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https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-05 02:51:59 +00:00
reuse comm-group
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@@ -94,9 +94,6 @@ class BaseConsumer:
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if self.rank == 0:
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cc.init_collective_group(self.num_producers + 1, self.num_producers, group_name="sync_model")
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for i in range(self.num_producers):
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cc.init_collective_group(self.world_size + 1, self.rank + 1, group_name=f"sync_eval_statistics_{i}")
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self.buffer = []
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self.recv_cnt = 0
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@@ -116,11 +113,14 @@ class BaseConsumer:
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i = 0
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if self.eval_interval > 0 and step % self.eval_interval == 0:
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eval_statistics = None
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eval_global_step = None
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for r in range(self.num_producers):
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print(f"[T{dist.get_rank()}] Recv eval result episode {episode} step {step} from {r}")
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local_eval_result = ray_broadcast_tensor_dict(
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None, src=0, device=self.device, group_name=f"sync_eval_statistics_{r}"
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None, src=0, device=self.device, group_name=f"sync_data_{r}"
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)
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assert "consumer_global_step" in local_eval_result
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eval_global_step = local_eval_result.pop("consumer_global_step").item()
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if eval_statistics is None:
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eval_statistics = local_eval_result
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else:
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@@ -129,8 +129,8 @@ class BaseConsumer:
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}
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eval_statistics = {k: (v[0] / v[1]).item() for k, v in eval_statistics.items()}
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if dist.get_rank() == 0:
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if hasattr(self, "wandb_run") and hasattr(self, "global_step"):
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self.wandb_run.log(eval_statistics, step=self.global_step)
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if hasattr(self, "wandb_run"):
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self.wandb_run.log(eval_statistics, step=eval_global_step)
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print(f"Eval statistics: {eval_statistics}")
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for _ in range(self.num_recv_per_update):
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# receive data from producers
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