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Update xmisc.scheduler/sampler

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This commit is contained in:
D-X-Y
2021-06-11 11:46:18 +08:00
parent 9bf0fa5f04
commit 48163c792c
17 changed files with 807 additions and 201 deletions
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0
.github/workflows/basic_test.yml → .github/workflows/test-basic.yml vendored
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41
.github/workflows/test-misc.yml vendored Normal file
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@@ -0,0 +1,41 @@
name: Test Xmisc
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
strategy:
matrix:
os: [ubuntu-16.04, ubuntu-18.04, ubuntu-20.04, macos-latest]
python-version: [3.6, 3.7, 3.8, 3.9]
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install XAutoDL from source
run: |
python setup.py install
- name: Test Xmisc
run: |
python -m pip install pytest numpy
python -m pip install torch torchvision
python -m pip install parameterized
echo $PWD
echo "Show what we have here:"
ls
python --version
python -m pytest ./tests/test_misc* -s
shell: bash

0
.github/workflows/super_model_test.yml → .github/workflows/test-super-layer_model.yml vendored
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10
exps/basic/xmain.py
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@@ -46,8 +46,7 @@ def main(args):
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
shuffle=True,
batch_sampler=xmisc.BatchSampler(train_data, args.batch_size, args.steps),
num_workers=args.workers,
pin_memory=True,
)
@@ -57,6 +56,7 @@ def main(args):
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=False,
)
logger.log("The training loader: {:}".format(train_loader))
@@ -73,6 +73,9 @@ def main(args):
logger.log("The loss is {:}".format(loss))
model, loss = torch.nn.DataParallel(model).cuda(), loss.cuda()
scheduler = xmisc.LRMultiplier(
optimizer, xmisc.get_scheduler(args.scheduler, args.lr), args.steps
)
import pdb
@@ -241,10 +244,11 @@ if __name__ == "__main__":
"--valid_data_config", type=str, help="The validation dataset config path."
)
parser.add_argument("--data_path", type=str, help="The path to the dataset.")
parser.add_argument("--algorithm", type=str, help="The algorithm.")
# Optimization options
parser.add_argument("--lr", type=float, help="The learning rate")
parser.add_argument("--weight_decay", type=float, help="The weight decay")
parser.add_argument("--scheduler", type=str, help="The scheduler indicator.")
parser.add_argument("--steps", type=int, help="The total number of steps.")
parser.add_argument("--batch_size", type=int, default=2, help="The batch size.")
parser.add_argument("--workers", type=int, default=4, help="The number of workers")
# Random Seed

0
notebooks/spaces/random-search-transformer.ipynb → notebooks/spaces-xmisc/random-search-transformer.ipynb
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119
notebooks/spaces-xmisc/scheduler.ipynb Normal file
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0
notebooks/LFNA/synthetic-data.ipynb → notebooks/spaces-xmisc/synthetic-data.ipynb
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0
notebooks/LFNA/synthetic-env.ipynb → notebooks/spaces-xmisc/synthetic-env.ipynb
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0
notebooks/LFNA/synthetic-visualize-env.ipynb → notebooks/spaces-xmisc/synthetic-visualize-env.ipynb
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0
notebooks/spaces/test-transformer-encoder.ipynb → notebooks/spaces-xmisc/test-transformer-encoder.ipynb
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76
notebooks/spaces/test.py
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@@ -1,76 +0,0 @@
import os
import sys
import qlib
import pprint
import numpy as np
import pandas as pd
from pathlib import Path
import torch
__file__ = os.path.dirname(os.path.realpath("__file__"))
lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
print("library path: {:}".format(lib_dir))
assert lib_dir.exists(), "{:} does not exist".format(lib_dir)
if str(lib_dir) not in sys.path:
sys.path.insert(0, str(lib_dir))
from trade_models import get_transformer
from qlib import config as qconfig
from qlib.utils import init_instance_by_config
from qlib.model.base import Model
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP
qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region=qconfig.REG_CN)
dataset_config = {
"class": "DatasetH",
"module_path": "qlib.data.dataset",
"kwargs": {
"handler": {
"class": "Alpha360",
"module_path": "qlib.contrib.data.handler",
"kwargs": {
"start_time": "2008-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2008-01-01",
"fit_end_time": "2014-12-31",
"instruments": "csi100",
},
},
"segments": {
"train": ("2008-01-01", "2014-12-31"),
"valid": ("2015-01-01", "2016-12-31"),
"test": ("2017-01-01", "2020-08-01"),
},
},
}
pprint.pprint(dataset_config)
dataset = init_instance_by_config(dataset_config)
df_train, df_valid, df_test = dataset.prepare(
["train", "valid", "test"],
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
)
model = get_transformer(None)
print(model)
features = torch.from_numpy(df_train["feature"].values).float()
labels = torch.from_numpy(df_train["label"].values).squeeze().float()
batch = list(range(2000))
predicts = model(features[batch])
mask = ~torch.isnan(labels[batch])
pred = predicts[mask]
label = labels[batch][mask]
loss = torch.nn.functional.mse_loss(pred, label)
from sklearn.metrics import mean_squared_error
mse_loss = mean_squared_error(pred.numpy(), label.numpy())

2
scripts/experimental/train-vit.sh
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@@ -28,4 +28,4 @@ python ./exps/basic/xmain.py --save_dir ${save_dir} --rand_seed ${rseed} \
--model_config ./configs/yaml.model/vit-cifar10.s0 \
--optim_config ./configs/yaml.opt/vit.cifar \
--loss_config ./configs/yaml.loss/cross-entropy \
--lr 0.003 --weight_decay 0.3
--lr 0.003 --weight_decay 0.3 --scheduler warm-cos --steps 10000

73
tests/test_misc_scheduler.py Normal file
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@@ -0,0 +1,73 @@
####################################################
# Copyright (c) Facebook, Inc. and its affiliates. #
####################################################
# Inspired from https://github.com/facebookresearch/detectron2/blob/master/tests/test_scheduler.py
####################################################
import math
import numpy as np
from unittest import TestCase
import torch
from xautodl.xmisc.scheduler_utils import CosineParamScheduler, MultiStepParamScheduler
from xautodl.xmisc.scheduler_utils import LRMultiplier, WarmupParamScheduler
class TestScheduler(TestCase):
"""Test the scheduler."""
def test_warmup_multistep(self):
p = torch.nn.Parameter(torch.zeros(0))
opt = torch.optim.SGD([p], lr=5)
multiplier = WarmupParamScheduler(
MultiStepParamScheduler(
[1, 0.1, 0.01, 0.001],
milestones=[10, 15, 20],
num_updates=30,
),
0.001,
5 / 30,
)
sched = LRMultiplier(opt, multiplier, 30)
# This is an equivalent of:
# sched = WarmupMultiStepLR(
# opt, milestones=[10, 15, 20], gamma=0.1, warmup_factor=0.001, warmup_iters=5)
p.sum().backward()
opt.step()
lrs = [0.005]
for _ in range(30):
sched.step()
lrs.append(opt.param_groups[0]["lr"])
self.assertTrue(np.allclose(lrs[:5], [0.005, 1.004, 2.003, 3.002, 4.001]))
self.assertTrue(np.allclose(lrs[5:10], 5.0))
self.assertTrue(np.allclose(lrs[10:15], 0.5))
self.assertTrue(np.allclose(lrs[15:20], 0.05))
self.assertTrue(np.allclose(lrs[20:], 0.005))
def test_warmup_cosine(self):
p = torch.nn.Parameter(torch.zeros(0))
opt = torch.optim.SGD([p], lr=5)
multiplier = WarmupParamScheduler(
CosineParamScheduler(1, 0),
0.001,
5 / 30,
)
sched = LRMultiplier(opt, multiplier, 30)
p.sum().backward()
opt.step()
self.assertEqual(opt.param_groups[0]["lr"], 0.005)
lrs = [0.005]
for _ in range(30):
sched.step()
lrs.append(opt.param_groups[0]["lr"])
for idx, lr in enumerate(lrs):
expected_cosine = 2.5 * (1.0 + math.cos(math.pi * idx / 30))
if idx >= 5:
self.assertAlmostEqual(lr, expected_cosine)
else:
self.assertNotAlmostEqual(lr, expected_cosine)

20
xautodl/xmisc/__init__.py
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@@ -10,3 +10,23 @@ from .yaml_utils import load_yaml
from .torch_utils import count_parameters
from .logger_utils import Logger
# sampler
from .sampler_utils import BatchSampler
# scheduler related
from .scheduler_utils import CosineParamScheduler, WarmupParamScheduler, LRMultiplier
def get_scheduler(indicator, lr):
if indicator == "warm-cos":
multiplier = WarmupParamScheduler(
CosineParamScheduler(lr, lr * 1e-3),
warmup_factor=0.001,
warmup_length=0.05,
warmup_method="linear",
)
else:
raise ValueError("Unknown indicator: {:}".format(indicator))
return multiplier

32
xautodl/xmisc/sampler_utils.py Normal file
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@@ -0,0 +1,32 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
#####################################################
import random
class BatchSampler:
"""A batch sampler used for single machine training."""
def __init__(self, dataset, batch, steps):
self._num_per_epoch = len(dataset)
self._iter_per_epoch = self._num_per_epoch // batch
self._steps = steps
self._batch = batch
if self._num_per_epoch < self._batch:
raise ValueError(
"The dataset size must be larger than batch={:}".format(batch)
)
self._indexes = list(range(self._num_per_epoch))
def __iter__(self):
"""
yield a batch of indexes using random sampling
"""
for i in range(self._steps):
if i % self._iter_per_epoch == 0:
random.shuffle(self._indexes)
j = i % self._iter_per_epoch
yield self._indexes[j * self._batch : (j + 1) * self._batch]
def __len__(self):
return self._steps

632
xautodl/xmisc/scheduler_utils.py
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@@ -1,136 +1,532 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.06 #
#####################################################
from torch.optim.lr_scheduler import _LRScheduler
####################################################
# Copyright (c) Facebook, Inc. and its affiliates. #
####################################################
# Borrowed from https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/param_scheduler.py
# and https://github.com/facebookresearch/detectron2/blob/master/detectron2/solver/lr_scheduler.py
####################################################
import torch
import bisect
import math
from typing import List, Optional, Sequence, Union
__all__ = [
"ParamScheduler",
"ConstantParamScheduler",
"CosineParamScheduler",
"ExponentialParamScheduler",
"LinearParamScheduler",
"CompositeParamScheduler",
"MultiStepParamScheduler",
"StepParamScheduler",
"StepWithFixedGammaParamScheduler",
"PolynomialDecayParamScheduler",
"WarmupParamScheduler",
"LRMultiplier",
]
class CosineDecayWithWarmup(_LRScheduler):
r"""Set the learning rate of each parameter group using a cosine annealing
schedule, where :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
is the number of epochs since the last restart and :math:`T_{i}` is the number
of epochs between two warm restarts in SGDR:
.. math::
\eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
\cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)
When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.
It has been proposed in
`SGDR: Stochastic Gradient Descent with Warm Restarts`_.
Args:
optimizer (Optimizer): Wrapped optimizer.
T_0 (int): Number of iterations for the first restart.
T_mult (int, optional): A factor increases :math:`T_{i}` after a restart. Default: 1.
eta_min (float, optional): Minimum learning rate. Default: 0.
last_epoch (int, optional): The index of last epoch. Default: -1.
verbose (bool): If ``True``, prints a message to stdout for
each update. Default: ``False``.
.. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
https://arxiv.org/abs/1608.03983
class ParamScheduler:
"""
Base class for parameter schedulers.
A parameter scheduler defines a mapping from a progress value in [0, 1) to
a number (e.g. learning rate).
"""
# To be used for comparisons with where
WHERE_EPSILON = 1e-6
def __call__(self, where: float) -> float:
"""
Get the value of the param for a given point at training.
We update params (such as learning rate) based on the percent progress
of training completed. This allows a scheduler to be agnostic to the
exact length of a particular run (e.g. 120 epochs vs 90 epochs), as
long as the relative progress where params should be updated is the same.
However, it assumes that the total length of training is known.
Args:
where: A float in [0,1) that represents how far training has progressed
"""
raise NotImplementedError("Param schedulers must override __call__")
class ConstantParamScheduler(ParamScheduler):
"""
Returns a constant value for a param.
"""
def __init__(self, value: float) -> None:
self._value = value
def __call__(self, where: float) -> float:
if where >= 1.0:
raise RuntimeError(
f"where in ParamScheduler must be in [0, 1]: got {where}"
)
return self._value
class CosineParamScheduler(ParamScheduler):
"""
Cosine decay or cosine warmup schedules based on start and end values.
The schedule is updated based on the fraction of training progress.
The schedule was proposed in 'SGDR: Stochastic Gradient Descent with
Warm Restarts' (https://arxiv.org/abs/1608.03983). Note that this class
only implements the cosine annealing part of SGDR, and not the restarts.
Example:
.. code-block:: python
CosineParamScheduler(start_value=0.1, end_value=0.0001)
"""
def __init__(
self, optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1, verbose=False
):
if T_0 <= 0 or not isinstance(T_0, int):
raise ValueError("Expected positive integer T_0, but got {}".format(T_0))
if T_mult < 1 or not isinstance(T_mult, int):
raise ValueError("Expected integer T_mult >= 1, but got {}".format(T_mult))
self.T_0 = T_0
self.T_i = T_0
self.T_mult = T_mult
self.eta_min = eta_min
self,
start_value: float,
end_value: float,
) -> None:
self._start_value = start_value
self._end_value = end_value
super(CosineDecayWithWarmup, self).__init__(optimizer, last_epoch, verbose)
def __call__(self, where: float) -> float:
return self._end_value + 0.5 * (self._start_value - self._end_value) * (
1 + math.cos(math.pi * where)
)
self.T_cur = self.last_epoch
def get_lr(self):
if not self._get_lr_called_within_step:
warnings.warn(
"To get the last learning rate computed by the scheduler, "
"please use `get_last_lr()`.",
UserWarning,
class ExponentialParamScheduler(ParamScheduler):
"""
Exponetial schedule parameterized by a start value and decay.
The schedule is updated based on the fraction of training
progress, `where`, with the formula
`param_t = start_value * (decay ** where)`.
Example:
.. code-block:: python
ExponentialParamScheduler(start_value=2.0, decay=0.02)
Corresponds to a decreasing schedule with values in [2.0, 0.04).
"""
def __init__(
self,
start_value: float,
decay: float,
) -> None:
self._start_value = start_value
self._decay = decay
def __call__(self, where: float) -> float:
return self._start_value * (self._decay ** where)
class LinearParamScheduler(ParamScheduler):
"""
Linearly interpolates parameter between ``start_value`` and ``end_value``.
Can be used for either warmup or decay based on start and end values.
The schedule is updated after every train step by default.
Example:
.. code-block:: python
LinearParamScheduler(start_value=0.0001, end_value=0.01)
Corresponds to a linear increasing schedule with values in [0.0001, 0.01)
"""
def __init__(
self,
start_value: float,
end_value: float,
) -> None:
self._start_value = start_value
self._end_value = end_value
def __call__(self, where: float) -> float:
# interpolate between start and end values
return self._end_value * where + self._start_value * (1 - where)
class MultiStepParamScheduler(ParamScheduler):
"""
Takes a predefined schedule for a param value, and a list of epochs or steps
which stand for the upper boundary (excluded) of each range.
Example:
.. code-block:: python
MultiStepParamScheduler(
values=[0.1, 0.01, 0.001, 0.0001],
milestones=[30, 60, 80, 120]
)
Then the param value will be 0.1 for epochs 0-29, 0.01 for
epochs 30-59, 0.001 for epochs 60-79, 0.0001 for epochs 80-120.
Note that the length of values must be equal to the length of milestones
plus one.
"""
def __init__(
self,
values: List[float],
num_updates: Optional[int] = None,
milestones: Optional[List[int]] = None,
) -> None:
"""
Args:
values: param value in each range
num_updates: the end of the last range. If None, will use ``milestones[-1]``
milestones: the boundary of each range. If None, will evenly split ``num_updates``
For example, all the following combinations define the same scheduler:
* num_updates=90, milestones=[30, 60], values=[1, 0.1, 0.01]
* num_updates=90, values=[1, 0.1, 0.01]
* milestones=[30, 60, 90], values=[1, 0.1, 0.01]
* milestones=[3, 6, 9], values=[1, 0.1, 0.01] (ParamScheduler is scale-invariant)
"""
if num_updates is None and milestones is None:
raise ValueError("num_updates and milestones cannot both be None")
if milestones is None:
# Default equispaced drop_epochs behavior
milestones = []
step_width = math.ceil(num_updates / float(len(values)))
for idx in range(len(values) - 1):
milestones.append(step_width * (idx + 1))
else:
if not (
isinstance(milestones, Sequence)
and len(milestones) == len(values) - int(num_updates is not None)
):
raise ValueError(
"MultiStep scheduler requires a list of %d miletones"
% (len(values) - int(num_updates is not None))
)
if num_updates is None:
num_updates, milestones = milestones[-1], milestones[:-1]
if num_updates < len(values):
raise ValueError(
"Total num_updates must be greater than length of param schedule"
)
return [
self.eta_min
+ (base_lr - self.eta_min)
* (1 + math.cos(math.pi * self.T_cur / self.T_i))
/ 2
for base_lr in self.base_lrs
]
self._param_schedule = values
self._num_updates = num_updates
self._milestones: List[int] = milestones
def step(self, epoch=None):
"""Step could be called after every batch update
Example:
>>> scheduler = CosineDecayWithWarmup(optimizer, T_0, T_mult)
>>> iters = len(dataloader)
>>> for epoch in range(20):
>>> for i, sample in enumerate(dataloader):
>>> inputs, labels = sample['inputs'], sample['labels']
>>> optimizer.zero_grad()
>>> outputs = net(inputs)
>>> loss = criterion(outputs, labels)
>>> loss.backward()
>>> optimizer.step()
>>> scheduler.step(epoch + i / iters)
This function can be called in an interleaved way.
Example:
>>> scheduler = CosineDecayWithWarmup(optimizer, T_0, T_mult)
>>> for epoch in range(20):
>>> scheduler.step()
>>> scheduler.step(26)
>>> scheduler.step() # scheduler.step(27), instead of scheduler(20)
"""
if epoch is None and self.last_epoch < 0:
epoch = 0
if epoch is None:
epoch = self.last_epoch + 1
self.T_cur = self.T_cur + 1
if self.T_cur >= self.T_i:
self.T_cur = self.T_cur - self.T_i
self.T_i = self.T_i * self.T_mult
else:
if epoch < 0:
start_epoch = 0
for milestone in self._milestones:
# Do not exceed the total number of epochs
if milestone >= self._num_updates:
raise ValueError(
"Expected non-negative epoch, but got {}".format(epoch)
"Milestone must be smaller than total number of updates: "
"num_updates=%d, milestone=%d" % (self._num_updates, milestone)
)
if epoch >= self.T_0:
if self.T_mult == 1:
self.T_cur = epoch % self.T_0
else:
n = int(
math.log(
(epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult
)
)
self.T_cur = epoch - self.T_0 * (self.T_mult ** n - 1) / (
self.T_mult - 1
)
self.T_i = self.T_0 * self.T_mult ** (n)
else:
self.T_i = self.T_0
self.T_cur = epoch
self.last_epoch = math.floor(epoch)
# Must be in ascending order
if start_epoch >= milestone:
raise ValueError(
"Milestone must be smaller than start epoch: start_epoch=%d, milestone=%d"
% (start_epoch, milestone)
)
start_epoch = milestone
class _enable_get_lr_call:
def __init__(self, o):
self.o = o
def __call__(self, where: float) -> float:
if where > 1.0:
raise RuntimeError(
f"where in ParamScheduler must be in [0, 1]: got {where}"
)
epoch_num = int((where + self.WHERE_EPSILON) * self._num_updates)
return self._param_schedule[bisect.bisect_right(self._milestones, epoch_num)]
def __enter__(self):
self.o._get_lr_called_within_step = True
return self
def __exit__(self, type, value, traceback):
self.o._get_lr_called_within_step = False
return self
class PolynomialDecayParamScheduler(ParamScheduler):
"""
Decays the param value after every epoch according to a
polynomial function with a fixed power.
The schedule is updated after every train step by default.
with _enable_get_lr_call(self):
for i, data in enumerate(zip(self.optimizer.param_groups, self.get_lr())):
param_group, lr = data
param_group["lr"] = lr
self.print_lr(self.verbose, i, lr, epoch)
Example:
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
.. code-block:: python
PolynomialDecayParamScheduler(base_value=0.1, power=0.9)
Then the param value will be 0.1 for epoch 0, 0.099 for epoch 1, and
so on.
"""
def __init__(
self,
base_value: float,
power: float,
) -> None:
self._base_value = base_value
self._power = power
def __call__(self, where: float) -> float:
return self._base_value * (1 - where) ** self._power
class StepParamScheduler(ParamScheduler):
"""
Takes a fixed schedule for a param value. If the length of the
fixed schedule is less than the number of epochs, then the epochs
are divided evenly among the param schedule.
The schedule is updated after every train epoch by default.
Example:
.. code-block:: python
StepParamScheduler(values=[0.1, 0.01, 0.001, 0.0001], num_updates=120)
Then the param value will be 0.1 for epochs 0-29, 0.01 for
epochs 30-59, 0.001 for epoch 60-89, 0.0001 for epochs 90-119.
"""
def __init__(
self,
num_updates: Union[int, float],
values: List[float],
) -> None:
if num_updates <= 0:
raise ValueError("Number of updates must be larger than 0")
if not (isinstance(values, Sequence) and len(values) > 0):
raise ValueError(
"Step scheduler requires a list of at least one param value"
)
self._param_schedule = values
def __call__(self, where: float) -> float:
ind = int((where + self.WHERE_EPSILON) * len(self._param_schedule))
return self._param_schedule[ind]
class StepWithFixedGammaParamScheduler(ParamScheduler):
"""
Decays the param value by gamma at equal number of steps so as to have the
specified total number of decays.
Example:
.. code-block:: python
StepWithFixedGammaParamScheduler(
base_value=0.1, gamma=0.1, num_decays=3, num_updates=120)
Then the param value will be 0.1 for epochs 0-29, 0.01 for
epochs 30-59, 0.001 for epoch 60-89, 0.0001 for epochs 90-119.
"""
def __init__(
self,
base_value: float,
num_decays: int,
gamma: float,
num_updates: int,
) -> None:
for k in [base_value, gamma]:
if not (isinstance(k, (int, float)) and k > 0):
raise ValueError("base_value and gamma must be positive numbers")
for k in [num_decays, num_updates]:
if not (isinstance(k, int) and k > 0):
raise ValueError("num_decays and num_updates must be positive integers")
self.base_value = base_value
self.num_decays = num_decays
self.gamma = gamma
self.num_updates = num_updates
values = [base_value]
for _ in range(num_decays):
values.append(values[-1] * gamma)
self._step_param_scheduler = StepParamScheduler(
num_updates=num_updates, values=values
)
def __call__(self, where: float) -> float:
return self._step_param_scheduler(where)
class CompositeParamScheduler(ParamScheduler):
"""
Composite parameter scheduler composed of intermediate schedulers.
Takes a list of schedulers and a list of lengths corresponding to
percentage of training each scheduler should run for. Schedulers
are run in order. All values in lengths should sum to 1.0.
Each scheduler also has a corresponding interval scale. If interval
scale is 'fixed', the intermediate scheduler will be run without any rescaling
of the time. If interval scale is 'rescaled', intermediate scheduler is
run such that each scheduler will start and end at the same values as it
would if it were the only scheduler. Default is 'rescaled' for all schedulers.
Example:
.. code-block:: python
schedulers = [
ConstantParamScheduler(value=0.42),
CosineParamScheduler(start_value=0.42, end_value=1e-4)
]
CompositeParamScheduler(
schedulers=schedulers,
interval_scaling=['rescaled', 'rescaled'],
lengths=[0.3, 0.7])
The parameter value will be 0.42 for the first [0%, 30%) of steps,
and then will cosine decay from 0.42 to 0.0001 for [30%, 100%) of
training.
"""
def __init__(
self,
schedulers: Sequence[ParamScheduler],
lengths: List[float],
interval_scaling: Sequence[str],
) -> None:
if len(schedulers) != len(lengths):
raise ValueError("Schedulers and lengths must be same length")
if len(schedulers) == 0:
raise ValueError(
"There must be at least one scheduler in the composite scheduler"
)
if abs(sum(lengths) - 1.0) >= 1e-3:
raise ValueError("The sum of all values in lengths must be 1")
if sum(lengths) != 1.0:
lengths[-1] = 1.0 - sum(lengths[:-1])
for s in interval_scaling:
if s not in ["rescaled", "fixed"]:
raise ValueError(f"Unsupported interval_scaling: {s}")
self._lengths = lengths
self._schedulers = schedulers
self._interval_scaling = interval_scaling
def __call__(self, where: float) -> float:
# Find scheduler corresponding to where
i = 0
running_total = self._lengths[i]
while (where + self.WHERE_EPSILON) > running_total and i < len(
self._schedulers
) - 1:
i += 1
running_total += self._lengths[i]
scheduler = self._schedulers[i]
scheduler_where = where
interval_scale = self._interval_scaling[i]
if interval_scale == "rescaled":
# Calculate corresponding where % for scheduler
scheduler_start = running_total - self._lengths[i]
scheduler_where = (where - scheduler_start) / self._lengths[i]
return scheduler(scheduler_where)
class WarmupParamScheduler(CompositeParamScheduler):
"""
Add an initial warmup stage to another scheduler.
"""
def __init__(
self,
scheduler: ParamScheduler,
warmup_factor: float,
warmup_length: float,
warmup_method: str = "linear",
):
"""
Args:
scheduler: warmup will be added at the beginning of this scheduler
warmup_factor: the factor w.r.t the initial value of ``scheduler``, e.g. 0.001
warmup_length: the relative length (in [0, 1]) of warmup steps w.r.t the entire
training, e.g. 0.01
warmup_method: one of "linear" or "constant"
"""
end_value = scheduler(warmup_length) # the value to reach when warmup ends
start_value = warmup_factor * scheduler(0.0)
if warmup_method == "constant":
warmup = ConstantParamScheduler(start_value)
elif warmup_method == "linear":
warmup = LinearParamScheduler(start_value, end_value)
else:
raise ValueError("Unknown warmup method: {}".format(warmup_method))
super().__init__(
[warmup, scheduler],
interval_scaling=["rescaled", "fixed"],
lengths=[warmup_length, 1 - warmup_length],
)
##### LR Scheduler
class LRMultiplier(torch.optim.lr_scheduler._LRScheduler):
"""
A LRScheduler which uses fvcore :class:`ParamScheduler` to multiply the
learning rate of each param in the optimizer.
Every step, the learning rate of each parameter becomes its initial value
multiplied by the output of the given :class:`ParamScheduler`.
The absolute learning rate value of each parameter can be different.
This scheduler can be used as long as the relative scale among them do
not change during training.
Examples:
::
LRMultiplier(
opt,
WarmupParamScheduler(
MultiStepParamScheduler(
[1, 0.1, 0.01],
milestones=[60000, 80000],
num_updates=90000,
), 0.001, 100 / 90000
),
max_iter=90000
)
"""
# NOTES: in the most general case, every LR can use its own scheduler.
# Supporting this requires interaction with the optimizer when its parameter
# group is initialized. For example, classyvision implements its own optimizer
# that allows different schedulers for every parameter group.
# To avoid this complexity, we use this class to support the most common cases
# where the relative scale among all LRs stay unchanged during training. In this
# case we only need a total of one scheduler that defines the relative LR multiplier.
def __init__(
self,
optimizer: torch.optim.Optimizer,
multiplier: ParamScheduler,
max_iter: int,
last_iter: int = -1,
):
"""
Args:
optimizer, last_iter: See ``torch.optim.lr_scheduler._LRScheduler``.
``last_iter`` is the same as ``last_epoch``.
multiplier: a fvcore ParamScheduler that defines the multiplier on
every LR of the optimizer
max_iter: the total number of training iterations
"""
if not isinstance(multiplier, ParamScheduler):
raise ValueError(
"_LRMultiplier(multiplier=) must be an instance of fvcore "
f"ParamScheduler. Got {multiplier} instead."
)
self._multiplier = multiplier
self._max_iter = max_iter
super().__init__(optimizer, last_epoch=last_iter)
def state_dict(self):
# fvcore schedulers are stateless. Only keep pytorch scheduler states
return {"base_lrs": self.base_lrs, "last_epoch": self.last_epoch}
def get_lr(self) -> List[float]:
multiplier = self._multiplier(self.last_epoch / self._max_iter)
return [base_lr * multiplier for base_lr in self.base_lrs]

3
xautodl/xmodels/__init__.py
View File

@@ -5,6 +5,3 @@
#####################################################
from .transformers import get_transformer
def obtain_model(config):
raise NotImplementedError