upgrade transformers to 5.2.0 and torchao to 0.16.0 (#3407)

* upgrade transformers to 5.1.0 and torchao to 0.16.0

* upgrade trl for parity

* handle trl api changes

* orpo doesn't have max_prompt_len to check anymore

* cpoconfig doesn't take max_prompt_length and fix cpu offload

* slow fsdp1 test

* triton min 3.4.0 and liger to 0.7.0

* use transformers main for now for zero3 fix

* handle group_by_length change

* fix changes upstream

* mark skip flaky test

* use transformers latest release 5.2.0

src/axolotl/core/builders/causal.py

@@ -246,7 +246,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             ddp_find_unused_parameters
         )
 
-        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
+        if self.cfg.group_by_length:
+            training_arguments_kwargs["train_sampling_strategy"] = "group_by_length"
         training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
 
         training_arguments_kwargs["sample_packing"] = bool(self.cfg.sample_packing)

src/axolotl/core/builders/rl.py

@@ -11,7 +11,6 @@ from axolotl.core.trainers import (
 )
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
-from axolotl.core.trainers.grpo import GRPOStrategy
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders.utils import ensure_dtype
 from axolotl.utils.callbacks.qat import QATCallback
@@ -53,6 +52,8 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         trainer_cls_args = [self.model]
 
         if self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
+            from axolotl.core.trainers.grpo import GRPOStrategy
+
             trainer_cls = GRPOStrategy.get_trainer_class(
                 sequence_parallel=self.cfg.context_parallel_size > 1
             )
@@ -133,21 +134,17 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-            # Handle when max_prompt_length == max_length from defaults
-            # CPOTrainer requires strictly less than
-            if (
-                training_args_kwargs["max_prompt_length"]
-                == training_args_kwargs["max_length"]
-            ):
-                training_args_kwargs["max_prompt_length"] -= 1
+            blocklist_args_kwargs.append("max_prompt_length")
 
         elif self.cfg.rl is RLType.ORPO:
             training_args_cls = AxolotlORPOConfig
 
+            blocklist_args_kwargs.append("max_prompt_length")
+
         elif self.cfg.rl is RLType.KTO:
             training_args_cls = AxolotlKTOConfig
             # KTOConfig in TRL >= 0.27.0 no longer accepts max_prompt_length
-            blocklist_args_kwargs = ["max_prompt_length"]
+            blocklist_args_kwargs.append("max_prompt_length")
 
             training_args_kwargs["desirable_weight"] = (
                 self.cfg.kto_desirable_weight or 1.0
@@ -157,6 +154,8 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             )
 
         elif self.cfg.rl in {RLType.GRPO, RLType.GDPO}:
+            from axolotl.core.trainers.grpo import GRPOStrategy
+
             training_args_cls = GRPOStrategy.get_training_args_class()
             training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
             blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()

src/axolotl/core/trainers/dpo/trainer.py

@@ -57,16 +57,18 @@ class AxolotlDPOTrainer(
     def tokenize_row(
         features,
         processing_class,
-        max_prompt_length,
-        max_completion_length,
-        add_special_tokens,
+        max_prompt_length: int | None = None,
+        max_completion_length: int | None = None,
+        add_special_tokens: bool = True,
+        is_chat: bool = False,
     ) -> Dict:
         res = DPOTrainer.tokenize_row(
             features,
             processing_class,
-            max_prompt_length,
-            max_completion_length,
-            add_special_tokens,
+            max_prompt_length=max_prompt_length,
+            max_completion_length=max_completion_length,
+            add_special_tokens=add_special_tokens,
+            is_chat=is_chat,
         )
         # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
         if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:

src/axolotl/loaders/patch_manager.py

@@ -10,6 +10,7 @@ from functools import cached_property
 import addict
 import transformers
 from transformers import PretrainedConfig, PreTrainedModel
+from transformers.modeling_flash_attention_utils import is_flash_attn_available
 
 from axolotl.integrations.base import PluginManager
 from axolotl.monkeypatch.multipack import (
@@ -500,6 +501,7 @@ class PatchManager:
             and not self.cfg.trust_remote_code
             and not self.cfg.gptq
             and self.cfg.flash_attention
+            and is_flash_attn_available()
             and not self.inference
         ):
             # TODO(MengqingCao): split these patches separately

src/axolotl/monkeypatch/gradient_checkpointing/offload_cpu.py

@@ -59,7 +59,12 @@ class CPU_Offloaded_Gradient_Checkpointer(torch.autograd.Function):
         hidden_states = hidden_states.to("cuda", non_blocking=True).detach()
         hidden_states.requires_grad = True
         with torch.enable_grad():
-            (output,) = ctx.forward_function(hidden_states, *ctx.args)
+            output = ctx.forward_function(hidden_states, *ctx.args)
+            # Newer HF models (e.g. Qwen3MoE) using GradientCheckpointingLayer
+            # return a plain tensor, not a tuple.  Older models return tuples
+            # like (hidden_states, present_kv, ...).  Unwrap if needed.
+            if isinstance(output, (tuple, list)):
+                (output,) = output
         torch.autograd.backward(output, dY)
         return (
             None,

src/axolotl/monkeypatch/transformers/trainer_loss_calc.py

@@ -28,8 +28,12 @@ PATCHED_EVAL_CODE = {
     "array": 'metrics[f"{metric_key_prefix}_loss"] = np.nanmean(all_losses).item()',
 }
 
-ORIGINAL_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).mean().item()"
-PATCHED_MAYBE_CODE = "tr_loss_scalar = self._nested_gather(tr_loss).nanmean().item()"
+ORIGINAL_MAYBE_CODE = (
+    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).mean().item()"
+)
+PATCHED_MAYBE_CODE = (
+    "tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).nanmean().item()"
+)
 
 
 def check_evaluation_loop_is_patchable() -> bool: