Lisa

examples/llama-2/lisa.yml

@@ -0,0 +1,75 @@
+base_model: NousResearch/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./lisa-out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+adapter:
+lora_model_dir:
+lora_r:
+lora_alpha:
+lora_dropout:
+lora_target_linear:
+lora_fan_in_fan_out:
+
+lisa_n_layers: 4
+lisa_step_interval: 20
+lisa_layers_attribute: model.layers
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 5e-5 # recommendation from lisa paper for 7b
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+flash_attn_cross_entropy: false
+flash_attn_rms_norm: true
+flash_attn_fuse_qkv: false
+flash_attn_fuse_mlp: true
+
+warmup_steps: 100
+evals_per_epoch: 4
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

src/axolotl/core/trainer_builder.py

@@ -45,6 +45,7 @@
     causal_lm_bench_eval_callback_factory,
     log_prediction_callback_factory,
 )
+from axolotl.utils.callbacks.lisa import lisa_callback_factory
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
     DataCollatorForSeq2Seq,
@@ -200,6 +201,18 @@ class AxolotlTrainingArguments(TrainingArguments):
     orpo_alpha: Optional[float] = field(
         default=None,
     )
+    lisa_n_layers: Optional[int] = field(
+        default=None,
+        metadata={"help": "the number of activate layers in LISA"},
+    )
+    lisa_step_interval: Optional[int] = field(
+        default=None,
+        metadata={"help": "how often to switch layers in LISA"},
+    )
+    lisa_layers_attribute: Optional[str] = field(
+        default=None,
+        metadata={"help": "path under the model to access the layers"},
+    )
 
 
 class AxolotlTrainer(Trainer):
@@ -938,6 +951,8 @@ def get_post_trainer_create_callbacks(self, trainer):
             )
             callbacks.append(early_stop_cb)
 
+        if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
+            callbacks.append(lisa_callback_factory(trainer))
         return callbacks
 
     def _get_trainer_cls(self):
@@ -1229,6 +1244,15 @@ def build(self, total_num_steps):
                     "relora_prune_ratio"
                 ] = self.cfg.relora_prune_ratio
 
+        if self.cfg.lisa_step_interval and self.cfg.lisa_n_layers:
+            training_arguments_kwargs["lisa_n_layers"] = self.cfg.lisa_n_layers
+            training_arguments_kwargs[
+                "lisa_step_interval"
+            ] = self.cfg.lisa_step_interval
+            training_arguments_kwargs[
+                "lisa_layers_attribute"
+            ] = self.cfg.lisa_layers_attribute
+
         training_arguments_kwargs = self.hook_pre_create_training_args(
             training_arguments_kwargs
         )

src/axolotl/utils/callbacks/lisa.py

@@ -0,0 +1,91 @@
+"""
+module for LISA
+
+Adapted from https://github.com/OptimalScale/LMFlow/pull/701 for HF transformers & Axolotl
+Arxiv: https://arxiv.org/abs/2403.17919
+License: Apache 2.0
+"""
+
+import logging
+from functools import reduce
+from typing import TYPE_CHECKING
+
+import numpy as np
+from transformers import TrainerCallback
+
+if TYPE_CHECKING:
+    from axolotl.core.trainer_builder import AxolotlTrainer
+
+LOG = logging.getLogger("axolotl.callbacks.lisa")
+
+
+def lisa_callback_factory(trainer: "AxolotlTrainer"):
+    class LISACallback(TrainerCallback):
+        """trainer callback for lisa layer switching"""
+
+        def __init__(
+            self, n_layers, step_interval, trainer, layers_attribute="model.layers"
+        ):
+            super().__init__()
+            self.n_layers = n_layers
+            self.step_interval = step_interval
+            self.layers_attribute = layers_attribute
+            self.trainer = trainer
+
+            reduce(getattr, self.layers_attribute.split("."), self.trainer.model)
+
+            self.total_layers = len(
+                reduce(getattr, self.layers_attribute.split("."), self.trainer.model)
+            )
+            self.active_layers_indices = []
+
+            layers = reduce(
+                getattr, self.layers_attribute.split("."), self.trainer.model
+            )
+            LOG.info(
+                f"LISA will activate {self.n_layers}/{len(layers)} layers ({self.n_layers*100/len(layers)}%) every {self.step_interval} steps"
+            )
+
+        def freeze_all_layers(self):
+            layers = reduce(
+                getattr, self.layers_attribute.split("."), self.trainer.model
+            )
+            for layer in layers:
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+        def on_step_begin(
+            self, args, state, control, **kwargs
+        ):  # pylint: disable=unused-argument
+            # Check if it's time to switch active layers, including at step 0
+            if state.global_step % self.step_interval == 0 or state.global_step == 1:
+                self.switch_active_layers()
+
+        def switch_active_layers(self):
+            # First, disable gradients for all layers
+            self.freeze_all_layers()
+
+            # Randomly select n_layers to activate
+            layers = reduce(
+                getattr, self.layers_attribute.split("."), self.trainer.model
+            )
+            self.active_layers_indices = np.random.choice(
+                range(self.total_layers), self.n_layers, replace=False
+            )
+            LOG.info(
+                f"Activating layers at indices: {self.active_layers_indices} for the next steps."
+            )
+
+            # Enable gradients only for the selected layers
+            for idx in self.active_layers_indices:
+                for param in layers[idx].parameters():
+                    param.requires_grad = True
+
+    lisa_callback = LISACallback(
+        n_layers=trainer.args.lisa_n_layers,
+        step_interval=trainer.args.lisa_step_interval,
+        trainer=trainer,
+        layers_attribute=trainer.args.lisa_layers_attribute,
+    )
+
+    return lisa_callback

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

@@ -370,6 +370,23 @@ class MLFlowConfig(BaseModel):
     hf_mlflow_log_artifacts: Optional[bool] = None
 
 
+class LISAConfig(BaseModel):
+    """LISA options"""
+
+    lisa_n_layers: Optional[int] = Field(
+        default=None,
+        metadata={"help": "the number of activate layers in LISA"},
+    )
+    lisa_step_interval: Optional[int] = Field(
+        default=None,
+        metadata={"help": "how often to switch layers in LISA"},
+    )
+    lisa_layers_attribute: Optional[str] = Field(
+        default="model.layers",
+        metadata={"help": "path under the model to access the layers"},
+    )
+
+
 class WandbConfig(BaseModel):
     """wandb configuration subset"""
 
@@ -404,6 +421,7 @@ class AxolotlInputConfig(
     HyperparametersConfig,
     WandbConfig,
     MLFlowConfig,
+    LISAConfig,
     RemappedParameters,
     DeprecatedParameters,
     BaseModel,