202 lines
7.1 KiB
Python
202 lines
7.1 KiB
Python
# 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.
|
|
|
|
from abc import ABC
|
|
import inspect
|
|
from typing import Union, Mapping
|
|
|
|
import torch
|
|
|
|
from pytorch_lightning.loggers import LightningLoggerBase
|
|
from pytorch_lightning.utilities.memory import recursive_detach
|
|
from pytorch_lightning.utilities.distributed import rank_zero_warn
|
|
|
|
|
|
class TrainerLoggingMixin(ABC):
|
|
|
|
# this is just a summary on variables used in this abstract class,
|
|
# the proper values/initialisation should be done in child class
|
|
current_epoch: int
|
|
on_gpu: bool
|
|
log_gpu_memory: ...
|
|
logger: Union[LightningLoggerBase, bool]
|
|
global_step: int
|
|
global_rank: int
|
|
use_dp: bool
|
|
use_ddp2: bool
|
|
default_root_dir: str
|
|
slurm_job_id: int
|
|
num_gpus: int
|
|
logged_metrics: ...
|
|
|
|
def metrics_to_scalars(self, metrics):
|
|
new_metrics = {}
|
|
for k, v in metrics.items():
|
|
if isinstance(v, torch.Tensor):
|
|
v = v.item()
|
|
|
|
if isinstance(v, dict):
|
|
v = self.metrics_to_scalars(v)
|
|
|
|
new_metrics[k] = v
|
|
|
|
return new_metrics
|
|
|
|
def process_dict_result(self, output, train=False):
|
|
"""Reduces output according to the training mode.
|
|
|
|
Separates loss from logging and progress bar metrics
|
|
"""
|
|
# --------------------
|
|
# WARN DEPRECATED KEYS
|
|
# --------------------
|
|
# TODO: 1.0.0 remove
|
|
if isinstance(output, dict):
|
|
for k, v in output.items():
|
|
if k in ['log', 'progress_bar']:
|
|
m = inspect.cleandoc(
|
|
f"""The {{{k}:dict keyword}} was deprecated in 0.9.1 and will be removed in 1.0.0
|
|
Please use self.log(...) inside the lightningModule instead.
|
|
|
|
# log on a step or aggregate epoch metric to the logger and/or progress bar
|
|
# (inside LightningModule)
|
|
self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)
|
|
""")
|
|
rank_zero_warn(m)
|
|
|
|
# --------------------------
|
|
# handle single scalar only
|
|
# --------------------------
|
|
# single scalar returned from a xx_step
|
|
if isinstance(output, torch.Tensor):
|
|
progress_bar_metrics = {}
|
|
log_metrics = {}
|
|
callback_metrics = {}
|
|
hiddens = None
|
|
return output, progress_bar_metrics, log_metrics, callback_metrics, hiddens
|
|
|
|
# ---------------
|
|
# EXTRACT CALLBACK KEYS
|
|
# ---------------
|
|
# all keys not progress_bar or log are candidates for callbacks
|
|
callback_metrics = {}
|
|
if isinstance(output, Mapping):
|
|
for k, v in output.items():
|
|
if k not in ['progress_bar', 'log', 'hiddens']:
|
|
callback_metrics[k] = v
|
|
|
|
if train and (self.use_dp or self.use_ddp2):
|
|
num_gpus = self.num_gpus
|
|
callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)
|
|
|
|
# ---------------
|
|
# EXTRACT PROGRESS BAR KEYS
|
|
# ---------------
|
|
try:
|
|
progress_output = output['progress_bar']
|
|
|
|
# reduce progress metrics for progress bar when using dp
|
|
if train and (self.use_dp or self.use_ddp2):
|
|
num_gpus = self.num_gpus
|
|
progress_output = self.reduce_distributed_output(progress_output, num_gpus)
|
|
|
|
progress_bar_metrics = progress_output
|
|
# todo: specify the possible exception
|
|
except Exception:
|
|
progress_bar_metrics = {}
|
|
|
|
# ---------------
|
|
# EXTRACT LOGGING KEYS
|
|
# ---------------
|
|
# extract metrics to log to experiment
|
|
try:
|
|
log_output = output['log']
|
|
|
|
# reduce progress metrics for progress bar when using dp
|
|
if train and (self.use_dp or self.use_ddp2):
|
|
num_gpus = self.num_gpus
|
|
log_output = self.reduce_distributed_output(log_output, num_gpus)
|
|
|
|
log_metrics = log_output
|
|
# todo: specify the possible exception
|
|
except Exception:
|
|
log_metrics = {}
|
|
|
|
# ---------------
|
|
# EXTRACT LOSS
|
|
# ---------------
|
|
# if output dict doesn't have the keyword loss
|
|
# then assume the output=loss if scalar
|
|
loss = None
|
|
if train:
|
|
try:
|
|
loss = output['loss']
|
|
# todo: specify the possible exception
|
|
except Exception as exp:
|
|
if isinstance(output, torch.Tensor):
|
|
loss = output
|
|
else:
|
|
raise RuntimeError(
|
|
'No `loss` value in the dictionary returned from `model.training_step()`.'
|
|
) from exp
|
|
|
|
# when using dp need to reduce the loss
|
|
if self.use_dp or self.use_ddp2:
|
|
loss = self.reduce_distributed_output(loss, self.num_gpus)
|
|
|
|
# ---------------
|
|
# EXTRACT HIDDEN
|
|
# ---------------
|
|
hiddens = output.get('hiddens', None) if isinstance(output, Mapping) else None
|
|
|
|
# use every metric passed in as a candidate for callback
|
|
callback_metrics.update(progress_bar_metrics)
|
|
callback_metrics.update(log_metrics)
|
|
|
|
# detach all metrics for callbacks to prevent memory leaks
|
|
# no .item() because it will slow things down
|
|
callback_metrics = recursive_detach(callback_metrics)
|
|
progress_bar_metrics = recursive_detach(progress_bar_metrics)
|
|
log_metrics = recursive_detach(log_metrics)
|
|
|
|
return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens
|
|
|
|
def reduce_distributed_output(self, output, num_gpus):
|
|
if num_gpus <= 1:
|
|
return output
|
|
|
|
# when using DP, we get one output per gpu
|
|
# average outputs and return
|
|
if isinstance(output, torch.Tensor):
|
|
return output.mean()
|
|
|
|
for k, v in output.items():
|
|
# recurse on nested dics
|
|
if isinstance(output[k], dict):
|
|
output[k] = self.reduce_distributed_output(output[k], num_gpus)
|
|
|
|
# compute the average of scalars
|
|
elif isinstance(output[k], list):
|
|
output[k] = sum(output[k]) / len(output[k])
|
|
|
|
# do nothing when there's a scalar
|
|
elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
|
|
pass
|
|
|
|
# do not reduce metrics that have batch size > num gpus
|
|
elif output[k].size(0) <= num_gpus:
|
|
output[k] = torch.mean(output[k])
|
|
|
|
return output
|