From 46f470bea65978309434cb78196ab7edaa7d9977 Mon Sep 17 00:00:00 2001 From: Daniel Han Date: Mon, 17 Feb 2025 21:08:32 -0800 Subject: [PATCH] Update rl_replacements.py --- unsloth/models/rl_replacements.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/unsloth/models/rl_replacements.py b/unsloth/models/rl_replacements.py index 9a1cf4b4c5..b1a2ba8f70 100644 --- a/unsloth/models/rl_replacements.py +++ b/unsloth/models/rl_replacements.py @@ -177,7 +177,7 @@ def grpo_trainer__get_per_token_logps(function_name, function): if function_name != "_get_per_token_logps": return function def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep): - # return None + return None if not hasattr(self, '_autocast_dtype'): self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16 with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype): @@ -234,13 +234,13 @@ def grpo_trainer_compute_loss(function_name, function): # per_token_loss = -(per_token_loss - self.beta * per_token_kl) # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean() input_ids = input_ids[:, -logits_to_keep:] - loss, completion_length, mean_kl = grpo_compute_loss( - ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages, bsz, - ) - # accumulated_loss, accumulated_completion_length, accumulated_mean_kl = grpo_accumulated_loss( - # self, _input_ids, logits_to_keep, completion_mask, advantages, n_chunks = 2, + # loss, completion_length, mean_kl = grpo_compute_loss( + # ref_per_token_logps, per_token_logps, input_ids, completion_mask, self.beta, advantages, bsz, # ) - # loss, completion_length, mean_kl = accumulated_loss, accumulated_completion_length, accumulated_mean_kl + accumulated_loss, accumulated_completion_length, accumulated_mean_kl = grpo_accumulated_loss( + self, _input_ids, logits_to_keep, completion_mask, advantages, n_chunks = 2, + ) + loss, completion_length, mean_kl = accumulated_loss, accumulated_completion_length, accumulated_mean_kl # Log the metrics # completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item() self._metrics["completion_length"].append(completion_length.item())