feat: add kto example (#2158) [skip ci]

docs/rlhf.qmd

@@ -52,6 +52,26 @@ datasets:
     type: chat_template.argilla
 ```
 
+
+#### KTO
+
+```yaml
+rl: kto
+rl_beta: 0.5
+kto_desirable_weight: 0.2
+
+remove_unused_columns: false
+
+datasets:
+  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
+    type: llama3.ultra
+    split: train
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+```
+
 #### Using local dataset files
 ```yaml
 datasets:

examples/llama-3/qlora-1b-kto.yaml

@@ -0,0 +1,75 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+rl: kto
+rl_beta: 0.5
+kto_desirable_weight: 0.2
+
+datasets:
+  - path: argilla/ultrafeedback-binarized-preferences-cleaned-kto
+    type: llama3.ultra
+    split: train
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/qlora-out
+
+remove_unused_columns: false
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: false  # not supported with kto
+eval_sample_packing: false
+pad_to_sequence_len: false
+
+lora_r: 32
+lora_alpha: 64
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 20
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -1475,6 +1475,27 @@ class AxolotlInputConfig(
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_kto_config(cls, data):
+        if data.get("rl") == "kto":
+            if data.get("sample_packing") or data.get("eval_sample_packing"):
+                raise ValueError("sample_packing is not supported with kto")
+
+            if data.get("remove_unused_columns") is not False:
+                raise ValueError("Set `remove_unused_columns: False` when using kto")
+
+            if data.get("gradient_checkpointing") and not (
+                data.get("gradient_checkpointing_kwargs")
+                and isinstance(data.get("gradient_checkpointing_kwargs"), dict)
+                and data["gradient_checkpointing_kwargs"].get("use_reentrant")
+            ):
+                raise ValueError(
+                    "Set `gradient_checkpointing_kwargs: {use_reentrant: true}` for when kto is enabled"
+                )
+
+        return data
+
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """wrapper to valdiate gpu capabilities with the configured options"""