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[hotfix] Add overlength count for wandb #6332

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Merged
TongLi3701 merged 1 commit into from
May 28, 2025
Merged

[hotfix] Add overlength count for wandb #6332

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14 changes: 14 additions & 0 deletions applications/ColossalChat/coati/distributed/grpo_consumer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,6 +85,8 @@ def __init__(
self.effective_sample_count = 0
self.effective_prompt_count = 0
self.total_sample_count = 0
self.overlength_samples = 0
self.total_overlength_samples = 0
self.project_name = project_name
self.run_name = run_name
self.wandb_group_name = wandb_group_name
Expand Down Expand Up @@ -227,10 +229,18 @@ def step(self, step_idx: int, pbar: Any, **kwargs) -> Optional[float]:

# filter out overlength samples
if self.filter_truncated_response and action_mask.size(1) == self.max_length:
old_loss_mask = loss_mask.clone()
loss_mask = torch.logical_and(
loss_mask,
action_mask[:, -1] == False,
)

self.overlength_samples = (old_loss_mask & ~loss_mask).sum().item()
self.overlength_samples = all_reduce_sum(
torch.tensor(self.overlength_samples, device=loss_mask.device), self.plugin
)
self.total_overlength_samples += self.overlength_samples.item()

prompt_level_mask = loss_mask.view(self.minibatch_size, self.num_generations)

# [minibatch_size] -> calculate the number of effective prompts
Expand Down Expand Up @@ -484,9 +494,11 @@ def _criterion(outputs, inputs):
self.optimizer.zero_grad()
self.global_step += 1
sample_utilization = self.effective_sample_count / self.total_sample_count
overlength_samples_percentage = self.total_overlength_samples / self.total_sample_count
self.effective_prompt_count = 0
self.effective_sample_count = 0
self.total_sample_count = 0
self.total_overlength_samples = 0
loss_scalar = self.accum_loss.item()
if not self.plugin.pp_size > 1 or (
self.plugin.pp_size > 1 and self.booster.plugin.stage_manager.is_last_stage() and self.tp_rank == 0
Expand All @@ -502,6 +514,7 @@ def _criterion(outputs, inputs):
f"Advantages: {self.accum_advantages.item() / self.accum_count:.4f}",
f"Response Length: {self.accum_response_length.item() / self.accum_count:.4f}",
f"Sample_utilization: {sample_utilization:.4f}",
f"Percentage of overlength samples: {overlength_samples_percentage:.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 @@ -513,6 +526,7 @@ 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/percentage_overlength_samples": overlength_samples_percentage,
"rollout/temperature": data["temperature"].cpu().numpy()[0][0],
}
if self.policy_loss_fn.beta > 0:
Expand Down