Source code for catalyst.utils.components

from typing import Dict, Tuple
import copy

import torch
from torch import nn
import torch.distributed

from import IS_XLA_AVAILABLE
from import (
from catalyst.utils.distributed import (
from catalyst.utils.misc import maybe_recursive_call
from catalyst.utils.torch import get_device

[docs]def process_components( model: Model, criterion: Criterion = None, optimizer: Optimizer = None, scheduler: Scheduler = None, distributed_params: Dict = None, device: Device = None, ) -> Tuple[Model, Criterion, Optimizer, Scheduler, Device]: """ Returns the processed model, criterion, optimizer, scheduler and device. Args: model (Model): torch model criterion (Criterion): criterion function optimizer (Optimizer): optimizer scheduler (Scheduler): scheduler distributed_params (dict, optional): dict with the parameters for distributed and FP16 method device (Device, optional): device Returns: tuple with processed model, criterion, optimizer, scheduler and device. Raises: ValueError: if device is None and TPU available, for using TPU need to manualy move model/optimizer/scheduler to a TPU device and pass device to a function. NotImplementedError: if model is not nn.Module or dict for multi-gpu, nn.ModuleDict for DataParallel not implemented yet """ distributed_params = distributed_params or {} distributed_params = copy.deepcopy(distributed_params) distributed_params.update(get_distributed_params()) if device is None and IS_XLA_AVAILABLE: raise ValueError( "TPU device is available. " "Please move model, optimizer and scheduler (if present) " "to TPU device manualy and specify a device or " "use CPU device." ) if device is None: device = get_device() elif isinstance(device, str): device = torch.device(device) is_apex_available = ( distributed_params.pop("apex", True) and check_apex_available() ) model: Model = maybe_recursive_call(model, "to", device=device) if check_ddp_wrapped(model): pass # distributed data parallel run (ddp) (with apex support) elif get_rank() >= 0: assert isinstance( model, nn.Module ), "Distributed training is not available for KV model" local_rank = distributed_params.pop("local_rank", 0) or 0 device = f"cuda:{local_rank}" model = maybe_recursive_call(model, "to", device=device) syncbn = distributed_params.pop("syncbn", False) if is_apex_available: import apex model, optimizer = initialize_apex( model, optimizer, **distributed_params ) model = apex.parallel.DistributedDataParallel(model) if syncbn: model = apex.parallel.convert_syncbn_model(model) else: model = nn.parallel.DistributedDataParallel( model, device_ids=[local_rank], output_device=local_rank ) # data parallel run (dp) (with apex support) else: # apex issue use_apex = (is_apex_available and torch.cuda.device_count() == 1) or ( is_apex_available and torch.cuda.device_count() > 1 and distributed_params.get("opt_level", "O0") == "O1" ) if use_apex: assert isinstance( model, nn.Module ), "Apex training is not available for KV model" model, optimizer = initialize_apex( model, optimizer, **distributed_params ) if ( torch.cuda.device_count() > 1 and device.type != "cpu" and device.index is None ): if isinstance(model, nn.Module): model = nn.DataParallel(model) elif isinstance(model, dict): model = {k: nn.DataParallel(v) for k, v in model.items()} else: raise NotImplementedError() model: Model = maybe_recursive_call(model, "to", device=device) return model, criterion, optimizer, scheduler, device
__all__ = ["process_components"]