deeplearning/modulus/modulus-sym-v130/_modules/modulus/sym/hydra/utils.html

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Source code for modulus.sym.hydra.utils

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. 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
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import functools
import hydra
import os
import torch
import logging
import copy
import pprint

from termcolor import colored
from pathlib import Path
from omegaconf import DictConfig, OmegaConf, MISSING
from typing import Optional, Any, Union, List
from hydra._internal.utils import _run_hydra, get_args_parser
from hydra.core.hydra_config import HydraConfig
from hydra.utils import get_original_cwd

from modulus.sym.key import Key
from modulus.sym.models.arch import Arch
from modulus.sym.distributed import DistributedManager
from modulus.sym.models.utils import ModulusModels

from .arch import ModelConf
from .config import register_modulus_configs, ModulusConfig
from .hydra import register_hydra_configs
from .loss import register_loss_configs
from .metric import register_metric_configs
from .arch import register_arch_configs
from .optimizer import register_optimizer_configs
from .pde import register_pde_configs
from .profiler import register_profiler_configs
from .scheduler import register_scheduler_configs
from .training import register_training_configs
from .callbacks import register_callbacks_configs
from .graph import register_graph_configs


logger = logging.getLogger(__name__)


[docs]def main(config_path: str, config_name: str = "config"): """Modified decorator for loading hydra configs in modulus See: https://github.com/facebookresearch/hydra/blob/main/hydra/main.py """ def register_decorator(func): @functools.wraps(func) def func_decorated(cfg_passthrough: Optional[DictConfig] = None) -> Any: # Register all modulus groups before calling hydra main register_hydra_configs() register_callbacks_configs() register_loss_configs() register_metric_configs() register_arch_configs() register_optimizer_configs() register_pde_configs() register_profiler_configs() register_scheduler_configs() register_training_configs() register_modulus_configs() register_graph_configs() # Set number of intraop torch CPU threads torch.set_num_threads(1) # TODO: define this as a hydra config somehow # Setup distributed process config DistributedManager.initialize() # Create model parallel process group model_parallel_size = os.getenv( "MODEL_PARALLEL_SIZE" ) # TODO: get this from config instead if model_parallel_size: # Create model parallel process group DistributedManager.create_process_subgroup( "model_parallel", int(model_parallel_size), verbose=True ) # Create data parallel process group for DDP allreduce DistributedManager.create_orthogonal_process_group( "data_parallel", "model_parallel", verbose=True ) # Pass through dict config if cfg_passthrough is not None: return func(cfg_passthrough) else: args_parser = get_args_parser() args = args_parser.parse_args() # multiple times (--multirun) _run_hydra( args=args_parser.parse_args(), args_parser=args_parser, task_function=func, config_path=config_path, config_name=config_name, ) return func_decorated return register_decorator
[docs]def compose( config_name: Optional[str] = None, config_path: Optional[str] = None, overrides: List[str] = [], return_hydra_config: bool = False, job_name: Optional[str] = "app", caller_stack_depth: int = 2, ) -> DictConfig: """Internal Modulus config initializer and compose function. This is an alternative for initializing a Hydra config which should be used as a last ditch effort in cases where @modulus.main() cannot work. For more info see: https://hydra.cc/docs/advanced/compose_api/ Parameters ---------- config_name : str Modulus config name config_path : str Path to config file relative to the caller at location caller_stack_depth overrides : list of strings List of overrides return_hydra_config : bool Return the hydra options in the dict config job_name : string Name of program run instance caller_stack_depth : int Stack depth of this function call (needed for finding config relative to python). """ # Clear already initialized hydra hydra.core.global_hydra.GlobalHydra.instance().clear() hydra.initialize( config_path, job_name, caller_stack_depth, ) register_hydra_configs() register_callbacks_configs() register_loss_configs() register_metric_configs() register_arch_configs() register_optimizer_configs() register_pde_configs() register_profiler_configs() register_scheduler_configs() register_training_configs() register_modulus_configs() register_graph_configs() cfg = hydra.compose( config_name=config_name, overrides=overrides, return_hydra_config=return_hydra_config, ) return cfg

def instantiate_arch( cfg: ModelConf, input_keys: Union[List[Key], None] = None, output_keys: Union[List[Key], None] = None, detach_keys: Union[List[Key], None] = None, verbose: bool = False, **kwargs, ) -> Arch: # Function for instantiating a modulus architecture with hydra assert hasattr( cfg, "arch_type" ), "Model configs are required to have an arch_type defined. \ Improper architecture supplied, please make sure config \ provided is a single arch config NOT the full hydra config!" try: # Convert to python dictionary model_cfg = OmegaConf.to_container(cfg, resolve=True) # Get model class beased on arch type modulus_models = ModulusModels() model_arch = modulus_models[model_cfg["arch_type"]] del model_cfg["arch_type"] # Add keys if present if not input_keys is None: model_cfg["input_keys"] = input_keys if not output_keys is None: model_cfg["output_keys"] = output_keys if not detach_keys is None: model_cfg["detach_keys"] = detach_keys # Add any additional kwargs for key, value in kwargs.items(): model_cfg[key] = value # Init model from config dictionary model, param = model_arch.from_config(model_cfg) # Verbose printing if verbose: pp = pprint.PrettyPrinter(indent=4) logger.info(f"Initialized models with parameters: \n") pp.pprint(param) except Exception as e: fail = colored(f"Failed to initialize architecture.\n {model_cfg}", "red") raise Exception(fail) from e return model def instantiate_optim( cfg: DictConfig, model: torch.nn.Module, verbose: bool = False ) -> torch.optim.Optimizer: # Function for instantiating an optimizer with hydra # Remove custom parameters used internally in modulus optim_cfg = copy.deepcopy(cfg.optimizer) del optim_cfg._params_ try: optimizer = hydra.utils.instantiate(optim_cfg, params=model.parameters()) except Exception as e: fail = colored("Failed to initialize optimizer: \n", "red") logger.error(fail + to_yaml(optim_cfg)) raise Exception(fail) from e if verbose: pp = pprint.PrettyPrinter(indent=4) logger.info(f"Initialized optimizer: \n") pp.pprint(optimizer) return optimizer def instantiate_sched( cfg: DictConfig, optimizer: torch.optim ) -> torch.optim.lr_scheduler: # Function for instantiating a scheduler with hydra sched_cfg = copy.deepcopy(cfg.scheduler) # Default is no scheduler, so just make fixed LR if sched_cfg is MISSING: sched_cfg = { "_target_": "torch.optim.lr_scheduler.ConstantLR", "factor": 1.0, } # Handle custom cases if sched_cfg._target_ == "custom": if "tf.ExponentialLR" in sched_cfg._name_: sched_cfg = { "_target_": "torch.optim.lr_scheduler.ExponentialLR", "gamma": sched_cfg.decay_rate ** (1.0 / sched_cfg.decay_steps), } else: logger.warn("Detected unsupported custom scheduler", sched_cfg) try: scheduler = hydra.utils.instantiate(sched_cfg, optimizer=optimizer) except Exception as e: fail = colored("Failed to initialize scheduler: \n", "red") logger.error(fail + to_yaml(sched_cfg)) raise Exception(fail) from e return scheduler def instantiate_agg(cfg: DictConfig, model: torch.nn.Module, num_losses: int = 1): # Function for instantiating a loss aggregator with hydra try: aggregator = hydra.utils.instantiate( cfg.loss, model, num_losses, _convert_="all", ) except Exception as e: fail = colored("Failed to initialize loss aggregator: \n", "red") logger.error(fail + to_yaml(cfg.loss)) raise Exception(fail) from e return aggregator

[docs]def to_yaml(cfg: DictConfig): """Converges dict config into a YML string""" return OmegaConf.to_yaml(cfg)
[docs]def add_hydra_run_path(path: Union[str, Path]) -> Path: """Prepends current hydra run path""" working_dir = Path(os.getcwd()) # Working directory only present with @modulus.main() if HydraConfig.initialized(): org_dir = Path(get_original_cwd()) hydra_dir = working_dir.relative_to(org_dir) / Path(path) else: hydra_dir = working_dir / Path(path) if isinstance(path, str): hydra_dir = str(hydra_dir) return hydra_dir
[docs]def to_absolute_path(*args: Union[str, Path]): """Converts file path to absolute path based on run file location Modified from: https://github.com/facebookresearch/hydra/blob/main/hydra/utils.py """ out = () for path in args: p = Path(path) if not HydraConfig.initialized(): base = Path(os.getcwd()) else: ret = HydraConfig.get().runtime.cwd base = Path(ret) if p.is_absolute(): ret = p else: ret = base / p if isinstance(path, str): out = out + (str(ret),) else: out = out + (ret,) if len(args) == 1: out = out[0] return out
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.