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https://github.com/hpcaitech/ColossalAI.git
synced 2025-07-23 19:49:30 +00:00
fix bug, tested
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4
.gitignore
vendored
4
.gitignore
vendored
@ -167,3 +167,7 @@ applications/ColossalChat/wandb
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applications/ColossalChat/model
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applications/ColossalChat/model
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applications/ColossalChat/eval
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applications/ColossalChat/eval
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applications/ColossalChat/rollouts
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applications/ColossalChat/rollouts
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applications/ColossalChat/*.txt
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applications/ColossalChat/*.db
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applications/ColossalChat/stdin
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applications/ColossalChat/*.zip
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@ -132,9 +132,7 @@ class BaseConsumer:
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format_acc = raw_batch["format_acc"][:, :, 0]
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format_acc = raw_batch["format_acc"][:, :, 0]
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ans_acc = raw_batch["ans_acc"][:, :, 0]
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ans_acc = raw_batch["ans_acc"][:, :, 0]
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response_len = (
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response_len = (
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raw_batch["response_idx"][:, :, 1]
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raw_batch["response_idx"][:, :, 1] - raw_batch["response_idx"][:, :, 0] + 1
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- raw_batch["response_idx"][:, :, 0]
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+ 1
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).type(torch.float32)
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).type(torch.float32)
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effective_group_mask = None
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effective_group_mask = None
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if self.filter_range is not None and self.grpo_config.get("dynamic_batching", True):
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if self.filter_range is not None and self.grpo_config.get("dynamic_batching", True):
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@ -160,7 +158,7 @@ class BaseConsumer:
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)
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)
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if effective_group_mask is not None:
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if effective_group_mask is not None:
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print(
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print(
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f"[T{dist.get_rank()}] Filter recv data: {len(raw_batch_with_reward)} -> {torch.sum(effective_group_mask).cpu().item()} effective groups"
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f"[T{dist.get_rank()}] Filter recv data: {len(raw_batch)} -> {torch.sum(effective_group_mask).cpu().item()} effective groups"
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)
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)
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# mapping the effective group to the raw group for indexing
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# mapping the effective group to the raw group for indexing
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effective_group_to_raw_group_mapping = {}
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effective_group_to_raw_group_mapping = {}
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@ -291,7 +291,7 @@ class BaseProducer:
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reward_model_output = self.reward_model(
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reward_model_output = self.reward_model(
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outputs["input_ids"].view((-1, outputs["input_ids"].size(-1))),
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outputs["input_ids"].view((-1, outputs["input_ids"].size(-1))),
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gt_answer=gt_answer,
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gt_answer=gt_answer,
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response_idx=outputs["response_idx"],
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response_idx=outputs["response_idx"].view((-1, 2)),
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)
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)
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outputs["reward"] = (
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outputs["reward"] = (
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torch.tensor([value[0] for value in reward_model_output])
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torch.tensor([value[0] for value in reward_model_output])
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