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Support Callable type for client optimizer and lr_scheduler (#1316)
* Callable option for optimizer and scheduler * Add unit test * Formatting * Disable debug prints * Use base optimizer to construct lr scheduler * Formatting * Remove dead import
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,135 @@ | ||
| import pytest | ||
| from typing import Callable | ||
| import torch | ||
| from torch.optim import Optimizer, Adam, AdamW | ||
| from torch.optim.lr_scheduler import _LRScheduler, LambdaLR | ||
|
|
||
| from simple_model import args_from_dict, SimpleModel | ||
| from common import distributed_test | ||
|
|
||
| import deepspeed | ||
| from deepspeed.ops.adam import FusedAdam | ||
| from deepspeed.runtime.lr_schedules import WARMUP_LR, WarmupLR | ||
| from deepspeed.runtime.config import ADAM_OPTIMIZER | ||
|
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||
|
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||
| @pytest.mark.parametrize('optimizer_type', [None, Optimizer, Callable]) | ||
| def test_client_optimizer(tmpdir, optimizer_type): | ||
| def _optimizer_callable(params) -> Optimizer: | ||
| return AdamW(params=params) | ||
|
|
||
| hidden_dim = 10 | ||
| model = SimpleModel(hidden_dim) | ||
|
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| config_dict = {'train_batch_size': 1} | ||
| if optimizer_type is None: | ||
| client_optimizer = None | ||
| config_dict['optimizer'] = {'type': ADAM_OPTIMIZER} | ||
| elif optimizer_type is Optimizer: | ||
| client_optimizer = Adam(model.parameters()) | ||
| else: | ||
| client_optimizer = _optimizer_callable | ||
|
|
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| args = args_from_dict(tmpdir, config_dict) | ||
|
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| @distributed_test(world_size=[1]) | ||
| def _test_client_optimizer(args, model, client_optimizer): | ||
| _, ds_optimizer, _, _ = deepspeed.initialize(args=args, | ||
| model=model, | ||
| model_parameters=list(model.parameters()), | ||
| optimizer=client_optimizer) | ||
| if client_optimizer is None: | ||
| assert isinstance(ds_optimizer, FusedAdam) | ||
| elif isinstance(client_optimizer, Optimizer): | ||
| assert ds_optimizer == client_optimizer | ||
| else: | ||
| assert isinstance(ds_optimizer, AdamW) | ||
|
|
||
| _test_client_optimizer(args=args, model=model, client_optimizer=client_optimizer) | ||
|
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|
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| @pytest.mark.parametrize('scheduler_type, optimizer_type', | ||
| [(None, | ||
| None), | ||
| (None, | ||
| Optimizer), | ||
| (None, | ||
| Callable), | ||
| (_LRScheduler, | ||
| None), | ||
| (_LRScheduler, | ||
| Optimizer), | ||
| (_LRScheduler, | ||
| Callable), | ||
| (Callable, | ||
| None), | ||
| (Callable, | ||
| Optimizer), | ||
| (Callable, | ||
| Callable)]) | ||
| def test_client_lr_scheduler(tmpdir, scheduler_type, optimizer_type): | ||
| def _my_lambda(epoch): | ||
| return epoch // 10 | ||
|
|
||
| def _optimizer_callable(params) -> Optimizer: | ||
| return torch.optim.AdamW(params=params) | ||
|
|
||
| def _lr_scheduler_callable(optimizer) -> _LRScheduler: | ||
| return LambdaLR(optimizer, _my_lambda) | ||
|
|
||
| hidden_dim = 10 | ||
| model = SimpleModel(hidden_dim) | ||
|
|
||
| config_dict = {'train_batch_size': 1} | ||
|
|
||
| client_optimizer = None | ||
| client_scheduler = None | ||
|
|
||
| if optimizer_type is None: | ||
| config_dict['optimizer'] = {'type': ADAM_OPTIMIZER} | ||
| elif optimizer_type is Optimizer: | ||
| client_optimizer = torch.optim.Adam(model.parameters()) | ||
| else: | ||
| client_optimizer = _optimizer_callable | ||
|
|
||
| if scheduler_type is None: | ||
| config_dict['scheduler'] = {'type': WARMUP_LR, 'params': {}} | ||
| elif scheduler_type == _LRScheduler: | ||
| if isinstance(client_optimizer, Optimizer): | ||
| client_scheduler = LambdaLR(client_optimizer, _my_lambda) | ||
| else: | ||
| # Verify invalid combination is correctly handled | ||
| client_scheduler = LambdaLR(torch.optim.Adam(model.parameters()), _my_lambda) | ||
| else: | ||
| client_scheduler = _lr_scheduler_callable | ||
|
|
||
| args = args_from_dict(tmpdir, config_dict) | ||
|
|
||
| @distributed_test(world_size=[1]) | ||
| def _test_client_lr_scheduler(args, model, optimizer, lr_scheduler): | ||
| if isinstance(lr_scheduler, | ||
| _LRScheduler) and not isinstance(optimizer, | ||
| Optimizer): | ||
| with pytest.raises(AssertionError): | ||
| _, _, _, _ = deepspeed.initialize(args=args, | ||
| model=model, | ||
| model_parameters=list(model.parameters()), | ||
| optimizer=optimizer, | ||
| lr_scheduler=lr_scheduler) | ||
| else: | ||
| _, _, _, ds_lr_scheduler = deepspeed.initialize(args=args, | ||
| model=model, | ||
| model_parameters=list(model.parameters()), | ||
| optimizer=optimizer, | ||
| lr_scheduler=lr_scheduler) | ||
| if lr_scheduler is None: | ||
| assert isinstance(ds_lr_scheduler, WarmupLR) | ||
| elif isinstance(lr_scheduler, _LRScheduler): | ||
| assert ds_lr_scheduler == lr_scheduler | ||
| else: | ||
| assert isinstance(ds_lr_scheduler, LambdaLR) | ||
|
|
||
| _test_client_lr_scheduler(args=args, | ||
| model=model, | ||
| optimizer=client_optimizer, | ||
| lr_scheduler=client_scheduler) |