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fix behind
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@ -131,10 +131,10 @@ class BaseConsumer:
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batch = post_recv(batch)
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return batch, raw_mini_batches_metric_dict
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def calculate_effective_group_to_raw_group_mapping(self):
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def calculate_effective_group_to_raw_group_mapping(self, step):
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effective_group_to_raw_group_mapping = {}
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for buffer_idx in range(len(self.buffer)):
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if self.buffer[buffer_idx][0] is not None:
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if self.buffer[buffer_idx][0] is not None and (self.buffer[buffer_idx][-1] <= step - self.n_behind):
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effective_group_to_raw_group_mapping[len(effective_group_to_raw_group_mapping)] = buffer_idx
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return effective_group_to_raw_group_mapping
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@ -152,37 +152,40 @@ class BaseConsumer:
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torch.cuda.reset_peak_memory_stats()
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i = 0
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for _ in range(self.num_recv_per_update):
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# after sync model, do not wait for more data to arrive as rollout takes time, use buffered data
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping()
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while len(effective_group_to_raw_group_mapping) > max(
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self.dp_size * self.batch_size
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- self.dp_size
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* self.minibatch_size
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* self.grpo_config.get("num_minibatch_during_rollout", 1),
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self.dp_size * self.minibatch_size,
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):
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self.profiler.log(
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f"Still have {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.minibatch_size}, start training"
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if self.n_behind > 0:
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# after sync model, do not wait for more data to arrive as rollout takes time, use buffered data
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping(
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step=step
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)
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batch, raw_mini_batches_metric_dict = self.prepare_mini_batch(
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effective_group_to_raw_group_mapping
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)
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self.profiler.enter("step")
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loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
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self.profiler.exit("step")
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self.buffer = self.buffer[
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effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1 :
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]
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# recalculate the effective group to raw group mapping
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effective_group_to_raw_group_mapping_size_before = len(effective_group_to_raw_group_mapping)
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping()
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assert (
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len(effective_group_to_raw_group_mapping)
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== effective_group_to_raw_group_mapping_size_before - self.dp_size * self.minibatch_size
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)
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if loss is not None:
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pbar.set_postfix({"loss": loss})
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i += 1
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while len(effective_group_to_raw_group_mapping) >= self.dp_size * self.minibatch_size:
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self.profiler.log(
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f"Still have {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.minibatch_size}, start training"
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)
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batch, raw_mini_batches_metric_dict = self.prepare_mini_batch(
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effective_group_to_raw_group_mapping
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)
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self.profiler.enter("step")
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loss = self.step(i, pbar, **batch, **raw_mini_batches_metric_dict)
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self.profiler.exit("step")
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self.buffer = self.buffer[
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effective_group_to_raw_group_mapping[self.dp_size * self.minibatch_size - 1] + 1 :
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]
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# recalculate the effective group to raw group mapping
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effective_group_to_raw_group_mapping_size_before = len(
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effective_group_to_raw_group_mapping
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)
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effective_group_to_raw_group_mapping = (
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self.calculate_effective_group_to_raw_group_mapping(step=step)
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)
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assert (
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len(effective_group_to_raw_group_mapping)
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== effective_group_to_raw_group_mapping_size_before
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- self.dp_size * self.minibatch_size
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)
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if loss is not None:
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pbar.set_postfix({"loss": loss})
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i += 1
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# receive data from producers
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for r in range(self.num_producers):
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@ -226,6 +229,7 @@ class BaseConsumer:
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format_acc[group_idx],
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ans_acc[group_idx],
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response_len[group_idx],
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step,
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]
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)
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if effective_group_mask is not None:
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@ -233,17 +237,22 @@ class BaseConsumer:
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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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# mapping the effective group to the raw group for indexing
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping()
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping(
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step=step
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)
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print(
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f"[T{dist.get_rank()}] Collect Effective Prompt: {len(effective_group_to_raw_group_mapping)}/{self.dp_size * self.minibatch_size}"
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)
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while len(effective_group_to_raw_group_mapping) > self.dp_size * self.batch_size:
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if self.n_behind == 0:
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# If n_behind is 0, we start training after receiving data from producers.
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping(
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step=step
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)
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while len(effective_group_to_raw_group_mapping) >= self.dp_size * self.minibatch_size:
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self.profiler.log(
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f"Received {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.batch_size}, start training after recv"
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f"Still have {len(effective_group_to_raw_group_mapping)} effective groups, greater than {self.dp_size * self.minibatch_size}, start training"
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)
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# always keep at least dp_size * batch_size effective samples in the buffer for training during the rollout times after each sync model
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# on each dp_rank, we use minibatch_size effective samples to form a batch
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batch, raw_mini_batches_metric_dict = self.prepare_mini_batch(
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effective_group_to_raw_group_mapping
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)
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@ -255,7 +264,9 @@ class BaseConsumer:
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]
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# recalculate the effective group to raw group mapping
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effective_group_to_raw_group_mapping_size_before = len(effective_group_to_raw_group_mapping)
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping()
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effective_group_to_raw_group_mapping = self.calculate_effective_group_to_raw_group_mapping(
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step=step
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)
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assert (
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len(effective_group_to_raw_group_mapping)
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== effective_group_to_raw_group_mapping_size_before - self.dp_size * self.minibatch_size
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@ -263,6 +274,7 @@ class BaseConsumer:
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if loss is not None:
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pbar.set_postfix({"loss": loss})
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i += 1
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if self.lr_scheduler is not None:
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self.lr_scheduler.step()
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if (step + 1) % self.save_interval == 0 or (step + 1) == self.num_update_per_episode:
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@ -337,9 +337,8 @@ 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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self.profiler.enter("sync_model")
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torch.cuda.empty_cache()
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self.profiler.enter("sync_model")
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if self.consumer_pp_size > 1:
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for pp_idx in range(self.consumer_pp_size):
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print(
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@ -361,9 +360,9 @@ class BaseProducer:
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if "consumer_global_step" in state_dict:
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self.consumer_global_step = state_dict.pop("consumer_global_step").item()
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self.load_state_dict(state_dict)
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self.profiler.exit("sync_model")
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del state_dict
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torch.cuda.empty_cache()
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self.profiler.exit("sync_model")
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if isinstance(self.model, BACKEND_MAP["vllm"]) and self.model.model_config.get(
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"enable_sleep_mode", False
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):
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@ -263,7 +263,6 @@ if __name__ == "__main__":
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grpo_config = {
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"lr": args.learning_rate,
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"train_microbatch_size": args.train_microbatch_size,
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"num_minibatch_during_rollout": 1, # number of mini batches to pop out from buffer and used for training during rollout of the producer after it syncs the model. Hint, set to a proper value close to the number of mini batches for training that takes roughly the same time as the rollout of the producer. A value that is too large or too small will cause bubble time on the trainer or the producer.
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"beta": args.kl_coeff, # KL penalty coefficient
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"loss_variation": "sample_level",
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"reward_fn_type": args.reward_type,
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