# Copyright 2022 The Plenoptix 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. """ Code to train model. """ from __future__ import annotations import functools import logging import os import typing from pathlib import Path from typing import Any, Dict, List import torch import torch.distributed as dist from torch.cuda.amp.grad_scaler import GradScaler from torch.nn.parallel import DistributedDataParallel as DDP from nerfactory.configs import base as cfg from nerfactory.dataloaders.structs import DatasetInputs from nerfactory.optimizers.optimizers import Optimizers, setup_optimizers from nerfactory.pipelines.base import Pipeline from nerfactory.utils import profiler, writer from nerfactory.utils.callbacks import Callback from nerfactory.utils.decorators import check_main_thread from nerfactory.utils.writer import EventName, TimeWriter from nerfactory.viewer.server import viewer_utils logging.getLogger("PIL").setLevel(logging.WARNING) def train_loop(local_rank: int, world_size: int, config: cfg.Config) -> Any: """Main training function that sets up and runs the trainer per process Args: local_rank (int): current rank of process world_size (int): total number of gpus available config (Config): config file specifying training regimen Returns: Any: TODO(): determine the return type """ trainer = Trainer(config, local_rank, world_size) trainer.setup() trainer.train() return 0 class Trainer: """Trainer class Args: config (Config): The configuration object. local_rank (int, optional): Local rank of the process. Defaults to 0. world_size (int, optional): World size of the process. Defaults to 1. """ def __init__(self, config: cfg.Config, local_rank: int = 0, world_size: int = 1): self.config = config self.local_rank = local_rank self.world_size = world_size self.device = "cpu" if world_size == 0 else f"cuda:{local_rank}" self.mixed_precision = self.config.trainer.mixed_precision if self.device == "cpu": self.mixed_precision = False logging.warning("Mixed precision is disabled for CPU training.") # dataset variables self.dataset_inputs_train: DatasetInputs # model variables self.pipeline: Pipeline self.optimizers: Optimizers self.start_step = 0 # visualizer variable banner_messages = None self.visualizer_state = viewer_utils.VisualizerState(config.viewer) if config.viewer.enable: banner_messages = [f"Viewer at: {self.visualizer_state.viewer_url}"] self.grad_scaler = GradScaler(enabled=self.mixed_precision) # training callbacks self.callbacks: List[Callback] # logging variables writer.setup_event_writers( config.logging, max_iter=config.trainer.max_num_iterations, banner_messages=banner_messages ) profiler.setup_profiler(config.logging) def setup(self, test_mode=False): """Setup the Trainer by calling other setup functions. Args: test_mode (bool, optional): Whether to setup for testing. Defaults to False. """ self.pipeline: Pipeline = self.config.pipeline.setup(device=self.device, test_mode=test_mode) self.optimizers = setup_optimizers(self.config.optimizers, self.pipeline.get_param_groups()) self._load_checkpoint() if self.world_size > 1: self.pipeline = typing.cast( Pipeline, typing.cast(Pipeline, DDP(self.pipeline, device_ids=[self.local_rank])) ) dist.barrier(device_ids=[self.local_rank]) # TODO(ethan): do this for pipeline, not pipeline.model self.callbacks = self.pipeline.model.get_training_callbacks() def train(self) -> None: """Train the model.""" self.visualizer_state.init_scene( image_dataset=self.pipeline.dataloader.train_image_dataset, dataset_inputs=self.pipeline.dataloader.train_datasetinputs, ) with TimeWriter(writer, EventName.TOTAL_TRAIN_TIME): num_iterations = self.config.trainer.max_num_iterations for step in range(self.start_step, self.start_step + num_iterations): # Note: if visualizer used, the rendering of the visualizer will be included in the iteration train time with TimeWriter(writer, EventName.ITER_TRAIN_TIME, step=step) as t: loss_metric_dict = self.train_iteration(step) with TimeWriter(writer, EventName.ITER_VIS_TIME, step=step) as t: self.visualizer_state.update_scene(step, self.pipeline.model) train_num_rays_per_batch = self.config.pipeline.dataloader.train_num_rays_per_batch writer.put_scalar(name=EventName.RAYS_PER_SEC, scalar=train_num_rays_per_batch / t.duration, step=step) if step != 0 and step % self.config.logging.steps_per_log == 0: writer.put_dict(name="Loss/train-loss_metrics_dict", scalar_dict=loss_metric_dict, step=step) if step != 0 and self.config.trainer.steps_per_save and step % self.config.trainer.steps_per_save == 0: self._save_checkpoint(self.config.trainer.model_dir, step) if step % self.config.trainer.steps_per_test == 0: self.pipeline.get_eval_loss_dict(step=step) self._write_out_storage(step) self._write_out_storage(num_iterations) def _write_out_storage(self, step: int) -> None: """Perform writes only during appropriate time steps Args: step (int): Current training step. """ if ( step % self.config.logging.steps_per_log == 0 or (self.config.trainer.steps_per_save and step % self.config.trainer.steps_per_save == 0) or step % self.config.trainer.steps_per_test == 0 or step == self.config.trainer.max_num_iterations ): writer.write_out_storage() def _load_checkpoint(self) -> None: """Helper function to load pipeline and optimizer from prespecified checkpoint""" load_dir = self.config.trainer.load_dir load_step = self.config.trainer.load_step if load_dir is not None and load_step is not None: load_path = os.path.join(load_dir, f"step-{load_step:09d}.ckpt") assert os.path.exists(load_path), f"Checkpoint {load_path} does not exist" loaded_state = torch.load(load_path, map_location="cpu") self.start_step = loaded_state["step"] + 1 # load the checkpoints for pipeline, optimizers, and gradient scalar self.pipeline.load_pipeline(loaded_state["pipeline"]) self.optimizers.load_optimizers(loaded_state["optimizers"]) self.grad_scaler.load_state_dict(loaded_state["scalers"]) logging.info("done loading checkpoint from %s", load_path) else: logging.info("No checkpoints to load, training from scratch") @check_main_thread def _save_checkpoint(self, output_dir: Path, step: int) -> None: """Save the model and optimizers Args: output_dir: directory to save the checkpoint step: number of steps in training for given checkpoint """ if not output_dir.exists(): output_dir.mkdir(parents=True, exist_ok=True) ckpt_path = output_dir / f"step-{step:09d}.ckpt" if hasattr(self.pipeline, "module"): pipeline = self.pipeline.module.state_dict() # type: ignore else: pipeline = self.pipeline.state_dict() torch.save( { "step": step, "pipeline": pipeline, "optimizers": {k: v.state_dict() for (k, v) in self.optimizers.optimizers.items()}, "scalers": self.grad_scaler.state_dict(), }, ckpt_path, ) @profiler.time_function def train_iteration(self, step: int) -> Dict[str, torch.Tensor]: """Run one iteration with a batch of inputs. Args: step: Current training step. Returns: Dictionary of model losses. """ self.optimizers.zero_grad_all() cpu_or_cuda_str = self.device.split(":")[0] with torch.autocast(device_type=cpu_or_cuda_str, enabled=self.mixed_precision): _, loss_dict, metrics_dict = self.pipeline.get_train_loss_dict(step=step) loss = functools.reduce(torch.add, loss_dict.values()) self.grad_scaler.scale(loss).backward() # type: ignore self.optimizers.optimizer_scaler_step_all(self.grad_scaler) self.grad_scaler.update() self.optimizers.scheduler_step_all(step) for callback in self.callbacks: callback.after_step(step) # Merging loss and metrics dict into a single output. loss_dict["loss"] = loss loss_dict.update(metrics_dict) return loss_dict