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@ -132,19 +132,21 @@ class BaseProducer:
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):
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self.model.llm.sleep() # revict KV_cache to avoid OOM
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# don't sync model for last iteration
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print(
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f"[P{self.producer_idx}] Sync model episode {episode} step {(i + 1) // self.num_microbatches - 1}"
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)
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torch.cuda.empty_cache()
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if self.consumer_pp_size > 1:
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for i in range(self.consumer_pp_size):
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print(f"[P{self.producer_idx}] Sync model PP stage {i}")
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print(
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f"[P{self.producer_idx}] Sync model PP stage {i} episode {episode} step {(i + 1) // self.num_microbatches - 1}"
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)
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state_dict = ray_broadcast_tensor_dict(
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None, self.num_producers, device=self.device, group_name=f"sync_model_{i}"
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)
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self.load_state_dict(state_dict)
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else:
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print(
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f"[P{self.producer_idx}] Sync model episode {episode} step {(i + 1) // self.num_microbatches - 1}"
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)
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state_dict = ray_broadcast_tensor_dict(
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None, self.num_producers, device=self.device, group_name="sync_model"
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)
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