200 lines
7.0 KiB
Python
200 lines
7.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from abc import ABC
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import inspect
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from typing import Union, Iterable
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import torch
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from pytorch_lightning.core import memory
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from pytorch_lightning.loggers import TensorBoardLogger, LightningLoggerBase, LoggerCollection
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from pytorch_lightning.utilities.memory import recursive_detach
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from pytorch_lightning.utilities.distributed import rank_zero_warn
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class TrainerLoggingMixin(ABC):
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# this is just a summary on variables used in this abstract class,
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# the proper values/initialisation should be done in child class
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current_epoch: int
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on_gpu: bool
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log_gpu_memory: ...
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logger: Union[LightningLoggerBase, bool]
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global_step: int
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global_rank: int
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use_dp: bool
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use_ddp2: bool
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default_root_dir: str
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slurm_job_id: int
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num_gpus: int
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logged_metrics: ...
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def metrics_to_scalars(self, metrics):
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new_metrics = {}
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for k, v in metrics.items():
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if isinstance(v, torch.Tensor):
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v = v.item()
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if isinstance(v, dict):
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v = self.metrics_to_scalars(v)
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new_metrics[k] = v
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return new_metrics
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def process_dict_result(self, output, train=False):
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"""Reduces output according to the training mode.
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Separates loss from logging and progress bar metrics
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"""
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# --------------------
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# WARN DEPRECATED KEYS
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# --------------------
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# TODO: 1.0.0 remove
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if isinstance(output, dict):
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for k, v in output.items():
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if k in ['log', 'progress_bar']:
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m = inspect.cleandoc(
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f"""The {{{k}:dict keyword}} was deprecated in 0.9.1 and will be removed in 1.0.0
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Please use self.log(...) inside the lightningModule instead.
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# log on a step or aggregate epoch metric to the logger and/or progress bar
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# (inside LightningModule)
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self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)
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""")
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rank_zero_warn(m)
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# --------------------------
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# handle single scalar only
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# --------------------------
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# single scalar returned from a xx_step
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if isinstance(output, torch.Tensor):
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progress_bar_metrics = {}
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log_metrics = {}
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callback_metrics = {}
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hiddens = None
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return output, progress_bar_metrics, log_metrics, callback_metrics, hiddens
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# ---------------
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# EXTRACT CALLBACK KEYS
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# ---------------
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# all keys not progress_bar or log are candidates for callbacks
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callback_metrics = {}
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if output:
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for k, v in output.items():
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if k not in ['progress_bar', 'log', 'hiddens']:
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callback_metrics[k] = v
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if train and (self.use_dp or self.use_ddp2):
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num_gpus = self.num_gpus
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callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)
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# ---------------
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# EXTRACT PROGRESS BAR KEYS
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# ---------------
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try:
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progress_output = output['progress_bar']
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# reduce progress metrics for progress bar when using dp
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if train and (self.use_dp or self.use_ddp2):
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num_gpus = self.num_gpus
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progress_output = self.reduce_distributed_output(progress_output, num_gpus)
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progress_bar_metrics = progress_output
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except Exception:
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progress_bar_metrics = {}
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# ---------------
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# EXTRACT LOGGING KEYS
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# ---------------
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# extract metrics to log to experiment
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try:
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log_output = output['log']
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# reduce progress metrics for progress bar when using dp
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if train and (self.use_dp or self.use_ddp2):
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num_gpus = self.num_gpus
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log_output = self.reduce_distributed_output(log_output, num_gpus)
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log_metrics = log_output
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except Exception:
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log_metrics = {}
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# ---------------
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# EXTRACT LOSS
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# ---------------
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# if output dict doesn't have the keyword loss
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# then assume the output=loss if scalar
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loss = None
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if train:
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try:
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loss = output['loss']
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except Exception as exp:
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if isinstance(output, torch.Tensor):
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loss = output
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else:
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raise RuntimeError(
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'No `loss` value in the dictionary returned from `model.training_step()`.'
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) from exp
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# when using dp need to reduce the loss
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if self.use_dp or self.use_ddp2:
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loss = self.reduce_distributed_output(loss, self.num_gpus)
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# ---------------
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# EXTRACT HIDDEN
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# ---------------
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hiddens = output.get('hiddens') if output else None
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# use every metric passed in as a candidate for callback
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callback_metrics.update(progress_bar_metrics)
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callback_metrics.update(log_metrics)
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# detach all metrics for callbacks to prevent memory leaks
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# no .item() because it will slow things down
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callback_metrics = recursive_detach(callback_metrics)
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progress_bar_metrics = recursive_detach(progress_bar_metrics)
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log_metrics = recursive_detach(log_metrics)
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return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens
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def reduce_distributed_output(self, output, num_gpus):
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if num_gpus <= 1:
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return output
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# when using DP, we get one output per gpu
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# average outputs and return
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if isinstance(output, torch.Tensor):
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return output.mean()
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for k, v in output.items():
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# recurse on nested dics
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if isinstance(output[k], dict):
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output[k] = self.reduce_distributed_output(output[k], num_gpus)
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# compute the average of scalars
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elif isinstance(output[k], list):
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output[k] = sum(output[k]) / len(output[k])
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# do nothing when there's a scalar
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elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:
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pass
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# do not reduce metrics that have batch size > num gpus
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elif output[k].size(0) <= num_gpus:
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output[k] = torch.mean(output[k])
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return output
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