TRL Updated version of VLM GRPO update along with GSPO

unsloth/models/rl_replacements.py

@@ -25,7 +25,7 @@
 import torch
 import inspect
 from collections import defaultdict
-from unsloth_zoo.rl_replacements import RL_REPLACEMENTS
+from unsloth_zoo.rl_replacements import RL_REPLACEMENTS, left_pack_padding
 from unsloth import DEVICE_TYPE
 
 RL_EXTRA_ARGS      = defaultdict(list)
@@ -245,6 +245,18 @@ def grpo_trainer__generate_and_score_completions(function_name, function):
         "prompt_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False",
     )
 
+    # Left pad prompt before calculation old and ref hidden states
+    line_to_replace = "batch_size = self.args.per_device_train_batch_size if mode == \"train\" else self.args.per_device_eval_batch_size"
+
+    # The new multi-line string that will replace the line above
+    replacement_lines = """
+        batch_size = self.args.per_device_train_batch_size if mode == "train" else self.args.per_device_eval_batch_size
+        if not has_images:
+            # Left pad prompt before calculation old and ref hidden states
+            prompt_completion_ids = left_pack_padding(prompt_completion_ids, self.processing_class.pad_token_id)"""
+
+    function = function.replace(line_to_replace, replacement_lines)
+
     # Always between max_prompt_length and use_vllm
     found = re.findall(
         r"\n(([ ]{8,})if self\.max_prompt_length is not None:.*?"\
@@ -263,26 +275,27 @@ def grpo_trainer__generate_and_score_completions(function_name, function):
             # If max_prompt_length is set, we trim the prompt to keep only the last `max_prompt_length` tokens.
             # Then we decode those tokens back into text. We manually remove leading pad tokens from the decoded text,
             # because we can't use `skip_special_tokens=True` (some special tokens are still needed for generation).
-            prompt_ids = prompt_ids[:, -self.max_prompt_length :]
-            prompt_mask = prompt_mask[:, -self.max_prompt_length :]
-            prompts_text = self.processing_class.batch_decode(
-                prompt_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
+            protected = [self.image_token_id, self.vision_start_token_id, self.vision_end_token_id]
+            protected = [token for token in protected if token is not None]
+            prompt_ids, prompt_mask = truncate_with_protected_tokens(
+                prompt_ids, prompt_mask, self.max_prompt_length, protected
             )
-            pad_token = self.processing_class.pad_token
-            def strip_leading_tokens(text):
-                while text.startswith(pad_token):
-                    text = text.removeprefix(pad_token)
-                return text
 
-            if pad_token is not None:
+            prompts_text = [re.sub(rf"^({{re.escape(self.pad_token)}})+", "", text) for text in prompts_text]
+
+            # The chat template inserts a single image token into the prompt text. However, when this text is later
+            # tokenized, the single image token string is expanded into multiple image token IDs, depending on the
+            # image size. Since we're detokenizing here, we may see repeated image tokens in the decoded text. We
+            # collapse them back into a single token string to match the original template.
+            if self.image_token is not None:
                 prompts_text = [
-                    strip_leading_tokens(text) for text in prompts_text
+                    re.sub(rf"({{re.escape(self.image_token)}})+", self.image_token, text) for text in prompts_text
                 ]
-
         # Generate completions using either vLLM or regular generation
         if self.use_vllm:"""
             function = function.replace(replace_part, new_replacement)
-    pass
+
+
     return function
 pass
 RL_FUNCTIONS["grpo_trainer"].append(grpo_trainer__generate_and_score_completions)
@@ -304,7 +317,7 @@ def _move_model_to_vllm(self, *args, **kwargs): return None
 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):
+    def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep, compute_efficient = False):
         if True: # os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
             return None # Unsloth efficient GRPO
         # Otherwise, calculate normally:
@@ -350,28 +363,59 @@ 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, *args, **kwargs):
-        if True: # os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
-            return None, None  # logps, entropies 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)
-
+    def _get_per_token_logps_and_entropies(self, model, input_ids, attention_mask, logits_to_keep, batch_size = None, 
+                                           compute_entropy = False, compute_efficient = False, *args, **kwargs):
+        # if True: # os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
+        #     return None, None  # logps, entropies Unsloth efficient GRPO
+        if compute_efficient:
+            return None, None
+        else: 
+            # 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
+
+            pixel_values, image_grid_thw = kwargs.get("pixel_values", None), kwargs.get("image_grid_thw", None)
+            pixel_attention_mask, image_sizes = kwargs.get('pixel_attention_mask',None), kwargs.get('image_sizes',None)
+
+            os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
+
+            unwrapped_model = self.accelerator.unwrap_model(model, keep_fp32_wrapper=False)
+
+            with torch.amp.autocast(device_type = 'cuda', dtype = self._autocast_dtype):
+                with torch.inference_mode():
+                    if pixel_values is None: 
+                        attention_mask =  input_ids != self.processing_class.pad_token_id
+                        attention_mask = attention_mask.to(attention_mask.dtype)
+                        # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
+                        logits = unwrapped_model(
+                            input_ids = input_ids,
+                            attention_mask = attention_mask,
+                            pixel_values = pixel_values,
+                            image_grid_thw = image_grid_thw,
+                            pixel_attention_mask = pixel_attention_mask,
+                            image_sizes = image_sizes,
+                            #logits_to_keep = logits_to_keep + 1,
+                        ).logits
+                    else:
+                        logits = unwrapped_model(
+                            input_ids = input_ids,
+                            attention_mask = attention_mask,
+                            pixel_values = pixel_values,
+                            image_grid_thw = image_grid_thw,
+                            pixel_attention_mask = pixel_attention_mask,
+                            image_sizes = image_sizes,
+                            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)
+
+
+            os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "0"
             # logits = logits[:, :-1, :]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
             return logits, entropies  # logps, entropies
             # input_ids = input_ids[:, -logits_to_keep:]
@@ -416,8 +460,12 @@ def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch
             raise ValueError("The GRPOTrainer does not support returning outputs")
         # Compute the per-token log probabilities for the model
 
+
         prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
         completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
+        pixel_values, image_grid_thw = inputs.get("pixel_values", None), inputs.get("image_grid_thw", None)
+        pixel_attention_mask, image_sizes = inputs.get('pixel_attention_mask',None), inputs.get('image_sizes',None)
+
         input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
         bsz, qlen = input_ids.shape
         attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
@@ -427,28 +475,29 @@ def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch
         _logits_to_keep = 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) \
+            lambda model, input_ids, attention_mask, logits_to_keep, batch_size=None, compute_entropy=False, compute_efficient = False: \
+            self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep, compute_efficient) \
             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)[0]  # logps
-
-        per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep)
-
+            self._get_per_token_logps_and_entropies(model, input_ids, attention_mask, logits_to_keep, batch_size, compute_entropy, compute_efficient)[0]  # logps
+        #breakpoint()
+        per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep, compute_efficient = True)
         # 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 = per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep)
-        else:
-            ref_per_token_logps = None
+        # if self.beta != 0.0:
+        #     with torch.inference_mode(), model.disable_adapter():
+        #         ref_per_token_logps = per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep)
+        # else:
+        #     ref_per_token_logps = None
+        ref_hidden_states = inputs.get("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
         # x - x.detach() allows for preserving gradients from x
         advantages = inputs["advantages"]
         # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
         # 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()
         old_hidden_states = inputs.get("old_per_token_logps", None)
+
         input_ids = input_ids[:, -logits_to_keep:]
 
         # Get logit softcapping and logit scale
@@ -459,22 +508,26 @@ def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch
         logit_scale_divide = getattr(model.config, "logits_scaling", 0) # Granite
         if logit_scale_divide is None: logit_scale_divide = 0
 
-
         if per_token_logps is not None:
 
-            if ref_per_token_logps is not None:
-                ref_per_token_logps = ref_per_token_logps[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+            if ref_hidden_states is not None:
+                ref_hidden_states = ref_hidden_states[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+            if old_hidden_states is not None:
+                old_hidden_states = old_hidden_states[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
             per_token_logps = per_token_logps[:, :-1, :] # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
 
             loss, completion_length, mean_kl = grpo_compute_loss_slow(
-                ref_per_token_logps,
+                ref_hidden_states,
                 per_token_logps,
                 old_hidden_states,
                 input_ids,
                 completion_mask,
                 self.beta,
                 advantages,
+                pixel_values = pixel_values,
+                image_grid_thw = image_grid_thw,
                 loss_type = self.args.loss_type,
+                importance_sampling_level = self.importance_sampling_level,
                 epsilon_low = self.epsilon_low,
                 epsilon_high = self.epsilon_high,
                 max_completion_length = self.args.max_completion_length,
@@ -489,12 +542,16 @@ def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch
                 loss, completion_length, mean_kl = grpo_accumulated_loss(
                     trainer = self,
                     input_ids = _input_ids,
+                    pixel_values = pixel_values,
+                    image_grid_thw = image_grid_thw,
                     logits_to_keep = logits_to_keep,
                     completion_mask = completion_mask,
                     advantages = advantages,
                     old_hidden_states = old_hidden_states,
+                    ref_hidden_states = ref_hidden_states,
                     n_chunks = self.args.unsloth_num_chunks,
                     loss_type = self.args.loss_type,
+                    importance_sampling_level = self.importance_sampling_level,
                     epsilon_low = self.epsilon_low,
                     epsilon_high = self.epsilon_high,
                     max_completion_length = self.args.max_completion_length,
@@ -514,6 +571,7 @@ def compute_loss(self, model, inputs, return_outputs = False, num_items_in_batch
                     completion_mask = completion_mask,
                     advantages = advantages,
                     old_hidden_states = old_hidden_states,
+                    ref_hidden_states = ref_hidden_states,
                     n_chunks = self.args.unsloth_num_chunks,
                     temperature = self.args.temperature,
                     logit_softcapping = logit_softcapping,