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set adamw_mode default true (follows FusedAdam and < 0.3.11 logic) #844

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merged 4 commits into from
Mar 11, 2021
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set adamw_mode default true (follows FusedAdam and < 0.3.11 logic) #844

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37e4e74
set adamw_mode default true (follows FusedAdam and < 0.3.11 logic)
jeffra Mar 9, 2021
c4df508
refactor of adam(w) logic, still same effect as before but hopefully …
jeffra Mar 9, 2021
a23891a
add unit tests sweeping the space of adam/adamw
jeffra Mar 11, 2021
2e391d2
Merge branch 'master' into jeffra/adamw_default_true
jeffra Mar 11, 2021
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2 changes: 1 addition & 1 deletion deepspeed/ops/adam/cpu_adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ def __init__(self,

self.opt_id = DeepSpeedCPUAdam.optimizer_id
DeepSpeedCPUAdam.optimizer_id = DeepSpeedCPUAdam.optimizer_id + 1

self.adam_w_mode = adamw_mode
self.ds_opt_adam = CPUAdamBuilder().load()

self.ds_opt_adam.create_adam(self.opt_id,
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4 changes: 4 additions & 0 deletions deepspeed/runtime/config.py
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Expand Up @@ -40,6 +40,10 @@
# extra optimizer parameters for adam/adamw
TORCH_ADAM_PARAM = "torch_adam"

# default to adamw logic for adam/adamw optimizers unless user explictly opts out
ADAM_W_MODE = "adam_w_mode"
ADAM_W_MODE_DEFAULT = True


class DeepSpeedConfigError(Exception):
pass
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38 changes: 21 additions & 17 deletions deepspeed/runtime/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
from deepspeed.runtime.fp16.unfused_optimizer import FP16_UnfusedOptimizer
from deepspeed.runtime.config import DeepSpeedConfig, DEEPSPEED_OPTIMIZERS, \
ADAM_OPTIMIZER, ADAMW_OPTIMIZER, LAMB_OPTIMIZER, ONEBIT_ADAM_OPTIMIZER, \
TORCH_ADAM_PARAM
TORCH_ADAM_PARAM, ADAM_W_MODE, ADAM_W_MODE_DEFAULT

from deepspeed.runtime.dataloader import DeepSpeedDataLoader
from deepspeed.runtime.constants import \
Expand Down Expand Up @@ -640,26 +640,30 @@ def _configure_basic_optimizer(self, model_parameters):

if self.optimizer_name() in [ADAM_OPTIMIZER, ADAMW_OPTIMIZER]:
torch_adam = optimizer_parameters.pop(TORCH_ADAM_PARAM, False)
adam_w_mode = self.optimizer_name() == ADAMW_OPTIMIZER
# zero-offload torch-adam adam_w_mode optimizer
# T|F T T torch.optim.AdamW
# T|F T F torch.optim.Adam
# T F T|F DeepSpeedCPUAdam(adam_w_mode)
# F F T|F FusedAdam(adam_w_mode)
adam_w_mode = optimizer_parameters.pop(ADAM_W_MODE, ADAM_W_MODE_DEFAULT)

# Optimizer name of Adam forces AdamW logic unless adam_w_mode is explictly set
effective_adam_w_mode = self.optimizer_name(
) == ADAMW_OPTIMIZER or adam_w_mode

if torch_adam:
if adam_w_mode:
optimizer = torch.optim.AdamW(model_parameters,
**optimizer_parameters)
else:
if not effective_adam_w_mode:
optimizer = torch.optim.Adam(model_parameters,
**optimizer_parameters)
elif self.zero_cpu_offload():
optimizer = DeepSpeedCPUAdam(model_parameters,
**optimizer_parameters,
adamw_mode=adam_w_mode)
else:
optimizer = torch.optim.AdamW(model_parameters,
**optimizer_parameters)
else:
optimizer_parameters['adam_w_mode'] = adam_w_mode
optimizer = FusedAdam(model_parameters, **optimizer_parameters)
if self.zero_cpu_offload():
from deepspeed.ops.adam import DeepSpeedCPUAdam
optimizer = DeepSpeedCPUAdam(model_parameters,
**optimizer_parameters,
adamw_mode=effective_adam_w_mode)
else:
from deepspeed.ops.adam import FusedAdam
optimizer = FusedAdam(model_parameters,
**optimizer_parameters,
adam_w_mode=effective_adam_w_mode)

elif self.optimizer_name() == LAMB_OPTIMIZER:
from deepspeed.ops.lamb import FusedLamb
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73 changes: 73 additions & 0 deletions tests/unit/test_adamw.py
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@@ -0,0 +1,73 @@
import deepspeed
import torch
import pytest

from common import distributed_test
from deepspeed.ops.adam import FusedAdam
from deepspeed.ops.adam import DeepSpeedCPUAdam
from simple_model import SimpleModel, args_from_dict

# yapf: disable
#'optimizer, zero_offload, torch_adam, adam_w_mode, resulting_optimizer
adam_configs = [["AdamW", False, False, False, (FusedAdam, True)],
["AdamW", False, True, False, (torch.optim.AdamW, None)],
["AdamW", True, False, False, (DeepSpeedCPUAdam, True)],
["AdamW", True, True, False, (torch.optim.AdamW, None)],
["AdamW", False, False, True, (FusedAdam, True)],
["AdamW", False, True, True, (torch.optim.AdamW, None)],
["AdamW", True, False, True, (DeepSpeedCPUAdam, True)],
["AdamW", True, True, True, (torch.optim.AdamW, None)],
["Adam", False, False, False, (FusedAdam, False)],
["Adam", False, True, False, (torch.optim.Adam, None)],
["Adam", True, False, False, (DeepSpeedCPUAdam, False)],
["Adam", True, True, False, (torch.optim.Adam, None)],
["Adam", False, False, True, (FusedAdam, True)],
["Adam", False, True, True, (torch.optim.AdamW, None)],
["Adam", True, False, True, (DeepSpeedCPUAdam, True)],
["Adam", True, True, True, (torch.optim.AdamW, None)]]

@pytest.mark.parametrize(
'optimizer, zero_offload, torch_adam, adam_w_mode, resulting_optimizer',
adam_configs)
def test_adam_configs(tmpdir,
optimizer,
zero_offload,
torch_adam,
adam_w_mode,
resulting_optimizer):
config_dict = {
"train_batch_size": 2,
"steps_per_print": 1,
"optimizer": {
"type": optimizer,
"params": {
"lr": 0.00015,
"torch_adam": torch_adam,
"adam_w_mode": adam_w_mode
}
},
"gradient_clipping": 1.0,
"fp16": {
"enabled": True
},
"zero_optimization": {
"stage": 2,
"cpu_offload": zero_offload
}
}
args = args_from_dict(tmpdir, config_dict)

@distributed_test(world_size=[1])
def helper(args):
model = SimpleModel(10)
model, _, _, _ = deepspeed.initialize(args=args,
model=model,
model_parameters=model.parameters())
# get base optimizer under zero
ds_optimizer = model.optimizer.optimizer
opt_class, adam_w_mode = resulting_optimizer
assert isinstance(ds_optimizer, opt_class)
if adam_w_mode in [True, False]:
assert ds_optimizer.adam_w_mode == adam_w_mode

helper(args)