from typing import Any, Dict, Iterable, Mapping, Optional, Tuple
from abc import ABC, abstractmethod
from collections import defaultdict, OrderedDict
from functools import lru_cache
import logging
import torch
import torch.distributed
import torch.multiprocessing
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from catalyst.core.callback import Callback, ICallback
from catalyst.core.engine import IEngine
from catalyst.core.logger import ILogger
from catalyst.core.misc import filter_callbacks_by_node, sort_callbacks_by_order, validate_loaders
from catalyst.core.trial import ITrial
from catalyst.typing import (
Criterion,
Device,
Model,
Optimizer,
RunnerCriterion,
RunnerModel,
RunnerOptimizer,
RunnerScheduler,
Sampler,
Scheduler,
)
from catalyst.utils.distributed import ddp_sync_run
from catalyst.utils.misc import maybe_recursive_call, set_global_seed
LOGGER = logging.getLogger(__name__)
BATCH_METRICS = Dict[str, float]
LOADER_METRICS = Dict[str, BATCH_METRICS]
EPOCH_METRICS = Dict[str, LOADER_METRICS]
@lru_cache(maxsize=42)
def _has_str_intersections(origin_string: str, strings: Tuple):
return any(x in origin_string for x in strings)
def _get_batch_size(loader: DataLoader):
batch_size = loader.batch_size
if batch_size is not None:
return batch_size
batch_size = loader.batch_sampler.batch_size
if batch_size is not None:
return batch_size
raise NotImplementedError(
"No `batch_size` found,"
"please specity it throught `loader.batch_size`, or `loader.batch_sampler.batch_size`"
)
[docs]class RunnerException(Exception):
"""Exception class for all runner errors."""
pass
[docs]class IRunner(ICallback, ILogger, ABC):
"""
An abstraction that contains all the logic of how to run the experiment,
stages, epochs, loaders and batches.
IRunner supports the logic for deep learning pipeline configuration with pure python code.
Please check the examples for intuition.
Args:
model: Torch model object
engine: IEngine instance
Abstraction, please check out implementations for more details:
- :py:mod:`catalyst.runners.runner.Runner`
- :py:mod:`catalyst.runners.config.ConfigRunner`
- :py:mod:`catalyst.runners.hydra.HydraRunner`
.. note::
To learn more about Catalyst Core concepts, please check out
- :py:mod:`catalyst.core.runner.IRunner`
- :py:mod:`catalyst.core.engine.IEngine`
- :py:mod:`catalyst.core.callback.Callback`
.. note::
Please follow the `minimal examples`_ sections for use cases.
.. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples
Examples:
.. code-block:: python
import os
from torch import nn, optim
from torch.utils.data import DataLoader
from catalyst import dl, utils
from catalyst.contrib.datasets import MNIST
from catalyst.data import ToTensor
class CustomRunner(dl.IRunner):
def __init__(self, logdir, device):
super().__init__()
self._logdir = logdir
self._device = device
def get_engine(self):
return dl.DeviceEngine(self._device)
def get_loggers(self):
return {
"console": dl.ConsoleLogger(),
"csv": dl.CSVLogger(logdir=self._logdir),
"tensorboard": dl.TensorboardLogger(logdir=self._logdir),
}
@property
def stages(self):
return ["train_freezed", "train_unfreezed"]
def get_stage_len(self, stage: str) -> int:
return 3
def get_loaders(self, stage: str):
loaders = {
"train": DataLoader(
MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()),
batch_size=32
),
"valid": DataLoader(
MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()),
batch_size=32
),
}
return loaders
def get_model(self, stage: str):
model = (
self.model
if self.model is not None
else nn.Sequential(
nn.Flatten(), nn.Linear(784, 128), nn.ReLU(), nn.Linear(128, 10)
)
)
if stage == "train_freezed":
# freeze layer
utils.set_requires_grad(model[1], False)
else:
utils.set_requires_grad(model, True)
return model
def get_criterion(self, stage: str):
return nn.CrossEntropyLoss()
def get_optimizer(self, stage: str, model):
if stage == "train_freezed":
return optim.Adam(model.parameters(), lr=1e-3)
else:
return optim.SGD(model.parameters(), lr=1e-1)
def get_scheduler(self, stage: str, optimizer):
return None
def get_callbacks(self, stage: str):
return {
"criterion": dl.CriterionCallback(
metric_key="loss", input_key="logits", target_key="targets"
),
"optimizer": dl.OptimizerCallback(metric_key="loss"),
"accuracy": dl.AccuracyCallback(
input_key="logits", target_key="targets", topk_args=(1, 3, 5)
),
"classification": dl.PrecisionRecallF1SupportCallback(
input_key="logits", target_key="targets", num_classes=10
),
"checkpoint": dl.CheckpointCallback(
self._logdir,
loader_key="valid",
metric_key="loss",
minimize=True,
save_n_best=3,
),
}
def handle_batch(self, batch):
x, y = batch
logits = self.model(x)
self.batch = {
"features": x,
"targets": y,
"logits": logits,
}
runner = CustomRunner("./logs", "cpu")
runner.run()
"""
def __init__(
self, model: RunnerModel = None, engine: IEngine = None,
):
"""Init."""
# the core
self.model: RunnerModel = model
self.engine: IEngine = engine
self.trial: ITrial = None
# the data
self.loaders: Dict[str, DataLoader] = None
# the components
self.criterion: RunnerCriterion = None
self.optimizer: RunnerOptimizer = None
self.scheduler: RunnerScheduler = None
# the callbacks
self.callbacks: Dict[str, Callback] = {}
# the loggers
self.loggers: Dict[str, ILogger] = {}
# the dataflow - model input/output and other batch tensors
self.batch: Dict[str, torch.Tensor] = None
# metrics flow - batch, loader and epoch metrics
self.batch_metrics: BATCH_METRICS = defaultdict(None)
self.loader_metrics: LOADER_METRICS = defaultdict(None)
self.epoch_metrics: EPOCH_METRICS = defaultdict(None)
# experiment info
self.run_key: str = None
self.global_epoch_step: int = 0
self.global_batch_step: int = 0
self.global_sample_step: int = 0
# stage info
self.stage_key: str = "infer"
self.is_infer_stage: bool = self.stage_key.startswith("infer")
self.stage_epoch_len: int = 0
self.stage_epoch_step: int = 0
self.stage_batch_step: int = 0
self.stage_sample_step: int = 0
# loader info
self.loader: DataLoader = None
self.loader_key: str = None
self.is_train_loader: bool = False
self.is_valid_loader: bool = False
self.is_infer_loader: bool = True
self.loader_batch_size: int = 0
self.loader_batch_len: int = 0
self.loader_sample_len: int = 0
self.loader_batch_step: int = 0
self.loader_sample_step: int = 0
# batch info
self.batch_size: int = 0
# extra
self.exception: Exception = None
self.need_early_stop: bool = False
self._stage_rank: int = -1
self._stage_world_size: int = -1
# @TODO: remove hotfix
@property
def device(self) -> Device:
"""Returns the runner's device instance."""
return self.engine.device
@property
def seed(self) -> int:
"""Experiment's seed for reproducibility."""
return 42
@property
def name(self) -> str:
"""Returns run name for monitoring tools."""
return "IRunner"
@property
def hparams(self) -> OrderedDict:
"""
Returns hyper-parameters for current run.
Example::
>>> runner.hparams
OrderedDict([('optimizer', 'Adam'),
('lr', 0.02),
('betas', (0.9, 0.999)),
('eps', 1e-08),
('weight_decay', 0),
('amsgrad', False),
('train_batch_size', 32)])
Returns:
dictionary with hyperparameters
"""
return {}
@property
def _log_defaults(self) -> Dict:
return {
# experiment info
"run_key": self.run_key,
"global_sample_step": self.global_sample_step,
"global_batch_step": self.global_batch_step,
"global_epoch_step": self.global_epoch_step,
# stage info
"stage_key": self.stage_key,
"stage_epoch_len": self.stage_epoch_len,
"stage_epoch_step": self.stage_epoch_step,
"stage_batch_step": self.stage_batch_step,
"stage_sample_step": self.stage_sample_step,
# loader info
"loader_key": self.loader_key,
"loader_batch_len": self.loader_batch_len,
"loader_sample_len": self.loader_sample_len,
"loader_batch_step": self.loader_batch_step,
"loader_sample_step": self.loader_sample_step,
}
@property
@abstractmethod
def stages(self) -> Iterable[str]:
"""Run's stage names.
Example::
>>> runner.stages
["pretraining", "finetuning"]
"""
pass
[docs] def get_stage_len(self, stage: str) -> int:
"""Returns number of epochs for the selected stage.
Args:
stage: current stage
Returns:
number of epochs in stage
Example::
>>> runner.get_stage_len("pretraining")
3
"""
return 1
[docs] def get_trial(self) -> Optional[ITrial]:
"""Returns the trial for the run."""
return None # noqa: WPS324
[docs] @abstractmethod
def get_engine(self) -> IEngine:
"""Returns the engine for the run."""
return None # noqa: WPS324
[docs] def get_loggers(self) -> Dict[str, ILogger]:
"""Returns the loggers for the run."""
return {}
[docs] def get_datasets(self, stage: str) -> "OrderedDict[str, Dataset]":
"""Returns the datasets for a given stage and epoch. # noqa: DAR401
.. note::
For Deep Learning cases you have the same dataset
during whole stage.
For Reinforcement Learning it's common to change the dataset
(experiment) every training epoch.
Args:
stage: stage name of interest, like "pretrain" / "train" / "finetune" / etc
Returns: # noqa: DAR202
OrderedDict[str, Dataset]: Ordered dictionary
with datasets for current stage and epoch.
.. note::
We need ordered dictionary to guarantee the correct dataflow
and order of our training datasets.
For example, to run train loader before validation one :)
Example::
>>> runner.get_datasets(stage="training")
OrderedDict({
"train": CsvDataset(in_csv=in_csv_train, ...),
"valid": CsvDataset(in_csv=in_csv_valid, ...),
})
"""
raise NotImplementedError
def get_samplers(self, stage: str = None) -> "OrderedDict[str, Sampler]":
"""Returns samplers for a given stage. # noqa: DAR401
Args:
stage: stage name of interest, like "pretrain" / "train" / "finetune" / etc
Returns: # noqa: DAR201, DAR202
OrderedDict[str, Sampler]: Ordered dictionary
with samplers for current stage and epoch.
"""
raise NotImplementedError
# def get_transforms(self, stage: str = None):
# """Returns the data transforms for a given stage and dataset.
#
# Args:
# stage: stage name of interest,
# like "pretrain" / "train" / "finetune" / etc
# dataset: dataset name of interest,
# like "train" / "valid" / "infer"
#
# .. note::
# For datasets/loaders naming please follow
# :py:mod:`catalyst.core.runner` documentation.
#
# Returns: # noqa: DAR202
# Data transformations to use for specified dataset.
#
# """
# raise NotImplementedError
[docs] @abstractmethod # noqa: WPS463
def get_loaders(self, stage: str) -> "OrderedDict[str, DataLoader]":
"""Returns the loaders for a given stage. # noqa: DAR401
.. note::
Wrapper for
:py:mod:`catalyst.core.experiment.IExperiment.get_datasets`.
For most of your experiments you need to rewrite `get_datasets`
method only.
Args:
stage: stage name of interest,
like "pretrain" / "train" / "finetune" / etc
Returns: # noqa: DAR201, DAR202
OrderedDict[str, DataLoader]: Ordered dictionary
with loaders for current stage and epoch.
"""
pass
[docs] @abstractmethod # noqa: WPS463
def get_model(self, stage: str) -> Model:
"""Returns the model for a given stage and epoch.
Example::
# suppose we have typical MNIST model, like
# nn.Sequential(nn.Linear(28*28, 128), nn.Linear(128, 10))
>>> runner.get_model(stage="train")
Sequential(
: Linear(in_features=784, out_features=128, bias=True)
: Linear(in_features=128, out_features=10, bias=True)
)
Args:
stage: stage name of interest
like "pretrain" / "train" / "finetune" / etc
Returns: # noqa: DAR201, DAR202
Model: model for a given stage.
"""
pass
[docs] def get_criterion(self, stage: str) -> Optional[Criterion]:
"""Returns the criterion for a given stage and epoch.
Example::
# for typical classification task
>>> runner.get_criterion(stage="train")
nn.CrossEntropyLoss()
Args:
stage: stage name of interest
like "pretrain" / "train" / "finetune" / etc
Returns: # noqa: DAR201, DAR202
Criterion: criterion for a given stage.
"""
return None # noqa: WPS324
[docs] def get_optimizer(self, stage: str, model: Model) -> Optional[Optimizer]:
"""Returns the optimizer for a given stage and model.
Example::
>>> runner.get_optimizer(model=model, stage="train")
torch.optim.Adam(model.parameters())
Args:
stage: stage name of interest
like "pretrain" / "train" / "finetune" / etc
model: model to optimize with stage optimizer
Returns: # noqa: DAR201, DAR202
Optimizer: optimizer for a given stage and model.
"""
return None # noqa: WPS324
[docs] def get_scheduler(self, stage: str, optimizer: Optimizer) -> Optional[Scheduler]:
"""Returns the scheduler for a given stage and optimizer.
Example::
>>> runner.get_scheduler(stage="training", optimizer=optimizer)
torch.optim.lr_scheduler.StepLR(optimizer)
Args:
stage: stage name of interest
like "pretrain" / "train" / "finetune" / etc
optimizer: optimizer to schedule with stage scheduler
Returns: # noqa: DAR201, DAR202
Scheduler: scheduler for a given stage and optimizer.
"""
return None # noqa: WPS324
def _get_model(self) -> Model:
self.model = self.get_model(stage=self.stage_key)
return self.model
def _get_criterion(self) -> Criterion:
self.criterion = self.get_criterion(stage=self.stage_key)
return self.criterion
def _get_optimizer(self, model: Model = None) -> Optimizer:
if model is not None:
self.model = model
# assert self.model is not None, "You need to setup model first"
self.optimizer = self.get_optimizer(stage=self.stage_key, model=self.model)
return self.optimizer
def _get_scheduler(self, optimizer: Optimizer = None) -> Scheduler:
if optimizer is not None:
self.optimizer = optimizer
# assert self.optimizer is not None, "You need to setup optimizer first"
self.scheduler = self.get_scheduler(stage=self.stage_key, optimizer=self.optimizer)
return self.scheduler
[docs] def get_callbacks(self, stage: str) -> "OrderedDict[str, ICallback]":
"""Returns callbacks for a given stage.
Args:
stage: stage name of interest like "pretrain" / "train" / "finetune" / etc
Returns:
OrderedDict[str, Callback]: Ordered dictionary # noqa: DAR202
with callbacks for current stage.
"""
return {}
[docs] def log_hparams(self, *args, **kwargs) -> None:
"""Logs hyperparameters to available loggers."""
for logger in self.loggers.values():
logger.log_hparams(
*args,
**kwargs,
# experiment info
run_key=self.run_key,
stage_key=self.stage_key,
)
[docs] def log_metrics(self, *args, **kwargs) -> None:
"""Logs batch, loader and epoch metrics to available loggers."""
for logger in self.loggers.values():
logger.log_metrics(*args, **kwargs, **self._log_defaults)
[docs] def log_image(self, *args, **kwargs) -> None:
"""Logs image to available loggers."""
for logger in self.loggers.values():
logger.log_image(*args, **kwargs, **self._log_defaults)
def log_artifact(self, *args, **kwargs) -> None:
"""Logs artifact (file like audio, video, csv, etc.) to available loggers."""
for logger in self.loggers.values():
logger.log_artifact(*args, **kwargs, **self._log_defaults)
def flush_log(self) -> None:
"""Flushes the loggers."""
for logger in self.loggers.values():
logger.flush_log()
def close_log(self, *args, **kwargs) -> None:
"""Closes the loggers."""
for logger in self.loggers.values():
logger.close_log(*args, **kwargs)
def _setup_loaders(self) -> None:
set_global_seed(self.seed + self.engine.rank + self.global_epoch_step)
loaders = self.get_loaders(stage=self.stage_key)
loaders = validate_loaders(loaders)
self.loaders = loaders
def _setup_components(self) -> None:
set_global_seed(self.seed + self.engine.rank + self.global_epoch_step)
self.model, self.criterion, self.optimizer, self.scheduler = self.engine.init_components(
model_fn=self._get_model,
criterion_fn=self._get_criterion,
optimizer_fn=self._get_optimizer,
scheduler_fn=self._get_scheduler,
)
def _setup_callbacks(self):
set_global_seed(self.seed + self.engine.rank + self.global_epoch_step)
callbacks = self.get_callbacks(self.stage_key)
callbacks = filter_callbacks_by_node(callbacks)
callbacks = sort_callbacks_by_order(callbacks)
self.callbacks = callbacks
def on_experiment_start(self, runner: "IRunner"):
"""Event handler."""
self.run_key = self.name
self.global_epoch_step: int = 0
self.global_batch_step: int = 0
self.global_sample_step: int = 0
self.exception: Exception = None
self.need_early_stop: bool = False
self.trial = self.get_trial()
self.engine = self.get_engine()
self.loggers = self.get_loggers()
self.log_hparams(hparams=self.hparams, scope="experiment")
def on_stage_start(self, runner: "IRunner"):
"""Event handler."""
assert self.stage_key is not None
self.is_infer_stage: bool = self.stage_key.startswith("infer")
self.stage_epoch_len = self.get_stage_len(stage=self.stage_key)
self.stage_epoch_step: int = 0
self.stage_batch_step: int = 0
self.stage_sample_step: int = 0
if self.engine.is_ddp:
self.engine.setup_process(rank=self._stage_rank, world_size=self._stage_world_size)
if not self.engine.is_master_process:
del self.loggers
self.loggers = {}
ddp_sync_run(self._setup_loaders)
self._setup_components()
self._setup_callbacks()
self.log_hparams(hparams=self.hparams, scope="stage")
def on_epoch_start(self, runner: "IRunner"):
"""Event handler."""
self.global_epoch_step += 1
self.stage_epoch_step += 1
self.epoch_metrics: Dict = defaultdict(None)
# storage for pure epoch-based metrics, like lr/momentum
self.epoch_metrics["_epoch_"] = {}
assert self.loaders is not None
for loader_key, loader in self.loaders.items():
if len(loader) == 0:
raise RunnerException(f"DataLoader with name {loader_key} is empty.")
set_global_seed(self.seed + self.engine.rank + self.global_epoch_step)
def on_loader_start(self, runner: "IRunner"):
"""Event handler."""
assert self.loader is not None
self.is_train_loader: bool = self.loader_key.startswith("train")
self.is_valid_loader: bool = self.loader_key.startswith("valid")
self.is_infer_loader: bool = self.loader_key.startswith("infer")
assert self.is_train_loader or self.is_valid_loader or self.is_infer_loader
self.loader_batch_size: int = _get_batch_size(self.loader)
self.loader_batch_len: int = len(self.loader)
self.loader_sample_len: int = len(self.loader.dataset)
self.loader_batch_step: int = 0
self.loader_sample_step: int = 0
self.loader_metrics: Dict = defaultdict(None)
if self.loader_batch_len == 0:
raise NotImplementedError(f"DataLoader with name {self.loader_key} is empty.")
set_global_seed(self.seed + self.engine.rank + self.global_epoch_step)
maybe_recursive_call(self.model, "train", mode=self.is_train_loader)
if isinstance(self.loader.sampler, DistributedSampler):
self.loader.sampler.set_epoch(self.stage_epoch_step)
def on_batch_start(self, runner: "IRunner"):
"""Event handler."""
self.batch = self.engine.sync_device(tensor_or_module=self.batch)
if isinstance(self.batch, dict):
self.batch_size = len(next(iter(self.batch.values())))
else:
self.batch_size = len(self.batch[0])
# we have an batch per each worker...
self.global_batch_step += self.engine.world_size
self.stage_batch_step += self.engine.world_size
self.loader_batch_step += self.engine.world_size
self.global_sample_step += self.batch_size * self.engine.world_size
self.stage_sample_step += self.batch_size * self.engine.world_size
self.loader_sample_step += self.batch_size * self.engine.world_size
self.batch_metrics: Dict = defaultdict(None)
def on_batch_end(self, runner: "IRunner"):
"""Event handler."""
# as far as we could `backward` anything from `batch_metrics` on the nodes during training,
# they could not be synced before, so we have to sync them in the end of the batch
# @TODO: could be done better
if self.engine.is_ddp:
self.batch_metrics = {
k: runner.engine.sync_tensor(torch.tensor(v, device=runner.device), "mean")
for k, v in self.batch_metrics.items()
}
self.log_metrics(metrics=self.batch_metrics, scope="batch")
def on_loader_end(self, runner: "IRunner"):
"""Event handler."""
self.log_metrics(metrics=self.loader_metrics, scope="loader")
self.epoch_metrics[self.loader_key] = {
key: float(value) for key, value in self.loader_metrics.items()
}
def on_epoch_end(self, runner: "IRunner"):
"""Event handler."""
self.log_metrics(metrics=self.epoch_metrics, scope="epoch")
self.flush_log()
def on_stage_end(self, runner: "IRunner"):
"""Event handler."""
del self.callbacks
self.callbacks = {}
del self.loaders
self.loaders = {}
self.engine.deinit_components(runner=self)
self.close_log(scope="stage")
# due to multiprocessing setup we have to close current loggers
# to prevent EOF-like errors
if self.engine.is_ddp:
self.flush_log()
self.close_log()
self.engine.cleanup_process()
def on_experiment_end(self, runner: "IRunner"):
"""Event handler."""
self.flush_log()
self.close_log(scope="experiment")
def on_exception(self, runner: "IRunner"):
"""Event handler."""
raise self.exception
def _run_event(self, event: str) -> None:
if _has_str_intersections(event, ("_start",)):
getattr(self, event)(self)
for callback in self.callbacks.values():
getattr(callback, event)(self)
if _has_str_intersections(event, ("_end", "_exception")):
getattr(self, event)(self)
[docs] @abstractmethod
def handle_batch(self, batch: Mapping[str, Any]) -> None:
"""
Inner method to handle specified data batch.
Used to make a train/valid/infer stage during Experiment run.
Args:
batch (Mapping[str, Any]): dictionary with data batches from DataLoader.
"""
pass
def _run_batch(self) -> None:
self._run_event("on_batch_start")
self.handle_batch(batch=self.batch)
self.batch = self.engine.sync_device(self.batch)
self._run_event("on_batch_end")
def _run_loader(self) -> None:
# NOTE: wrapped forward because need to scale forward propagation
# as it was noted in docs:
# https://pytorch.org/docs/stable/notes/amp_examples.html#typical-mixed-precision-training
self._run_event("on_loader_start")
with torch.set_grad_enabled(self.is_train_loader):
for self.loader_batch_step, self.batch in enumerate(self.loader):
with self.engine.autocast():
self._run_batch()
if self.need_early_stop:
self.need_early_stop = False
break
self._run_event("on_loader_end")
def _run_epoch(self) -> None:
self._run_event("on_epoch_start")
for self.loader_key, self.loader in self.loaders.items():
self._run_loader()
self._run_event("on_epoch_end")
def _run_stage(self, rank: int = -1, world_size: int = 1) -> None:
self._stage_rank, self._stage_world_size = rank, world_size
self._run_event("on_stage_start")
while self.stage_epoch_step < self.stage_epoch_len:
self._run_epoch()
if self.need_early_stop:
self.need_early_stop = False
break
self._run_event("on_stage_end")
def _run_experiment(self) -> None:
self._run_event("on_experiment_start")
for self.stage_key in self.stages:
if self.engine.is_ddp:
# ddp-device branch
world_size = self.engine.world_size
torch.multiprocessing.spawn(
self._run_stage, args=(world_size,), nprocs=world_size, join=True,
)
else:
# single-device branch (cpu, gpu, dp)
self._run_stage()
self._run_event("on_experiment_end")
[docs] def run(self) -> "IRunner":
"""Runs the experiment.
Returns:
self, `IRunner` instance after the experiment
"""
try:
self._run_experiment()
except (Exception, KeyboardInterrupt) as ex:
self.exception = ex
self._run_event("on_exception")
return self
__all__ = ["IRunner", "RunnerException"]