trainer_base_mul_vanilla.py

# coding=utf-8
#
# Copyright 2020 Heinrich Heine University Duesseldorf
#
# Part of this code is based on the source code of BERT-DST
# (arXiv:1907.03040)
# Part of this code is based on the source code of Transformers
# (arXiv:1910.03771)
#
# 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 glob
import json
import logging
import os
import random
import sys
from typing import Dict, Union

import hydra
import numpy as np
import torch
from omegaconf import DictConfig, OmegaConf
from torch import distributed as dist
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset, Dataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from transformers import (AdamW, get_linear_schedule_with_warmup, AutoTokenizer, PreTrainedTokenizer)

from general_util.logger import setting_logger
from general_util.training_utils import set_seed, batch_to_device, unwrap_model

try:
    from tensorboardX import SummaryWriter
except ImportError:
    from torch.utils.tensorboard import SummaryWriter

logger: logging.Logger


def save_model(model: torch.nn.Module, cfg: DictConfig, output_dir: str, tokenizer: PreTrainedTokenizer = None):
    # Save model checkpoint.
    if cfg.local_rank != -1:
        state_dict = model.state_dict()
        if cfg.local_rank == 0:
            unwrap_model(model).save_pretrained(output_dir, state_dict=state_dict)
    else:
        model.save_pretrained(output_dir)

    # Save tokenizer and training args.
    if cfg.local_rank in [-1, 0]:
        if tokenizer is not None:
            tokenizer.save_pretrained(output_dir)
        OmegaConf.save(cfg, os.path.join(output_dir, "training_config.yaml"))
        logger.info("Saving model checkpoint to %s", output_dir)


def forward_step(model, inputs: Dict[str, torch.Tensor], cfg, scaler):
    if cfg.fp16:
        with torch.cuda.amp.autocast():
            outputs = model(**inputs)
            loss = outputs["loss"]  # model outputs are always tuple in transformers (see doc)
    else:
        outputs = model(**inputs)
        loss = outputs["loss"]  # model outputs are always tuple in pytorch-transformers (see doc)

    if cfg.n_gpu > 1:
        loss = loss.mean()  # mean() to average on multi-gpu parallel (not distributed) training
    if cfg.gradient_accumulation_steps > 1:
        loss = loss / cfg.gradient_accumulation_steps

    if cfg.fp16:
        scaler.scale(loss).backward()
    else:
        loss.backward()

    return loss.item()


def train(cfg, model, tokenizer, continue_from_global_step=0):
    """ Train the model """
    if cfg.local_rank in [-1, 0]:
        _dir_splits = cfg.output_dir.split('/')
        _log_dir = '/'.join([_dir_splits[0], 'runs'] + _dir_splits[1:])
        tb_writer = SummaryWriter(log_dir=_log_dir)
    else:
        tb_writer = None

    cfg.train_batch_size = cfg.per_gpu_train_batch_size * max(1, cfg.n_gpu)

    num_examples = 0
    if os.path.exists(cfg.train_file):
        train_files = [cfg.train_file]
    else:
        train_files = list(glob.glob(cfg.train_file))

    logger.info("Pre-loading dataset(s) to count the total steps.")
    for _train_file in train_files:
        _sub_train_dataset, _ = load_and_cache_examples(cfg, tokenizer, _split="train", _file=_train_file)
        num_examples += len(_sub_train_dataset)
        del _sub_train_dataset

    if "do_preprocess" in cfg and cfg.do_preprocess:
        exit(0)

    if cfg.local_rank != -1:
        cum_steps = int(num_examples * 1.0 / cfg.train_batch_size / dist.get_world_size())
    else:
        cum_steps = int(num_examples * 1.0 / cfg.train_batch_size)

    if "extended_vocab" in cfg and cfg.extended_vocab:
        model.resize_token_embeddings(model.config.vocab_size + hydra.utils.call(cfg.extended_vocab))

    if cfg.max_steps > 0:
        t_total = cfg.max_steps
        cfg.num_train_epochs = cfg.max_steps // (cum_steps // cfg.gradient_accumulation_steps) + 1
    else:
        t_total = cum_steps // cfg.gradient_accumulation_steps * cfg.num_train_epochs

    num_warmup_steps = int(t_total * cfg.warmup_proportion) if cfg.warmup_proportion else cfg.warmup_steps

    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ['bias', 'LayerNorm.weight', 'layer_norm.weight']
    optimizer_grouped_parameters = [
        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
         'weight_decay': cfg.weight_decay},
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
         'weight_decay': 0.0}
    ]

    if cfg.fp16:
        from torch.cuda.amp.grad_scaler import GradScaler

        scaler = GradScaler()
    else:
        scaler = None

    # multi-gpu training (should be after apex fp16 initialization)
    model_single_gpu = model
    if cfg.n_gpu > 1:
        model = torch.nn.DataParallel(model_single_gpu)

    # Distributed training (should be after apex fp16 initialization)
    if cfg.local_rank == -1:
        if "optimizer" in cfg and cfg.optimizer == 'lamb':
            from deepspeed.ops.lamb import FusedLamb

            optimizer = FusedLamb(optimizer_grouped_parameters,
                                  lr=cfg.learning_rate,
                                  betas=eval(cfg.adam_betas),
                                  eps=cfg.adam_epsilon,
                                  max_grad_norm=cfg.max_grad_norm)
        else:
            optimizer = AdamW(optimizer_grouped_parameters, lr=cfg.learning_rate, eps=cfg.adam_epsilon, betas=eval(cfg.adam_betas))
    else:
        model = torch.nn.parallel.DistributedDataParallel(
            model, device_ids=[cfg.local_rank], output_device=cfg.local_rank, find_unused_parameters=True
        )

        from torch.distributed.optim import ZeroRedundancyOptimizer

        if "optimizer" in cfg and cfg.optimizer == 'lamb':
            from deepspeed.ops.lamb import FusedLamb

            optimizer_class = FusedLamb
            optimizer_defaults = {
                "lr": cfg.learning_rate,
                "betas": eval(cfg.adam_betas),
                "eps": cfg.adam_epsilon,
                "max_grad_norm": cfg.max_grad_norm
            }
        else:
            optimizer_class = AdamW
            optimizer_defaults = {
                "lr": cfg.learning_rate,
                "eps": cfg.adam_epsilon,
                "betas": eval(cfg.adam_betas)
            }

        optimizer = ZeroRedundancyOptimizer(
            optimizer_grouped_parameters,
            optimizer_class=optimizer_class,
            parameters_as_bucket_view=True,
            **optimizer_defaults
        )
    scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=t_total)

    logger.info(optimizer)

    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", num_examples)
    logger.info("  Num Epochs = %d", cfg.num_train_epochs)
    logger.info("  Instantaneous batch size per GPU = %d", cfg.per_gpu_train_batch_size)
    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
                cfg.train_batch_size * cfg.gradient_accumulation_steps * (dist.get_world_size() if cfg.local_rank != -1 else 1))
    logger.info("  Gradient Accumulation steps = %d", cfg.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)
    logger.info("  Warmup steps = %d", num_warmup_steps)

    if continue_from_global_step > 0:
        logger.info("Fast forwarding to global step %d to resume training from latest checkpoint...", continue_from_global_step)

    global_step = 0
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    train_iterator = trange(int(cfg.num_train_epochs), desc="Epoch", disable=cfg.local_rank not in [-1, 0])
    set_seed(cfg)  # Added here for reproducibility (even between python 2 and 3)

    train_collator = hydra.utils.instantiate(cfg.collator) if "collator" in cfg and cfg.collator else None
    for epoch in train_iterator:

        random.shuffle(train_files)

        for _file_index, _train_file in enumerate(train_files):
            logger.info(f"Loading tensors from {_train_file}")
            _sub_train_dataset, _ = load_and_cache_examples(cfg, tokenizer, _split="train", _file=_train_file)
            _sub_train_sampler = RandomSampler(_sub_train_dataset) if cfg.local_rank == -1 else DistributedSampler(_sub_train_dataset)
            train_dataloader = DataLoader(dataset=_sub_train_dataset, sampler=_sub_train_sampler, batch_size=cfg.train_batch_size,
                                          collate_fn=train_collator, num_workers=cfg.num_workers, pin_memory=True,
                                          prefetch_factor=cfg.prefetch_factor)

            epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=cfg.local_rank not in [-1, 0], dynamic_ncols=True)
            if cfg.local_rank != -1:
                train_dataloader.sampler.set_epoch(epoch * len(train_files) + _file_index)

            for step, batch in enumerate(epoch_iterator):
                # If training is continued from a checkpoint, fast forward
                # to the state of that checkpoint.
                if global_step < continue_from_global_step:
                    if (step + 1) % cfg.gradient_accumulation_steps == 0:
                        scheduler.step()  # Update learning rate schedule
                        global_step += 1
                    continue

                model.train()
                batch = batch_to_device(batch, cfg.device)

                if (step + 1) % cfg.gradient_accumulation_steps != 0 and cfg.local_rank != -1:
                    # Avoid unnecessary DDP synchronization since there will be no backward pass on this example.
                    with model.no_sync():
                        loss = forward_step(model, batch, cfg, scaler)
                else:
                    loss = forward_step(model, batch, cfg, scaler)

                tr_loss += loss
                if (step + 1) % cfg.gradient_accumulation_steps == 0:
                    if cfg.fp16:
                        scaler.unscale_(optimizer)

                    if cfg.max_grad_norm and not ("optimizer" in cfg and cfg.optimizer == "lamb"):
                        if hasattr(optimizer, "clip_grad_norm"):
                            optimizer.clip_grad_norm(cfg.max_grad_norm)
                        elif hasattr(model, "clip_grad_norm_"):
                            model.clip_grad_norm_(cfg.max_grad_norm)
                        else:
                            torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.max_grad_norm)

                    if cfg.fp16:
                        scaler.step(optimizer)
                        scaler.update()
                    else:
                        optimizer.step()

                    scheduler.step()  # Update learning rate schedule
                    model.zero_grad(set_to_none=True)
                    global_step += 1

                    # Log metrics
                    if cfg.local_rank in [-1, 0] and cfg.logging_steps > 0 and global_step % cfg.logging_steps == 0:
                        tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
                        tb_writer.add_scalar('loss', (tr_loss - logging_loss) / cfg.logging_steps, global_step)
                        logging_loss = tr_loss

                    # Save model checkpoint
                    if cfg.save_steps > 0 and global_step % cfg.save_steps == 0:
                        output_dir = os.path.join(cfg.output_dir, 'checkpoint-{}'.format(global_step))
                        if cfg.local_rank in [-1, 0] and not os.path.exists(output_dir):
                            os.makedirs(output_dir)
                        save_model(model, cfg, output_dir, tokenizer)

                    # Evaluation
                    if cfg.evaluate_during_training and cfg.eval_steps > 0 and global_step % cfg.eval_steps == 0:
                        if cfg.local_rank in [-1, 0]:
                            results = evaluate(cfg, model, tokenizer, prefix=str(global_step), _split="dev")
                            for key, value in results.items():
                                tb_writer.add_scalar(f"eval/{key}", value, global_step)

                if 0 < cfg.max_steps < global_step:
                    epoch_iterator.close()
                    break

            del _sub_train_dataset
            del _sub_train_sampler
            del train_dataloader

            if 0 < cfg.max_steps < global_step:
                train_iterator.close()
                break

        if 0 < cfg.max_steps < global_step:
            train_iterator.close()
            break

    if cfg.local_rank in [-1, 0]:
        tb_writer.close()

    return global_step, tr_loss / global_step


def evaluate(cfg, model, tokenizer: PreTrainedTokenizer, prefix="", _split="dev"):
    dataset, features = load_and_cache_examples(cfg, tokenizer, _split=_split)

    # if not os.path.exists(cfg.output_dir) and cfg.local_rank in [-1, 0]:
    #     os.makedirs(cfg.output_dir)
    if not os.path.exists(os.path.join(cfg.output_dir, prefix)):
        os.makedirs(os.path.join(cfg.output_dir, prefix))

    cfg.eval_batch_size = cfg.per_gpu_eval_batch_size
    eval_sampler = SequentialSampler(dataset)  # Note that DistributedSampler samples randomly
    eval_collator = hydra.utils.instantiate(cfg.collator) if "collator" in cfg and cfg.collator else None
    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=cfg.eval_batch_size,
                                 collate_fn=eval_collator)
    single_model_gpu = unwrap_model(model)
    single_model_gpu.get_eval_log(reset=True)
    # Eval!
    logger.info("***** Running evaluation {}.{} *****".format(_split, prefix))
    logger.info("  Num examples = %d", len(dataset))
    logger.info("  Batch size = %d", cfg.eval_batch_size)
    # Seems FSDP does not need to unwrap the model for evaluating.
    model.eval()
    pred_list = []
    prob_list = []
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        batch = batch_to_device(batch, cfg.device)

        with torch.no_grad():
            outputs = model(**batch)
            # logits = outputs["logits"].detach().cpu()
            probs = outputs["logits"].softmax(dim=-1).detach().cpu().float()
            prob, pred = probs.max(dim=-1)
            pred_list.extend(pred.tolist())
            prob_list.extend(prob.tolist())

    metric_log, results = single_model_gpu.get_eval_log(reset=True)
    logger.info("****** Evaluation Results ******")
    logger.info(f"Global Steps: {prefix}")
    logger.info(metric_log)

    prediction_file = os.path.join(cfg.output_dir, prefix, "eval_predictions.npy")
    np.save(prediction_file, pred_list)
    json.dump(prob_list, open(os.path.join(cfg.output_dir, prefix, "eval_probs.json"), "w"))

    return results


def load_and_cache_examples(cfg, tokenizer: PreTrainedTokenizer, _split="train", _file=None):
    if cfg.local_rank not in [-1, 0] and _split == "train":
        dist.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    if _file is not None:
        input_file = _file
    elif _split == "train":
        input_file = cfg.train_file
    elif _split == "dev":
        input_file = cfg.dev_file
    elif _split == "test":
        input_file = cfg.test_file
    else:
        raise RuntimeError(_split)

    examples, features, res = hydra.utils.call(cfg.read_tensor, file_path=input_file, tokenizer=tokenizer)

    if cfg.local_rank == 0 and _split == "train":
        dist.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    if isinstance(res, Dataset):
        return res, features

    dataset = TensorDataset(*res)

    return dataset, features


@hydra.main(config_path="conf", config_name="config")
def main(cfg: DictConfig):
    if cfg.local_rank == -1 or cfg.no_cuda:
        device = str(torch.device("cuda" if torch.cuda.is_available() and not cfg.no_cuda else "cpu"))
        cfg.n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
        torch.cuda.set_device(cfg.local_rank)
        device = str(torch.device("cuda", cfg.local_rank))
        dist.init_process_group(backend='nccl')
        cfg.n_gpu = 1
    cfg.device = device

    global logger
    logger = setting_logger(cfg.output_dir, local_rank=cfg.local_rank)
    logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
                   cfg.local_rank, device, cfg.n_gpu, bool(cfg.local_rank != -1), cfg.fp16)

    # Set seed
    set_seed(cfg)

    # Load pre-trained model and tokenizer
    if cfg.local_rank not in [-1, 0]:
        dist.barrier()  # Make sure only the first process in distributed training will download model & vocab

    if cfg.pretrain:
        pretrain_state_dict = torch.load(cfg.pretrain, map_location='cpu')
    else:
        pretrain_state_dict = None

    tokenizer = AutoTokenizer.from_pretrained(cfg.model_name_or_path)
    model = hydra.utils.call(cfg.model, cfg.model_name_or_path, state_dict=pretrain_state_dict)

    if cfg.local_rank == 0:
        dist.barrier()  # Make sure only the first process in distributed training will download model & vocab

    if cfg.local_rank == -1:  # For FullyShardedDDP, place the model on cpu first.
        model.to(cfg.device)

    # logger.info("Training/evaluation parameters %s", OmegaConf.to_yaml(cfg))
    if cfg.local_rank in [-1, 0]:
        if not os.path.exists(cfg.output_dir):
            os.makedirs(cfg.output_dir)
        OmegaConf.save(cfg, os.path.join(cfg.output_dir, "training_config.yaml"))

    # Training
    if cfg.do_train:
        # TODO: Add option for continuously training from checkpoint.
        #  The operation should be introduced in ``train`` method since both the state dict
        #  of schedule and optimizer (and scaler, if any) should be loaded.
        # If output files already exists, assume to continue training from latest checkpoint (unless overwrite_output_dir is set)
        continue_from_global_step = 0  # If set to 0, start training from the beginning
        # if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
        #     checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/*/' + WEIGHTS_NAME, recursive=True)))
        #     if len(checkpoints) > 0:
        #         checkpoint = checkpoints[-1]
        #         logger.info("Resuming training from the latest checkpoint: %s", checkpoint)
        #         continue_from_global_step = int(checkpoint.split('-')[-1])
        #         model = model_class.from_pretrained(checkpoint)
        #         model.to(args.device)

        # train_dataset, features = load_and_cache_examples(cfg, tokenizer, _split="train")
        global_step, tr_loss = train(cfg, model, tokenizer, continue_from_global_step)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

    # Save the trained model and the tokenizer
    if cfg.do_train:
        # Create output directory if needed
        if not os.path.exists(cfg.output_dir) and cfg.local_rank in [-1, 0]:
            os.makedirs(cfg.output_dir)

        logger.info("Saving model checkpoint to %s", cfg.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        # model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
        # model_to_save.save_pretrained(cfg.output_dir)
        save_model(model, cfg, cfg.output_dir)
        if cfg.local_rank == -1 or dist.get_rank() == 0:
            tokenizer.save_pretrained(cfg.output_dir)

            # Good practice: save your training arguments together with the trained model
            # torch.save(cfg, os.path.join(cfg.output_dir, 'training_args.bin'))
            OmegaConf.save(cfg, os.path.join(cfg.output_dir, "training_args.yaml"))

    # Test
    results = {}
    if cfg.do_eval and cfg.local_rank in [-1, 0]:
        checkpoints = [cfg.output_dir]
        if cfg.eval_sub_path:
            checkpoints = list(
                os.path.dirname(c) for c in
                sorted(glob.glob(cfg.output_dir + f"/{cfg.eval_sub_path}/" + "pytorch_model.bin", recursive=True))
            )
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
        logger.info(" the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
            split = "dev"

            model = hydra.utils.call(cfg.model, checkpoint)
            model.to(device)

            if cfg.test_file:
                prefix = 'test-' + prefix
                split = "test"

            result = evaluate(cfg, model, tokenizer, prefix=prefix, _split=split)
            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
            results.update(result)

    return results


if __name__ == "__main__":
    hydra_formatted_args = []
    # convert the cli params added by torch.distributed.launch into Hydra format
    for arg in sys.argv:
        if arg.startswith("--"):
            hydra_formatted_args.append(arg[len("--"):])
        else:
            hydra_formatted_args.append(arg)
    sys.argv = hydra_formatted_args

    main()