Merge pull request #813 from rwightman/opt_cleanup

Optimizer cleanup and additions

.github/workflows/tests.yml

@@ -17,8 +17,8 @@ jobs:
       matrix:
         os: [ubuntu-latest, macOS-latest]
         python: ['3.8']
-        torch: ['1.8.1']
-        torchvision: ['0.9.1']
+        torch: ['1.9.0']
+        torchvision: ['0.10.0']
     runs-on: ${{ matrix.os }}
 
     steps:
@@ -43,7 +43,7 @@ jobs:
     - name: Install requirements
       run: |
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-        pip install --no-cache-dir git+https://github.com/mapillary/inplace_abn.git@v1.0.12
+        pip install --no-cache-dir git+https://github.com/mapillary/inplace_abn.git@v1.1.0
     - name: Run tests
       env:
         LD_PRELOAD: /usr/lib/x86_64-linux-gnu/libtcmalloc.so.4

benchmark.py

@@ -255,8 +255,8 @@ class TrainBenchmarkRunner(BenchmarkRunner):
 
         self.optimizer = create_optimizer_v2(
             self.model,
-            optimizer_name=kwargs.pop('opt', 'sgd'),
-            learning_rate=kwargs.pop('lr', 1e-4))
+            opt=kwargs.pop('opt', 'sgd'),
+            lr=kwargs.pop('lr', 1e-4))
 
     def _gen_target(self, batch_size):
         return torch.empty(

tests/test_optim.py

@@ -0,0 +1,704 @@
+""" Optimzier Tests
+
+These tests were adapted from PyTorch' optimizer tests.
+
+"""
+import math
+import pytest
+import functools
+from copy import deepcopy
+
+import torch
+from torch.testing._internal.common_utils import TestCase
+from torch.autograd import Variable
+from timm.scheduler import PlateauLRScheduler
+
+from timm.optim import create_optimizer_v2
+
+
+# HACK relying on internal PyTorch test functionality for comparisons that I don't want to write
+torch_tc = TestCase()
+
+
+def _test_basic_cases_template(weight, bias, input, constructor, scheduler_constructors):
+    weight = Variable(weight, requires_grad=True)
+    bias = Variable(bias, requires_grad=True)
+    input = Variable(input)
+    optimizer = constructor(weight, bias)
+    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 _i in range(200):
+        for scheduler in schedulers:
+            if isinstance(scheduler, PlateauLRScheduler):
+                val_loss = fn()
+                scheduler.step(val_loss)
+            else:
+                scheduler.step()
+        optimizer.step(fn)
+
+    assert fn().item() < initial_value
+
+
+def _test_state_dict(weight, bias, input, constructor):
+    weight = Variable(weight, requires_grad=True)
+    bias = Variable(bias, requires_grad=True)
+    input = Variable(input)
+
+    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
+    weight_c = Variable(weight.data.clone(), requires_grad=True)
+    bias_c = Variable(bias.data.clone(), requires_grad=True)
+    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 _i in range(20):
+        optimizer.step(fn)
+        optimizer_c.step(fn_c)
+        #assert torch.equal(weight, weight_c)
+        #assert torch.equal(bias, bias_c)
+        torch_tc.assertEqual(weight, weight_c)
+        torch_tc.assertEqual(bias, bias_c)
+    # Make sure state dict wasn't modified
+    torch_tc.assertEqual(state_dict, state_dict_c)
+    # Make sure state dict is deterministic with equal but not identical parameters
+    torch_tc.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)
+    torch_tc.assertEqual(optimizer.state_dict()['param_groups'][-1], optimizer_c.state_dict()['param_groups'][-1])
+
+    # 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
+
+    input_cuda = Variable(input.data.float().cuda())
+    weight_cuda = Variable(weight.data.float().cuda(), requires_grad=True)
+    bias_cuda = Variable(bias.data.float().cuda(), requires_grad=True)
+    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
+    torch_tc.assertEqual(state_dict, state_dict_c)
+
+    for _i in range(20):
+        optimizer.step(fn)
+        optimizer_cuda.step(fn_cuda)
+        torch_tc.assertEqual(weight, weight_cuda)
+        torch_tc.assertEqual(bias, bias_cuda)
+
+    # validate deepcopy() copies all public attributes
+    def getPublicAttr(obj):
+        return set(k for k in obj.__dict__ if not k.startswith('_'))
+
+    assert getPublicAttr(optimizer) == getPublicAttr(deepcopy(optimizer))
+
+
+def _test_basic_cases(constructor, scheduler_constructors=None):
+    if scheduler_constructors is None:
+        scheduler_constructors = []
+    _test_state_dict(
+        torch.randn(10, 5),
+        torch.randn(10),
+        torch.randn(5),
+        constructor
+    )
+    _test_basic_cases_template(
+        torch.randn(10, 5),
+        torch.randn(10),
+        torch.randn(5),
+        constructor,
+        scheduler_constructors
+    )
+    # non-contiguous parameters
+    _test_basic_cases_template(
+        torch.randn(10, 5, 2)[..., 0],
+        torch.randn(10, 2)[..., 0],
+        torch.randn(5),
+        constructor,
+        scheduler_constructors
+    )
+    # CUDA
+    if not torch.cuda.is_available():
+        return
+    _test_basic_cases_template(
+        torch.randn(10, 5).cuda(),
+        torch.randn(10).cuda(),
+        torch.randn(5).cuda(),
+        constructor,
+        scheduler_constructors
+    )
+
+
+def _test_model(optimizer, params, device=torch.device('cpu')):
+    weight = torch.tensor(
+        [[-0.2109, -0.4976], [-0.1413, -0.3420], [-0.2524, 0.6976]],
+        device=device, requires_grad=True)
+    bias = torch.tensor([-0.1085, -0.2979, 0.6892], device=device, requires_grad=True)
+    weight2 = torch.tensor([[-0.0508, -0.3941, -0.2843]], device=device, requires_grad=True)
+    bias2 = torch.tensor([-0.0711], device=device, requires_grad=True)
+    input = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], device=device).reshape(3, 2)
+
+    model = torch.nn.Sequential(torch.nn.Linear(2, 3),
+                                torch.nn.Sigmoid(),
+                                torch.nn.Linear(3, 1),
+                                torch.nn.Sigmoid())
+    model.to(device)
+
+    pretrained_dict = model.state_dict()
+    pretrained_dict['0.weight'] = weight
+    pretrained_dict['0.bias'] = bias
+    pretrained_dict['2.weight'] = weight2
+    pretrained_dict['2.bias'] = bias2
+    model.load_state_dict(pretrained_dict)
+
+    optimizer = create_optimizer_v2(model, opt=optimizer, **params)
+
+    prev_loss = float('inf')
+    for i in range(20):
+        optimizer.zero_grad()
+        output = model(input)
+        loss = output.sum()
+        loss.backward()
+        loss = loss.item()
+        assert loss < prev_loss
+        prev_loss = loss
+        optimizer.step()
+
+
+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)))
+
+
+def _test_rosenbrock(constructor, scheduler_constructors=None):
+    if scheduler_constructors is None:
+        scheduler_constructors = []
+    params_t = torch.tensor([1.5, 1.5])
+
+    params = Variable(params_t, requires_grad=True)
+    optimizer = constructor([params])
+    schedulers = []
+    for scheduler_constructor in scheduler_constructors:
+        schedulers.append(scheduler_constructor(optimizer))
+
+    solution = torch.tensor([1, 1])
+    initial_dist = params.data.dist(solution)
+
+    def eval(params, 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., x - x / 4.])
+        else:
+            i = torch.LongTensor([[1, 1]])
+            y = grad[1]
+            v = torch.tensor([y - y / 4., y / 4.])
+        x = torch.sparse.DoubleTensor(i, v, torch.Size([2])).to(dtype=v.dtype)
+        with torch.no_grad():
+            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, w))
+        for scheduler in schedulers:
+            if isinstance(scheduler, PlateauLRScheduler):
+                scheduler.step(rosenbrock(params))
+            else:
+                scheduler.step()
+
+    torch_tc.assertLessEqual(params.data.dist(solution), initial_dist)
+
+
+def _build_params_dict(weight, bias, **kwargs):
+    return [{'params': [weight]}, dict(params=[bias], **kwargs)]
+
+
+def _build_params_dict_single(weight, bias, **kwargs):
+    return [dict(params=bias, **kwargs)]
+
+
+@pytest.mark.parametrize('optimizer', ['sgd', 'momentum'])
+def test_sgd(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=1e-2),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=1e-2),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=1e-2), optimizer)
+    )
+    # _test_basic_cases(
+    #     lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3),
+    #     [lambda opt: StepLR(opt, gamma=0.9, step_size=10)]
+    # )
+    # _test_basic_cases(
+    #     lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3),
+    #     [lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4, warmup_method="linear")]
+    # )
+    # _test_basic_cases(
+    #     lambda weight, bias: optimizer([weight, bias], lr=1e-3),
+    #     [lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4, warmup_method="constant")]
+    # )
+    # _test_basic_cases(
+    #     lambda weight, bias: optimizer([weight, bias], lr=1e-3),
+    #     [lambda opt: StepLR(opt, gamma=0.9, step_size=10),
+    #      lambda opt: WarmUpLR(opt, warmup_factor=0.4, warmup_iters=4)]
+    # )
+    # _test_basic_cases(
+    #     lambda weight, bias: optimizer([weight, bias], lr=1e-3),
+    #     [lambda opt: StepLR(opt, gamma=0.9, step_size=10),
+    #      lambda opt: ReduceLROnPlateau(opt)]
+    # )
+    # _test_basic_cases(
+    #     lambda weight, bias: optimizer([weight, bias], lr=1e-3),
+    #     [lambda opt: StepLR(opt, gamma=0.99, step_size=10),
+    #      lambda opt: ExponentialLR(opt, gamma=0.99),
+    #      lambda opt: ReduceLROnPlateau(opt)]
+    # )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, momentum=1)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, momentum=1, weight_decay=1)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
+    )
+    _test_model(optimizer, dict(lr=1e-3))
+
+
+@pytest.mark.parametrize('optimizer',  ['adamw', 'adam', 'nadam', 'adamax'])
+def test_adam(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
+    )
+    _test_model(optimizer, dict(lr=5e-2))
+
+
+@pytest.mark.parametrize('optimizer',  ['adabelief'])
+def test_adabelief(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
+    )
+    _test_model(optimizer, dict(lr=5e-2))
+
+
+@pytest.mark.parametrize('optimizer',  ['radam', 'radabelief'])
+def test_rectified(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
+    )
+    _test_model(optimizer, dict(lr=1e-3))
+
+
+@pytest.mark.parametrize('optimizer',   ['adadelta', 'adagrad'])
+def test_adaother(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-1)
+    )
+    _test_model(optimizer, dict(lr=5e-2))
+
+
+@pytest.mark.parametrize('optimizer',   ['adafactor'])
+def test_adafactor(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(_build_params_dict_single(weight, bias), optimizer)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3, weight_decay=1)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
+    )
+    _test_model(optimizer, dict(lr=5e-2))
+
+
+@pytest.mark.parametrize('optimizer',  ['lamb'])
+def test_lamb(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
+    )
+    _test_model(optimizer, dict(lr=1e-3))
+
+
+@pytest.mark.parametrize('optimizer',  ['madgrad', 'madgradw'])
+def test_madgrad(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-2)
+    )
+    _test_model(optimizer, dict(lr=1e-2))
+
+
+@pytest.mark.parametrize('optimizer',  ['novograd'])
+def test_novograd(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
+    )
+    _test_model(optimizer, dict(lr=1e-3))
+
+
+@pytest.mark.parametrize('optimizer', ['rmsprop', 'rmsproptf'])
+def test_rmsprop(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-2)
+    )
+    _test_model(optimizer, dict(lr=1e-2))
+
+
+@pytest.mark.parametrize('optimizer', ['adamp'])
+def test_adamp(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
+    )
+    _test_model(optimizer, dict(lr=5e-2))
+
+
+@pytest.mark.parametrize('optimizer', ['sgdp'])
+def test_sgdp(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
+    )
+    _test_model(optimizer, dict(lr=1e-3))
+
+
+@pytest.mark.parametrize('optimizer', ['lookahead_sgd', 'lookahead_momentum'])
+def test_lookahead_sgd(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-3)
+    )
+
+
+@pytest.mark.parametrize('optimizer', ['lookahead_adamw', 'lookahead_adam'])
+def test_lookahead_adam(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=5e-2)
+    )
+
+
+@pytest.mark.parametrize('optimizer', ['lookahead_radam'])
+def test_lookahead_radam(optimizer):
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3),
+            optimizer,
+            lr=1e-3)
+    )
+    _test_basic_cases(
+        lambda weight, bias: create_optimizer_v2(
+            _build_params_dict_single(weight, bias, lr=3e-3), optimizer)
+    )
+    _test_rosenbrock(
+        lambda params: create_optimizer_v2(params, optimizer, lr=1e-4)
+    )
+

timm/optim/__init__.py

@@ -3,8 +3,8 @@ from .adamw import AdamW
 from .adafactor import Adafactor
 from .adahessian import Adahessian
 from .lookahead import Lookahead
+from .madgrad import MADGRAD
 from .nadam import Nadam
-from .novograd import NovoGrad
 from .nvnovograd import NvNovoGrad
 from .radam import RAdam
 from .rmsprop_tf import RMSpropTF

timm/optim/adabelief.py

@@ -18,7 +18,7 @@ class AdaBelief(Optimizer):
         amsgrad (boolean, optional): whether to use the AMSGrad variant of this
             algorithm from the paper `On the Convergence of Adam and Beyond`_
             (default: False)
-        weight_decouple (boolean, optional): ( default: True) If set as True, then
+        decoupled_decay (boolean, optional): (default: True) If set as True, then
             the optimizer uses decoupled weight decay as in AdamW
         fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
             is set as True.
@@ -39,9 +39,9 @@ class AdaBelief(Optimizer):
       - link to args.yaml: https://gist.github.com/juntang-zhuang/517ce3c27022b908bb93f78e4f786dc3
     """
 
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
-                 weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True,
-                 degenerated_to_sgd=True):
+    def __init__(
+            self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16, weight_decay=0, amsgrad=False,
+            decoupled_decay=True, fixed_decay=False, rectify=True, degenerated_to_sgd=True):
 
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
@@ -52,21 +52,17 @@ class AdaBelief(Optimizer):
         if not 0.0 <= betas[1] < 1.0:
             raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
 
-        self.degenerated_to_sgd = degenerated_to_sgd
         if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
             for param in params:
                 if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
                     param['buffer'] = [[None, None, None] for _ in range(10)]
 
-        defaults = dict(lr=lr, betas=betas, eps=eps,
-                        weight_decay=weight_decay, amsgrad=amsgrad, buffer=[[None, None, None] for _ in range(10)])
+        defaults = dict(
+            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad,
+            degenerated_to_sgd=degenerated_to_sgd, decoupled_decay=decoupled_decay, rectify=rectify,
+            fixed_decay=fixed_decay, buffer=[[None, None, None] for _ in range(10)])
         super(AdaBelief, self).__init__(params, defaults)
 
-        self.degenerated_to_sgd = degenerated_to_sgd
-        self.weight_decouple = weight_decouple
-        self.rectify = rectify
-        self.fixed_decay = fixed_decay
-
     def __setstate__(self, state):
         super(AdaBelief, self).__setstate__(state)
         for group in self.param_groups:
@@ -133,8 +129,8 @@ class AdaBelief(Optimizer):
                         state['max_exp_avg_var'] = torch.zeros_like(p.data)
                 
                 # perform weight decay, check if decoupled weight decay
-                if self.weight_decouple:
-                    if not self.fixed_decay:
+                if group['decoupled_decay']:
+                    if not group['fixed_decay']:
                         p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
                     else:
                         p.data.mul_(1.0 - group['weight_decay'])
@@ -152,7 +148,7 @@ class AdaBelief(Optimizer):
                 # Update first and second moment running average
                 exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
                 grad_residual = grad - exp_avg
-                exp_avg_var.mul_(beta2).addcmul_( grad_residual, grad_residual, value=1 - beta2)
+                exp_avg_var.mul_(beta2).addcmul_(grad_residual, grad_residual, value=1 - beta2)
 
                 if amsgrad:
                     max_exp_avg_var = state['max_exp_avg_var']
@@ -165,38 +161,40 @@ class AdaBelief(Optimizer):
                     denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
                 
                 # update
-                if not self.rectify:
+                if not group['rectify']:
                     # Default update
                     step_size = group['lr'] / bias_correction1
-                    p.data.addcdiv_( exp_avg, denom, value=-step_size)
-
-                else:  # Rectified update, forked from RAdam
+                    p.data.addcdiv_(exp_avg, denom, value=-step_size)
+                else:
+                    # Rectified update, forked from RAdam
                     buffered = group['buffer'][int(state['step'] % 10)]
                     if state['step'] == buffered[0]:
-                        N_sma, step_size = buffered[1], buffered[2]
+                        num_sma, step_size = buffered[1], buffered[2]
                     else:
                         buffered[0] = state['step']
                         beta2_t = beta2 ** state['step']
-                        N_sma_max = 2 / (1 - beta2) - 1
-                        N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
-                        buffered[1] = N_sma
+                        num_sma_max = 2 / (1 - beta2) - 1
+                        num_sma = num_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                        buffered[1] = num_sma
 
                         # more conservative since it's an approximated value
-                        if N_sma >= 5:
+                        if num_sma >= 5:
                             step_size = math.sqrt(
-                                (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
-                                        N_sma_max - 2)) / (1 - beta1 ** state['step'])
-                        elif self.degenerated_to_sgd:
+                                (1 - beta2_t) *
+                                (num_sma - 4) / (num_sma_max - 4) *
+                                (num_sma - 2) / num_sma *
+                                num_sma_max / (num_sma_max - 2)) / (1 - beta1 ** state['step'])
+                        elif group['degenerated_to_sgd']:
                             step_size = 1.0 / (1 - beta1 ** state['step'])
                         else:
                             step_size = -1
                         buffered[2] = step_size
 
-                    if N_sma >= 5:
+                    if num_sma >= 5:
                         denom = exp_avg_var.sqrt().add_(group['eps'])
                         p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
                     elif step_size > 0:
-                        p.data.add_( exp_avg, alpha=-step_size * group['lr'])
+                        p.data.add_(exp_avg, alpha=-step_size * group['lr'])
                 
                 if half_precision:
                     p.data = p.data.half()

timm/optim/adafactor.py

@@ -34,15 +34,13 @@ class Adafactor(torch.optim.Optimizer):
         beta1 (float): coefficient used for computing running averages of gradient (default: None)
         weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
         scale_parameter (bool): if True, learning rate is scaled by root mean square of parameter (default: True)
-        relative_step (bool): if True, time-dependent learning rate is computed
-            instead of external learning rate (default: True)
         warmup_init (bool): time-dependent learning rate computation depends on
             whether warm-up initialization is being used (default: False)
     """
 
     def __init__(self, params, lr=None, eps=1e-30, eps_scale=1e-3, clip_threshold=1.0,
                  decay_rate=-0.8, betas=None, weight_decay=0.0, scale_parameter=True, warmup_init=False):
-        relative_step = lr is None
+        relative_step = not lr
         if warmup_init and not relative_step:
             raise ValueError('warmup_init requires relative_step=True')
 
@@ -138,10 +136,8 @@ class Adafactor(torch.optim.Optimizer):
                     exp_avg_sq_row = state['exp_avg_sq_row']
                     exp_avg_sq_col = state['exp_avg_sq_col']
 
-                    exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1))
-                    exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2))
-                    #exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t)  # pytorch 1.6+
-                    #exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t)
+                    exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t)
+                    exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t)
 
                     # Approximation of exponential moving average of square of gradient
                     update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
@@ -149,8 +145,7 @@ class Adafactor(torch.optim.Optimizer):
                 else:
                     exp_avg_sq = state['exp_avg_sq']
 
-                    exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update)
-                    #exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t)  # pytorch 1.6+
+                    exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t)
                     update = exp_avg_sq.rsqrt().mul_(grad)
 
                 update.div_((self._rms(update) / group['clip_threshold']).clamp_(min=1.0))
@@ -158,17 +153,15 @@ class Adafactor(torch.optim.Optimizer):
 
                 if use_first_moment:
                     exp_avg = state['exp_avg']
-                    exp_avg.mul_(group["beta1"]).add_(1 - group["beta1"], update)
-                    #exp_avg.mul_(group['beta1']).add_(update, alpha=1 - group['beta1'])  # pytorch 1.6+
+                    exp_avg.mul_(group['beta1']).add_(update, alpha=1 - group['beta1'])
                     update = exp_avg
 
                 if group['weight_decay'] != 0:
-                    p_data_fp32.add_(-group["weight_decay"] * lr_t, p_data_fp32)
-                    #p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * lr_t)  # pytorch 1.6+
+                    p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * lr_t)
 
                 p_data_fp32.add_(-update)
 
                 if p.data.dtype in {torch.float16, torch.bfloat16}:
                     p.data.copy_(p_data_fp32)
 
-        return loss
+        return loss

timm/optim/adamp.py

@@ -9,48 +9,43 @@ MIT license
 """
 
 import torch
-import torch.nn as nn
-from torch.optim.optimizer import Optimizer, required
+import torch.nn.functional as F
+from torch.optim.optimizer import Optimizer
 import math
 
-class AdamP(Optimizer):
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
-                 weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
-                        delta=delta, wd_ratio=wd_ratio, nesterov=nesterov)
-        super(AdamP, self).__init__(params, defaults)
-
-    def _channel_view(self, x):
-        return x.view(x.size(0), -1)
 
-    def _layer_view(self, x):
-        return x.view(1, -1)
+def _channel_view(x) -> torch.Tensor:
+    return x.reshape(x.size(0), -1)
 
-    def _cosine_similarity(self, x, y, eps, view_func):
-        x = view_func(x)
-        y = view_func(y)
 
-        x_norm = x.norm(dim=1).add_(eps)
-        y_norm = y.norm(dim=1).add_(eps)
-        dot = (x * y).sum(dim=1)
+def _layer_view(x) -> torch.Tensor:
+    return x.reshape(1, -1)
 
-        return dot.abs() / x_norm / y_norm
 
-    def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
-        wd = 1
-        expand_size = [-1] + [1] * (len(p.shape) - 1)
-        for view_func in [self._channel_view, self._layer_view]:
+def projection(p, grad, perturb, delta: float, wd_ratio: float, eps: float):
+    wd = 1.
+    expand_size = (-1,) + (1,) * (len(p.shape) - 1)
+    for view_func in [_channel_view, _layer_view]:
+        param_view = view_func(p.data)
+        grad_view = view_func(grad)
+        cosine_sim = F.cosine_similarity(grad_view, param_view, dim=1, eps=eps).abs_()
 
-            cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
+        if cosine_sim.max() < delta / math.sqrt(param_view.size(1)):
+            p_n = p.data / param_view.norm(p=2, dim=1).add_(eps).reshape(expand_size)
+            perturb -= p_n * view_func(p_n * perturb).sum(dim=1).reshape(expand_size)
+            wd = wd_ratio
+            return perturb, wd
 
-            if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
-                p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)
-                perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)
-                wd = wd_ratio
+    return perturb, wd
 
-                return perturb, wd
 
-        return perturb, wd
+class AdamP(Optimizer):
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
+                 weight_decay=0, delta=0.1, wd_ratio=0.1, nesterov=False):
+        defaults = dict(
+            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
+            delta=delta, wd_ratio=wd_ratio, nesterov=nesterov)
+        super(AdamP, self).__init__(params, defaults)
 
     def step(self, closure=None):
         loss = None
@@ -81,8 +76,8 @@ class AdamP(Optimizer):
                 bias_correction1 = 1 - beta1 ** state['step']
                 bias_correction2 = 1 - beta2 ** state['step']
 
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
 
                 denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
                 step_size = group['lr'] / bias_correction1
@@ -93,15 +88,15 @@ class AdamP(Optimizer):
                     perturb = exp_avg / denom
 
                 # Projection
-                wd_ratio = 1
+                wd_ratio = 1.
                 if len(p.shape) > 1:
-                    perturb, wd_ratio = self._projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps'])
+                    perturb, wd_ratio = projection(p, grad, perturb, group['delta'], group['wd_ratio'], group['eps'])
 
                 # Weight decay
                 if group['weight_decay'] > 0:
-                    p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio)
+                    p.data.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio)
 
                 # Step
-                p.data.add_(-step_size, perturb)
+                p.data.add_(perturb, alpha=-step_size)
 
         return loss

timm/optim/adamw.py

@@ -1,5 +1,8 @@
 """ AdamW Optimizer
 Impl copied from PyTorch master
+
+NOTE: Builtin optim.AdamW is used by the factory, this impl only serves as a Python based reference, will be removed
+someday
 """
 import math
 import torch
@@ -100,8 +103,8 @@ class AdamW(Optimizer):
                 bias_correction2 = 1 - beta2 ** state['step']
 
                 # Decay the first and second moment running average coefficient
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
                 if amsgrad:
                     # Maintains the maximum of all 2nd moment running avg. till now
                     torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
@@ -112,6 +115,6 @@ class AdamW(Optimizer):
 
                 step_size = group['lr'] / bias_correction1
 
-                p.data.addcdiv_(-step_size, exp_avg, denom)
+                p.data.addcdiv_(exp_avg, denom, value=-step_size)
 
         return loss

timm/optim/lamb.py

@@ -47,12 +47,13 @@ Original copyrights for above sources are below.
 # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
+import math
 
 import torch
 from torch.optim import Optimizer
 
 
-class NvLamb(Optimizer):
+class Lamb(Optimizer):
     """Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer from apex.optimizers.FusedLAMB
     reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
 
@@ -82,25 +83,13 @@ class NvLamb(Optimizer):
         https://openreview.net/forum?id=ryQu7f-RZ
     """
 
-    def __init__(self, params, lr=1e-3, bias_correction=True,
-                 betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
-                 grad_averaging=True, set_grad_none=True,
-                 max_grad_norm=1.0, use_nvlamb=False):
-        defaults = dict(lr=lr, bias_correction=bias_correction,
-                        betas=betas, eps=eps, weight_decay=weight_decay,
-                        grad_averaging=grad_averaging,
-                        max_grad_norm=max_grad_norm)
+    def __init__(
+            self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-6,
+            weight_decay=0.01, grad_averaging=True, max_grad_norm=1.0, use_nvlamb=False):
+        defaults = dict(
+            lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay,
+            grad_averaging=grad_averaging, max_grad_norm=max_grad_norm, use_nvlamb=use_nvlamb)
         super().__init__(params, defaults)
-        self.set_grad_none = set_grad_none
-        self.use_nvlamb = use_nvlamb
-
-    def zero_grad(self):
-        if self.set_grad_none:
-            for group in self.param_groups:
-                for p in group['params']:
-                    p.grad = None
-        else:
-            super(NvLamb, self).zero_grad()
 
     def step(self, closure=None):
         """Performs a single optimization step.
@@ -109,6 +98,7 @@ class NvLamb(Optimizer):
                 and returns the loss.
         """
         device = self.param_groups[0]["params"][0].device
+        one_tensor = torch.tensor(1.0, device=device)
 
         loss = None
         if closure is not None:
@@ -124,22 +114,18 @@ class NvLamb(Optimizer):
                     raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')
                 global_grad_norm.add_(grad.pow(2).sum())
 
-        global_grad_norm_ = torch.sqrt(global_grad_norm)
+        global_grad_norm = torch.sqrt(global_grad_norm)
         max_grad_norm = self.defaults['max_grad_norm']
-
-        if global_grad_norm_ > max_grad_norm:
-            clip_global_grad_norm = global_grad_norm_ / max_grad_norm
-        else:
-            clip_global_grad_norm = 1.0
+        clip_global_grad_norm = torch.where(
+            global_grad_norm > max_grad_norm,
+            global_grad_norm / max_grad_norm,
+            one_tensor)
 
         for group in self.param_groups:
             bias_correction = 1 if group['bias_correction'] else 0
             beta1, beta2 = group['betas']
             grad_averaging = 1 if group['grad_averaging'] else 0
-            if grad_averaging:
-                beta3 = 1 - beta1
-            else:
-                beta3 = 1.0
+            beta3 = 1 - beta1 if grad_averaging else 1.0
 
             # assume same step across group now to simplify things
             # per parameter step can be easily support by making it tensor, or pass list into kernel
@@ -148,8 +134,6 @@ class NvLamb(Optimizer):
             else:
                 group['step'] = 1
 
-            step_size = group['lr']
-
             if bias_correction:
                 bias_correction1 = 1 - beta1 ** group['step']
                 bias_correction2 = 1 - beta2 ** group['step']
@@ -169,36 +153,31 @@ class NvLamb(Optimizer):
                     # Exponential moving average of squared gradient values
                     state['exp_avg_sq'] = torch.zeros_like(p.data)
 
-                exp_avg_, exp_avg_sq_ = state['exp_avg'], state['exp_avg_sq']
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
 
                 # Decay the first and second moment running average coefficient
-                # m_t
-                exp_avg_.mul_(beta1).add_(grad, alpha=beta3)
-                # v_t
-                exp_avg_sq_.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-                # create clones to avoid modifying runner stats
-                exp_avg = exp_avg_.div(bias_correction1)
-                exp_avg_sq = exp_avg_sq_.div(bias_correction2)
-
-                # || w_t ||
-                weight_norm = p.data.norm(2.0)
-                # u_t
-                exp_avg_sq_sqrt = torch.sqrt(exp_avg_sq)
-                adam_step = exp_avg.div_(exp_avg_sq_sqrt.add_(group['eps']))
-                if group['weight_decay'] != 0:
-                    adam_step.add_(p.data, alpha=group['weight_decay'])
-                # || u_t ||
-                adam_norm = adam_step.norm(2.0)
-                if (group['weight_decay'] != 0 or self.use_nvlamb) and adam_norm > 0 and weight_norm > 0:
-                    trust_ratio = weight_norm / adam_norm
-                    trust_ratio = trust_ratio.item()
-                else:
-                    trust_ratio = 1
-
-                state['weight_norm'] = weight_norm
-                state['adam_norm'] = adam_norm
-                state['trust_ratio'] = trust_ratio
-
-                p.data.add_(adam_step, alpha=-step_size * trust_ratio)
+                exp_avg.mul_(beta1).add_(grad, alpha=beta3)  # m_t
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)  # v_t
+
+                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
+                update = (exp_avg / bias_correction1).div_(denom)
+
+                weight_decay = group['weight_decay']
+                if weight_decay != 0:
+                    update.add_(p.data, alpha=weight_decay)
+
+                trust_ratio = one_tensor
+                if weight_decay != 0 or group['use_nvlamb']:
+                    # Layer adaptation. By default, skip layer adaptation on parameters that are
+                    # excluded from weight norm, unless use_nvlamb == True, then always enabled.
+                    w_norm = p.data.norm(2.0)
+                    g_norm = update.norm(2.0)
+                    trust_ratio = torch.where(
+                        w_norm > 0,
+                        torch.where(g_norm > 0, w_norm / g_norm, one_tensor),
+                        one_tensor,
+                    )
+                update.mul_(trust_ratio)
+                p.data.add_(update, alpha=-group['lr'])
 
         return loss

timm/optim/lookahead.py

@@ -11,82 +11,49 @@ from collections import defaultdict
 
 class Lookahead(Optimizer):
     def __init__(self, base_optimizer, alpha=0.5, k=6):
+        # NOTE super().__init__() not called on purpose
         if not 0.0 <= alpha <= 1.0:
             raise ValueError(f'Invalid slow update rate: {alpha}')
         if not 1 <= k:
             raise ValueError(f'Invalid lookahead steps: {k}')
         defaults = dict(lookahead_alpha=alpha, lookahead_k=k, lookahead_step=0)
-        self.base_optimizer = base_optimizer
-        self.param_groups = self.base_optimizer.param_groups
+        self._base_optimizer = base_optimizer
+        self.param_groups = base_optimizer.param_groups
         self.defaults = base_optimizer.defaults
         self.defaults.update(defaults)
         self.state = defaultdict(dict)
         # manually add our defaults to the param groups
         for name, default in defaults.items():
-            for group in self.param_groups:
+            for group in self._base_optimizer.param_groups:
                 group.setdefault(name, default)
 
     def update_slow(self, group):
         for fast_p in group["params"]:
             if fast_p.grad is None:
                 continue
-            param_state = self.state[fast_p]
-            if 'slow_buffer' not in param_state:
-                param_state['slow_buffer'] = torch.empty_like(fast_p.data)
-                param_state['slow_buffer'].copy_(fast_p.data)
-            slow = param_state['slow_buffer']
-            slow.add_(group['lookahead_alpha'], fast_p.data - slow)
+            param_state = self._base_optimizer.state[fast_p]
+            if 'lookahead_slow_buff' not in param_state:
+                param_state['lookahead_slow_buff'] = torch.empty_like(fast_p.data)
+                param_state['lookahead_slow_buff'].copy_(fast_p.data)
+            slow = param_state['lookahead_slow_buff']
+            slow.add_(fast_p.data - slow, alpha=group['lookahead_alpha'])
             fast_p.data.copy_(slow)
 
     def sync_lookahead(self):
-        for group in self.param_groups:
+        for group in self._base_optimizer.param_groups:
             self.update_slow(group)
 
     def step(self, closure=None):
-        #assert id(self.param_groups) == id(self.base_optimizer.param_groups)
-        loss = self.base_optimizer.step(closure)
-        for group in self.param_groups:
+        loss = self._base_optimizer.step(closure)
+        for group in self._base_optimizer.param_groups:
             group['lookahead_step'] += 1
             if group['lookahead_step'] % group['lookahead_k'] == 0:
                 self.update_slow(group)
         return loss
 
     def state_dict(self):
-        fast_state_dict = self.base_optimizer.state_dict()
-        slow_state = {
-            (id(k) if isinstance(k, torch.Tensor) else k): v
-            for k, v in self.state.items()
-        }
-        fast_state = fast_state_dict['state']
-        param_groups = fast_state_dict['param_groups']
-        return {
-            'state': fast_state,
-            'slow_state': slow_state,
-            'param_groups': param_groups,
-        }
+        return self._base_optimizer.state_dict()
 
     def load_state_dict(self, state_dict):
-        fast_state_dict = {
-            'state': state_dict['state'],
-            'param_groups': state_dict['param_groups'],
-        }
-        self.base_optimizer.load_state_dict(fast_state_dict)
-
-        # We want to restore the slow state, but share param_groups reference
-        # with base_optimizer. This is a bit redundant but least code
-        slow_state_new = False
-        if 'slow_state' not in state_dict:
-            print('Loading state_dict from optimizer without Lookahead applied.')
-            state_dict['slow_state'] = defaultdict(dict)
-            slow_state_new = True
-        slow_state_dict = {
-            'state': state_dict['slow_state'],
-            'param_groups': state_dict['param_groups'],  # this is pointless but saves code
-        }
-        super(Lookahead, self).load_state_dict(slow_state_dict)
-        self.param_groups = self.base_optimizer.param_groups  # make both ref same container
-        if slow_state_new:
-            # reapply defaults to catch missing lookahead specific ones
-            for name, default in self.defaults.items():
-                for group in self.param_groups:
-                    group.setdefault(name, default)
+        self._base_optimizer.load_state_dict(state_dict)
+        self.param_groups = self._base_optimizer.param_groups

timm/optim/madgrad.py

@@ -0,0 +1,190 @@
+""" PyTorch MADGRAD optimizer
+
+MADGRAD: https://arxiv.org/abs/2101.11075
+
+Code from: https://github.com/facebookresearch/madgrad
+"""
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from typing import TYPE_CHECKING, Any, Callable, Optional
+
+import torch
+import torch.optim
+
+if TYPE_CHECKING:
+    from torch.optim.optimizer import _params_t
+else:
+    _params_t = Any
+
+
+class MADGRAD(torch.optim.Optimizer):
+    """
+    MADGRAD_: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic
+    Optimization.
+
+    .. _MADGRAD: https://arxiv.org/abs/2101.11075
+
+    MADGRAD is a general purpose optimizer that can be used in place of SGD or
+    Adam may converge faster and generalize better. Currently GPU-only.
+    Typically, the same learning rate schedule that is used for SGD or Adam may
+    be used. The overall learning rate is not comparable to either method and
+    should be determined by a hyper-parameter sweep.
+
+    MADGRAD requires less weight decay than other methods, often as little as
+    zero. Momentum values used for SGD or Adam's beta1 should work here also.
+
+    On sparse problems both weight_decay and momentum should be set to 0.
+
+    Arguments:
+        params (iterable):
+            Iterable of parameters to optimize or dicts defining parameter groups.
+        lr (float):
+            Learning rate (default: 1e-2).
+        momentum (float):
+            Momentum value in  the range [0,1) (default: 0.9).
+        weight_decay (float):
+            Weight decay, i.e. a L2 penalty (default: 0).
+        eps (float):
+            Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-6).
+    """
+
+    def __init__(
+            self,
+            params: _params_t,
+            lr: float = 1e-2,
+            momentum: float = 0.9,
+            weight_decay: float = 0,
+            eps: float = 1e-6,
+            decoupled_decay: bool = False,
+    ):
+        if momentum < 0 or momentum >= 1:
+            raise ValueError(f"Momentum {momentum} must be in the range [0,1]")
+        if lr <= 0:
+            raise ValueError(f"Learning rate {lr} must be positive")
+        if weight_decay < 0:
+            raise ValueError(f"Weight decay {weight_decay} must be non-negative")
+        if eps < 0:
+            raise ValueError(f"Eps must be non-negative")
+
+        defaults = dict(
+            lr=lr, eps=eps, momentum=momentum, weight_decay=weight_decay, decoupled_decay=decoupled_decay)
+        super().__init__(params, defaults)
+
+    @property
+    def supports_memory_efficient_fp16(self) -> bool:
+        return False
+
+    @property
+    def supports_flat_params(self) -> bool:
+        return True
+
+    def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        # step counter must be stored in state to ensure correct behavior under
+        # optimizer sharding
+        if 'k' not in self.state:
+            self.state['k'] = torch.tensor([0], dtype=torch.long)
+        k = self.state['k'].item()
+
+        for group in self.param_groups:
+            eps = group["eps"]
+            lr = group["lr"] + eps
+            weight_decay = group["weight_decay"]
+            momentum = group["momentum"]
+
+            ck = 1 - momentum
+            lamb = lr * math.pow(k + 1, 0.5)
+
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                state = self.state[p]
+
+                if "grad_sum_sq" not in state:
+                    state["grad_sum_sq"] = torch.zeros_like(p.data).detach()
+                    state["s"] = torch.zeros_like(p.data).detach()
+                    if momentum != 0:
+                        state["x0"] = torch.clone(p.data).detach()
+
+                if momentum != 0.0 and grad.is_sparse:
+                    raise RuntimeError("momentum != 0 is not compatible with sparse gradients")
+
+                grad_sum_sq = state["grad_sum_sq"]
+                s = state["s"]
+
+                # Apply weight decay
+                if weight_decay != 0:
+                    if group['decoupled_decay']:
+                        p.data.mul_(1.0 - group['lr'] * weight_decay)
+                    else:
+                        if grad.is_sparse:
+                            raise RuntimeError("weight_decay option is not compatible with sparse gradients")
+                        grad.add_(p.data, alpha=weight_decay)
+
+                if grad.is_sparse:
+                    grad = grad.coalesce()
+                    grad_val = grad._values()
+
+                    p_masked = p.sparse_mask(grad)
+                    grad_sum_sq_masked = grad_sum_sq.sparse_mask(grad)
+                    s_masked = s.sparse_mask(grad)
+
+                    # Compute x_0 from other known quantities
+                    rms_masked_vals = grad_sum_sq_masked._values().pow(1 / 3).add_(eps)
+                    x0_masked_vals = p_masked._values().addcdiv(s_masked._values(), rms_masked_vals, value=1)
+
+                    # Dense + sparse op
+                    grad_sq = grad * grad
+                    grad_sum_sq.add_(grad_sq, alpha=lamb)
+                    grad_sum_sq_masked.add_(grad_sq, alpha=lamb)
+
+                    rms_masked_vals = grad_sum_sq_masked._values().pow_(1 / 3).add_(eps)
+
+                    s.add_(grad, alpha=lamb)
+                    s_masked._values().add_(grad_val, alpha=lamb)
+
+                    # update masked copy of p
+                    p_kp1_masked_vals = x0_masked_vals.addcdiv(s_masked._values(), rms_masked_vals, value=-1)
+                    # Copy updated masked p to dense p using an add operation
+                    p_masked._values().add_(p_kp1_masked_vals, alpha=-1)
+                    p.data.add_(p_masked, alpha=-1)
+                else:
+                    if momentum == 0:
+                        # Compute x_0 from other known quantities
+                        rms = grad_sum_sq.pow(1 / 3).add_(eps)
+                        x0 = p.data.addcdiv(s, rms, value=1)
+                    else:
+                        x0 = state["x0"]
+
+                    # Accumulate second moments
+                    grad_sum_sq.addcmul_(grad, grad, value=lamb)
+                    rms = grad_sum_sq.pow(1 / 3).add_(eps)
+
+                    # Update s
+                    s.data.add_(grad, alpha=lamb)
+
+                    # Step
+                    if momentum == 0:
+                        p.data.copy_(x0.addcdiv(s, rms, value=-1))
+                    else:
+                        z = x0.addcdiv(s, rms, value=-1)
+
+                        # p is a moving average of z
+                        p.data.mul_(1 - ck).add_(z, alpha=ck)
+
+        self.state['k'] += 1
+        return loss

timm/optim/nadam.py

@@ -1,5 +1,5 @@
 import torch
-from torch.optim import Optimizer
+from torch.optim.optimizer import Optimizer
 
 
 class Nadam(Optimizer):
@@ -27,8 +27,10 @@ class Nadam(Optimizer):
 
     def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
                  weight_decay=0, schedule_decay=4e-3):
-        defaults = dict(lr=lr, betas=betas, eps=eps,
-                        weight_decay=weight_decay, schedule_decay=schedule_decay)
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        defaults = dict(
+            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, schedule_decay=schedule_decay)
         super(Nadam, self).__init__(params, defaults)
 
     def step(self, closure=None):
@@ -53,8 +55,8 @@ class Nadam(Optimizer):
                 if len(state) == 0:
                     state['step'] = 0
                     state['m_schedule'] = 1.
-                    state['exp_avg'] = grad.new().resize_as_(grad).zero_()
-                    state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_()
+                    state['exp_avg'] = torch.zeros_like(p.data)
+                    state['exp_avg_sq'] = torch.zeros_like(p.data)
 
                 # Warming momentum schedule
                 m_schedule = state['m_schedule']
@@ -66,23 +68,21 @@ class Nadam(Optimizer):
                 t = state['step']
 
                 if group['weight_decay'] != 0:
-                    grad = grad.add(group['weight_decay'], p.data)
+                    grad = grad.add(p.data, alpha=group['weight_decay'])
 
-                momentum_cache_t = beta1 * \
-                    (1. - 0.5 * (0.96 ** (t * schedule_decay)))
-                momentum_cache_t_1 = beta1 * \
-                    (1. - 0.5 * (0.96 ** ((t + 1) * schedule_decay)))
+                momentum_cache_t = beta1 * (1. - 0.5 * (0.96 ** (t * schedule_decay)))
+                momentum_cache_t_1 = beta1 * (1. - 0.5 * (0.96 ** ((t + 1) * schedule_decay)))
                 m_schedule_new = m_schedule * momentum_cache_t
                 m_schedule_next = m_schedule * momentum_cache_t * momentum_cache_t_1
                 state['m_schedule'] = m_schedule_new
 
                 # Decay the first and second moment running average coefficient
-                exp_avg.mul_(beta1).add_(1. - beta1, grad)
-                exp_avg_sq.mul_(beta2).addcmul_(1. - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(grad, alpha=1. - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1. - beta2)
                 exp_avg_sq_prime = exp_avg_sq / (1. - beta2 ** t)
                 denom = exp_avg_sq_prime.sqrt_().add_(eps)
 
-                p.data.addcdiv_(-group['lr'] * (1. - momentum_cache_t) / (1. - m_schedule_new), grad, denom)
-                p.data.addcdiv_(-group['lr'] * momentum_cache_t_1 / (1. - m_schedule_next), exp_avg, denom)
+                p.data.addcdiv_(grad, denom, value=-group['lr'] * (1. - momentum_cache_t) / (1. - m_schedule_new))
+                p.data.addcdiv_(exp_avg, denom, value=-group['lr'] * momentum_cache_t_1 / (1. - m_schedule_next))
 
         return loss

timm/optim/novograd.py

@@ -1,77 +0,0 @@
-"""NovoGrad Optimizer.
-Original impl by Masashi Kimura (Convergence Lab): https://github.com/convergence-lab/novograd
-Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
-    - https://arxiv.org/abs/1905.11286
-"""
-
-import torch
-from torch.optim.optimizer import Optimizer
-import math
-
-
-class NovoGrad(Optimizer):
-    def __init__(self, params, grad_averaging=False, lr=0.1, betas=(0.95, 0.98), eps=1e-8, weight_decay=0):
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-        super(NovoGrad, self).__init__(params, defaults)
-        self._lr = lr
-        self._beta1 = betas[0]
-        self._beta2 = betas[1]
-        self._eps = eps
-        self._wd = weight_decay
-        self._grad_averaging = grad_averaging
-
-        self._momentum_initialized = False
-
-    def step(self, closure=None):
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        if not self._momentum_initialized:
-            for group in self.param_groups:
-                for p in group['params']:
-                    if p.grad is None:
-                        continue
-                    state = self.state[p]
-                    grad = p.grad.data
-                    if grad.is_sparse:
-                        raise RuntimeError('NovoGrad does not support sparse gradients')
-
-                    v = torch.norm(grad)**2
-                    m = grad/(torch.sqrt(v) + self._eps) + self._wd * p.data
-                    state['step'] = 0
-                    state['v'] = v
-                    state['m'] = m
-                    state['grad_ema'] = None
-            self._momentum_initialized = True
-
-        for group in self.param_groups:
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                state = self.state[p]
-                state['step'] += 1
-
-                step, v, m = state['step'], state['v'], state['m']
-                grad_ema = state['grad_ema']
-
-                grad = p.grad.data
-                g2 = torch.norm(grad)**2
-                grad_ema = g2 if grad_ema is None else grad_ema * \
-                    self._beta2 + g2 * (1. - self._beta2)
-                grad *= 1.0 / (torch.sqrt(grad_ema) + self._eps)
-
-                if self._grad_averaging:
-                    grad *= (1. - self._beta1)
-
-                g2 = torch.norm(grad)**2
-                v = self._beta2*v + (1. - self._beta2)*g2
-                m = self._beta1*m + (grad / (torch.sqrt(v) + self._eps) + self._wd * p.data)
-                bias_correction1 = 1 - self._beta1 ** step
-                bias_correction2 = 1 - self._beta2 ** step
-                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
-
-                state['v'], state['m']  = v, m
-                state['grad_ema'] = grad_ema
-                p.data.add_(-step_size, m)
-        return loss

timm/optim/nvnovograd.py

@@ -96,7 +96,7 @@ class NvNovoGrad(Optimizer):
                 if exp_avg_sq == 0:
                     exp_avg_sq.copy_(norm)
                 else:
-                    exp_avg_sq.mul_(beta2).add_(1 - beta2, norm)
+                    exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)
 
                 if amsgrad:
                     # Maintains the maximum of all 2nd moment running avg. till now
@@ -108,11 +108,11 @@ class NvNovoGrad(Optimizer):
 
                 grad.div_(denom)
                 if group['weight_decay'] != 0:
-                    grad.add_(group['weight_decay'], p.data)
+                    grad.add_(p.data, alpha=group['weight_decay'])
                 if group['grad_averaging']:
                     grad.mul_(1 - beta1)
                 exp_avg.mul_(beta1).add_(grad)
 
-                p.data.add_(-group['lr'], exp_avg)
+                p.data.add_(exp_avg, alpha=-group['lr'])
 
         return loss

timm/optim/optim_factory.py

@@ -1,5 +1,5 @@
 """ Optimizer Factory w/ Custom Weight Decay
-Hacked together by / Copyright 2020 Ross Wightman
+Hacked together by / Copyright 2021 Ross Wightman
 """
 from typing import Optional
 
@@ -11,10 +11,10 @@ from .adabelief import AdaBelief
 from .adafactor import Adafactor
 from .adahessian import Adahessian
 from .adamp import AdamP
-from .lamb import NvLamb
+from .lamb import Lamb
 from .lookahead import Lookahead
+from .madgrad import MADGRAD
 from .nadam import Nadam
-from .novograd import NovoGrad
 from .nvnovograd import NvNovoGrad
 from .radam import RAdam
 from .rmsprop_tf import RMSpropTF
@@ -47,8 +47,8 @@ def optimizer_kwargs(cfg):
     Convert optimizer args in argparse args or cfg like object to keyword args for updated create fn.
     """
     kwargs = dict(
-        optimizer_name=cfg.opt,
-        learning_rate=cfg.lr,
+        opt=cfg.opt,
+        lr=cfg.lr,
         weight_decay=cfg.weight_decay,
         momentum=cfg.momentum)
     if getattr(cfg, 'opt_eps', None) is not None:
@@ -72,9 +72,9 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
 
 
 def create_optimizer_v2(
-        model: nn.Module,
-        optimizer_name: str = 'sgd',
-        learning_rate: Optional[float] = None,
+        model_or_params,
+        opt: str = 'sgd',
+        lr: Optional[float] = None,
         weight_decay: float = 0.,
         momentum: float = 0.9,
         filter_bias_and_bn: bool = True,
@@ -87,9 +87,9 @@ def create_optimizer_v2(
       * expose the parameters interface and leave it up to caller
 
     Args:
-        model (nn.Module): model containing parameters to optimize
-        optimizer_name: name of optimizer to create
-        learning_rate: initial learning rate
+        model_or_params (nn.Module): model containing parameters to optimize
+        opt: name of optimizer to create
+        lr: initial learning rate
         weight_decay: weight decay to apply in optimizer
         momentum:  momentum for momentum based optimizers (others may use betas via kwargs)
         filter_bias_and_bn:  filter out bias, bn and other 1d params from weight decay
@@ -98,59 +98,85 @@ def create_optimizer_v2(
     Returns:
         Optimizer
     """
-    opt_lower = optimizer_name.lower()
-    if weight_decay and filter_bias_and_bn:
-        skip = {}
-        if hasattr(model, 'no_weight_decay'):
-            skip = model.no_weight_decay()
-        parameters = add_weight_decay(model, weight_decay, skip)
-        weight_decay = 0.
+    if isinstance(model_or_params, nn.Module):
+        # a model was passed in, extract parameters and add weight decays to appropriate layers
+        if weight_decay and filter_bias_and_bn:
+            skip = {}
+            if hasattr(model_or_params, 'no_weight_decay'):
+                skip = model_or_params.no_weight_decay()
+            parameters = add_weight_decay(model_or_params, weight_decay, skip)
+            weight_decay = 0.
+        else:
+            parameters = model_or_params.parameters()
     else:
-        parameters = model.parameters()
-    if 'fused' in opt_lower:
-        assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
+        # iterable of parameters or param groups passed in
+        parameters = model_or_params
 
-    opt_args = dict(lr=learning_rate, weight_decay=weight_decay, **kwargs)
+    opt_lower = opt.lower()
     opt_split = opt_lower.split('_')
     opt_lower = opt_split[-1]
+    if 'fused' in opt_lower:
+        assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
+
+    opt_args = dict(weight_decay=weight_decay, **kwargs)
+    if lr is not None:
+        opt_args.setdefault('lr', lr)
+
+    # basic SGD & related
     if opt_lower == 'sgd' or opt_lower == 'nesterov':
+        # NOTE 'sgd' refers to SGD + nesterov momentum for legacy / backwards compat reasons
         opt_args.pop('eps', None)
         optimizer = optim.SGD(parameters, momentum=momentum, nesterov=True, **opt_args)
     elif opt_lower == 'momentum':
         opt_args.pop('eps', None)
         optimizer = optim.SGD(parameters, momentum=momentum, nesterov=False, **opt_args)
+    elif opt_lower == 'sgdp':
+        optimizer = SGDP(parameters, momentum=momentum, nesterov=True, **opt_args)
+
+    # adaptive
     elif opt_lower == 'adam':
         optimizer = optim.Adam(parameters, **opt_args) 
-    elif opt_lower == 'adabelief':
-        optimizer = AdaBelief(parameters, rectify=False, **opt_args)
     elif opt_lower == 'adamw':
         optimizer = optim.AdamW(parameters, **opt_args)
+    elif opt_lower == 'adamp':
+        optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
     elif opt_lower == 'nadam':
-        optimizer = Nadam(parameters, **opt_args)
+        try:
+            # NOTE PyTorch >= 1.10 should have native NAdam
+            optimizer = optim.Nadam(parameters, **opt_args)
+        except AttributeError:
+            optimizer = Nadam(parameters, **opt_args)
     elif opt_lower == 'radam':
         optimizer = RAdam(parameters, **opt_args)
-    elif opt_lower == 'adamp':        
-        optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
-    elif opt_lower == 'sgdp':
-        optimizer = SGDP(parameters, momentum=momentum, nesterov=True, **opt_args)
+    elif opt_lower == 'adamax':
+        optimizer = optim.Adamax(parameters, **opt_args)
+    elif opt_lower == 'adabelief':
+        optimizer = AdaBelief(parameters, rectify=False, **opt_args)
+    elif opt_lower == 'radabelief':
+        optimizer = AdaBelief(parameters, rectify=True, **opt_args)
     elif opt_lower == 'adadelta':
         optimizer = optim.Adadelta(parameters, **opt_args)
+    elif opt_lower == 'adagrad':
+        opt_args.setdefault('eps', 1e-8)
+        optimizer = optim.Adagrad(parameters, **opt_args)
     elif opt_lower == 'adafactor':
-        if not learning_rate:
-            opt_args['lr'] = None
         optimizer = Adafactor(parameters, **opt_args)
-    elif opt_lower == 'adahessian':
-        optimizer = Adahessian(parameters, **opt_args)
+    elif opt_lower == 'lamb':
+        optimizer = Lamb(parameters, **opt_args)
+    elif opt_lower == 'madgrad':
+        optimizer = MADGRAD(parameters, momentum=momentum, **opt_args)
+    elif opt_lower == 'madgradw':
+        optimizer = MADGRAD(parameters, momentum=momentum, decoupled_decay=True, **opt_args)
+    elif opt_lower == 'novograd' or opt_lower == 'nvnovograd':
+        optimizer = NvNovoGrad(parameters, **opt_args)
     elif opt_lower == 'rmsprop':
         optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=momentum, **opt_args)
     elif opt_lower == 'rmsproptf':
         optimizer = RMSpropTF(parameters, alpha=0.9, momentum=momentum, **opt_args)
-    elif opt_lower == 'novograd':
-        optimizer = NovoGrad(parameters, **opt_args)
-    elif opt_lower == 'nvnovograd':
-        optimizer = NvNovoGrad(parameters, **opt_args)
-    elif opt_lower == 'lamb':
-        optimizer = NvLamb(parameters, **opt_args)
+
+    # second order
+    elif opt_lower == 'adahessian':
+        optimizer = Adahessian(parameters, **opt_args)
 
     # NVIDIA fused optimizers, require APEX to be installed
     elif opt_lower == 'fusedsgd':

timm/optim/radam.py

@@ -4,21 +4,21 @@ Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxi
 """
 import math
 import torch
-from torch.optim.optimizer import Optimizer, required
+from torch.optim.optimizer import Optimizer
 
 
 class RAdam(Optimizer):
 
     def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-        self.buffer = [[None, None, None] for ind in range(10)]
+        defaults = dict(
+            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
+            buffer=[[None, None, None] for _ in range(10)])
         super(RAdam, self).__init__(params, defaults)
 
     def __setstate__(self, state):
         super(RAdam, self).__setstate__(state)
 
     def step(self, closure=None):
-
         loss = None
         if closure is not None:
             loss = closure()
@@ -47,105 +47,40 @@ class RAdam(Optimizer):
                 exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                 beta1, beta2 = group['betas']
 
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
 
                 state['step'] += 1
-                buffered = self.buffer[int(state['step'] % 10)]
+                buffered = group['buffer'][int(state['step'] % 10)]
                 if state['step'] == buffered[0]:
-                    N_sma, step_size = buffered[1], buffered[2]
+                    num_sma, step_size = buffered[1], buffered[2]
                 else:
                     buffered[0] = state['step']
                     beta2_t = beta2 ** state['step']
-                    N_sma_max = 2 / (1 - beta2) - 1
-                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
-                    buffered[1] = N_sma
+                    num_sma_max = 2 / (1 - beta2) - 1
+                    num_sma = num_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                    buffered[1] = num_sma
 
                     # more conservative since it's an approximated value
-                    if N_sma >= 5:
+                    if num_sma >= 5:
                         step_size = group['lr'] * math.sqrt(
-                            (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
-                                        N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                            (1 - beta2_t) *
+                            (num_sma - 4) / (num_sma_max - 4) *
+                            (num_sma - 2) / num_sma *
+                            num_sma_max / (num_sma_max - 2)) / (1 - beta1 ** state['step'])
                     else:
                         step_size = group['lr'] / (1 - beta1 ** state['step'])
                     buffered[2] = step_size
 
                 if group['weight_decay'] != 0:
-                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-
-                # more conservative since it's an approximated value
-                if N_sma >= 5:
-                    denom = exp_avg_sq.sqrt().add_(group['eps'])
-                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
-                else:
-                    p_data_fp32.add_(-step_size, exp_avg)
-
-                p.data.copy_(p_data_fp32)
-
-        return loss
-
-
-class PlainRAdam(Optimizer):
-
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-
-        super(PlainRAdam, self).__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super(PlainRAdam, self).__setstate__(state)
-
-    def step(self, closure=None):
-
-        loss = None
-        if closure is not None:
-            loss = closure()
-
-        for group in self.param_groups:
-
-            for p in group['params']:
-                if p.grad is None:
-                    continue
-                grad = p.grad.data.float()
-                if grad.is_sparse:
-                    raise RuntimeError('RAdam does not support sparse gradients')
-
-                p_data_fp32 = p.data.float()
-
-                state = self.state[p]
-
-                if len(state) == 0:
-                    state['step'] = 0
-                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
-                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-                else:
-                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
-                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
-
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
-                exp_avg.mul_(beta1).add_(1 - beta1, grad)
-
-                state['step'] += 1
-                beta2_t = beta2 ** state['step']
-                N_sma_max = 2 / (1 - beta2) - 1
-                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
-
-                if group['weight_decay'] != 0:
-                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+                    p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr'])
 
                 # more conservative since it's an approximated value
-                if N_sma >= 5:
-                    step_size = group['lr'] * math.sqrt(
-                        (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
-                                    N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                if num_sma >= 5:
                     denom = exp_avg_sq.sqrt().add_(group['eps'])
-                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+                    p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
                 else:
-                    step_size = group['lr'] / (1 - beta1 ** state['step'])
-                    p_data_fp32.add_(-step_size, exp_avg)
+                    p_data_fp32.add_(exp_avg, alpha=-step_size)
 
                 p.data.copy_(p_data_fp32)
 

timm/optim/rmsprop_tf.py

@@ -58,8 +58,9 @@ class RMSpropTF(Optimizer):
         if not 0.0 <= alpha:
             raise ValueError("Invalid alpha value: {}".format(alpha))
 
-        defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay,
-                        decoupled_decay=decoupled_decay, lr_in_momentum=lr_in_momentum)
+        defaults = dict(
+            lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay,
+            decoupled_decay=decoupled_decay, lr_in_momentum=lr_in_momentum)
         super(RMSpropTF, self).__init__(params, defaults)
 
     def __setstate__(self, state):
@@ -103,34 +104,34 @@ class RMSpropTF(Optimizer):
                 state['step'] += 1
 
                 if group['weight_decay'] != 0:
-                    if 'decoupled_decay' in group and group['decoupled_decay']:
-                        p.data.add_(-group['weight_decay'], p.data)
+                    if group['decoupled_decay']:
+                        p.data.mul_(1. - group['lr'] * group['weight_decay'])
                     else:
-                        grad = grad.add(group['weight_decay'], p.data)
+                        grad = grad.add(p.data, alpha=group['weight_decay'])
 
                 # Tensorflow order of ops for updating squared avg
-                square_avg.add_(one_minus_alpha, grad.pow(2) - square_avg)
-                # square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)  # PyTorch original
+                square_avg.add_(grad.pow(2) - square_avg, alpha=one_minus_alpha)
+                # square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)  # PyTorch original
 
                 if group['centered']:
                     grad_avg = state['grad_avg']
-                    grad_avg.add_(one_minus_alpha, grad - grad_avg)
-                    # grad_avg.mul_(alpha).add_(1 - alpha, grad)  # PyTorch original
-                    avg = square_avg.addcmul(-1, grad_avg, grad_avg).add(group['eps']).sqrt_()  # eps moved in sqrt
+                    grad_avg.add_(grad - grad_avg, alpha=one_minus_alpha)
+                    avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).add(group['eps']).sqrt_()  # eps in sqrt
+                    # grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha)  # PyTorch original
                 else:
                     avg = square_avg.add(group['eps']).sqrt_()  # eps moved in sqrt
 
                 if group['momentum'] > 0:
                     buf = state['momentum_buffer']
                     # Tensorflow accumulates the LR scaling in the momentum buffer
-                    if 'lr_in_momentum' in group and group['lr_in_momentum']:
-                        buf.mul_(group['momentum']).addcdiv_(group['lr'], grad, avg)
+                    if group['lr_in_momentum']:
+                        buf.mul_(group['momentum']).addcdiv_(grad, avg, value=group['lr'])
                         p.data.add_(-buf)
                     else:
                         # PyTorch scales the param update by LR
                         buf.mul_(group['momentum']).addcdiv_(grad, avg)
-                        p.data.add_(-group['lr'], buf)
+                        p.data.add_(buf, alpha=-group['lr'])
                 else:
-                    p.data.addcdiv_(-group['lr'], grad, avg)
+                    p.data.addcdiv_(grad, avg, value=-group['lr'])
 
         return loss

timm/optim/sgdp.py

@@ -9,49 +9,21 @@ MIT license
 """
 
 import torch
-import torch.nn as nn
+import torch.nn.functional as F
 from torch.optim.optimizer import Optimizer, required
 import math
 
+from .adamp import projection
+
+
 class SGDP(Optimizer):
     def __init__(self, params, lr=required, momentum=0, dampening=0,
                  weight_decay=0, nesterov=False, eps=1e-8, delta=0.1, wd_ratio=0.1):
-        defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
-                        nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)
+        defaults = dict(
+            lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay,
+            nesterov=nesterov, eps=eps, delta=delta, wd_ratio=wd_ratio)
         super(SGDP, self).__init__(params, defaults)
 
-    def _channel_view(self, x):
-        return x.view(x.size(0), -1)
-
-    def _layer_view(self, x):
-        return x.view(1, -1)
-
-    def _cosine_similarity(self, x, y, eps, view_func):
-        x = view_func(x)
-        y = view_func(y)
-
-        x_norm = x.norm(dim=1).add_(eps)
-        y_norm = y.norm(dim=1).add_(eps)
-        dot = (x * y).sum(dim=1)
-
-        return dot.abs() / x_norm / y_norm
-
-    def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
-        wd = 1
-        expand_size = [-1] + [1] * (len(p.shape) - 1)
-        for view_func in [self._channel_view, self._layer_view]:
-
-            cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
-
-            if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
-                p_n = p.data / view_func(p.data).norm(dim=1).view(expand_size).add_(eps)
-                perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(expand_size)
-                wd = wd_ratio
-
-                return perturb, wd
-
-        return perturb, wd
-
     def step(self, closure=None):
         loss = None
         if closure is not None:
@@ -75,22 +47,22 @@ class SGDP(Optimizer):
 
                 # SGD
                 buf = state['momentum']
-                buf.mul_(momentum).add_(1 - dampening, grad)
+                buf.mul_(momentum).add_(grad, alpha=1. - dampening)
                 if nesterov:
                     d_p = grad + momentum * buf
                 else:
                     d_p = buf
 
                 # Projection
-                wd_ratio = 1
+                wd_ratio = 1.
                 if len(p.shape) > 1:
-                    d_p, wd_ratio = self._projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])
+                    d_p, wd_ratio = projection(p, grad, d_p, group['delta'], group['wd_ratio'], group['eps'])
 
                 # Weight decay
                 if weight_decay != 0:
-                    p.data.mul_(1 - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))
+                    p.data.mul_(1. - group['lr'] * group['weight_decay'] * wd_ratio / (1-momentum))
 
                 # Step
-                p.data.add_(-group['lr'], d_p)
+                p.data.add_(d_p, alpha=-group['lr'])
 
         return loss