collator cls for plugins

src/axolotl/core/builders/causal.py

@@ -429,18 +429,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ]
         ]
         collator_args = [self.tokenizer]
-        if self.cfg.reward_model:
-            collator = RewardDataCollatorWithPadding
-        elif self.cfg.kd_trainer:
-            from axolotl.integrations.kd.collator import (
-                DataCollatorForKD,
-                KDBatchSamplerDataCollatorForSeq2Seq,
-            )
 
-            if self.cfg.sample_packing and use_batch_sampler_collator:
-                collator = KDBatchSamplerDataCollatorForSeq2Seq
-            else:
-                collator = DataCollatorForKD
+        if self.cfg.plugins:
+            plugin_manager = PluginManager.get_instance()
+            collator_cls = plugin_manager.get_collator_cls(self.cfg, is_eval=is_eval)
+
+        if collator_cls:
+            pass
+        elif self.cfg.reward_model:
+            collator = RewardDataCollatorWithPadding
         elif use_batch_sampler_collator:
             # Use V2BatchSamplerDataCollatorForSeq2Seq for flex attention,
             # supported multipack models, or non-flash-attention llama

src/axolotl/integrations/base.py

@@ -167,7 +167,82 @@ class BasePlugin:
             trainer: The trainer object for training.
 
         Returns:
+<<<<<<< HEAD
             The created optimizer.
+=======
+            None
+        """
+
+    def pre_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions before LoRA weights are loaded.
+
+        Args:
+            cfg (dict): The configuration for the plugin.
+            model (object): The loaded model.
+
+        Returns:
+            None
+        """
+
+    def post_lora_load(self, cfg, model):  # pylint: disable=unused-argument
+        """
+        Performs actions after LoRA weights are loaded.
+
+        Args:
+            cfg (dict): The configuration for the plugin.
+            model (object): The loaded model.
+
+        Returns:
+            None
+        """
+
+    def get_trainer_cls(self, cfg):  # pylint: disable=unused-argument):
+        """
+        Returns a custom class for the trainer.
+
+        Args:
+            cfg (dict): The global axolotl configuration.
+
+        Returns:
+            class: The class for the trainer.
+        """
+
+    def get_collator_cls(self, cfg, is_eval=False):  # pylint: disable=unused-argument):
+        """
+        Returns a custom class for the collator.
+
+        Args:
+            cfg (dict): The global axolotl configuration.
+            is_eval (bool): Whether this is an eval split.
+
+        Returns:
+            class: The class for the collator.
+        """
+
+    def post_trainer_create(self, cfg, trainer):  # pylint: disable=unused-argument
+        """
+        Performs actions after the trainer is created.
+
+        Args:
+            cfg (dict): The configuration for the plugin.
+            trainer (object): The trainer object for training.
+
+        Returns:
+            None
+        """
+
+    def create_optimizer(self, cfg, trainer):  # pylint: disable=unused-argument
+        """
+        Creates and returns an optimizer for training.
+
+        Args:
+            cfg (dict): The configuration for the plugin.
+            trainer (object): The trainer object for training.
+
+        Returns:
+            object: The created optimizer.
+>>>>>>> f8df1563d (collator cls for plugins)
         """
 
     # pylint: disable=unused-argument
@@ -442,6 +517,23 @@ class PluginManager:
                 return trainer_cls
         return None
 
+    def get_collator_cls(self, cfg, is_eval=False):
+        """
+        Calls the get_collator_cls method of all registered plugins and returns the first non-None collator class.
+
+        Parameters:
+        cfg (dict): The configuration for the plugins.
+        is_eval (bool): Whether this is an eval split.
+
+        Returns:
+        object: The collator class, or None if none was found.
+        """
+        for plugin in self.plugins.values():
+            collator_cls = plugin.get_collator_cls(cfg, is_eval=is_eval)
+            if collator_cls is not None:
+                return collator_cls
+        return None
+
     def post_trainer_create(self, cfg: DictDefault, trainer: Trainer):
         """Calls the `post_trainer_create` method of all registered plugins.
 

src/axolotl/integrations/kd/__init__.py

@@ -35,6 +35,22 @@ class KDPlugin(BasePlugin):
             return AxolotlKDTrainer
         return None
 
+    def get_collator_cls(self, cfg, is_eval=False):
+        if not cfg.kd_trainer:
+            return None
+
+        from .collator import DataCollatorForKD, KDBatchSamplerDataCollatorForSeq2Seq
+
+        use_batch_sampler_collator = False
+        if is_eval is False and cfg.sample_packing:
+            use_batch_sampler_collator = True
+        if cfg.eval_sample_packing and is_eval:
+            use_batch_sampler_collator = True
+
+        if use_batch_sampler_collator:
+            return KDBatchSamplerDataCollatorForSeq2Seq
+        return DataCollatorForKD
+
     def pre_model_load(self, cfg):
         from .kernels.models import apply_kernel
 

src/axolotl/integrations/kd/kernels/liger.py

@@ -305,8 +305,6 @@ class LigerFusedLinearKLTopKLogprobFunction(LigerFusedLinearDistillationBase):
             grad_inputs_list.append(grad_input_chunk)
 
         grad_inputs_combined = torch.cat(grad_inputs_list, dim=0)
-        print("grad_inputs_combined")
-        print(grad_inputs_combined.shape)
         ctx.save_for_backward(grad_inputs_combined, grad_weight_acc, grad_bias_acc)
 
         # For matching None returns in backward for non-tensor/non-grad_requiring inputs

src/axolotl/utils/collators/batching.py

@@ -1,7 +1,7 @@
 """Data collators for axolotl to pad labels and position_ids for packed sequences"""
 
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, List
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
@@ -161,7 +161,7 @@ class V2BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
 
     def __call__(self, features, return_tensors=None):
         if not isinstance(features[0], list):
-            features = [features]
+            features: List[List[dict]] = [features]
         out_features = [{} for _ in features]
         for i, features_ in enumerate(features):
             for feature in features_[0].keys():