# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""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
import mindspore.common.dtype as mstype
reduce_opt = C.MultitypeFuncGraph("reduce_opt")
_all_reduce = AllReduce()
def _init_optimizer_allreduce():
global _all_reduce
_all_reduce = AllReduce(ReduceOp.SUM, GlobalComm.WORLD_COMM_GROUP)
_all_reduce.add_prim_attr('fusion', 1)
@reduce_opt.register("Function", "Number", "Bool", "Tensor")
def _tensors_allreduce_mean(mul, degree, allreduce_filter, grad):
"""
Apply mean and allreduce on gradient. Allreduce is a communication operation used for distributed deep learning.
Args:
mul (Primitive): Div operation.
degree (int): The mean coefficient.
allreduce_filter (bool): When it is true, allreduce would apply.
grad (Tensor): The gradient tensor before operation.
Returns:
Tensor, the gradient tensor after operation.
"""
if allreduce_filter:
degree = F.scalar_cast(degree, F.dtype(grad))
grad = _all_reduce(grad)
cast_op = P.Cast()
return mul(grad, cast_op(F.scalar_to_array(1.0/degree), F.dtype(grad)))
return grad
@reduce_opt.register("Bool", "Tensor")
def _tensors_allreduce(allreduce_filter, grad):
"""
Apply allreduce on gradient.
Args:
allreduce_filter (bool): When it is true, allreduce would apply.
grad (Tensor): The gradient tensor before operation.
Returns:
Tensor, the gradient tensor after operation.
"""
if allreduce_filter:
return _all_reduce(grad)
return grad
_get_datatype = C.MultitypeFuncGraph("_get_datatype")
@_get_datatype.register("Tensor")
def _tensors_get_datatype(grad):
"""
Acquire gradient datatype.
Args:
grad (Tensor): The gradient tensor before operation.
Returns:
mstype, the datatype of gradient.
"""
return F.dtype(grad)
_cast_datatype = C.MultitypeFuncGraph("_cast_datatype")
@_cast_datatype.register("TypeType", "Tensor")
def _tensors_cast_datatype(datatype, grad):
"""
Cast gradient to datatype.
Args:
datatype (mstype): the destination datatype of gradient.
grad (Tensor): The gradient tensor before operation.
Returns:
Tensor, the gradient tensor after operation.
"""
return F.cast(grad, datatype)
[docs]class DistributedGradReducer(Cell):
"""
A distributed optimizer.
Constructs a gradient reducer Cell, which applies communication and average operations on
single-process gradient 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.
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 ParallelMode, ParameterTuple
>>>
>>> device_id = int(os.environ["DEVICE_ID"])
>>> context.set_context(mode=context.GRAPH_MODE, device_target="Davinci", save_graphs=True,
>>> device_id=int(devie_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('grad', 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("mirror_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, mean=True, degree=None):
super(DistributedGradReducer, self).__init__(auto_prefix=False)
self.hyper_map = C.HyperMap()
self.mul = P.Mul()
if degree is None:
self.degree = get_group_size()
else:
if not isinstance(degree, int) or degree <= 0:
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()
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), self.allreduce_filter, 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