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!5782 change allreduce fusion function

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Merge pull request !5782 from wangmin0104/master
pull/5782/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent 28c42d5534 b035890128
commit 4e7346509f
7 changed files with 148 additions and 247 deletions
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2
model_zoo/official/cv/resnet_thor/README.md
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@ -217,7 +217,7 @@ Inference result will be stored in the example path, whose folder name is "eval"
```
Inference result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.
```
result: {'top_5_accuracy': 0.9286771766965429, 'top_1_accuracy': 0.7613036171574904} ckpt=train_parallel/resnet-36_5004.ckpt
result: {'top_5_accuracy': 0.9287972151088348, 'top_1_accuracy': 0.7597031049935979} ckpt=train_parallel/resnet-36_5004.ckpt
```
## Model Description

173
model_zoo/official/cv/resnet_thor/src/grad_reducer_thor.py
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@ -12,149 +12,109 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""grad_reducer_thor"""
import mindspore.common.dtype as mstype
from mindspore.communication.management import GlobalComm, get_group_size
"""grad reducer cell for distributed training"""
from mindspore.nn.cell import Cell
from mindspore.communication.management import GlobalComm, get_group_size
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.ops.operations.comm_ops import AllReduce, ReduceOp
from mindspore.ops.operations.comm_ops import AllReduce
import mindspore.common.dtype as mstype
reduce_opt = C.MultitypeFuncGraph("reduce_opt")
_all_reduce_A = AllReduce()
def _init_allreduce_operators(length, split_indices):
""" initialize allreduce communication operators"""
indices = split_indices[0]
fusion = split_indices[1]
op_list = ()
j = 0
for i in range(length):
if j <= len(indices)-1:
temp = indices[j]
else:
temp = length
if i >= temp:
j = j + 1
fusion = fusion + 1
op = AllReduce('sum', GlobalComm.WORLD_COMM_GROUP)
op.add_prim_attr('fusion', fusion)
op_list = op_list + (op,)
return op_list
@reduce_opt.register("Function", "Number", "Function", "Tensor")
def _tensors_allreduce_mean(mul, degree, allreduce, parameters):
"""
Apply allreduce on parameters.
def _init_optimizer_allreduce(group):
global _all_reduce_A
_all_reduce_A = AllReduce(ReduceOp.SUM, GlobalComm.WORLD_COMM_GROUP)
_all_reduce_A.add_prim_attr('fusion', group)
Args:
mul(Primitive): The mul operator for parameters.
degree (int): The mean coefficient.
allreduce (Primitive): The communication operator for parameters.
parameters (Tensor): The parameters before operation.
@reduce_opt.register("Function", "Number", "Tensor")
def _tensors_allreduce_mean(mul, degree, grad):
degree = F.scalar_cast(degree, F.dtype(grad))
grad = _all_reduce_A(grad)
Returns:
Tensor, the parameters after operation.
"""
degree = F.scalar_cast(degree, F.dtype(parameters))
parameters = allreduce(parameters)
cast_op = P.Cast()
return mul(grad, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(grad)))
@reduce_opt.register("Bool", "Tensor")
def _tensors_allreduce(allreduce_filter, grad):
if allreduce_filter:
return _all_reduce_A(grad)
return grad
return mul(parameters, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(parameters)))
_get_datatype = C.MultitypeFuncGraph("_get_datatype")
@_get_datatype.register("Tensor")
def _tensors_get_datatype(grad):
def _tensors_get_datatype(parameters):
"""
Acquire gradient datatype.
Acquire parameters datatype.
Args:
grad (Tensor): The gradient tensor before operation.
parameters (Tensor): The parameters before operation.
Returns:
mstype, the datatype of gradient.
mstype, the datatype of parameters.
"""
return F.dtype(grad)
return F.dtype(parameters)
_cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@_cast_datatype.register("TypeType", "Tensor")
def _tensors_cast_datatype(datatype, grad):
def _tensors_cast_datatype(datatype, parameters):
"""
Cast gradient to datatype.
Cast parameters to datatype.
Args:
datatype (mstype): the destination datatype of gradient.
grad (Tensor): The gradient tensor before operation.
datatype (mstype): the destination datatype of parameters.
parameters (Tensor): The parameters before operation.
Returns:
Tensor, the gradient tensor after operation.
Tensor, the parameters after operation.
"""
return F.cast(grad, datatype)
return F.cast(parameters, datatype)
class DistributedGradReducerThor(Cell):
"""
A distributed optimizer.
Constructs a gradient reducer Cell, which applies communication and average operations on
single-process gradient values.
Constructs a parameters reducer Cell, which applies communication and average operations on
single-process parameters values.
Args:
parameters (list): the parameters to be updated.
mean (bool): When mean is true, the mean coefficient (degree) would apply on gradients. Default: False.
parameter_length (int): length of the parameters to be updated.
split_indices(tuple): parameter split indices.
mean (bool): When mean is true, the mean coefficient (degree) would apply on parameters. Default: False.
degree (int): The mean coefficient. Usually it equals to device number. Default: None.
Raises:
ValueError: If degree is not a int or less than 0.
Examples:
>>> from mindspore.communication import init, get_group_size
>>> from mindspore.ops import composite as C
>>> from mindspore.ops import operations as P
>>> from mindspore.ops import functional as F
>>> from mindspore import context
>>> from mindspore import nn
>>> from mindspore import ParameterTuple
>>> from mindspore.context import ParallelMode
>>>
>>> device_id = int(os.environ["DEVICE_ID"])
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
>>> device_id=int(device_id), enable_hccl=True)
>>> init()
>>> context.reset_auto_parallel_context()
>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
>>>
>>>
>>> class TrainingWrapper(nn.Cell):
>>> def __init__(self, network, optimizer, sens=1.0):
>>> super(TrainingWrapper, self).__init__(auto_prefix=False)
>>> self.network = network
>>> self.network.add_flags(defer_inline=True)
>>> self.weights = ParameterTuple(network.trainable_params())
>>> self.optimizer = optimizer
>>> self.grad = C.GradOperation(get_by_list=True, sens_param=True)
>>> self.sens = sens
>>> self.reducer_flag = False
>>> self.grad_reducer = None
>>> self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
>>> if self.parallel_mode in [ParallelMode.DATA_PARALLEL,
>>> ParallelMode.HYBRID_PARALLEL]:
>>> self.reducer_flag = True
>>> if self.reducer_flag:
>>> mean = context.get_auto_parallel_context("gradients_mean")
>>> if mean.get_device_num_is_set():
>>> degree = context.get_auto_parallel_context("device_num")
>>> else:
>>> degree = get_group_size()
>>> self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
>>>
>>> def construct(self, *args):
>>> weights = self.weights
>>> loss = self.network(*args)
>>> sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
>>> grads = self.grad(self.network, weights)(*args, sens)
>>> if self.reducer_flag:
>>> # apply grad reducer on grads
>>> grads = self.grad_reducer(grads)
>>> return F.depend(loss, self.optimizer(grads))
>>>
>>> network = Net()
>>> optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> train_cell = TrainingWrapper(network, optimizer)
>>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
>>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
>>> grads = train_cell(inputs, label)
"""
def __init__(self, parameters, group, mean=True, degree=None):
def __init__(self, parameter_length, split_indices, mean=True, degree=None):
super(DistributedGradReducerThor, self).__init__(auto_prefix=False)
self.hyper_map = C.HyperMap()
self.mul = P.Mul()
@ -165,16 +125,11 @@ class DistributedGradReducerThor(Cell):
raise ValueError("Parameter 'degree' in DistributedGradReducer should large than 0 and be int")
self.degree = degree
self.mean = mean
self.allreduce_filter = tuple(x.layerwise_parallel is False for x in parameters)
_init_optimizer_allreduce(group)
def construct(self, grads):
# In some circumstances, the data precision of grads could be mixed with float16 and float32. Thus, the
# result of AllReduce is unreliable. To solve the problem, grads should be cast to float32 before AllReduce,
# and cast back after the operation.
datatypes = self.hyper_map(F.partial(_get_datatype), grads)
grads = self.hyper_map(F.partial(_cast_datatype, mstype.float32), grads)
new_grad = self.hyper_map(F.partial(reduce_opt, self.mul, self.degree), grads)
new_grad = self.hyper_map(F.partial(_cast_datatype), datatypes, new_grad)
return new_grad
self.op_list = _init_allreduce_operators(parameter_length, split_indices)
def construct(self, parameters):
datatypes = self.hyper_map(F.partial(_get_datatype), parameters)
parameters = self.hyper_map(F.partial(_cast_datatype, mstype.float32), parameters)
new_parameters = self.hyper_map(F.partial(reduce_opt, self.mul, self.degree), self.op_list, parameters)
new_parameters = self.hyper_map(F.partial(_cast_datatype), datatypes, new_parameters)
return new_parameters

21
model_zoo/official/cv/resnet_thor/src/thor.py
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@ -22,7 +22,7 @@ import mindspore.common.dtype as mstype
from mindspore._checkparam import check_bool
from mindspore._checkparam import Validator as validator
from mindspore.nn.optim.optimizer import Optimizer
from mindspore.parallel._utils import _get_device_num, _get_gradients_mean
from mindspore.parallel._utils import _get_device_num, _get_mirror_mean
from src.grad_reducer_thor import DistributedGradReducerThor
_momentum_opt = C.MultitypeFuncGraph("momentum_opt")
@ -85,10 +85,12 @@ class THOR_GPU(Optimizer):
self.assign = P.Assign()
self.mul = P.Mul()
mean = _get_gradients_mean()
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer_thorA = DistributedGradReducerThor(self.parameters, 0, mean, degree)
self.grad_reducer_thorG = DistributedGradReducerThor(self.parameters, 0, mean, degree)
parameter_length = len(self.feature_map)
self.grad_reducer_thorA = DistributedGradReducerThor(parameter_length, ((parameter_length,), 0), mean, degree)
self.grad_reducer_thorG = DistributedGradReducerThor(parameter_length, ((parameter_length,), 0), mean, degree)
self.weight_decay = weight_decay
self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
self.update_gradient = P.UpdateThorGradient(split_dim=128)
@ -191,12 +193,13 @@ class THOR(Optimizer):
1.0 / 196, 1.0 / 196, 1.0 / 196,
1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49, 1.0 / 49,
1.0]
mean = _get_gradients_mean()
mean = _get_mirror_mean()
degree = _get_device_num()
self.grad_reducer_Amax = DistributedGradReducerThor(self.parameters, 2, mean, degree)
self.grad_reducer_Gmax = DistributedGradReducerThor(self.parameters, 5, mean, degree)
self.grad_reducer_A = DistributedGradReducerThor(self.parameters, 3, mean, degree)
self.grad_reducer_G = DistributedGradReducerThor(self.parameters, 4, mean, degree)
parameter_length = len(self.feature_map)
self.grad_reducer_Amax = DistributedGradReducerThor(parameter_length, ((27,), 2), mean, degree)
self.grad_reducer_Gmax = DistributedGradReducerThor(parameter_length, ((27,), 4), mean, degree)
self.grad_reducer_A = DistributedGradReducerThor(parameter_length, ((27,), 6), mean, degree)
self.grad_reducer_G = DistributedGradReducerThor(parameter_length, ((27,), 8), mean, degree)
self.matrix_A_inv = ()
self.matrix_G_inv = ()
self.matrix_max_inv = ()

6
model_zoo/official/cv/resnet_thor/train.py
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@ -95,11 +95,7 @@ if __name__ == '__main__':
context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([107], "hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum2")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum3")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum4")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum5")
auto_parallel_context().set_all_reduce_fusion_split_indices([107])
init()
# GPU target
else:

178
tests/st/networks/models/resnet50/src_thor/grad_reducer_thor.py
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@ -12,150 +12,109 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""grad_reducer_thor"""
import mindspore.common.dtype as mstype
from mindspore.communication.management import GlobalComm, get_group_size
"""grad reducer cell for distributed training"""
from mindspore.nn.cell import Cell
from mindspore.communication.management import GlobalComm, get_group_size
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.ops.operations.comm_ops import AllReduce, ReduceOp
from mindspore.ops.operations.comm_ops import AllReduce
import mindspore.common.dtype as mstype
reduce_opt = C.MultitypeFuncGraph("reduce_opt")
_all_reduce_A = AllReduce()
def _init_allreduce_operators(length, split_indices):
""" initialize allreduce communication operators"""
indices = split_indices[0]
fusion = split_indices[1]
op_list = ()
j = 0
for i in range(length):
if j <= len(indices)-1:
temp = indices[j]
else:
temp = length
if i >= temp:
j = j + 1
fusion = fusion + 1
op = AllReduce('sum', GlobalComm.WORLD_COMM_GROUP)
op.add_prim_attr('fusion', fusion)
op_list = op_list + (op,)
return op_list
@reduce_opt.register("Function", "Number", "Function", "Tensor")
def _tensors_allreduce_mean(mul, degree, allreduce, parameters):
"""
Apply allreduce on parameters.
def _init_optimizer_allreduce(group):
global _all_reduce_A
_all_reduce_A = AllReduce(ReduceOp.SUM, GlobalComm.WORLD_COMM_GROUP)
_all_reduce_A.add_prim_attr('fusion', group)
Args:
mul(Primitive): The mul operator for parameters.
degree (int): The mean coefficient.
allreduce (Primitive): The communication operator for parameters.
parameters (Tensor): The parameters before operation.
@reduce_opt.register("Function", "Number", "Tensor")
def _tensors_allreduce_mean(mul, degree, grad):
degree = F.scalar_cast(degree, F.dtype(grad))
grad = _all_reduce_A(grad)
Returns:
Tensor, the parameters after operation.
"""
degree = F.scalar_cast(degree, F.dtype(parameters))
parameters = allreduce(parameters)
cast_op = P.Cast()
return mul(grad, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(grad)))
@reduce_opt.register("Bool", "Tensor")
def _tensors_allreduce(allreduce_filter, grad):
if allreduce_filter:
return _all_reduce_A(grad)
return grad
return mul(parameters, cast_op(F.scalar_to_array(1.0 / degree), F.dtype(parameters)))
_get_datatype = C.MultitypeFuncGraph("_get_datatype")
@_get_datatype.register("Tensor")
def _tensors_get_datatype(grad):
def _tensors_get_datatype(parameters):
"""
Acquire gradient datatype.
Acquire parameters datatype.
Args:
grad (Tensor): The gradient tensor before operation.
parameters (Tensor): The parameters before operation.
Returns:
mstype, the datatype of gradient.
mstype, the datatype of parameters.
"""
return F.dtype(grad)
return F.dtype(parameters)
_cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@_cast_datatype.register("TypeType", "Tensor")
def _tensors_cast_datatype(datatype, grad):
def _tensors_cast_datatype(datatype, parameters):
"""
Cast gradient to datatype.
Cast parameters to datatype.
Args:
datatype (mstype): the destination datatype of gradient.
grad (Tensor): The gradient tensor before operation.
datatype (mstype): the destination datatype of parameters.
parameters (Tensor): The parameters before operation.
Returns:
Tensor, the gradient tensor after operation.
Tensor, the parameters after operation.
"""
return F.cast(grad, datatype)
return F.cast(parameters, datatype)
class DistributedGradReducerThor(Cell):
"""
A distributed optimizer.
Constructs a gradient reducer Cell, which applies communication and average operations on
single-process gradient values.
Constructs a parameters reducer Cell, which applies communication and average operations on
single-process parameters values.
Args:
parameters (list): the parameters to be updated.
group (int): the different group to allreduce.
mean (bool): When mean is true, the mean coefficient (degree) would apply on gradients. Default: False.
parameter_length (int): length of the parameters to be updated.
split_indices(tuple): parameter split indices.
mean (bool): When mean is true, the mean coefficient (degree) would apply on parameters. Default: False.
degree (int): The mean coefficient. Usually it equals to device number. Default: None.
Raises:
ValueError: If degree is not a int or less than 0.
Examples:
>>> from mindspore.communication import init, get_group_size
>>> from mindspore.ops import composite as C
>>> from mindspore.ops import operations as P
>>> from mindspore.ops import functional as F
>>> from mindspore import context
>>> from mindspore import nn
>>> from mindspore import ParameterTuple
>>> from mindspore.context import ParallelMode
>>>
>>> device_id = int(os.environ["DEVICE_ID"])
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
>>> device_id=int(device_id), enable_hccl=True)
>>> init()
>>> context.reset_auto_parallel_context()
>>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)
>>>
>>>
>>> class TrainingWrapper(nn.Cell):
>>> def __init__(self, network, optimizer, sens=1.0):
>>> super(TrainingWrapper, self).__init__(auto_prefix=False)
>>> self.network = network
>>> self.network.add_flags(defer_inline=True)
>>> self.weights = ParameterTuple(network.trainable_params())
>>> self.optimizer = optimizer
>>> self.grad = C.GradOperation(get_by_list=True, sens_param=True)
>>> self.sens = sens
>>> self.reducer_flag = False
>>> self.grad_reducer = None
>>> self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
>>> if self.parallel_mode in [ParallelMode.DATA_PARALLEL,
>>> ParallelMode.HYBRID_PARALLEL]:
>>> self.reducer_flag = True
>>> if self.reducer_flag:
>>> mean = context.get_auto_parallel_context("gradients_mean")
>>> if mean.get_device_num_is_set():
>>> degree = context.get_auto_parallel_context("device_num")
>>> else:
>>> degree = get_group_size()
>>> self.grad_reducer = nn.DistributedGradReducer(optimizer.parameters, mean, degree)
>>>
>>> def construct(self, *args):
>>> weights = self.weights
>>> loss = self.network(*args)
>>> sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
>>> grads = self.grad(self.network, weights)(*args, sens)
>>> if self.reducer_flag:
>>> # apply grad reducer on grads
>>> grads = self.grad_reducer(grads)
>>> return F.depend(loss, self.optimizer(grads))
>>>
>>> network = Net()
>>> optimizer = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> train_cell = TrainingWrapper(network, optimizer)
>>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
>>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
>>> grads = train_cell(inputs, label)
"""
def __init__(self, parameters, group, mean=True, degree=None):
def __init__(self, parameter_length, split_indices, mean=True, degree=None):
super(DistributedGradReducerThor, self).__init__(auto_prefix=False)
self.hyper_map = C.HyperMap()
self.mul = P.Mul()
@ -166,20 +125,11 @@ class DistributedGradReducerThor(Cell):
raise ValueError("Parameter 'degree' in DistributedGradReducer should large than 0 and be int")
self.degree = degree
self.mean = mean
self.allreduce_filter = tuple(x.layerwise_parallel is False for x in parameters)
_init_optimizer_allreduce(group)
def construct(self, grads):
# In some circumstances, the data precision of grads could be mixed with float16 and float32. Thus, the
# result of AllReduce is unreliable. To solve the problem, grads should be cast to float32 before AllReduce,
# and cast back after the operation.
datatypes = self.hyper_map(F.partial(_get_datatype), grads)
grads = self.hyper_map(F.partial(_cast_datatype, mstype.float32), grads)
if self.mean:
new_grad = self.hyper_map(F.partial(reduce_opt, self.mul, self.degree), grads)
else:
new_grad = self.hyper_map(F.partial(reduce_opt), self.allreduce_filter, grads)
new_grad = self.hyper_map(F.partial(_cast_datatype), datatypes, new_grad)
return new_grad
self.op_list = _init_allreduce_operators(parameter_length, split_indices)
def construct(self, parameters):
datatypes = self.hyper_map(F.partial(_get_datatype), parameters)
parameters = self.hyper_map(F.partial(_cast_datatype, mstype.float32), parameters)
new_parameters = self.hyper_map(F.partial(reduce_opt, self.mul, self.degree), self.op_list, parameters)
new_parameters = self.hyper_map(F.partial(_cast_datatype), datatypes, new_parameters)
return new_parameters

9
tests/st/networks/models/resnet50/src_thor/thor.py
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@ -89,10 +89,11 @@ class THOR(Optimizer):
1.0]
mean = _get_gradients_mean()
degree = _get_device_num()
self.grad_reducer_Amax = DistributedGradReducerThor(self.parameters, 2, mean, degree)
self.grad_reducer_Gmax = DistributedGradReducerThor(self.parameters, 5, mean, degree)
self.grad_reducer_A = DistributedGradReducerThor(self.parameters, 3, mean, degree)
self.grad_reducer_G = DistributedGradReducerThor(self.parameters, 4, mean, degree)
parameter_length = len(self.feature_map)
self.grad_reducer_Amax = DistributedGradReducerThor(parameter_length, ((27,), 2), mean, degree)
self.grad_reducer_Gmax = DistributedGradReducerThor(parameter_length, ((27,), 4), mean, degree)
self.grad_reducer_A = DistributedGradReducerThor(parameter_length, ((27,), 6), mean, degree)
self.grad_reducer_G = DistributedGradReducerThor(parameter_length, ((27,), 8), mean, degree)
self.matrix_A_inv = ()
self.matrix_G_inv = ()
self.matrix_max_inv = ()

6
tests/st/networks/models/resnet50/test_resnet50_imagenet.py
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@ -241,11 +241,7 @@ def train_process_thor(q, device_id, epoch_size, device_num, enable_hccl):
if enable_hccl:
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
gradients_mean=True, parameter_broadcast=True)
auto_parallel_context().set_all_reduce_fusion_split_indices([107], "hccl_world_groupsum1")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum2")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum3")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum4")
auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum5")
auto_parallel_context().set_all_reduce_fusion_split_indices([107])
init()
# network

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