input_ids"],
Expand Down Expand Up @@ -359,6 +363,20 @@ def _criterion(outputs, inputs):
kl = all_reduce_mean(torch.mean(torch.stack(kl)).to(loss.device), self.plugin).data
mean_kl.append(kl)
mean_loss.append(all_reduce_mean(loss, self.plugin).data)
mini_batch_entropies.append(
all_reduce_mean(
(
(
(
entropy_from_logits(policy_model_logits[:, -num_action:])
* action_mask_forward_micro_batch
).sum(-1)
)
/ action_mask_forward_micro_batch.sum(-1)
).detach(),
self.plugin,
)
)
else:
policy_model_logits = self.policy_model(
input_ids=input_ids_forward_micro_batch,
Expand Down Expand Up @@ -412,6 +430,20 @@ def _criterion(outputs, inputs):
kl = all_reduce_mean(kl.mean(), self.plugin)
mean_kl.append(kl.data)
mean_loss.append(loss.data)
mini_batch_entropies.append(
all_reduce_mean(
(
(
(
entropy_from_logits(policy_model_logits[:, -num_action:])
* action_mask_forward_micro_batch
).sum(-1)
)
/ action_mask_forward_micro_batch.sum(-1)
).detach(),
self.plugin,
)
)
if not self.plugin.pp_size > 1 or (
self.plugin.pp_size > 1
and self.booster.plugin.stage_manager.is_last_stage()
Expand All @@ -423,7 +455,9 @@ def _criterion(outputs, inputs):
ans_acc = all_reduce_mean(ans_acc.mean(), self.plugin)
advantages = all_reduce_mean(advantages.mean(), self.plugin)
response_length = all_reduce_mean(response_length.mean(), self.plugin)
entropy = torch.cat(mini_batch_entropies, dim=0).mean()
self.accum_loss.add_(sum(mean_loss) / len(mean_loss))
self.accum_entropy.add_(entropy.data)
if self.policy_loss_fn.beta > 0:
self.accum_kl.add_(sum(mean_kl) / len(mean_kl))
self.accum_advantages.add_(advantages.data)
Expand Down Expand Up @@ -464,6 +498,7 @@ def _criterion(outputs, inputs):
f"Response Length: {raw_batch_response_len_mean:.4f}",
f"Sample_utilization: {sample_utilization:.4f}",
f"Overlength samples ratio: {overlength_samples_ratio:.4f}",
f"Entropy: {self.accum_entropy.item() / self.accum_count:.4f}",
] + ([f"KL: {self.accum_kl.item() / self.accum_count:.4f}"] if self.policy_loss_fn.beta > 0 else [])
print("\n".join(to_log_msg))
metrics = {
Expand All @@ -475,6 +510,7 @@ def _criterion(outputs, inputs):
"train/advantages": self.accum_advantages.item() / self.accum_count,
"train/learning_rate": self.lr_scheduler.get_last_lr()[0],
"train/sample_utilization": sample_utilization,
"train/entropy": self.accum_entropy.item() / self.accum_count,
"train/overlength_samples_ratio": overlength_samples_ratio,
"rollout/temperature": data["temperature"].cpu().numpy()[0][0],
}
Expand All @@ -484,6 +520,7 @@ def _criterion(outputs, inputs):
self.wandb_run.log(metrics)
self.accum_loss.zero_()
self.accum_kl.zero_()
self.accum_entropy.zero_()
self.accum_advantages.zero_()
self.accum_count = 0
return loss_scalar
Expand Down
10 changes: 10 additions & 0 deletions applications/ColossalChat/coati/distributed/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,16 @@ def memory_efficient_logprob(
return action_log_probs


def entropy_from_logits(logits: torch.Tensor) -> torch.Tensor:
"""
Calculate entropy
Reference: https://github.com/volcengine/verl/blob/96b730bbed80292a439f0c0057d3920ab8b28d52/verl/utils/torch_functional.py#L145
"""
p = torch.nn.functional.softmax(logits, dim=-1)
entropy = torch.logsumexp(logits, dim=-1) - torch.sum(p * logits, dim=-1)
return entropy


def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor:
"""
Compute the masked mean of a tensor along a specified dimension.
Expand Down