mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
fe5bdb2fb5
Summary:
Adds the ability to have different learning rates for different parts of the model. The trainable parts of the implicitron have a new member
param_groups: dictionary where keys are names of individual parameters,
or module’s members and values are the parameter group where the
parameter/member will be sorted to. "self" key is used to denote the
parameter group at the module level. Possible keys, including the "self" key
do not have to be defined. By default all parameters are put into "default"
parameter group and have the learning rate defined in the optimizer,
it can be overriden at the:
- module level with “self” key, all the parameters and child
module s parameters will be put to that parameter group
- member level, which is the same as if the `param_groups` in that
member has key=“self” and value equal to that parameter group.
This is useful if members do not have `param_groups`, for
example torch.nn.Linear.
- parameter level, parameter with the same name as the key
will be put to that parameter group.
And in the optimizer factory, parameters and their learning rates are recursively gathered.
Reviewed By: shapovalov
Differential Revision: D40145802
fbshipit-source-id: 631c02b8d79ee1c0eb4c31e6e42dbd3d2882078a
326 lines
13 KiB
Python
326 lines
13 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import inspect
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import logging
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import os
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from collections import defaultdict
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from dataclasses import field
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from typing import Any, Dict, List, Optional, Tuple
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import torch.optim
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from accelerate import Accelerator
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from pytorch3d.implicitron.models.base_model import ImplicitronModelBase
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from pytorch3d.implicitron.tools import model_io
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from pytorch3d.implicitron.tools.config import (
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registry,
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ReplaceableBase,
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run_auto_creation,
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)
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logger = logging.getLogger(__name__)
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class OptimizerFactoryBase(ReplaceableBase):
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def __call__(
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self, model: ImplicitronModelBase, **kwargs
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) -> Tuple[torch.optim.Optimizer, Any]:
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"""
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Initialize the optimizer and lr scheduler.
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Args:
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model: The model with optionally loaded weights.
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Returns:
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An optimizer module (optionally loaded from a checkpoint) and
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a learning rate scheduler module (should be a subclass of torch.optim's
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lr_scheduler._LRScheduler).
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"""
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raise NotImplementedError()
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@registry.register
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class ImplicitronOptimizerFactory(OptimizerFactoryBase):
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"""
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A factory that initializes the optimizer and lr scheduler.
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Members:
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betas: Beta parameters for the Adam optimizer.
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breed: The type of optimizer to use. We currently support SGD, Adagrad
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and Adam.
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exponential_lr_step_size: With Exponential policy only,
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lr = lr * gamma ** (epoch/step_size)
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gamma: Multiplicative factor of learning rate decay.
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lr: The value for the initial learning rate.
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lr_policy: The policy to use for learning rate. We currently support
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MultiStepLR and Exponential policies.
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momentum: A momentum value (for SGD only).
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multistep_lr_milestones: With MultiStepLR policy only: list of
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increasing epoch indices at which the learning rate is modified.
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momentum: Momentum factor for SGD optimizer.
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weight_decay: The optimizer weight_decay (L2 penalty on model weights).
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foreach: Whether to use new "foreach" implementation of optimizer where
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available (e.g. requires PyTorch 1.12.0 for Adam)
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group_learning_rates: Parameters or modules can be assigned to parameter
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groups. This dictionary has names of those parameter groups as keys
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and learning rates as values. All parameter group names have to be
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defined in this dictionary. Parameters which do not have predefined
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parameter group are put into "default" parameter group which has
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`lr` as its learning rate.
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"""
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betas: Tuple[float, ...] = (0.9, 0.999)
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breed: str = "Adam"
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exponential_lr_step_size: int = 250
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gamma: float = 0.1
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lr: float = 0.0005
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lr_policy: str = "MultiStepLR"
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momentum: float = 0.9
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multistep_lr_milestones: tuple = ()
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weight_decay: float = 0.0
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linear_exponential_lr_milestone: int = 200
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linear_exponential_start_gamma: float = 0.1
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foreach: Optional[bool] = True
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group_learning_rates: Dict[str, float] = field(default_factory=lambda: {})
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def __post_init__(self):
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run_auto_creation(self)
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def __call__(
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self,
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last_epoch: int,
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model: ImplicitronModelBase,
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accelerator: Optional[Accelerator] = None,
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exp_dir: Optional[str] = None,
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resume: bool = True,
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resume_epoch: int = -1,
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**kwargs,
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) -> Tuple[torch.optim.Optimizer, Any]:
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"""
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Initialize the optimizer (optionally from a checkpoint) and the lr scheduluer.
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Args:
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last_epoch: If the model was loaded from checkpoint this will be the
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number of the last epoch that was saved.
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model: The model with optionally loaded weights.
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accelerator: An optional Accelerator instance.
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exp_dir: Root experiment directory.
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resume: If True, attempt to load optimizer checkpoint from exp_dir.
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Failure to do so will return a newly initialized optimizer.
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resume_epoch: If `resume` is True: Resume optimizer at this epoch. If
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`resume_epoch` <= 0, then resume from the latest checkpoint.
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Returns:
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An optimizer module (optionally loaded from a checkpoint) and
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a learning rate scheduler module (should be a subclass of torch.optim's
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lr_scheduler._LRScheduler).
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"""
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# Get the parameters to optimize
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if hasattr(model, "_get_param_groups"): # use the model function
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# pyre-ignore[29]
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p_groups = model._get_param_groups(self.lr, wd=self.weight_decay)
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else:
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p_groups = [
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{"params": params, "lr": self._get_group_learning_rate(group)}
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for group, params in self._get_param_groups(model).items()
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]
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# Intialize the optimizer
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optimizer_kwargs: Dict[str, Any] = {
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"lr": self.lr,
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"weight_decay": self.weight_decay,
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}
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if self.breed == "SGD":
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optimizer_class = torch.optim.SGD
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optimizer_kwargs["momentum"] = self.momentum
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elif self.breed == "Adagrad":
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optimizer_class = torch.optim.Adagrad
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elif self.breed == "Adam":
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optimizer_class = torch.optim.Adam
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optimizer_kwargs["betas"] = self.betas
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else:
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raise ValueError(f"No such solver type {self.breed}")
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if "foreach" in inspect.signature(optimizer_class.__init__).parameters:
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optimizer_kwargs["foreach"] = self.foreach
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optimizer = optimizer_class(p_groups, **optimizer_kwargs)
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logger.info(f"Solver type = {self.breed}")
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# Load state from checkpoint
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optimizer_state = self._get_optimizer_state(
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exp_dir,
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accelerator,
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resume_epoch=resume_epoch,
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resume=resume,
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)
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if optimizer_state is not None:
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logger.info("Setting loaded optimizer state.")
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optimizer.load_state_dict(optimizer_state)
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# Initialize the learning rate scheduler
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if self.lr_policy.casefold() == "MultiStepLR".casefold():
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scheduler = torch.optim.lr_scheduler.MultiStepLR(
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optimizer,
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milestones=self.multistep_lr_milestones,
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gamma=self.gamma,
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)
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elif self.lr_policy.casefold() == "Exponential".casefold():
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scheduler = torch.optim.lr_scheduler.LambdaLR(
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optimizer,
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lambda epoch: self.gamma ** (epoch / self.exponential_lr_step_size),
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verbose=False,
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)
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elif self.lr_policy.casefold() == "LinearExponential".casefold():
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# linear learning rate progression between epochs 0 to
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# self.linear_exponential_lr_milestone, followed by exponential
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# lr decay for the rest of the epochs
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def _get_lr(epoch: int):
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m = self.linear_exponential_lr_milestone
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if epoch < m:
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w = (m - epoch) / m
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gamma = w * self.linear_exponential_start_gamma + (1 - w)
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else:
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epoch_rest = epoch - m
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gamma = self.gamma ** (epoch_rest / self.exponential_lr_step_size)
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return gamma
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scheduler = torch.optim.lr_scheduler.LambdaLR(
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optimizer, _get_lr, verbose=False
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)
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else:
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raise ValueError("no such lr policy %s" % self.lr_policy)
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# When loading from checkpoint, this will make sure that the
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# lr is correctly set even after returning.
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for _ in range(last_epoch):
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scheduler.step()
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optimizer.zero_grad()
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return optimizer, scheduler
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def _get_optimizer_state(
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self,
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exp_dir: Optional[str],
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accelerator: Optional[Accelerator] = None,
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resume: bool = True,
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resume_epoch: int = -1,
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) -> Optional[Dict[str, Any]]:
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"""
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Load an optimizer state from a checkpoint.
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resume: If True, attempt to load the last checkpoint from `exp_dir`
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passed to __call__. Failure to do so will return a newly initialized
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optimizer.
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resume_epoch: If `resume` is True: Resume optimizer at this epoch. If
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`resume_epoch` <= 0, then resume from the latest checkpoint.
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"""
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if exp_dir is None or not resume:
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return None
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if resume_epoch > 0:
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save_path = model_io.get_checkpoint(exp_dir, resume_epoch)
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if not os.path.isfile(save_path):
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raise FileNotFoundError(
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f"Cannot find optimizer from epoch {resume_epoch}."
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)
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else:
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save_path = model_io.find_last_checkpoint(exp_dir)
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optimizer_state = None
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if save_path is not None:
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logger.info(f"Found previous optimizer state {save_path} -> resuming.")
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opt_path = model_io.get_optimizer_path(save_path)
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if os.path.isfile(opt_path):
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map_location = None
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if accelerator is not None and not accelerator.is_local_main_process:
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map_location = {
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"cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
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}
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optimizer_state = torch.load(opt_path, map_location)
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else:
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raise FileNotFoundError(f"Optimizer state {opt_path} does not exist.")
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return optimizer_state
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def _get_param_groups(
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self, module: torch.nn.Module
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) -> Dict[str, List[torch.nn.Parameter]]:
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"""
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Recursively visits all the modules inside the `module` and sorts all the
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parameters in parameter groups.
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Uses `param_groups` dictionary member, where keys are names of individual
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parameters or module members and values are the names of the parameter groups
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for those parameters or members. "self" key is used to denote the parameter groups
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at the module level. Possible keys, including the "self" key do not have to
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be defined. By default all parameters have the learning rate defined in the
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optimizer. This can be overridden by setting the parameter group in `param_groups`
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member of a specific module, it can be overridden at the:
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- module level with “self” key, all the parameters and child
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module's parameters will inherit it
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- member level, which is the same as if the `param_groups` in that
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member has key=“self” and value equal to that parameter group.
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This is useful if members do not have `param_groups`, for
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example torch.nn.Linear.
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- parameter level, only parameter with the same name as the key
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will have it.
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Args:
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module: module from which to extract the parameters and their parameter
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groups
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Returns:
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dictionary with parameter groups as keys and lists of parameters as values
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"""
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param_groups = defaultdict(list)
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def traverse(module, default_group):
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# If key self is defined in param_groups then chenge the default param
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# group for all parameters and children in the module.
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if hasattr(module, "param_groups") and "self" in module.param_groups:
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default_group = module.param_groups["self"]
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# Collect all the parameters that are directly inside the `module`,
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# they will be in the default param group if they don't have
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# defined group.
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for name, param in module.named_parameters(recurse=False):
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if param.requires_grad:
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if hasattr(module, "param_groups") and name in module.param_groups:
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param_groups[module.param_groups[name]].append(param)
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else:
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param_groups[default_group].append(param)
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# If children have defined default param group then use it else pass
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# own default.
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for child_name, child in module.named_children():
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if (
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hasattr(module, "param_groups")
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and child_name in module.param_groups
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):
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traverse(child, module.param_groups[child_name])
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else:
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traverse(child, default_group)
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traverse(module, "default")
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return param_groups
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def _get_group_learning_rate(self, group_name: str) -> float:
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"""
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Wraps the `group_learning_rates` dictionary providing errors and returns
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`self.lr` for "default" group_name.
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Args:
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group_name: a string representing the name of the group
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Returns:
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learning rate for a specific group
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"""
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if group_name == "default":
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return self.lr
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lr = self.group_learning_rates.get(group_name, None)
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if lr is None:
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raise ValueError(f"no learning rate given for group {group_name}")
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return lr
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