[core / DDP] Fix RM trainer + DDP + quantization + propagate gradient_checkpointing_kwargs in SFT & DPO

examples/scripts/dpo.py

@@ -69,6 +69,15 @@ class ScriptArguments:
             "https://github.com/huggingface/transformers/issues/22482#issuecomment-1595790992"
         },
     )
+    gradient_checkpointing: Optional[bool] = field(
+        default=False, metadata={"help": "Whether to use gradient checkpointing or no"}
+    )
+    gradient_checkpointing_kwargs: Optional[dict] = field(
+        default=None,
+        metadata={
+            "help": "key word arguments to be passed along `torch.utils.checkpoint.checkpoint` method - e.g. `use_reentrant=False`"
+        },
+    )
 
 
 def extract_anthropic_prompt(prompt_and_response):
@@ -149,6 +158,9 @@ def split_prompt_and_responses(sample) -> Dict[str, str]:
         warmup_steps=150,
         report_to=script_args.report_to,
         bf16=True,
+        gradient_checkpointing=script_args.gradient_checkpointing,
+        # TODO: uncomment that on the next transformers release
+        # gradient_checkpointing_kwargs=script_args.gradient_checkpointing_kwargs,
     )
 
     # 5. initialize the DPO trainer

examples/scripts/reward_modeling.py

@@ -51,6 +51,7 @@ class ScriptArguments:
             num_train_epochs=1,
             gradient_accumulation_steps=16,
             gradient_checkpointing=True,
+            gradient_checkpointing_kwargs={"use_reentrant": False},
             learning_rate=1.41e-5,
             report_to="tensorboard",
             remove_unused_columns=False,

examples/scripts/sft.py

@@ -63,6 +63,15 @@ class ScriptArguments:
     )
     save_total_limit: Optional[int] = field(default=10, metadata={"help": "Limits total number of checkpoints."})
     push_to_hub: Optional[bool] = field(default=False, metadata={"help": "Push the model to HF Hub"})
+    gradient_checkpointing: Optional[bool] = field(
+        default=False, metadata={"help": "Whether to use gradient checkpointing or no"}
+    )
+    gradient_checkpointing_kwargs: Optional[dict] = field(
+        default=None,
+        metadata={
+            "help": "key word arguments to be passed along `torch.utils.checkpoint.checkpoint` method - e.g. `use_reentrant=False`"
+        },
+    )
     hub_model_id: Optional[str] = field(default=None, metadata={"help": "The name of the model on HF Hub"})
 
 
@@ -114,6 +123,9 @@ class ScriptArguments:
     save_total_limit=script_args.save_total_limit,
     push_to_hub=script_args.push_to_hub,
     hub_model_id=script_args.hub_model_id,
+    gradient_checkpointing=script_args.gradient_checkpointing,
+    # TODO: uncomment that on the next release
+    # gradient_checkpointing_kwargs=script_args.gradient_checkpointing_kwargs,
 )
 
 # Step 4: Define the LoraConfig

trl/trainer/dpo_trainer.py

@@ -12,6 +12,7 @@
 # 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 inspect
 import random
 import warnings
 from collections import defaultdict
@@ -137,7 +138,18 @@ 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_kbit_training(model, use_gradient_checkpointing=args.gradient_checkpointing)
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                preprare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    preprare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **preprare_model_kwargs)
             elif getattr(args, "gradient_checkpointing", False):
                 # For backward compatibility with older versions of transformers
                 if hasattr(model, "enable_input_require_grads"):

trl/trainer/reward_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 inspect
 import warnings
 from dataclasses import FrozenInstanceError, replace
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
@@ -128,10 +129,22 @@ def __init__(
         elif is_peft_available() and peft_config is not None:
             if not isinstance(model, PeftModel):
                 if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_quantized", False):
-                    model = prepare_model_for_kbit_training(
-                        model, use_gradient_checkpointing=args.gradient_checkpointing
+                    _supports_gc_kwargs = "gradient_checkpointing_kwargs" in list(
+                        inspect.signature(prepare_model_for_kbit_training).parameters
                     )
 
+                    preprare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                    if not _supports_gc_kwargs and args.gradient_checkpointing_kwargs is not None:
+                        warnings.warn(
+                            "You passed `gradient_checkpointing_kwargs` in the trainer's kwargs, but your peft version does not support it. "
+                            "please update to the latest version of peft to use `gradient_checkpointing_kwargs`."
+                        )
+                    elif _supports_gc_kwargs and args.gradient_checkpointing_kwargs is not None:
+                        preprare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                    model = prepare_model_for_kbit_training(model, **preprare_model_kwargs)
+
                 model = get_peft_model(model, peft_config)
 
         if is_peft_available() and isinstance(model, PeftModel):

trl/trainer/sft_trainer.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import dataclasses
+import inspect
 import warnings
 from functools import wraps
 from typing import Callable, Dict, List, Optional, Tuple, Union
@@ -159,10 +160,19 @@ def __init__(
                     )
 
                 if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
-                    model = prepare_model_for_kbit_training(
-                        model, use_gradient_checkpointing=args.gradient_checkpointing
+                    _support_gc_kwargs = hasattr(
+                        args, "gradient_checkpointing_kwargs"
+                    ) and "gradient_checkpointing_kwargs" in list(
+                        inspect.signature(prepare_model_for_kbit_training).parameters
                     )
 
+                    preprare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                    if _support_gc_kwargs:
+                        preprare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                    model = prepare_model_for_kbit_training(model, **preprare_model_kwargs)
+
                     args = dataclasses.replace(args, gradient_checkpointing=False)
 
                 model = get_peft_model(model, peft_config)

trl/trainer/training_configs.py

@@ -39,3 +39,5 @@ class RewardConfig(TrainingArguments):
     """The maximum length of the sequences in the batch. This argument is required if you want to use the default data collator."""
     gradient_checkpointing: Optional[bool] = True
     """If True, use gradient checkpointing to save memory at the expense of slower backward pass."""
+    gradient_checkpointing_kwargs: Optional[dict] = None
+    """Keyword arguments to pass to the gradient checkpointing function."""