Loading vagen/trainer/ppo/ray_trainer.py +2 −2 Original line number Diff line number Diff line Loading @@ -174,10 +174,10 @@ def compute_advantage(data: DataProto, adv_estimator, gamma=1.0, lam=1.0, num_re response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] assert "multi_turn_token_level_rewards" in data.batch.keys() #assert "multi_turn_token_level_rewards" in data.batch.keys() # assert "loss_mask" in data.batch.keys() # loss_mask = data.batch['loss_mask'][:, -response_length:] if "loss_mask" in in data.batch.keys(): if "loss_mask" in data.batch.keys(): loss_mask = data.batch['loss_mask'][:, -response_length:] else: loss_mask=data.batch['attention_mask'][:, -response_length:] Loading Loading
vagen/trainer/ppo/ray_trainer.py +2 −2 Original line number Diff line number Diff line Loading @@ -174,10 +174,10 @@ def compute_advantage(data: DataProto, adv_estimator, gamma=1.0, lam=1.0, num_re response_length = responses.size(-1) attention_mask = data.batch['attention_mask'] response_mask = attention_mask[:, -response_length:] assert "multi_turn_token_level_rewards" in data.batch.keys() #assert "multi_turn_token_level_rewards" in data.batch.keys() # assert "loss_mask" in data.batch.keys() # loss_mask = data.batch['loss_mask'][:, -response_length:] if "loss_mask" in in data.batch.keys(): if "loss_mask" in data.batch.keys(): loss_mask = data.batch['loss_mask'][:, -response_length:] else: loss_mask=data.batch['attention_mask'][:, -response_length:] Loading
