OSSForks/pytorch
1
0
Fork 0
mirror of https://github.com/zebrajr/pytorch.git synced 2025-12-06 12:20:52 +01:00
Code Issues Actions 38 Packages Projects Releases Wiki Activity
67d9790985
pytorch/test/test_optim.py
Aaron Gokaslan 67d9790985 [BE] Apply almost all remaining flake8-comprehension checks (#94676) 
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
2023-02-12 01:01:25 +00:00

4542 lines
171 KiB
Python
Raw Blame History

# Owner(s): ["module: optimizer"]
import warnings
import math
import unittest
import functools
import itertools
import pickle
from copy import deepcopy
import weakref
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter
from torch.optim import Adam, SGD, Optimizer
from torch import sparse
from torch.optim.lr_scheduler import (
LambdaLR,
MultiplicativeLR,
SequentialLR,
StepLR,
MultiStepLR,
ConstantLR,
LinearLR,
ExponentialLR,
CosineAnnealingLR,
ReduceLROnPlateau,
LRScheduler,
CyclicLR,
CosineAnnealingWarmRestarts,
OneCycleLR,
ChainedScheduler,
PolynomialLR,
EPOCH_DEPRECATION_WARNING,
)
from torch.optim.swa_utils import AveragedModel, SWALR, update_bn
from torch.testing._internal.common_utils import (
TestCase,
run_tests,
TEST_WITH_UBSAN,
load_tests,
parametrize,
instantiate_parametrized_tests,
gradcheck,
skipIfRocm,
skipIfTorchDynamo
)
from torch.testing._internal.common_cuda import TEST_MULTIGPU
from typing import Dict, Any, Tuple
from torch.optim.optimizer import register_optimizer_step_pre_hook, register_optimizer_step_post_hook
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
def rosenbrock(tensor):
x, y = tensor
return (1 - x) ** 2 + 100 * (y - x**2) ** 2
def drosenbrock(tensor):
x, y = tensor
return torch.tensor((-400 * x * (y - x**2) - 2 * (1 - x), 200 * (y - x**2)))
class TestOptim(TestCase):
exact_dtype = True
def _test_rosenbrock_sparse(
self,
constructor,
scheduler_constructors=None,
sparse_only=False,
maximize=False,
):
if scheduler_constructors is None:
scheduler_constructors = []
params_t = torch.tensor([1.5, 1.5])
params = Parameter(params_t)
optimizer = constructor([params])
schedulers = []
for scheduler_constructor in scheduler_constructors:
schedulers.append(scheduler_constructor(optimizer))
if not sparse_only:
params_c = Parameter(params_t.clone())
optimizer_c = constructor([params_c])
solution = torch.tensor([1, 1])
with torch.no_grad():
initial_dist = params.dist(solution)
def eval(params, sparse_grad, w):
# Depending on w, provide only the x or y gradient
optimizer.zero_grad()
loss = rosenbrock(params)
loss.backward()
grad = drosenbrock(params.data)
# NB: We torture test the optimizer by returning an
# uncoalesced sparse tensor
if w:
i = torch.LongTensor([[0, 0]])
x = grad[0]
v = torch.tensor([x / 4.0, x - x / 4.0])
else:
i = torch.LongTensor([[1, 1]])
y = grad[1]
v = torch.tensor([y - y / 4.0, y / 4.0])
x = sparse.DoubleTensor(i, v, torch.Size([2])).to(dtype=v.dtype)
with torch.no_grad():
if sparse_grad:
params.grad = x
else:
params.grad = x.to_dense()
return loss
for i in range(2000):
# Do cyclic coordinate descent
w = i % 2
optimizer.step(functools.partial(eval, params, True, w))
for scheduler in schedulers:
if isinstance(scheduler, ReduceLROnPlateau):
scheduler.step(rosenbrock(params))
else:
scheduler.step()
if not sparse_only:
optimizer_c.step(functools.partial(eval, params_c, False, w))
self.assertEqual(params, params_c)
if not maximize:
self.assertLessEqual(params.data.dist(solution), initial_dist)
else:
self.assertGreaterEqual(rosenbrock(params), rosenbrock(params_t))
def _test_basic_cases_template(
self,
weight_tensor,
bias_tensor,
input_tensor,
constructor,
scheduler_constructors,
constructor_accepts_maximize=True,
constructor_accepts_foreach=False,
):
maximize_options = {False, constructor_accepts_maximize}
foreach_options = {False, constructor_accepts_foreach}
four_arg_constructor = constructor
if constructor_accepts_maximize and constructor_accepts_foreach:
pass
elif constructor_accepts_maximize:
def four_arg_constructor(weight, bias, maximize, foreach):
self.assertFalse(foreach)
return constructor(weight, bias, maximize)
elif constructor_accepts_foreach:
def four_arg_constructor(weight, bias, maximize, foreach):
self.assertFalse(maximize)
return constructor(weight, bias, foreach)
else:
def four_arg_constructor(weight, bias, maximize, foreach):
self.assertFalse(maximize or foreach)
return constructor(weight, bias)
for maximize, foreach in itertools.product(maximize_options, foreach_options):
with torch.no_grad():
weight = Parameter(weight_tensor.clone().detach())
bias = Parameter(bias_tensor.clone().detach())
input = input_tensor.clone().detach().requires_grad_()
optimizer = four_arg_constructor(weight, bias, maximize, foreach)
schedulers = []
for scheduler_constructor in scheduler_constructors:
schedulers.append(scheduler_constructor(optimizer))
# to check if the optimizer can be printed as a string
optimizer.__repr__()
def fn():
optimizer.zero_grad()
y = weight.mv(input)
if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device():
y = y.cuda(bias.get_device())
loss = (y + bias).pow(2).sum()
loss.backward()
return loss
initial_value = fn().item()
for _ in range(200):
for scheduler in schedulers:
if isinstance(scheduler, ReduceLROnPlateau):
val_loss = fn()
scheduler.step(val_loss)
else:
scheduler.step()
optimizer.step(fn)
if maximize:
self.assertGreater(fn().item(), initial_value)
else:
self.assertLess(fn().item(), initial_value)
def _test_state_dict(self, weight, bias, input, constructor, atol=None, rtol=None):
weight = Parameter(weight)
bias = Parameter(bias)
with torch.no_grad():
input = input.clone().detach().requires_grad_()
def fn_base(optimizer, weight, bias):
optimizer.zero_grad()
i = input_cuda if weight.is_cuda else input
loss = (weight.mv(i) + bias).pow(2).sum()
loss.backward()
return loss
optimizer = constructor(weight, bias)
fn = functools.partial(fn_base, optimizer, weight, bias)
# Prime the optimizer
for _i in range(20):
optimizer.step(fn)
# Clone the weights and construct new optimizer for them
with torch.no_grad():
weight_c = Parameter(weight.clone().detach())
bias_c = Parameter(bias.clone().detach())
optimizer_c = constructor(weight_c, bias_c)
fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c)
# Load state dict
state_dict = deepcopy(optimizer.state_dict())
state_dict_c = deepcopy(optimizer.state_dict())
optimizer_c.load_state_dict(state_dict_c)
# Run both optimizations in parallel
for _ in range(20):
optimizer.step(fn)
optimizer_c.step(fn_c)
self.assertEqual(weight, weight_c)
self.assertEqual(bias, bias_c)
# Make sure state dict wasn't modified
self.assertEqual(state_dict, state_dict_c)
# Make sure state dict is deterministic with equal but not identical parameters
self.assertEqual(optimizer.state_dict(), optimizer_c.state_dict())
# Make sure repeated parameters have identical representation in state dict
optimizer_c.param_groups.extend(optimizer_c.param_groups)
self.assertEqual(
optimizer.state_dict()["param_groups"][-1],
optimizer_c.state_dict()["param_groups"][-1],
)
# Make sure that optimizers that support maximize can load older models
state_dict = optimizer.state_dict()
if "maximize" in state_dict["param_groups"][0]:
for group in state_dict["param_groups"]:
del group["maximize"]
optimizer.load_state_dict(state_dict)
# Make sure we can still step
optimizer.step()
# Make sure that optimizers that support foreach can load older models
state_dict = optimizer.state_dict()
if "foreach" in state_dict["param_groups"][0]:
for group in state_dict["param_groups"]:
del group["foreach"]
optimizer.load_state_dict(state_dict)
# Make sure we can still step
optimizer.step()
# Make sure that loading optimizers with step not wrapped in tensor can work
state_dict = optimizer.state_dict()
if "step" in state_dict["state"][0] and torch.is_tensor(
state_dict["state"][0]["step"]
):
for state in state_dict["state"].values():
state["step"] = state["step"].item()
optimizer.load_state_dict(state_dict)
optimizer.step()
# Check that state dict can be loaded even when we cast parameters
# to a different type and move to a different device.
if not torch.cuda.is_available():
return
with torch.no_grad():
input_cuda = input.clone().detach().to(dtype=torch.float32, device="cuda")
weight_cuda = Parameter(
weight.clone().detach().to(dtype=torch.float32, device="cuda")
)
bias_cuda = Parameter(
bias.clone().detach().to(dtype=torch.float32, device="cuda")
)
optimizer_cuda = constructor(weight_cuda, bias_cuda)
fn_cuda = functools.partial(fn_base, optimizer_cuda, weight_cuda, bias_cuda)
state_dict = deepcopy(optimizer.state_dict())
state_dict_c = deepcopy(optimizer.state_dict())
optimizer_cuda.load_state_dict(state_dict_c)
# Make sure state dict wasn't modified
self.assertEqual(state_dict, state_dict_c)
# Make sure that device of state['step'] is still CPU
new_state_dict = optimizer_cuda.state_dict()
if "step" in state_dict["state"][0] and torch.is_tensor(
state_dict["state"][0]["step"]
):
for state in new_state_dict["state"].values():
self.assertEqual(state["step"].device.type, "cpu")
for _i in range(20):
optimizer.step(fn)
optimizer_cuda.step(fn_cuda)
self.assertEqual(weight, weight_cuda)
self.assertEqual(bias, bias_cuda, atol=atol, rtol=rtol)
# validate deepcopy() copies all public attributes
def getPublicAttr(obj):
return {k for k in obj.__dict__ if not k.startswith("_")}
self.assertEqual(getPublicAttr(optimizer), getPublicAttr(deepcopy(optimizer)))
def _test_basic_cases(
self,
constructor,
scheduler_constructors=None,
ignore_multidevice=False,
constructor_accepts_maximize=False,
constructor_accepts_foreach=False,
atol=None,
rtol=None,
):
if scheduler_constructors is None:
scheduler_constructors = []
def make_two_arg_constructor(
constructor, maximize: bool = False, foreach: bool = False
):
if constructor_accepts_maximize and constructor_accepts_foreach:
return lambda weight, bias: constructor(weight, bias, maximize, foreach)
if constructor_accepts_maximize:
return lambda weight, bias: constructor(weight, bias, maximize)
if constructor_accepts_foreach:
return lambda weight, bias: constructor(weight, bias, foreach)
return constructor
for maximize, foreach in itertools.product(
{False, constructor_accepts_maximize},
{False, constructor_accepts_foreach},
):
self._test_state_dict(
torch.randn(10, 5),
torch.randn(10),
torch.randn(5),
make_two_arg_constructor(constructor, maximize, foreach),
atol=atol,
rtol=rtol,
)
self._test_basic_cases_template(
torch.randn(10, 5),
torch.randn(10),
torch.randn(5),
constructor,
scheduler_constructors,
constructor_accepts_maximize,
constructor_accepts_foreach,
)
# non-contiguous parameters
self._test_basic_cases_template(
torch.randn(10, 5, 2)[..., 0],
torch.randn(10, 2)[..., 0],
torch.randn(5),
constructor,
scheduler_constructors,
constructor_accepts_maximize,
constructor_accepts_foreach,
)
# CUDA
if not torch.cuda.is_available():
return
self._test_basic_cases_template(
torch.randn(10, 5).cuda(),
torch.randn(10).cuda(),
torch.randn(5).cuda(),
constructor,
scheduler_constructors,
constructor_accepts_maximize,
constructor_accepts_foreach,
)
# Multi-GPU
if not torch.cuda.device_count() > 1 or ignore_multidevice:
return
self._test_basic_cases_template(
torch.randn(10, 5).cuda(0),
torch.randn(10).cuda(1),
torch.randn(5).cuda(0),
constructor,
scheduler_constructors,
constructor_accepts_maximize,
constructor_accepts_foreach,
)
def _test_complex_optimizer(self, optimizer_constructor):
complex_param = torch.randn(5, 5, dtype=torch.complex64, requires_grad=True)
real_param = torch.view_as_real(complex_param).detach().clone().requires_grad_()
complex_opt = optimizer_constructor(complex_param)
real_opt = optimizer_constructor(real_param)
for _ in range(3):
complex_param.grad = torch.randn_like(complex_param)
real_param.grad = torch.view_as_real(complex_param.grad)
complex_opt.step()
real_opt.step()
self.assertEqual(torch.view_as_real(complex_param), real_param)
def _test_complex_2d(self, optimizer_constructor, f=None):
if f is None:
f = rosenbrock
a1 = torch.randn(2, dtype=torch.complex64, requires_grad=True)
a1_real = a1.real.clone().detach()
a1_imag = a1.imag.clone().detach()
a1_real.requires_grad_()
a1_imag.requires_grad_()
optim1 = optimizer_constructor([a1])
optim2 = optimizer_constructor([a1_real, a1_imag])
for _ in range(10):
optim1.zero_grad()
optim2.zero_grad()
a2 = torch.complex(a1_real, a1_imag)
f(a1).backward()
f(a2).backward()
self.assertEqual(a1.grad.real, a1_real.grad)
self.assertEqual(a1.grad.imag, a1_imag.grad)
optim1.step()
optim2.step()
self.assertEqual(a1.real, a1_real)
self.assertEqual(a1.imag, a1_imag)
def _build_params_dict(self, weight, bias, **kwargs):
return [{"params": [weight]}, dict(params=[bias], **kwargs)]
def _build_params_dict_single(self, weight, bias, **kwargs):
return [dict(params=bias, **kwargs)]
def test_sgd(self):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
self._build_params_dict_single(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
self._build_params_dict_single(weight, bias, lr=1e-2),
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[lambda opt: StepLR(opt, gamma=0.9, step_size=10)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[
lambda opt: LinearLR(
opt, start_factor=0.4, end_factor=0.8, total_iters=4
)
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[lambda opt: ConstantLR(opt, factor=0.4, total_iters=4)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[
lambda opt: StepLR(opt, gamma=0.9, step_size=10),
lambda opt: LinearLR(
opt, start_factor=0.4, end_factor=0.6, total_iters=4
),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[
lambda opt: StepLR(opt, gamma=0.9, step_size=10),
lambda opt: ReduceLROnPlateau(opt),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[
lambda opt: StepLR(opt, gamma=0.99, step_size=10),
lambda opt: ExponentialLR(opt, gamma=0.99),
lambda opt: ReduceLROnPlateau(opt),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias],
lr=1e-3,
momentum=0.5,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias],
lr=1e-3,
momentum=0.5,
weight_decay=1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias],
nesterov=True,
lr=1e-3,
momentum=0.5,
weight_decay=1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.SGD(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
[lambda opt: PolynomialLR(opt, power=0.9, total_iters=4)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
with self.assertRaisesRegex(ValueError, "Invalid momentum value: -0.5"):
optim.SGD(None, lr=1e-2, momentum=-0.5)
def test_sgd_sparse(self):
for foreach in (False, True):
self._test_rosenbrock_sparse(
lambda params: optim.SGD(params, lr=4.8e-3, foreach=foreach)
)
self._test_rosenbrock_sparse(
lambda params: optim.SGD(params, lr=0.0048, foreach=foreach),
[lambda opt: StepLR(opt, gamma=0.99999, step_size=300)],
)
def test_sgd_complex(self):
for foreach in (False, True):
self._test_complex_optimizer(
lambda param: optim.SGD([param], lr=0.001, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.SGD([param], lr=0.001, momentum=1, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.SGD(
[param], lr=0.001, momentum=1, weight_decay=1, foreach=foreach
)
)
self._test_complex_optimizer(
lambda param: optim.SGD(
[param],
lr=0.001,
nesterov=True,
momentum=1,
weight_decay=1,
foreach=foreach,
)
)
self._test_complex_optimizer(
lambda param: optim.SGD(
[param],
lr=0.001,
momentum=1,
dampening=0.5,
weight_decay=1,
foreach=foreach,
)
)
def _test_derived_optimizers_varying_tensors(self, optimizer_with_kwargs, kwarg):
if not torch.cuda.is_available():
return
assert kwarg in ("foreach", "fused")
# Specifically test that inputting params of different dtypes and devices
# is handled equivalently on the foreach and fused implementations as the
# single tensor implementations. We need multiple GPUs (vs just a CPU and
# GPU) because fused adam only works on GPUs. (Thus we only run the tests
# that call into this helper when TEST_MULTIGPU.)
params = [
torch.rand(2, 3, dtype=torch.float64, device='cuda:0', requires_grad=True),
torch.rand(2, 3, dtype=torch.float32, device='cuda:0', requires_grad=True),
torch.rand(2, 3, dtype=torch.float16, device='cuda:0', requires_grad=True),
torch.rand(2, 3, dtype=torch.bfloat16, device='cuda:0', requires_grad=True),
torch.rand(2, 3, dtype=torch.float64, device='cuda:1', requires_grad=True),
torch.rand(2, 3, dtype=torch.float32, device='cuda:1', requires_grad=True),
torch.rand(2, 3, dtype=torch.float16, device='cuda:1', requires_grad=True),
torch.rand(2, 3, dtype=torch.bfloat16, device='cuda:1', requires_grad=True),
torch.randint(1024, (2, 3), dtype=torch.int64, device='cuda:1', requires_grad=False),
]
for p in params:
if p.requires_grad:
p.grad = torch.rand_like(p, device=p.device, dtype=p.dtype)
kIterations = 7 if kwarg == "foreach" else 1
for optimizer_constructor, kwargs in optimizer_with_kwargs:
res, state = [], []
for enabled in (False, True):
kwargs_clone = deepcopy(kwargs)
kwargs_clone[kwarg] = enabled
params_clone = []
for p in params:
p_clone = p.clone().detach()
if p.requires_grad:
p_clone.requires_grad = True
p_clone.grad = p.grad.clone().detach()
params_clone.append(p_clone)
optimizer = optimizer_constructor(params_clone, **kwargs_clone)
for _ in range(kIterations):
optimizer.step()
state.append(optimizer.state)
res.append(params_clone)
st_state = state[0]
mt_state = state[1]
for st_p, mt_p in zip(res[0], res[1]):
self.assertEqual(st_p, mt_p)
# check that optimizer states are the same
st_p_state = st_state[st_p]
mt_p_state = mt_state[mt_p]
for k in st_p_state:
actual = mt_p_state[k]
# If `torch.optim.Adam` is `__init__`ed with either `fused=True` or `capturable=True`,
# `step` Tensor is 1D while usually it's 0D.
if (
k == "step"
and isinstance(actual, torch.Tensor)
and actual.ndim == 1
):
actual = actual[0]
self.assertEqual(st_p_state[k], actual)
def _test_derived_optimizers(self, optimizer_pairs_with_flags, flag):
if not torch.cuda.is_available():
return
assert flag in ("foreach", "fused")
# why 7? iteration 7 is where we start to see differences for RAdam
# params interacting with the small eps value, because that's right
# after rho_t becomes greater than 5 in step 6.
kIterations = 7
device = "cuda"
for optimizer_constructor, params in optimizer_pairs_with_flags:
res, state = [], []
for flag_value in (False, True):
input = torch.tensor(
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype=torch.float64, device=device
).reshape(3, 2)
torch.manual_seed(1)
model = torch.nn.Sequential(
torch.nn.Linear(2, 3),
torch.nn.Sigmoid(),
torch.nn.Linear(3, 1),
torch.nn.Sigmoid(),
)
model.to(dtype=torch.float64, device=device)
params_with_flags = deepcopy(params)
params_with_flags[flag] = flag_value
optimizer = optimizer_constructor(
model.parameters(), **params_with_flags
)
for _ in range(kIterations):
optimizer.zero_grad()
output = model(input)
loss = output.sum()
loss.backward()
# Test that step behaves as expected (a no-op) when grads are set to None
if iter == 0:
optimizer.zero_grad(set_to_none=True)
optimizer.step()
state.append(optimizer.state)
res.append(model.parameters())
st_state = state[0]
mt_state = state[1]
for st_p, mt_p in zip(res[0], res[1]):
self.assertEqual(st_p, mt_p)
# check that optimizer states are the same
st_p_state = st_state[st_p]
mt_p_state = mt_state[mt_p]
for k in st_p_state:
actual = mt_p_state[k]
# If `torch.optim.Adam` is `__init__`ed with either `fused=True` or `capturable=True`,
# `step` Tensor is 1D while usually it's 0D.
if (
k == "step"
and isinstance(actual, torch.Tensor)
and actual.ndim == 1
):
actual = actual[0]
self.assertEqual(st_p_state[k], actual)
def test_multi_tensor_optimizers(self):
optimizer_pairs_with_flags = [
(optim.Adam, dict(weight_decay=1.0, amsgrad=True, fused=False)),
(optim.Adam, dict(weight_decay=1.0, amsgrad=False, fused=False)),
(optim.Adam, dict(weight_decay=0.0, amsgrad=True, fused=False)),
(optim.Adam, dict(weight_decay=0.0, amsgrad=False, fused=False)),
(optim.AdamW, dict(weight_decay=1.0, amsgrad=True)),
(optim.AdamW, dict(weight_decay=1.0, amsgrad=False)),
(optim.AdamW, dict(weight_decay=0.0, amsgrad=True)),
(optim.AdamW, dict(weight_decay=0.0, amsgrad=False)),
(optim.NAdam, dict(weight_decay=0.0, momentum_decay=6e-3)),
(optim.NAdam, dict(weight_decay=1.0, momentum_decay=6e-3)),
(optim.NAdam, dict(weight_decay=0.0, momentum_decay=4e-3)),
(optim.NAdam, dict(weight_decay=0.01, momentum_decay=4e-3)),
(
optim.SGD,
dict(lr=0.2, momentum=1, dampening=0, weight_decay=1, nesterov=True),
),
(
optim.SGD,
dict(lr=0.2, momentum=1, dampening=0.5, weight_decay=1, nesterov=False),
),
(optim.RAdam, dict(weight_decay=0, eps=1e-6)),
(optim.RAdam, dict(weight_decay=0)),
(optim.RAdam, dict(weight_decay=1, eps=1e-6)),
(optim.RAdam, dict(weight_decay=1)),
(optim.RMSprop, dict(weight_decay=1, momentum=1, centered=True)),
(optim.RMSprop, dict(weight_decay=1, momentum=0, centered=True)),
(optim.RMSprop, dict(weight_decay=1, momentum=1, centered=False)),
(optim.RMSprop, dict(weight_decay=0, momentum=1, centered=False)),
(optim.Rprop, dict(lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50))),
(optim.ASGD, dict(weight_decay=0)),
(optim.ASGD, dict(weight_decay=1)),
(optim.Adamax, dict(weight_decay=0)),
(optim.Adamax, dict(weight_decay=1)),
(optim.Adadelta, dict(weight_decay=0)),
(optim.Adadelta, dict(weight_decay=1)),
(optim.Adagrad, dict(weight_decay=0)),
(optim.Adagrad, dict(weight_decay=1)),
]
self._test_derived_optimizers(optimizer_pairs_with_flags, "foreach")
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_multi_tensor_optimizers_with_varying_tensors(self):
optimizer_pairs_with_flags = [
(optim.Adam, dict(weight_decay=1.0, amsgrad=True, fused=False)),
(optim.Adam, dict(weight_decay=1.0, amsgrad=False, fused=False)),
(optim.Adam, dict(weight_decay=0.0, amsgrad=True, fused=False)),
(optim.Adam, dict(weight_decay=0.0, amsgrad=False, fused=False)),
(optim.AdamW, dict(weight_decay=1.0, amsgrad=True)),
(optim.AdamW, dict(weight_decay=1.0, amsgrad=False)),
(optim.AdamW, dict(weight_decay=0.0, amsgrad=True)),
(optim.AdamW, dict(weight_decay=0.0, amsgrad=False)),
(optim.NAdam, dict(weight_decay=0.0, momentum_decay=6e-3)),
(optim.NAdam, dict(weight_decay=1.0, momentum_decay=6e-3)),
(optim.NAdam, dict(weight_decay=0.0, momentum_decay=4e-3)),
(optim.NAdam, dict(weight_decay=0.01, momentum_decay=4e-3)),
(
optim.SGD,
dict(lr=0.2, momentum=1, dampening=0, weight_decay=1, nesterov=True),
),
(
optim.SGD,
dict(lr=0.2, momentum=1, dampening=0.5, weight_decay=1, nesterov=False),
),
(optim.RAdam, dict(weight_decay=0, eps=1e-6)),
(optim.RAdam, dict(weight_decay=0)),
(optim.RAdam, dict(weight_decay=1, eps=1e-6)),
(optim.RAdam, dict(weight_decay=1)),
(optim.RMSprop, dict(weight_decay=1, momentum=1, centered=True)),
(optim.RMSprop, dict(weight_decay=1, momentum=0, centered=True)),
(optim.RMSprop, dict(weight_decay=1, momentum=1, centered=False)),
(optim.RMSprop, dict(weight_decay=0, momentum=1, centered=False)),
(optim.Rprop, dict(lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50))),
(optim.ASGD, dict(weight_decay=0)),
(optim.ASGD, dict(weight_decay=1)),
(optim.Adamax, dict(weight_decay=0)),
(optim.Adamax, dict(weight_decay=1)),
(optim.Adadelta, dict(weight_decay=0)),
(optim.Adadelta, dict(weight_decay=1)),
(optim.Adagrad, dict(weight_decay=0)),
(optim.Adagrad, dict(weight_decay=1)),
]
self._test_derived_optimizers_varying_tensors(optimizer_pairs_with_flags, "foreach")
def test_fused_optimizers(self):
optimizer_pairs_with_flags = tuple(itertools.product(
(optim.Adam, optim.AdamW),
(
dict(weight_decay=1., amsgrad=False),
dict(weight_decay=1., amsgrad=True),
dict(weight_decay=0., amsgrad=False),
dict(weight_decay=0., amsgrad=True),
),
))
self._test_derived_optimizers(optimizer_pairs_with_flags, "fused")
@unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
def test_fused_optimizers_with_varying_tensors(self):
optimizer_pairs_with_flags = tuple(itertools.product(
(optim.Adam, optim.AdamW),
(
dict(weight_decay=1., amsgrad=False),
dict(weight_decay=1., amsgrad=True),
dict(weight_decay=0., amsgrad=False),
dict(weight_decay=0., amsgrad=True),
),
))
self._test_derived_optimizers_varying_tensors(optimizer_pairs_with_flags, "fused")
def test_adam(self):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
[weight, bias],
lr=1e-3,
amsgrad=True,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
[weight, bias],
lr=1e-3,
weight_decay=0.1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
amsgrad=True,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
[lambda opt: ExponentialLR(opt, gamma=0.9)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
[lambda opt: LinearLR(opt, start_factor=0.4, total_iters=4)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
[lambda opt: ConstantLR(opt, factor=0.4, total_iters=4)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
[weight, bias],
lr=1e-3,
amsgrad=True,
maximize=maximize,
foreach=foreach,
),
[
lambda opt: ConstantLR(opt, factor=0.4, total_iters=4),
lambda opt: ExponentialLR(opt, gamma=0.9),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
[weight, bias],
lr=1e-3,
amsgrad=True,
maximize=maximize,
foreach=foreach,
),
[
lambda opt: ExponentialLR(opt, gamma=0.9),
lambda opt: ReduceLROnPlateau(opt),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
amsgrad=True,
maximize=maximize,
foreach=foreach,
),
[
lambda opt: StepLR(opt, gamma=0.9, step_size=10),
lambda opt: ReduceLROnPlateau(opt),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adam(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
[lambda opt: PolynomialLR(opt, total_iters=4, power=0.9)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_complex_2d(optim.Adam)
self._test_complex_2d(functools.partial(optim.Adam, foreach=True))
with self.assertRaisesRegex(
ValueError, "Invalid beta parameter at index 0: 1.0"
):
optim.Adam(None, lr=1e-2, betas=(1.0, 0.0))
with self.assertRaisesRegex(ValueError, "Invalid weight_decay value: -1"):
optim.Adam(None, lr=1e-2, weight_decay=-1)
def test_adamw(self):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.AdamW(
[weight, bias], lr=1e-3, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.AdamW(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.AdamW(
[weight, bias],
lr=1e-3,
weight_decay=1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.AdamW(
[weight, bias],
lr=1e-3,
weight_decay=1,
amsgrad=True,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_complex_2d(optim.AdamW)
self._test_complex_2d(functools.partial(optim.AdamW, foreach=True))
with self.assertRaisesRegex(ValueError, "Invalid weight_decay value: -1"):
optim.AdamW(None, lr=1e-2, weight_decay=-1)
def test_sparse_adam(self):
self._test_rosenbrock_sparse(
lambda params: optim.SparseAdam(params, lr=4e-2), [], True
)
self._test_rosenbrock_sparse(
lambda params: optim.SparseAdam(params, lr=4e-2, maximize=True),
[],
True,
True,
)
with self.assertRaisesRegex(
ValueError, "Invalid beta parameter at index 0: 1.0"
):
optim.SparseAdam(None, lr=1e-2, betas=(1.0, 0.0))
with self.assertRaisesRegex(
ValueError, "SparseAdam requires dense parameter tensors"
):
optim.SparseAdam([torch.zeros(3, layout=torch.sparse_coo)])
with self.assertRaisesRegex(
ValueError, "SparseAdam requires dense parameter tensors"
):
optim.SparseAdam([{"params": [torch.zeros(3, layout=torch.sparse_coo)]}])
# ROCm precision is too low to pass this test
def test_adadelta(self):
# Handles https://github.com/pytorch/pytorch/issues/69698
self.rel_tol = 4e-3
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adadelta(
[weight, bias], maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adadelta(
self._build_params_dict(weight, bias, rho=0.95),
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adadelta(
self._build_params_dict(weight, bias, rho=0.95),
maximize=maximize,
foreach=foreach,
),
[
lambda opt: StepLR(opt, gamma=0.9, step_size=10),
lambda opt: ReduceLROnPlateau(opt),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adadelta(
[weight, bias], weight_decay=1, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
with self.assertRaisesRegex(ValueError, "Invalid rho value: 1.1"):
optim.Adadelta(None, lr=1e-2, rho=1.1)
def test_adadelta_complex(self):
# Handles https://github.com/pytorch/pytorch/issues/69698
self.rel_tol = 2e-2
for optimizer in [optim.Adadelta]:
self._test_complex_optimizer(lambda weight: optimizer([weight]))
self._test_complex_optimizer(lambda weight: optimizer([weight], rho=0.95))
self._test_complex_optimizer(
lambda weight: optimizer([weight], rho=0.95, weight_decay=1)
)
def test_nadam(self):
self._test_basic_cases(
lambda weight, bias, foreach: optim.NAdam(
[weight, bias], lr=1e-3, foreach=foreach
),
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, foreach: optim.NAdam(
self._build_params_dict(weight, bias, lr=1e-2), lr=1e-3, foreach=foreach
),
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, foreach: optim.NAdam(
[weight, bias],
lr=1e-3,
weight_decay=0.1,
momentum_decay=6e-3,
foreach=foreach,
),
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, foreach: optim.NAdam(
[weight, bias],
lr=1e-3,
weight_decay=0.1,
momentum_decay=6e-3,
foreach=foreach,
),
[lambda opt: ExponentialLR(opt, gamma=0.9)],
constructor_accepts_foreach=True,
)
with self.assertRaisesRegex(
ValueError, "Invalid beta parameter at index 0: 1.0"
):
optim.NAdam(None, lr=1e-2, betas=(1.0, 0.0))
with self.assertRaisesRegex(ValueError, "Invalid momentum_decay value: -0.2"):
optim.NAdam(None, lr=1e-2, momentum_decay=-0.2)
def test_adagrad(self):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adagrad(
[weight, bias], lr=1e-1, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adagrad(
[weight, bias],
lr=1e-1,
initial_accumulator_value=0.1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adagrad(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adagrad(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-1,
maximize=maximize,
foreach=foreach,
),
[lambda opt: ReduceLROnPlateau(opt)],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adagrad(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-1,
maximize=maximize,
foreach=foreach,
),
[
lambda opt: ReduceLROnPlateau(opt),
lambda opt: ExponentialLR(opt, gamma=0.99),
],
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
with self.assertRaisesRegex(ValueError, "Invalid lr_decay value: -0.5"):
optim.Adagrad(None, lr=1e-2, lr_decay=-0.5)
def test_adagrad_sparse(self):
for foreach in (False, True):
self._test_rosenbrock_sparse(
lambda params: optim.Adagrad(params, lr=1e-1, foreach=foreach)
)
self._test_rosenbrock_sparse(
lambda params: optim.Adagrad(params, lr=0.1, foreach=foreach),
[
lambda opt: StepLR(opt, gamma=1 - 1e-5, step_size=500),
lambda opt: ReduceLROnPlateau(opt, threshold=1e-4),
],
)
def test_adagrad_complex(self):
for foreach in (False, True):
self._test_complex_optimizer(
lambda param: optim.Adagrad([param], lr=1e-1, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.Adagrad(
[param],
lr=1e-1,
initial_accumulator_value=0.1,
foreach=foreach,
)
)
def test_adamax(self):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adamax(
[weight, bias], lr=1e-1, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adamax(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Adamax(
[weight, bias],
lr=1e-1,
weight_decay=1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_complex_2d(optim.Adamax)
self._test_complex_2d(functools.partial(optim.Adamax, foreach=True))
with self.assertRaisesRegex(
ValueError, "Invalid beta parameter at index 1: 1.0"
):
optim.Adamax(None, lr=1e-2, betas=(0.0, 1.0))
def test_radam(self):
self._test_basic_cases(
lambda weight, bias, foreach: optim.RAdam(
[weight, bias], lr=1e-3, foreach=foreach
),
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, foreach: optim.RAdam(
self._build_params_dict(weight, bias, lr=1e-2), lr=1e-3, foreach=foreach
),
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, foreach: optim.RAdam(
[weight, bias], lr=1e-3, weight_decay=0.1, foreach=foreach
),
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, foreach: optim.RAdam(
[weight, bias], lr=1e-3, foreach=foreach
),
[
lambda opt: ExponentialLR(opt, gamma=0.9),
lambda opt: ReduceLROnPlateau(opt),
],
constructor_accepts_foreach=True,
)
with self.assertRaisesRegex(
ValueError, "Invalid beta parameter at index 0: 1.0"
):
optim.RAdam(None, lr=1e-2, betas=(1.0, 0.0))
with self.assertRaisesRegex(ValueError, "Invalid weight_decay value: -1"):
optim.RAdam(None, lr=1e-2, weight_decay=-1)
def test_rmsprop(self):
for foreach in (False, True):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.RMSprop(
[weight, bias], lr=1e-2, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.RMSprop(
self._build_params_dict(weight, bias, lr=1e-3),
lr=1e-2,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.RMSprop(
self._build_params_dict(weight, bias, lr=1e-3),
lr=1e-2,
centered=True,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.RMSprop(
self._build_params_dict(weight, bias, lr=1e-3),
lr=1e-2,
centered=True,
momentum=0.1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.RMSprop(
self._build_params_dict(weight, bias, lr=1e-3),
lr=1e-2,
momentum=0.1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.RMSprop(
self._build_params_dict(weight, bias, lr=1e-3),
lr=1e-2,
momentum=0.1,
weight_decay=1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_complex_2d(lambda param: optim.RMSprop(param, foreach=foreach))
self._test_complex_2d(
lambda param: optim.RMSprop(param, centered=True, foreach=foreach)
)
self._test_complex_2d(
lambda param: optim.RMSprop(param, momentum=0.1, foreach=foreach)
)
self._test_complex_2d(
lambda param: optim.RMSprop(param, maximize=True, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.RMSprop([param], foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.RMSprop([param], centered=True, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.RMSprop([param], momentum=0.1, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.RMSprop([param], maximize=True, foreach=foreach)
)
with self.assertRaisesRegex(ValueError, "Invalid momentum value: -1.0"):
optim.RMSprop(None, lr=1e-2, momentum=-1.0, foreach=foreach)
def test_asgd(self):
for foreach in (False, True):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.ASGD(
[weight, bias], lr=1e-3, t0=100, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.ASGD(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
t0=100,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.ASGD(
self._build_params_dict(weight, bias, lr=1e-2),
lr=1e-3,
weight_decay=1,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
# Ref: https://github.com/pytorch/pytorch/issues/84560
# self._test_complex_2d(optimizer)
self._test_complex_optimizer(
lambda params: optim.ASGD([params], foreach=foreach)
)
self._test_complex_optimizer(
lambda params: optim.ASGD([params], maximize=True, foreach=foreach)
)
self._test_complex_optimizer(
lambda params: optim.ASGD(
[params], maximize=True, weight_decay=0.9, foreach=foreach
)
)
self._test_complex_optimizer(
lambda params: optim.ASGD(
[params], maximize=False, weight_decay=0.9, foreach=foreach
)
)
self._test_complex_optimizer(
lambda params: optim.ASGD([params], weight_decay=0.9, foreach=foreach)
)
with self.assertRaisesRegex(ValueError, "Invalid weight_decay value: -0.5"):
optim.ASGD(None, lr=1e-2, weight_decay=-0.5, foreach=foreach)
@skipIfRocm
def test_rprop(self):
is_cuda_sm86 = torch.cuda.is_available() and torch.cuda.get_device_capability(
0
) == (8, 6)
for foreach in (False, True):
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Rprop(
[weight, bias], lr=2e-4, maximize=maximize, foreach=foreach
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
)
self._test_basic_cases(
lambda weight, bias, maximize, foreach: optim.Rprop(
self._build_params_dict(weight, bias, lr=1e-2),
lr=2e-4,
maximize=maximize,
foreach=foreach,
),
constructor_accepts_maximize=True,
constructor_accepts_foreach=True,
atol=4e-5 if is_cuda_sm86 else None,
rtol=3e-5 if is_cuda_sm86 else None,
)
self._test_complex_2d(lambda param: optim.Rprop(param, foreach=foreach))
self._test_complex_optimizer(
lambda param: optim.Rprop([param], lr=0.001, foreach=foreach)
)
self._test_complex_optimizer(
lambda param: optim.Rprop(
[param], lr=0.001, maximize=True, foreach=foreach
)
)
with self.assertRaisesRegex(ValueError, "Invalid eta values: 1.0, 0.5"):
optim.Rprop(None, lr=1e-2, etas=(1.0, 0.5), foreach=foreach)
def test_lbfgs(self):
self._test_basic_cases(
lambda weight, bias: optim.LBFGS([weight, bias]), ignore_multidevice=True
)
self._test_basic_cases(
lambda weight, bias: optim.LBFGS(
[weight, bias], line_search_fn="strong_wolfe"
),
ignore_multidevice=True,
)
@unittest.skipIf(TEST_WITH_UBSAN, "division-by-zero error with UBSAN")
def test_lbfgs_return_type(self):
params = [torch.randn(10, 5), torch.randn(10)]
opt1 = optim.LBFGS(params, 0.01, tolerance_grad=math.inf)
opt2 = optim.LBFGS(params, 0.01, tolerance_grad=-math.inf)
def closure():
return torch.tensor([10])
res1 = opt1.step(closure)
res2 = opt2.step(closure)
self.assertEqual(type(res1), type(res2))
def test_invalid_param_type(self):
with self.assertRaises(TypeError):
optim.SGD(Parameter(torch.randn(5, 5)), lr=3)
def test_duplicate_params_in_param_group(self):
param = Parameter(torch.randn(5, 5))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
optim.SGD([param, param], lr=0.1)
self.assertEqual(len(w), 1)
self.assertIn(
"a parameter group with duplicate parameters", str(w[0].message)
)
def test_no_grad_for_all_params(self):
params = [torch.randn(5, 5, requires_grad=False) for _ in range(2)]
optimizer_list = [
optim.Adadelta,
optim.AdamW,
optim.Adam,
optim.Adagrad,
optim.Adamax,
optim.RMSprop,
optim.SGD,
optim.SparseAdam,
optim.ASGD,
]
for optim_ctr in optimizer_list:
opt = optim_ctr(params, lr=0.1)
# make sure step can still run even if
# all params have no grad
opt.step()
# make sure that `state_steps` is correctly either updated or not updated when `found_inf`.
def test_functional_fused_optimizer_with_foundinf(self):
if not torch.cuda.is_available():
self.skipTest("CUDA is required.")
from torch.optim import adam, adamw
num_tensors = 5
for functional_optim, amsgrad in itertools.product((adam.adam, adamw.adamw), (False, True)):
params, grads, exp_avgs, exp_avg_sqs = [[torch.ones((1,), device="cuda") for _ in range(num_tensors)] for _ in range(4)]
max_exp_avg_sqs = [torch.ones((1,), device="cuda") for _ in range(num_tensors)] if amsgrad else []
state_steps = [torch.ones((1,), dtype=torch.float32, device="cuda") for _ in range(num_tensors)]
grad_scale = torch.ones((1,), dtype=torch.float32, device="cuda")
found_inf = torch.ones((1,), dtype=torch.float32, device="cuda")
functional_optim(
params,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
foreach=False,
capturable=False,
fused=True,
amsgrad=amsgrad,
beta1=0.9,
beta2=0.99,
lr=1e-2,
weight_decay=0.0,
eps=1e-8,
maximize=False,
grad_scale=grad_scale,
found_inf=found_inf,
)
self.assertEqual(
state_steps,
[
torch.ones((1,), dtype=torch.float32, device="cuda")
for _ in range(num_tensors)
],
)
def test_empty_grad(self):
optimizers = [
torch.optim.Adadelta,
torch.optim.Adagrad,
torch.optim.Adam,
torch.optim.AdamW,
torch.optim.Adamax,
torch.optim.ASGD,
torch.optim.NAdam,
torch.optim.RAdam,
torch.optim.RMSprop,
torch.optim.Rprop,
torch.optim.SGD,
torch.optim.SparseAdam,
]
for optimizer in optimizers:
net = torch.nn.Embedding(
5, 1, padding_idx=0, sparse=optimizer is torch.optim.SparseAdam
)
original_params = (param.detach().clone() for param in net.parameters())
# Simulate a batch that only indexes the embedding at padding_idx
x = torch.tensor([[0, 0]]).int()
y = torch.tensor([[[3.0], [4.0]]])
opt = optimizer(net.parameters(), lr=1e-5)
torch.nn.MSELoss()(net.forward(x), y).backward()
opt.step()
for original_param, param in zip(original_params, net.parameters()):
# assert that the parameters have not changed
self.assertEqual(original_param, param)
@skipIfTorchDynamo()
def test_post_hook(self):
def post_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
nonlocal data
data += 2
params = [torch.Tensor([1, 1])]
opt = SGD(params, lr=0.001)
data = 2
hook_handle = opt.register_step_post_hook(post_hook)
opt.step()
opt.step()
# check if pre hooks were registered
self.assertEqual(data, 6)
# remove handles, take step and verify that hook is no longer registered
hook_handle.remove()
opt.step()
self.assertEqual(data, 6)
@skipIfTorchDynamo()
def test_pre_hook(self):
def pre_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
nonlocal data
data += 2
params = [torch.Tensor([1, 1])]
opt = SGD(params, lr=0.001)
data = 5
hook_handle = opt.register_step_pre_hook(pre_hook)
opt.step()
opt.step()
# check if pre hooks were registered
self.assertEqual(data, 9)
# remove handles, take step and verify that hook is no longer registered
hook_handle.remove()
opt.step()
self.assertEqual(data, 9)
@skipIfTorchDynamo()
def test_pre_and_post_hook(self):
def global_pre_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
nonlocal data
data.append(0)
def global_post_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
nonlocal data
data.append(5)
def local_pre_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
nonlocal data
data.append(1)
def local_post_hook(opt: Optimizer, args: Tuple[Any], kwargs: Dict[Any, Any]):
nonlocal data
data.append(2)
params = [torch.Tensor([1, 1])]
opt1 = SGD(params, lr=0.001)
opt2 = Adam(params, lr=0.01)
data = []
# register global hooks to both optimizers
global_pre_handle = register_optimizer_step_pre_hook(global_pre_hook)
global_post_handle = register_optimizer_step_post_hook(global_post_hook)
# register local hooks
first_pre_handle = opt1.register_step_pre_hook(local_pre_hook)
first_post_handle = opt1.register_step_post_hook(local_post_hook)
second_pre_handle = opt2.register_step_pre_hook(local_pre_hook)
second_post_handle = opt2.register_step_post_hook(local_post_hook)
opt1.step()
self.assertListEqual(data, [0, 1, 2, 5])
opt2.step()
self.assertListEqual(data, [0, 1, 2, 5, 0, 1, 2, 5])
opt1.step()
self.assertListEqual(data, [0, 1, 2, 5, 0, 1, 2, 5, 0, 1, 2, 5])
# remove all hooks
global_pre_handle.remove()
global_post_handle.remove()
first_pre_handle.remove()
first_post_handle.remove()
second_pre_handle.remove()
second_post_handle.remove()
opt1.step()
opt2.step()
self.assertListEqual(data, [0, 1, 2, 5, 0, 1, 2, 5, 0, 1, 2, 5])
def test_fused_optimizer_raises(self):
if not torch.cuda.is_available():
self.skipTest("Requires CUDA devices")
for optimizer_ctor in (torch.optim.Adam, torch.optim.AdamW):
with self.assertRaisesRegex(RuntimeError, "`fused` and `foreach` cannot be `True` together."):
optimizer_ctor([torch.empty((), device="cuda")], foreach=True, fused=True)
with self.assertRaisesRegex(RuntimeError, "`fused` does not support `differentiable`"):
optimizer_ctor([torch.empty((), device="cuda")], differentiable=True, fused=True)
class SchedulerTestNet(torch.nn.Module):
def __init__(self):
super(SchedulerTestNet, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 1, 1)
self.conv2 = torch.nn.Conv2d(1, 1, 1)
def forward(self, x):
return self.conv2(F.relu(self.conv1(x)))
class LambdaLRTestObject:
def __init__(self, value):
self.value = value
def __call__(self, epoch):
return self.value * epoch
def __eq__(self, other):
if isinstance(other, self.__class__):
return self.__dict__ == other.__dict__
else:
return False
class TestLRScheduler(TestCase):
exact_dtype = True
def setUp(self):
super(TestLRScheduler, self).setUp()
self.net = SchedulerTestNet()
self.opt = SGD(
[
{"params": self.net.conv1.parameters()},
{"params": self.net.conv2.parameters(), "lr": 0.5},
],
lr=0.05,
)
def _check_warning_is_epoch_deprecation_warning(self, w, *, num_warnings: int = 1):
"""This function swallows the epoch deprecation warning which is produced when we
call `scheduler.step(epoch)` with some not `None` value of `epoch`.
this is deprecated, and this function will need to be removed/updated when
the schedulers no longer accept the parameter at all.
"""
self.assertEqual(len(w), num_warnings)
for warning in w:
self.assertEqual(len(warning.message.args), 1)
self.assertEqual(warning.message.args[0], EPOCH_DEPRECATION_WARNING)
def test_error_when_getlr_has_epoch(self):
class MultiStepLR(torch.optim.lr_scheduler.LRScheduler):
def __init__(self, optimizer, gamma, milestones, last_epoch=-1):
self.init_lr = [group["lr"] for group in optimizer.param_groups]
self.gamma = gamma
self.milestones = milestones
super().__init__(optimizer, last_epoch)
def get_lr(self, step):
global_step = self.last_epoch
gamma_power = (
[0]
+ [i + 1 for i, m in enumerate(self.milestones) if global_step >= m]
)[-1]
return [
init_lr * (self.gamma**gamma_power) for init_lr in self.init_lr
]
optimizer = torch.optim.SGD([torch.rand(1)], lr=1)
with self.assertRaises(TypeError):
scheduler = MultiStepLR(optimizer, gamma=1, milestones=[10, 20])
@skipIfTorchDynamo("Torchdynamo keeps references to optim in the guards and the stack of the graph break frames")
def test_no_cyclic_references(self):
import gc
param = Parameter(torch.empty(10))
optim = SGD([param], lr=0.5)
scheduler = LambdaLR(optim, lambda epoch: 1.0)
del scheduler
self.assertTrue(
len(gc.get_referrers(optim)) == 0,
"Optimizer should contain no cyclic references",
)
gc.collect()
del optim
self.assertEqual(
gc.collect(), 0, msg="Optimizer should be garbage-collected on __del__"
)
@skipIfTorchDynamo("Torchdynamo keeps references to optim in the guards and the stack of the graph break frames")
def test_no_cyclic_references_in_step(self):
import gc
import weakref
def run():
param = torch.empty(10, requires_grad=True)
optim = SGD(params=[param], lr=0.5)
scheduler = LambdaLR(optim, lambda epoch: 1.0)
param.sum().backward()
optim.step()
scheduler.step()
return weakref.ref(scheduler)
# To ensure that there are no reference cycles in scheduler,
# we need to turn off the garbage collector. Since gc will
# automatically collect unreachable objects.
gc.disable()
ref = run()
assert ref() is None
gc.enable() # restore
def test_old_pattern_warning(self):
epochs = 35
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self.assertTrue(len(ws) == 0, "No warning should be raised")
def old_pattern():
for _ in range(epochs):
scheduler.step()
self.opt.step()
self.assertWarnsRegex(UserWarning, r"how-to-adjust-learning-rate", old_pattern)
def test_old_pattern_warning_with_arg(self):
epochs = 35
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self.assertTrue(len(ws) == 0, "No warning should be raised")
def old_pattern2():
for _ in range(epochs):
scheduler.step()
self.opt.step()
self.assertWarnsRegex(UserWarning, r"how-to-adjust-learning-rate", old_pattern2)
def test_old_pattern_warning_resuming(self):
epochs = 35
for i, group in enumerate(self.opt.param_groups):
group["initial_lr"] = 0.01
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3, last_epoch=10)
self.assertTrue(len(ws) == 0, "No warning should be raised")
def old_pattern():
for _ in range(epochs):
scheduler.step()
self.opt.step()
self.assertWarnsRegex(UserWarning, r"how-to-adjust-learning-rate", old_pattern)
def test_old_pattern_warning_resuming_with_arg(self):
epochs = 35
for i, group in enumerate(self.opt.param_groups):
group["initial_lr"] = 0.01
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3, last_epoch=10)
self.assertTrue(len(ws) == 0, "No warning should be raised")
def old_pattern2():
for _ in range(epochs):
scheduler.step()
self.opt.step()
self.assertWarnsRegex(UserWarning, r"how-to-adjust-learning-rate", old_pattern2)
def test_old_pattern_warning_with_overridden_optim_step(self):
epochs = 35
for i, group in enumerate(self.opt.param_groups):
group["initial_lr"] = 0.01
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3, last_epoch=10)
self.assertTrue(len(ws) == 0, "No warning should be raised")
# emulate use-case with optimizer.step overridden
import types
old_step = self.opt.step
def new_step(o, *args, **kwargs):
retval = old_step(*args, **kwargs)
return retval
self.opt.step = types.MethodType(new_step, self.opt)
def old_pattern2():
for _ in range(epochs):
scheduler.step()
self.opt.step()
self.assertWarnsRegex(UserWarning, r"how-to-adjust-learning-rate", old_pattern2)
def test_new_pattern_no_warning(self):
epochs = 35
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self.assertTrue(len(ws) == 0, "No warning should be raised")
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
for _ in range(epochs):
self.opt.step()
scheduler.step()
self.assertTrue(len(ws) == 0, "No warning should be raised")
def test_new_pattern_no_warning_with_arg(self):
epochs = 35
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self.assertTrue(len(ws) == 0, "No warning should be raised")
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
for _ in range(epochs):
self.opt.step()
scheduler.step()
self.assertTrue(len(ws) == 0, "No warning should be raised")
def test_new_pattern_no_warning_with_overridden_optim_step(self):
epochs = 35
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always") # allow any warning to be raised
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self.assertTrue(len(ws) == 0, "No warning should be raised")
# emulate use-case with optimizer.step overridden
import types
old_step = self.opt.step
def new_step(o, *args, **kwargs):
retval = old_step(*args, **kwargs)
return retval
self.opt.step = types.MethodType(new_step, self.opt)
def new_pattern():
for e in range(epochs):
self.opt.step()
scheduler.step()
self.assertWarnsRegex(
UserWarning, r"`optimizer.step\(\)` has been overridden", new_pattern
)
def _test_lr_is_constant_for_constant_epoch(self, scheduler):
l = []
for _ in range(10):
scheduler.optimizer.step()
with warnings.catch_warnings(record=True) as w:
scheduler.step(2)
self._check_warning_is_epoch_deprecation_warning(w)
l.append(self.opt.param_groups[0]["lr"])
self.assertEqual(min(l), max(l))
def test_step_lr_is_constant_for_constant_epoch(self):
scheduler = StepLR(self.opt, 2)
self._test_lr_is_constant_for_constant_epoch(scheduler)
def test_exponential_lr_is_constant_for_constant_epoch(self):
scheduler = ExponentialLR(self.opt, gamma=0.9)
self._test_lr_is_constant_for_constant_epoch(scheduler)
def test_constantlr_is_constant_for_constant_epoch(self):
scheduler = ConstantLR(self.opt)
self._test_lr_is_constant_for_constant_epoch(scheduler)
def test_linear_linearlr_is_constant_for_constant_epoch(self):
scheduler = LinearLR(self.opt)
self._test_lr_is_constant_for_constant_epoch(scheduler)
def test_polynomial_lr_is_constant_for_constant_epoch(self):
scheduler = PolynomialLR(self.opt, power=0.9)
self._test_lr_is_constant_for_constant_epoch(scheduler)
def test_step_lr(self):
# lr = 0.05 if epoch < 3
# lr = 0.005 if 30 <= epoch < 6
# lr = 0.0005 if epoch >= 9
epochs = 10
single_targets = [0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005] * 3
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self._test(scheduler, targets, epochs)
def test_get_last_lr_step_lr(self):
from torch.nn import Parameter
epochs = 10
optimizer = torch.optim.SGD(
[Parameter(torch.randn(2, 2, requires_grad=True))], 0.1
)
targets = [[0.1] * 3 + [0.01] * 3 + [0.001] * 3 + [0.0001]]
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 3, gamma=0.1)
self._test_get_last_lr(scheduler, targets, epochs)
def test_get_last_lr_multi_step_lr(self):
# lr = 0.05 if epoch < 2
# lr = 0.005 if 2 <= epoch < 5
# lr = 0.0005 if 5 <= epoch < 9
# lr = 0.00005 if 9 <= epoch
epochs = 10
single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005] * 1
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
self._test_get_last_lr(scheduler, targets, epochs)
def test_multi_step_lr(self):
# lr = 0.05 if epoch < 2
# lr = 0.005 if 2 <= epoch < 5
# lr = 0.0005 if epoch < 9
# lr = 0.00005 if epoch >= 9
epochs = 10
single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005] * 3
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
self._test(scheduler, targets, epochs)
def test_multi_step_lr_with_epoch(self):
# lr = 0.05 if epoch < 2
# lr = 0.005 if 2 <= epoch < 5
# lr = 0.0005 if epoch < 9
# lr = 0.00005 if epoch >= 9
epochs = 10
single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005] * 3
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
self._test_with_epoch(scheduler, targets, epochs)
def test_get_last_lr_constantlr(self):
# lr = 0.025 if epoch < 5
# lr = 0.005 if 5 <= epoch
epochs = 10
single_targets = [0.025] * 5 + [0.05] * 5
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = ConstantLR(self.opt, factor=1.0 / 2, total_iters=5)
self._test_get_last_lr(scheduler, targets, epochs)
def test_get_last_lr_linearlr(self):
# lr = 0.025 if epoch == 0
# lr = 0.03125 if epoch == 1
# lr = 0.0375 if epoch == 2
# lr = 0.04375 if epoch == 3
# lr = 0.005 if 4 <= epoch
epochs = 10
start_factor = 1.0 / 4
end_factor = 3.0 / 5
iters = 4
interpolation = [
start_factor + i * (end_factor - start_factor) / iters for i in range(iters)
]
single_targets = [x * 0.05 for x in interpolation] + [0.05 * end_factor] * (
epochs - iters
)
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = LinearLR(
self.opt,
start_factor=start_factor,
end_factor=end_factor,
total_iters=iters,
)
self._test_get_last_lr(scheduler, targets, epochs)
def test_constantlr(self):
# lr = 0.025 if epoch < 5
# lr = 0.005 if 5 <= epoch
epochs = 10
single_targets = [0.025] * 5 + [0.05] * 5
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = ConstantLR(self.opt, factor=1.0 / 2, total_iters=5)
self._test(scheduler, targets, epochs)
def test_linearlr(self):
# lr = 0.025 if epoch == 0
# lr = 0.03125 if epoch == 1
# lr = 0.0375 if epoch == 2
# lr = 0.04375 if epoch == 3
# lr = 0.005 if 4 <= epoch
epochs = 10
start_factor = 1.0 / 2
iters = 4
interpolation = [
start_factor + i * (1 - start_factor) / iters for i in range(iters)
]
single_targets = [x * 0.05 for x in interpolation] + [0.05] * (epochs - iters)
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
self._test(scheduler, targets, epochs)
def test_linearlr_start_factor_limits1(self):
start_factor = 0.0
iters = 4
with self.assertRaises(ValueError):
LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
def test_linearlr_start_factor_limits2(self):
start_factor = 1.1
iters = 4
with self.assertRaises(ValueError):
LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
def test_constantlr_with_epoch(self):
# lr = 0.025 if epoch < 5
# lr = 0.005 if 5 <= epoch
epochs = 10
single_targets = [0.025] * 5 + [0.05] * 5
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = ConstantLR(self.opt, factor=1.0 / 2, total_iters=5)
self._test_with_epoch(scheduler, targets, epochs)
def test_linearlr_with_epoch(self):
# lr = 0.025 if epoch == 0
# lr = 0.03125 if epoch == 1
# lr = 0.0375 if epoch == 2
# lr = 0.04375 if epoch == 3
# lr = 0.005 if 4 <= epoch
epochs = 10
start_factor = 1.0 / 2
end_factor = 1.0
iters = 4
interpolation = [
start_factor + i * (end_factor - start_factor) / iters for i in range(iters)
]
single_targets = [x * 0.05 for x in interpolation] + [0.05] * (epochs - iters)
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
self._test_with_epoch(scheduler, targets, epochs)
def test_exp_lr(self):
epochs = 10
single_targets = [0.05 * (0.9**x) for x in range(epochs)]
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = ExponentialLR(self.opt, gamma=0.9)
self._test(scheduler, targets, epochs)
def test_poly_lr(self):
epochs = 10
power = 0.9
total_iters = 5
single_targets = [
(1.0 - x / total_iters) ** power * 0.05 for x in range(total_iters)
] + [0.0] * (epochs - total_iters)
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = PolynomialLR(self.opt, power=power, total_iters=total_iters)
self._test(scheduler, targets, epochs)
def test_cos_anneal_lr(self):
epochs = 10
eta_min = 1e-10
single_targets = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
for x in range(epochs)
]
targets = [single_targets, [x * epochs for x in single_targets]]
scheduler = CosineAnnealingLR(self.opt, T_max=epochs, eta_min=eta_min)
self._test(scheduler, targets, epochs)
def test_closed_form_step_lr(self):
scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
closed_form_scheduler = StepLR(self.opt, gamma=0.1, step_size=3)
self._test_against_closed_form(scheduler, closed_form_scheduler, 20)
def test_closed_form_linearlr(self):
scheduler = LinearLR(
self.opt, start_factor=1.0 / 3, end_factor=0.7, total_iters=4
)
closed_form_scheduler = LinearLR(
self.opt, start_factor=1.0 / 3, end_factor=0.7, total_iters=4
)
self._test_against_closed_form(scheduler, closed_form_scheduler, 20)
def test_closed_form_constantlr(self):
scheduler = ConstantLR(self.opt, factor=1.0 / 3, total_iters=4)
closed_form_scheduler = ConstantLR(self.opt, factor=1.0 / 3, total_iters=4)
self._test_against_closed_form(scheduler, closed_form_scheduler, 20)
def test_closed_form_multi_step_lr(self):
scheduler = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
closed_form_scheduler = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
self._test_against_closed_form(scheduler, closed_form_scheduler, 20)
def test_closed_form_exp_lr(self):
scheduler = ExponentialLR(self.opt, gamma=0.9)
closed_form_scheduler = ExponentialLR(self.opt, gamma=0.9)
self._test_against_closed_form(scheduler, closed_form_scheduler, 20)
def test_closed_form_poly_lr(self):
scheduler = PolynomialLR(self.opt, power=0.9)
closed_form_scheduler = PolynomialLR(self.opt, power=0.9)
self._test_against_closed_form(scheduler, closed_form_scheduler, 20)
def test_closed_form_cos_anneal_lr(self):
eta_min = 1e-10
epochs = 20
T_max = 5
scheduler = CosineAnnealingLR(self.opt, T_max=T_max, eta_min=eta_min)
closed_form_scheduler = CosineAnnealingLR(
self.opt, T_max=T_max, eta_min=eta_min
)
self._test_against_closed_form(scheduler, closed_form_scheduler, epochs)
def test_cos_anneal_lr_continue(self):
eta_min = 0.1
T_max = 5
scheduler = CosineAnnealingLR(self.opt, T_max=T_max, eta_min=eta_min)
self.opt.step()
scheduler.step()
original_lrs = scheduler._last_lr
new_scheduler = CosineAnnealingLR(
self.opt, T_max=T_max, eta_min=eta_min, last_epoch=0
)
new_lrs = new_scheduler._last_lr
torch.testing.assert_close(original_lrs, new_lrs, rtol=1e-4, atol=1e-5)
def test_reduce_lr_on_plateau1(self):
epochs = 10
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 20]
metrics = [10 - i * 0.0167 for i in range(20)]
scheduler = ReduceLROnPlateau(
self.opt,
threshold_mode="abs",
mode="min",
threshold=0.01,
patience=5,
cooldown=5,
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau2(self):
epochs = 22
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 6 + [0.05] * 7 + [0.005] * 7 + [0.0005] * 2]
metrics = [10 - i * 0.0165 for i in range(22)]
scheduler = ReduceLROnPlateau(
self.opt,
patience=5,
cooldown=0,
threshold_mode="abs",
mode="min",
threshold=0.1,
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau3(self):
epochs = 22
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * (2 + 6) + [0.05] * (5 + 6) + [0.005] * 4]
metrics = [-0.8] * 2 + [-0.234] * 20
scheduler = ReduceLROnPlateau(
self.opt, mode="max", patience=5, cooldown=5, threshold_mode="abs"
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau4(self):
epochs = 20
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 20]
metrics = [1.5 * (1.025**i) for i in range(20)] # 1.025 > 1.1**0.25
scheduler = ReduceLROnPlateau(
self.opt, mode="max", patience=3, threshold_mode="rel", threshold=0.1
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau5(self):
epochs = 20
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 6 + [0.05] * (5 + 6) + [0.005] * 4]
metrics = [1.5 * (1.005**i) for i in range(20)]
scheduler = ReduceLROnPlateau(
self.opt,
mode="max",
threshold_mode="rel",
threshold=0.1,
patience=5,
cooldown=5,
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau6(self):
epochs = 20
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 20]
metrics = [1.5 * (0.85**i) for i in range(20)]
scheduler = ReduceLROnPlateau(
self.opt, mode="min", threshold_mode="rel", threshold=0.1
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau7(self):
epochs = 20
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 6 + [0.05] * (5 + 6) + [0.005] * 4]
metrics = [1] * 7 + [0.6] + [0.5] * 12
scheduler = ReduceLROnPlateau(
self.opt,
mode="min",
threshold_mode="rel",
threshold=0.1,
patience=5,
cooldown=5,
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_reduce_lr_on_plateau8(self):
epochs = 20
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
targets = [[0.5] * 6 + [0.4] * 14, [0.5] * 6 + [0.3] * 14]
metrics = [1.5 * (1.005**i) for i in range(20)]
scheduler = ReduceLROnPlateau(
self.opt,
mode="max",
threshold_mode="rel",
min_lr=[0.4, 0.3],
threshold=0.1,
patience=5,
cooldown=5,
)
self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs)
def test_sequentiallr1(self):
epochs = 19
schedulers = [None] * 2
targets = [
[0.05, 0.04, 0.032]
+ [0.05 for x in range(4)]
+ [0.05 * 0.1 for x in range(4)]
+ [0.05 * 0.01 for x in range(4)]
+ [0.05 * 0.001 for x in range(4)]
]
milestones = [3]
schedulers[0] = ExponentialLR(self.opt, gamma=0.8)
schedulers[1] = StepLR(self.opt, gamma=0.1, step_size=4)
scheduler = SequentialLR(self.opt, schedulers=schedulers, milestones=milestones)
self._test(scheduler, targets, epochs)
def test_sequentiallr2(self):
epochs = 13
schedulers = [None] * 2
targets = [[0.005, 0.005, 0.005] + [0.05 * 0.9**x for x in range(10)]]
milestones = [3]
schedulers[0] = ConstantLR(self.opt, factor=0.1, total_iters=3)
schedulers[1] = ExponentialLR(self.opt, gamma=0.9)
scheduler = SequentialLR(self.opt, schedulers=schedulers, milestones=milestones)
self._test(scheduler, targets, epochs)
def test_sequentiallr3(self):
epochs = 12
schedulers = [None] * 3
targets = [
[0.005, 0.005, 0.005]
+ [0.05, 0.04, 0.032]
+ [0.05, 0.05, 0.005, 0.005, 0.0005, 0.0005]
]
milestones = [3, 6]
schedulers[0] = ConstantLR(self.opt, factor=0.1, total_iters=3)
schedulers[1] = ExponentialLR(self.opt, gamma=0.8)
schedulers[2] = StepLR(self.opt, gamma=0.1, step_size=2)
scheduler = SequentialLR(self.opt, schedulers=schedulers, milestones=milestones)
self._test(scheduler, targets, epochs)
def test_sequentiallr4(self):
optimizer = torch.optim.SGD([torch.tensor(0.5)], lr=0.1)
prev_lr = optimizer.param_groups[0]["lr"]
schedulers = [
torch.optim.lr_scheduler.ConstantLR(optimizer, factor=1),
torch.optim.lr_scheduler.ConstantLR(optimizer, factor=0.1),
]
scheduler = torch.optim.lr_scheduler.SequentialLR(
optimizer, schedulers, milestones=[10]
)
new_lr = optimizer.param_groups[0]["lr"]
# Ensure that multiple schedulers does not affect the initial learning rate
self.assertEqual(prev_lr, new_lr)
def test_get_last_lr_sequentiallr(self):
epochs = 12
milestones = [3, 6]
schedulers = [None] * 3
schedulers[0] = ConstantLR(self.opt, factor=0.1, total_iters=3)
schedulers[1] = ExponentialLR(self.opt, gamma=0.8)
schedulers[2] = StepLR(self.opt, gamma=0.1, step_size=2)
scheduler = SequentialLR(self.opt, schedulers=schedulers, milestones=milestones)
constant_lr_target = [0.005] * 3
exponential_lr_target = [0.05, 0.04, 0.032]
step_lr_target = [0.05, 0.05, 0.005, 0.005, 0.0005, 0.0005]
single_targets = constant_lr_target + exponential_lr_target + step_lr_target
targets = [single_targets, [x * 10 for x in single_targets]]
self._test_get_last_lr(scheduler, targets, epochs)
def test_chained_lr2_get_last_lr_before_step(self):
schedulers = [
LinearLR(self.opt, start_factor=0.4, total_iters=3),
MultiStepLR(self.opt, milestones=[4, 8, 10], gamma=0.1),
]
scheduler = ChainedScheduler(schedulers)
self.assertEqual(scheduler.get_last_lr(), schedulers[-1].get_last_lr())
def test_chained_lr1(self):
epochs = 10
schedulers = [None] * 1
targets = [[0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005] * 3]
schedulers[0] = StepLR(self.opt, gamma=0.1, step_size=3)
scheduler = ChainedScheduler(schedulers)
self._test([scheduler], targets, epochs)
self.assertEqual(scheduler.get_last_lr(), schedulers[-1].get_last_lr())
def test_chained_lr2(self):
epochs = 10
schedulers = [None] * 1
targets = [[0.02, 0.03, 0.04] + [0.05] * 9]
schedulers[0] = LinearLR(self.opt, start_factor=0.4, total_iters=3)
scheduler = ChainedScheduler(schedulers)
self._test([scheduler], targets, epochs)
self.assertEqual(scheduler.get_last_lr(), schedulers[-1].get_last_lr())
def test_chained_lr3(self):
epochs = 10
schedulers = [None] * 2
targets = [
[0.02, 0.03, 0.04, 0.05] + [0.005] * 4 + [0.0005] * 3 + [0.00005] * 3
]
schedulers[0] = LinearLR(self.opt, start_factor=0.4, total_iters=3)
schedulers[1] = MultiStepLR(self.opt, milestones=[4, 8, 10], gamma=0.1)
scheduler = ChainedScheduler(schedulers)
self._test([scheduler], targets, epochs)
self.assertEqual(scheduler.get_last_lr(), schedulers[-1].get_last_lr())
def test_chained_lr4(self):
epochs = 9
schedulers = [None] * 3
targets = [
[0.05 * 0.2 * 0.9**x for x in range(3)]
+ [0.05 * 0.2 * 0.9**3 * 0.1]
+ [0.05 * 0.9**x * 0.1 for x in range(4, 6)]
+ [0.05 * 0.9**x * 0.01 for x in range(6, 9)]
]
schedulers[0] = ExponentialLR(self.opt, gamma=0.9)
schedulers[1] = ConstantLR(self.opt, factor=0.2, total_iters=4)
schedulers[2] = StepLR(self.opt, gamma=0.1, step_size=3)
scheduler = ChainedScheduler(schedulers)
self._test([scheduler], targets, epochs)
self.assertEqual(scheduler.get_last_lr(), schedulers[-1].get_last_lr())
def test_chained_lr5(self):
def poly_lr(lr: float):
return [
(lr * ((1.0 - x / total_iters) ** power)) for x in range(total_iters)
] + [0.0] * (epochs - total_iters)
schedulers = [None] * 2
epochs = 10
power = 0.9
total_iters = 5
const_factor = 0.1
single_targets = [x * const_factor for x in poly_lr(lr=0.05)]
targets = [single_targets, [x * const_factor for x in poly_lr(0.5)]]
schedulers[0] = PolynomialLR(self.opt, power=power, total_iters=total_iters)
schedulers[1] = ConstantLR(self.opt, factor=const_factor)
scheduler = ChainedScheduler(schedulers)
self._test(scheduler, targets, epochs)
self.assertEqual(scheduler.get_last_lr(), schedulers[-1].get_last_lr())
def test_compound_step_and_multistep_lr(self):
epochs = 10
schedulers = [None] * 2
schedulers[0] = StepLR(self.opt, gamma=0.1, step_size=3)
schedulers[1] = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
targets = [[0.05] * 2 + [0.005] * 1 + [5e-4] * 2 + [5e-5] + [5e-6] * 3 + [5e-8]]
self._test(schedulers, targets, epochs)
def test_compound_step_and_exp_lr(self):
epochs = 10
schedulers = [None] * 2
single_targets = [0.05 * (0.9**x) for x in range(3)]
single_targets += [0.005 * (0.9**x) for x in range(3, 6)]
single_targets += [0.0005 * (0.9**x) for x in range(6, 9)]
single_targets += [0.00005 * (0.9**x) for x in range(9, 12)]
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers[0] = StepLR(self.opt, gamma=0.1, step_size=3)
schedulers[1] = ExponentialLR(self.opt, gamma=0.9)
self._test(schedulers, targets, epochs)
def test_compound_exp_and_multistep_lr(self):
epochs = 10
schedulers = [None] * 2
single_targets = [0.05 * (0.9**x) for x in range(2)]
single_targets += [0.005 * (0.9**x) for x in range(2, 5)]
single_targets += [0.0005 * (0.9**x) for x in range(5, 9)]
single_targets += [0.00005 * (0.9**x) for x in range(9, 11)]
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers[0] = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
schedulers[1] = ExponentialLR(self.opt, gamma=0.9)
self._test(schedulers, targets, epochs)
def test_compound_exp_and_linearlr(self):
epochs = 10
iters = 4
start_factor = 0.4
end_factor = 0.9
schedulers = [None] * 2
single_targets = [0.05 * (0.9**x) for x in range(11)]
for i in range(iters):
single_targets[i] *= start_factor + i / iters * (end_factor - start_factor)
for i in range(iters, 11):
single_targets[i] *= end_factor
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers[0] = LinearLR(
self.opt,
start_factor=start_factor,
end_factor=end_factor,
total_iters=iters,
)
schedulers[1] = ExponentialLR(self.opt, gamma=0.9)
self._test(schedulers, targets, epochs)
def test_compound_step_and_constantlr(self):
epochs = 10
iters = 4
factor = 0.4
schedulers = [None] * 2
single_targets = (
[0.05 * 0.4] * 3
+ [0.005 * 0.4]
+ [0.005] * 2
+ [0.0005] * 3
+ [0.00005] * 3
)
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers[0] = StepLR(self.opt, gamma=0.1, step_size=3)
schedulers[1] = ConstantLR(self.opt, factor=0.4, total_iters=4)
self._test(schedulers, targets, epochs)
def test_compound_linearlr_and_multistep_lr(self):
epochs = 10
iters = 4
start_factor = 0.4
schedulers = [None] * 2
single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005] * 2
for i in range(iters):
single_targets[i] *= start_factor + i / iters * (1 - start_factor)
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers[0] = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
schedulers[1] = LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
self._test(schedulers, targets, epochs)
def test_compound_cosanneal_and_step_lr(self):
epochs = 10
eta_min = 1e-10
single_targets = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
for x in range(epochs)
]
single_targets = [x * 0.1 ** (i // 3) for i, x in enumerate(single_targets)]
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers = [None] * 2
schedulers[0] = CosineAnnealingLR(self.opt, T_max=epochs, eta_min=eta_min)
schedulers[1] = StepLR(self.opt, gamma=0.1, step_size=3)
self._test(schedulers, targets, epochs)
def test_compound_cosanneal_and_multistep_lr(self):
epochs = 10
eta_min = 1e-10
single_targets = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
for x in range(epochs)
]
multipliers = [1] * 2 + [0.1] * 3 + [0.01] * 4 + [0.001]
single_targets = [x * y for x, y in zip(single_targets, multipliers)]
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers = [None] * 2
schedulers[0] = CosineAnnealingLR(self.opt, T_max=epochs, eta_min=eta_min)
schedulers[1] = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9])
self._test(schedulers, targets, epochs)
def test_compound_cosanneal_and_linearlr(self):
epochs = 10
iters = 4
start_factor = 0.4
eta_min = 1e-10
schedulers = [None] * 2
single_targets = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
for x in range(epochs)
]
for i in range(iters):
single_targets[i] *= start_factor + i / iters * (1 - start_factor)
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers[0] = LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
schedulers[1] = CosineAnnealingLR(self.opt, T_max=epochs, eta_min=eta_min)
self._test(schedulers, targets, epochs)
def test_compound_cosanneal_and_exp_lr(self):
epochs = 10
eta_min = 1e-10
single_targets = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
for x in range(epochs)
]
multipliers = [0.1**i for i in range(epochs)]
single_targets = [x * y for x, y in zip(single_targets, multipliers)]
targets = [single_targets, [x * epochs for x in single_targets]]
schedulers = [None] * 2
schedulers[0] = CosineAnnealingLR(self.opt, T_max=epochs, eta_min=eta_min)
schedulers[1] = ExponentialLR(self.opt, gamma=0.1)
self._test(schedulers, targets, epochs)
def test_compound_reduce_lr_on_plateau1(self):
epochs = 10
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
single_targets = [0.5] * 20
multipliers = [0.1 ** (i // 3) for i in range(20)]
single_targets = [x * y for x, y in zip(multipliers, single_targets)]
targets = [single_targets]
targets = targets[1:] # test runs step before checking lr
metrics = [10 - i * 0.0167 for i in range(20)]
schedulers = [None, None]
schedulers[0] = ReduceLROnPlateau(
self.opt,
threshold_mode="abs",
mode="min",
threshold=0.01,
patience=5,
cooldown=5,
)
schedulers[1] = StepLR(self.opt, gamma=0.1, step_size=3)
self._test_reduce_lr_on_plateau(schedulers, targets, metrics, epochs)
def test_compound_reduce_lr_on_plateau2(self):
epochs = 22
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
single_targets = [0.5] * 6 + [0.05] * 7 + [0.005] * 7 + [0.0005] * 2
multipliers = [1] * 3 + [0.1] * 5 + [0.01] * 4 + [0.001] * 10
single_targets = [x * y for x, y in zip(single_targets, multipliers)]
targets = [single_targets]
targets = targets[1:] # test runs step before checking lr
metrics = [10 - i * 0.0165 for i in range(22)]
schedulers = [None] * 2
schedulers[0] = ReduceLROnPlateau(
self.opt,
patience=5,
cooldown=0,
threshold_mode="abs",
mode="min",
threshold=0.1,
)
schedulers[1] = MultiStepLR(self.opt, gamma=0.1, milestones=[3, 8, 12])
self._test_reduce_lr_on_plateau(schedulers, targets, metrics, epochs)
def test_compound_reduce_lr_on_plateau3(self):
epochs = 22
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
single_targets = [0.5] * (2 + 6) + [0.05] * (5 + 6) + [0.005] * 4
multipliers = [0.1**i for i in range(epochs)]
single_targets = [x * y for x, y in zip(multipliers, single_targets)]
targets = [single_targets]
targets = targets[1:] # test runs step before checking lr
metrics = [-0.8] * 2 + [-0.234] * 20
schedulers = [None, None]
schedulers[0] = ReduceLROnPlateau(
self.opt, mode="max", patience=5, cooldown=5, threshold_mode="abs"
)
schedulers[1] = ExponentialLR(self.opt, gamma=0.1)
self._test_reduce_lr_on_plateau(schedulers, targets, metrics, epochs)
def test_compound_reduce_lr_on_plateau4(self):
epochs = 20
for param_group in self.opt.param_groups:
param_group["lr"] = 0.05
epochs = 10
eta_min = 1e-10
single_targets = [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / epochs)) / 2
for x in range(epochs)
]
targets = [single_targets]
targets = targets[1:] # test runs step before checking lr
metrics = [1.5 * (1.025**i) for i in range(20)] # 1.025 > 1.1**0.25
schedulers = [None, None]
schedulers[0] = ReduceLROnPlateau(
self.opt, mode="max", patience=3, threshold_mode="rel", threshold=0.1
)
schedulers[1] = CosineAnnealingLR(self.opt, epochs, eta_min)
self._test_reduce_lr_on_plateau(schedulers, targets, metrics, epochs)
def test_compound_reduce_lr_on_plateau5(self):
iters = 4
start_factor = 0.4
epochs = 22
for param_group in self.opt.param_groups:
param_group["lr"] = 0.5
single_targets = [0.5] * 6 + [0.05] * 7 + [0.005] * 7 + [0.0005] * 2
multipliers = [1] * 22
for i in range(iters):
multipliers[i] *= start_factor + i / iters * (1 - start_factor)
single_targets = [x * y for x, y in zip(single_targets, multipliers)]
targets = [single_targets]
targets = targets[1:] # test runs step before checking lr
metrics = [10 - i * 0.0165 for i in range(22)]
schedulers = [None] * 2
schedulers[0] = ReduceLROnPlateau(
self.opt,
patience=5,
cooldown=0,
threshold_mode="abs",
mode="min",
threshold=0.1,
)
schedulers[1] = LinearLR(self.opt, start_factor=start_factor, total_iters=iters)
self._test_reduce_lr_on_plateau(schedulers, targets, metrics, epochs)
def test_cycle_lr_invalid_mode(self):
with self.assertRaises(ValueError):
scheduler = CyclicLR(self.opt, base_lr=0, max_lr=0, mode="CATS")
def test_cycle_lr_triangular_mode_one_lr(self):
lr_target = [1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3]
momentum_target = [5, 4, 3, 2, 1, 2, 3, 4, 5, 4, 3]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=1,
max_lr=5,
step_size_up=4,
cycle_momentum=True,
base_momentum=1,
max_momentum=5,
mode="triangular",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
def test_cycle_lr_triangular_mode_one_lr_no_momentum(self):
lr_target = [1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3]
lr_targets = [lr_target, lr_target]
momentum_target = [self.opt.defaults["momentum"]] * len(lr_target)
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=1,
max_lr=5,
step_size_up=4,
cycle_momentum=False,
mode="triangular",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
def test_cycle_lr_triangular2_mode_one_lr(self):
lr_target = [
1,
2,
3,
4,
5,
4,
3,
2,
1,
1.5,
2.0,
2.5,
3.0,
2.5,
2.0,
1.5,
1,
1.25,
1.50,
1.75,
2.00,
1.75,
]
momentum_target = [
5.0,
4.0,
3.0,
2.0,
1.0,
2.0,
3.0,
4.0,
5.0,
4.5,
4.0,
3.5,
3.0,
3.5,
4.0,
4.5,
5.0,
4.75,
4.5,
4.25,
4.0,
4.25,
]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=1,
max_lr=5,
step_size_up=4,
cycle_momentum=True,
base_momentum=1,
max_momentum=5,
mode="triangular2",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
def test_cycle_lr_exp_range_mode_one_lr(self):
base_lr, max_lr = 1, 5
diff_lr = max_lr - base_lr
gamma = 0.9
xs = [0, 0.25, 0.5, 0.75, 1, 0.75, 0.50, 0.25, 0, 0.25, 0.5, 0.75, 1]
lr_target = [base_lr + x * diff_lr * gamma**i for i, x in enumerate(xs)]
momentum_target = [max_lr - x * diff_lr * gamma**i for i, x in enumerate(xs)]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=base_lr,
max_lr=max_lr,
step_size_up=4,
cycle_momentum=True,
base_momentum=base_lr,
max_momentum=max_lr,
mode="exp_range",
gamma=gamma,
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
def test_cycle_lr_triangular_mode(self):
lr_target_1 = [1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3]
lr_target_2 = [x + 1 for x in lr_target_1]
lr_targets = [lr_target_1, lr_target_2]
momentum_target_1 = [5, 4, 3, 2, 1, 2, 3, 4, 5, 4, 3]
momentum_target_2 = [x + 1 for x in momentum_target_1]
momentum_targets = [momentum_target_1, momentum_target_2]
scheduler = CyclicLR(
self.opt,
base_lr=[1, 2],
max_lr=[5, 6],
step_size_up=4,
cycle_momentum=True,
base_momentum=[1, 2],
max_momentum=[5, 6],
mode="triangular",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target_1))
def test_cycle_lr_triangular2_mode(self):
lr_target_1 = [
1,
2,
3,
4,
5,
4,
3,
2,
1,
1.5,
2.0,
2.5,
3.0,
2.5,
2.0,
1.5,
1,
1.25,
1.50,
1.75,
2.00,
1.75,
]
lr_target_2 = [x + 2 for x in lr_target_1]
lr_targets = [lr_target_1, lr_target_2]
momentum_target_1 = [
5.0,
4.0,
3.0,
2.0,
1.0,
2.0,
3.0,
4.0,
5.0,
4.5,
4.0,
3.5,
3.0,
3.5,
4.0,
4.5,
5.0,
4.75,
4.5,
4.25,
4.0,
4.25,
]
momentum_target_2 = [x + 2 for x in momentum_target_1]
momentum_targets = [momentum_target_1, momentum_target_2]
scheduler = CyclicLR(
self.opt,
base_lr=[1, 3],
max_lr=[5, 7],
step_size_up=4,
cycle_momentum=True,
base_momentum=[1, 3],
max_momentum=[5, 7],
mode="triangular2",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target_1))
def test_cycle_lr_exp_range_mode(self):
base_lr_1, max_lr_1 = 1, 5
base_lr_2, max_lr_2 = 5, 12
diff_lr_1 = max_lr_1 - base_lr_1
diff_lr_2 = max_lr_2 - base_lr_2
gamma = 0.9
xs = [0, 0.25, 0.5, 0.75, 1, 0.75, 0.50, 0.25, 0, 0.25, 0.5, 0.75, 1]
lr_target_1 = [base_lr_1 + x * diff_lr_1 * gamma**i for i, x in enumerate(xs)]
lr_target_2 = [base_lr_2 + x * diff_lr_2 * gamma**i for i, x in enumerate(xs)]
lr_targets = [lr_target_1, lr_target_2]
momentum_target_1 = [
max_lr_1 - x * diff_lr_1 * gamma**i for i, x in enumerate(xs)
]
momentum_target_2 = [
max_lr_2 - x * diff_lr_2 * gamma**i for i, x in enumerate(xs)
]
momentum_targets = [momentum_target_1, momentum_target_2]
scheduler = CyclicLR(
self.opt,
base_lr=[base_lr_1, base_lr_2],
max_lr=[max_lr_1, max_lr_2],
step_size_up=4,
cycle_momentum=True,
base_momentum=[base_lr_1, base_lr_2],
max_momentum=[max_lr_1, max_lr_2],
mode="exp_range",
gamma=gamma,
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target_1))
def test_cycle_lr_triangular_mode_step_size_up_down(self):
lr_target = [
1.0,
2.0,
3.0,
4.0,
5.0,
13.0 / 3,
11.0 / 3,
9.0 / 3,
7.0 / 3,
5.0 / 3,
1.0,
]
lr_targets = [lr_target, lr_target]
momentum_target = [
5.0,
4.0,
3.0,
2.0,
1.0,
5.0 / 3,
7.0 / 3,
3.0,
11.0 / 3,
13.0 / 3,
5.0,
]
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=1,
max_lr=5,
step_size_up=4,
step_size_down=6,
cycle_momentum=True,
base_momentum=1,
max_momentum=5,
mode="triangular",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
def test_cycle_lr_triangular2_mode_step_size_up_down(self):
lr_base_target = [
1.0,
3.0,
5.0,
13.0 / 3,
11.0 / 3,
9.0 / 3,
7.0 / 3,
5.0 / 3,
1.0,
2.0,
3.0,
8.0 / 3,
7.0 / 3,
6.0 / 3,
5.0 / 3,
4.0 / 3,
1.0,
3.0 / 2,
2.0,
11.0 / 6,
10.0 / 6,
9.0 / 6,
8.0 / 6,
7.0 / 6,
]
momentum_base_target = [
5.0,
3.0,
1.0,
5.0 / 3,
7.0 / 3,
3.0,
11.0 / 3,
13.0 / 3,
5.0,
4.0,
3.0,
10.0 / 3,
11.0 / 3,
4.0,
13.0 / 3,
14.0 / 3,
5.0,
4.5,
4.0,
25.0 / 6,
13.0 / 3,
4.5,
14.0 / 3,
29.0 / 6,
]
deltas = [2 * i for i in range(0, 2)]
base_lrs = [1 + delta for delta in deltas]
max_lrs = [5 + delta for delta in deltas]
lr_targets = [[x + delta for x in lr_base_target] for delta in deltas]
momentum_targets = [
[x + delta for x in momentum_base_target] for delta in deltas
]
scheduler = CyclicLR(
self.opt,
base_lr=base_lrs,
max_lr=max_lrs,
step_size_up=2,
step_size_down=6,
cycle_momentum=True,
base_momentum=base_lrs,
max_momentum=max_lrs,
mode="triangular2",
)
self._test_cycle_lr(
scheduler, lr_targets, momentum_targets, len(lr_base_target)
)
def test_cycle_lr_exp_range_mode_step_size_up_down(self):
base_lr, max_lr = 1, 5
diff_lr = max_lr - base_lr
gamma = 0.9
xs = [
0.0,
0.5,
1.0,
5.0 / 6,
4.0 / 6,
3.0 / 6,
2.0 / 6,
1.0 / 6,
0.0,
0.5,
1.0,
5.0 / 6,
4.0 / 6,
]
lr_target = [base_lr + x * diff_lr * gamma**i for i, x in enumerate(xs)]
lr_targets = [lr_target, lr_target]
momentum_target = [max_lr - x * diff_lr * gamma**i for i, x in enumerate(xs)]
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=base_lr,
max_lr=max_lr,
step_size_up=2,
step_size_down=6,
cycle_momentum=True,
base_momentum=base_lr,
max_momentum=max_lr,
mode="exp_range",
gamma=gamma,
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
def test_cycle_lr_with_momentumless_optimizer(self):
# Note [Temporarily set optimizer to Adam]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The TestLRScheduler object carries around an SGD optimizer to avoid having to
# instantiate one for every test. This gets in the way for our very specific case
# in which we need to use Adam (or really any optimizer that doesn't use momentum)
# in order to test that the momentum bug in CyclicLR is fixed (the bug is described
# in more detail in https://github.com/pytorch/pytorch/issues/19003 ).
old_opt = self.opt
self.opt = optim.Adam(
[
{"params": self.net.conv1.parameters()},
{"params": self.net.conv2.parameters(), "lr": 0.5},
],
lr=0.05,
)
lr_target = [1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3]
lr_targets = [lr_target, lr_target]
momentum_target = [None] * len(lr_target)
momentum_targets = [momentum_target, momentum_target]
scheduler = CyclicLR(
self.opt,
base_lr=1,
max_lr=5,
step_size_up=4,
cycle_momentum=False,
mode="triangular",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, len(lr_target))
self.opt = old_opt # set optimizer back to SGD
def test_cycle_lr_cycle_momentum_fail_with_momentumless_optimizer(self):
with self.assertRaises(ValueError):
adam_opt = optim.Adam(self.net.parameters())
scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=True)
def test_cycle_lr_removed_after_out_of_scope(self):
import gc
import weakref
gc.disable()
def test():
adam_opt = optim.Adam(self.net.parameters())
scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=False)
return weakref.ref(scheduler)
ref = test()
assert ref() is None
gc.enable()
def test_cycle_lr_state_dict_picklable(self):
adam_opt = optim.Adam(self.net.parameters())
scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=False)
self.assertIsInstance(scheduler._scale_fn_ref, weakref.WeakMethod)
state = scheduler.state_dict()
self.assertNotIn("_scale_fn_ref", state)
pickle.dumps(state)
def test_cycle_lr_scale_fn_restored_from_state_dict(self):
adam_opt = optim.Adam(self.net.parameters())
# Case 1: Built-in mode
scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=False, mode="triangular2")
restored_scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=False)
restored_scheduler.load_state_dict(scheduler.state_dict())
self.assertTrue(restored_scheduler.mode == scheduler.mode == "triangular2")
self.assertIsNotNone(restored_scheduler._scale_fn_ref) and self.assertIsNotNone(scheduler._scale_fn_ref)
self.assertIs(restored_scheduler._scale_fn_custom, None)
self.assertIs(scheduler._scale_fn_custom, None)
# Case 2: Custom `scale_fn`
def scale_fn(_):
return 0.5
scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=False, scale_fn=scale_fn)
restored_scheduler = CyclicLR(adam_opt, base_lr=1, max_lr=5, cycle_momentum=False, scale_fn=scale_fn)
restored_scheduler.load_state_dict(scheduler.state_dict())
self.assertIs(scheduler._scale_fn_custom, scale_fn)
self.assertIs(restored_scheduler._scale_fn_custom, scale_fn)
def test_onecycle_lr_invalid_anneal_strategy(self):
with self.assertRaises(ValueError):
scheduler = OneCycleLR(
self.opt, max_lr=1e-3, total_steps=10, anneal_strategy="CATS"
)
def test_onecycle_lr_invalid_pct_start(self):
with self.assertRaises(ValueError):
scheduler = OneCycleLR(self.opt, max_lr=1e-3, total_steps=10, pct_start=1.1)
def test_onecycle_lr_cannot_calculate_total_steps(self):
with self.assertRaises(ValueError):
scheduler = OneCycleLR(self.opt, max_lr=1e-3)
def test_onecycle_lr_linear_annealing(self):
lr_target = [1, 13, 25, 21.5, 18, 14.5, 11, 7.5, 4, 0.5]
momentum_target = [22, 11.5, 1, 4, 7, 10, 13, 16, 19, 22]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = OneCycleLR(
self.opt,
max_lr=25,
final_div_factor=2,
base_momentum=1,
max_momentum=22,
total_steps=10,
anneal_strategy="linear",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, 10)
def test_onecycle_lr_linear_annealing_three_phases(self):
lr_target = [1, 9, 17, 25, 17, 9, 1, 0.75, 0.5, 0.25]
momentum_target = [22, 15, 8, 1, 8, 15, 22, 22, 22, 22]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = OneCycleLR(
self.opt,
max_lr=25,
div_factor=25,
base_momentum=1,
max_momentum=22,
total_steps=10,
anneal_strategy="linear",
pct_start=0.4,
final_div_factor=4,
three_phase=True,
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, 10)
def test_onecycle_lr_cosine_annealing(self):
def annealing_cos(start, end, pct):
cos_out = math.cos(math.pi * pct) + 1
return end + (start - end) / 2.0 * cos_out
lr_target = [
1,
13,
25,
annealing_cos(25, 0.5, 1 / 7.0),
annealing_cos(25, 0.5, 2 / 7.0),
annealing_cos(25, 0.5, 3 / 7.0),
annealing_cos(25, 0.5, 4 / 7.0),
annealing_cos(25, 0.5, 5 / 7.0),
annealing_cos(25, 0.5, 6 / 7.0),
0.5,
]
momentum_target = [
22,
11.5,
1,
annealing_cos(1, 22, 1 / 7.0),
annealing_cos(1, 22, 2 / 7.0),
annealing_cos(1, 22, 3 / 7.0),
annealing_cos(1, 22, 4 / 7.0),
annealing_cos(1, 22, 5 / 7.0),
annealing_cos(1, 22, 6 / 7.0),
22,
]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = OneCycleLR(
self.opt,
max_lr=25,
final_div_factor=2,
base_momentum=1,
max_momentum=22,
total_steps=10,
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, 10)
def test_cycle_lr_with_adam(self):
old_opt = self.opt
self.opt = optim.Adam(
[
{"params": self.net.conv1.parameters()},
{"params": self.net.conv2.parameters(), "lr": 0.5},
],
lr=0.05,
)
lr_target = [1, 13, 25, 21.5, 18, 14.5, 11, 7.5, 4, 0.5]
momentum_target = [22, 11.5, 1, 4, 7, 10, 13, 16, 19, 22]
lr_targets = [lr_target, lr_target]
momentum_targets = [momentum_target, momentum_target]
scheduler = OneCycleLR(
self.opt,
max_lr=25,
final_div_factor=2,
base_momentum=1,
max_momentum=22,
total_steps=10,
anneal_strategy="linear",
)
self._test_cycle_lr(scheduler, lr_targets, momentum_targets, 10, use_beta1=True)
self.opt = old_opt # set optimizer back to SGD
def test_lambda_lr(self):
epochs = 10
self.opt.param_groups[0]["lr"] = 0.05
self.opt.param_groups[1]["lr"] = 0.4
targets = [
[0.05 * (0.9**x) for x in range(epochs)],
[0.4 * (0.8**x) for x in range(epochs)],
]
scheduler = LambdaLR(
self.opt, lr_lambda=[lambda x1: 0.9**x1, lambda x2: 0.8**x2]
)
self._test(scheduler, targets, epochs)
def test_multiplicative_lr(self):
epochs = 10
self.opt.param_groups[0]["lr"] = 0.05
self.opt.param_groups[1]["lr"] = 0.4
targets = [
[0.05 * (0.9**x) for x in range(epochs)],
[0.4 * (0.8**x) for x in range(epochs)],
]
scheduler = MultiplicativeLR(
self.opt, lr_lambda=[lambda x1: 0.9, lambda x2: 0.8]
)
self._test(scheduler, targets, epochs)
@parametrize("T_mult", [1, 2, 4])
def test_CosineAnnealingWarmRestarts_lr1(self, T_mult):
iters = 100
eta_min = 1e-10
T_i = 10
T_cur = 0
targets = [[0.05], [0.5]]
scheduler = CosineAnnealingWarmRestarts(
self.opt, T_0=T_i, T_mult=T_mult, eta_min=eta_min
)
for _ in range(1, iters, 1):
T_cur += 1
if T_cur >= T_i:
T_cur = T_cur - T_i
T_i = int(T_mult) * T_i
targets[0] += [
eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2
]
targets[1] += [
eta_min + (0.5 - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2
]
self._test(scheduler, targets, iters)
def test_CosineAnnealingWarmRestarts_lr2(self):
iters = 30
eta_min = 1e-10
T_mults = [1, 2, 4]
for T_mult in T_mults:
T_i = 10
T_cur = 0
targets = [[0.05], [0.5]]
scheduler = CosineAnnealingWarmRestarts(
self.opt, T_0=T_i, T_mult=T_mult, eta_min=eta_min
)
for _ in torch.arange(0.1, iters, 0.1):
T_cur = round(T_cur + 0.1, 1)
if T_cur >= T_i:
T_cur = T_cur - T_i
T_i = int(T_mult) * T_i
targets[0] += [
eta_min
+ (0.05 - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2
]
targets[1] += [
eta_min
+ (0.5 - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2
]
self._test_CosineAnnealingWarmRestarts(scheduler, targets, iters)
def test_CosineAnnealingWarmRestarts_lr3(self):
epochs_for_T_mults = [
[0, 1, 2, 3, 4, 5, 12, 27, 3, 4, 5, 6, 13],
[0, 1, 2, 3, 4, 5, 25, 32, 33, 34, 80, 81, 3],
[0, 0.1, 0.2, 0.3, 1.3, 2.3, 17.5, 18.5, 19.5, 29.5, 30.5, 31.5, 50],
]
T_curs_for_T_mults = [
[1, 2, 3, 4, 5, 2, 7, 3, 4, 5, 6, 3],
[1, 2, 3, 4, 5, 15, 2, 3, 4, 10, 11, 3],
[0.1, 0.2, 0.3, 1.3, 2.3, 7.5, 8.5, 9.5, 19.5, 20.5, 21.5, 10],
]
T_is_for_T_mults = [
[10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10],
[10, 10, 10, 10, 10, 20, 40, 40, 40, 80, 80, 10],
[10, 10, 10, 10, 10, 30, 30, 30, 30, 30, 30, 90],
]
eta_min = 1e-10
T_mults = [1, 2, 3]
for epochs, T_mult, T_curs, T_is in zip(
epochs_for_T_mults, T_mults, T_curs_for_T_mults, T_is_for_T_mults
):
targets = [[0.05], [0.5]]
scheduler = CosineAnnealingWarmRestarts(
self.opt, T_0=10, T_mult=T_mult, eta_min=eta_min
)
for T_cur, T_i in zip(T_curs, T_is):
targets[0] += [
eta_min
+ (0.05 - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2
]
targets[1] += [
eta_min
+ (0.5 - eta_min) * (1 + math.cos(math.pi * T_cur / T_i)) / 2
]
self._test_interleaved_CosineAnnealingWarmRestarts(
scheduler, targets, epochs
)
def test_swalr_no_anneal(self):
epochs, swa_start, swa_lr = 10, 5, 0.01
initial_lrs = [group["lr"] for group in self.opt.param_groups]
targets = [
[lr] * (swa_start + 1) + [swa_lr] * (epochs - swa_start - 1)
for lr in initial_lrs
]
swa_scheduler = SWALR(self.opt, anneal_epochs=1, swa_lr=swa_lr)
self._test_swalr(swa_scheduler, None, targets, swa_start, epochs)
def test_swalr_cosine_anneal_after_multiplicative(self):
# same swa_lr for different param_groups
epochs, swa_start, swa_lr, anneal_epochs = 15, 5, 0.01, 5
mult_factor = 0.9
scheduler = MultiplicativeLR(self.opt, lr_lambda=lambda epoch: mult_factor)
swa_scheduler = SWALR(self.opt, anneal_epochs=anneal_epochs, swa_lr=swa_lr)
def anneal_coef(t):
if t + 1 >= anneal_epochs:
return 0.0
return (1 + math.cos(math.pi * (t + 1) / anneal_epochs)) / 2
initial_lrs = [group["lr"] for group in self.opt.param_groups]
targets_before_swa = [
[lr * mult_factor**i for i in range(swa_start + 1)] for lr in initial_lrs
]
swa_epochs = epochs - swa_start - 1
targets = [
lrs
+ [
lrs[-1] * anneal_coef(t) + swa_lr * (1 - anneal_coef(t))
for t in range(swa_epochs)
]
for lrs in targets_before_swa
]
self._test_swalr(swa_scheduler, scheduler, targets, swa_start, epochs)
def test_swalr_linear_anneal_after_multiplicative(self):
# separate swa_lr for different param_groups
epochs, swa_start, swa_lrs, anneal_epochs = 15, 5, [0.01, 0.02], 4
mult_factor = 0.9
scheduler = MultiplicativeLR(self.opt, lr_lambda=lambda epoch: mult_factor)
swa_scheduler = SWALR(
self.opt,
anneal_epochs=anneal_epochs,
anneal_strategy="linear",
swa_lr=swa_lrs,
)
def anneal_coef(t):
if t + 1 >= anneal_epochs:
return 0.0
return 1 - (t + 1) / anneal_epochs
initial_lrs = [group["lr"] for group in self.opt.param_groups]
targets_before_swa = [
[lr * mult_factor**i for i in range(swa_start + 1)] for lr in initial_lrs
]
swa_epochs = epochs - swa_start - 1
targets = [
lrs
+ [
lrs[-1] * anneal_coef(t) + swa_lr * (1 - anneal_coef(t))
for t in range(swa_epochs)
]
for lrs, swa_lr in zip(targets_before_swa, swa_lrs)
]
self._test_swalr(swa_scheduler, scheduler, targets, swa_start, epochs)
def _test_swalr(self, swa_scheduler, scheduler, targets, swa_start, epochs):
for epoch in range(epochs):
for param_group, target in zip(self.opt.param_groups, targets):
self.assertEqual(
target[epoch],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, target[epoch], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
if epoch >= swa_start:
self.opt.step()
swa_scheduler.step()
elif scheduler is not None:
self.opt.step()
scheduler.step()
def test_swalr_hypers(self):
# Test that SWALR raises errors for incorrect hyper-parameters
with self.assertRaisesRegex(ValueError, "anneal_strategy must"):
swa_scheduler = SWALR(self.opt, anneal_strategy="exponential", swa_lr=1.0)
with self.assertRaisesRegex(ValueError, "anneal_epochs must"):
swa_scheduler = SWALR(self.opt, anneal_epochs=-1, swa_lr=1.0)
with self.assertRaisesRegex(ValueError, "anneal_epochs must"):
swa_scheduler = SWALR(self.opt, anneal_epochs=1.7, swa_lr=1.0)
with self.assertRaisesRegex(ValueError, "swa_lr must"):
swa_scheduler = SWALR(self.opt, swa_lr=[1.0, 0.1, 0.01])
def test_step_lr_state_dict(self):
self._check_scheduler_state_dict(
lambda: StepLR(self.opt, gamma=0.1, step_size=3),
lambda: StepLR(self.opt, gamma=0.01 / 2, step_size=1),
)
def test_multi_step_lr_state_dict(self):
self._check_scheduler_state_dict(
lambda: MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9]),
lambda: MultiStepLR(self.opt, gamma=0.01, milestones=[1, 4, 6]),
)
def test_exp_step_lr_state_dict(self):
self._check_scheduler_state_dict(
lambda: ExponentialLR(self.opt, gamma=0.1),
lambda: ExponentialLR(self.opt, gamma=0.01),
)
def test_cosine_lr_state_dict(self):
epochs = 10
eta_min = 1e-10
self._check_scheduler_state_dict(
lambda: CosineAnnealingLR(self.opt, T_max=epochs, eta_min=eta_min),
lambda: CosineAnnealingLR(self.opt, T_max=epochs // 2, eta_min=eta_min / 2),
epochs=epochs,
)
def test_reduce_lr_on_plateau_state_dict(self):
scheduler = ReduceLROnPlateau(self.opt, mode="min", factor=0.1, patience=2)
for score in [1.0, 2.0, 3.0, 4.0, 3.0, 4.0, 5.0, 3.0, 2.0, 1.0]:
scheduler.step(score)
scheduler_copy = ReduceLROnPlateau(
self.opt, mode="max", factor=0.5, patience=10
)
scheduler_copy.load_state_dict(scheduler.state_dict())
for key in scheduler.__dict__.keys():
if key not in {"optimizer", "is_better"}:
self.assertEqual(scheduler.__dict__[key], scheduler_copy.__dict__[key])
def test_lambda_lr_state_dict_fn(self):
scheduler = LambdaLR(self.opt, lr_lambda=lambda x: x)
state = scheduler.state_dict()
self.assertIsNone(state["lr_lambdas"][0])
scheduler_copy = LambdaLR(self.opt, lr_lambda=lambda x: x)
scheduler_copy.load_state_dict(state)
for key in scheduler.__dict__.keys():
if key not in {"optimizer", "lr_lambdas"}:
self.assertEqual(scheduler.__dict__[key], scheduler_copy.__dict__[key])
def test_lambda_lr_state_dict_obj(self):
scheduler = LambdaLR(self.opt, lr_lambda=LambdaLRTestObject(10))
state = scheduler.state_dict()
self.assertIsNotNone(state["lr_lambdas"][0])
scheduler_copy = LambdaLR(self.opt, lr_lambda=LambdaLRTestObject(-1))
scheduler_copy.load_state_dict(state)
for key in scheduler.__dict__.keys():
if key not in {"optimizer"}:
self.assertEqual(scheduler.__dict__[key], scheduler_copy.__dict__[key])
def test_CosineAnnealingWarmRestarts_lr_state_dict(self):
self._check_scheduler_state_dict(
lambda: CosineAnnealingWarmRestarts(self.opt, T_0=10, T_mult=2),
lambda: CosineAnnealingWarmRestarts(self.opt, T_0=100),
)
def test_swa_lr_state_dict(self):
self._check_scheduler_state_dict(
lambda: SWALR(self.opt, anneal_epochs=3, swa_lr=0.5),
lambda: SWALR(
self.opt, anneal_epochs=10, anneal_strategy="linear", swa_lr=5.0
),
)
def _check_scheduler_state_dict(self, constr, constr2, epochs=10):
scheduler = constr()
for _ in range(epochs):
scheduler.optimizer.step()
scheduler.step()
scheduler_copy = constr2()
scheduler_copy.load_state_dict(scheduler.state_dict())
for key in scheduler.__dict__.keys():
if key != "optimizer":
self.assertEqual(scheduler.__dict__[key], scheduler_copy.__dict__[key])
self.assertEqual(scheduler.get_last_lr(), scheduler_copy.get_last_lr())
def _test_get_last_lr(self, schedulers, targets, epochs=10):
if isinstance(schedulers, LRScheduler):
schedulers = [schedulers]
optimizers = {scheduler.optimizer for scheduler in schedulers}
for epoch in range(epochs):
result = [scheduler.get_last_lr() for scheduler in schedulers]
[optimizer.step() for optimizer in optimizers]
[scheduler.step() for scheduler in schedulers]
target = [[t[epoch] for t in targets]] * len(schedulers)
for t, r in zip(target, result):
self.assertEqual(
target,
result,
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, t, r
),
atol=1e-5,
rtol=0,
)
def _test_with_epoch(self, schedulers, targets, epochs=10):
if isinstance(schedulers, LRScheduler):
schedulers = [schedulers]
optimizers = {scheduler.optimizer for scheduler in schedulers}
for epoch in range(epochs):
[optimizer.step() for optimizer in optimizers]
with warnings.catch_warnings(record=True) as w:
[
scheduler.step(epoch) for scheduler in schedulers
] # step before assert: skip initial lr
self._check_warning_is_epoch_deprecation_warning(
w, num_warnings=len(schedulers)
)
for param_group, target in zip(self.opt.param_groups, targets):
self.assertEqual(
target[epoch],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, target[epoch], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
def _test(self, schedulers, targets, epochs=10):
if isinstance(schedulers, LRScheduler):
schedulers = [schedulers]
for epoch in range(epochs):
for param_group, target in zip(self.opt.param_groups, targets):
self.assertEqual(
target[epoch],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, target[epoch], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
[scheduler.step() for scheduler in schedulers]
def _test_CosineAnnealingWarmRestarts(self, scheduler, targets, epochs=10):
for index, epoch in enumerate(torch.arange(0, epochs, 0.1)):
epoch = round(epoch.item(), 1)
scheduler.step(epoch)
for param_group, target in zip(self.opt.param_groups, targets):
self.assertEqual(
target[index],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, target[index], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
def _test_interleaved_CosineAnnealingWarmRestarts(self, scheduler, targets, epochs):
for index, epoch in enumerate(epochs):
scheduler.step(epoch)
for param_group, target in zip(self.opt.param_groups, targets):
self.assertEqual(
target[index],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, target[index], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
def _test_against_closed_form(self, scheduler, closed_form_scheduler, epochs=10):
self.setUp()
targets = []
for epoch in range(epochs):
closed_form_scheduler.optimizer.step()
with warnings.catch_warnings(record=True) as w:
closed_form_scheduler.step(epoch)
self._check_warning_is_epoch_deprecation_warning(w)
targets.append([group["lr"] for group in self.opt.param_groups])
self.setUp()
for epoch in range(epochs):
self.opt.step()
scheduler.step()
for i, param_group in enumerate(self.opt.param_groups):
self.assertEqual(
targets[epoch][i],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, targets[epoch][i], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
def _test_reduce_lr_on_plateau(
self, schedulers, targets, metrics, epochs=10, verbose=False
):
if isinstance(schedulers, (LRScheduler, ReduceLROnPlateau)):
schedulers = [schedulers]
for epoch in range(epochs):
self.opt.step()
for scheduler in schedulers:
if isinstance(scheduler, ReduceLROnPlateau):
scheduler.step(metrics[epoch])
else:
scheduler.step()
if verbose:
print("epoch{}:\tlr={}".format(epoch, self.opt.param_groups[0]["lr"]))
for param_group, target in zip(self.opt.param_groups, targets):
self.assertEqual(
target[epoch],
param_group["lr"],
msg="LR is wrong in epoch {}: expected {}, got {}".format(
epoch, target[epoch], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
def _test_cycle_lr(
self,
scheduler,
lr_targets,
momentum_targets,
batch_iterations,
verbose=False,
use_beta1=False,
):
for batch_num in range(batch_iterations):
if verbose:
if "momentum" in self.opt.param_groups[0].keys():
print(
"batch{}:\tlr={},momentum={}".format(
batch_num,
self.opt.param_groups[0]["lr"],
self.opt.param_groups[0]["momentum"],
)
)
elif use_beta1 and "betas" in self.opt.param_groups[0].keys():
print(
"batch{}:\tlr={},beta1={}".format(
batch_num,
self.opt.param_groups[0]["lr"],
self.opt.param_groups[0]["betas"][0],
)
)
else:
print(
"batch{}:\tlr={}".format(
batch_num, self.opt.param_groups[0]["lr"]
)
)
for param_group, lr_target, momentum_target in zip(
self.opt.param_groups, lr_targets, momentum_targets
):
self.assertEqual(
lr_target[batch_num],
param_group["lr"],
msg="LR is wrong in batch_num {}: expected {}, got {}".format(
batch_num, lr_target[batch_num], param_group["lr"]
),
atol=1e-5,
rtol=0,
)
if use_beta1 and "betas" in param_group.keys():
self.assertEqual(
momentum_target[batch_num],
param_group["betas"][0],
msg="Beta1 is wrong in batch_num {}: expected {}, got {}".format(
batch_num,
momentum_target[batch_num],
param_group["betas"][0],
),
atol=1e-5,
rtol=0,
)
elif "momentum" in param_group.keys():
self.assertEqual(
momentum_target[batch_num],
param_group["momentum"],
msg="Momentum is wrong in batch_num {}: expected {}, got {}".format(
batch_num,
momentum_target[batch_num],
param_group["momentum"],
),
atol=1e-5,
rtol=0,
)
self.opt.step()
scheduler.step()
def test_cosine_then_cyclic(self):
# https://github.com/pytorch/pytorch/issues/21965
max_lr = 0.3
base_lr = 0.1
optim_lr = 0.5
model = torch.nn.Linear(2, 1)
optimizer = torch.optim.SGD(model.parameters(), lr=optim_lr)
lr_scheduler_1 = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=20, eta_min=0.1
)
lr_scheduler_2 = torch.optim.lr_scheduler.CyclicLR(
optimizer, base_lr=base_lr, max_lr=max_lr, step_size_up=1, step_size_down=3
)
for i in range(40):
optimizer.step()
if i <= lr_scheduler_1.T_max:
lr_scheduler_1.step()
else:
lr_scheduler_2.step()
last_lr = optimizer.param_groups[0]["lr"]
self.assertLessEqual(last_lr, max_lr)
class SWATestDNN(torch.nn.Module):
def __init__(self, input_features):
super(SWATestDNN, self).__init__()
self.n_features = 100
self.fc1 = torch.nn.Linear(input_features, self.n_features)
self.bn = torch.nn.BatchNorm1d(self.n_features)
def compute_preactivation(self, x):
return self.fc1(x)
def forward(self, x):
x = self.fc1(x)
x = self.bn(x)
return x
class SWATestCNN(torch.nn.Module):
def __init__(self, input_channels):
super(SWATestCNN, self).__init__()
self.n_features = 10
self.conv1 = torch.nn.Conv2d(
input_channels, self.n_features, kernel_size=3, padding=1
)
self.bn = torch.nn.BatchNorm2d(self.n_features, momentum=0.3)
def compute_preactivation(self, x):
return self.conv1(x)
def forward(self, x):
x = self.conv1(x)
x = self.bn(x)
return x
class TestSWAUtils(TestCase):
def _test_averaged_model(self, net_device, swa_device):
dnn = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.ReLU(),
torch.nn.MaxPool2d(kernel_size=2),
torch.nn.BatchNorm2d(5, momentum=0.3),
torch.nn.Conv2d(5, 2, kernel_size=3),
torch.nn.ReLU(),
torch.nn.Linear(5, 5),
torch.nn.ReLU(),
torch.nn.Linear(5, 10),
).to(net_device)
averaged_dnn = AveragedModel(dnn, device=swa_device)
averaged_params = [torch.zeros_like(param) for param in dnn.parameters()]
n_updates = 10
for i in range(n_updates):
for p, p_avg in zip(dnn.parameters(), averaged_params):
p.detach().add_(torch.randn_like(p))
p_avg += p.detach() / n_updates
averaged_dnn.update_parameters(dnn)
for p_avg, p_swa in zip(averaged_params, averaged_dnn.parameters()):
self.assertEqual(p_avg, p_swa)
# Check that AveragedModel is on the correct device
self.assertTrue(p_swa.device == swa_device)
self.assertTrue(p.device == net_device)
self.assertTrue(averaged_dnn.n_averaged.device == swa_device)
def test_averaged_model_all_devices(self):
cpu = torch.device("cpu")
self._test_averaged_model(cpu, cpu)
if torch.cuda.is_available():
cuda = torch.device(0)
self._test_averaged_model(cuda, cpu)
self._test_averaged_model(cpu, cuda)
self._test_averaged_model(cuda, cuda)
def test_averaged_model_mixed_device(self):
if not torch.cuda.is_available():
return
dnn = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10)
)
dnn[0].cuda()
dnn[1].cpu()
averaged_dnn = AveragedModel(dnn)
averaged_params = [torch.zeros_like(param) for param in dnn.parameters()]
n_updates = 10
for i in range(n_updates):
for p, p_avg in zip(dnn.parameters(), averaged_params):
p.detach().add_(torch.randn_like(p))
p_avg += p.detach() / n_updates
averaged_dnn.update_parameters(dnn)
for p_avg, p_swa in zip(averaged_params, averaged_dnn.parameters()):
self.assertEqual(p_avg, p_swa)
# Check that AveragedModel is on the correct device
self.assertTrue(p_avg.device == p_swa.device)
def test_averaged_model_state_dict(self):
dnn = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10)
)
averaged_dnn = AveragedModel(dnn)
averaged_dnn2 = AveragedModel(dnn)
n_updates = 10
for i in range(n_updates):
for p in dnn.parameters():
p.detach().add_(torch.randn_like(p))
averaged_dnn.update_parameters(dnn)
averaged_dnn2.load_state_dict(averaged_dnn.state_dict())
for p_swa, p_swa2 in zip(averaged_dnn.parameters(), averaged_dnn2.parameters()):
self.assertEqual(p_swa, p_swa2)
self.assertTrue(averaged_dnn.n_averaged == averaged_dnn2.n_averaged)
def test_averaged_model_exponential(self):
# Test AveragedModel with EMA as avg_fn
dnn = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.BatchNorm2d(5, momentum=0.3),
torch.nn.Linear(5, 10),
)
alpha = 0.9
def avg_fn(p_avg, p, n_avg):
return alpha * p_avg + (1 - alpha) * p
averaged_dnn = AveragedModel(dnn, avg_fn=avg_fn)
averaged_params = [torch.zeros_like(param) for param in dnn.parameters()]
n_updates = 10
for i in range(n_updates):
updated_averaged_params = []
for p, p_avg in zip(dnn.parameters(), averaged_params):
p.detach().add_(torch.randn_like(p))
if i == 0:
updated_averaged_params.append(p.clone())
else:
updated_averaged_params.append(
(p_avg * alpha + p * (1 - alpha)).clone()
)
for b in dnn.buffers():
if b.size() != torch.Size([]):
b.detach_().add_(torch.randn_like(b))
averaged_dnn.update_parameters(dnn)
averaged_params = updated_averaged_params
for p_avg, p_swa in zip(averaged_params, averaged_dnn.parameters()):
self.assertEqual(p_avg, p_swa)
for b_avg, b_swa in zip(dnn.buffers(), averaged_dnn.module.buffers()):
self.assertEqual(b_avg, b_swa)
def test_averaged_model_exponential_buffers(self):
# Test AveragedModel with EMA as avg_fn and use_buffers as True.
dnn = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.BatchNorm2d(5, momentum=0.3),
torch.nn.Linear(5, 10),
)
alpha = 0.9
def avg_fn(p_avg, p, n_avg):
return alpha * p_avg + (1 - alpha) * p
averaged_dnn = AveragedModel(dnn, avg_fn=avg_fn, use_buffers=True)
dnn_params = itertools.chain(dnn.parameters(), dnn.buffers())
averaged_params = [
torch.zeros_like(param)
for param in dnn_params
if param.size() != torch.Size([])
]
n_updates = 10
for i in range(n_updates):
updated_averaged_params = []
for p, p_avg in zip(dnn_params, averaged_params):
if p.size() == torch.Size([]):
continue
p.detach().add_(torch.randn_like(p))
if i == 0:
updated_averaged_params.append(p.clone())
else:
updated_averaged_params.append(
(p_avg * alpha + p * (1 - alpha)).clone()
)
averaged_dnn.update_parameters(dnn)
averaged_params = updated_averaged_params
for p_avg, p_swa in zip(
averaged_params,
itertools.chain(
averaged_dnn.module.parameters(), averaged_dnn.module.buffers()
),
):
self.assertEqual(p_avg, p_swa)
def _test_update_bn(self, dnn, dl_x, dl_xy, cuda):
preactivation_sum = torch.zeros(dnn.n_features)
preactivation_squared_sum = torch.zeros(dnn.n_features)
if cuda:
preactivation_sum = preactivation_sum.cuda()
preactivation_squared_sum = preactivation_squared_sum.cuda()
total_num = 0
for x in dl_x:
x = x[0]
if cuda:
x = x.cuda()
dnn.forward(x)
preactivations = dnn.compute_preactivation(x)
if len(preactivations.shape) == 4:
preactivations = preactivations.transpose(1, 3)
preactivations = preactivations.contiguous().view(-1, dnn.n_features)
total_num += preactivations.shape[0]
preactivation_sum += torch.sum(preactivations, dim=0)
preactivation_squared_sum += torch.sum(preactivations**2, dim=0)
preactivation_mean = preactivation_sum / total_num
preactivation_var = preactivation_squared_sum / total_num
preactivation_var = preactivation_var - preactivation_mean**2
update_bn(dl_xy, dnn, device=x.device)
self.assertEqual(preactivation_mean, dnn.bn.running_mean)
self.assertEqual(preactivation_var, dnn.bn.running_var, atol=1e-1, rtol=0)
def _reset_bn(module):
if issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
module.running_mean = torch.zeros_like(module.running_mean)
module.running_var = torch.ones_like(module.running_var)
# reset batch norm and run update_bn again
dnn.apply(_reset_bn)
update_bn(dl_xy, dnn, device=x.device)
self.assertEqual(preactivation_mean, dnn.bn.running_mean)
self.assertEqual(preactivation_var, dnn.bn.running_var, atol=1e-1, rtol=0)
# using the dl_x loader instead of dl_xy
dnn.apply(_reset_bn)
update_bn(dl_x, dnn, device=x.device)
self.assertEqual(preactivation_mean, dnn.bn.running_mean)
self.assertEqual(preactivation_var, dnn.bn.running_var, atol=1e-1, rtol=0)
def test_update_bn_dnn(self):
# Test update_bn for a fully-connected network with BatchNorm1d
objects, input_features = 100, 5
x = torch.rand(objects, input_features)
y = torch.rand(objects)
ds_x = torch.utils.data.TensorDataset(x)
ds_xy = torch.utils.data.TensorDataset(x, y)
dl_x = torch.utils.data.DataLoader(ds_x, batch_size=5, shuffle=True)
dl_xy = torch.utils.data.DataLoader(ds_xy, batch_size=5, shuffle=True)
dnn = SWATestDNN(input_features=input_features)
dnn.train()
self._test_update_bn(dnn, dl_x, dl_xy, False)
if torch.cuda.is_available():
dnn = SWATestDNN(input_features=input_features)
dnn.train()
self._test_update_bn(dnn.cuda(), dl_x, dl_xy, True)
self.assertTrue(dnn.training)
def test_update_bn_cnn(self):
# Test update_bn for convolutional network and BatchNorm2d
objects = 100
input_channels = 3
height, width = 5, 5
x = torch.rand(objects, input_channels, height, width)
y = torch.rand(objects)
ds_x = torch.utils.data.TensorDataset(x)
ds_xy = torch.utils.data.TensorDataset(x, y)
dl_x = torch.utils.data.DataLoader(ds_x, batch_size=5, shuffle=True)
dl_xy = torch.utils.data.DataLoader(ds_xy, batch_size=5, shuffle=True)
dnn = SWATestCNN(input_channels=input_channels)
dnn.train()
self._test_update_bn(dnn, dl_x, dl_xy, False)
if torch.cuda.is_available():
dnn = SWATestCNN(input_channels=input_channels)
dnn.train()
self._test_update_bn(dnn.cuda(), dl_x, dl_xy, True)
self.assertTrue(dnn.training)
def test_bn_update_eval_momentum(self):
# check that update_bn preserves eval mode
objects = 100
input_channels = 3
height, width = 5, 5
x = torch.rand(objects, input_channels, height, width)
ds_x = torch.utils.data.TensorDataset(x)
dl_x = torch.utils.data.DataLoader(ds_x, batch_size=5, shuffle=True)
dnn = SWATestCNN(input_channels=input_channels)
dnn.eval()
update_bn(dl_x, dnn)
self.assertFalse(dnn.training)
# check that momentum is preserved
self.assertEqual(dnn.bn.momentum, 0.3)
instantiate_parametrized_tests(TestLRScheduler)
def _diff_fn(p, grad, opt_differentiable_state, opt_class, kwargs, *ignored):
# Ignored is the list of values in `opt_differentiable_state`, we do this
# for `gradcheck` to correctly track the state tensors as function inputs
# because otherwise it can't unpack the values in the `opt_differentiable_state`
# dict
p = p.clone()
p.grad = grad
opt_differentiable_state = {
k: v.clone() if isinstance(v, torch.Tensor) else v
for k, v in opt_differentiable_state.items()
}
opt = opt_class([p], **kwargs)
opt.state[p].update(opt_differentiable_state)
opt.step()
return (p,) + tuple(
v
for v in opt.state[p].values()
if isinstance(v, torch.Tensor) and v.requires_grad
)
class TestDifferentiableOptimizer(TestCase):
def test_sgd(self):
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
mbuff = torch.rand(10, requires_grad=True, dtype=torch.float64)
state = {"momentum_buffer": mbuff}
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.SGD,
{"lr": 0.9, "differentiable": True},
*state.values(),
),
)
def test_adam(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["exp_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["exp_avg_sq"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["max_exp_avg_sq"] = torch.rand(
10, requires_grad=True, dtype=torch.float64
)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.Adam,
{"lr": 0.9, "differentiable": True, "amsgrad": True},
*state.values(),
),
)
def test_rmsprop(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["step"] = 0
state["square_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["momentum_buffer"] = torch.rand(
10, requires_grad=True, dtype=torch.float64
)
# This can cause issues with large values and nan due to sqrt ops
state["grad_avg"] = 1e-2 * torch.rand(
10, requires_grad=True, dtype=torch.float64
)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.RMSprop,
{
"lr": 0.9,
"maximize": True,
"momentum": 0.9,
"differentiable": True,
"centered": True,
"weight_decay": 0.1,
},
*state.values(),
),
)
def test_adadelta(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["square_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["acc_delta"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.Adadelta,
{"lr": 0.9, "weight_decay": 0.1, "differentiable": True},
*state.values(),
),
)
def test_adagrad(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["sum"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.Adagrad,
{"lr": 0.9, "weight_decay": 0.1, "differentiable": True},
*state.values(),
),
)
def test_adamax(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["exp_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["exp_inf"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.Adamax,
{"lr": 0.9, "weight_decay": 0.1, "differentiable": True},
*state.values(),
),
)
@skipIfTorchDynamo("The inplace mu update fails with dynamo, "
"since this is only happening when differentiable is enabled, skipping for now")
def test_asgd(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` `eta` & `mu` are not continuous variables (even though we define them as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["eta"] = torch.tensor(0.9, requires_grad=False, dtype=torch.float64)
state["mu"] = torch.tensor(1.0, requires_grad=False, dtype=torch.float64)
state["ax"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.ASGD,
{"lr": 0.9, "differentiable": True},
*state.values(),
),
)
def test_rprop(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["prev"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["step_size"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.Rprop,
{"lr": 0.9, "differentiable": True},
*state.values(),
),
)
def test_adamw(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["exp_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["exp_avg_sq"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["max_exp_avg_sq"] = torch.rand(
10, requires_grad=True, dtype=torch.float64
)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.AdamW,
{"lr": 0.9, "differentiable": True, "amsgrad": True},
*state.values(),
),
)
def test_nadam(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["exp_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["exp_avg_sq"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["mu_product"] = torch.tensor(1.0, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.NAdam,
{"lr": 0.9, "differentiable": True},
*state.values(),
),
)
def test_radam(self):
state = {}
p = torch.rand(10, requires_grad=True, dtype=torch.float64)
grad = torch.rand(10, requires_grad=True, dtype=torch.float64)
# `step` is not a continuous variable (even though we define it as a float)
# and so it shouldn't require gradients.
state["step"] = torch.tensor(10.0, requires_grad=False, dtype=torch.float64)
state["exp_avg"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
state["exp_avg_sq"] = torch.rand(10, requires_grad=True, dtype=torch.float64)
gradcheck(
_diff_fn,
(
p,
grad,
state,
torch.optim.RAdam,
{"lr": 0.9, "differentiable": True},
*state.values(),
),
)
if __name__ == "__main__":
run_tests()
Reference in New Issue View Git Blame Copy Permalink