feat: adds REINFORCE algorithm (#357)

Signed-off-by: Terry Kong <terryk@nvidia.com> Signed-off-by: NeMo-Aligner CI <nemo-aligner-ci@nvidia.com> Signed-off-by: abukharin <abukharin@nvidia.com> Co-authored-by: abukharin <abukharin@nvidia.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Terry Kong <terryk@nvidia.com>
NVIDIA · Nov 22, 2024 · 716e503 · 716e503
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diff --git a/.github/workflows/cicd-main.yml b/.github/workflows/cicd-main.yml
@@ -90,6 +90,7 @@ jobs:
       matrix:
         test_case:
           - ppo-llama3-pp2-reshard
+          - reinforce-llama3-pp2-reshard
           - dpo-llama3
           - kd-llama3
           - sft-llama3

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -36,6 +36,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
     durations = timer.consume_durations()
     ```
 - Add code and instructions for replicating Reward Modeling training in HelpSteer2 and HelpSteer2-Preference
+- Implement REINFORCE algorithm.
 
 ### Breaking Changes
 - Upgrade TRTLLM dependency from v0.10.0 to v0.12.0 and migrate from `GPTSession` cpp runtime to `ModelRunner` python runtime. Please use the latest Dockerfile.

diff --git a/README.md b/README.md
@@ -23,6 +23,8 @@ The toolkit is currently in it's early stages. We are committed to improving the
 * **Reward Model Training**
 * **Reinforcement Learning from Human Feedback using the [PPO](https://arxiv.org/pdf/1707.06347.pdf) Algorithm**
     * [Llama3-70B-PPO-Chat](https://huggingface.co/nvidia/Llama3-70B-PPO-Chat) aligned with NeMo-Aligner using TRT-LLM.
+* **Reinforcement Learning from Human Feedback using the REINFORCE Algorithm**
+    * [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) aligned with NeMo-Aligner using TRT-LLM.
 * **Direct Preference Optimization** as described in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/pdf/2305.18290)
     * [Llama3-70B-DPO-Chat](https://huggingface.co/nvidia/Llama3-70B-DPO-Chat) aligned with NeMo Aligner.
 * **Self-Play Fine-Tuning (SPIN)** as described in [Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models](https://arxiv.org/pdf/2401.01335)

diff --git a/docs/user-guide/reinforce.rst b/docs/user-guide/reinforce.rst
@@ -0,0 +1,256 @@
+.. include:: /content/nemo.rsts
+
+.. _model-aligner-reinforce:
+
+Model Alignment by REINFORCE
+@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
+
+In this tutorial, we will guide you through the process of aligning a NeMo Framework model using REINFORCE. This method can be applied to various models, including LLaMa2 and Mistral, with our scripts functioning consistently across different models.
+
+REINFORCE is usually preceded by a Supervised Fine-Tuning (SFT). We should first follow the :ref:`Prerequisite guide <prerequisite>` and the :ref:`SFT guide <sft>`. After obtaining the SFT model, we will also need to train a reward model as in :ref:`PPO guide <ppo>`. We will use the REINFORCE algorithm on the `Anthropic-HH-RLHF <https://huggingface.co/datasets/Anthropic/hh-rlhf>`__ dataset.
+
+REINFORCE Training
+############
+
+After you have fine-tuned a GPT model using Supervised Fine-Tuning (SFT), and trained a reward model as explained in the preceding section, you can start aligning the policy using REINFORCE.
+
+During REINFORCE training, we have three models interacting with each other, which Aligner runs in two separate jobs:
+
+#. The Policy Network: This is the model we are training and it should start from an SFT model.
+#. The Reward Model (RM): This model accepts a prompt combined with a response as input and produces a single scalar value, known as the reward. The REINFORCE algorithm aims to maximize this reward.
+#. The Initial Policy Network (also known as the Reference Model): We use this model to compute a KL Divergence penalty term that ensures that the PPO Actor does not diverge too much from the Initial Policy. This way, we prevent the REINFORCE Actor from overfitting to the rewards given by the RM, and ensure it does not forget the knowledge it acquired during pretraining and SFT. This model should be the one used to initialize the REINFORCE Actor Network.
+
+The next section discusses how to launch each of these two jobs.
+
+Launching the Reward Model and Critic Server
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+To launch the server:
+
+.. code-block:: bash 
+
+   #!/bin/bash
+   RM_NEMO_FILE="/path/to/trained_rm.nemo"
+   GPFS="/path/to/nemo-aligner-repo"
+
+   RESULTS_DIR="critic_results_dir"
+
+   cd ${GPFS}
+   export PYTHONPATH="${GPFS}:${PYTHONPATH}" \
+   && export HYDRA_FULL_ERROR=1 \
+   && python -u examples/nlp/gpt/serve_reward_model.py \
+      trainer.num_nodes=1 \
+      trainer.devices=8 \
+      ++model.tensor_model_parallel_size=4 \
+      rm_model_file=${RM_NEMO_FILE}
+
+
+The above example launches the reward model server on eight GPUs and one node. Make sure to change trainer.devices, trainer.num_nodes depending on your model size and scale. Aligner will work on any scale. Also, make sure to tune the trainer.reinforce.inference_micro_batch_size argument. This argument sets the size of the batch the REINFORCE actor is allowed to send to the reward per DP rank.
+
+Launch the Initial Policy and REINFORCE Actor Training
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+The REINFORCE Actor training job contains the master controller that makes the HTTP calls to all servers when needed. To launch the REINFORCE Actor and Initial Policy server:
+
+.. code-block:: bash 
+
+   GPFS="/path/to/nemo-aligner-repo"
+   TRAIN_DATA_PATH="/path/to/train_prompts.jsonl"
+   VALID_DATA_PATH="/path/to/test_prompts.jsonl"
+
+   PRETRAINED_ACTOR_NEMO_FILE="/path/to/sft_checkpoint.nemo"
+   RESULTS_DIR="/path/to/actor_results_dir"
+
+   USE_FLASK=False
+   ACTOR_LR=1e-6
+   KL=0.01
+   NUM_ROLLOUTS=32
+   ACTOR_GBS=32
+   REWARD_PORT=5555
+   # Change this to the hostname of server hosting the reward model 
+   host_reward="localhost"
+
+   cd ${GPFS}
+   export PYTHONPATH="${GPFS}:${PYTHONPATH}" \
+   && export HYDRA_FULL_ERROR=1 \
+   && python -u examples/nlp/gpt/train_gpt_reinforce_actor.py \
+      "model.data.data_prefix={train: [${TRAIN_DATA_PATH}], validation: [${VALID_DATA_PATH}], test: [${VALID_DATA_PATH}]}" \
+      pretrained_checkpoint.restore_from_path=\"${ACTOR_NEMO_FILE}\" \
+      exp_manager.checkpoint_callback_params.save_top_k=1 \
+      exp_manager.explicit_log_dir=\"${RESULTS_DIR}\" \
+      trainer.reinforce.max_epochs=1 \
+      trainer.reinforce.max_steps=313 \
+      trainer.reinforce.val_check_interval=4 \
+      trainer.num_nodes=1 \
+      trainer.devices=8 \
+      trainer.reinforce.trt_llm.enable=True \
+      trainer.reinforce.trt_llm.reshard=True \
+      trainer.reinforce.trt_llm.unload_engine_train=False \
+      ++model.tensor_model_parallel_size=4 \
+      ++model.reinforce.num_rollout_samples=${NUM_ROLLOUTS} \
+      model.global_batch_size=${ACTOR_GBS} \
+      model.micro_batch_size=1 \
+      model.optim.lr=\"\\\$\{multiply:${ACTOR_LR},1.001\}\" \
+      model.optim.sched.warmup_steps=0 \
+      model.optim.sched.constant_steps=312 \
+      model.optim.sched.min_lr=${ACTOR_LR} \
+      model.optim.weight_decay=0.01 \
+      model.reinforce.rollout_micro_batch_size=16 \
+      model.reinforce.forward_micro_batch_size=16 \
+      model.reinforce.val_rollout_micro_batch_size=8 \
+      model.data.data_impl=jsonl \
+      remote_rm.reward_model.ip=${host_reward} \
+      remote_rm.reward_model.port=${REWARD_PORT} \
+      ++model.reinforce.length_params.max_length=2048 \
+      trainer.reinforce.initial_policy_kl_penalty="${KL}" \
+      ++model.optim.bucket_cap_mb=200 \
+      ++model.dist_ckpt_format=zarr \
+      ++model.optim.overlap_grad_sync=False \
+      ++model.optim.contiguous_grad_buffer=True \
+      ++model.enable_nge=True \
+      trainer.reinforce.batch_iterator.use_flask=${USE_FLASK} \
+      trainer.reinforce.rollout_batch_seq_length=4096
+
+The above command launches the initial and actor server on one node with eight GPUs.
+
+Launching Both Servers for REINFORCE training
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+You can use slurm to launch the two jobs and get them to coordinate together in a full REINFORCE job through the following:
+
+.. code-block:: bash 
+
+   #!/bin/bash
+   #SBATCH -N 1 --ntasks-per-node 8 -A <<ACCOUNT>> -p <<PARTITION>> --job-name <<JOBNAME>> -t 4:00:00 --exclusive
+   #SBATCH hetjob
+   #SBATCH -N 1 --ntasks-per-node 8 -A <<ACCOUNT>> -p <<PARTITION>> --job-name <<JOBNAME>> -t 4:00:00 --exclusive
+
+   NAME="reinforce"
+
+   # PARAMETERS
+   RM_NEMO_FILE="/path/to/trained_rm.nemo"
+
+   ACTOR_NEMO_FILE="/path/to/sft_model.nemo"
+
+   TRAIN_DATA_PATH="/path/to/train_prompts.jsonl"
+   VALID_DATA_PATH="/path/to/test_prompts.jsonl"
+
+   RESULTS_DIR="/path/to/results_dir"
+   mkdir -p $RESULTS_DIR
+
+   GPFS="/path/to/nemo-aligner-repo"
+   MOUNTS="--container-mounts=MOUNTS" # mounts
+
+   CONTAINER=<<<CONTAINER>>> # use the latest NeMo Training container, Aligner will work there
+
+   PROJECT=reinforce_run
+
+   CRITIC_LOG_DIR="${RESULTS_DIR}/critic_results"
+   CRITIC_OUTFILE="${CRITIC_LOG_DIR}/critic_output_%j_%t.log"
+   CRITIC_ERRFILE="${CRITIC_LOG_DIR}/critic_error_%j_%t.err"
+   REWARD_PORT=5567
+   CRITIC_CONFIG_PATH="${GPFS}/examples/nlp/gpt/conf"
+   CRITIC_CONFIG_NAME="inference_rm"
+
+   CONF_DIR="${GPFS}/examples/nlp/gpt/conf"
+   CONFIG_NAME="gpt_reinforce_actor"
+
+   mkdir -p $CRITIC_LOG_DIR
+
+   CRITIC_NAME="${NAME}_critic"
+
+   read -r -d '' cmd_critic_inference <<EOF
+   cd ${GPFS} \
+   && export PYTHONPATH="${GPFS}:${PYTHONPATH}" \
+   && export HYDRA_FULL_ERROR=1 \
+   && python -u examples/nlp/gpt/serve_reward_model.py \
+      --config-path=${CRITIC_CONFIG_PATH} \
+      --config-name=${CRITIC_CONFIG_NAME} \
+      trainer.num_nodes=1 \
+      trainer.devices=8 \
+      ++model.tensor_model_parallel_size=4 \
+      rm_model_file=${RM_NEMO_FILE} \
+      inference.port=${REWARD_PORT}
+   EOF
+
+   srun --het-group=0 -o $CRITIC_OUTFILE -e $CRITIC_ERRFILE --container-image=${CONTAINER} $MOUNTS bash -c "${cmd_critic_inference}" &
+
+   sleep 30
+
+   ACTOR_LOG_DIR="${RESULTS_DIR}/actor_results"
+   CHECKPOINT_DIR="${ACTOR_LOG_DIR}/checkpoints"
+   TENSOBOARD_DIR="${ACTOR_LOG_DIR}/tensorboard"
+
+   NUM_ROLLOUTS=32
+   NORMALIZE="True"
+   ACTOR_LR="1e-6"
+   ACTOR_GBS=32
+   KL=0.01
+   USE_FLASK=False
+
+   mkdir -p $ACTOR_LOG_DIR
+   mkdir -p $TENSOBOARD_DIR
+   mkdir -p $CHECKPOINT_DIR
+
+   ACTOR_NAME="${NAME}_actor"
+
+   host_reward="$(scontrol show hostnames=$SLURM_JOB_NODELIST_HET_GROUP_0 | head -n1)"
+
+   read -r -d '' cmd_reinforce <<EOF
+   cd ${GPFS}
+   export PYTHONPATH="${GPFS}:${PYTHONPATH}" \
+   && export HYDRA_FULL_ERROR=1 \
+   && python -u examples/nlp/gpt/train_gpt_reinforce_actor.py \
+      "model.data.data_prefix={train: [${TRAIN_DATA_PATH}], validation: [${VALID_DATA_PATH}], test: [${VALID_DATA_PATH}]}" \
+      pretrained_checkpoint.restore_from_path=\"${ACTOR_NEMO_FILE}\" \
+      exp_manager.checkpoint_callback_params.save_top_k=1 \
+      exp_manager.explicit_log_dir=\"${RESULTS_DIR}\" \
+      trainer.reinforce.max_epochs=1 \
+      trainer.reinforce.max_steps=313 \
+      trainer.reinforce.val_check_interval=4 \
+      trainer.num_nodes=1 \
+      trainer.devices=8 \
+      trainer.reinforce.trt_llm.enable=True \
+      trainer.reinforce.trt_llm.reshard=True \
+      trainer.reinforce.trt_llm.unload_engine_train=False \
+      ++model.tensor_model_parallel_size=4 \
+      ++model.reinforce.num_rollout_samples=${NUM_ROLLOUTS} \
+      model.global_batch_size=${ACTOR_GBS} \
+      model.micro_batch_size=1 \
+      model.optim.lr=\"\\\$\{multiply:${ACTOR_LR},1.001\}\" \
+      model.optim.sched.warmup_steps=0 \
+      model.optim.sched.constant_steps=312 \
+      model.optim.sched.min_lr=${ACTOR_LR} \
+      model.optim.weight_decay=0.01 \
+      model.reinforce.rollout_micro_batch_size=16 \
+      model.reinforce.forward_micro_batch_size=16 \
+      model.reinforce.val_rollout_micro_batch_size=8 \
+      model.data.data_impl=jsonl \
+      remote_rm.reward_model.ip=${host_reward} \
+      remote_rm.reward_model.port=${REWARD_PORT} \
+      ++model.reinforce.length_params.max_length=2048 \
+      trainer.reinforce.initial_policy_kl_penalty="${KL}" \
+      ++model.optim.bucket_cap_mb=200 \
+      ++model.dist_ckpt_format=zarr \
+      ++model.optim.overlap_grad_sync=False \
+      ++model.optim.contiguous_grad_buffer=True \
+      ++model.enable_nge=True \
+      trainer.reinforce.batch_iterator.use_flask=${USE_FLASK} \
+      trainer.reinforce.rollout_batch_seq_length=4096
+   EOF
+
+   srun --het-group=1 -o $PPO_OUTFILE -e $PPO_ERRFILE --container-image=${CONTAINER} $MOUNTS bash -c "${cmd_reinforce}" &
+
+   wait
+
+The above script runs the reward model server on one node and the actor on one node.
+
+It is important to launch all jobs with ``&`` after the srun command to ensure they do not block each other. 
+
+.. Note::
+   Make sure to change the reward model arg ``trainer.reinforce.inference_micro_batch_size`` such that ``trainer.reinforce.inference_micro_batch_size * DP size <= model.reinforce.rollout_micro_batch_size``.
+
+REINFORCE Results
+%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+After you've completed reinforce training, you can serve your model using the `megatron_gpt_eval.py <https://github.com/NVIDIA/NeMo/blob/8cd5f1c8e7d4fed9f4f946028cd02047c5d2296f/examples/nlp/language_modeling/megatron_gpt_eval.py#L4>`__ script from the NeMo codebase to run more rigorous evaluation of your trained model.