🧑‍🍳 Add precompute batch size argument in DPOTrainer for reference …

…model (huggingface#2426)

* added precompute_batch

* review-fixes

* moving up

* Update trl/trainer/dpo_config.py

Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>

* Update trl/trainer/dpo_config.py

* Update trl/trainer/dpo_config.py [ci skip]

---------

Co-authored-by: Quentin Gallouédec <45557362+qgallouedec@users.noreply.github.com>

tests/test_dpo_trainer.py

@@ -350,6 +350,40 @@ def test_dpo_trainer_with_ref_model_is_model(self):
                     train_dataset=dummy_dataset["train"],
                 )
 
+    def test_precompute_ref_batch_size(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            training_args = DPOConfig(
+                output_dir=tmp_dir,
+                per_device_train_batch_size=2,
+                precompute_ref_log_probs=True,
+                precompute_ref_batch_size=4,
+                report_to="none",
+            )
+
+            dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
+
+            trainer = DPOTrainer(
+                model=self.model,
+                ref_model=self.ref_model,
+                args=training_args,
+                processing_class=self.tokenizer,
+                train_dataset=dummy_dataset["train"],
+                eval_dataset=dummy_dataset["test"],
+            )
+
+            previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
+
+            trainer.train()
+
+            self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
+
+            # check the params have changed
+            for n, param in previous_trainable_params.items():
+                new_param = trainer.model.get_parameter(n)
+                # check the params have changed - ignore 0 biases
+                if param.sum() != 0:
+                    self.assertFalse(torch.allclose(param, new_param, rtol=1e-12, atol=1e-12))
+
     @require_peft
     def test_dpo_trainer_without_providing_ref_model_with_lora(self):
         from peft import LoraConfig

trl/trainer/dpo_config.py

@@ -94,6 +94,10 @@ class DPOConfig(TrainingArguments):
         precompute_ref_log_probs (`bool`, *optional*, defaults to `False`):
             Whether to precompute reference model log probabilities for training and evaluation datasets. This is
             useful when training without the reference model to reduce the total GPU memory needed.
+        precompute_ref_batch_size (`Optional[int]`, *optional*, defaults to `None`):
+            Batch size to use when precomputing reference model log probabilities. This can be set higher than the
+            training batch size to speed up preprocessing. If `None`, defaults to `per_device_train_batch_size` for
+            training and `per_device_eval_batch_size` for evaluation.
         dataset_num_proc (`Optional[int]`, *optional*, defaults to `None`):
             Number of processes to use for processing the dataset.
         model_init_kwargs (`Optional[dict[str, Any]]`, *optional*, defaults to `None`):
@@ -173,6 +177,7 @@ class DPOConfig(TrainingArguments):
     disable_dropout: bool = True
     generate_during_eval: bool = False
     precompute_ref_log_probs: bool = False
+    precompute_ref_batch_size: Optional[int] = None
     dataset_num_proc: Optional[int] = None
     model_init_kwargs: Optional[dict[str, Any]] = None
     ref_model_init_kwargs: Optional[dict[str, Any]] = None

trl/trainer/dpo_trainer.py

@@ -684,8 +684,9 @@ def get_train_dataloader(self) -> DataLoader:
         """
 
         if self.precompute_ref_log_probs and not self._precomputed_train_ref_log_probs:
+            batch_size = self.args.precompute_ref_batch_size or self.args.per_device_train_batch_size
             dataloader_params = {
-                "batch_size": self.args.per_device_train_batch_size,
+                "batch_size": batch_size,
                 "collate_fn": self.data_collator,
                 "num_workers": self.args.dataloader_num_workers,
                 "pin_memory": self.args.dataloader_pin_memory,
@@ -737,8 +738,9 @@ def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoa
         eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
 
         if self.precompute_ref_log_probs and not self._precomputed_eval_ref_log_probs:
+            batch_size = self.args.precompute_ref_batch_size or self.args.per_device_eval_batch_size
             dataloader_params = {
-                "batch_size": self.args.per_device_eval_batch_size,
+                "batch_size": batch_size,
                 "collate_fn": self.data_collator,
                 "num_workers": self.args.dataloader_num_workers,
                 "pin_memory": self.args.dataloader_pin_memory,