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1182 lines
41 KiB
Python
1182 lines
41 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import random
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import lightning.pytorch as pl
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import omegaconf
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import pytest
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import torch
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import torch.optim
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from lightning.pytorch.utilities import rank_zero_only
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from nemo.core import config, optim
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from nemo.core.optim.lr_scheduler import AVAILABLE_SCHEDULERS
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from nemo.core.optim.optimizers import AVAILABLE_OPTIMIZERS
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from nemo.utils import logging
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class TempModel(torch.nn.Module):
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def __init__(self):
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super(TempModel, self).__init__()
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self.layer = torch.nn.Linear(5, 1)
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def forward(self, x):
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x = self.layer(x)
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return x
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class OptCounter(torch.optim.SGD):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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for group in self.param_groups:
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group.setdefault('count', 0)
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def step(self, closure=None):
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for group in self.param_groups:
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group['count'] += 1
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super().step(closure)
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class RandomDataset(torch.utils.data.Dataset):
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def __init__(self, dataset_len):
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super().__init__()
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self.__dataset_len = dataset_len
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def __getitem__(self, *args):
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return torch.randn(2)
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def __len__(self):
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return self.__dataset_len
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class ExampleModel(pl.LightningModule):
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def __init__(self, batch_size, dataset_len, drop_last, max_steps):
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super().__init__()
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self.l1 = torch.nn.modules.Linear(in_features=2, out_features=1)
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self.batch_size = batch_size
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self.dataset_len = dataset_len
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self.drop_last = drop_last
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self.max_steps = max_steps
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def train_dataloader(self):
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dataset = RandomDataset(self.dataset_len)
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return torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, drop_last=self.drop_last)
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def training_step(self, batch, batch_idx):
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output = self.l1(batch)
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output = torch.nn.functional.l1_loss(output, torch.ones(output.size()).to(output.device))
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return {"loss": output}
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def configure_optimizers(self):
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self.my_opt = OptCounter(self.parameters(), lr=0.02)
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return self.my_opt
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class Callback(pl.callbacks.Callback):
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@rank_zero_only
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def on_train_end(self, trainer, module):
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count = module.my_opt.param_groups[0]['count']
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if trainer.global_step != count or trainer.global_step != module.max_steps:
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logging.debug(f"max_epochs: {trainer.max_epochs}")
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logging.debug(f"accumulate_grad_batches: {trainer.accumulate_grad_batches}")
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logging.debug(f"limit_train_batches: {trainer.limit_train_batches}")
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logging.debug(f"num_devices: {trainer.num_devices}")
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logging.debug(f"batch_size: {module.batch_size}")
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logging.debug(f"dataset_len: {module.dataset_len}")
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logging.debug(f"drop_last: {module.drop_last}")
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logging.debug(f"{len(trainer.train_dataloader)}")
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logging.debug(f"{trainer.num_training_batches }")
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self.assert_counts(trainer, module, count)
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def assert_counts(self, trainer, module, count):
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assert trainer.global_step == count, f"{trainer.global_step} != {count} != {module.max_steps}"
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assert trainer.global_step == module.max_steps, f"{trainer.global_step} != {count} != {module.max_steps}"
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class SchedulerNoOpCallback(Callback):
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def on_train_batch_end(self, trainer: pl.Trainer, pl_module, outputs, batch, batch_idx):
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# pl_module.max_steps is "original" max steps without trainer extra steps.
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if (trainer.global_step + 1) % 3 == 0 and (trainer.global_step + 1) < pl_module.max_steps:
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schedulers = trainer.lr_scheduler_configs
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for scheduler in schedulers:
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# Decrement the counter by 2, then perform a scheduler.step() to perform a no-up
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# as well as update the optimizer lr in all param groups
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scheduler.scheduler.last_epoch -= 2
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scheduler.scheduler.step()
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# Increase the max step count by 1
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trainer.fit_loop.epoch_loop.max_steps = trainer.fit_loop.epoch_loop.max_steps + 1
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def assert_counts(self, trainer, module, count):
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num_skips = module.max_steps // 3
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extra_steps = module.max_steps + num_skips
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assert trainer.global_step == count, f"{trainer.global_step} != {count} != {extra_steps}"
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assert trainer.global_step == extra_steps, f"{trainer.global_step} != {count} != {extra_steps}"
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class TestOptimizersSchedulers:
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INITIAL_LR = 0.1
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MIN_LR = 1e-3
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MAX_STEPS = 10
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D_MODEL = 16
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# Apex optimizers require CUDA and this test is being run on CPU only tests
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@pytest.mark.unit
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def test_get_optimizer(self):
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model = TempModel()
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if torch.cuda.is_available():
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model.cuda()
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for opt_name in AVAILABLE_OPTIMIZERS.keys():
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if opt_name == 'fused_adam' or opt_name == 'megatron_fused_adam':
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if not torch.cuda.is_available():
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continue
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if opt_name == 'distributed_fused_adam':
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# TODO: this test fails when run with all other tests, we need to move this test to nightly or CI
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continue
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# if not torch.cuda.is_available() or not torch.distributed.is_nccl_available():
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# continue
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# if not torch.distributed.is_initialized():
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# torch.distributed.init_process_group(
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# 'nccl', world_size=1, rank=0, store=torch.distributed.HashStore(),
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# )
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opt_cls = optim.get_optimizer(opt_name)
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if opt_name == 'adafactor':
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# Adafactor's default mode uses relative_step without any lr.
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opt = opt_cls(model.parameters())
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else:
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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assert isinstance(opt, AVAILABLE_OPTIMIZERS[opt_name])
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@pytest.mark.unit
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def test_register_optimizer(self):
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class TempOpt(torch.optim.SGD):
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pass
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class TempOptParams(config.optimizers.SGDParams):
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pass
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optim.register_optimizer('TempOpt', TempOpt, TempOptParams)
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model = TempModel()
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opt_cls = optim.get_optimizer('TempOpt')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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assert isinstance(opt, TempOpt)
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@pytest.mark.unit
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def test_optim_config_parse_bypass(self):
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basic_optim_config = {'weight_decay': 0.001, 'betas': [0.8, 0.5]}
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parsed_params = optim.parse_optimizer_args('novograd', basic_optim_config)
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assert parsed_params['weight_decay'] == basic_optim_config['weight_decay']
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assert parsed_params['betas'][0] == basic_optim_config['betas'][0]
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assert parsed_params['betas'][1] == basic_optim_config['betas'][1]
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dict_config = omegaconf.OmegaConf.create(basic_optim_config)
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parsed_params = optim.parse_optimizer_args('novograd', dict_config)
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assert parsed_params['weight_decay'] == dict_config['weight_decay']
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assert parsed_params['betas'][0] == dict_config['betas'][0]
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assert parsed_params['betas'][1] == dict_config['betas'][1]
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@pytest.mark.unit
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def test_optim_config_parse_arg_by_name(self):
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basic_optim_config = {'name': 'auto', 'weight_decay': 0.001, 'betas': [0.8, 0.5]}
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parsed_params = optim.parse_optimizer_args('novograd', basic_optim_config)
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assert parsed_params['weight_decay'] == basic_optim_config['weight_decay']
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assert parsed_params['betas'][0] == basic_optim_config['betas'][0]
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assert parsed_params['betas'][1] == basic_optim_config['betas'][1]
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dict_config = omegaconf.OmegaConf.create(basic_optim_config)
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parsed_params = optim.parse_optimizer_args('novograd', dict_config)
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assert parsed_params['weight_decay'] == dict_config['weight_decay']
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assert parsed_params['betas'][0] == dict_config['betas'][0]
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assert parsed_params['betas'][1] == dict_config['betas'][1]
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with pytest.raises(omegaconf.errors.ConfigKeyError):
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optim.parse_optimizer_args('sgd', dict_config)
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@pytest.mark.unit
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def test_optim_config_parse_arg_by_target(self):
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basic_optim_config = {
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'_target_': 'nemo.core.config.NovogradParams',
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'params': {'weight_decay': 0.001, 'betas': [0.8, 0.5]},
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}
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basic_optim_config = omegaconf.OmegaConf.create(basic_optim_config)
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parsed_params = optim.parse_optimizer_args('novograd', basic_optim_config)
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assert parsed_params['weight_decay'] == basic_optim_config['params']['weight_decay']
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assert parsed_params['betas'][0] == basic_optim_config['params']['betas'][0]
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assert parsed_params['betas'][1] == basic_optim_config['params']['betas'][1]
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dict_config = omegaconf.OmegaConf.create(basic_optim_config)
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parsed_params = optim.parse_optimizer_args('novograd', dict_config)
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assert parsed_params['weight_decay'] == dict_config['params']['weight_decay']
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assert parsed_params['betas'][0] == dict_config['params']['betas'][0]
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assert parsed_params['betas'][1] == dict_config['params']['betas'][1]
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# Names are ignored when passing class path
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# This will be captured during optimizer instantiation
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output_config = optim.parse_optimizer_args('sgd', dict_config)
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sgd_config = vars(config.SGDParams())
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novograd_config = vars(config.NovogradParams())
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assert set(output_config.keys()) != set(sgd_config.keys())
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assert set(output_config.keys()) == set(novograd_config)
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@pytest.mark.unit
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def test_get_scheduler(self):
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model = TempModel()
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optimizer = optim.Novograd(model.parameters(), lr=self.INITIAL_LR)
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for sched_name in AVAILABLE_SCHEDULERS.keys():
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sched_cls = optim.lr_scheduler.get_scheduler(sched_name)
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try:
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sched = sched_cls(optimizer)
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assert isinstance(sched, AVAILABLE_SCHEDULERS[sched_name])
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continue
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except Exception:
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pass
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try:
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sched = sched_cls(optimizer, max_steps=self.MAX_STEPS)
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assert isinstance(sched, AVAILABLE_SCHEDULERS[sched_name])
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continue
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except Exception:
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pass
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@pytest.mark.unit
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def test_register_scheduler(self):
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class TempSched(optim.lr_scheduler.CosineAnnealing):
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pass
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class TempSchedParams(config.schedulers.CosineAnnealingParams):
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pass
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optim.lr_scheduler.register_scheduler('TempSched', TempSched, TempSchedParams)
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model = TempModel()
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opt_cls = optim.get_optimizer('novograd')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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sched_cls = optim.lr_scheduler.get_scheduler('TempSched')
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sched = sched_cls(opt, max_steps=self.MAX_STEPS)
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assert isinstance(sched, TempSched)
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@pytest.mark.unit
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def test_sched_config_parse_simple(self):
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model = TempModel()
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opt_cls = optim.get_optimizer('novograd')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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basic_sched_config = {'name': 'CosineAnnealing', 'max_steps': 10}
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scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
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assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
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dict_config = omegaconf.OmegaConf.create(basic_sched_config)
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scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
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assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
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@pytest.mark.unit
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def test_sched_config_parse_from_cls(self):
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model = TempModel()
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opt_cls = optim.get_optimizer('novograd')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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basic_sched_config = {
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'_target_': 'nemo.core.config.CosineAnnealingParams',
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'params': {'min_lr': 0.1},
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'max_steps': self.MAX_STEPS,
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}
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scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
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assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
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dict_config = omegaconf.OmegaConf.create(basic_sched_config)
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scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
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assert isinstance(scheduler_setup['scheduler'], optim.lr_scheduler.CosineAnnealing)
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@pytest.mark.unit
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def test_sched_config_parse_reduce_on_plateau(self):
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model = TempModel()
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opt_cls = optim.get_optimizer('novograd')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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reduce_on_plateau_parameters = {
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'mode': 'min',
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'factor': 0.5,
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'patience': 1,
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'threshold': 1e-4,
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'threshold_mode': 'rel',
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'min_lr': 1e-6,
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'eps': 1e-7,
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'cooldown': 1,
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}
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basic_sched_config = {
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'name': 'ReduceLROnPlateau',
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'monitor': 'val_loss',
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'reduce_on_plateau': True,
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'max_steps': self.MAX_STEPS,
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}
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basic_sched_config.update(reduce_on_plateau_parameters)
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scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
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assert isinstance(scheduler_setup['scheduler'], torch.optim.lr_scheduler.ReduceLROnPlateau)
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for k, v in reduce_on_plateau_parameters.items():
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if k == 'min_lr':
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k += 's'
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v = [v]
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found_v = getattr(scheduler_setup['scheduler'], k)
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assert (
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found_v == v
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), f"Wrong value `{repr(found_v)}` for `ReduceLROnPlateau` parameter `{k}`. Expected `{repr(v)}`."
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dict_config = omegaconf.OmegaConf.create(basic_sched_config)
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scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
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assert isinstance(scheduler_setup['scheduler'], torch.optim.lr_scheduler.ReduceLROnPlateau)
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for k, v in reduce_on_plateau_parameters.items():
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if k == 'min_lr':
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k += 's'
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v = [v]
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found_v = getattr(scheduler_setup['scheduler'], k)
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assert (
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found_v == v
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), f"Wrong value `{repr(found_v)}` for `ReduceLROnPlateau` parameter `{k}`. Expected `{repr(v)}`."
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@pytest.mark.unit
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def test_WarmupPolicy(self):
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model = TempModel()
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opt_cls = optim.get_optimizer('novograd')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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# No warmup case
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policy = optim.lr_scheduler.WarmupPolicy(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
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initial_lr = policy.get_last_lr()[0]
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assert initial_lr == self.INITIAL_LR
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for i in range(self.MAX_STEPS):
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assert policy.get_last_lr()[0] == self.INITIAL_LR
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opt.step()
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policy.step()
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policy.step()
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final_lr = policy.get_last_lr()[0]
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assert final_lr == self.MIN_LR
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# Warmup steps available
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policy = optim.lr_scheduler.WarmupPolicy(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
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initial_lr = policy.get_last_lr()[0]
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assert initial_lr < self.INITIAL_LR
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for i in range(self.MAX_STEPS):
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if i <= 4:
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assert policy.get_last_lr()[0] <= self.INITIAL_LR
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else:
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assert policy.get_last_lr()[0] == self.INITIAL_LR
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opt.step()
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policy.step()
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policy.step()
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final_lr = policy.get_last_lr()[0]
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assert final_lr == self.MIN_LR
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@pytest.mark.unit
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def test_WarmupHoldPolicy(self):
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model = TempModel()
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opt_cls = optim.get_optimizer('novograd')
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opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
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# No warmup case
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policy = optim.lr_scheduler.WarmupHoldPolicy(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
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initial_lr = policy.get_last_lr()[0]
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assert initial_lr == self.INITIAL_LR
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for i in range(self.MAX_STEPS):
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assert policy.get_last_lr()[0] == self.INITIAL_LR
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opt.step()
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policy.step()
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policy.step()
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final_lr = policy.get_last_lr()[0]
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assert final_lr == self.MIN_LR
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|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.WarmupHoldPolicy(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 4:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] == self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup + Hold steps available
|
|
policy = optim.lr_scheduler.WarmupHoldPolicy(
|
|
opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 4:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] == self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_WarmupAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.WarmupAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.WarmupAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup + Hold steps available
|
|
policy = optim.lr_scheduler.WarmupHoldPolicy(
|
|
opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 4:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] == self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_SquareAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.SquareAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.SquareAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_SquareRootAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.SquareRootAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.SquareRootAnnealing(
|
|
opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_CosineAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.CosineAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.CosineAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup + Constant steps available
|
|
policy = optim.lr_scheduler.CosineAnnealing(
|
|
opt, warmup_steps=3, constant_steps=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 3:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR + 1e-5
|
|
elif i > 3 and i <= 8:
|
|
assert policy.get_last_lr()[0] == policy._get_lr(i)[0]
|
|
else:
|
|
assert policy.get_last_lr()[0] == self.MIN_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Noam scheduler should decay past MAX_STEPS - run two schedulers in parallel to test it
|
|
@pytest.mark.unit
|
|
def test_NoamAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt1 = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
opt2 = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy1 = optim.lr_scheduler.NoamAnnealing(
|
|
opt1, d_model=self.D_MODEL, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
policy2 = optim.lr_scheduler.NoamAnnealing(
|
|
opt2, d_model=self.D_MODEL, max_steps=self.MAX_STEPS * 2, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy1.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.D_MODEL ** (-0.5) * self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS * 2):
|
|
assert self.MIN_LR < policy1.get_last_lr()[0] <= self.INITIAL_LR
|
|
assert policy1.get_last_lr()[0] == policy2.get_last_lr()[0]
|
|
opt1.step()
|
|
opt2.step()
|
|
policy1.step()
|
|
policy2.step()
|
|
|
|
# Warmup steps available
|
|
policy1 = optim.lr_scheduler.NoamAnnealing(
|
|
opt1, d_model=self.D_MODEL, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
policy2 = optim.lr_scheduler.NoamAnnealing(
|
|
opt2, d_model=self.D_MODEL, warmup_steps=5, max_steps=self.MAX_STEPS * 2, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy1.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS * 2):
|
|
if i <= 5:
|
|
assert policy1.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert self.MIN_LR < policy1.get_last_lr()[0] < self.INITIAL_LR
|
|
assert policy1.get_last_lr()[0] == policy2.get_last_lr()[0]
|
|
|
|
opt1.step()
|
|
opt2.step()
|
|
policy1.step()
|
|
policy2.step()
|
|
|
|
@pytest.mark.unit
|
|
def test_PolynomialDecayAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.PolynomialDecayAnnealing(
|
|
opt, power=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.PolynomialDecayAnnealing(
|
|
opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_PolynomialHoldDecayAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
|
|
opt, power=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
|
|
opt, power=2, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup + Hold steps available
|
|
policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
|
|
opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR, power=2
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 4:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
elif i <= 8:
|
|
assert policy.get_last_lr()[0] == self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_InverseSquareRootAnnealing(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.InverseSquareRootAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Warmup steps available
|
|
policy = optim.lr_scheduler.InverseSquareRootAnnealing(
|
|
opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr < self.INITIAL_LR
|
|
|
|
for i in range(self.MAX_STEPS):
|
|
if i <= 5:
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
else:
|
|
assert policy.get_last_lr()[0] < self.INITIAL_LR
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
|
|
assert final_lr == self.MIN_LR
|
|
|
|
|
|
class TestWarmupHoldAnnealSchedulers:
|
|
INITIAL_LR = 0.1
|
|
MIN_LR = 0.01
|
|
MAX_STEPS = 100
|
|
|
|
@pytest.mark.unit
|
|
def test_WarmupHoldAnnealOneMinusSquareRoot(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# Test case 1: No warmup, no hold
|
|
policy = optim.lr_scheduler.WarmupHoldAnnealOneMinusSquareRoot(
|
|
opt, warmup_ratio=None, hold_ratio=None, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
# Simulate training steps
|
|
lrs = []
|
|
for i in range(self.MAX_STEPS):
|
|
current_lr = policy.get_last_lr()[0]
|
|
lrs.append(current_lr)
|
|
assert current_lr <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
# Check final LR
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Test case 2: With warmup and hold
|
|
warmup_ratio = 0.1 # 10% warmup
|
|
hold_ratio = 0.2 # 20% hold
|
|
warmup_steps = int(warmup_ratio * self.MAX_STEPS)
|
|
hold_steps = int(hold_ratio * self.MAX_STEPS)
|
|
|
|
policy = optim.lr_scheduler.WarmupHoldAnnealOneMinusSquareRoot(
|
|
opt, warmup_ratio=warmup_ratio, hold_ratio=hold_ratio, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
|
|
initial_lr = policy.get_last_lr()[0]
|
|
assert initial_lr < self.INITIAL_LR # Should start at a lower LR
|
|
|
|
# Simulate training steps
|
|
lrs = []
|
|
for i in range(self.MAX_STEPS):
|
|
current_lr = policy.get_last_lr()[0]
|
|
lrs.append(current_lr)
|
|
|
|
# During warmup, LR should increase
|
|
if i < warmup_steps:
|
|
if i > 0:
|
|
assert current_lr >= lrs[i - 1]
|
|
assert current_lr <= self.INITIAL_LR
|
|
|
|
# During hold, LR should remain constant
|
|
elif i < warmup_steps + hold_steps:
|
|
assert abs(current_lr - self.INITIAL_LR) < 1e-6
|
|
|
|
# During annealing, LR should decrease
|
|
else:
|
|
if i > warmup_steps + hold_steps:
|
|
assert current_lr <= lrs[i - 1]
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
# Check final LR
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_WarmupHoldAnnealLinear(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# Test case 1: No warmup, no hold
|
|
policy = optim.lr_scheduler.WarmupHoldAnnealLinear(
|
|
opt, warmup_ratio=None, hold_ratio=None, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
# Simulate training steps
|
|
lrs = []
|
|
for i in range(self.MAX_STEPS):
|
|
current_lr = policy.get_last_lr()[0]
|
|
lrs.append(current_lr)
|
|
assert current_lr <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
# Check final LR
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
assert final_lr == self.MIN_LR
|
|
|
|
# Test case 2: With warmup and hold
|
|
warmup_ratio = 0.1 # 10% warmup
|
|
hold_ratio = 0.2 # 20% hold
|
|
warmup_steps = int(warmup_ratio * self.MAX_STEPS)
|
|
hold_steps = int(hold_ratio * self.MAX_STEPS)
|
|
|
|
policy = optim.lr_scheduler.WarmupHoldAnnealLinear(
|
|
opt, warmup_ratio=warmup_ratio, hold_ratio=hold_ratio, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
|
|
)
|
|
|
|
initial_lr = policy.get_last_lr()[0]
|
|
assert initial_lr < self.INITIAL_LR # Should start at a lower LR
|
|
|
|
# Simulate training steps
|
|
lrs = []
|
|
for i in range(self.MAX_STEPS):
|
|
current_lr = policy.get_last_lr()[0]
|
|
lrs.append(current_lr)
|
|
|
|
# During warmup, LR should increase
|
|
if i < warmup_steps:
|
|
if i > 0:
|
|
assert current_lr >= lrs[i - 1]
|
|
assert current_lr <= self.INITIAL_LR
|
|
|
|
# During hold, LR should remain constant
|
|
elif i < warmup_steps + hold_steps:
|
|
assert abs(current_lr - self.INITIAL_LR) < 1e-6
|
|
|
|
# During annealing, LR should decrease
|
|
else:
|
|
if i > warmup_steps + hold_steps:
|
|
assert current_lr <= lrs[i - 1]
|
|
|
|
opt.step()
|
|
policy.step()
|
|
|
|
# Check final LR
|
|
policy.step()
|
|
final_lr = policy.get_last_lr()[0]
|
|
assert final_lr == self.MIN_LR
|
|
|
|
@pytest.mark.unit
|
|
def test_CosineAnnealing_with_noop_steps(self):
|
|
model = TempModel()
|
|
opt_cls = optim.get_optimizer('novograd')
|
|
opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
|
|
|
|
# No warmup case
|
|
policy = optim.lr_scheduler.CosineAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
|
|
initial_lr = policy.get_last_lr()[0]
|
|
|
|
assert initial_lr == self.INITIAL_LR
|
|
|
|
update_steps = 0
|
|
for i in range(self.MAX_STEPS):
|
|
assert policy.get_last_lr()[0] <= self.INITIAL_LR
|
|
opt.step()
|
|
policy.step()
|
|
|
|
# Perform a No-Op for scheduler every 2 steps
|
|
if i % 2 == 0:
|
|
policy.last_epoch -= 1
|
|
else:
|
|
update_steps += 1
|
|
|
|
policy.step()
|
|
update_steps += 1
|
|
|
|
assert update_steps < self.MAX_STEPS
|
|
|
|
final_lr = policy.get_last_lr()[0]
|
|
assert final_lr > self.MIN_LR
|
|
|
|
# update step = true number of updates performed after some number of skipped steps
|
|
true_end_lr = policy._get_lr(step=update_steps)[0]
|
|
assert final_lr == true_end_lr
|
|
|
|
@pytest.mark.unit
|
|
@pytest.mark.run_only_on('CPU')
|
|
def test_max_step_computation(self):
|
|
def train(
|
|
max_epochs, accumulate_grad_batches, limit_train_batches, devices, batch_size, dataset_len, drop_last
|
|
):
|
|
trainer = pl.Trainer(
|
|
max_epochs=max_epochs,
|
|
strategy="ddp_spawn",
|
|
accelerator="cpu",
|
|
devices=devices,
|
|
accumulate_grad_batches=accumulate_grad_batches,
|
|
limit_train_batches=limit_train_batches,
|
|
enable_checkpointing=False,
|
|
enable_progress_bar=False,
|
|
)
|
|
max_steps = optim.lr_scheduler.compute_max_steps(
|
|
max_epochs,
|
|
accumulate_grad_batches,
|
|
limit_train_batches,
|
|
devices,
|
|
dataset_len,
|
|
batch_size,
|
|
drop_last,
|
|
)
|
|
model = ExampleModel(batch_size, dataset_len, drop_last, max_steps)
|
|
trainer.callbacks.append(Callback())
|
|
trainer.fit(model)
|
|
|
|
# This test will break once we and lightning upgrade to pytorch 1.7.0 due to a bug fix in pytorch 1.7.0
|
|
train(
|
|
31,
|
|
accumulate_grad_batches=1,
|
|
limit_train_batches=1.0,
|
|
devices=9,
|
|
batch_size=60,
|
|
dataset_len=1613,
|
|
drop_last=True,
|
|
)
|
|
train(
|
|
5,
|
|
accumulate_grad_batches=1,
|
|
limit_train_batches=0.5,
|
|
devices=4,
|
|
batch_size=97,
|
|
dataset_len=498,
|
|
drop_last=False,
|
|
)
|
|
train(
|
|
5,
|
|
accumulate_grad_batches=8,
|
|
limit_train_batches=0.5,
|
|
devices=4,
|
|
batch_size=54,
|
|
dataset_len=629,
|
|
drop_last=True,
|
|
)
|
|
train(
|
|
5,
|
|
accumulate_grad_batches=1,
|
|
limit_train_batches=0.5,
|
|
devices=1,
|
|
batch_size=68,
|
|
dataset_len=488,
|
|
drop_last=False,
|
|
)
|
|
for _ in range(5):
|
|
drop_last = bool(random.randint(0, 1))
|
|
accumulate_grad_batches = random.randint(1, 10)
|
|
|
|
limit_train_batches_int = random.randint(1, 10)
|
|
limit_train_batches_float = random.uniform(0.5, 1)
|
|
limit_train_batches = random.choice([limit_train_batches_int, limit_train_batches_float])
|
|
max_epochs = random.randint(4, 20)
|
|
devices = random.randint(1, 5)
|
|
dataset_len = random.randint(20, devices * 500)
|
|
batch_size = random.randint(math.ceil(5.0 / devices), min(dataset_len // devices, 128))
|
|
train(
|
|
max_epochs,
|
|
accumulate_grad_batches,
|
|
limit_train_batches,
|
|
devices,
|
|
batch_size,
|
|
dataset_len,
|
|
drop_last,
|
|
)
|
|
|
|
@pytest.mark.unit
|
|
@pytest.mark.run_only_on('CPU')
|
|
def test_max_step_computation_with_sched_no_ops(self):
|
|
def train(
|
|
max_steps, accumulate_grad_batches, limit_train_batches, devices, batch_size, dataset_len, drop_last
|
|
):
|
|
trainer = pl.Trainer(
|
|
max_steps=max_steps,
|
|
strategy="ddp_spawn",
|
|
accelerator="cpu",
|
|
devices=devices,
|
|
accumulate_grad_batches=accumulate_grad_batches,
|
|
limit_train_batches=limit_train_batches,
|
|
enable_checkpointing=False,
|
|
enable_progress_bar=False,
|
|
)
|
|
model = ExampleModel(batch_size, dataset_len, drop_last, max_steps)
|
|
trainer.callbacks.append(SchedulerNoOpCallback())
|
|
trainer.fit(model)
|
|
|
|
# This test will break once we and lightning upgrade to pytorch 1.7.0 due to a bug fix in pytorch 1.7.0
|
|
train(
|
|
max_steps=20,
|
|
accumulate_grad_batches=1,
|
|
limit_train_batches=1.0,
|
|
devices=4,
|
|
batch_size=60,
|
|
dataset_len=2000,
|
|
drop_last=True,
|
|
)
|