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# Copyright Lightning AI.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Houses the methods used to set up the Trainer."""
from typing import Optional, Union
import pytorch_lightning as pl
from lightning_fabric.utilities.warnings import PossibleUserWarning
from pytorch_lightning.accelerators import CUDAAccelerator, MPSAccelerator, XLAAccelerator
from pytorch_lightning.loggers.logger import DummyLogger
from pytorch_lightning.profilers import (
AdvancedProfiler,
PassThroughProfiler,
Profiler,
PyTorchProfiler,
SimpleProfiler,
XLAProfiler,
)
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _habana_available_and_importable
from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
def _init_debugging_flags(
trainer: "pl.Trainer",
limit_train_batches: Optional[Union[int, float]],
limit_val_batches: Optional[Union[int, float]],
limit_test_batches: Optional[Union[int, float]],
limit_predict_batches: Optional[Union[int, float]],
fast_dev_run: Union[int, bool],
overfit_batches: Union[int, float],
val_check_interval: Optional[Union[int, float]],
num_sanity_val_steps: int,
) -> None:
# init debugging flags
if isinstance(fast_dev_run, int) and (fast_dev_run < 0):
raise MisconfigurationException(
f"fast_dev_run={fast_dev_run!r} is not a valid configuration. It should be >= 0."
)
trainer.fast_dev_run = fast_dev_run
# set fast_dev_run=True when it is 1, used while logging
if fast_dev_run == 1:
trainer.fast_dev_run = True
trainer.overfit_batches = _determine_batch_limits(overfit_batches, "overfit_batches")
overfit_batches_enabled = overfit_batches > 0
if fast_dev_run:
num_batches = int(fast_dev_run)
if not overfit_batches_enabled:
trainer.limit_train_batches = num_batches
trainer.limit_val_batches = num_batches
trainer.limit_test_batches = num_batches
trainer.limit_predict_batches = num_batches
trainer.fit_loop.epoch_loop.max_steps = num_batches
trainer.num_sanity_val_steps = 0
trainer.fit_loop.max_epochs = 1
trainer.val_check_interval = 1.0
trainer.check_val_every_n_epoch = 1
trainer.loggers = [DummyLogger()] if trainer.loggers else []
rank_zero_info(
f"Running in `fast_dev_run` mode: will run the requested loop using {num_batches} batch(es). "
"Logging and checkpointing is suppressed."
)
else:
if not overfit_batches_enabled:
trainer.limit_train_batches = _determine_batch_limits(limit_train_batches, "limit_train_batches")
trainer.limit_val_batches = _determine_batch_limits(limit_val_batches, "limit_val_batches")
trainer.limit_test_batches = _determine_batch_limits(limit_test_batches, "limit_test_batches")
trainer.limit_predict_batches = _determine_batch_limits(limit_predict_batches, "limit_predict_batches")
trainer.num_sanity_val_steps = float("inf") if num_sanity_val_steps == -1 else num_sanity_val_steps
trainer.val_check_interval = _determine_batch_limits(val_check_interval, "val_check_interval")
if overfit_batches_enabled:
trainer.limit_train_batches = overfit_batches
trainer.limit_val_batches = overfit_batches
def _determine_batch_limits(batches: Optional[Union[int, float]], name: str) -> Union[int, float]:
if batches is None:
# batches is optional to know if the user passed a value so that we can show the above info messages only to the
# users that set a value explicitly
return 1.0
# differentiating based on the type can be error-prone for users. show a message describing the chosen behaviour
if isinstance(batches, int) and batches == 1:
if name == "limit_train_batches":
message = "1 batch per epoch will be used."
elif name == "val_check_interval":
message = "validation will run after every batch."
else:
message = "1 batch will be used."
rank_zero_info(f"`Trainer({name}=1)` was configured so {message}")
elif isinstance(batches, float) and batches == 1.0:
if name == "limit_train_batches":
message = "100% of the batches per epoch will be used."
elif name == "val_check_interval":
message = "validation will run at the end of the training epoch."
else:
message = "100% of the batches will be used."
rank_zero_info(f"`Trainer({name}=1.0)` was configured so {message}.")
if 0 <= batches <= 1:
return batches
if batches > 1 and batches % 1.0 == 0:
return int(batches)
raise MisconfigurationException(
f"You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int."
)
def _init_profiler(trainer: "pl.Trainer", profiler: Optional[Union[Profiler, str]]) -> None:
if isinstance(profiler, str):
PROFILERS = {
"simple": SimpleProfiler,
"advanced": AdvancedProfiler,
"pytorch": PyTorchProfiler,
"xla": XLAProfiler,
}
profiler = profiler.lower()
if profiler not in PROFILERS:
raise MisconfigurationException(
"When passing string value for the `profiler` parameter of `Trainer`,"
f" it can only be one of {list(PROFILERS.keys())}"
)
profiler_class = PROFILERS[profiler]
profiler = profiler_class()
trainer.profiler = profiler or PassThroughProfiler()
def _log_device_info(trainer: "pl.Trainer") -> None:
if CUDAAccelerator.is_available():
gpu_available = True
gpu_type = " (cuda)"
elif MPSAccelerator.is_available():
gpu_available = True
gpu_type = " (mps)"
else:
gpu_available = False
gpu_type = ""
gpu_used = isinstance(trainer.accelerator, (CUDAAccelerator, MPSAccelerator))
rank_zero_info(f"GPU available: {gpu_available}{gpu_type}, used: {gpu_used}")
num_tpu_cores = trainer.num_devices if isinstance(trainer.accelerator, XLAAccelerator) else 0
rank_zero_info(f"TPU available: {XLAAccelerator.is_available()}, using: {num_tpu_cores} TPU cores")
if _habana_available_and_importable():
from lightning_habana import HPUAccelerator
num_hpus = trainer.num_devices if isinstance(trainer.accelerator, HPUAccelerator) else 0
hpu_available = HPUAccelerator.is_available()
else:
num_hpus = 0
hpu_available = False
rank_zero_info(f"HPU available: {hpu_available}, using: {num_hpus} HPUs")
if (
CUDAAccelerator.is_available()
and not isinstance(trainer.accelerator, CUDAAccelerator)
or MPSAccelerator.is_available()
and not isinstance(trainer.accelerator, MPSAccelerator)
):
rank_zero_warn(
"GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.",
category=PossibleUserWarning,
)
if XLAAccelerator.is_available() and not isinstance(trainer.accelerator, XLAAccelerator):
rank_zero_warn("TPU available but not used. You can set it by doing `Trainer(accelerator='tpu')`.")
if _habana_available_and_importable():
from lightning_habana import HPUAccelerator
if HPUAccelerator.is_available() and not isinstance(trainer.accelerator, HPUAccelerator):
rank_zero_warn("HPU available but not used. You can set it by doing `Trainer(accelerator='hpu')`.")
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