257 lines
8.3 KiB
Python
257 lines
8.3 KiB
Python
"""Trainer module."""
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import sys
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import torch
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import lightning
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from .utils import check_consistency
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from .data import PinaDataModule
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from .solver import SolverInterface, PINNInterface
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class Trainer(lightning.pytorch.Trainer):
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"""
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PINA custom Trainer class which allows to customize standard Lightning
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Trainer class for PINNs training.
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"""
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def __init__(
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self,
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solver,
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batch_size=None,
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train_size=1.0,
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test_size=0.0,
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val_size=0.0,
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compile=None,
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automatic_batching=None,
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num_workers=None,
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pin_memory=None,
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shuffle=None,
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**kwargs,
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):
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"""
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Initialize the Trainer class for by calling Lightning costructor and
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adding many other functionalities.
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:param solver: A pina:class:`SolverInterface` solver for the
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differential problem.
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:type solver: SolverInterface
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:param batch_size: How many samples per batch to load.
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If ``batch_size=None`` all
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samples are loaded and data are not batched, defaults to None.
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:type batch_size: int | None
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:param train_size: Percentage of elements in the train dataset.
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:type train_size: float
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:param test_size: Percentage of elements in the test dataset.
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:type test_size: float
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:param val_size: Percentage of elements in the val dataset.
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:type val_size: float
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:param compile: if True model is compiled before training,
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default False. For Windows users compilation is always disabled.
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:type compile: bool
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:param automatic_batching: if True automatic PyTorch batching is
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performed. Please avoid using automatic batching when batch_size is
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large, default False.
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:type automatic_batching: bool
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:param num_workers: Number of worker threads for data loading.
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Default 0 (serial loading).
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:type num_workers: int
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:param pin_memory: Whether to use pinned memory for faster data
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transfer to GPU. Default False.
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:type pin_memory: bool
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:param shuffle: Whether to shuffle the data for training. Default True.
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:type pin_memory: bool
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:Keyword Arguments:
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The additional keyword arguments specify the training setup
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and can be choosen from the `pytorch-lightning
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Trainer API <https://lightning.ai/docs/pytorch/stable/common/
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trainer.html#trainer-class-api>`_
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"""
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# check consistency for init types
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self._check_input_consistency(
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solver,
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train_size,
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test_size,
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val_size,
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automatic_batching,
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compile,
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)
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pin_memory, num_workers, shuffle, batch_size = (
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self._check_consistency_and_set_defaults(
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pin_memory, num_workers, shuffle, batch_size
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)
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)
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# inference mode set to false when validating/testing PINNs otherwise
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# gradient is not tracked and optimization_cycle fails
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if isinstance(solver, PINNInterface):
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kwargs["inference_mode"] = False
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# Logging depends on the batch size, when batch_size is None then
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# log_every_n_steps should be zero
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if batch_size is None:
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kwargs["log_every_n_steps"] = 0
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else:
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kwargs.setdefault("log_every_n_steps", 50) # default for lightning
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# Setting default kwargs, overriding lightning defaults
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kwargs.setdefault("enable_progress_bar", True)
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kwargs.setdefault("logger", None)
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super().__init__(**kwargs)
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# checking compilation and automatic batching
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if compile is None or sys.platform == "win32":
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compile = False
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self.automatic_batching = (
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automatic_batching if automatic_batching is not None else False
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)
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# set attributes
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self.compile = compile
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self.solver = solver
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self.batch_size = batch_size
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self._move_to_device()
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self.data_module = None
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self._create_datamodule(
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train_size,
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test_size,
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val_size,
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batch_size,
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automatic_batching,
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pin_memory,
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num_workers,
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shuffle,
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)
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# logging
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self.logging_kwargs = {
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"logger": bool(
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kwargs["logger"] is not None or kwargs["logger"] is True
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),
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"sync_dist": bool(
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len(self._accelerator_connector._parallel_devices) > 1
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),
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"on_step": bool(kwargs["log_every_n_steps"] > 0),
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"prog_bar": bool(kwargs["enable_progress_bar"]),
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"on_epoch": True,
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}
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def _move_to_device(self):
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device = self._accelerator_connector._parallel_devices[0]
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# move parameters to device
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pb = self.solver.problem
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if hasattr(pb, "unknown_parameters"):
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for key in pb.unknown_parameters:
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pb.unknown_parameters[key] = torch.nn.Parameter(
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pb.unknown_parameters[key].data.to(device)
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)
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def _create_datamodule(
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self,
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train_size,
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test_size,
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val_size,
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batch_size,
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automatic_batching,
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pin_memory,
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num_workers,
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shuffle,
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):
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"""
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This method is used here because is resampling is needed
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during training, there is no need to define to touch the
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trainer dataloader, just call the method.
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"""
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if not self.solver.problem.are_all_domains_discretised:
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error_message = "\n".join(
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[
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f"""{" " * 13} ---> Domain {key} {
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"sampled" if key in self.solver.problem.discretised_domains
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else
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"not sampled"}"""
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for key in self.solver.problem.domains.keys()
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]
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)
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raise RuntimeError(
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"Cannot create Trainer if not all conditions "
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"are sampled. The Trainer got the following:\n"
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f"{error_message}"
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)
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self.data_module = PinaDataModule(
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self.solver.problem,
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train_size=train_size,
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test_size=test_size,
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val_size=val_size,
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batch_size=batch_size,
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automatic_batching=automatic_batching,
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num_workers=num_workers,
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pin_memory=pin_memory,
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shuffle=shuffle,
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)
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def train(self, **kwargs):
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"""
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Train the solver method.
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"""
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return super().fit(self.solver, datamodule=self.data_module, **kwargs)
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def test(self, **kwargs):
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"""
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Test the solver method.
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"""
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return super().test(self.solver, datamodule=self.data_module, **kwargs)
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@property
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def solver(self):
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"""
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Returning trainer solver.
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"""
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return self._solver
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@solver.setter
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def solver(self, solver):
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self._solver = solver
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@staticmethod
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def _check_input_consistency(
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solver, train_size, test_size, val_size, automatic_batching, compile
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):
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"""
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Check the consistency of the input parameters."
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"""
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check_consistency(solver, SolverInterface)
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check_consistency(train_size, float)
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check_consistency(test_size, float)
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check_consistency(val_size, float)
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if automatic_batching is not None:
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check_consistency(automatic_batching, bool)
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if compile is not None:
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check_consistency(compile, bool)
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@staticmethod
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def _check_consistency_and_set_defaults(
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pin_memory, num_workers, shuffle, batch_size
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):
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"""
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Check the consistency of the input parameters and set the default
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values.
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"""
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if pin_memory is not None:
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check_consistency(pin_memory, bool)
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else:
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pin_memory = False
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if num_workers is not None:
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check_consistency(pin_memory, int)
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else:
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num_workers = 0
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if shuffle is not None:
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check_consistency(shuffle, bool)
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else:
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shuffle = True
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if batch_size is not None:
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check_consistency(batch_size, int)
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return pin_memory, num_workers, shuffle, batch_size
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