remove xformers changes

docs/fsdp_qlora.qmd

@@ -20,7 +20,7 @@ To enable `QLoRA` with `FSDP`, you need to perform the following steps:
 > See the [example config](#example-config) file in addition to reading these instructions.
 
 1. Set `adapter: qlora` in your axolotl config file.
-2. Enable FSDP in your axolotl config, as [described here](https://github.com/axolotl-ai-cloud/axolotl?tab=readme-ov-file#fsdp).
+2. Enable FSDP in your axolotl config, as [described here](multi-gpu.qmd#sec-fsdp).
 3. Use one of the supported model types: `llama`, `mistral` or `mixtral`.
 
 ## Example Config

requirements.txt

@@ -11,7 +11,7 @@ liger-kernel==0.5.10
 
 packaging==23.2
 
-huggingface_hub==0.32.2
+huggingface_hub[hf_xet]==0.33.0
 peft==0.15.2
 transformers==4.53.1
 tokenizers>=0.21.1

setup.py

@@ -65,15 +65,13 @@ def parse_requirements(extras_require_map):
                 raise ValueError("Invalid version format")
 
             if (major, minor) >= (2, 7):
-                _install_requires.pop(_install_requires.index(xformers_version))
                 # _install_requires.append("xformers==0.0.29.post3")  # xformers seems to be hard pinned to 2.6.0
-                extras_require_map["vllm"] = ["vllm==0.8.5.post1"]
+                extras_require_map["vllm"] = ["vllm==0.9.2"]
             elif (major, minor) >= (2, 6):
-                _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append(
                     "xformers==0.0.29.post2"
                 )  # vllm needs post2 w torch 2.6
-                extras_require_map["vllm"] = ["vllm==0.8.5.post1"]
+                extras_require_map["vllm"] = ["vllm==0.9.2"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:

src/axolotl/core/builders/rl.py

@@ -6,7 +6,6 @@ from pathlib import Path
 from axolotl.core.builders.base import TrainerBuilderBase
 from axolotl.core.trainers import (
     AxolotlCPOTrainer,
-    AxolotlDPOTrainer,
     AxolotlKTOTrainer,
     AxolotlORPOTrainer,
 )
@@ -37,23 +36,33 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         callbacks = super().get_post_trainer_create_callbacks(trainer=trainer)
         return callbacks
 
-    def _get_trainer_cls(self):
+    def _get_trainer_cls(self, trainer_kwargs: dict):
-        """Returns trainer_cls"""
+        """
+        Returns trainer_cls and trainer_cls_args
+        """
         if self.cfg.plugins:
             plugin_manager = PluginManager.get_instance()
             trainer_cls = plugin_manager.get_trainer_cls(self.cfg)
+            trainer_cls_args = []  # type: ignore
 
             if trainer_cls is not None:
-                return trainer_cls
+                return trainer_cls, trainer_cls_args
 
         trainer_cls = None
+        trainer_cls_args = [self.model]
 
         if self.cfg.rl is RLType.GRPO:
             trainer_cls = GRPOStrategy.get_trainer_class(
                 sequence_parallel=self.cfg.sequence_parallel_degree > 1
             )
+            trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
+
+            trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
+
         elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
-            trainer_cls = AxolotlDPOTrainer
+            trainer_cls = DPOStrategy.get_trainer_class()
+            trainer_cls_args.append(self.model_ref)
+
         elif self.cfg.rl is RLType.ORPO:
             trainer_cls = AxolotlORPOTrainer
         elif self.cfg.rl is RLType.KTO:
@@ -63,7 +72,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         else:
             raise ValueError(f"Unsupported RL: {self.cfg.rl}")
 
-        return trainer_cls
+        return trainer_cls, trainer_cls_args
 
     def _build_training_arguments(self, total_num_steps):
         """
@@ -173,15 +182,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 self.cfg.precompute_ref_log_probs
             )
 
-        trainer_cls = self._get_trainer_cls()
+        trainer_cls, trainer_cls_args = self._get_trainer_cls(trainer_kwargs)
-        trainer_cls_args = [self.model]
-
-        if self.cfg.rl is RLType.GRPO:
-            trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
-            trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
-
-        if self.cfg.rl in [RLType.DPO, RLType.IPO]:
-            trainer_cls_args.append(self.model_ref)
 
         sig = inspect.signature(trainer_cls)
         if "tokenizer" in sig.parameters:
@@ -189,7 +190,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         else:
             trainer_kwargs["processing_class"] = self.tokenizer
 
-        if self.cfg.datasets is not None and (trainer_cls is AxolotlDPOTrainer):
+        if self.cfg.datasets is not None and (
+            trainer_cls is DPOStrategy.get_trainer_class()
+        ):
             trainer_kwargs["dataset_tags"] = [
                 d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
             ]