"""Core SpeechBrain code for running experiments.
Authors
* Peter Plantinga 2020
* Abdel Heba 2020
* Mirco Ravanelli 2020
"""
import os
import sys
import yaml
import time
import torch
import shutil
import logging
import inspect
import pathlib
import argparse
import tempfile
import speechbrain as sb
from datetime import date
from enum import Enum, auto
from tqdm.contrib import tqdm
from types import SimpleNamespace
from torch.nn import SyncBatchNorm
from torch.utils.data import DataLoader
from torch.nn import DataParallel as DP
from torch.utils.data import IterableDataset
from torch.utils.data import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from hyperpyyaml import resolve_references
from speechbrain.utils.distributed import run_on_main
from speechbrain.dataio.dataloader import SaveableDataLoader
from speechbrain.dataio.sampler import DistributedSamplerWrapper
from speechbrain.dataio.sampler import ReproducibleRandomSampler
logger = logging.getLogger(__name__)
DEFAULT_LOG_CONFIG = os.path.dirname(os.path.abspath(__file__))
DEFAULT_LOG_CONFIG = os.path.join(DEFAULT_LOG_CONFIG, "log-config.yaml")
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
INTRA_EPOCH_CKPT_FLAG = "brain_intra_epoch_ckpt"
PYTHON_VERSION_MAJOR = 3
PYTHON_VERSION_MINOR = 7
[docs]def create_experiment_directory(
experiment_directory,
hyperparams_to_save=None,
overrides={},
log_config=DEFAULT_LOG_CONFIG,
save_env_desc=True,
):
"""Create the output folder and relevant experimental files.
Arguments
---------
experiment_directory : str
The place where the experiment directory should be created.
hyperparams_to_save : str
A filename of a yaml file representing the parameters for this
experiment. If passed, references are resolved, and the result is
written to a file in the experiment directory called "hyperparams.yaml".
overrides : dict
A mapping of replacements made in the yaml file, to save in yaml.
log_config : str
A yaml filename containing configuration options for the logger.
save_env_desc : bool
If True, an environment state description is saved to the experiment
directory, in a file called env.log in the experiment directory.
"""
try:
# all writing command must be done with the main_process
if sb.utils.distributed.if_main_process():
if not os.path.isdir(experiment_directory):
os.makedirs(experiment_directory)
# Write the parameters file
if hyperparams_to_save is not None:
hyperparams_filename = os.path.join(
experiment_directory, "hyperparams.yaml"
)
with open(hyperparams_to_save) as f:
resolved_yaml = resolve_references(f, overrides)
with open(hyperparams_filename, "w") as w:
print("# Generated %s from:" % date.today(), file=w)
print("# %s" % os.path.abspath(hyperparams_to_save), file=w)
print("# yamllint disable", file=w)
shutil.copyfileobj(resolved_yaml, w)
# Copy executing file to output directory
module = inspect.getmodule(inspect.currentframe().f_back)
if module is not None:
callingfile = os.path.realpath(module.__file__)
shutil.copy(callingfile, experiment_directory)
# Log exceptions to output automatically
log_file = os.path.join(experiment_directory, "log.txt")
logger_overrides = {
"handlers": {"file_handler": {"filename": log_file}}
}
sb.utils.logger.setup_logging(log_config, logger_overrides)
sys.excepthook = _logging_excepthook
# Log beginning of experiment!
logger.info("Beginning experiment!")
logger.info(f"Experiment folder: {experiment_directory}")
# Save system description:
if save_env_desc:
description_str = sb.utils.logger.get_environment_description()
with open(
os.path.join(experiment_directory, "env.log"), "w"
) as fo:
fo.write(description_str)
finally:
# wait for main_process if ddp is used
sb.utils.distributed.ddp_barrier()
def _logging_excepthook(exc_type, exc_value, exc_traceback):
"""Interrupt exception raising to log the error."""
logger.error("Exception:", exc_info=(exc_type, exc_value, exc_traceback))
[docs]def parse_arguments(arg_list):
r"""Parse command-line arguments to the experiment.
Arguments
---------
arg_list : list
A list of arguments to parse, most often from `sys.argv[1:]`.
Returns
-------
param_file : str
The location of the parameters file.
run_opts : dict
Run options, such as distributed, device, etc.
overrides : dict
The overrides to pass to ``load_hyperpyyaml``.
Example
-------
>>> argv = ['hyperparams.yaml', '--device', 'cuda:1', '--seed', '10']
>>> filename, run_opts, overrides = parse_arguments(argv)
>>> filename
'hyperparams.yaml'
>>> run_opts["device"]
'cuda:1'
>>> overrides
'seed: 10'
"""
parser = argparse.ArgumentParser(
description="Run a SpeechBrain experiment",
)
parser.add_argument(
"param_file",
type=str,
help="A yaml-formatted file using the extended YAML syntax. "
"defined by SpeechBrain.",
)
parser.add_argument(
"--debug",
default=False,
action="store_true",
help="Run the experiment with only a few batches for all "
"datasets, to ensure code runs without crashing.",
)
parser.add_argument(
"--debug_batches",
type=int,
default=2,
help="Number of batches to run in debug mode.",
)
parser.add_argument(
"--debug_epochs",
type=int,
default=2,
help="Number of epochs to run in debug mode. "
"If a non-positive number is passed, all epochs are run.",
)
parser.add_argument(
"--log_config",
type=str,
help="A file storing the configuration options for logging",
)
# if use_env = False in torch.distributed.lunch then local_rank arg is given
parser.add_argument(
"--local_rank", type=int, help="Rank on local machine",
)
parser.add_argument(
"--device",
type=str,
default="cuda:0",
help="The device to run the experiment on (e.g. 'cuda:0')",
)
parser.add_argument(
"--data_parallel_backend",
default=False,
action="store_true",
help="This flag enables training with data_parallel.",
)
parser.add_argument(
"--distributed_launch",
default=False,
action="store_true",
help="This flag enables training with DDP. Assumes script run with "
"`torch.distributed.launch`",
)
parser.add_argument(
"--distributed_backend",
type=str,
default="nccl",
help="One of {nccl, gloo, mpi}",
)
parser.add_argument(
"--find_unused_parameters",
default=False,
action="store_true",
help="This flag disable unused parameters detection",
)
parser.add_argument(
"--jit_module_keys",
type=str,
nargs="*",
help="A list of keys in the 'modules' dict to jitify",
)
parser.add_argument(
"--auto_mix_prec",
default=False,
action="store_true",
help="This flag enables training with automatic mixed-precision.",
)
parser.add_argument(
"--max_grad_norm",
type=float,
help="Gradient norm will be clipped to this value, "
"enter negative value to disable.",
)
parser.add_argument(
"--nonfinite_patience",
type=int,
help="Max number of batches per epoch to skip if loss is nonfinite.",
)
parser.add_argument(
"--noprogressbar",
default=False,
action="store_true",
help="This flag disables the data loop progressbars.",
)
parser.add_argument(
"--ckpt_interval_minutes",
type=float,
help="Amount of time between saving intra-epoch checkpoints "
"in minutes. If non-positive, intra-epoch checkpoints are not saved.",
)
# Accept extra args to override yaml
run_opts, overrides = parser.parse_known_args(arg_list)
# Ignore items that are "None", they were not passed
run_opts = {k: v for k, v in vars(run_opts).items() if v is not None}
param_file = run_opts["param_file"]
del run_opts["param_file"]
overrides = _convert_to_yaml(overrides)
# Checking that DataParallel use the right number of GPU
if run_opts["data_parallel_backend"]:
if torch.cuda.device_count() == 0:
raise ValueError("You must have at least 1 GPU.")
# For DDP, the device args must equal to local_rank used by
# torch.distributed.launch. If run_opts["local_rank"] exists,
# use os.environ["LOCAL_RANK"]
local_rank = None
if "local_rank" in run_opts:
local_rank = run_opts["local_rank"]
else:
if "LOCAL_RANK" in os.environ and os.environ["LOCAL_RANK"] != "":
local_rank = int(os.environ["LOCAL_RANK"])
# force device arg to be the same as local_rank from torch.distributed.lunch
if local_rank is not None and "cuda" in run_opts["device"]:
run_opts["device"] = run_opts["device"][:-1] + str(local_rank)
return param_file, run_opts, overrides
def _convert_to_yaml(overrides):
"""Convert args to yaml for overrides"""
yaml_string = ""
# Handle '--arg=val' type args
joined_args = "=".join(overrides)
split_args = joined_args.split("=")
for arg in split_args:
if arg.startswith("--"):
yaml_string += "\n" + arg[len("--") :] + ":"
else:
yaml_string += " " + arg
return yaml_string.strip()
[docs]class Stage(Enum):
"""Simple enum to track stage of experiments."""
TRAIN = auto()
VALID = auto()
TEST = auto()
[docs]@sb.utils.checkpoints.register_checkpoint_hooks
class Brain:
r"""Brain class abstracts away the details of data loops.
The primary purpose of the `Brain` class is the implementation of
the ``fit()`` method, which iterates epochs and datasets for the
purpose of "fitting" a set of modules to a set of data.
In order to use the ``fit()`` method, one should sub-class the ``Brain``
class and override any methods for which the default behavior does not
match the use case. For a simple use case (e.g., training a single model
with a single dataset) the only methods that need to be overridden are:
* ``compute_forward()``
* ``compute_objectives()``
The example below illustrates how overriding these two methods is done.
For more complicated use cases, such as multiple modules that need to
be updated, the following methods can be overridden:
* ``fit_batch()``
* ``evaluate_batch()``
Arguments
---------
modules : dict of str:torch.nn.Module pairs
These modules are passed to the optimizer by default if they have
trainable parameters, and will have ``train()``/``eval()`` called on them.
opt_class : torch.optim class
A torch optimizer constructor that has takes only the list of
parameters (e.g. a lambda or partial function definition). By default,
this will be passed all modules in ``modules`` at the
beginning of the ``fit()`` method. This behavior can be changed
by overriding the ``configure_optimizers()`` method.
hparams : dict
Each key:value pair should consist of a string key and a hyperparameter
that is used within the overridden methods. These will
be accessible via an ``hparams`` attribute, using "dot" notation:
e.g., self.hparams.model(x).
run_opts : dict
A set of options to change the runtime environment, including
debug (bool)
If ``True``, this will only iterate a few batches for all
datasets, to ensure code runs without crashing.
debug_batches (int)
Number of batches to run in debug mode, Default ``2``.
debug_epochs (int)
Number of epochs to run in debug mode, Default ``2``.
If a non-positive number is passed, all epochs are run.
jit_module_keys (list of str)
List of keys in ``modules`` that should be jit compiled.
distributed_count (int)
Number of devices to run on.
distributed_backend (str)
One of ``ddp_nccl``, ``ddp_gloo``, ``ddp_mpi``, ``data_parallel``.
device (str)
The location for performing computations.
auto_mix_prec (bool)
If ``True``, automatic mixed-precision is used.
Activate it only with cuda.
max_grad_norm (float)
Default implementation of ``fit_batch()`` uses
``clip_grad_norm_`` with this value. Default: ``5``.
nonfinite_patience (int)
Number of times to ignore non-finite losses before stopping.
Default: ``3``.
noprogressbar (bool)
Whether to turn off progressbar when training. Default: ``False``.
ckpt_interval_minutes (float)
Amount of time between saving intra-epoch checkpoints,
in minutes, default: ``15.0``. If non-positive, these are not saved.
checkpointer : speechbrain.Checkpointer
By default, this will be used to load checkpoints, and will have the
optimizer added to continue training if interrupted.
Example
-------
>>> from torch.optim import SGD
>>> class SimpleBrain(Brain):
... def compute_forward(self, batch, stage):
... return self.modules.model(batch[0])
... def compute_objectives(self, predictions, batch, stage):
... return torch.nn.functional.l1_loss(predictions, batch[0])
>>> model = torch.nn.Linear(in_features=10, out_features=10)
>>> brain = SimpleBrain({"model": model}, opt_class=lambda x: SGD(x, 0.1))
>>> brain.fit(range(1), ([torch.rand(10, 10), torch.rand(10, 10)],))
"""
def __init__( # noqa: C901
self,
modules=None,
opt_class=None,
hparams=None,
run_opts=None,
checkpointer=None,
):
self.opt_class = opt_class
self.checkpointer = checkpointer
# Arguments passed via the run opts dictionary
run_opt_defaults = {
"debug": False,
"debug_batches": 2,
"debug_epochs": 2,
"device": "cpu",
"data_parallel_backend": False,
"distributed_launch": False,
"distributed_backend": "nccl",
"find_unused_parameters": False,
"jit_module_keys": None,
"auto_mix_prec": False,
"max_grad_norm": 5.0,
"nonfinite_patience": 3,
"noprogressbar": False,
"ckpt_interval_minutes": 0,
}
for arg, default in run_opt_defaults.items():
if run_opts is not None and arg in run_opts:
if hparams is not None and arg in hparams:
logger.info(
"Info: " + arg + " arg overridden by command line input"
)
setattr(self, arg, run_opts[arg])
else:
# If any arg from run_opt_defaults exist in hparams and
# not in command line args "run_opts"
if hparams is not None and arg in hparams:
logger.info(
"Info: " + arg + " arg from hparam file is used"
)
setattr(self, arg, hparams[arg])
else:
setattr(self, arg, default)
# Check Python version
if not (
sys.version_info.major == PYTHON_VERSION_MAJOR
and sys.version_info.minor >= PYTHON_VERSION_MINOR
):
logger.warn(
"Detected Python "
+ str(sys.version_info.major)
+ "."
+ str(sys.version_info.minor)
+ ". We suggest using SpeechBrain with Python >="
+ str(PYTHON_VERSION_MAJOR)
+ "."
+ str(PYTHON_VERSION_MINOR)
)
if self.data_parallel_backend and self.distributed_launch:
sys.exit(
"To use data_parallel backend, start your script with:\n\t"
"python experiment.py hyperparams.yaml "
"--data_parallel_backend=True"
"To use DDP backend, start your script with:\n\t"
"python -m torch.distributed.lunch [args]\n"
"experiment.py hyperparams.yaml --distributed_launch=True "
"--distributed_backend=nccl"
)
# Switch to the right context
if "cuda" in self.device:
torch.cuda.set_device(int(self.device[-1]))
# Put modules on the right device, accessible with dot notation
self.modules = torch.nn.ModuleDict(modules).to(self.device)
# Make hyperparams available with dot notation too
if hparams is not None:
self.hparams = SimpleNamespace(**hparams)
# Checkpointer should point at a temporary directory in debug mode
if (
self.debug
and self.checkpointer is not None
and hasattr(self.checkpointer, "checkpoints_dir")
):
tempdir = tempfile.TemporaryDirectory()
logger.info(
"Since debug mode is active, switching checkpointer "
f"output to temporary directory: {tempdir.name}"
)
self.checkpointer.checkpoints_dir = pathlib.Path(tempdir.name)
# Keep reference to tempdir as long as checkpointer exists
self.checkpointer.tempdir = tempdir
# Sampler should be handled by `make_dataloader`
# or if you provide a DataLoader directly, you can set
# this.train_sampler = your_sampler
# to have your_sampler.set_epoch() called on each epoch.
self.train_sampler = None
# Automatic mixed precision init
if self.auto_mix_prec:
self.scaler = torch.cuda.amp.GradScaler()
# List parameter count for the user
total_params = sum(
p.numel() for p in self.modules.parameters() if p.requires_grad
)
if total_params > 0:
clsname = self.__class__.__name__
fmt_num = sb.utils.logger.format_order_of_magnitude(total_params)
logger.info(f"{fmt_num} trainable parameters in {clsname}")
if self.distributed_launch:
self.rank = int(os.environ["RANK"])
if not torch.distributed.is_initialized():
if self.rank > 0:
sys.exit(
" ================ WARNING ==============="
"Please add sb.ddp_init_group() into your exp.py"
"To use DDP backend, start your script with:\n\t"
"python -m torch.distributed.launch [args]\n\t"
"experiment.py hyperparams.yaml "
"--distributed_launch=True --distributed_backend=nccl"
)
else:
logger.warn(
"To use DDP, please add "
"sb.utils.distributed.ddp_init_group() into your exp.py"
)
logger.info(
"Only the main process is alive, "
"all other subprocess were killed."
)
# force the models to start and remain synchronized
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Prepare iterating variables
self.avg_train_loss = 0.0
self.step = 0
# Add this class to the checkpointer for intra-epoch checkpoints
if self.checkpointer is not None:
self.checkpointer.add_recoverable("brain", self)
[docs] def compute_forward(self, batch, stage):
"""Forward pass, to be overridden by sub-classes.
Arguments
---------
batch : torch.Tensor or tensors
An element from the dataloader, including inputs for processing.
stage : Stage
The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
Returns
-------
torch.Tensor or Tensors
The outputs after all processing is complete.
Directly passed to ``compute_objectives()``.
"""
raise NotImplementedError
[docs] def compute_objectives(self, predictions, batch, stage):
"""Compute loss, to be overridden by sub-classes.
Arguments
---------
predictions : torch.Tensor or Tensors
The output tensor or tensors to evaluate.
Comes directly from ``compute_forward()``.
batch : torch.Tensor or tensors
An element from the dataloader, including targets for comparison.
stage : Stage
The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
Returns
-------
loss : torch.Tensor
A tensor with the computed loss.
"""
raise NotImplementedError
[docs] def on_stage_start(self, stage, epoch=None):
"""Gets called when a stage starts.
Useful for defining class variables used during the stage.
Arguments
---------
stage : Stage
The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
epoch : int
The current epoch count.
"""
pass
[docs] def on_stage_end(self, stage, stage_loss, epoch=None):
"""Gets called at the end of a stage.
Useful for computing stage statistics, saving checkpoints, etc.
Arguments
---------
stage : Stage
The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
stage_loss : float
The average loss over the completed stage.
epoch : int
The current epoch count.
"""
pass
[docs] def make_dataloader(
self, dataset, stage, ckpt_prefix="dataloader-", **loader_kwargs,
):
"""Creates DataLoaders for Datasets.
This is used by ``fit()`` and ``evaluate()`` if they just receive
Datasets.
Alternatively, this can be called from outside the Brain subclass.
In that case, the DataLoader should be passed to ``fit()`` in place
of the dataset.
The Stage.TRAIN DataLoader is handled specially. It has extra args for
shuffle and drop_last. In DDP a DistributedSampler is created (unless
the dataset is an IterableDataset).
NOTE
----
Some important DataLoader arguments are passed via **loader_kwargs,
e.g., batch_size, num_workers, pin_memory.
NOTE
----
By default, ``evaluate()`` specifies ckpt_prefix=None to stop the test
DataLoader being added to the checkpointer. If you need to add a
recoverable after saving checkpoints (e.g., at test time, after
checkpointing the training), and still be able to recover reasonably,
you should probably specify ``allow_partial_load=True``.
Arguments
---------
dataset : Dataset
A set of data to use to create data loader. If the Dataset is a
DynamicItemDataset, PaddedBatch is used as the default collate_fn,
unless specified in loader_kwargs.
stage : Stage
The stage of the experiment: Stage.TRAIN, Stage.VALID, Stage.TEST
ckpt_prefix : str, None
Prefix to use for SaveableDataLoader Checkpoint name. The Stage
name is added to this to create the full key. Set to None to not
save the DataLoader.
**loader_kwargs : dict
Additional keyword arguments to the DataLoader.
E.g., batch_size, num_workers, pin_memory.
"""
# TRAIN stage is handled specially.
if stage == sb.Stage.TRAIN:
loader_kwargs = self._train_loader_specifics(dataset, loader_kwargs)
dataloader = sb.dataio.dataloader.make_dataloader(
dataset, **loader_kwargs
)
if (
self.checkpointer is not None
and ckpt_prefix is not None
and isinstance(dataloader, SaveableDataLoader)
):
ckpt_key = ckpt_prefix + stage.name
self.checkpointer.add_recoverable(ckpt_key, dataloader)
return dataloader
def _train_loader_specifics(self, dataset, loader_kwargs):
sampler = loader_kwargs.get("sampler", None)
# Shuffling should really only matter for the train stage. Shuffling
# will also lead to more padding in batches if the order was otherwise
# sorted by length.
shuffle = loader_kwargs.get("shuffle", False)
if shuffle and not self.distributed_launch:
if sampler is not None:
raise ValueError(
"Cannot specify both shuffle=True "
"and a sampler in loader_kwargs"
)
sampler = ReproducibleRandomSampler(dataset)
self.train_sampler = sampler
loader_kwargs["sampler"] = self.train_sampler
# Delete the shuffle flag, since you cannot specify both a sampler and
# shuffling:
del loader_kwargs["shuffle"]
# Possibly make a DistributedSampler or a wrapper for some other sampler
if self.distributed_launch and not isinstance(dataset, IterableDataset):
drop_last = loader_kwargs.get("drop_last", False)
# num_replicas arg is equal to world_size
# and retrieved automatically within
# DistributedSampler obj.
if sampler is not None:
self.train_sampler = DistributedSamplerWrapper(
sampler,
rank=self.rank,
drop_last=drop_last,
shuffle=shuffle,
)
# with DistributedSamplerWrapper, one must disable shuffling for dataloader
loader_kwargs["shuffle"] = False
elif loader_kwargs.get("batch_sampler") is None:
# Currently to get here, shuffle == False, so not passing it.
# Otherwise we'd have to handle deleting it (but it is already
# deleted).
self.train_sampler = DistributedSampler(
dataset,
rank=self.rank,
shuffle=shuffle,
drop_last=drop_last,
)
# with DistributedSamplerWrapper, one must disable shuffling for dataloader
loader_kwargs["shuffle"] = False
else: # batch_sampler was specified
# TODO: Could a DistributedSamplerWrapper actually work
# just fine for wrapping a BatchSampler, as well?
logger.warning(
"Cannot automatically solve distributed sampling "
"when using a BatchSampler."
)
loader_kwargs["sampler"] = self.train_sampler
elif self.distributed_launch and isinstance(dataset, IterableDataset):
logger.warning(
"Cannot automatically solve distributed sampling "
"for IterableDataset."
)
return loader_kwargs
[docs] def on_fit_start(self):
"""Gets called at the beginning of ``fit()``, on multiple processes
if ``distributed_count > 0`` and backend is ddp.
Default implementation compiles the jit modules, initializes
optimizers, and loads the latest checkpoint to resume training.
"""
# Run this *after* starting all processes since jit modules cannot be
# pickled.
self._compile_jit()
# Wrap modules with parallel backend after jit
self._wrap_distributed()
# Initialize optimizers after parameters are configured
self.init_optimizers()
# Load latest checkpoint to resume training if interrupted
if self.checkpointer is not None:
self.checkpointer.recover_if_possible(
device=torch.device(self.device)
)
[docs] def init_optimizers(self):
"""Called during ``on_fit_start()``, initialize optimizers
after parameters are fully configured (e.g. DDP, jit).
The default implementation of this method depends on an optimizer
class being passed at initialization that takes only a list
of parameters (e.g., a lambda or a partial function definition).
This creates a single optimizer that optimizes all trainable params.
Override this class if there are multiple optimizers.
"""
if self.opt_class is not None:
self.optimizer = self.opt_class(self.modules.parameters())
if self.checkpointer is not None:
self.checkpointer.add_recoverable("optimizer", self.optimizer)
[docs] def on_evaluate_start(self, max_key=None, min_key=None):
"""Gets called at the beginning of ``evaluate()``
Default implementation loads the best-performing checkpoint for
evaluation, based on stored metrics.
Arguments
---------
max_key : str
Key to use for finding best checkpoint (higher is better).
By default, passed to ``self.checkpointer.recover_if_possible()``.
min_key : str
Key to use for finding best checkpoint (lower is better).
By default, passed to ``self.checkpointer.recover_if_possible()``.
"""
# Recover best checkpoint for evaluation
if self.checkpointer is not None:
self.checkpointer.recover_if_possible(
max_key=max_key,
min_key=min_key,
device=torch.device(self.device),
)
[docs] def fit_batch(self, batch):
"""Fit one batch, override to do multiple updates.
The default implementation depends on a few methods being defined
with a particular behavior:
* ``compute_forward()``
* ``compute_objectives()``
Also depends on having optimizers passed at initialization.
Arguments
---------
batch : list of torch.Tensors
Batch of data to use for training. Default implementation assumes
this batch has two elements: inputs and targets.
Returns
-------
detached loss
"""
# Managing automatic mixed precision
if self.auto_mix_prec:
self.optimizer.zero_grad()
with torch.cuda.amp.autocast():
outputs = self.compute_forward(batch, Stage.TRAIN)
loss = self.compute_objectives(outputs, batch, Stage.TRAIN)
self.scaler.scale(loss).backward()
self.scaler.unscale_(self.optimizer)
if self.check_gradients(loss):
self.scaler.step(self.optimizer)
self.scaler.update()
else:
outputs = self.compute_forward(batch, Stage.TRAIN)
loss = self.compute_objectives(outputs, batch, Stage.TRAIN)
loss.backward()
if self.check_gradients(loss):
self.optimizer.step()
self.optimizer.zero_grad()
return loss.detach().cpu()
[docs] def check_gradients(self, loss):
"""Check if gradients are finite and not too large.
Automatically clips large gradients.
Arguments
---------
loss : tensor
The loss tensor after ``backward()`` has been called but
before the optimizers ``step()``.
Returns
-------
bool
Whether or not the optimizer step should be carried out.
"""
if not torch.isfinite(loss):
self.nonfinite_count += 1
# Print helpful debug info
logger.warn(f"Loss is {loss}.")
for p in self.modules.parameters():
if not torch.isfinite(p).all():
logger.warn("Parameter is not finite: " + str(p))
# Check if patience is exhausted
if self.nonfinite_count > self.nonfinite_patience:
raise ValueError(
"Loss is not finite and patience is exhausted. "
"To debug, wrap `fit()` with "
"autograd's `detect_anomaly()`, e.g.\n\nwith "
"torch.autograd.detect_anomaly():\n\tbrain.fit(...)"
)
else:
logger.warn("Patience not yet exhausted, ignoring this batch.")
return False
# Clip gradient norm
torch.nn.utils.clip_grad_norm_(
(p for p in self.modules.parameters()), self.max_grad_norm
)
return True
[docs] def evaluate_batch(self, batch, stage):
"""Evaluate one batch, override for different procedure than train.
The default implementation depends on two methods being defined
with a particular behavior:
* ``compute_forward()``
* ``compute_objectives()``
Arguments
---------
batch : list of torch.Tensors
Batch of data to use for evaluation. Default implementation assumes
this batch has two elements: inputs and targets.
stage : Stage
The stage of the experiment: Stage.VALID, Stage.TEST
Returns
-------
detached loss
"""
out = self.compute_forward(batch, stage=stage)
loss = self.compute_objectives(out, batch, stage=stage)
return loss.detach().cpu()
[docs] def fit(
self,
epoch_counter,
train_set,
valid_set=None,
progressbar=None,
train_loader_kwargs={},
valid_loader_kwargs={},
):
"""Iterate epochs and datasets to improve objective.
Relies on the existence of multiple functions that can (or should) be
overridden. The following methods are used and expected to have a
certain behavior:
* ``fit_batch()``
* ``evaluate_batch()``
* ``update_average()``
If the initialization was done with distributed_count > 0 and the
distributed_backend is ddp, this will generally handle multiprocess
logic, like splitting the training data into subsets for each device and
only saving a checkpoint on the main process.
Arguments
---------
epoch_counter : iterable
Each call should return an integer indicating the epoch count.
train_set : Dataset, DataLoader
A set of data to use for training. If a Dataset is given, a
DataLoader is automatically created. If a DataLoader is given, it is
used directly.
valid_set : Dataset, DataLoader
A set of data to use for validation. If a Dataset is given, a
DataLoader is automatically created. If a DataLoader is given, it is
used directly.
train_loader_kwargs : dict
Kwargs passed to `make_dataloader()` for making the train_loader
(if train_set is a Dataset, not DataLoader).
E.G. batch_size, num_workers.
DataLoader kwargs are all valid.
valid_loader_kwargs : dict
Kwargs passed to `make_dataloader()` for making the valid_loader
(if valid_set is a Dataset, not DataLoader).
E.g., batch_size, num_workers.
DataLoader kwargs are all valid.
progressbar : bool
Whether to display the progress of each epoch in a progressbar.
"""
if not isinstance(train_set, DataLoader):
train_set = self.make_dataloader(
train_set, stage=sb.Stage.TRAIN, **train_loader_kwargs
)
if valid_set is not None and not isinstance(valid_set, DataLoader):
valid_set = self.make_dataloader(
valid_set,
stage=sb.Stage.VALID,
ckpt_prefix=None,
**valid_loader_kwargs,
)
self.on_fit_start()
if progressbar is None:
progressbar = not self.noprogressbar
# Iterate epochs
for epoch in epoch_counter:
# Training stage
self.on_stage_start(Stage.TRAIN, epoch)
self.modules.train()
# Reset nonfinite count to 0 each epoch
self.nonfinite_count = 0
if self.train_sampler is not None and hasattr(
self.train_sampler, "set_epoch"
):
self.train_sampler.set_epoch(epoch)
# Time since last intra-epoch checkpoint
last_ckpt_time = time.time()
# Only show progressbar if requested and main_process
enable = progressbar and sb.utils.distributed.if_main_process()
with tqdm(
train_set,
initial=self.step,
dynamic_ncols=True,
disable=not enable,
) as t:
for batch in t:
self.step += 1
loss = self.fit_batch(batch)
self.avg_train_loss = self.update_average(
loss, self.avg_train_loss
)
t.set_postfix(train_loss=self.avg_train_loss)
# Debug mode only runs a few batches
if self.debug and self.step == self.debug_batches:
break
if (
self.checkpointer is not None
and self.ckpt_interval_minutes > 0
and time.time() - last_ckpt_time
>= self.ckpt_interval_minutes * 60.0
):
run_on_main(self._save_intra_epoch_ckpt)
last_ckpt_time = time.time()
# Run train "on_stage_end" on all processes
self.on_stage_end(Stage.TRAIN, self.avg_train_loss, epoch)
self.avg_train_loss = 0.0
self.step = 0
# Validation stage
if valid_set is not None:
self.on_stage_start(Stage.VALID, epoch)
self.modules.eval()
avg_valid_loss = 0.0
with torch.no_grad():
for batch in tqdm(
valid_set, dynamic_ncols=True, disable=not enable
):
self.step += 1
loss = self.evaluate_batch(batch, stage=Stage.VALID)
avg_valid_loss = self.update_average(
loss, avg_valid_loss
)
# Debug mode only runs a few batches
if self.debug and self.step == self.debug_batches:
break
# Only run validation "on_stage_end" on main process
self.step = 0
run_on_main(
self.on_stage_end,
args=[Stage.VALID, avg_valid_loss, epoch],
)
# Debug mode only runs a few epochs
if self.debug and epoch == self.debug_epochs:
break
def _save_intra_epoch_ckpt(self):
"""Saves a CKPT with specific intra-epoch flag."""
self.checkpointer.save_and_keep_only(
end_of_epoch=False,
num_to_keep=1,
ckpt_predicate=lambda c: INTRA_EPOCH_CKPT_FLAG in c.meta,
meta={INTRA_EPOCH_CKPT_FLAG: True},
verbosity=logging.DEBUG,
)
def _compile_jit(self):
"""Compile requested modules with ``torch.jit.script``."""
if self.jit_module_keys is None:
return
for name in self.jit_module_keys:
if name not in self.modules:
raise ValueError(
"module" + name + " is not defined in your hparams file."
)
module = torch.jit.script(self.modules[name])
self.modules[name] = module.to(self.device)
def _wrap_distributed(self):
"""Wrap modules with distributed wrapper when requested."""
if not self.distributed_launch and not self.data_parallel_backend:
return
elif self.distributed_launch:
for name, module in self.modules.items():
if any(p.requires_grad for p in module.parameters()):
module = SyncBatchNorm.convert_sync_batchnorm(module)
module = DDP(
module,
device_ids=[self.device],
find_unused_parameters=self.find_unused_parameters,
)
self.modules[name] = module
else:
# data_parallel_backend
for name, module in self.modules.items():
if any(p.requires_grad for p in module.parameters()):
module = DP(module)
self.modules[name] = module
[docs] def evaluate(
self,
test_set,
max_key=None,
min_key=None,
progressbar=None,
test_loader_kwargs={},
):
"""Iterate test_set and evaluate brain performance. By default, loads
the best-performing checkpoint (as recorded using the checkpointer).
Arguments
---------
test_set : Dataset, DataLoader
If a DataLoader is given, it is iterated directly. Otherwise passed
to ``self.make_dataloader()``.
max_key : str
Key to use for finding best checkpoint, passed to
``on_evaluate_start()``.
min_key : str
Key to use for finding best checkpoint, passed to
``on_evaluate_start()``.
progressbar : bool
Whether to display the progress in a progressbar.
test_loader_kwargs : dict
Kwargs passed to ``make_dataloader()`` if ``test_set`` is not a
DataLoader. NOTE: ``loader_kwargs["ckpt_prefix"]`` gets
automatically overwritten to ``None`` (so that the test DataLoader
is not added to the checkpointer).
Returns
-------
average test loss
"""
if progressbar is None:
progressbar = not self.noprogressbar
if not isinstance(test_set, DataLoader):
test_loader_kwargs["ckpt_prefix"] = None
test_set = self.make_dataloader(
test_set, Stage.TEST, **test_loader_kwargs
)
self.on_evaluate_start(max_key=max_key, min_key=min_key)
self.on_stage_start(Stage.TEST, epoch=None)
self.modules.eval()
avg_test_loss = 0.0
with torch.no_grad():
for batch in tqdm(
test_set, dynamic_ncols=True, disable=not progressbar
):
self.step += 1
loss = self.evaluate_batch(batch, stage=Stage.TEST)
avg_test_loss = self.update_average(loss, avg_test_loss)
# Debug mode only runs a few batches
if self.debug and self.step == self.debug_batches:
break
# Only run evaluation "on_stage_end" on main process
run_on_main(
self.on_stage_end, args=[Stage.TEST, avg_test_loss, None]
)
self.step = 0
[docs] def update_average(self, loss, avg_loss):
"""Update running average of the loss.
Arguments
---------
loss : torch.tensor
detached loss, a single float value.
avg_loss : float
current running average.
Returns
-------
avg_loss : float
The average loss.
"""
if torch.isfinite(loss):
avg_loss -= avg_loss / self.step
avg_loss += float(loss) / self.step
return avg_loss
@sb.utils.checkpoints.mark_as_saver
def _save(self, path):
save_dict = {
"step": self.step,
"avg_train_loss": self.avg_train_loss,
}
with open(path, "w") as w:
w.write(yaml.dump(save_dict))
@sb.utils.checkpoints.mark_as_loader
def _recover(self, path, end_of_epoch, device):
del end_of_epoch
del device
with open(path) as f:
save_dict = yaml.safe_load(f)
self.step = save_dict["step"]
self.avg_train_loss = save_dict["avg_train_loss"]