train.py

# Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
import os
# os.environ['LD_LIBRARY_PATH'] = '/nfs/scistore19/alistgrp/imodoran/miniconda3/envs/llm_foundry/lib/'

import sys

PROJECTS_ROOT = os.environ.get('PROJECTS_ROOT', None)
if PROJECTS_ROOT is None or not os.path.isdir(PROJECTS_ROOT):
    print(f'Please set PROJECTS_ROOT environment variable')
    sys.exit(666)

PATH_LIB_LLM_FOUNDRY = os.path.join(PROJECTS_ROOT, 'MicroAdam', 'llm-foundry')
PATH_LM_EVAL_HARNESS = os.path.join(PROJECTS_ROOT, 'lm-evaluation-harness')

sys.path.append(PATH_LIB_LLM_FOUNDRY)
sys.path.append(PATH_LM_EVAL_HARNESS)

from evaluation import evaluate_model # this is from PATH_LM_EVAL_HARNESS

import copy
import gc
import logging
import time
import warnings
from typing import Any, Dict, List, Optional, Union

import torch
from composer import Trainer
from composer.core.callback import Callback
from composer.loggers import MosaicMLLogger
from composer.loggers.mosaicml_logger import (MOSAICML_ACCESS_TOKEN_ENV_VAR,
                                              MOSAICML_PLATFORM_ENV_VAR)
from composer.metrics.nlp import InContextLearningMetric
from composer.profiler import (JSONTraceHandler, Profiler, TraceHandler,
                               cyclic_schedule)
from composer.utils import dist, get_device, reproducibility
from omegaconf import DictConfig, ListConfig
from omegaconf import OmegaConf as om
from rich.traceback import install

install()

from transformers import PreTrainedTokenizerBase
from streaming.base.util import clean_stale_shared_memory

from llmfoundry import COMPOSER_MODEL_REGISTRY
from llmfoundry.callbacks import AsyncEval
from llmfoundry.data.dataloader import build_dataloader
from llmfoundry.registry import import_file
from llmfoundry.utils.builders import (add_metrics_to_eval_loaders,
                                       build_algorithm, build_callback,
                                       build_evaluators, build_logger,
                                       build_optimizer, build_scheduler,
                                       build_tokenizer)
from llmfoundry.utils.config_utils import (log_config, pop_config,
                                           process_init_device,
                                           update_batch_size_info)

log = logging.getLogger(__name__)


def validate_config(cfg: DictConfig):
    """Validates compatible model and dataloader selection."""
    loaders = [cfg.train_loader]
    if 'eval_loader' in cfg:
        eval_loader = cfg.eval_loader
        if isinstance(eval_loader, ListConfig):
            for loader in eval_loader:
                if loader.label is None:
                    raise ValueError(
                        'When specifying multiple evaluation datasets, each one must include the \
                            `label` attribute.')
                loaders.append(loader)
        else:
            loaders.append(eval_loader)
    for loader in loaders:
        if loader.name == 'text':
            if cfg.model.name in ['hf_prefix_lm', 'hf_t5']:
                raise ValueError(
                    f'Model type "{cfg.model.name}" is not supported when using the "text " ' +\
                    f'dataloader. Please use the "text_denoising" dataloader to pre-train that model type.')
        elif loader.name == 'text_denoising':
            if cfg.model.name == 'hf_causal_lm':
                raise ValueError(
                    f'Model type "{cfg.model.name}" is not supported when using the "text_denoising" ' +\
                    f'dataloader. Please use the "text" dataloader to pre-train that model type.')
            if loader.mixture_of_denoisers.decoder_only_format and cfg.model.name == 'hf_t5':
                warnings.warn(
                    'Model type "hf_t5" requires `decoder_only_format` to be ``False``. ' +\
                    'Overriding `decoder_only_format` from ``True`` to ``False``.')
                loader.mixture_of_denoisers.decoder_only_format = False
            if (not loader.mixture_of_denoisers.decoder_only_format
               ) and cfg.model.name == 'hf_prefix_lm':
                warnings.warn(
                    'Model type "hf_prefix_lm" requires `decoder_only_format` to be ``True``. ' +\
                    'Overriding `decoder_only_format` from ``False`` to ``True``.')
                loader.mixture_of_denoisers.decoder_only_format = True
        elif loader.name == 'finetuning':
            if cfg.model.name == 'hf_prefix_lm':
                is_prefix_lm = True
            elif cfg.model.name == 'mpt_causal_lm':
                is_prefix_lm = cfg.model.get('attn_config',
                                             {}).get('prefix_lm', False)
            else:
                # Note: This only covers the two prefix-lms introduced in this repo
                is_prefix_lm = False
            target_responses = loader.dataset.get('target_responses', 'last')
            target_prompts = loader.dataset.get('target_prompts', 'none')
            prefix_lm_safe = target_responses == 'last' and target_prompts == 'none'
            if is_prefix_lm and not prefix_lm_safe:
                raise ValueError(
                    'The model configuration is building a Prefix-LM, which requires that the finetuning ' +\
                    'dataloader uses `target_responses`="last" and `target_prompts`="none".'
                )

    if 'icl_tasks' in cfg:
        if cfg.model.name == 'hf_t5':
            raise ValueError(
                'ICL evaluation does not currently support Encoder-Decoder models, such as "hf_t5".'
            )

    if (cfg.model.get('fc_type', 'torch') != 'te' and 'te' not in cfg.model.get(
            'ffn_config', {}).get('ffn_type', 'mptmlp') and
            'fp8' in cfg.precision):
        warnings.warn(
            "fp8 only supported for te.Linear layers. Either set `cfg.model.fc_typ='te'` or "
            +
            "`cfg.model.ffn_config.ffn_type='te_ln_mlp'` to enable layers using fp8 precision."
        )

    if (cfg.model.get('fc_type', 'torch') == 'te' or
            'te' in cfg.model.get('ffn_config', {}).get('ffn_type', 'mptmlp')):
        fsdp_config = cfg.get('fsdp_config', None)
        act_ckpt = fsdp_config.get('activation_checkpointing', False)
        act_ckpt_reentrant = fsdp_config.get(
            'activation_checkpointing_reentrant', True)
        if fsdp_config is not None and act_ckpt == True and act_ckpt_reentrant == False:
            warnings.warn(
                '`te.Linear` layers do not support activation_checkpointing with '
                + '`activation_checkpointing_reentrant = False`. ' +
                'Setting cfg.fsdp_config.activation_checkpointing_reentrant=True.'
            )
            cfg.fsdp_config.activation_checkpointing_reentrant = True

    if 'te' in cfg.model.get('ffn_config', {}).get('ffn_type', 'mptmlp'):
        warnings.warn(
            '`te.LayerNormMLP` requires has issues with torch._dynamo. ' +
            'Setting `torch._dynamo.config.suppress_errors = True` and falling back to eager.'
        )
        torch._dynamo.config.suppress_errors = True  # type: ignore (third-party)

    if cfg.model.get('load_in_8bit', False):
        raise ValueError(
            '`load_in_8bit` is only supported for evaluation rather than training.'
        )


def build_composer_model(model_cfg: DictConfig,
                         tokenizer: PreTrainedTokenizerBase):
    warnings.filterwarnings(
        action='ignore',
        message='Torchmetrics v0.9 introduced a new argument class property')
    if model_cfg.name not in COMPOSER_MODEL_REGISTRY:
        raise ValueError(
            f'Not sure how to build model with name={model_cfg.name}')
    return COMPOSER_MODEL_REGISTRY[model_cfg.name](model_cfg, tokenizer)


def main(cfg: DictConfig):
    # Filter deprecation warning from torch internal usage
    warnings.filterwarnings(
        action='ignore',
        category=UserWarning,
        message=
        'torch.distributed.*_base is a private function and will be deprecated.*'
    )

    task: str = pop_config(cfg, 'task', must_exist=True)

    # Run user provided code if specified
    code_paths = pop_config(cfg,
                            'code_paths',
                            must_exist=False,
                            default_value=[],
                            convert=True)
    # Import any user provided code
    for code_path in code_paths:
        import_file(code_path)

    # Check for incompatibilities between the model and data loaders
    validate_config(cfg)

    # Resolve all interpolation variables as early as possible
    om.resolve(cfg)

    # Create copy of config for logging
    logged_cfg: DictConfig = copy.deepcopy(cfg)

    # Get max split size mb
    max_split_size_mb: Optional[int] = cfg.pop('max_split_size_mb', None)
    if max_split_size_mb is not None:
        os.environ[
            'PYTORCH_CUDA_ALLOC_CONF'] = f'max_split_size_mb:{max_split_size_mb}'

    # Set CUDA lazy loading
    # This can save a bit of memory if not all modules are needed
    cuda_load_lazy: bool = cfg.pop('cuda_load_lazy', False)
    if cuda_load_lazy:
        os.environ['CUDA_MODULE_LOADING'] = 'LAZY'

    # Set seed first
    seed: int = pop_config(cfg, 'seed', must_exist=True)
    reproducibility.seed_all(seed)

    # Initialize pytorch distributed training process groups
    dist_timeout: Union[int, float] = pop_config(cfg,
                                                 'dist_timeout',
                                                 must_exist=False,
                                                 default_value=600.0)

    dist.initialize_dist(get_device(None), timeout=dist_timeout)

    # Get global and device batch size information from distributed/single node setting
    cfg = update_batch_size_info(cfg)
    logged_cfg.update(cfg, merge=True)

    # Mandatory model training configs
    model_config: DictConfig = pop_config(cfg, 'model', must_exist=True)
    tokenizer_config: Dict[str, Any] = pop_config(cfg,
                                                  'tokenizer',
                                                  must_exist=True,
                                                  convert=True)
    optimizer_config: Dict[str, Any] = pop_config(cfg,
                                                  'optimizer',
                                                  must_exist=True,
                                                  convert=True)
    scheduler_config: Dict[str, Any] = pop_config(cfg,
                                                  'scheduler',
                                                  must_exist=True,
                                                  convert=True)
    train_loader_config: DictConfig = pop_config(cfg,
                                                 'train_loader',
                                                 must_exist=True)

    # Optional fsdp data, fine-tuning, and eval configs
    fsdp_config: Optional[Dict[str, Any]] = pop_config(cfg,
                                                       'fsdp_config',
                                                       must_exist=False,
                                                       default_value=None,
                                                       convert=True)
    eval_loader_config: Optional[Union[DictConfig, ListConfig]] = pop_config(
        cfg, 'eval_loader', must_exist=False, default_value=None)
    icl_tasks_config: Optional[Union[ListConfig,
                                     str]] = pop_config(cfg,
                                                        'icl_tasks',
                                                        must_exist=False,
                                                        default_value=None)
    eval_gauntlet_config: Optional[Union[DictConfig,
                                         str]] = pop_config(cfg,
                                                            'eval_gauntlet',
                                                            must_exist=False,
                                                            default_value=None)
    icl_subset_num_batches: Optional[int] = pop_config(cfg,
                                                       'icl_subset_num_batches',
                                                       must_exist=False,
                                                       default_value=None)
    icl_seq_len: Optional[int] = pop_config(cfg,
                                            'icl_seq_len',
                                            must_exist=False,
                                            default_value=None)
    # Optional logging, evaluation and callback configs
    logger_configs: Optional[DictConfig] = pop_config(cfg,
                                                      'loggers',
                                                      must_exist=False,
                                                      default_value=None,
                                                      convert=True)
    callback_configs: Optional[DictConfig] = pop_config(cfg,
                                                        'callbacks',
                                                        must_exist=False,
                                                        default_value=None,
                                                        convert=True)
    algorithm_configs: Optional[DictConfig] = pop_config(cfg,
                                                         'algorithms',
                                                         must_exist=False,
                                                         default_value=None)

    # Mandatory hyperparameters for training
    device_train_batch_size: int = pop_config(cfg,
                                              'device_train_batch_size',
                                              must_exist=True)
    device_eval_batch_size: int = pop_config(cfg,
                                             'device_eval_batch_size',
                                             must_exist=True)
    max_duration: Union[int, str] = pop_config(cfg,
                                               'max_duration',
                                               must_exist=True)
    eval_interval: Union[int, str] = pop_config(cfg,
                                                'eval_interval',
                                                default_value=1,
                                                must_exist=False)
    precision: str = pop_config(cfg, 'precision', must_exist=True)
    max_seq_len: int = pop_config(cfg, 'max_seq_len', must_exist=True)

    # Optional parameters will be set to default values if not specified.
    default_run_name: str = os.environ.get('RUN_NAME', 'llm')
    run_name: str = pop_config(cfg,
                               'run_name',
                               must_exist=False,
                               default_value=default_run_name)
    save_folder: Optional[str] = pop_config(cfg,
                                            'save_folder',
                                            must_exist=False,
                                            default_value=None)
    is_state_dict_sharded: bool = (fsdp_config.get('state_dict_type', 'full')
                                   == 'sharded') if fsdp_config else False
    save_latest_filename: str = pop_config(
        cfg,
        'save_latest_filename',
        must_exist=False,
        default_value='latest-sharded-rank{rank}'
        if is_state_dict_sharded else 'latest-rank{rank}.pt')
    save_overwrite: bool = pop_config(cfg,
                                      'save_overwrite',
                                      must_exist=False,
                                      default_value=False)
    save_weights_only: bool = pop_config(cfg,
                                         'save_weights_only',
                                         must_exist=False,
                                         default_value=False)
    save_filename: str = pop_config(
        cfg,
        'save_filename',
        must_exist=False,
        default_value='ep{epoch}-ba{batch}-rank{rank}.pt')
    save_interval: Union[str, int] = pop_config(cfg,
                                                'save_interval',
                                                must_exist=False,
                                                default_value='1000ba')
    save_num_checkpoints_to_keep: int = pop_config(
        cfg, 'save_num_checkpoints_to_keep', must_exist=False, default_value=-1)
    progress_bar = pop_config(cfg,
                              'progress_bar',
                              must_exist=False,
                              default_value=False)
    log_to_console: bool = pop_config(cfg,
                                      'log_to_console',
                                      must_exist=False,
                                      default_value=True)
    python_log_level: Optional[str] = pop_config(cfg,
                                                 'python_log_level',
                                                 must_exist=False,
                                                 default_value='debug')
    console_log_interval: Union[int, str] = pop_config(cfg,
                                                       'console_log_interval',
                                                       must_exist=False,
                                                       default_value='1ba')
    device_train_microbatch_size: Union[str, int] = pop_config(
        cfg,
        'device_train_microbatch_size',
        must_exist=False,
        default_value='auto')
    eval_subset_num_batches: int = pop_config(cfg,
                                              'eval_subset_num_batches',
                                              must_exist=False,
                                              default_value=-1)
    eval_first: bool = pop_config(cfg,
                                  'eval_first',
                                  must_exist=False,
                                  default_value=False)
    load_path: str = pop_config(cfg,
                                'load_path',
                                must_exist=False,
                                default_value=None)
    load_weights_only: bool = pop_config(cfg,
                                         'load_weights_only',
                                         must_exist=False,
                                         default_value=False)
    load_strict_model_weights: bool = pop_config(cfg,
                                                 'load_strict_model_weights',
                                                 must_exist=False,
                                                 default_value=True)
    load_ignore_keys: Optional[List[str]] = pop_config(cfg,
                                                       'load_ignore_keys',
                                                       must_exist=False,
                                                       default_value=None)
    compile_config: Optional[Dict[str, Any]] = pop_config(cfg,
                                                          'compile_config',
                                                          must_exist=False,
                                                          default_value=None)
    metadata: Optional[Dict[str, str]] = pop_config(cfg,
                                                    'metadata',
                                                    must_exist=False,
                                                    default_value=None,
                                                    convert=True)
    should_log_config: bool = pop_config(cfg,
                                         'log_config',
                                         must_exist=False,
                                         default_value=True)

    wandb_groups_config: Dict[str, Any] = pop_config(cfg,
                                              'wandb_groups',
                                              must_exist=True,
                                              convert=True)

    optimizer_name: str = optimizer_config.pop('name')
    logger_configs['wandb']['group'] = wandb_groups_config[optimizer_name]['group']
    wandb_project = logger_configs['wandb']['project']
    wandb_group = logger_configs['wandb']['group']
    wandb_job_type = logger_configs['wandb']['job_type']
    wandb_name = logger_configs['wandb']['name']
    run_name = f'{wandb_group}_{wandb_job_type}_{wandb_name}'
    save_folder = os.path.join(save_folder, wandb_project, run_name)
    callback_configs['hf_checkpointer']['save_folder'] = save_folder

    print()
    print(f'{save_folder=}')
    print(f'{run_name=}')
    print()

    # Enable autoresume from model checkpoints if possible
    autoresume_default: bool = False
    if logged_cfg.get('run_name', None) is not None \
        and save_folder is not None \
        and not save_overwrite \
        and not save_weights_only:
        autoresume_default = True

    if cfg.get('autoresume') is None and autoresume_default:
        log.info('As run_name, save_folder, and save_latest_filename are set, \
                changing autoresume default to True...')

    autoresume: bool = pop_config(cfg,
                                  'autoresume',
                                  must_exist=False,
                                  default_value=autoresume_default)

    # Pop known unused parameters that are used as interpolation variables or
    # created by update_batch_size_info.
    pop_config(cfg, 'data_local', must_exist=False)
    pop_config(cfg, 'data_remote', must_exist=False)
    pop_config(cfg, 'global_seed', must_exist=False)
    pop_config(cfg, 'global_train_batch_size', must_exist=False)
    pop_config(cfg, 'n_gpus', must_exist=False)
    pop_config(cfg, 'device_train_grad_accum', must_exist=False)

    # Warn users for unused parameters
    for key in cfg:
        warnings.warn(
            f'Unused parameter {key} found in cfg. Please check your yaml to ensure this parameter is necessary.'
        )

    # Warn if fsdp is enabled but user only has 1 GPU
    if dist.get_world_size() == 1 and fsdp_config is not None:
        warnings.warn(
            'FSDP is not applicable for single-GPU training. Reverting to DDP.')
        fsdp_config = None

    # set logging level
    if python_log_level is not None:
        logging.basicConfig(
            # Example of format string
            # 2022-06-29 11:22:26,152: rank0[822018][MainThread]: INFO: Message here
            format=
            f'%(asctime)s: rank{dist.get_global_rank()}[%(process)d][%(threadName)s]: %(levelname)s: %(name)s: %(message)s'
        )
        logging.getLogger('llmfoundry').setLevel(
            python_log_level.upper())  # Foundry module
        logging.getLogger(__name__).setLevel(
            python_log_level.upper())  # Train script

    # Initialize context
    init_context = process_init_device(model_config, fsdp_config)
    logged_cfg.update({'fsdp_config': fsdp_config}, merge=True)

    clean_stale_shared_memory()

    # Build tokenizer
    log.info('Building tokenizer...')
    tokenizer_name = tokenizer_config['name']
    tokenizer_kwargs = tokenizer_config.get('kwargs', {})
    tokenizer = build_tokenizer(tokenizer_name, tokenizer_kwargs)

    # Scheduler
    scheduler_name: str = scheduler_config.pop('name')
    scheduler = build_scheduler(scheduler_name, scheduler_config)

    # Loggers
    loggers = [
        build_logger(str(name), logger_cfg)
        for name, logger_cfg in logger_configs.items()
    ] if logger_configs else []

    mosaicml_logger = next(
        (logger for logger in loggers if isinstance(logger, MosaicMLLogger)),
        None)
    if mosaicml_logger is None:
        if os.environ.get(MOSAICML_PLATFORM_ENV_VAR, 'false').lower(
        ) == 'true' and os.environ.get(MOSAICML_ACCESS_TOKEN_ENV_VAR):
            # Adds mosaicml logger to composer if the run was sent from Mosaic platform, access token is set, and mosaic logger wasn't previously added
            mosaicml_logger = MosaicMLLogger()
            loggers.append(mosaicml_logger)

    if metadata is not None:
        # Flatten the metadata for logging
        logged_cfg.pop('metadata', None)
        logged_cfg.update(metadata, merge=True)
        if mosaicml_logger is not None:
            mosaicml_logger.log_metrics(metadata)
            mosaicml_logger._flush_metadata(force_flush=True)

    # Profiling
    profiler: Optional[Profiler] = None
    profiler_cfg: Optional[DictConfig] = pop_config(cfg,
                                                    'profiler',
                                                    must_exist=False,
                                                    convert=False,
                                                    default_value=None)
    if profiler_cfg:
        profiler_schedule_cfg: Dict = pop_config(profiler_cfg,
                                                 'schedule',
                                                 must_exist=True,
                                                 convert=True)
        profiler_schedule = cyclic_schedule(**profiler_schedule_cfg)
        # Only support json trace handler
        profiler_trace_handlers: List[TraceHandler] = []
        profiler_trace_cfg: Optional[Dict] = pop_config(profiler_cfg,
                                                        'json_trace_handler',
                                                        must_exist=False,
                                                        default_value=None,
                                                        convert=True)
        if profiler_trace_cfg:
            profiler_trace_handlers.append(
                JSONTraceHandler(**profiler_trace_cfg))
        profiler = Profiler(**profiler_cfg,
                            trace_handlers=profiler_trace_handlers,
                            schedule=profiler_schedule)

    # Callbacks
    callbacks: List[Callback] = [
        build_callback(str(name), callback_cfg, om.to_container(logged_cfg))
        for name, callback_cfg in callback_configs.items()
    ] if callback_configs else []

    use_async_eval = any(isinstance(c, AsyncEval) for c in callbacks)

    # Algorithms
    algorithms = [
        build_algorithm(str(name), algorithm_cfg)
        for name, algorithm_cfg in algorithm_configs.items()
    ] if algorithm_configs else None

    # Dataloaders
    log.info('Building train loader...')
    train_loader = build_dataloader(
        train_loader_config,
        tokenizer,
        device_train_batch_size,
    )

    if mosaicml_logger is not None:
        mosaicml_logger.log_metrics({'data_validated': time.time()})

    ## Evaluation
    if use_async_eval:
        evaluators = []
        if eval_first:
            warnings.warn(
                'AsyncEval callback does not support eval_first=True. Ignoring.'
            )
            eval_first = False

    else:
        log.info('Building eval loader...')
        eval_icl_seq_len: int = icl_seq_len if icl_seq_len else max_seq_len
        evaluators, _, eval_gauntlet_callback = build_evaluators(
            eval_loader_config,
            icl_tasks_config,
            eval_gauntlet_config,
            tokenizer=tokenizer,
            device_eval_batch_size=device_eval_batch_size,
            icl_seq_len=eval_icl_seq_len,
            icl_subset_num_batches=icl_subset_num_batches,
        )
        if eval_gauntlet_callback is not None:
            callbacks.append(eval_gauntlet_callback)

    # Build Model
    log.info('Initializing model...')
    with init_context:
        model = build_composer_model(model_config, tokenizer)

        if model_config.get('master_weights_dtype') in ('bf16', 'bfloat16'):
            model = model.to(dtype=torch.bfloat16)
        elif model_config.get('master_weights_dtype') in ('f16', 'float16'):
            model = model.to(dtype=torch.float16)

    # Log number of parameters
    n_params = sum(p.numel() for p in model.parameters())
    n_trainable_params = sum(
        p.numel() for p in model.parameters() if p.requires_grad)
    logged_cfg.update({
        'n_params': n_params,
        'n_trainable_params': n_trainable_params,
    })

    # Optimizer
    optimizer = build_optimizer(model, optimizer_name, optimizer_config)

    # Now add the eval metrics
    if eval_loader_config is not None and not use_async_eval:
        eval_metrics = model.get_metrics(is_train=False)
        non_icl_metrics = [
            metric_name for metric_name, metric in eval_metrics.items()
            if not isinstance(metric, InContextLearningMetric)
        ]
        evaluators = add_metrics_to_eval_loaders(evaluators, non_icl_metrics)

    # from helpers.tools import print_model_architecture
    # print_model_architecture(model,
    #                          file=f'/nfs/scistore19/alistgrp/imodoran/workplace/M-FAC_extensions/architectures/llama2-7b_gsm8k.txt',
    #                          exit666=False)

    # Build the Trainer
    log.info('Building trainer...')
    trainer = Trainer(
        run_name=run_name,
        seed=seed,
        model=model,
        train_dataloader=train_loader,
        eval_dataloader=evaluators,
        optimizers=optimizer,
        schedulers=scheduler,
        max_duration=max_duration,
        eval_interval=eval_interval,
        eval_subset_num_batches=eval_subset_num_batches,
        progress_bar=progress_bar,
        log_to_console=log_to_console,
        console_log_interval=console_log_interval,
        loggers=loggers,
        callbacks=callbacks,
        precision=precision,
        algorithms=algorithms,
        device_train_microbatch_size=device_train_microbatch_size,
        fsdp_config=fsdp_config,
        save_folder=save_folder,
        save_filename=save_filename,
        save_latest_filename=save_latest_filename,
        save_interval=save_interval,
        save_num_checkpoints_to_keep=save_num_checkpoints_to_keep,
        save_overwrite=save_overwrite,
        save_weights_only=save_weights_only,
        load_path=load_path,
        load_weights_only=load_weights_only,
        load_strict_model_weights=load_strict_model_weights,
        load_ignore_keys=load_ignore_keys,
        autoresume=autoresume,
        python_log_level=python_log_level,
        dist_timeout=dist_timeout,
        profiler=profiler,
        compile_config=compile_config,
    )

    if should_log_config:
        log.info('Logging config')
        log_config(logged_cfg)

    with torch.cuda.device(f'cuda:{torch.distributed.get_rank()}'):
        torch.cuda.empty_cache()
        gc.collect()

    # Eval first if requested
    if eval_first and trainer.state.timestamp.batch.value == 0:
        trainer.eval()

    log.info('Starting training...')
    time_train_start = time.time()
    trainer.fit()
    time_train_end = time.time()

    log.info('Done.')

    with torch.cuda.device(f'cuda:{torch.distributed.get_rank()}'):
        torch.cuda.empty_cache()
        gc.collect()

    delete_pt_files(save_folder)

    def convert_seconds_to_hours_minutes_seconds(ss):
        ss = int(ss)
        hh = ss // 3600
        ss %= 3600
        mm = ss // 60
        ss %= 60
        return f'{hh}h {mm}m {ss}s'

    time_eval_start = time.time()
    acc, acc_std = evaluate_model(ft_model_path=os.path.join(save_folder, 'huggingface'), task=task)
    time_eval_end = time.time()

    elapsed_train = convert_seconds_to_hours_minutes_seconds(time_train_end - time_train_start)
    elapsed_eval = convert_seconds_to_hours_minutes_seconds(time_eval_end - time_eval_start)

    loggers[0].log_metrics(dict(acc=acc, acc_std=acc_std, elapsed_train=elapsed_train, elapsed_eval=elapsed_eval))


def delete_pt_files(folder):
    """
        This method deletes the pt files that are created in save_folder.
        For evaluation we use the HF format saved in save_folder/huggingface
    """
    for file in os.listdir(folder):
        if file.endswith('.pt'):
            f = os.path.join(folder, file)
            os.remove(f)
            print(f'Deleted {f}')

if __name__ == '__main__':
    yaml_path, args_list = sys.argv[1], sys.argv[2:]

    # Disable resolving environment variables through omegaconf.
    om.clear_resolver('oc.env')

    # Load yaml and cli arguments.
    with open(yaml_path) as f:
        yaml_cfg = om.load(f)
    cli_cfg = om.from_cli(args_list)
    cfg = om.merge(yaml_cfg, cli_cfg)
    om.resolve(cfg)
    assert isinstance(cfg, DictConfig)

    main(cfg)