[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()
 ```
 

docs/source/reward_trainer.mdx

@@ -6,18 +6,10 @@ Check out a complete flexible example at [`examples/scripts/reward_modeling.py`]
 
 ## Expected dataset format
 
-The [`RewardTrainer`] expects a very specific format for the dataset since the model will be trained on pairs of examples to predict which of the two is preferred. We provide an example from the [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset below:
+The [`RewardTrainer`] requires a [*implicit prompt* preference dataset](dataset_formats#preference). It means that the dataset should only contain the columns `chosen` and `rejected` (and not `prompt`).
+The [`RewardTrainer`] supports both [conversational](dataset_formats#conversational-dataset-format) and [standard](dataset_formats#standard-dataset-format) dataset format. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.
 
-<div style="text-align: center">
-<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/rlhf-antropic-example.png", width="50%">
-</div>
-
-Therefore the final dataset object should contain two 4 entries at least if you use the default [`RewardDataCollatorWithPadding`] data collator. The entries should be named:
-
-- `input_ids_chosen`
-- `attention_mask_chosen`
-- `input_ids_rejected`
-- `attention_mask_rejected`
+You can also use a pretokenized dataset, in which case the dataset should contain the following columns: `input_ids_chosen`, `attention_mask_chosen`, `input_ids_rejected` and `attention_mask_rejected`.
 
 ## Using the `RewardTrainer`
 

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
  ##########