[RewardTrainer] Tokenize inputs within trainer

README.md

@@ -103,15 +103,12 @@ from datasets import load_dataset
 
 dataset = load_dataset("trl-lib/Capybara", split="train")
 
-# configure trainer
 training_args = SFTConfig(output_dir="Qwen/Qwen2.5-0.5B-SFT")
 trainer = SFTTrainer(
     args=training_args,
     model="Qwen/Qwen2.5-0.5B",
     train_dataset=dataset,
 )
-
-# train
 trainer.train()
 ```
 
@@ -121,7 +118,6 @@ Here is a basic example on how to use the `RewardTrainer`:
 
 ```python
 from trl import RewardConfig, RewardTrainer
-from trl.extras.dataset_formatting import conversations_formatting_function
 from datasets import load_dataset
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
@@ -131,48 +127,15 @@ model = AutoModelForSequenceClassification.from_pretrained(
 )
 model.config.pad_token_id = tokenizer.pad_token_id
 
-dataset = load_dataset("trl-lib/Capybara-Preferences", split="train")
-
-def preprocess_function(examples):
-    new_examples = {
-        "input_ids_chosen": [],
-        "attention_mask_chosen": [],
-        "input_ids_rejected": [],
-        "attention_mask_rejected": [],
-    }
-    for chosen, rejected in zip(examples["chosen"], examples["rejected"]):
-        tokenized_chosen = tokenizer(chosen)
-        tokenized_rejected = tokenizer(rejected)
-        new_examples["input_ids_chosen"].append(tokenized_chosen["input_ids"])
-        new_examples["attention_mask_chosen"].append(tokenized_chosen["attention_mask"])
-        new_examples["input_ids_rejected"].append(tokenized_rejected["input_ids"])
-        new_examples["attention_mask_rejected"].append(
-            tokenized_rejected["attention_mask"]
-        )
-
-    return new_examples
-
-chosen_fn = conversations_formatting_function(tokenizer, "chosen")
-rejected_fn = conversations_formatting_function(tokenizer, "rejected")
-dataset = dataset.map(lambda x: {"chosen": chosen_fn(x), "rejected": rejected_fn(x)})
-dataset = dataset.map(
-    preprocess_function,
-    batched=True,
-)
+dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
 
-training_args = RewardConfig(
-    per_device_train_batch_size=2,
-    remove_unused_columns=False,
-    output_dir="Qwen2.5-0.5B-Reward",
-)
+training_args = RewardConfig(output_dir="Qwen2.5-0.5B-Reward", per_device_train_batch_size=2)
 trainer = RewardTrainer(
     args=training_args,
     model=model,
     tokenizer=tokenizer,
     train_dataset=dataset,
 )
-
-# train
 trainer.train()
 ```
 
@@ -210,7 +173,6 @@ trainer = RLOOTrainer(
     train_dataset=dataset["train"],
     eval_dataset=dataset["test"],
 )
-# train
 trainer.train()
 ```
 
@@ -219,30 +181,24 @@ trainer.train()
 `DPOTrainer` implements the popular [Direct Preference Optimization (DPO) algorithm](https://huggingface.co/papers/2305.18290) that was used to post-train Llama 3 and many other models. Here is a basic example on how to use the `DPOTrainer`:
 
 ```python
-# imports
 from trl import DPOConfig, DPOTrainer, maybe_extract_prompt, maybe_apply_chat_template
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
 model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
 
-# load preference dataset - needs to be in a specific format
 dataset = load_dataset("trl-lib/Capybara-Preferences", split="train")
 dataset = dataset.map(maybe_extract_prompt)
 dataset = dataset.map(maybe_apply_chat_template, fn_kwargs={"tokenizer": tokenizer})
 
-# load trainer
 training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
 trainer = DPOTrainer(
     args=training_args,
     model=model,
     tokenizer=tokenizer,
     train_dataset=dataset,
 )
-
-# train
 trainer.train()
 ```
 

examples/scripts/reward_modeling.py

@@ -19,8 +19,6 @@
     --output_dir Qwen2-0.5B-Reward \
     --per_device_train_batch_size 8 \
     --num_train_epochs 1 \
-    --gradient_accumulation_steps 1 \
-    --remove_unused_columns False \
     --gradient_checkpointing True \
     --learning_rate 1.0e-5 \
     --logging_steps 25 \
@@ -32,17 +30,15 @@
 python examples/scripts/reward_modeling.py \
     --model_name_or_path Qwen/Qwen2-0.5B-Instruct \
     --dataset_name trl-lib/ultrafeedback_binarized \
-    --output_dir Qwen2-0.5B-Reward \
+    --output_dir Qwen2-0.5B-Reward-LoRA \
     --per_device_train_batch_size 8 \
     --num_train_epochs 1 \
-    --gradient_accumulation_steps 1 \
-    --remove_unused_columns False \
     --gradient_checkpointing True \
-    --learning_rate 1.0e-5 \
+    --learning_rate 1.0e-4 \
     --logging_steps 25 \
     --eval_strategy steps \
     --eval_steps 50 \
-    --max_length 2048 /
+    --max_length 2048 \
     --use_peft \
     --lora_r 32 \
     --lora_alpha 16
@@ -51,9 +47,7 @@
 import warnings
 
 import torch
-from accelerate import PartialState
 from datasets import load_dataset
-from tqdm import tqdm
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser
 
 from trl import (
@@ -66,10 +60,6 @@
     setup_chat_format,
 )
 from trl.commands.cli_utils import RewardScriptArguments
-from trl.extras.dataset_formatting import conversations_formatting_function
-
-
-tqdm.pandas()
 
 
 if __name__ == "__main__":
@@ -90,6 +80,7 @@
         revision=model_config.model_revision,
         device_map=get_kbit_device_map() if quantization_config is not None else None,
         quantization_config=quantization_config,
+        use_cache=False if training_args.gradient_checkpointing else True,
     )
     tokenizer = AutoTokenizer.from_pretrained(
         model_config.model_name_or_path, trust_remote_code=model_config.trust_remote_code, use_fast=True
@@ -110,49 +101,11 @@
             " Make sure to pass --lora_task_type SEQ_CLS when using this script with PEFT."
         )
 
-    #############################
-    # Load and preprocess dataset
-    #############################
+    ##############
+    # Load dataset
+    ##############
     dataset = load_dataset(args.dataset_name)
 
-    def preprocess_function(examples):
-        new_examples = {
-            "input_ids_chosen": [],
-            "attention_mask_chosen": [],
-            "input_ids_rejected": [],
-            "attention_mask_rejected": [],
-        }
-        for chosen, rejected in zip(examples["chosen"], examples["rejected"]):
-            tokenized_chosen = tokenizer(chosen)
-            tokenized_rejected = tokenizer(rejected)
-            new_examples["input_ids_chosen"].append(tokenized_chosen["input_ids"])
-            new_examples["attention_mask_chosen"].append(tokenized_chosen["attention_mask"])
-            new_examples["input_ids_rejected"].append(tokenized_rejected["input_ids"])
-            new_examples["attention_mask_rejected"].append(tokenized_rejected["attention_mask"])
-
-        return new_examples
-
-    with PartialState().local_main_process_first():
-        # Wrap inputs with chat template.
-        # This assumes the chosen/rejected columns are in the OpenAI messages format.
-        chosen_fn = conversations_formatting_function(tokenizer, "chosen")
-        rejected_fn = conversations_formatting_function(tokenizer, "rejected")
-        dataset = dataset.map(
-            lambda x: {"chosen": chosen_fn(x), "rejected": rejected_fn(x)}, num_proc=training_args.dataset_num_proc
-        )
-        # Tokenize inputs
-        dataset = dataset.map(
-            preprocess_function,
-            batched=True,
-            num_proc=training_args.dataset_num_proc,
-        )
-        # Filter out examples that are too long
-        dataset = dataset.filter(
-            lambda x: len(x["input_ids_chosen"]) <= training_args.max_length
-            and len(x["input_ids_rejected"]) <= training_args.max_length,
-            num_proc=training_args.dataset_num_proc,
-        )
-
     ##########
     # Training
     ##########