fixes to accelerator so that iterable pretraining datasets work (#1759)

* fixes to accelerator so that iterable pretraining datasets work

* fix the pretraining test params

* split batches, not dispatch batches needs to be set

* update c4 datasets

* set epochs in pretrain config test

* need to set both split_batches and dispatch_batches to false for pretraining

* fix bool val in comment

src/axolotl/core/trainer_builder.py

@@ -1481,6 +1481,11 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
+        if self.cfg.accelerator_config:
+            training_arguments_kwargs[
+                "accelerator_config"
+            ] = self.cfg.accelerator_config
+
         training_args = (
             AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
                 **training_arguments_kwargs,

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

@@ -77,6 +77,7 @@ class PretrainingDataset(BaseModel):
     split: Optional[str] = "train"
     text_column: Optional[str] = "text"
     type: Optional[str] = "pretrain"
+    trust_remote_code: Optional[bool] = False
 
 
 class UserDefinedPrompterType(BaseModel):
@@ -118,6 +119,8 @@ class SFTDataset(BaseModel):
     roles: Optional[Dict[str, List[str]]] = None
     drop_system_message: Optional[bool] = None
 
+    trust_remote_code: Optional[bool] = False
+
 
 class UserDefinedDPOType(BaseModel):
     """User defined typing for DPO"""
@@ -158,6 +161,7 @@ class KTODataset(BaseModel):
     split: Optional[str] = None
     type: Optional[Union[UserDefinedKTOType, str]] = None
     data_files: Optional[List[str]] = None
+    trust_remote_code: Optional[bool] = False
 
 
 class RLType(str, Enum):
@@ -504,6 +508,8 @@ class AxolotlInputConfig(
     dataloader_prefetch_factor: Optional[int] = None
     dataloader_drop_last: Optional[bool] = None
 
+    accelerator_config: Optional[Dict[str, Any]] = None
+
     remove_unused_columns: Optional[bool] = None
 
     push_dataset_to_hub: Optional[str] = None
@@ -702,6 +708,24 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_pretraining_split_batches_accelerate(cls, data):
+        # alternatively set ACCELERATE_SPLIT_BATCHES=False
+        if data.get("pretraining_dataset"):
+            accelerator_config = data.get("accelerator_config", {})
+            if not accelerator_config:
+                data["accelerator_config"] = {
+                    "split_batches": False,
+                    "dispatch_batches": False,
+                }
+            else:
+                if accelerator_config.get("split_batches") is None:
+                    data["accelerator_config"]["split_batches"] = False
+                if accelerator_config.get("dispatch_batches") is None:
+                    data["accelerator_config"]["dispatch_batches"] = False
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_gptq_w_revision(cls, data):