FIX Use SFTConfig instead of SFTTrainer keyword args (huggingface…

…#2150)

Update training script using trl to fix deprecations in argument usage.

docs/source/accelerate/deepspeed.md

@@ -128,7 +128,7 @@ Notice that we are using LoRA with  rank=8, alpha=16 and targeting all linear la
 
 Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
 
-The first thing to know is that the script uses DeepSpeed for distributed training as the DeepSpeed config has been passed. The `SFTTrainer` class handles all the heavy lifting of creating the PEFT model using the peft config that is passed. After that, when you call `trainer.train()`, `SFTTrainer` internally uses 🤗 Accelerate to prepare the model, optimizer and trainer using the DeepSpeed config to create DeepSpeed engine which is then trained. The main code snippet is below:
+The first thing to know is that the script uses DeepSpeed for distributed training as the DeepSpeed config has been passed. The [`~trl.SFTTrainer`] class handles all the heavy lifting of creating the PEFT model using the peft config that is passed. After that, when you call `trainer.train()`, [`~trl.SFTTrainer`] internally uses 🤗 Accelerate to prepare the model, optimizer and trainer using the DeepSpeed config to create DeepSpeed engine which is then trained. The main code snippet is below:
 
 ```python
 # trainer
@@ -139,13 +139,6 @@ trainer = SFTTrainer(
     train_dataset=train_dataset,
     eval_dataset=eval_dataset,
     peft_config=peft_config,
-    packing=data_args.packing,
-    dataset_kwargs={
-        "append_concat_token": data_args.append_concat_token,
-        "add_special_tokens": data_args.add_special_tokens,
-    },
-    dataset_text_field=data_args.dataset_text_field,
-    max_seq_length=data_args.max_seq_length,
 )
 trainer.accelerator.print(f"{trainer.model}")
 

docs/source/accelerate/fsdp.md

@@ -108,7 +108,7 @@ Notice that we are using LoRA with  rank=8, alpha=16 and targeting all linear la
 
 Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
 
-The first thing to know is that the script uses FSDP for distributed training as the FSDP config has been passed. The `SFTTrainer` class handles all the heavy lifting of creating PEFT model using the peft config that is passed. After that when you call `trainer.train()`, Trainer internally uses 🤗 Accelerate to prepare model, optimizer and trainer using the FSDP config to create FSDP wrapped model which is then trained. The main code snippet is below:
+The first thing to know is that the script uses FSDP for distributed training as the FSDP config has been passed. The [`~trl.SFTTrainer`] class handles all the heavy lifting of creating PEFT model using the peft config that is passed. After that when you call `trainer.train()`, Trainer internally uses 🤗 Accelerate to prepare model, optimizer and trainer using the FSDP config to create FSDP wrapped model which is then trained. The main code snippet is below:
 
 ```python
 # trainer
@@ -119,13 +119,6 @@ trainer = SFTTrainer(
     train_dataset=train_dataset,
     eval_dataset=eval_dataset,
     peft_config=peft_config,
-    packing=data_args.packing,
-    dataset_kwargs={
-        "append_concat_token": data_args.append_concat_token,
-        "add_special_tokens": data_args.add_special_tokens,
-    },
-    dataset_text_field=data_args.dataset_text_field,
-    max_seq_length=data_args.max_seq_length,
 )
 trainer.accelerator.print(f"{trainer.model}")
 if model_args.use_peft_lora:

examples/olora_finetuning/README.md

@@ -8,7 +8,7 @@
 import torch
 from peft import LoraConfig, get_peft_model
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from trl import SFTTrainer
+from trl import SFTConfig, SFTTrainer
 from datasets import load_dataset
 
 model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.bfloat16, device_map="auto")
@@ -18,11 +18,10 @@ lora_config = LoraConfig(
     init_lora_weights="olora"
 )
 peft_model = get_peft_model(model, lora_config)
+training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
 trainer = SFTTrainer(
     model=peft_model,
     train_dataset=dataset,
-    dataset_text_field="text",
-    max_seq_length=512,
     tokenizer=tokenizer,
 )
 trainer.train()

examples/pissa_finetuning/README.md

@@ -6,8 +6,7 @@ PiSSA represents a matrix $W\in\mathbb{R}^{m\times n}$ within the model by the p
 ```python
 import torch
 from peft import LoraConfig, get_peft_model
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from trl import SFTTrainer
+from transformers import AutoTokenizer, AutoModelForCausalLMfrom trl import SFTConfig, SFTTrainer
 from datasets import load_dataset
 
 model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.bfloat16, device_map="auto")
@@ -23,11 +22,11 @@ peft_model.print_trainable_parameters()
 
 dataset = load_dataset("imdb", split="train[:1%]")
 
+training_args = SFTConfig(dataset_text_field="text", max_seq_length=128)
 trainer = SFTTrainer(
     model=peft_model,
+    args=training_args,
     train_dataset=dataset,
-    dataset_text_field="text",
-    max_seq_length=128,
     tokenizer=tokenizer,
 )
 trainer.train()

examples/pissa_finetuning/pissa_finetuning.py

@@ -14,18 +14,18 @@
 
 import os
 from dataclasses import dataclass, field
-from typing import List, Optional
+from typing import Optional
 
 import torch
 from datasets import load_dataset
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
-from trl import SFTTrainer
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser
+from trl import SFTConfig, SFTTrainer
 
 from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training
 
 
 @dataclass
-class TrainingArguments(TrainingArguments):
+class ScriptArguments(SFTConfig):
     # model configs
     base_model_name_or_path: Optional[str] = field(
         default=None, metadata={"help": "The name or path of the fp32/16 base model."}
@@ -46,14 +46,9 @@ class TrainingArguments(TrainingArguments):
     # dataset configs
     data_path: str = field(default="imdb", metadata={"help": "Path to the training data."})
     dataset_split: str = field(default="train[:1%]", metadata={"help": "(`['train', 'test', 'eval']`):"})
-    dataset_field: List[str] = field(default=None, metadata={"help": "Fields of dataset input and output."})
-    max_seq_length: int = field(
-        default=512,
-        metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
-    )
 
 
-parser = HfArgumentParser(TrainingArguments)
+parser = HfArgumentParser(ScriptArguments)
 script_args = parser.parse_args_into_dataclasses()[0]
 print(script_args)
 
@@ -133,8 +128,6 @@ class TrainingArguments(TrainingArguments):
     model=peft_model,
     args=script_args,
     train_dataset=dataset,
-    dataset_text_field="text",
-    max_seq_length=script_args.max_seq_length,
     tokenizer=tokenizer,
 )
 trainer.train()

examples/sft/train.py

@@ -3,8 +3,8 @@
 from dataclasses import dataclass, field
 from typing import Optional
 
-from transformers import HfArgumentParser, TrainingArguments, set_seed
-from trl import SFTTrainer
+from transformers import HfArgumentParser, set_seed
+from trl import SFTConfig, SFTTrainer
 from utils import create_and_prepare_model, create_datasets
 
 
@@ -79,12 +79,6 @@ class DataTrainingArguments:
         default="timdettmers/openassistant-guanaco",
         metadata={"help": "The preference dataset to use."},
     )
-    packing: Optional[bool] = field(
-        default=False,
-        metadata={"help": "Use packing dataset creating."},
-    )
-    dataset_text_field: str = field(default="text", metadata={"help": "Dataset field to use as input text."})
-    max_seq_length: Optional[int] = field(default=512)
     append_concat_token: Optional[bool] = field(
         default=False,
         metadata={"help": "If True, appends `eos_token_id` at the end of each sample being packed."},
@@ -112,6 +106,11 @@ def main(model_args, data_args, training_args):
     if training_args.gradient_checkpointing:
         training_args.gradient_checkpointing_kwargs = {"use_reentrant": model_args.use_reentrant}
 
+    training_args.dataset_kwargs = {
+        "append_concat_token": data_args.append_concat_token,
+        "add_special_tokens": data_args.add_special_tokens,
+    }
+
     # datasets
     train_dataset, eval_dataset = create_datasets(
         tokenizer,
@@ -128,13 +127,6 @@ def main(model_args, data_args, training_args):
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         peft_config=peft_config,
-        packing=data_args.packing,
-        dataset_kwargs={
-            "append_concat_token": data_args.append_concat_token,
-            "add_special_tokens": data_args.add_special_tokens,
-        },
-        dataset_text_field=data_args.dataset_text_field,
-        max_seq_length=data_args.max_seq_length,
     )
     trainer.accelerator.print(f"{trainer.model}")
     if hasattr(trainer.model, "print_trainable_parameters"):
@@ -153,7 +145,7 @@ def main(model_args, data_args, training_args):
 
 
 if __name__ == "__main__":
-    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, SFTConfig))
     if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
         # If we pass only one argument to the script and it's the path to a json file,
         # let's parse it to get our arguments.