trainer.py

# coding:utf-8
# Copyright (c) 2020  PaddlePaddle Authors. 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.

import os
import pickle
import time
from collections import defaultdict
from typing import Any, Callable, Generic, List

import paddle
from visualdl import LogWriter

from paddlehub.utils.log import logger
from paddlehub.utils.utils import Timer


class Trainer(object):
    '''
    Model trainer

    Args:
        model(paddle.nn.Layer) : Model to train or evaluate.
        optimizer(paddle.optimizer.Optimizer) : Optimizer for loss.
        use_vdl(bool) : Whether to use visualdl to record training data.
        checkpoint_dir(str) : Directory where the checkpoint is saved, and the trainer will restore the
            state and model parameters from the checkpoint.
        compare_metrics(callable) : The method of comparing the model metrics. If not specified, the main
            metric return by `validation_step` will be used for comparison by default, the larger the
            value, the better the effect. This method will affect the saving of the best model. If the
            default behavior does not meet your requirements, please pass in a custom method.

            Example:
                .. code-block:: python

                    def compare_metrics(old_metric: dict, new_metric: dict):
                        mainkey = list(new_metric.keys())[0]
                        return old_metric[mainkey] < new_metric[mainkey]
    '''

    def __init__(self,
                 model: paddle.nn.Layer,
                 optimizer: paddle.optimizer.Optimizer,
                 use_vdl: bool = True,
                 checkpoint_dir: str = None,
                 compare_metrics: Callable = None):
        self.nranks = paddle.distributed.get_world_size()
        self.local_rank = paddle.distributed.get_rank()
        self.model = model
        self.optimizer = optimizer
        self.checkpoint_dir = checkpoint_dir if checkpoint_dir else 'ckpt_{}'.format(time.time())

        if not isinstance(self.model, paddle.nn.Layer):
            raise TypeError('The model {} is not a `paddle.nn.Layer` object.'.format(self.model.__name__))

        if self.local_rank == 0 and not os.path.exists(self.checkpoint_dir):
            os.makedirs(self.checkpoint_dir)

        self.use_vdl = use_vdl
        if self.local_rank == 0 and self.use_vdl:
            vdl_dir = os.path.join(self.checkpoint_dir, 'visualization')
            self.log_writer = LogWriter(vdl_dir)

        self.current_epoch = 0
        self.best_metrics = defaultdict(int)

        if self.nranks > 1:
            paddle.distributed.init_parallel_env()
            strategy = paddle.distributed.prepare_context()
            self.model = paddle.DataParallel(self.model, strategy)
        self.compare_metrics = self._compare_metrics if not compare_metrics else compare_metrics
        self._load_checkpoint()

    def _load_checkpoint(self):
        '''Load checkpoint and state dict'''
        max_epoch = -1

        for file in os.listdir(self.checkpoint_dir):
            if not file.startswith('epoch_'):
                continue

            _epoch = file.split('_')[-1]
            if not _epoch.isdigit():
                continue

            max_epoch = max(max_epoch, int(_epoch))

        if max_epoch == -1:
            if self.local_rank == 0:
                logger.warning('PaddleHub model checkpoint not found, start from scratch...')
            return

        # load best metrics
        self._load_metrics()

        self.current_epoch = max_epoch
        metric_msg = ['{}={:.4f}'.format(metric, value) for metric, value in self.best_metrics.items()]
        metric_msg = ' '.join(metric_msg)
        if self.local_rank == 0:
            logger.info('PaddleHub model checkpoint loaded. current_epoch={} [{}]'.format(
                self.current_epoch, metric_msg))

        # load model checkpoint
        model_params_path = os.path.join(self.checkpoint_dir, 'epoch_{}'.format(self.current_epoch), 'model.pdparams')
        state_dict = paddle.load(model_params_path)
        self.model.set_state_dict(state_dict)

        # load optimizer checkpoint
        optim_params_path = os.path.join(self.checkpoint_dir, 'epoch_{}'.format(self.current_epoch), 'model.pdopt')
        state_dict = paddle.load(optim_params_path)
        self.optimizer.set_state_dict(state_dict)

    def _save_checkpoint(self):
        '''Save model checkpoint and state dict'''
        model_path = os.path.join(self.checkpoint_dir, 'epoch_{}'.format(self.current_epoch))
        logger.info('Saving model checkpoint to {}'.format(model_path))
        self.save_model(model_path)

    def save_model(self, save_dir: str):
        '''Save model'''
        model_params_path = os.path.join(save_dir, 'model.pdparams')
        optim_params_path = os.path.join(save_dir, 'model.pdopt')
        paddle.save(self.model.state_dict(), model_params_path)
        paddle.save(self.optimizer.state_dict(), optim_params_path)

    def _save_metrics(self):
        with open(os.path.join(self.checkpoint_dir, 'metrics.pkl'), 'wb') as file:
            pickle.dump(self.best_metrics, file)

    def _load_metrics(self):
        with open(os.path.join(self.checkpoint_dir, 'metrics.pkl'), 'rb') as file:
            self.best_metrics = pickle.load(file)

    def train(self,
              train_dataset: paddle.io.Dataset,
              epochs: int = 1,
              batch_size: int = 1,
              num_workers: int = 0,
              eval_dataset: paddle.io.Dataset = None,
              log_interval: int = 10,
              save_interval: int = 10):
        '''
        Train a model with specific config.

        Args:
            train_dataset(paddle.io.Dataset) : Dataset to train the model
            epochs(int) : Number of training loops, default is 1.
            batch_size(int) : Batch size of per step, default is 1.
            num_workers(int) : Number of subprocess to load data, default is 0.
            eval_dataset(paddle.io.Dataset) : The validation dataset, deafult is None. If set, the Trainer will
                execute evaluate function every `save_interval` epochs.
            log_interval(int) : Log the train infomation every `log_interval` steps.
            save_interval(int) : Save the checkpoint every `save_interval` epochs.
        '''
        batch_sampler = paddle.io.DistributedBatchSampler(
            train_dataset, batch_size=batch_size, shuffle=True, drop_last=False)
        loader = paddle.io.DataLoader(
            train_dataset,
            batch_sampler=batch_sampler,
            num_workers=num_workers,
            return_list=True,
            use_buffer_reader=True)

        steps_per_epoch = len(batch_sampler)
        timer = Timer(steps_per_epoch * epochs)
        timer.start()

        for i in range(epochs):
            self.current_epoch += 1
            avg_loss = 0
            avg_metrics = defaultdict(int)
            self.model.train()

            for batch_idx, batch in enumerate(loader):
                loss, metrics = self.training_step(batch, batch_idx)
                self.optimizer_step(self.current_epoch, batch_idx, self.optimizer, loss)
                self.optimizer_zero_grad(self.current_epoch, batch_idx, self.optimizer)

                # calculate metrics and loss
                avg_loss += loss.numpy()[0]
                for metric, value in metrics.items():
                    avg_metrics[metric] += value.numpy()[0]

                timer.count()

                if (batch_idx + 1) % log_interval == 0 and self.local_rank == 0:
                    lr = self.optimizer.get_lr()
                    avg_loss /= log_interval
                    if self.use_vdl:
                        self.log_writer.add_scalar(tag='TRAIN/loss', step=timer.current_step, value=avg_loss)

                    print_msg = 'Epoch={}/{}, Step={}/{}'.format(self.current_epoch, epochs, batch_idx + 1,
                                                                 steps_per_epoch)
                    print_msg += ' loss={:.4f}'.format(avg_loss)

                    for metric, value in avg_metrics.items():
                        value /= log_interval
                        if self.use_vdl:
                            self.log_writer.add_scalar(
                                tag='TRAIN/{}'.format(metric), step=timer.current_step, value=value)
                        print_msg += ' {}={:.4f}'.format(metric, value)

                    print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(lr, timer.timing, timer.eta)

                    logger.train(print_msg)

                    avg_loss = 0
                    avg_metrics = defaultdict(int)

                if self.current_epoch % save_interval == 0 and batch_idx + 1 == steps_per_epoch and self.local_rank == 0:
                    if eval_dataset:
                        result = self.evaluate(eval_dataset, batch_size, num_workers)
                        eval_loss = result.get('loss', None)
                        eval_metrics = result.get('metrics', {})
                        if self.use_vdl:
                            if eval_loss:
                                self.log_writer.add_scalar(tag='EVAL/loss', step=timer.current_step, value=eval_loss)

                            for metric, value in eval_metrics.items():
                                self.log_writer.add_scalar(
                                    tag='EVAL/{}'.format(metric), step=timer.current_step, value=value)

                        if not self.best_metrics or self.compare_metrics(self.best_metrics, eval_metrics):
                            self.best_metrics = eval_metrics
                            best_model_path = os.path.join(self.checkpoint_dir, 'best_model')
                            self.save_model(best_model_path)
                            self._save_metrics()

                            metric_msg = [
                                '{}={:.4f}'.format(metric, value) for metric, value in self.best_metrics.items()
                            ]
                            metric_msg = ' '.join(metric_msg)
                            logger.eval('Saving best model to {} [best {}]'.format(best_model_path, metric_msg))

                    self._save_checkpoint()

    def evaluate(self, eval_dataset: paddle.io.Dataset, batch_size: int = 1, num_workers: int = 0):
        '''
        Run evaluation and returns metrics.

        Args:
            eval_dataset(paddle.io.Dataset) : The validation dataset
            batch_size(int) : Batch size of per step, default is 1.
            num_workers(int) : Number of subprocess to load data, default is 0.
        '''
        batch_sampler = paddle.io.DistributedBatchSampler(
            eval_dataset, batch_size=batch_size, shuffle=False, drop_last=False)

        loader = paddle.io.DataLoader(
            eval_dataset, batch_sampler=batch_sampler, num_workers=num_workers, return_list=True)

        self.model.eval()
        avg_loss = num_samples = 0
        sum_metrics = defaultdict(int)
        avg_metrics = defaultdict(int)

        with logger.processing('Evaluation on validation dataset'):
            for batch_idx, batch in enumerate(loader):
                result = self.validation_step(batch, batch_idx)
                loss = result.get('loss', None)
                metrics = result.get('metrics', {})
                bs = batch[0].shape[0]
                num_samples += bs

                if loss:
                    avg_loss += loss.numpy()[0] * bs

                for metric, value in metrics.items():
                    sum_metrics[metric] += value.numpy()[0] * bs

        # print avg metrics and loss
        print_msg = '[Evaluation result]'
        if loss:
            avg_loss /= num_samples
            print_msg += ' avg_loss={:.4f}'.format(avg_loss)

        for metric, value in sum_metrics.items():
            avg_metrics[metric] = value / num_samples
            print_msg += ' avg_{}={:.4f}'.format(metric, avg_metrics[metric])

        logger.eval(print_msg)

        if loss:
            return {'loss': avg_loss, 'metrics': avg_metrics}
        return {'metrics': avg_metrics}

    def training_step(self, batch: List[paddle.Tensor], batch_idx: int):
        '''
        One step for training, which should be called as forward computation.

        Args:
            batch(list[paddle.Tensor]) : The one batch data
            batch_idx(int) : The index of batch.
        '''
        if self.nranks > 1:
            result = self.model._layers.training_step(batch, batch_idx)
        else:
            result = self.model.training_step(batch, batch_idx)

        # process result
        if not isinstance(result, dict):
            raise RuntimeError('The return value of `trainning_step` in {} is not a dict'.format(self.model.__class__))

        loss = result.get('loss', None)
        if not loss:
            raise RuntimeError('Cannot find loss attribute in the return value of `trainning_step` of {}'.format(
                self.model.__class__))

        metrics = result.get('metrics', {})

        # back prop
        loss.backward()

        return loss, metrics

    def validation_step(self, batch: Any, batch_idx: int):
        '''
        One step for validation, which should be called as forward computation.

        Args:
            batch(list[paddle.Tensor]) : The one batch data
            batch_idx(int) : The index of batch.
        '''
        if self.nranks > 1:
            result = self.model._layers.validation_step(batch, batch_idx)
        else:
            result = self.model.validation_step(batch, batch_idx)
        return result

    def optimizer_step(self, epoch_idx: int, batch_idx: int, optimizer: paddle.optimizer.Optimizer,
                       loss: paddle.Tensor):
        '''
        One step for optimize.

        Args:
            epoch_idx(int) : The index of epoch.
            batch_idx(int) : The index of batch.
            optimizer(paddle.optimizer.Optimizer) : Optimizer used.
            loss(paddle.Tensor) : Loss tensor.
        '''
        self.optimizer.step()
        self.learning_rate_step(epoch_idx, batch_idx, self.optimizer._learning_rate, loss)

    def learning_rate_step(self, epoch_idx: int, batch_idx: int, learning_rate: Generic, loss: paddle.Tensor):
        if isinstance(learning_rate, paddle.optimizer.lr.LRScheduler):
            learning_rate.step()

    def optimizer_zero_grad(self, epoch_idx: int, batch_idx: int, optimizer: paddle.optimizer.Optimizer):
        '''
        One step for clear gradients.

        Args:
            epoch_idx(int) : The index of epoch.
            batch_idx(int) : The index of batch.
            optimizer(paddle.optimizer.Optimizer) : Optimizer used.
            loss(paddle.Tensor) : Loss tensor.
        '''
        self.model.clear_gradients()

    def _compare_metrics(self, old_metric: dict, new_metric: dict):
        '''Compare the whether the new metric value is better than the old one'''
        mainkey = list(new_metric.keys())[0]
        return old_metric[mainkey] < new_metric[mainkey]