Dynamically adjust get_per_token_logps [trl main upgrade]

unsloth/models/rl_replacements.py

@@ -291,6 +291,58 @@ def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep)
 pass
 RL_FUNCTIONS["grpo_trainer"].append(grpo_trainer__get_per_token_logps)
 
+def grpo_trainer__get_per_token_logps_and_entropies(function_name, function):
+    if function_name != "_get_per_token_logps_and_entropies": return function
+
+    # Just copy over from _get_per_token_logps replacement function above. For now this returns None anyway
+    def _get_per_token_logps_and_entropies(self, model, input_ids, attention_mask, logits_to_keep, batch_size = None, compute_entropy = False):
+        if True: # os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
+            return {"logps": None, "entropies": None} # Unsloth efficient GRPO
+        # Otherwise, calculate normally:
+        if not hasattr(self, '_autocast_dtype'):
+            self._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
+            if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': self._autocast_dtype = torch.float16
+
+        os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
+        with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype):
+            # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
+            logits = model(
+                input_ids = input_ids,
+                attention_mask = attention_mask,
+                logits_to_keep = logits_to_keep + 1,
+            ).logits
+
+            entropies = None
+            if compute_entropy:
+                from trl.trainer.utils import entropy_from_logits
+                entropies = entropy_from_logits(logits)
+
+            # logits = logits[:, :-1, :]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+            return {"logps": logits, "entropies": entropies}
+            # input_ids = input_ids[:, -logits_to_keep:]
+            # For transformers<=4.48, logits_to_keep argument isn't supported, so here we drop logits ourselves.
+            # See https://github.com/huggingface/trl/issues/2770
+            # logits = logits[:, -logits_to_keep:]
+            # return logits
+            # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details
+            # logits = logits / self.temperature
+            # logps = selective_log_softmax(logits, input_ids)
+
+            # row_indices, col_indices = torch.where(logps < -20)
+
+            # # Method 1: Check if tensors have elements
+            # if len(row_indices) > 0 and len(col_indices) > 0:
+            #     breakpoint()  # Breakpoint triggered here
+            #     print("Found high values!")
+            # return  logps #  compute logprobs for the input tokens
+        pass
+    pass
+
+    function = inspect.getsource(_get_per_token_logps_and_entropies)
+    return function
+pass
+RL_FUNCTIONS["grpo_trainer"].append(grpo_trainer__get_per_token_logps_and_entropies)
+
 grpo_compute_loss      = RL_REPLACEMENTS["grpo_compute_loss"]
 grpo_compute_loss_slow = RL_REPLACEMENTS["grpo_compute_loss_slow"]
 UnslothEfficientGRPO   = RL_REPLACEMENTS["UnslothEfficientGRPO"]
@@ -319,14 +371,16 @@ def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch
         _input_ids = input_ids
         _logits_to_keep = logits_to_keep
 
-        per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
+        get_logps_func = lambda model, input_ids, attention_mask, logits_to_keep, batch_size=None, compute_entropy=False: self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep, batch_size) if hasattr(self, "_get_per_token_logps") else self._get_per_token_logps_and_entropies(model, input_ids, attention_mask, logits_to_keep, batch_size, compute_entropy)['logps']
+
+        per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep)
 
         # Compute the KL divergence between the model and the reference model
         # _prepare_inputs doesn't return reference log probs anymore. We need to calculate it ourselves.
         # https://github.com/huggingface/trl/blob/05bc43e960396581e458195b8388efe6b82cae1f/trl/trainer/grpo_trainer.py#L1328
         if self.beta != 0.0:
             with torch.inference_mode(), model.disable_adapter():
-                ref_per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
+                ref_per_token_logps = per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep)
         else:
             ref_per_token_logps = None
         # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1