optimizer_factory.py 13.8 KB
Newer Older
1
2
3
4
5
6
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
7
import inspect
8
9
import logging
import os
10
11
12
from collections import defaultdict
from dataclasses import field
from typing import Any, Dict, List, Optional, Tuple
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66

import torch.optim

from accelerate import Accelerator

from pytorch3d.implicitron.models.base_model import ImplicitronModelBase
from pytorch3d.implicitron.tools import model_io
from pytorch3d.implicitron.tools.config import (
    registry,
    ReplaceableBase,
    run_auto_creation,
)

logger = logging.getLogger(__name__)


class OptimizerFactoryBase(ReplaceableBase):
    def __call__(
        self, model: ImplicitronModelBase, **kwargs
    ) -> Tuple[torch.optim.Optimizer, Any]:
        """
        Initialize the optimizer and lr scheduler.

        Args:
            model: The model with optionally loaded weights.

        Returns:
            An optimizer module (optionally loaded from a checkpoint) and
            a learning rate scheduler module (should be a subclass of torch.optim's
            lr_scheduler._LRScheduler).
        """
        raise NotImplementedError()


@registry.register
class ImplicitronOptimizerFactory(OptimizerFactoryBase):
    """
    A factory that initializes the optimizer and lr scheduler.

    Members:
        betas: Beta parameters for the Adam optimizer.
        breed: The type of optimizer to use. We currently support SGD, Adagrad
            and Adam.
        exponential_lr_step_size: With Exponential policy only,
            lr = lr * gamma ** (epoch/step_size)
        gamma: Multiplicative factor of learning rate decay.
        lr: The value for the initial learning rate.
        lr_policy: The policy to use for learning rate. We currently support
            MultiStepLR and Exponential policies.
        momentum: A momentum value (for SGD only).
        multistep_lr_milestones: With MultiStepLR policy only: list of
            increasing epoch indices at which the learning rate is modified.
        momentum: Momentum factor for SGD optimizer.
        weight_decay: The optimizer weight_decay (L2 penalty on model weights).
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
67
68
        foreach: Whether to use new "foreach" implementation of optimizer where
            available (e.g. requires PyTorch 1.12.0 for Adam)
69
70
71
72
73
74
        group_learning_rates: Parameters or modules can be assigned to parameter
            groups. This dictionary has names of those parameter groups as keys
            and learning rates as values. All parameter group names have to be
            defined in this dictionary. Parameters which do not have predefined
            parameter group are put into "default" parameter group which has
            `lr` as its learning rate.
75
76
77
78
79
80
81
82
83
84
85
    """

    betas: Tuple[float, ...] = (0.9, 0.999)
    breed: str = "Adam"
    exponential_lr_step_size: int = 250
    gamma: float = 0.1
    lr: float = 0.0005
    lr_policy: str = "MultiStepLR"
    momentum: float = 0.9
    multistep_lr_milestones: tuple = ()
    weight_decay: float = 0.0
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
86
87
    linear_exponential_lr_milestone: int = 200
    linear_exponential_start_gamma: float = 0.1
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
88
    foreach: Optional[bool] = True
89
    group_learning_rates: Dict[str, float] = field(default_factory=lambda: {})
90
91
92
93
94
95
96
97
98
99

    def __post_init__(self):
        run_auto_creation(self)

    def __call__(
        self,
        last_epoch: int,
        model: ImplicitronModelBase,
        accelerator: Optional[Accelerator] = None,
        exp_dir: Optional[str] = None,
100
101
        resume: bool = True,
        resume_epoch: int = -1,
102
103
104
105
106
107
108
109
110
111
112
        **kwargs,
    ) -> Tuple[torch.optim.Optimizer, Any]:
        """
        Initialize the optimizer (optionally from a checkpoint) and the lr scheduluer.

        Args:
            last_epoch: If the model was loaded from checkpoint this will be the
                number of the last epoch that was saved.
            model: The model with optionally loaded weights.
            accelerator: An optional Accelerator instance.
            exp_dir: Root experiment directory.
113
114
115
116
            resume: If True, attempt to load optimizer checkpoint from exp_dir.
                Failure to do so will return a newly initialized optimizer.
            resume_epoch: If `resume` is True: Resume optimizer at this epoch. If
                `resume_epoch` <= 0, then resume from the latest checkpoint.
117
118
119
120
121
122
123
124
125
        Returns:
            An optimizer module (optionally loaded from a checkpoint) and
            a learning rate scheduler module (should be a subclass of torch.optim's
            lr_scheduler._LRScheduler).
        """
        # Get the parameters to optimize
        if hasattr(model, "_get_param_groups"):  # use the model function
            p_groups = model._get_param_groups(self.lr, wd=self.weight_decay)
        else:
126
127
128
129
            p_groups = [
                {"params": params, "lr": self._get_group_learning_rate(group)}
                for group, params in self._get_param_groups(model).items()
            ]
130
131

        # Intialize the optimizer
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
132
133
134
135
        optimizer_kwargs: Dict[str, Any] = {
            "lr": self.lr,
            "weight_decay": self.weight_decay,
        }
136
        if self.breed == "SGD":
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
137
138
            optimizer_class = torch.optim.SGD
            optimizer_kwargs["momentum"] = self.momentum
139
        elif self.breed == "Adagrad":
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
140
            optimizer_class = torch.optim.Adagrad
141
        elif self.breed == "Adam":
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
142
143
            optimizer_class = torch.optim.Adam
            optimizer_kwargs["betas"] = self.betas
144
        else:
145
            raise ValueError(f"No such solver type {self.breed}")
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
146
147
148
149

        if "foreach" in inspect.signature(optimizer_class.__init__).parameters:
            optimizer_kwargs["foreach"] = self.foreach
        optimizer = optimizer_class(p_groups, **optimizer_kwargs)
150
        logger.info(f"Solver type = {self.breed}")
151
152

        # Load state from checkpoint
153
154
155
156
157
158
        optimizer_state = self._get_optimizer_state(
            exp_dir,
            accelerator,
            resume_epoch=resume_epoch,
            resume=resume,
        )
159
        if optimizer_state is not None:
160
            logger.info("Setting loaded optimizer state.")
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
            optimizer.load_state_dict(optimizer_state)

        # Initialize the learning rate scheduler
        if self.lr_policy.casefold() == "MultiStepLR".casefold():
            scheduler = torch.optim.lr_scheduler.MultiStepLR(
                optimizer,
                milestones=self.multistep_lr_milestones,
                gamma=self.gamma,
            )
        elif self.lr_policy.casefold() == "Exponential".casefold():
            scheduler = torch.optim.lr_scheduler.LambdaLR(
                optimizer,
                lambda epoch: self.gamma ** (epoch / self.exponential_lr_step_size),
                verbose=False,
            )
Jeremy Reizenstein's avatar
Jeremy Reizenstein committed
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
        elif self.lr_policy.casefold() == "LinearExponential".casefold():
            # linear learning rate progression between epochs 0 to
            # self.linear_exponential_lr_milestone, followed by exponential
            # lr decay for the rest of the epochs
            def _get_lr(epoch: int):
                m = self.linear_exponential_lr_milestone
                if epoch < m:
                    w = (m - epoch) / m
                    gamma = w * self.linear_exponential_start_gamma + (1 - w)
                else:
                    epoch_rest = epoch - m
                    gamma = self.gamma ** (epoch_rest / self.exponential_lr_step_size)
                return gamma

            scheduler = torch.optim.lr_scheduler.LambdaLR(
                optimizer, _get_lr, verbose=False
            )
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
        else:
            raise ValueError("no such lr policy %s" % self.lr_policy)

        # When loading from checkpoint, this will make sure that the
        # lr is correctly set even after returning.
        for _ in range(last_epoch):
            scheduler.step()

        optimizer.zero_grad()

        return optimizer, scheduler

    def _get_optimizer_state(
        self,
        exp_dir: Optional[str],
        accelerator: Optional[Accelerator] = None,
209
210
        resume: bool = True,
        resume_epoch: int = -1,
211
212
213
    ) -> Optional[Dict[str, Any]]:
        """
        Load an optimizer state from a checkpoint.
214
215
216
217
218
219

        resume: If True, attempt to load the last checkpoint from `exp_dir`
            passed to __call__. Failure to do so will return a newly initialized
            optimizer.
        resume_epoch: If `resume` is True: Resume optimizer at this epoch. If
            `resume_epoch` <= 0, then resume from the latest checkpoint.
220
        """
221
        if exp_dir is None or not resume:
222
            return None
223
224
225
226
227
228
        if resume_epoch > 0:
            save_path = model_io.get_checkpoint(exp_dir, resume_epoch)
            if not os.path.isfile(save_path):
                raise FileNotFoundError(
                    f"Cannot find optimizer from epoch {resume_epoch}."
                )
229
230
231
232
        else:
            save_path = model_io.find_last_checkpoint(exp_dir)
        optimizer_state = None
        if save_path is not None:
233
            logger.info(f"Found previous optimizer state {save_path} -> resuming.")
234
235
236
237
238
239
240
241
242
243
            opt_path = model_io.get_optimizer_path(save_path)

            if os.path.isfile(opt_path):
                map_location = None
                if accelerator is not None and not accelerator.is_local_main_process:
                    map_location = {
                        "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                    }
                optimizer_state = torch.load(opt_path, map_location)
            else:
244
                raise FileNotFoundError(f"Optimizer state {opt_path} does not exist.")
245
        return optimizer_state
246
247
248
249
250
251
252
253
254
255
256
257
258
259

    def _get_param_groups(
        self, module: torch.nn.Module
    ) -> Dict[str, List[torch.nn.Parameter]]:
        """
        Recursively visits all the modules inside the `module` and sorts all the
        parameters in parameter groups.

        Uses `param_groups` dictionary member, where keys are names of individual
        parameters or module members and values are the names of the parameter groups
        for those parameters or members. "self" key is used to denote the parameter groups
        at the module level. Possible keys, including the "self" key do not have to
        be defined. By default all parameters have the learning rate defined in the
        optimizer. This can be overridden by setting the parameter group in `param_groups`
260
261
262
263
264
265
        member of a specific module. Values are a parameter group name. The keys
        specify what parameters will be affected as follows:
            - “self”: All the parameters of the module and its child modules
            - name of a parameter: A parameter with that name.
            - name of a module member: All the parameters of the module and its
                child modules.
266
267
                This is useful if members do not have `param_groups`, for
                example torch.nn.Linear.
268
269
            - <name of module member>.<something>: recursive. Same as if <something>
                was used in param_groups of that submodule/member.
270
271
272
273
274
275
276
277
278
279

        Args:
            module: module from which to extract the parameters and their parameter
                groups
        Returns:
            dictionary with parameter groups as keys and lists of parameters as values
        """

        param_groups = defaultdict(list)

280
281
282
283
284
285
286
287
288
289
290
291
        def traverse(module, default_group: str, mapping: Dict[str, str]) -> None:
            """
            Visitor for module to assign its parameters to the relevant member of
            param_groups.

            Args:
                module: the module being visited in a depth-first search
                default_group: the param group to assign parameters to unless
                                otherwise overriden.
                mapping: known mappings of parameters to groups for this module,
                    destructively modified by this function.
            """
292
293
294
295
296
297
298
299
            # If key self is defined in param_groups then chenge the default param
            # group for all parameters and children in the module.
            if hasattr(module, "param_groups") and "self" in module.param_groups:
                default_group = module.param_groups["self"]

            # Collect all the parameters that are directly inside the `module`,
            # they will be in the default param group if they don't have
            # defined group.
300
301
302
            if hasattr(module, "param_groups"):
                mapping.update(module.param_groups)

303
304
            for name, param in module.named_parameters(recurse=False):
                if param.requires_grad:
305
                    group_name = mapping.get(name, default_group)
306
                    logger.debug(f"Assigning {name} to param_group {group_name}")
307
                    param_groups[group_name].append(param)
308
309
310
311

            # If children have defined default param group then use it else pass
            # own default.
            for child_name, child in module.named_children():
312
313
314
315
316
317
318
319
                mapping_to_add = {
                    name[len(child_name) + 1 :]: group
                    for name, group in mapping.items()
                    if name.startswith(child_name + ".")
                }
                traverse(child, mapping.get(child_name, default_group), mapping_to_add)

        traverse(module, "default", {})
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
        return param_groups

    def _get_group_learning_rate(self, group_name: str) -> float:
        """
        Wraps the `group_learning_rates` dictionary providing errors and returns
        `self.lr` for "default" group_name.

        Args:
            group_name: a string representing the name of the group
        Returns:
            learning rate for a specific group
        """
        if group_name == "default":
            return self.lr
        lr = self.group_learning_rates.get(group_name, None)
        if lr is None:
            raise ValueError(f"no learning rate given for group {group_name}")
        return lr