From dc986cd7e2aa6ca24c100e3b8791956f966e5ca0 Mon Sep 17 00:00:00 2001
From: danielhanchen <danielhanchen@users.noreply.github.com>
Date: Mon, 5 Jan 2026 13:29:58 +0000
Subject: [PATCH] Drop rl.py GRPO changes from this branch

---
 unsloth/models/rl.py | 86 ++++++++++----------------------------------
 1 file changed, 18 insertions(+), 68 deletions(-)

diff --git a/unsloth/models/rl.py b/unsloth/models/rl.py
index 23cbbf0256..fd0c69bb0e 100644
--- a/unsloth/models/rl.py
+++ b/unsloth/models/rl.py
@@ -43,31 +43,10 @@ torch_compile_options = {
     "triton.cudagraphs": False,
 }
 
-# vLLM compatibility shim (TRL expects GuidedDecodingParams even if vLLM doesn't provide it)
-try:
-    import vllm.sampling_params as _unsloth_vllm_sp
-
-    if not hasattr(_unsloth_vllm_sp, "GuidedDecodingParams"):
-
-        class GuidedDecodingParams:
-            def __init__(self, **kwargs):
-                self.kwargs = kwargs
-
-        _unsloth_vllm_sp.GuidedDecodingParams = GuidedDecodingParams
-except Exception:
-    pass
-
-from trl import __version__ as trl_version_raw
-from importlib.metadata import version as importlib_version
+from trl import __version__ as trl_version
 from unsloth_zoo.utils import Version
 
-try:
-    trl_version = Version(trl_version_raw)
-except Exception:
-    try:
-        trl_version = Version(importlib_version("trl"))
-    except Exception:
-        trl_version = Version("0.0.0")
+trl_version = Version(trl_version)
 
 
 def vLLMSamplingParams(**kwargs):
@@ -241,7 +220,7 @@ RLTrainer_replacement = '''
 import os
 from typing import *
 from dataclasses import dataclass, field
-from unsloth_zoo.utils import Version
+from packaging.version import Version
 import torch
 import numpy as np
 from contextlib import nullcontext
@@ -263,18 +242,12 @@ def prepare_for_training_mode(f):
     @functools.wraps(f)
     def wrapper(self, *args, **kwargs):
         # Enable training mode
-        _was_training = None
-        if hasattr(self, 'model') and hasattr(self.model, "training"):
-            _was_training = self.model.training
         if hasattr(self, 'model') and hasattr(self.model, "for_training"):
             self.model.for_training()
         output = f(self, *args, **kwargs)
-        # Restore previous mode when possible
+        # Return inference mode
         if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
-            if _was_training is False:
-                self.model.for_inference()
-            elif _was_training is True and hasattr(self.model, "for_training"):
-                self.model.for_training()
+            self.model.for_inference()
         # Reset gradient checkpointing buffers to free memory while staying ready for next run
         try:
             reset_unsloth_gradient_checkpointing_buffers()
@@ -359,32 +332,6 @@ pass
 '''
 
 
-def _wrap_grpo_generate_and_score(trainer_cls):
-    if not hasattr(trainer_cls, "_generate_and_score_completions"):
-        return
-    original = trainer_cls._generate_and_score_completions
-    if getattr(original, "_unsloth_restore_training_wrapped", False):
-        return
-
-    def wrapped(self, *args, **kwargs):
-        was_training = getattr(getattr(self, "model", None), "training", None)
-        try:
-            return original(self, *args, **kwargs)
-        finally:
-            if (
-                was_training is False
-                and hasattr(self, "model")
-                and hasattr(self.model, "for_inference")
-            ):
-                try:
-                    self.model.for_inference()
-                except Exception:
-                    pass
-
-    wrapped._unsloth_restore_training_wrapped = True
-    trainer_cls._generate_and_score_completions = wrapped
-
-
 def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
     # Patch for vLLM and Unsloth PEFT
     import trl
@@ -747,6 +694,19 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
         )
         RLTrainer_post += training_check
 
+    # Sync chat_template from processing_class to vLLM's tokenizer
+    # This fixes base models that have custom chat templates applied after loading
+    if "model" in call_args:
+        vllm_chat_template_sync = (
+            "if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):\n"
+            "    _vllm_tok = self.llm.get_tokenizer()\n"
+            "    _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)\n"
+            "    if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:\n"
+            "        _vllm_tok.chat_template = _pc.chat_template\n"
+            "pass\n"
+        )
+        RLTrainer_post += vllm_chat_template_sync
+
     # Edit optional metrics
     other_metrics_processor = ""
     if trainer_file in RL_METRICS_CHANGES:
@@ -1112,16 +1072,6 @@ def _patch_trl_rl_trainers(trainer_file = "grpo_trainer"):
         globals(),
     )
 
-    if trainer_file == "grpo_trainer":
-        try:
-            _wrap_grpo_generate_and_score(
-                getattr(created_module, f"Unsloth{RLTrainer_name}")
-            )
-        except Exception as e:
-            logger.info(
-                f"Unsloth: Could not wrap _generate_and_score_completions for {RLTrainer_name}: {e}"
-            )
-
 
 def patch_functions(RLTrainer, trainer_file, RLTrainer_name, all_imports, imports):
     init = inspect.getsource(RLTrainer.__init__)