lightning/pytorch_lightning/utilities/memory.py

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2020-08-20 02:03:22 +00:00
# Copyright The PyTorch Lightning team.
#
# 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.
import gc
import torch
def recursive_detach(in_dict: dict, to_cpu: bool = False) -> dict:
"""Detach all tensors in `in_dict`.
May operate recursively if some of the values in `in_dict` are dictionaries
which contain instances of `torch.Tensor`. Other types in `in_dict` are
not affected by this utility function.
Args:
in_dict: Dictionary with tensors to detach
to_cpu: Whether to move tensor to cpu
Return:
out_dict: Dictionary with detached tensors
"""
out_dict = {}
for k, v in in_dict.items():
if isinstance(v, dict):
v = recursive_detach(v, to_cpu=to_cpu)
elif callable(getattr(v, 'detach', None)):
v = v.detach()
if to_cpu:
v = v.cpu()
out_dict[k] = v
return out_dict
def is_oom_error(exception):
return is_cuda_out_of_memory(exception) \
or is_cudnn_snafu(exception) \
or is_out_of_cpu_memory(exception)
# based on https://github.com/BlackHC/toma/blob/master/toma/torch_cuda_memory.py
def is_cuda_out_of_memory(exception):
return isinstance(exception, RuntimeError) \
and len(exception.args) == 1 \
and "CUDA out of memory." in exception.args[0]
# based on https://github.com/BlackHC/toma/blob/master/toma/torch_cuda_memory.py
def is_cudnn_snafu(exception):
# For/because of https://github.com/pytorch/pytorch/issues/4107
return isinstance(exception, RuntimeError) \
and len(exception.args) == 1 \
and "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED." in exception.args[0]
# based on https://github.com/BlackHC/toma/blob/master/toma/cpu_memory.py
def is_out_of_cpu_memory(exception):
return isinstance(exception, RuntimeError) \
and len(exception.args) == 1 \
and "DefaultCPUAllocator: can't allocate memory" in exception.args[0]
# based on https://github.com/BlackHC/toma/blob/master/toma/torch_cuda_memory.py
def garbage_collection_cuda():
"""Garbage collection Torch (CUDA) memory."""
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()