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Fix multi_tensor adam/momentum bug when the parameter is list of dict #47352

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Oct 26, 2022
Merged

Fix multi_tensor adam/momentum bug when the parameter is list of dict #47352

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12 changes: 10 additions & 2 deletions python/paddle/fluid/tests/unittests/test_adam_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1104,14 +1104,22 @@ def _adam_optimize_dygraph(
multi_precision=use_amp,
)
else:
parameters = list(model.parameters())
param_num = len(parameters)
optimizer = paddle.optimizer.Adam(
parameters=[
{
'params': model.parameters(),
'params': parameters[: int(param_num / 2)],
'weight_decay': 0.001,
'beta1': 0.1,
'beta2': 0.99,
}
},
{
'params': parameters[int(param_num / 2) :],
'weight_decay': 0.001,
'beta1': 0.1,
'beta2': 0.99,
},
],
use_multi_tensor=use_multi_tensor,
multi_precision=use_amp,
Expand Down
12 changes: 10 additions & 2 deletions python/paddle/fluid/tests/unittests/test_momentum_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -889,14 +889,22 @@ def _momentum_optimize_dygraph(
multi_precision=use_amp,
)
else:
parameters = list(model.parameters())
n = len(parameters)
optimizer = paddle.optimizer.Momentum(
parameters=[
{
'params': model.parameters(),
'params': parameters[: int(n / 2)],
'weight_decay': 0.001,
'learning_rate': 0.1,
'momentum': 0.99,
}
},
{
'params': parameters[int(n / 2) :],
'weight_decay': 0.001,
'learning_rate': 0.1,
'momentum': 0.99,
},
],
use_multi_tensor=use_multi_tensor,
multi_precision=use_amp,
Expand Down
166 changes: 102 additions & 64 deletions python/paddle/optimizer/adam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -217,21 +217,13 @@ def __init__(

self._use_multi_tensor = use_multi_tensor
if self._use_multi_tensor:
self._param_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}
self._moment1_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}
self._moment2_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}
self._beta1_pow_acc_dict = {
'FP32_LODTensor': [],
'FP16_LODTensor': [],
}
self._beta2_pow_acc_dict = {
'FP32_LODTensor': [],
'FP16_LODTensor': [],
}
self._master_weight_dict = {
'FP32_LODTensor': None,
'FP16_LODTensor': [],
}
self._param_dict = self._create_multi_tensor_dict()
self._moment1_dict = self._create_multi_tensor_dict()
self._moment2_dict = self._create_multi_tensor_dict()
self._beta1_pow_acc_dict = self._create_multi_tensor_dict()
self._beta2_pow_acc_dict = self._create_multi_tensor_dict()
self._master_weight_dict = self._create_multi_tensor_dict()
self._master_weight_dict['FP32_LODTensor'] = None

def _create_master_weight(self, param):
if param.name in self._master_weights:
Expand Down Expand Up @@ -550,11 +542,14 @@ def step(self):
params_grads.append((param, grad_var))

optimize_ops = self._apply_optimize(
loss=None, startup_program=None, params_grads=params_grads
loss=None,
startup_program=None,
params_grads=params_grads,
param_group_idx=0,
)
else:
# optimize parameters in groups
for param_group in self._param_groups:
for idx, param_group in enumerate(self._param_groups):
params_grads = defaultdict(lambda: list())
for param in param_group['params']:
if param.stop_gradient:
Expand All @@ -566,10 +561,13 @@ def step(self):
{k: v for k, v in param_group.items() if k != 'params'}
)
self._apply_optimize(
loss=None, startup_program=None, params_grads=params_grads
loss=None,
startup_program=None,
params_grads=params_grads,
param_group_idx=idx,
)

def _multi_tensor_init(self, target_block, parameters):
def _multi_tensor_init(self, target_block, parameters, param_group_idx):
"""
All parameters used for optimizer (such as: parameters, master_weight, velocity_acc for momentum) calculations are grouped into a python list by data type (float16, float32).
This function will be overridden in the corresponding optimizer file.
Expand All @@ -589,21 +587,41 @@ def _multi_tensor_init(self, target_block, parameters):
)

if param.dtype == paddle.float32:
self._param_dict['FP32_LODTensor'].append(param)
self._moment1_dict['FP32_LODTensor'].append(moment1)
self._moment2_dict['FP32_LODTensor'].append(moment2)
self._beta1_pow_acc_dict['FP32_LODTensor'].append(beta1_pow_acc)
self._beta2_pow_acc_dict['FP32_LODTensor'].append(beta2_pow_acc)
self._param_dict['FP32_LODTensor'][param_group_idx].append(
param
)
self._moment1_dict['FP32_LODTensor'][param_group_idx].append(
moment1
)
self._moment2_dict['FP32_LODTensor'][param_group_idx].append(
moment2
)
self._beta1_pow_acc_dict['FP32_LODTensor'][
param_group_idx
].append(beta1_pow_acc)
self._beta2_pow_acc_dict['FP32_LODTensor'][
param_group_idx
].append(beta2_pow_acc)
elif param.dtype == paddle.float16:
self._param_dict['FP16_LODTensor'].append(param)
self._moment1_dict['FP16_LODTensor'].append(moment1)
self._moment2_dict['FP16_LODTensor'].append(moment2)
self._beta1_pow_acc_dict['FP16_LODTensor'].append(beta1_pow_acc)
self._beta2_pow_acc_dict['FP16_LODTensor'].append(beta2_pow_acc)
self._param_dict['FP16_LODTensor'][param_group_idx].append(
param
)
self._moment1_dict['FP16_LODTensor'][param_group_idx].append(
moment1
)
self._moment2_dict['FP16_LODTensor'][param_group_idx].append(
moment2
)
self._beta1_pow_acc_dict['FP16_LODTensor'][
param_group_idx
].append(beta1_pow_acc)
self._beta2_pow_acc_dict['FP16_LODTensor'][
param_group_idx
].append(beta2_pow_acc)
if self._multi_precision:
self._master_weight_dict['FP16_LODTensor'].append(
self._master_weights[param.name]
)
self._master_weight_dict['FP16_LODTensor'][
param_group_idx
].append(self._master_weights[param.name])
else:
self._master_weight_dict['FP16_LODTensor'] = None
else:
Expand All @@ -612,7 +630,10 @@ def _multi_tensor_init(self, target_block, parameters):
)

def _append_optimize_multi_tensor_op(
self, target_block, parameters_and_grads
self,
target_block,
parameters_and_grads,
param_group_idx,
):
"""
For Multi Tensor, append optimize merged_operator to block.
Expand Down Expand Up @@ -677,7 +698,7 @@ def _append_optimize_multi_tensor_op(

multi_tensor_list = ['FP32_LODTensor', 'FP16_LODTensor']
for key in multi_tensor_list:
if len(self._param_dict[key]) > 0:
if len(self._param_dict[key][param_group_idx]) > 0:
find_master = self._multi_precision and key == 'FP16_LODTensor'

_beta1 = (
Expand All @@ -692,16 +713,23 @@ def _append_optimize_multi_tensor_op(
)

if framework._non_static_mode():
master_weight = self._master_weight_dict[key]
master_weight = (
master_weight[param_group_idx]
if master_weight is not None
else None
)
if in_dygraph_mode():

_, _, _, _, _, _ = _C_ops.merged_adam_(
self._param_dict[key],
self._param_dict[key][param_group_idx],
grad_dict[key],
lr_dict[key],
self._moment1_dict[key],
self._moment2_dict[key],
self._beta1_pow_acc_dict[key],
self._beta2_pow_acc_dict[key],
self._master_weight_dict[key],
self._moment1_dict[key][param_group_idx],
self._moment2_dict[key][param_group_idx],
self._beta1_pow_acc_dict[key][param_group_idx],
self._beta2_pow_acc_dict[key][param_group_idx],
master_weight,
_beta1,
_beta2,
self._epsilon,
Expand All @@ -710,20 +738,20 @@ def _append_optimize_multi_tensor_op(
)
else:
_, _, _, _, _, _ = _legacy_C_ops.merged_adam(
self._param_dict[key],
self._param_dict[key][param_group_idx],
grad_dict[key],
lr_dict[key],
self._moment1_dict[key],
self._moment2_dict[key],
self._beta1_pow_acc_dict[key],
self._beta2_pow_acc_dict[key],
self._master_weight_dict[key],
self._param_dict[key],
self._moment1_dict[key],
self._moment2_dict[key],
self._beta1_pow_acc_dict[key],
self._beta2_pow_acc_dict[key],
self._master_weight_dict[key],
self._moment1_dict[key][param_group_idx],
self._moment2_dict[key][param_group_idx],
self._beta1_pow_acc_dict[key][param_group_idx],
self._beta2_pow_acc_dict[key][param_group_idx],
master_weight,
self._param_dict[key][param_group_idx],
self._moment1_dict[key][param_group_idx],
self._moment2_dict[key][param_group_idx],
self._beta1_pow_acc_dict[key][param_group_idx],
self._beta2_pow_acc_dict[key][param_group_idx],
master_weight,
'epsilon',
self._epsilon,
'beta1',
Expand All @@ -735,31 +763,41 @@ def _append_optimize_multi_tensor_op(
)
else:
inputs = {
"Param": self._param_dict[key],
"Param": self._param_dict[key][param_group_idx],
"Grad": grad_dict[key],
"LearningRate": lr_dict[key],
"Moment1": self._moment1_dict[key],
"Moment2": self._moment2_dict[key],
"Beta1Pow": self._beta1_pow_acc_dict[key],
"Beta2Pow": self._beta2_pow_acc_dict[key],
"Moment1": self._moment1_dict[key][param_group_idx],
"Moment2": self._moment2_dict[key][param_group_idx],
"Beta1Pow": self._beta1_pow_acc_dict[key][
param_group_idx
],
"Beta2Pow": self._beta2_pow_acc_dict[key][
param_group_idx
],
}
outputs = {
"ParamOut": self._param_dict[key],
"Moment1Out": self._moment1_dict[key],
"Moment2Out": self._moment2_dict[key],
"Beta1PowOut": self._beta1_pow_acc_dict[key],
"Beta2PowOut": self._beta2_pow_acc_dict[key],
"ParamOut": self._param_dict[key][param_group_idx],
"Moment1Out": self._moment1_dict[key][param_group_idx],
"Moment2Out": self._moment2_dict[key][param_group_idx],
"Beta1PowOut": self._beta1_pow_acc_dict[key][
param_group_idx
],
"Beta2PowOut": self._beta2_pow_acc_dict[key][
param_group_idx
],
}
attrs = {
"epsilon": self._epsilon,
"beta1": _beta1,
"beta2": _beta2,
}
if find_master:
inputs["MasterParam"] = self._master_weight_dict[key]
inputs["MasterParam"] = self._master_weight_dict[key][
param_group_idx
]
outputs["MasterParamOut"] = self._master_weight_dict[
key
]
][param_group_idx]
attrs["multi_precision"] = find_master
target_block.append_op(
type="merged_adam",
Expand Down
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