deeplearning/modulus/modulus-sym/_modules/modulus/sym/domain/constraint/constraint.html

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Source code for modulus.sym.domain.constraint.constraint

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Union, List

import torch
import logging
from torch.utils.data import DataLoader, BatchSampler, SequentialSampler, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel
from typing import Union, List

from modulus.sym.node import Node
from modulus.sym.constants import tf_dt
from modulus.sym.distributed.manager import DistributedManager
from modulus.sym.dataset import Dataset, IterableDataset
from modulus.sym.loss import Loss
from modulus.sym.graph import Graph
from modulus.sym.key import Key

logger = logging.getLogger(__name__)
Tensor = torch.Tensor


[docs]class Constraint: """Base class for constraints""" def __init__( self, nodes: List[Node], dataset: Union[Dataset, IterableDataset], loss: Loss, batch_size: int, shuffle: bool, drop_last: bool, num_workers: int, ): # Get DDP manager self.manager = DistributedManager() self.device = self.manager.device if not drop_last and self.manager.cuda_graphs: logger.info("drop_last must be true when using cuda graphs") drop_last = True # get dataset and dataloader self.dataset = dataset self.dataloader = iter( Constraint.get_dataloader( dataset=self.dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers, ) ) # construct model from nodes self.model = Graph( nodes, Key.convert_list(self.dataset.invar_keys), Key.convert_list(self.dataset.outvar_keys), ) self.model.to(self.device) if self.manager.distributed: # https://pytorch.org/docs/master/notes/cuda.html#id5 s = torch.cuda.Stream() s.wait_stream(torch.cuda.current_stream()) with torch.cuda.stream(s): self.model = DistributedDataParallel( self.model, device_ids=[self.manager.local_rank], output_device=self.device, broadcast_buffers=self.manager.broadcast_buffers, find_unused_parameters=self.manager.find_unused_parameters, process_group=self.manager.group( "data_parallel" ), # None by default ) torch.cuda.current_stream().wait_stream(s) self._input_names = Key.convert_list(dataset.invar_keys) self._output_names = Key.convert_list(dataset.outvar_keys) self._input_vars = None self._target_vars = None self._lambda_weighting = None # put loss on device self._loss = loss.to(self.device) @property def input_names(self) -> List[Key]: return self._input_names @property def output_names(self) -> List[Key]: return self._output_names def load_data(self): raise NotImplementedError("Subclass of Constraint needs to implement this") def load_data_static(self): raise NotImplementedError("Subclass of Constraint needs to implement this") def loss(self, step: int): raise NotImplementedError("Subclass of Constraint needs to implement this") def save_batch(self, filename: str): raise NotImplementedError("Subclass of Constraint needs to implement this") @staticmethod def _set_device(tensor_dict, device=None, requires_grad=False): # convert np to torch if needed tensor_dict = { key: torch.as_tensor(value, dtype=tf_dt, device=device) for key, value in tensor_dict.items() } # set requires_grad if needed if requires_grad: tensor_dict = { key: value.requires_grad_(requires_grad) for key, value in tensor_dict.items() } return tensor_dict
[docs] @staticmethod def get_dataloader( dataset: Union[Dataset, IterableDataset], batch_size: int, shuffle: bool, drop_last: bool, num_workers: int, distributed: bool = None, infinite: bool = True, ): "Return an appropriate dataloader given a dataset" assert isinstance(dataset, Dataset) or isinstance( dataset, IterableDataset ), "error, dataset must be a subclass of Dataset or IterableDataset" manager = DistributedManager() # use persistent workers # this is important for small datasets - torch would otherwise spend a lot of CPU overhead spawning workers each epoch persistent_workers = True if num_workers > 0 else False # map-style if isinstance(dataset, Dataset): assert batch_size is not None, "error, batch_size must be specified" assert shuffle is not None, "error, shuffle must be specified" assert drop_last is not None, "error, drop_last must be specified" # if distributed, use distributed sampler if distributed is not False and manager.distributed: sampler = DistributedSampler( dataset, num_replicas=manager.group_size("data_parallel"), rank=manager.group_rank("data_parallel"), shuffle=shuffle, drop_last=drop_last, ) # otherwise use standard sampler else: if shuffle: sampler = RandomSampler(dataset) else: sampler = SequentialSampler(dataset) # get batch sampler batch_sampler = BatchSampler(sampler, batch_size, drop_last) # if the dataset does auto collation, turn off automatic batching in dataloader # this passes batched indices directly to dataset # i.e. the dataloader yields default_convert(dataset[idx]) # see https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/fetch.py # note: may need to use torch.set_num_threads if array indexing tensors in dataset to avoid excessive threading if dataset.auto_collation: dataloader = DataLoader( dataset, batch_size=None, sampler=batch_sampler, pin_memory=True, num_workers=num_workers, worker_init_fn=dataset.worker_init_fn, persistent_workers=persistent_workers, ) # otherwise turn on automatic batching in dataloader # this passes single indices to the dataset # i.e. the dataloader yields default_collate([dataset[i] for i in idx]) else: dataloader = DataLoader( dataset, batch_sampler=batch_sampler, pin_memory=True, num_workers=num_workers, worker_init_fn=dataset.worker_init_fn, persistent_workers=persistent_workers, ) # iterable-style elif isinstance(dataset, IterableDataset): # for iterable datasets, must do batching/sampling within dataset dataloader = DataLoader( dataset, batch_size=None, pin_memory=True, num_workers=num_workers, worker_init_fn=dataset.worker_init_fn, persistent_workers=persistent_workers, ) # make dataloader infinite if infinite: dataloader = InfiniteDataLoader(dataloader) # initialise dataset if on main thread if num_workers == 0: dataset.worker_init_fn(0) return dataloader
[docs]class InfiniteDataLoader: "An infinite dataloader, for use with map-style datasets to avoid StopIteration after each epoch" def __init__(self, dataloader): self.dataloader = dataloader self.epoch = 0 def __iter__(self): while True: dataloader = iter(self.dataloader) for batch in dataloader: yield batch self.epoch += 1
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.