upgrade trl to 0.24.0 and liger to 0.6.3

cicd/Dockerfile.jinja

@@ -1,6 +1,6 @@
 FROM axolotlai/axolotl-base:{{ BASE_TAG }}
 
-ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
+ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
 ENV AXOLOTL_EXTRAS="{{ AXOLOTL_EXTRAS }}"
 ENV AXOLOTL_ARGS="{{ AXOLOTL_ARGS }}"
 ENV CUDA="{{ CUDA }}"

requirements.txt

@@ -5,27 +5,27 @@ bitsandbytes==0.47.0
 triton>=3.0.0
 mamba-ssm==1.2.0.post1
 xformers>=0.0.23.post1
-liger-kernel==0.6.1
+liger-kernel==0.6.3
 # END section
 
 packaging==23.2
 
-huggingface_hub>=0.33.0
+huggingface_hub>=0.36.0
 peft>=0.17.1
 tokenizers>=0.21.1
 transformers==4.57.1
 accelerate==1.10.1
 datasets==4.0.0
 deepspeed>=0.17.0
-trl==0.23.1
-hf_xet==1.1.5
-kernels==0.9.0
+trl==0.24.0
+hf_xet==1.2.0
+kernels>=0.9.0
 trackio
 
 optimum==1.16.2
 hf_transfer
 sentencepiece
-gradio==5.41.1
+gradio==5.49.1
 
 modal==1.0.2
 pydantic==2.10.6

setup.py

@@ -62,8 +62,12 @@ def parse_requirements(extras_require_map):
             else:
                 raise ValueError("Invalid version format")
 
-            if (major, minor) >= (2, 8):
-                pass
+            if (major, minor) >= (2, 9):
+                extras_require_map.pop("fbgemm-gpu")
+                extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.4.1"]
+            elif (major, minor) >= (2, 8):
+                extras_require_map.pop("fbgemm-gpu")
+                extras_require_map["fbgemm-gpu"] = ["fbgemm-gpu-genai==1.3.0"]
             elif (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:

src/axolotl/core/builders/causal.py

@@ -12,7 +12,7 @@
     EarlyStoppingCallback,
     Trainer,
 )
-from trl.trainer.utils import RewardDataCollatorWithPadding
+from trl.trainer.reward_trainer import DataCollatorForPreference
 
 from axolotl.core.builders.base import TrainerBuilderBase
 from axolotl.core.trainers import (
@@ -453,7 +453,7 @@ def build_collator(
                 BatchSamplerDataCollatorForSeq2Seq,
                 DataCollatorForSeq2Seq,
                 DataCollatorWithFlattening,
-                RewardDataCollatorWithPadding,
+                DataCollatorForPreference,
             ]
         ]
         collator_args = [self.tokenizer]
@@ -470,7 +470,10 @@ def build_collator(
             if kwargs and isinstance(kwargs, dict):
                 kwargs.update(collator_cls_and_kwargs[1])
         elif self.cfg.reward_model:
-            collator = RewardDataCollatorWithPadding
+            collator = DataCollatorForPreference
+            tokenizer = collator_args.pop(0)
+            kwargs["pad_token_id"] = tokenizer.pad_token_id
+            kwargs.pop("padding")
         elif use_batch_sampler_collator:
             # Use V2BatchSamplerDataCollatorForSeq2Seq for flex attention,
             # supported multipack models, or non-flash-attention llama

src/axolotl/prompt_strategies/bradley_terry/chat_template.py

@@ -71,10 +71,10 @@ def _tokenize_single_prompt(self, prompt):
             ]
 
         return {
-            "input_ids_chosen": chosen_tokenized["input_ids"],
+            "chosen_input_ids": chosen_tokenized["input_ids"],
             "attention_mask_chosen": chosen_tokenized["attention_mask"],
             "labels_chosen": 1.0,
-            "input_ids_rejected": rejected_tokenized["input_ids"],
+            "rejected_input_ids": rejected_tokenized["input_ids"],
             "attention_mask_rejected": rejected_tokenized["attention_mask"],
             "labels_rejected": 0.0,
         }