examples/split_placement/split_monkey_patch.py

# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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.
"""
An naive implementation of split placment example
"""
from pprint import pprint
from verl import DataProto
from verl.trainer.ppo.ray_trainer_new import compute_advantage, apply_kl_penalty, reduce_metrics, compute_data_metrics, _timer, compute_timing_metrics, AdvantageEstimator
from copy import deepcopy
import numpy as np
import torch
import uuid


def fit(self):
    """
    The training loop of PPO.
    The driver process only need to call the compute functions of the worker group through RPC to construct the PPO dataflow.
    The light-weight advantage computation is done on the driver process.
    """
    from verl.utils.tracking import Tracking
    from omegaconf import OmegaConf

    logger = Tracking(project_name=self.config.trainer.project_name,
                      experiment_name=self.config.trainer.experiment_name,
                      default_backend=self.config.trainer.logger,
                      config=OmegaConf.to_container(self.config, resolve=True))

    self.global_steps = 0

    # load checkpoint before doing anything
    self._load_checkpoint()

    # perform validation before training
    # currently, we only support validation using the reward_function.
    if self.val_reward_fn is not None and self.config.trainer.get('val_before_train', True):
        val_metrics = self._validate()
        pprint(f'Initial validation metrics: {val_metrics}')
        logger.log(data=val_metrics, step=self.global_steps)
        if self.config.trainer.get('val_only', False):
            return

    # we start from step 1
    self.global_steps += 1
    last_val_metrics = None

    for epoch in range(self.config.trainer.total_epochs):
        for batch_dict in self.train_dataloader:
            metrics = {}
            timing_raw = {}

            batch: DataProto = DataProto.from_single_dict(batch_dict)

            # pop those keys for generation
            gen_batch = batch.pop(batch_keys=['input_ids', 'attention_mask', 'position_ids'])
            is_last_step = self.global_steps >= self.total_training_steps

            with _timer('step', timing_raw):
                # generate a batch
                with _timer('gen', timing_raw):
                    gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch)

                if self.config.algorithm.adv_estimator == AdvantageEstimator.REMAX:
                    with _timer('gen_max', timing_raw):
                        gen_baseline_batch = deepcopy(gen_batch)
                        gen_baseline_batch.meta_info['do_sample'] = False
                        gen_baseline_output = self.actor_rollout_wg.generate_sequences(gen_baseline_batch)

                        batch = batch.union(gen_baseline_output)
                        reward_baseline_tensor = self.reward_fn(batch)
                        reward_baseline_tensor = reward_baseline_tensor.sum(dim=-1)

                        batch.pop(batch_keys=list(gen_baseline_output.batch.keys()))

                        batch.batch['reward_baselines'] = reward_baseline_tensor

                        del gen_baseline_batch, gen_baseline_output

                batch.non_tensor_batch['uid'] = np.array([str(uuid.uuid4()) for _ in range(len(batch.batch))],
                                                         dtype=object)
                # repeat to align with repeated responses in rollout
                batch = batch.repeat(repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True)
                batch = batch.union(gen_batch_output)

                # balance the number of valid tokens on each dp rank.
                # Note that this breaks the order of data inside the batch.
                # Please take care when you implement group based adv computation such as GRPO and rloo
                self._balance_batch(batch, metrics=metrics)

                # compute global_valid tokens
                batch.meta_info['global_token_num'] = torch.sum(batch.batch['attention_mask'], dim=-1).tolist()

                # recompute old_log_probs
                with _timer('old_log_prob', timing_raw):
                    old_log_prob = self.actor_rollout_wg.compute_log_prob(batch)
                    batch = batch.union(old_log_prob)

                if self.use_reference_policy:
                    # compute reference log_prob
                    with _timer('ref', timing_raw):
                        ref_log_prob = self.ref_policy_wg.compute_ref_log_prob(batch)
                        batch = batch.union(ref_log_prob)

                # compute values
                if self.use_critic:
                    with _timer('values', timing_raw):
                        values = self.critic_wg.compute_values(batch)
                        batch = batch.union(values)

                with _timer('adv', timing_raw):
                    # compute scores. Support both model and function-based.
                    # We first compute the scores using reward model. Then, we call reward_fn to combine
                    # the results from reward model and rule-based results.
                    if self.use_rm:
                        # we first compute reward model score
                        reward_tensor = self.rm_wg.compute_rm_score(batch)
                        batch = batch.union(reward_tensor)

                    # we combine with rule-based rm
                    reward_tensor = self.reward_fn(batch)
                    batch.batch['token_level_scores'] = reward_tensor

                    # compute rewards. apply_kl_penalty if available
                    if self.config.algorithm.use_kl_in_reward:
                        batch, kl_metrics = apply_kl_penalty(batch,
                                                             kl_ctrl=self.kl_ctrl_in_reward,
                                                             kl_penalty=self.config.algorithm.kl_penalty)
                        metrics.update(kl_metrics)
                    else:
                        batch.batch['token_level_rewards'] = batch.batch['token_level_scores']

                    # compute advantages, executed on the driver process
                    batch = compute_advantage(batch,
                                              adv_estimator=self.config.algorithm.adv_estimator,
                                              gamma=self.config.algorithm.gamma,
                                              lam=self.config.algorithm.lam,
                                              num_repeat=self.config.actor_rollout_ref.rollout.n)

                # update critic
                if self.use_critic:
                    with _timer('update_critic_call', timing_raw):
                        critic_output = self.critic_wg.update_critic(batch)

                # implement critic warmup
                if self.config.trainer.critic_warmup <= self.global_steps:
                    # update actor
                    with _timer('update_actor_call', timing_raw):
                        actor_output = self.actor_rollout_wg.update_actor(batch)

                # NOTE: make sure you set blocking=False in update_actor and update_crtic in the worker class
                with _timer('update_actor_critic', timing_raw):
                    critic_output = critic_output.get()
                    critic_output_metrics = reduce_metrics(critic_output.meta_info['metrics'])
                    metrics.update(critic_output_metrics)

                    actor_output = actor_output.get()
                    actor_output_metrics = reduce_metrics(actor_output.meta_info['metrics'])
                    metrics.update(actor_output_metrics)

                # validate
                if self.val_reward_fn is not None and self.config.trainer.test_freq > 0 and \
                        (is_last_step or  self.global_steps % self.config.trainer.test_freq == 0):
                    with _timer('testing', timing_raw):
                        val_metrics: dict = self._validate()
                        if is_last_step:
                            last_val_metrics = val_metrics
                    metrics.update(val_metrics)

                if self.config.trainer.save_freq > 0 and (is_last_step or \
                        self.global_steps % self.config.trainer.save_freq == 0):
                    with _timer('save_checkpoint', timing_raw):
                        self._save_checkpoint()

            # collect metrics
            metrics.update(compute_data_metrics(batch=batch, use_critic=self.use_critic))
            metrics.update(compute_timing_metrics(batch=batch, timing_raw=timing_raw))

            # TODO: make a canonical logger that supports various backend
            logger.log(data=metrics, step=self.global_steps)

            if self.global_steps >= self.total_training_steps:
                pprint(f'Final validation metrics: {last_val_metrics}')
                return

            self.global_steps += 1