Source code for composer.algorithms.selective_backprop.selective_backprop

# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0

"""Core SelectiveBackprop class and functions."""

from __future__ import annotations

import inspect
from typing import Any, Callable, Optional, Sequence, Tuple, Union

import numpy as np
import torch
from torch.nn import functional as F

from composer.core import Algorithm, Event, State, get_precision_context
from composer.loggers import Logger
from composer.models import ComposerModel

__all__ = ['SelectiveBackprop', 'select_using_loss', 'should_selective_backprop']


[docs]def should_selective_backprop( current_duration: float, batch_idx: int, start: float = 0.5, end: float = 0.9, interrupt: int = 2, ) -> bool: """Decides if selective backprop should be run based on time in training. Returns true if the ``current_duration`` is between ``start`` and ``end``. It is recommended that SB be applied during the later stages of a training run, once the model has already "learned" easy examples. To preserve convergence, SB can be interrupted with vanilla minibatch gradient steps every ``interrupt`` steps. When ``interrupt=0``, SB will be used at every step during the SB interval. When ``interrupt=2``, SB will alternate with vanilla minibatch steps. Args: current_duration (float): The elapsed training duration. Must be within ``[0.0, 1.0)``. batch_idx (int): The current batch within the epoch. start (float, optional): The duration at which selective backprop should be enabled, as a percentage. Default: ``0.5``. end (float, optional): The duration at which selective backprop should be disabled. Default: ``0.9``. interrupt (int, optional): The number of batches between vanilla minibatch gradient updates. Default: ``2``. Returns: bool: If selective backprop should be performed on this batch. """ is_interval = ((current_duration >= start) and (current_duration < end)) is_step = ((interrupt == 0) or ((batch_idx + 1) % interrupt != 0)) return is_interval and is_step
[docs]def select_using_loss(input: torch.Tensor, target: torch.Tensor, model: Callable[[Union[torch.Tensor, Sequence[torch.Tensor]]], torch.Tensor], loss_fun: Callable, keep: float = 0.5, scale_factor: float = 1) -> Tuple[torch.Tensor, torch.Tensor]: """Prunes minibatches as a subroutine of :class:`.SelectiveBackprop`. Computes the loss function on the provided training examples and runs minibatches according to the difficulty. The fraction of the minibatch that is kept for gradient computation is specified by the argument ``0 <= keep <= 1``. To speed up SB's selection forward pass, the argument ``scale_factor`` can be used to spatially downsample input tensors. The full-sized inputs will still be used for the weight gradient computation. Args: input (torch.Tensor): Input tensor to prune. target (torch.Tensor): Output tensor to prune. model (Callable): Model with which to predict outputs. loss_fun (Callable): Loss function of the form ``loss(outputs, targets, reduction='none')``. The function must take the keyword argument ``reduction='none'`` to ensure that per-sample losses are returned. keep (float, optional): Fraction of examples in the batch to keep. Default: ``0.5``. scale_factor (float, optional): Multiplier between 0 and 1 for spatial size. Downsampling requires the input tensor to be at least 3D. Default: ``1``. Returns: (torch.Tensor, torch.Tensor): The pruned batch of inputs and targets Raises: ValueError: If ``scale_factor > 1``. TypeError: If ``loss_fun > 1`` has the wrong signature or is not callable. .. note:: This function runs an extra forward pass through the model on the batch of data. If you are using a non-default precision, ensure that this forward pass runs in your desired precision. For example: .. testsetup:: N_sb, D_sb = 16, 8 X_sb, y_sb = torch.randn(N_sb, D_sb), torch.randint(2, (N_sb,)) lin_model = torch.nn.Linear(X_sb.shape[1], 1) .. doctest:: >>> import torch >>> from composer.algorithms.selective_backprop import select_using_loss >>> with torch.cuda.amp.autocast(True): ... X_new, y_new = select_using_loss( ... X_sb, ... y_sb, ... lin_model, ... loss_fun, ... keep=0.5, ... scale_factor=1 ... ) """ INTERPOLATE_MODES = {3: 'linear', 4: 'bilinear', 5: 'trilinear'} interp_mode = 'bilinear' if scale_factor != 1: if input.dim() not in INTERPOLATE_MODES: raise ValueError(f'Input must be 3D, 4D, or 5D if scale_factor != 1, got {input.dim()}') interp_mode = INTERPOLATE_MODES[input.dim()] if scale_factor > 1: raise ValueError('scale_factor must be <= 1') if callable(loss_fun): sig = inspect.signature(loss_fun) if not 'reduction' in sig.parameters: raise TypeError('Loss function `loss_fun` must take a keyword argument `reduction`.') else: raise TypeError('Loss function must be callable') with torch.no_grad(): N = input.shape[0] # Maybe interpolate if scale_factor < 1: X_scaled = F.interpolate(input, scale_factor=scale_factor, mode=interp_mode, align_corners=False, recompute_scale_factor=False) else: X_scaled = input # Get per-examples losses out = model(X_scaled) losses = loss_fun(out, target, reduction='none') # Sort losses sorted_idx = torch.argsort(losses) n_select = int(keep * N) # Sample by loss percs = np.arange(0.5, N, 1) / N probs = percs**((1.0 / keep) - 1.0) probs = probs / np.sum(probs) select_percs_idx = np.random.choice(N, n_select, replace=False, p=probs) select_idx = sorted_idx[select_percs_idx] return input[select_idx], target[select_idx]
[docs]class SelectiveBackprop(Algorithm): """Selectively backpropagate gradients from a subset of each batch. Based on (`Jiang et al, 2019`_), Selective Backprop (SB) prunes minibatches according to the difficulty of the individual training examples, and only computes weight gradients over the pruned subset, reducing iteration time, and speeding up training. The fraction of the minibatch that is kept for gradient computation is specified by the argument ``0 <= keep <= 1``. To speed up SB's selection forward pass, the argument ``scale_factor`` can be used to spatially downsample input image tensors. The full-sized inputs will still be used for the weight gradient computation. To preserve convergence, SB can be interrupted with vanilla minibatch gradient steps every ``interrupt`` steps. When ``interrupt=0``, SB will be used at every step during the SB interval. When ``interrupt=2``, SB will alternate with vanilla minibatch steps. .. _Jiang et al, 2019: https://arxiv.org/abs/1910.00762 Args: start (float, optional): SB interval start as fraction of training duration. Default: ``0.5``. end (float, optional): SB interval end as fraction of training duration. Default: ``0.9``. keep (float, optional): fraction of minibatch to select and keep for gradient computation. Default: ``0.5``. scale_factor (float, optional): scale for downsampling input for selection forward pass. Default: ``1.``. interrupt (int, optional): interrupt SB with a vanilla minibatch step every ``interrupt`` batches. Default: ``2``. input_key (str | int | Tuple[Callable, Callable] | Any, optional): A key that indexes to the input from the batch. Can also be a pair of get and set functions, where the getter is assumed to be first in the pair. The default is 0, which corresponds to any sequence, where the first element is the input. Default: ``0``. target_key (str | int | Tuple[Callable, Callable] | Any, optional): A key that indexes to the target from the batch. Can also be a pair of get and set functions, where the getter is assumed to be first in the pair. The default is 1, which corresponds to any sequence, where the second element is the target. Default: ``1``. Example: .. testcode:: from composer.algorithms import SelectiveBackprop algorithm = SelectiveBackprop(start=0.5, end=0.9, keep=0.5) trainer = Trainer( model=model, train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, max_duration="1ep", algorithms=[algorithm], optimizers=[optimizer] ) """ def __init__( self, start: float = 0.5, end: float = 0.9, keep: float = 0.5, scale_factor: float = 1., interrupt: int = 2, input_key: Union[str, int, Tuple[Callable, Callable], Any] = 0, target_key: Union[str, int, Tuple[Callable, Callable], Any] = 1, ): self.start = start self.end = end self.keep = keep self.scale_factor = scale_factor self.interrupt = interrupt self._loss_fn = None # set on Event.INIT self.input_key, self.target_key = input_key, target_key def match(self, event: Event, state: State) -> bool: if event == Event.INIT: return True if event != Event.AFTER_DATALOADER: return False is_keep = (self.keep < 1) if not is_keep: return False elapsed_duration = state.get_elapsed_duration() assert elapsed_duration is not None, 'elapsed duration should be set on Event.AFTER_DATALOADER' is_chosen = should_selective_backprop( current_duration=float(elapsed_duration), batch_idx=int(state.timestamp.batch_in_epoch), start=self.start, end=self.end, interrupt=self.interrupt, ) return is_chosen def apply(self, event: Event, state: State, logger: Optional[Logger] = None) -> None: if event == Event.INIT: if self._loss_fn is None: if not isinstance(state.model, ComposerModel): raise RuntimeError('Model must be of type ComposerModel') self._loss_fn = state.model.loss return input, target = state.batch_get_item(key=self.input_key), state.batch_get_item(key=self.target_key) assert isinstance(input, torch.Tensor) and isinstance(target, torch.Tensor), \ 'Multiple tensors not supported for this method yet.' # Model expected to only take in input, not the full batch model = lambda X: state.model((X, None)) def loss(p, y, reduction='none'): assert self._loss_fn is not None, 'loss_fn should be set on Event.INIT' return self._loss_fn(p, (torch.Tensor(), y), reduction=reduction) with get_precision_context(state.precision, state.precision_config): new_input, new_target = select_using_loss(input, target, model, loss, self.keep, self.scale_factor) state.batch_set_item(self.input_key, new_input) state.batch_set_item(self.target_key, new_target)