trainer_base_ds_mul_te.py

# coding=utf-8
#
# Copyright 2023 Nanyang Technological University Fangkai Jiao
#
# 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 datetime
import glob
import logging
import os
import sys
from typing import Dict, Union

import deepspeed
import hydra
import torch
import wandb
from omegaconf import DictConfig, OmegaConf
from torch import distributed as dist
from torch.utils.data import (DataLoader, RandomSampler)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from transformers import (AutoTokenizer, PreTrainedTokenizer, PreTrainedModel)

from general_util.evaluator import evaluate
from general_util.logger import setting_logger
from general_util.training_utils import batch_to_device, unwrap_model, set_seed, note_best_checkpoint, load_and_cache_examples, set_seed_int, \
    organize_multiple_dataset

logger: logging.Logger

try:
    import transformer_engine.pytorch as transformer_engine
    from transformer_engine.common import recipe
except ImportError:
    pytest.skip("Transformer Engine package is missing, skipping tests", allow_module_level=True)

torch.backends.cuda.matmul.allow_tf32 = True

GLOBAL_SEED = 1
GLOBAL_WORKER_ID = None


def get_zero_stage(cfg: DictConfig):
    if hasattr(cfg, "zero_optimization"):
        return int(getattr(cfg.zero_optimization, "stage", 0))
    return 0


def worker_init_fn(worker_id):
    global GLOBAL_WORKER_ID
    GLOBAL_WORKER_ID = worker_id
    set_seed_int(GLOBAL_SEED + worker_id)


def save_model(model: Union[deepspeed.DeepSpeedEngine, deepspeed.PipelineEngine],
               cfg: DictConfig, output_dir: str, tokenizer: PreTrainedTokenizer = None, state_dict: Dict = None):
    unwrapped_model = model.module
    assert isinstance(unwrapped_model, PreTrainedModel)

    save_ds_state = getattr(cfg, "save_ds_state", True)
    zero_stage = get_zero_stage(cfg.ds_cfg)

    if not save_ds_state:
        if zero_stage == 3:
            logger.warning("Deepspeed ZeRO-3 has to save checkpoint states since the model is sharded.")
            saving_ds_state = True

    if save_ds_state:
        model.save_checkpoint(cfg.output_dir)

    if zero_stage == 3:
        state_dict = model._zero3_consolidated_16bit_state_dict()
    else:
        state_dict = model.module.state_dict()

    if dist.is_initialized() and cfg.local_rank != 0:
        dist.barrier()

    if cfg.local_rank in [-1, 0]:
        unwrapped_model.save_pretrained(output_dir, state_dict=state_dict, safe_serialization=False)

        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)

        if dist.is_initialized():
            dist.barrier()


def forward_step(model, inputs: Dict[str, torch.Tensor], fp8_recipe: recipe.DelayedScaling = None):
    if fp8_recipe is not None:
        # Enables autocasting for the forward pass
        with transformer_engine.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
            outputs = model(**inputs)
    else:
        outputs = model(**inputs)
    if isinstance(outputs, tuple):
        loss = outputs[0]
    else:
        loss = outputs["loss"]
    model.backward(loss)
    model.step()

    return loss.item(), outputs


def train(cfg, model, tokenizer, continue_from_global_step=0):
    """ Train the model """
    if cfg.local_rank in [-1, 0]:
        tb_helper = hydra.utils.instantiate(cfg.summary_helper) if "summary_helper" in cfg and cfg.summary_helper else None
    else:
        tb_helper = None

    cfg.train_batch_size = cfg.per_gpu_train_batch_size

    files, total_dataset_len = organize_multiple_dataset(cfg, tokenizer, _split="train")

    logger.warning(f"Rank No. {dist.get_rank()} has {total_dataset_len} samples.")

    if getattr(cfg, "do_preprocess", False):
        return

    if getattr(cfg, "fp8", False):
        fp8_recipe = recipe.DelayedScaling(fp8_format=recipe.Format.HYBRID,
                                           amax_history_len=getattr(cfg, "fp8_amax_history_len", 16),
                                           amax_compute_algo=getattr(cfg, "fp8_amax_compute_algo", "max"))

        from general_util.transformer_engine import convert_model
        logger.info("Converting model to transformer_engine...")

        convert_model(model, to_transformer_engine=True, _convert_linear=True, _convert_ln=True)
    else:
        fp8_recipe = None

    if "extended_vocab" in cfg and cfg.extended_vocab:
        logger.info(f"Extended extra vocab size: {cfg.extended_vocab}")
        model.resize_token_embeddings(model.config.vocab_size + cfg.extended_vocab)

    dp_degree = dist.get_world_size() if cfg.local_rank != -1 else 1
    _actual_train_batch_size = cfg.train_batch_size * cfg.gradient_accumulation_steps * dp_degree
    if cfg.max_steps > 0:
        t_total = cfg.max_steps
        cfg.num_train_epochs = cfg.max_steps // (total_dataset_len // _actual_train_batch_size) + 1
    else:
        t_total = total_dataset_len // _actual_train_batch_size * cfg.num_train_epochs

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

    ds_config = cfg.ds_cfg
    if "total_num_steps" in ds_config.scheduler.params:
        ds_config.scheduler.params.total_num_steps = t_total
    ds_config.scheduler.params.warmup_num_steps = num_warmup_steps
    ds_config = OmegaConf.to_container(ds_config, resolve=True)

    if torch.__version__ >= "2" and (getattr(os.environ, "TORCH_COMPILE", False) or getattr(cfg, "compile", False)):
        model = torch.compile(model, mode="max-autotune")
    model, optimizer, _, scheduler = deepspeed.initialize(model=model,
                                                          model_parameters=[p for p in model.parameters() if p.requires_grad],
                                                          config=ds_config)
    logger.info(optimizer.optimizer)

    unwrapped_model = model.module
    assert isinstance(unwrapped_model, PreTrainedModel)

    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", total_dataset_len)
    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", _actual_train_batch_size)
    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)
        resume = os.path.dirname(cfg.resume)
        # resume = cfg.resume.replace("checkpoint-", "global_step")
        # resume_dir = os.path.dirname(cfg.resume)
        # tag = open(os.path.join(resume_dir, "latest"), "r").read().strip()
        model.load_checkpoint(resume)

    if cfg.local_rank in [-1, 0]:
        wandb.init(
            project="llm-reasoning",
            name=cfg.exp_name,
            notes=cfg.exp_notes,
            config=OmegaConf.to_container(cfg, resolve=True),
        )
        wandb.define_metric(cfg.prediction_cfg.metric, summary=("max" if cfg.prediction_cfg.measure > 0 else "min"))

    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)

    for epoch in train_iterator:
        for _file in files:
            sub_train_dataset = load_and_cache_examples(cfg, tokenizer, _split="train", _file=_file)
            sub_train_sampler = RandomSampler(sub_train_dataset) if cfg.local_rank == -1 else DistributedSampler(sub_train_dataset)
            sub_train_collator = hydra.utils.instantiate(cfg.collator) if "collator" in cfg and cfg.collator else None
            sub_train_dataloader = DataLoader(dataset=sub_train_dataset,
                                              sampler=sub_train_sampler,
                                              batch_size=cfg.train_batch_size,
                                              collate_fn=sub_train_collator,
                                              num_workers=cfg.num_workers,
                                              pin_memory=True,
                                              prefetch_factor=cfg.prefetch_factor,
                                              worker_init_fn=worker_init_fn)

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

            if dist.is_initialized():
                dist.barrier()

            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  # Done by `load_checkpoint` of DS.
                        global_step += 1
                    continue

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

                loss, outputs = forward_step(model, batch, fp8_recipe=fp8_recipe)
                loss /= cfg.gradient_accumulation_steps

                if tb_helper is not None:
                    tb_helper.update(last_batch=batch, last_outputs=outputs)

                tr_loss += loss
                if (step + 1) % cfg.gradient_accumulation_steps == 0:
                    global_step += 1

                    # Log metrics
                    log_metrics = {}
                    if cfg.local_rank in [-1, 0]:
                        log_metrics['lr'] = scheduler.get_lr()[0]
                        log_metrics['loss'] = tr_loss - logging_loss
                        logging_loss = tr_loss

                        if tb_helper is not None:
                            log_metrics.update(tb_helper(clear=True))

                    # 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, exist_ok=True)
                        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:
                        # state_dict = get_state_dict(model, cfg)

                        if cfg.ddp_eval or cfg.local_rank in [-1, 0]:
                            results = evaluate(cfg, model, tokenizer, prefix=str(global_step), _split="dev")

                            if cfg.local_rank in [-1, 0]:
                                for key, value in results.items():
                                    log_metrics[f"eval/{key}"] = value

                            sub_path = os.path.join(cfg.output_dir, 'checkpoint-{}'.format(global_step))
                            flag = note_best_checkpoint(cfg, results, sub_path)
                            if cfg.save_best and flag:
                                save_model(model, cfg, cfg.output_dir, tokenizer)

                    if len(log_metrics) > 0 and cfg.local_rank in [-1, 0]:
                        wandb.log(log_metrics)

                    del batch
                    del log_metrics

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

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

        if 0 < cfg.max_steps < global_step:
            break

    return global_step, tr_loss / global_step


@hydra.main(config_path="conf", config_name="config", version_base="1.2")
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))
        deepspeed.init_distributed(dist_backend="nccl", timeout=datetime.timedelta(seconds=9600))
        cfg.n_gpu = 1
        cfg.world_size = dist.get_world_size()
    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, cfg.device, cfg.n_gpu, bool(cfg.local_rank != -1), cfg.fp16)
    logger.warning(f"CPU cores: {os.cpu_count()}")

    # Set seed
    set_seed(cfg)

    # Training
    if cfg.do_train:
        use_barrier = not os.path.exists(cfg.model_name_or_path)
        # Load pre-trained model and tokenizer
        if use_barrier and 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)

        from general_util.tokenization_utils import expand_special_tokenizer

        expand_special_tokenizer(tokenizer)

        try:
            model = hydra.utils.call(cfg.model, cfg.model_name_or_path, state_dict=pretrain_state_dict)
        except Exception as e:
            logger.warning(e)
            model = hydra.utils.call(cfg.model)

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

        if dist.is_initialized():
            dist.barrier()

        # logger.info("Training/evaluation parameters %s", OmegaConf.to_yaml(cfg))
        if cfg.local_rank in [-1, 0] and cfg.do_train:
            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"))

        continue_from_global_step = 0  # If set to 0, start training from the beginning
        if os.path.exists(cfg.output_dir) and getattr(cfg, "resume", None):
            checkpoint = cfg.resume
            logger.info("Resuming training from the latest checkpoint: %s", checkpoint)
            continue_from_global_step = int(checkpoint.split('-')[-1])

        # Catch keyboard interrupts
        try:
            global_step, tr_loss = train(cfg, model, tokenizer, continue_from_global_step)
        except KeyboardInterrupt:
            logger.info("Keyboard interrupt, normally exiting...")
            exit()
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

    # Test
    results = {}
    if cfg.do_eval:
        if not cfg.ddp_eval and cfg.local_rank not in [-1, 0]:
            return results

        checkpoints = [cfg.output_dir]
        if cfg.save_best:
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
        elif cfg.prediction_cfg.best_checkpoint and os.path.exists(cfg.prediction_cfg.best_checkpoint):
            checkpoints = [cfg.prediction_cfg.best_checkpoint]
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
        elif cfg.eval_sub_path:
            checkpoints = list(sorted(list(set(
                os.path.dirname(c) for c in
                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"

            if "model_eval" in cfg:
                model = hydra.utils.call(cfg.model_eval, checkpoint)
            else:
                model = hydra.utils.call(cfg.model, checkpoint)
            if cfg.n_gpu == 1:
                model.to(cfg.device)
            else:
                # For model parallel (of mT5)
                if getattr(cfg, "get_device_map", None):
                    model.parallelize(hydra.utils.call(cfg.get_device_map))
                else:
                    model.parallelize()

            tokenizer = AutoTokenizer.from_pretrained(checkpoint)
            cfg.model_name_or_path = checkpoint

            if cfg.test_file:
                prefix = f'test' + (f'-{prefix}' if prefix != "" else "")
                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__":
    os.environ["HYDRA_FULL_ERROR"] = "1"
    os.environ["WANDB__SERVICE_WAIT"] = "1200"
    print(os.environ)
    os.environ["NCCL_BLOCKING_WAIT"] = "1"
    os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "1"

    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
    print(sys.argv)
    main()