update to prepare_model_for_kbit_training

docs/source/sft_trainer.mdx

@@ -336,7 +336,7 @@ trainer.train()
 Pay attention to the following best practices when training a model with that trainer:
 
 - [`SFTTrainer`] always pads by default the sequences to the `max_seq_length` argument of the [`SFTTrainer`]. If none is passed, the trainer will retrieve that value from the tokenizer. Some tokenizers do not provide default value, so there is a check to retrieve the minimum between 2048 and that value. Make sure to check it before training.
-- For training adapters in 8bit, you might need to tweak the arguments of the `prepare_model_for_int8_training` method from PEFT, hence we advise users to use `prepare_in_int8_kwargs` field, or create the `PeftModel` outside the [`SFTTrainer`] and pass it.
+- For training adapters in 8bit, you might need to tweak the arguments of the `prepare_model_for_kbit_training` method from PEFT, hence we advise users to use `prepare_in_int8_kwargs` field, or create the `PeftModel` outside the [`SFTTrainer`] and pass it.
 - For a more memory-efficient training using adapters, you can load the base model in 8bit, for that simply add `load_in_8bit` argument when creating the [`SFTTrainer`], or create a base model in 8bit outside the trainer and pass it.  
 - If you create a model outside the trainer, make sure to not pass to the trainer any additional keyword arguments that are relative to `from_pretrained()` method.
 
@@ -346,4 +346,4 @@ Pay attention to the following best practices when training a model with that tr
 
 ## ConstantLengthDataset
 
-[[autodoc]] trainer.ConstantLengthDataset
+[[autodoc]] trainer.ConstantLengthDataset

docs/source/using_llama_models.mdx

@@ -52,7 +52,7 @@ model = AutoModelForCausalLM.from_pretrained(
         load_in_8bit=True,
         device_map={"": Accelerator().local_process_index}
     )
-model = prepare_model_for_int8_training(model)
+model = prepare_model_for_kbit_training(model)
 
 # add LoRA to model
 lora_config = LoraConfig(
@@ -157,4 +157,4 @@ for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
     ppo_trainer.log_stats(stats, batch, rewards)
 ```
 
-For the rest of the details and evaluation, please refer to our [blog post on StackLLaMA](https://huggingface.co/blog/stackllama).
+For the rest of the details and evaluation, please refer to our [blog post on StackLLaMA](https://huggingface.co/blog/stackllama).

examples/research_projects/stack_llama_2/scripts/sft_llama2.py

@@ -4,6 +4,7 @@
 from typing import Optional
 
 import torch
+from accelerate import Accelerator
 from datasets import load_dataset
 from peft import AutoPeftModelForCausalLM, LoraConfig
 from tqdm import tqdm
@@ -148,7 +149,7 @@ def create_datasets(tokenizer, args):
 base_model = AutoModelForCausalLM.from_pretrained(
     script_args.model_name,
     quantization_config=bnb_config,
-    device_map={"": 0},
+    device_map={"": Accelerator().local_process_index},
     trust_remote_code=True,
     use_auth_token=True,
 )

trl/models/modeling_base.py

@@ -35,7 +35,7 @@
         PeftModelForSeq2SeqLM,
         PromptLearningConfig,
         get_peft_model,
-        prepare_model_for_int8_training,
+        prepare_model_for_kbit_training,
     )
     from peft.peft_model import set_peft_model_state_dict
 
@@ -108,7 +108,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                 `from_pretrained` method. We also pre-process the kwargs to extract
                 the arguments that are specific to the `transformers.PreTrainedModel`
                 class and the arguments that are specific to trl models. The kwargs
-                also support `prepare_model_for_int8_training` arguments from
+                also support `prepare_model_for_kbit_training` arguments from
                 `peft` library.
         """
         if kwargs is not None:
@@ -203,7 +203,7 @@ class and the arguments that are specific to trl models. The kwargs
                 if peft_config is not None:
                     # Initialize a new peft adapter with the given config
                     if is_loaded_in_8bit or is_loaded_in_4bit:
-                        pretrained_model = prepare_model_for_int8_training(
+                        pretrained_model = prepare_model_for_kbit_training(
                             pretrained_model,
                             **peft_quantization_kwargs,
                         )
@@ -216,7 +216,7 @@ class and the arguments that are specific to trl models. The kwargs
             if peft_config is not None and isinstance(pretrained_model, PreTrainedModel):
                 # Initialize a new peft adapter with the given config
                 if is_loaded_in_8bit or is_loaded_in_4bit:
-                    pretrained_model = prepare_model_for_int8_training(
+                    pretrained_model = prepare_model_for_kbit_training(
                         pretrained_model,
                         **peft_quantization_kwargs,
                     )
@@ -339,7 +339,7 @@ def _split_kwargs(cls, kwargs):
         check_peft_kwargs = False
 
         if is_peft_available():
-            from peft import prepare_model_for_int8_training
+            from peft import prepare_model_for_kbit_training
 
             check_peft_kwargs = True
 
@@ -354,7 +354,7 @@ def _split_kwargs(cls, kwargs):
                 unsupported_kwargs[key] = value
 
             if check_peft_kwargs:
-                if key in prepare_model_for_int8_training.__code__.co_varnames:
+                if key in prepare_model_for_kbit_training.__code__.co_varnames:
                     peft_kwargs[key] = value
                     if key in unsupported_kwargs:
                         unsupported_kwargs.pop(key)

trl/trainer/dpo_trainer.py

@@ -29,7 +29,7 @@
 
 
 if is_peft_available():
-    from peft import PeftModel, get_peft_model, prepare_model_for_int8_training
+    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training
 
 
 class DPOTrainer(Trainer):
@@ -116,7 +116,7 @@ def __init__(
             )
         elif is_peft_available() and peft_config is not None:
             if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
-                model = prepare_model_for_int8_training(model)
+                model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=args.gradient_checkpointing)
             model = get_peft_model(model, peft_config)
 
         if model is not None:

trl/trainer/reward_trainer.py

@@ -29,7 +29,7 @@
 
 
 if is_peft_available():
-    from peft import PeftModel, get_peft_model, prepare_model_for_int8_training
+    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training
 
 
 class RewardTrainer(Trainer):
@@ -113,7 +113,7 @@ def __init__(
             )
         elif is_peft_available() and peft_config is not None:
             if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_quantized", False):
-                model = prepare_model_for_int8_training(model, use_gradient_checkpointing=args.gradient_checkpointing)
+                model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=args.gradient_checkpointing)
 
             model = get_peft_model(model, peft_config)
 

trl/trainer/sft_trainer.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import dataclasses
 import warnings
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
@@ -35,7 +36,7 @@
 
 
 if is_peft_available():
-    from peft import PeftConfig, PeftModel, get_peft_model, prepare_model_for_int8_training
+    from peft import PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training
 
 
 class SFTTrainer(Trainer):
@@ -147,7 +148,11 @@ def __init__(
                     )
 
                 if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
-                    model = prepare_model_for_int8_training(model)
+                    model = prepare_model_for_kbit_training(
+                        model, use_gradient_checkpointing=args.gradient_checkpointing
+                    )
+
+                    args = dataclasses.replace(args, gradient_checkpointing=False)
 
                 model = get_peft_model(model, peft_config)