Add DistillationTrainer for efficient on-policy distillation (#5407)

Author: cmpatino

docs/source/paper_index.md

@@ -1563,21 +1563,21 @@ Papers relating to training a student model with the help of a teacher model.
 
 **📜 Paper**: https://huggingface.co/papers/2306.13649
 
-Introduces Generalized Knowledge Distillation (GKD), which addresses distribution mismatch in KD for auto-regressive models by training the student on its own generated outputs with teacher feedback, instead of a fixed set of sequences. GKD supports flexible loss functions (e.g. beyond KL when the student cannot match the teacher) and integrates with RL fine-tuning (RLHF). The paper reports results on summarization, translation, arithmetic reasoning, and instruction-tuning. Used in TRL via [`experimental.gkd.GKDTrainer`]. To reproduce the paper's setting, use this configuration:
+Introduces Generalized Knowledge Distillation (GKD), which addresses distribution mismatch in KD for auto-regressive models by training the student on its own generated outputs with teacher feedback, instead of a fixed set of sequences. GKD supports flexible loss functions (e.g. beyond KL when the student cannot match the teacher) and integrates with RL fine-tuning (RLHF). The paper reports results on summarization, translation, arithmetic reasoning, and instruction-tuning. Used in TRL via [`experimental.distillation.DistillationTrainer`] and [`experimental.gkd.GKDTrainer`]. To reproduce the paper's setting, use this configuration:
 
 ```python
-from trl.experimental.gkd import GKDConfig
+from trl.experimental.distillation import DistillationConfig
 
 # XSum summarization task (Table A.1 of the paper)
-training_args = GKDConfig(
+training_args = DistillationConfig(
     lmbda=0.5,  # λ student data fraction (Section 3 of the paper)
     beta=0.5,  # β Generalized JSD interpolation, 0=KL, 1=reverse KL (Section 3 of the paper)
     temperature=1.0,  # student training temperature (Appendix A of the paper)
     max_steps=40000,  # training steps (Table A.1 of the paper)
     learning_rate=3e-4,  # learning rate (Table A.1 of the paper)
     per_device_train_batch_size=32,  # batch size (Table A.1 of the paper)
     warmup_steps=2000,  # warm-up steps (Table A.1 of the paper)
-    max_new_tokens=64,  # max output tokens (Table A.1 of the paper)
+    max_completion_length=64,  # max output tokens (Table A.1 of the paper)
 )
 ```
 
@@ -1597,20 +1597,31 @@ On-Policy Distillation has been shown to outperform SFT, GRPO and can be used to
 
 Additionally on-policy distillation is more compute efficient and is less prone to overfitting when trained with limited data.
 
-To train a model with on-policy distillation using TRL, you can use the following configuration, with the [`experimental.gkd.GKDTrainer`] and [`experimental.gkd.GKDConfig`]:
+To train a model with on-policy distillation using TRL, you can use the following configuration, with the [`experimental.distillation.DistillationTrainer`] and [`experimental.distillation.DistillationConfig`]:
+
+```python
+from trl.experimental.distillation import DistillationConfig
+
+training_args = DistillationConfig(
+    lmbda=1.0,  # student produces rollouts for all batches
+    beta=1.0,  # to ensure reverse-kl as the loss function
+    teacher_model_name_or_path="teacher-model",  # specify the teacher model
+)
+```
+
+Alternatively, you can use the [`experimental.gkd.GKDTrainer`] and [`experimental.gkd.GKDConfig`]:
 
 ```python
 from trl.experimental.gkd import GKDConfig
 
 training_args = GKDConfig(
-    lmbda=1.0, # student produces rollouts for all batches
-    beta=1.0, # to ensure reverse-kl as the loss function
-    teacher_model_name_or_path="teacher-model", # specify the teacher model
-
+    lmbda=1.0,  # student produces rollouts for all batches
+    beta=1.0,  # to ensure reverse-kl as the loss function
+    teacher_model_name_or_path="teacher-model",  # specify the teacher model
 )
 ```
 
-Alternatively, you can use the [`GOLDTrainer`] and [`GOLDConfig`] to perform on-policy distillation with a similar configuration:
+You can also use the [`GOLDTrainer`] and [`GOLDConfig`] to perform on-policy distillation with a similar configuration:
 
 ```python
 from trl.experimental import GOLDConfig

trl/experimental/distillation/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2020-2026 The HuggingFace 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.
+
+from .distillation_config import DistillationConfig
+from .distillation_trainer import DistillationTrainer
+
+
+__all__ = ["DistillationConfig", "DistillationTrainer"]

trl/experimental/distillation/distillation.py

@@ -0,0 +1,179 @@
+# Copyright 2020-2026 The HuggingFace 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.
+
+# /// script
+# dependencies = [
+#     "trl",
+#     "peft",
+#     "trackio",
+#     "kernels",
+# ]
+# ///
+
+# docstyle-ignore
+"""
+# Full training (off-policy only, lmbda=0):
+```
+python trl/experimental/distillation/distillation.py \
+    --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
+    --teacher_model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+    --dataset_name trl-lib/chatbot_arena_completions \
+    --learning_rate 2e-5 \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 8 \
+    --lmbda 0.0 \
+    --output_dir distilled-model \
+    --num_train_epochs 1
+```
+
+# Mixed on/off-policy (lmbda=0.5):
+```
+python trl/experimental/distillation/distillation.py \
+    --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
+    --teacher_model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+    --dataset_name trl-lib/chatbot_arena_completions \
+    --learning_rate 2e-5 \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 8 \
+    --lmbda 0.5 \
+    --beta 0.5 \
+    --output_dir distilled-model \
+    --num_train_epochs 1
+```
+
+# LoRA:
+```
+python trl/experimental/distillation/distillation.py \
+    --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
+    --teacher_model_name_or_path Qwen/Qwen2.5-1.5B-Instruct \
+    --dataset_name trl-lib/chatbot_arena_completions \
+    --learning_rate 2e-4 \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 8 \
+    --lmbda 0.0 \
+    --output_dir distilled-model \
+    --num_train_epochs 1 \
+    --use_peft \
+    --lora_r 64 \
+    --lora_alpha 16
+```
+"""
+
+import argparse
+import os
+
+
+# Enable logging in a Hugging Face Space
+os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
+
+
+def main(script_args, training_args, model_args):
+    from datasets import load_dataset
+    from transformers import GenerationConfig
+
+    from trl import (
+        LogCompletionsCallback,
+        get_kbit_device_map,
+        get_peft_config,
+        get_quantization_config,
+    )
+    from trl.experimental.distillation import DistillationTrainer
+
+    ################
+    # Model init kwargs
+    ################
+    quantization_config = get_quantization_config(model_args)
+    model_kwargs = dict(
+        revision=model_args.model_revision,
+        trust_remote_code=model_args.trust_remote_code,
+        attn_implementation=model_args.attn_implementation,
+        torch_dtype=model_args.dtype,
+        use_cache=False if training_args.gradient_checkpointing else True,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+    training_args.model_init_kwargs = model_kwargs
+
+    teacher_model_kwargs = dict(
+        revision=training_args.teacher_model_revision,
+        trust_remote_code=model_args.trust_remote_code,
+        attn_implementation=model_args.attn_implementation,
+        torch_dtype=model_args.dtype,
+        use_cache=True,
+        device_map=get_kbit_device_map() if quantization_config is not None else None,
+        quantization_config=quantization_config,
+    )
+    if training_args.teacher_model_init_kwargs is not None:
+        teacher_model_kwargs.update(training_args.teacher_model_init_kwargs)
+    training_args.teacher_model_init_kwargs = teacher_model_kwargs
+
+    ################
+    # Dataset
+    ################
+    dataset = load_dataset(script_args.dataset_name, name=script_args.dataset_config)
+
+    ################
+    # Training
+    ################
+    eval_dataset = None
+    if training_args.eval_strategy != "no":
+        if script_args.dataset_test_split in dataset:
+            eval_dataset = dataset[script_args.dataset_test_split]
+        elif "validation" in dataset:
+            eval_dataset = dataset["validation"]
+        elif "dev" in dataset:
+            eval_dataset = dataset["dev"]
+
+    trainer = DistillationTrainer(
+        model=model_args.model_name_or_path,
+        teacher_model=training_args.teacher_model_name_or_path,
+        args=training_args,
+        train_dataset=dataset[script_args.dataset_train_split],
+        eval_dataset=eval_dataset,
+        peft_config=get_peft_config(model_args),
+    )
+
+    if training_args.eval_strategy != "no":
+        generation_config = GenerationConfig(
+            max_new_tokens=training_args.max_completion_length, do_sample=True, temperature=training_args.temperature
+        )
+        completions_callback = LogCompletionsCallback(trainer, generation_config, num_prompts=8)
+        trainer.add_callback(completions_callback)
+
+    trainer.train()
+
+    # Save and push to Hub
+    trainer.save_model(training_args.output_dir)
+    if training_args.push_to_hub:
+        trainer.push_to_hub(dataset_name=script_args.dataset_name)
+
+
+def make_parser(subparsers: argparse._SubParsersAction | None = None, prog: str | None = None):
+    from trl import ModelConfig, ScriptArguments, TrlParser
+    from trl.experimental.distillation import DistillationConfig
+
+    dataclass_types = (ScriptArguments, DistillationConfig, ModelConfig)
+    if subparsers is not None:
+        parser = subparsers.add_parser(
+            "distillation", help="Run the distillation training script", dataclass_types=dataclass_types
+        )
+    else:
+        parser = TrlParser(dataclass_types, prog=prog)
+    return parser
+
+
+if __name__ == "__main__":
+    parser = make_parser()
+    script_args, training_args, model_args = parser.parse_args_and_config(fail_with_unknown_args=False)
+    main(script_args, training_args, model_args)