704 lines
22 KiB
Python
704 lines
22 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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"""Result class for easier logging and epoch-wise reduction."""
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import numbers
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from copy import copy
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from typing import Any, Callable, Dict, Iterable, List, MutableMapping, Optional, Sequence, Tuple, Union
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import torch
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from torch import Tensor
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from torchmetrics import Metric
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from pytorch_lightning.utilities.distributed import sync_ddp_if_available
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class Result(Dict):
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def __init__(self, minimize: Optional[Tensor] = None):
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super().__init__()
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if minimize is not None:
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err = 'Minimize can only be used in training_step, training_step_end, training_epoch_end'
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self._assert_grad_tensor_metric('minimize', minimize, err)
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self.minimize = minimize
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self['meta'] = {'_internal': {'_reduce_on_epoch': False, 'batch_sizes': []}}
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def __getitem__(self, key: Union[str, Any]) -> Any:
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try:
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return super().__getitem__(key)
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except KeyError:
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return super().__getitem__(f'{key}_step')
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def __getattr__(self, key: str) -> Any:
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try:
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if key == 'batch_log_metrics':
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return self.get_batch_log_metrics()
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elif key == 'batch_pbar_metrics':
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return self.get_batch_pbar_metrics()
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elif key == 'epoch_log_metrics':
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return self.get_epoch_log_metrics()
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elif key == 'epoch_pbar_metrics':
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return self.get_epoch_pbar_metrics()
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else:
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return self[key]
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except KeyError:
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return None
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def __setattr__(self, key: str, val: Union[Tensor, Any]):
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# ensure tensors are detached
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if isinstance(val, torch.Tensor) and key != 'minimize':
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val = val.detach()
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self[key] = val
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def __getstate__(self):
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return self
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def __setstate__(self, d):
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self.update(d)
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def _assert_grad_tensor_metric(self, name: str, x: Union[torch.Tensor, Any], additional_err: str = ''):
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if x is not None:
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if not isinstance(x, Tensor):
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raise TypeError(f'{name} must be a torch.Tensor')
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m = f'{name} must have a computational graph.'
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if additional_err:
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m += f' {additional_err}'
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assert x.grad_fn is not None, m
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def log(
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self,
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name: str,
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value: Any,
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prog_bar: bool = False,
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logger: bool = True,
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on_step: bool = False,
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on_epoch: bool = True,
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reduce_fx: Callable = torch.mean,
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tbptt_reduce_fx: Callable = torch.mean,
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tbptt_pad_token: int = 0,
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enable_graph: bool = False,
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sync_dist: bool = False,
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sync_dist_op: Union[Any, str] = 'mean',
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sync_dist_group: Optional[Any] = None,
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sync_fn: Callable = None,
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dataloader_idx: Optional[int] = None,
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device: torch.device = None,
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):
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# no metrics should be logged with graphs
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if not enable_graph and isinstance(value, torch.Tensor):
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value = value.detach()
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# sync across workers when using distributed training
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sync_fn = sync_fn or sync_ddp_if_available
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if sync_dist and isinstance(value, (torch.Tensor, numbers.Number)):
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is_dist_initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
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# TODO: Find a way to make the reduction only once, so we don't need to clone.
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if is_dist_initialized and isinstance(value, torch.Tensor):
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value = value.clone()
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else:
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value = torch.tensor(value, device=device, dtype=torch.float)
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value = sync_fn(value, group=sync_dist_group, reduce_op=sync_dist_op)
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if isinstance(value, torch.Tensor) and value.device.type == "xla":
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value = value.cpu()
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if 'meta' not in self:
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self.__setitem__('meta', {})
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# if user requests both step and epoch, then we split the metric in two automatically
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# one will be logged per step. the other per epoch
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was_forked = False
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if on_step and on_epoch:
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was_forked = True
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# set step version
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step_name = f'{name}_step'
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self.__set_meta(
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step_name,
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value,
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prog_bar,
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logger,
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on_step=True,
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on_epoch=False,
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reduce_fx=reduce_fx,
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tbptt_reduce_fx=tbptt_reduce_fx,
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tbptt_pad_token=tbptt_pad_token,
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forked=False,
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dataloader_idx=dataloader_idx,
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)
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self.__setitem__(step_name, value)
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# set epoch version
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epoch_name = f'{name}_epoch'
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self.__set_meta(
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epoch_name,
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value,
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prog_bar,
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logger,
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on_step=False,
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on_epoch=True,
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reduce_fx=reduce_fx,
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tbptt_reduce_fx=tbptt_reduce_fx,
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tbptt_pad_token=tbptt_pad_token,
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forked=False,
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dataloader_idx=dataloader_idx,
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)
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self.__setitem__(epoch_name, value)
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# always log the original metric
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self.__set_meta(
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name,
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value,
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prog_bar,
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logger,
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on_step,
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on_epoch,
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reduce_fx,
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tbptt_reduce_fx=tbptt_reduce_fx,
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tbptt_pad_token=tbptt_pad_token,
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forked=was_forked,
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dataloader_idx=dataloader_idx,
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)
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# set the value
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self.__setitem__(name, value)
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def __set_meta(
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self,
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name: str,
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value: Any,
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prog_bar: bool,
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logger: bool,
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on_step: bool,
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on_epoch: bool,
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reduce_fx: Callable,
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tbptt_pad_token: int,
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tbptt_reduce_fx: Callable,
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forked: bool,
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dataloader_idx: Union[int, None],
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):
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# set the meta for the item
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meta_value = value
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meta = dict(
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prog_bar=prog_bar,
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logger=logger,
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on_step=on_step,
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on_epoch=on_epoch,
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reduce_fx=reduce_fx,
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value=meta_value,
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tbptt_reduce_fx=tbptt_reduce_fx,
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tbptt_pad_token=tbptt_pad_token,
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forked=forked,
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dataloader_idx=dataloader_idx,
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)
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self['meta'][name] = meta
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# track whether any input requires reduction on epoch end
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_internal = self['meta']['_internal']
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_internal['_reduce_on_epoch'] = max(_internal['_reduce_on_epoch'], on_epoch)
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def track_batch_size(self, batch):
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batch_size = Result.extract_batch_size(batch)
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Result.attach_batch_size(batch_size, self)
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@staticmethod
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def extract_batch_size(batch):
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try:
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batch_size = Result.unpack_batch_size(batch)
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except RecursionError:
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batch_size = 1
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return batch_size
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@staticmethod
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def attach_batch_size(batch_size: Union[int, None], result: 'Result') -> None:
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if batch_size is not None:
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meta = result['meta']
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meta['_internal']['batch_sizes'].append(batch_size)
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def get_batch_sizes(self):
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meta = self['meta']
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return torch.tensor(meta['_internal']['batch_sizes'])
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def _add_dataloader_idx(self, k: str, dataloader_idx: Union[int, None], add_dataloader_idx: bool) -> str:
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if dataloader_idx is not None and add_dataloader_idx:
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return f"{k}/dataloader_idx_{dataloader_idx}"
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return k
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def get_batch_log_metrics(self, include_forked_originals=True, add_dataloader_idx=False) -> dict:
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"""
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Gets the metrics to log at the end of the batch step
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"""
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result = {}
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meta = self['meta']
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for k, options in meta.items():
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if k == '_internal':
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continue
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if options['forked'] and not include_forked_originals:
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continue
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dl_key = self._add_dataloader_idx(k, options["dataloader_idx"], add_dataloader_idx)
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if options['logger'] and options['on_step']:
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if isinstance(self[k], Metric) and self[k]._forward_cache is not None:
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result[dl_key] = self[k]._forward_cache.detach()
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else:
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result[dl_key] = self[k]
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return result
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def get_epoch_log_metrics(self, add_dataloader_idx=False) -> dict:
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"""
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Gets the metrics to log at the end of epoch
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"""
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result = {}
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meta = self['meta']
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for k, options in meta.items():
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if k == '_internal':
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continue
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if options['forked']:
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continue
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dl_key = self._add_dataloader_idx(k, options["dataloader_idx"], add_dataloader_idx)
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if options['logger'] and options['on_epoch']:
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if isinstance(self[k], Metric):
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result[dl_key] = self[k].compute().detach()
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self[k].reset()
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else:
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result[dl_key] = self[k]
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if k in self and not options['on_epoch'] and isinstance(self[k], Metric):
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# reset metric anyway so state does not accumulate
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# NOTE: we must compute before reseting just in case the computed value is needed
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# later (i.e. if the step metric gets visited first, and then the epoch metric)
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self[k].compute()
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self[k].reset()
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return result
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def get_epoch_pbar_metrics(self, add_dataloader_idx=False):
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"""
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Gets the metrics to log at the end of epoch
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"""
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result = {}
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meta = self['meta']
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for k, options in meta.items():
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if k == '_internal':
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continue
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if options['forked']:
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continue
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dl_key = self._add_dataloader_idx(k, options["dataloader_idx"], add_dataloader_idx)
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if options['prog_bar'] and options['on_epoch']:
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if isinstance(self[k], Metric):
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result[dl_key] = self[k].compute().detach()
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self[k].reset()
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else:
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result[dl_key] = self[k]
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if k in self and not options['on_epoch'] and isinstance(self[k], Metric):
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# reset metric anyway so state does not accumulate
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# NOTE: we must compute before reseting just in case the computed value is needed
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# later (i.e. if the step metric gets visited first, and then the epoch metric)
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self[k].compute()
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self[k].reset()
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return result
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def get_forked_metrics(self, add_dataloader_idx=False):
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"""
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Gets the metrics to log at the end of epoch
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"""
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result = {}
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meta = self['meta']
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for k, options in meta.items():
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if k == '_internal':
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continue
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dl_key = self._add_dataloader_idx(k, options["dataloader_idx"], add_dataloader_idx)
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if options['forked']:
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if isinstance(self[k], Metric):
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result[dl_key] = self[k].compute().detach()
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self[k].reset()
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else:
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result[dl_key] = self[k]
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return result
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def get_batch_pbar_metrics(self, include_forked_originals=True, add_dataloader_idx=False):
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"""
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Gets the metrics to log at the end of the batch step
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"""
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result = {}
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meta = self['meta']
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for k, options in meta.items():
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if k == '_internal':
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continue
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if options['forked'] and not include_forked_originals:
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continue
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dl_key = self._add_dataloader_idx(k, options["dataloader_idx"], add_dataloader_idx)
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if options['prog_bar'] and options['on_step']:
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if isinstance(self[k], Metric) and self[k]._forward_cache is not None:
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result[dl_key] = self[k]._forward_cache
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else:
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result[dl_key] = self[k]
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return result
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def detach(self) -> 'Result':
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for k, v in self.items():
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if isinstance(v, torch.Tensor):
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self.__setitem__(k, v.detach())
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return self
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def to(self, *args, **kwargs) -> 'Result':
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"""Move all self attributes to the given device."""
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for k, v in self.items():
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if isinstance(v, torch.Tensor):
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self.__setitem__(k, v.to(*args, **kwargs))
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return self
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def cpu(self) -> 'Result':
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"""Move all self attributes to CPU."""
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return self.to(torch.device("cpu"))
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def __repr__(self):
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self_copy = self.copy()
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if 'meta' in self_copy:
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del self_copy['meta']
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return str(self_copy)
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def __str__(self):
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copy = self.copy()
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del copy['meta']
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return str(copy)
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def __copy__(self):
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newone = type(self)()
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for k, v in self.items():
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if isinstance(v, torch.Tensor):
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v = v.detach()
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newone[k] = copy(v)
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return newone
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@staticmethod
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def unpack_batch_size(sample):
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"""
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Recursively unpack sample to find a torch.Tensor.
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returns len(tensor) when found, or 1 when it hits an empty or non iterable.
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"""
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if isinstance(sample, torch.Tensor):
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size = sample.size(0)
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elif isinstance(sample, str):
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return len(sample)
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elif isinstance(sample, dict):
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sample = next(iter(sample.values()), 1)
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size = Result.unpack_batch_size(sample)
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elif isinstance(sample, Iterable):
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sample = next(iter(sample), 1)
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size = Result.unpack_batch_size(sample)
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else:
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size = 1
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return size
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@classmethod
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def gather(cls, outputs):
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meta = outputs[0].get('meta')
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result = cls()
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result = recursive_gather(outputs, result)
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recursive_stack(result)
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if meta:
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result['meta'] = meta
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return result
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@classmethod
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def padded_gather(cls, outputs):
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meta = outputs[0].get('meta')
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result = cls()
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result = recursive_gather(outputs, result)
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# find the padding used for other values
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default_padding_idx = 0
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for name, value in result.items():
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if (
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name != 'minimize' and isinstance(value, list) and len(value) > 0
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and isinstance(value[0], torch.Tensor)
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):
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default_padding_idx = meta[name]['tbptt_pad_token']
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break
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# pad across each key individually
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for name, value in result.items():
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if (isinstance(value, list) and len(value) > 0 and isinstance(value[0], torch.Tensor)):
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padding_key = default_padding_idx if name == 'minimize' else meta[name]['tbptt_pad_token']
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padded = torch.nn.utils.rnn.pad_sequence(value, batch_first=True, padding_value=padding_key)
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result[name] = padded
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# also update the result
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if meta and name != "minimize":
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meta[name]['value'] = padded
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if meta:
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result['meta'] = meta
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return result
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@classmethod
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def reduce_on_epoch_end(cls, outputs):
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# get the batch sizes for all outputs
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batch_sizes = []
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meta = {}
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for x in outputs:
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batch_sizes.append(x.get_batch_sizes())
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meta.update(x['meta'])
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batch_sizes = torch.stack(batch_sizes).view(-1)
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result = cls()
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result = recursive_gather(outputs, result)
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recursive_stack(result)
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for k, option in meta.items():
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if k == '_internal' or isinstance(result[k], Metric):
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continue
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# for forked metrics don't reduce, just take the last val
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if option['forked']:
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result[k] = choose_last(result[k])
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continue
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if option['on_epoch']:
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fx = option['reduce_fx']
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if fx == torch.mean:
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if isinstance(result[k], list):
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result[k] = torch.tensor(result[k]).float()
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try:
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reduced_val = weighted_mean(result[k], batch_sizes)
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# todo: specify the expected Exceptions to come
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except Exception:
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reduced_val = torch.mean(result[k])
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else:
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reduced_val = fx(result[k])
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result[k] = reduced_val
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else:
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del result[k]
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result['meta'] = meta
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return result
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@classmethod
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def reduce_across_time(cls, time_outputs):
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# auto-reduce across time for tbptt
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meta = time_outputs[0]['meta']
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# in 1.0 the results have 'extra'. Once we deprecate 0.10.0 we may not need this
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if 'extra' in time_outputs[0]:
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[x.pop('extra', None) for x in time_outputs]
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result = cls()
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|
result = recursive_gather(time_outputs, result)
|
|
recursive_stack(result)
|
|
|
|
for k, value in result.items():
|
|
if k in ['meta', 'extra'] or isinstance(value, Metric):
|
|
continue
|
|
|
|
# pick the reduce fx
|
|
tbptt_reduce_fx = torch.mean if k == "minimize" else meta[k]['tbptt_reduce_fx']
|
|
|
|
if isinstance(value, list):
|
|
value = torch.tensor(value)
|
|
|
|
if isinstance(value, dict):
|
|
# TODO: recursive reduce:
|
|
_recursive_fx_apply(value, tbptt_reduce_fx)
|
|
else:
|
|
result[k] = tbptt_reduce_fx(value.float())
|
|
|
|
result['meta'] = meta
|
|
return result
|
|
|
|
def dp_reduce(self):
|
|
for k, value in self.items():
|
|
if k == 'meta' or isinstance(value, Metric):
|
|
continue
|
|
|
|
if isinstance(value, list):
|
|
value = torch.tensor(value)
|
|
|
|
self[k] = value.mean(dim=-1)
|
|
|
|
@property
|
|
def should_reduce_on_epoch_end(self) -> bool:
|
|
return self['meta']['_internal']['_reduce_on_epoch']
|
|
|
|
def rename_keys(self, map_dict: dict):
|
|
"""
|
|
Maps key values to the target values. Useful when renaming variables in mass.
|
|
|
|
Args:
|
|
map_dict:
|
|
"""
|
|
meta = self.meta
|
|
for source, dest in map_dict.items():
|
|
# map the main keys
|
|
self[dest] = self[source]
|
|
del self[source]
|
|
|
|
# map meta
|
|
meta[dest] = meta[source]
|
|
del meta[source]
|
|
|
|
def get_non_metrics_keys(self):
|
|
"""
|
|
This function is used to filter metric keys for which the value isn't a Metric
|
|
"""
|
|
return [k for k, v in self.items() if not isinstance(v, Metric)]
|
|
|
|
|
|
def choose_last(x):
|
|
if isinstance(x, (torch.Tensor, list)):
|
|
return x[-1]
|
|
if isinstance(x, dict):
|
|
for k, v in x.items():
|
|
x[k] = x[k][-1]
|
|
|
|
|
|
def recursive_gather(outputs: Sequence[dict], result: Optional[MutableMapping] = None) -> Optional[MutableMapping]:
|
|
for out in outputs:
|
|
if 'meta' in out:
|
|
del out['meta']
|
|
|
|
for k, v in out.items():
|
|
# support manual opt where the user does not return a minimize key
|
|
if k == 'minimize' and v is None:
|
|
continue
|
|
|
|
if isinstance(v, dict):
|
|
in_d = result.get(k, {})
|
|
v = recursive_gather([v], in_d)
|
|
result[k] = v
|
|
else:
|
|
if isinstance(v, Metric):
|
|
# if v is a metric, just keep one of them,
|
|
# don't keep on adding a list of them
|
|
result[k] = v
|
|
else:
|
|
if k not in result:
|
|
result[k] = []
|
|
result[k].append(v)
|
|
|
|
return result
|
|
|
|
|
|
def recursive_stack(result: MutableMapping):
|
|
for k, v in result.items():
|
|
if isinstance(v, dict):
|
|
recursive_stack(v)
|
|
|
|
result[k] = collate_tensors(v)
|
|
|
|
|
|
def _recursive_fx_apply(input: dict, fx):
|
|
for k, v in input.items():
|
|
if isinstance(v, list):
|
|
v = torch.tensor(v)
|
|
|
|
if isinstance(v, torch.Tensor):
|
|
v = fx(v.float())
|
|
input[k] = v
|
|
else:
|
|
_recursive_fx_apply(v, fx)
|
|
|
|
|
|
def collate_tensors(items: Union[List, Tuple]) -> Union[Tensor, List, Tuple]:
|
|
if not items or not isinstance(items, (list, tuple)) or any(not isinstance(item, Tensor) for item in items):
|
|
# items is not a sequence, empty, or contains non-tensors
|
|
return items
|
|
|
|
if all(item.ndim == 0 for item in items):
|
|
# all tensors are scalars, we need to stack
|
|
return torch.stack(items)
|
|
|
|
if all(item.ndim >= 1 and item.shape[1:] == items[0].shape[1:] for item in items):
|
|
# we can concatenate along the first dimension
|
|
return torch.cat(items)
|
|
|
|
return items
|
|
|
|
|
|
def weighted_mean(result, weights):
|
|
|
|
if isinstance(result, dict):
|
|
_process_dataloader_aggregated_steps(result, weights)
|
|
else:
|
|
if isinstance(result, list):
|
|
result = torch.tensor(result)
|
|
|
|
weights = weights.to(result.device)[:result.size(0)]
|
|
numerator = torch.dot(result.float(), weights.transpose(-1, 0).float())
|
|
result = numerator / weights.sum().float()
|
|
return result
|
|
|
|
|
|
def _process_dataloader_aggregated_steps(result, weights):
|
|
internal_keys = {'meta'}
|
|
|
|
moved = False
|
|
|
|
for k, v in result.items():
|
|
if k in internal_keys:
|
|
continue
|
|
|
|
# make sure v is a tensor
|
|
if not isinstance(v, torch.Tensor):
|
|
v = torch.tensor(v)
|
|
|
|
# move to memory only once
|
|
if not moved:
|
|
weights = weights.to(v.device)
|
|
moved = True
|
|
|
|
# move weights to same device as value to reduce
|
|
weights_t = weights[:v.size(0)]
|
|
|
|
# weighted mean
|
|
numerator = torch.dot(v.float(), weights_t.transpose(-1, 0).float())
|
|
v = numerator / weights.sum().float()
|
|
result[k] = v
|