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synced 2025-08-09 11:58:06 +00:00
move prompt-level-filtering to buffer side
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@ -254,7 +254,6 @@ class GRPOConsumer(BaseConsumer):
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total_effective_tokens_count = all_reduce_sum(torch.sum(effective_tokens_count), self.plugin)
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total_samples = all_reduce_sum(torch.sum(torch.ones_like(loss_mask, device=loss_mask.device)), self.plugin)
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self.effective_sample_count += effective_samples.item()
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self.total_sample_count += total_samples.item()
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pbar.set_postfix(
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{
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"Global Step": self.global_step,
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@ -461,6 +460,9 @@ class GRPOConsumer(BaseConsumer):
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self.optimizer.step()
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self.optimizer.zero_grad()
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self.global_step += 1
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self.total_sample_count = all_reduce_sum(
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torch.tensor(self.total_sample_count).to(self.accum_loss.device), self.plugin
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).item()
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sample_utilization = self.effective_sample_count / self.total_sample_count
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self.effective_prompt_count = 0
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self.effective_sample_count = 0
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@ -564,6 +566,7 @@ class GRPOConsumer(BaseConsumer):
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"format_acc": torch.Tensor, [num_of_generation]
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"ans_acc": torch.Tensor, [num_of_generation]
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"""
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self.total_sample_count += rollout_group["input_ids"].size(0)
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if self.filter_range is not None:
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# filter prompt whoes accuracy is too high or too low (out of range)
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group_ans_acc = torch.mean(rollout_group["ans_acc"])
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