ORPO trainer

README.md

@@ -32,7 +32,7 @@ The library is built on top of the [`transformers`](https://github.com/huggingfa
     - [`PEFT`](https://github.com/huggingface/peft) is fully integrated and allows to train even the largest models on modest hardware with quantisation and methods such as LoRA or QLoRA.
     - [`unsloth`](https://github.com/unslothai/unsloth) is also integrated and allows to significantly speed up training with dedicated kernels.
 - **`CLI`**: With the [CLI](https://huggingface.co/docs/trl/clis) you can fine-tune and chat with LLMs without writing any code using a single command and a flexible config system.
-- **`Trainers`**: The Trainer classes are an abstraction to apply many fine-tuning methods with ease such as the [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer), [`DPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.DPOTrainer), [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer), [`PPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.PPOTrainer), and [`CPOTrainer`]((https://huggingface.co/docs/trl/trainer#trl.CPOTrainer).
+- **`Trainers`**: The Trainer classes are an abstraction to apply many fine-tuning methods with ease such as the [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer), [`DPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.DPOTrainer), [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer), [`PPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.PPOTrainer), [`CPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.CPOTrainer), and [`ORPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.ORPOTrainer).
 - **`AutoModels`**: The [`AutoModelForCausalLMWithValueHead`](https://huggingface.co/docs/trl/models#trl.AutoModelForCausalLMWithValueHead) & [`AutoModelForSeq2SeqLMWithValueHead`](https://huggingface.co/docs/trl/models#trl.AutoModelForSeq2SeqLMWithValueHead) classes add an additional value head to the model which allows to train them with RL algorithms such as PPO.
 - **`Examples`**: Train GPT2 to generate positive movie reviews with a BERT sentiment classifier, full RLHF using adapters only, train GPT-j to be less toxic, [StackLlama example](https://huggingface.co/blog/stackllama), etc. following the [examples](https://github.com/huggingface/trl/tree/main/examples).
 

docs/source/_toctree.yml

@@ -37,6 +37,8 @@
     title: CPO Trainer
   - local: ddpo_trainer
     title: Denoising Diffusion Policy Optimization
+  - local: orpo_trainer
+    title: ORPO Trainer
   - local: iterative_sft_trainer
     title: Iterative Supervised Fine-Tuning
   - local: text_environments

docs/source/orpo_trainer.md

@@ -0,0 +1,98 @@
+# ORPO Trainer
+
+[Odds Ratio Preference Optimization](https://arxiv.org/abs/2403.07691) (ORPO) by Jiwoo Hong, Noah Lee, and James Thorne studies the crucial role of SFT within the context of preference alignment. Using preference data the method posits that a minor penalty for the disfavored generation together with a strong adaption signal to the chosen response via a simple log odds ratio term appended to the NLL loss is sufficient for preference-aligned SFT.
+
+Thus ORPO is a reference model-free preference optimization algorithm eliminating the necessity for an additional preference alignment phase thus saving compute and memory.
+
+The official code can be found [xfactlab/orpo](https://github.com/xfactlab/orpo).
+
+## Expected dataset format
+
+The ORPO trainer expects a format identical to the DPO trainer, which should include three entries. These entries should be named as follows:
+
+- `prompt`
+- `chosen`
+- `rejected`
+
+for example:
+
+```py
+orpo_dataset_dict = {
+    "prompt": [
+        "hello",
+        "how are you",
+        "What is your name?",
+        "What is your name?",
+        "Which is the best programming language?",
+        "Which is the best programming language?",
+        "Which is the best programming language?",
+    ],
+    "chosen": [
+        "hi nice to meet you",
+        "I am fine",
+        "My name is Mary",
+        "My name is Mary",
+        "Python",
+        "Python",
+        "Java",
+    ],
+    "rejected": [
+        "leave me alone",
+        "I am not fine",
+        "Whats it to you?",
+        "I dont have a name",
+        "Javascript",
+        "C++",
+        "C++",
+    ],
+}
+```
+where the `prompt` contains the context inputs, `chosen` contains the corresponding chosen responses and `rejected` contains the corresponding negative (rejected) responses. Note that a prompt can have multiple responses and this is reflected in the entries being repeated in the dictionary's value arrays.
+
+## Expected model format
+The ORPO trainer expects a model of `AutoModelForCausalLM`, compared to PPO that expects `AutoModelForCausalLMWithValueHead` for the value function.
+
+## Using the `ORPOTrainer`
+For a detailed example have a look at the `examples/scripts/orpo.py` script. At a high level we need to initialize the `ORPOTrainer` with a `model` we wish to train. **Note that ORPOTrainer eliminates the need to use the reference model, simplifying the optimization process.** The `beta` refers to the hyperparameter `lambda` in eq. (6) of the paper and refers to the weighting of the relative odd ratio loss in the standard cross-entropy loss used for SFT.
+
+```py
+orpo_config = ORPOConfig(
+    beta=0.1, # the lambda/alpha hyperparameter in the paper/code
+)
+
+orpo_trainer = ORPOTrainer(
+    model,
+    args=orpo_config,
+    train_dataset=train_dataset,
+    tokenizer=tokenizer,
+)
+```
+After this one can then call:
+
+```py
+orpo_trainer.train()
+```
+
+## Logging
+
+While training and evaluating we record the following reward metrics:
+
+* `rewards/chosen`: the mean log probabilities of the policy model for the chosen responses scaled by beta
+* `rewards/rejected`: the mean log probabilities of the policy model for the rejected responses scaled by beta
+* `rewards/accuracies`: mean of how often the chosen rewards are > than the corresponding rejected rewards
+* `rewards/margins`: the mean difference between the chosen and corresponding rejected rewards
+
+* `log_odds_chosen`: the mean log odds ratio of the chosen responses over the rejected responses
+
+* `log_odds_ratio`: the mean of the `log(sigmoid(log_odds_chosen))`
+
+* `nll_loss`: the mean negative log likelihood loss from the SFT part of the loss over chosen responses
+
+## ORPOTrainer
+
+[[autodoc]] ORPOTrainer
+
+
+## ORPOConfig
+
+[[autodoc]] ORPOConfig

examples/scripts/orpo.py

@@ -0,0 +1,121 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Run the ORPO training script with the following command with some example arguments.
+In general, the optimal configuration for ORPO will be similar to that of DPO without the need for a reference model:
+
+# regular:
+python examples/scripts/orpo.py \
+    --model_name_or_path=gpt2 \
+    --per_device_train_batch_size 4 \
+    --max_steps 1000 \
+    --learning_rate 8e-6 \
+    --gradient_accumulation_steps 1 \
+    --logging_steps 10 \
+    --eval_steps 500 \
+    --output_dir="gpt2-aligned-orpo" \
+    --warmup_steps 150 \
+    --report_to wandb \
+    --bf16 \
+    --logging_first_step \
+    --no_remove_unused_columns
+
+# peft:
+python examples/scripts/orpo.py \
+    --model_name_or_path=gpt2 \
+    --per_device_train_batch_size 4 \
+    --max_steps 1000 \
+    --learning_rate 8e-5 \
+    --gradient_accumulation_steps 1 \
+    --logging_steps 10 \
+    --eval_steps 500 \
+    --output_dir="gpt2-lora-aligned-orpo" \
+    --optim rmsprop \
+    --warmup_steps 150 \
+    --report_to wandb \
+    --bf16 \
+    --logging_first_step \
+    --no_remove_unused_columns \
+    --use_peft \
+    --lora_r=16 \
+    --lora_alpha=16
+"""
+
+import multiprocessing
+from dataclasses import dataclass, field
+
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
+
+from trl import ModelConfig, ORPOConfig, ORPOTrainer, get_peft_config
+
+
+@dataclass
+class ScriptArguments:
+    dataset: str = field(
+        default="trl-internal-testing/hh-rlhf-trl-style", metadata={"help": "The name of the dataset to use."}
+    )
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser((ScriptArguments, ORPOConfig, ModelConfig))
+    args, orpo_args, model_config = parser.parse_args_into_dataclasses()
+
+    ################
+    # Model & Tokenizer
+    ################
+    model = AutoModelForCausalLM.from_pretrained(model_config.model_name_or_path)
+    peft_config = get_peft_config(model_config)
+    tokenizer = AutoTokenizer.from_pretrained(model_config.model_name_or_path)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    ################
+    # Dataset
+    ################
+    ds = load_dataset(args.dataset)
+    if orpo_args.debug:
+        for key in ds:
+            ds[key] = ds[key].select(range(50))
+    if tokenizer.chat_template is None:
+        tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
+
+    def process(row):
+        row["chosen"] = tokenizer.apply_chat_template(row["chosen"], tokenize=False)
+        row["rejected"] = tokenizer.apply_chat_template(row["rejected"], tokenize=False)
+        return row
+
+    ds = ds.map(
+        process,
+        num_proc=1 if orpo_args.debug else multiprocessing.cpu_count(),
+        load_from_cache_file=False,
+    )
+    train_dataset = ds["train"]
+    eval_dataset = ds["test"]
+
+    ################
+    # Training
+    ################
+    trainer = ORPOTrainer(
+        model,
+        args=orpo_args,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        tokenizer=tokenizer,
+        peft_config=get_peft_config(model_config),
+    )
+
+    # train and save the model
+    trainer.train()
+    trainer.save_model(orpo_args.output_dir)