# Copyright 2021 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.
# ============================================================================
"""Boost Mode Cell Wrapper."""
from mindspore.nn.wrap import TrainOneStepCell
import mindspore.context as context
from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_global_rank, _get_device_num, _get_gradients_mean
from mindspore.communication.management import get_group_size, create_group
from mindspore.nn.cell import Cell
from mindspore.common import Tensor, RowTensor
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
from mindspore.ops import functional as F
from mindspore.ops import composite as C
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from .boost import AutoBoost
from .grad_freeze import FreezeOpt, freeze_cell
from .adasum import AdaSum
from .dim_reduce import DimReduce
from .grad_accumulation import gradient_accumulation_op, gradient_clear_op
from .base import _load_local_pca_mat
__all__ = ["BoostTrainOneStepCell", "BoostTrainOneStepWithLossScaleCell"]
_get_delta_weight = C.MultitypeFuncGraph("_get_delta_weight")
@_get_delta_weight.register("Tensor", "Tensor")
def _get_delta_weight_process(new_parameter, old_parameter):
delta_w = old_parameter - new_parameter
return delta_w
_save_weight = C.MultitypeFuncGraph("_save_weight")
@_save_weight.register("Tensor", "Tensor")
def _save_weight_process(new_parameter, old_parameter):
return P.Assign()(new_parameter, old_parameter)
_grad_scale = C.MultitypeFuncGraph("grad_scale")
reciprocal = P.Reciprocal()
@_grad_scale.register("Tensor", "Tensor")
def tensor_grad_scale(scale, grad):
return grad * F.cast(reciprocal(scale), F.dtype(grad))
@_grad_scale.register("Tensor", "RowTensor")
def tensor_grad_scale_row_tensor(scale, grad):
return RowTensor(grad.indices,
grad.values * F.cast(reciprocal(scale), F.dtype(grad.values)),
grad.dense_shape)
_grad_overflow = C.MultitypeFuncGraph("_grad_overflow")
grad_overflow = P.FloatStatus()
@_grad_overflow.register("Tensor")
def _tensor_grad_overflow(grad):
return grad_overflow(grad)
@_grad_overflow.register("RowTensor")
def _tensor_grad_overflow_row_tensor(grad):
return grad_overflow(grad.values)
[docs]class BoostTrainOneStepCell(TrainOneStepCell):
r"""
Boost Network training package class.
Wraps the network with an optimizer. The resulting Cell is trained with input '\*inputs'.
The backward graph will be created in the construct function to update the parameter. Different
parallel modes are available for training.
Args:
network (Cell): The training network. The network only supports single output.
optimizer (Union[Cell]): Optimizer for updating the weights.
sens (numbers.Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
Inputs:
- **(\*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
Outputs:
Tensor, a tensor means the loss value, the shape of which is usually :math:`()`.
Raises:
TypeError: If `sens` is not a number.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import boost
>>> net = Net()
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
>>> optim = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> #1) Using the WithLossCell existing provide
>>> loss_net = nn.WithLossCell(net, loss_fn)
>>> train_net = boost.BoostTrainOneStepCell(loss_net, optim)
>>>
>>> #2) Using user-defined WithLossCell
>>> class MyWithLossCell(Cell):
... def __init__(self, backbone, loss_fn):
... super(MyWithLossCell, self).__init__(auto_prefix=False)
... self._backbone = backbone
... self._loss_fn = loss_fn
...
... def construct(self, x, y, label):
... out = self._backbone(x, y)
... return self._loss_fn(out, label)
...
... @property
... def backbone_network(self):
... return self._backbone
...
>>> loss_net = MyWithLossCell(net, loss_fn)
>>> train_net = boost.BoostTrainOneStepCell(loss_net, optim)
"""
def __init__(self, network, optimizer, sens=1.0):
super(BoostTrainOneStepCell, self).__init__(network, optimizer, sens)
self.hyper_map = C.HyperMap()
self.freeze = isinstance(optimizer, FreezeOpt)
if not self.freeze:
self.weights = self.optimizer.parameters
self.train_strategy = getattr(self.optimizer, 'train_strategy', None)
auto_boost = AutoBoost()
self.use_grad_accumulation = self.parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.STAND_ALONE)
self.use_grad_accumulation = self.use_grad_accumulation & auto_boost.boost_config["grad_accumulation"]
self.max_accumulation_step = 1
if self.use_grad_accumulation:
self.max_accumulation_step = auto_boost.grad_accumulation_step
if self.max_accumulation_step <= 1:
self.max_accumulation_step = 1
self.use_grad_accumulation = False
self.accumulation_step = Parameter(Tensor(0, dtype=mstype.int32), name="accumulation_step")
if self.use_grad_accumulation:
self.grad_accumulation = self.weights.clone(prefix="grad_accumulation", init='zeros')
self.enable_dim_reduce = self.check_dim_reduce_enable()
if self.enable_dim_reduce:
local_pca_mat_path = auto_boost.local_pca_mat_path
rho = auto_boost.rho
gamma = auto_boost.gamma
alpha = auto_boost.alpha
sigma = auto_boost.sigma
_rank = _get_global_rank()
_rank_size = 1 if self.parallel_mode == ParallelMode.STAND_ALONE else get_group_size()
_device_number = auto_boost.device_number
n_components = auto_boost.n_components
timeout = auto_boost.timeout
pca_mat = _load_local_pca_mat(local_pca_mat_path, timeout)
self.weights_clone = ParameterTuple(self.weights).clone(prefix="weights_clone", init="same")
self.dim_reduce = DimReduce(self.network, self.optimizer, self.weights, pca_mat, n_components, rho, gamma,
alpha, sigma, _rank, _rank_size)
self.freeze_nets = None
self.step = Parameter(Tensor(0, dtype=mstype.int32))
if self.freeze:
if self.reducer_flag:
self.mean = _get_gradients_mean()
self.degree = _get_device_num()
else:
self.mean = None
self.degree = None
self.freeze_nets = freeze_cell(self.reducer_flag, self.network, self.optimizer, self.sens,
self.grad, self.use_grad_accumulation, self.mean, self.degree,
self.max_accumulation_step)
self.enable_adasum = self.check_adasum_enable()
self.sync_tensor = Parameter(Tensor(0, dtype=mstype.int32))
if self.enable_adasum:
_rank = _get_global_rank()
_rank_size = get_group_size()
_device_number = auto_boost.device_number
self.device_number = _device_number
group_number = _rank_size // _device_number
self.server_rank = _rank % _device_number
parameter_rank_number = len(self.weights) // _device_number
self.start = [x * parameter_rank_number for x in range(_device_number)]
self.end = [(x + 1) * parameter_rank_number for x in range(_device_number)]
self.end[-1] = len(self.weights)
current_weights = self.weights[self.start[self.server_rank]: self.end[self.server_rank]]
self.grad_clone = ParameterTuple(current_weights).clone(prefix="delta_weight")
self.adasum = AdaSum(_rank, _device_number, group_number, self.grad_clone)
self.degree = int(self.degree / group_number)
group_list = [list(range(x * self.degree, (x + 1) * self.degree)) for x in range(group_number)]
current_index = _rank // _device_number
server_group_name = "allreduce_" + str(current_index)
create_group(server_group_name, group_list[current_index])
self.grad_reducer = DistributedGradReducer(self.weights, self.mean, self.degree, group=server_group_name)
def construct(self, *inputs):
if self.freeze:
loss = self.gradient_freeze_process(*inputs)
else:
loss = self.network(*inputs)
sens = F.fill(loss.dtype, loss.shape, self.sens)
grads = self.grad(self.network, self.weights)(*inputs, sens)
grads = self.grad_reducer(grads)
if self.use_grad_accumulation:
loss = self.gradient_accumulation_process(loss, grads, sens, *inputs)
else:
if self.enable_dim_reduce:
loss = F.depend(loss, self.dim_reduce(loss, grads, sens, self.weights, self.weights_clone, *inputs))
elif self.enable_adasum:
loss = F.depend(loss, self.adasum_process(loss, grads))
else:
loss = F.depend(loss, self.optimizer(grads))
return loss
[docs] def gradient_freeze_process(self, *inputs):
r"""
Gradient freeze algorithm process.
Args:
inputs (tuple(Tensor)): Tuple of input tensors with shape :math:`(N, \ldots)`.
Outputs:
- **loss** (Tensor) - Tensor with shape :math:`()`.
"""
if self.train_strategy is None:
step = self.step
max_index = len(self.freeze_nets)
else:
step = self.train_strategy[self.step]
max_index = len(self.train_strategy)
loss = self.freeze_nets[step](*inputs)
if self.step + 1 >= max_index:
self.step = 0
else:
self.step += 1
return loss
[docs] def gradient_accumulation_process(self, loss, grads, sens, *inputs):
r"""
Gradient accumulation algorithm process.
Args:
loss (Tensor): Tensor with shape :math:`()`.
grads (tuple(Tensor)): Tuple of gradient tensors.
sens (Tensor): Tensor with shape :math:`()`.
Outputs:
- **loss** (Tensor) - Tensor with shape :math:`()`.
"""
loss = F.depend(loss, self.hyper_map(F.partial(gradient_accumulation_op, self.max_accumulation_step),
self.grad_accumulation, grads))
self.accumulation_step += 1
if self.accumulation_step >= self.max_accumulation_step:
if self.enable_dim_reduce:
loss = F.depend(loss, self.dim_reduce(loss, self.grad_accumulation, sens, self.weights,
self.weights_clone, *inputs))
elif self.enable_adasum:
loss = F.depend(loss, self.adasum_process(loss, self.grad_accumulation))
else:
loss = F.depend(loss, self.optimizer(self.grad_accumulation))
self.accumulation_step = 0
if self.accumulation_step == 0:
loss = F.depend(loss, self.hyper_map(F.partial(gradient_clear_op), self.grad_accumulation))
return loss
[docs] def adasum_process(self, loss, grads):
r"""
Adasum algorithm process.
Args:
loss (Tensor): Tensor with shape :math:`()`.
grads (tuple(Tensor)): Tuple of gradient tensors.
Outputs:
- **loss** (Tensor) - Tensor with shape :math:`()`.
"""
loss = F.depend(loss, self.optimizer(grads))
rank_weights = self.weights[self.start[self.server_rank]: self.end[self.server_rank]]
grad_clone = F.depend(self.grad_clone, loss)
delta_w = self.hyper_map(F.partial(_get_delta_weight), rank_weights, grad_clone)
adasum_res = self.adasum(delta_w, rank_weights, grad_clone)
sync_tensor = F.depend(self.sync_tensor, adasum_res)
sync_flag = self.adasum.sync_barrier(sync_tensor)
for i in range(self.device_number):
weight_tuple = self.weights[self.start[i]: self.end[i]]
node_rank = F.depend(weight_tuple, sync_flag)
update_weights = self.adasum.broadcast_list[i](node_rank)
if i == self.server_rank:
self.hyper_map(F.partial(_save_weight), self.grad_clone, update_weights)
else:
self.hyper_map(F.partial(_save_weight), weight_tuple, update_weights)
return loss
[docs] def check_adasum_enable(self):
r"""
Check adasum enable.
"""
if not getattr(self.optimizer, "adasum", None) or not self.reducer_flag:
return False
_rank_size = get_group_size()
_device_number = 8
group_number = _rank_size // _device_number
is_enable = bool(group_number > 1 and group_number & (group_number - 1) == 0)
return is_enable
[docs] def check_dim_reduce_enable(self):
r"""
Check dim_reduce enable.
"""
if not getattr(self.optimizer, "dim_reduce", None):
return False
return True
[docs]class BoostTrainOneStepWithLossScaleCell(BoostTrainOneStepCell):
r"""
Boost Network training with loss scaling.
This is a training step with loss scaling. It takes a network, an optimizer and possibly a scale update
Cell as args. The loss scale value can be updated in both host side or device side. The
BoostTrainOneStepWithLossScaleCell will be compiled to be graph which takes `*inputs` as input data.
The Tensor type of `scale_sense` is acting as loss scaling value. If you want to update it on host side,
the value must be provided. If the Tensor type of `scale_sense` is not given, the loss scale update logic
must be provide by Cell type of `scale_sense`.
Args:
network (Cell): The training network. The network only supports single output.
optimizer (Cell): Optimizer for updating the weights.
scale_sense (Union[Tensor, Cell]): If this value is Cell type, the loss scaling update logic cell.If this value
is Tensor type, Tensor with shape :math:`()` or :math:`(1,)`.
Inputs:
- **(*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
Outputs:
Tuple of 3 Tensor, the loss, overflow flag and current loss scaling value.
- **loss** (Tensor) - Tensor with shape :math:`()`.
- **overflow** (Tensor) - Tensor with shape :math:`()`, type is bool.
- **loss scaling value** (Tensor) - Tensor with shape :math:`()`
Raises:
TypeError: If `scale_sense` is neither Cell nor Tensor.
ValueError: If shape of `scale_sense` is neither (1,) nor ().
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, Parameter, nn
>>> import mindspore.ops as ops
>>> from mindspore.nn import WithLossCell
>>> from mindspore import dtype as mstype
>>> from mindspore import boost
>>>
>>> class Net(nn.Cell):
... def __init__(self, in_features, out_features):
... super(Net, self).__init__()
... self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
... name='weight')
... self.matmul = ops.MatMul()
...
... def construct(self, x):
... output = self.matmul(x, self.weight)
... return output
...
>>> size, in_features, out_features = 16, 16, 10
>>> #1) when the type of scale_sense is Cell:
>>> net = Net(in_features, out_features)
>>> loss = nn.MSELoss()
>>> optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> net_with_loss = WithLossCell(net, loss)
>>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
>>> train_network = boost.BoostTrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
>>> input = Tensor(np.ones([out_features, in_features]), mstype.float32)
>>> labels = Tensor(np.ones([out_features,]), mstype.float32)
>>> output = train_network(input, labels)
>>>
>>> #2) when the type of scale_sense is Tensor:
>>> net = Net(in_features, out_features)
>>> loss = nn.MSELoss()
>>> optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
>>> net_with_loss = WithLossCell(net, loss)
>>> inputs = Tensor(np.ones([size, in_features]).astype(np.float32))
>>> label = Tensor(np.zeros([size, out_features]).astype(np.float32))
>>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mstype.float32)
>>> train_network = boost.BoostTrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=scaling_sens)
>>> output = train_network(inputs, label)
"""
def __init__(self, network, optimizer, scale_sense):
super(BoostTrainOneStepWithLossScaleCell, self).__init__(network, optimizer, sens=None)
self.base = Tensor(1, mstype.float32)
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.less_equal = P.LessEqual()
self.allreduce = P.AllReduce()
self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
self.gpu_target = (context.get_context("device_target") == "GPU")
self.loss_scaling_manager = None
if isinstance(scale_sense, Cell):
self.loss_scaling_manager = scale_sense
self.scale_sense = Parameter(Tensor(scale_sense.get_loss_scale(), dtype=mstype.float32),
name="scale_sense")
elif isinstance(scale_sense, Tensor):
if scale_sense.shape == (1,) or scale_sense.shape == ():
self.scale_sense = Parameter(scale_sense, name='scale_sense')
else:
raise ValueError("The shape of scale_sense must be (1,) or (), but got {}".format(scale_sense.shape))
else:
raise TypeError("The scale_sense must be Cell or Tensor, but got {}".format(type(scale_sense)))
def construct(self, *inputs):
weights = self.weights
loss = self.network(*inputs)
scaling_sens = self.scale_sense
status, scaling_sens = self._start_overflow_check(loss, scaling_sens)
scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss))
grads = self.grad(self.network, weights)(*inputs, scaling_sens_filled)
grads = self.hyper_map(F.partial(_grad_scale, scaling_sens), grads)
# get the overflow buffer
cond = self._get_overflow_status(status, grads)
overflow = self._process_loss_scale(cond)
# if there is no overflow, do optimize
if not overflow:
if self.use_grad_accumulation:
loss = self.gradient_accumulation_process(loss, grads, scaling_sens_filled, *inputs)
else:
if self.enable_dim_reduce:
loss = F.depend(loss, self.dim_reduce(loss, grads, scaling_sens_filled, self.weights,
self.weights_clone, *inputs))
elif self.enable_adasum:
loss = F.depend(loss, self.adasum_process(loss, grads))
else:
loss = F.depend(loss, self.optimizer(grads))
return loss, cond, scaling_sens
def _set_sense_scale(self, sens):
"""
If the user has set the sens in the training process and wants to reassign the value, he can call
this function again to make modification, and sens needs to be of type Tensor.
Inputs:
- **sens** (Tensor) - The new sense whose shape and type are the same with original `scale_sense`.
"""
if self.scale_sense and isinstance(sens, Tensor):
self.scale_sense.set_data(sens)
else:
raise TypeError("The input type must be Tensor, but got {}".format(type(sens)))
def _start_overflow_check(self, pre_cond, compute_input):
"""
Start floating-point overflow detection. Create and clear the overflow detection state.
Specify the argument 'pre_cond' and 'compute_input' to make sure overflow status is cleared at the right time.
Taking this situation as an example, we need to execute state clearing after loss calculation and then detect
overflow in the process of gradient calculation. In this case, pre_cond should be the output of the loss
function, and compute_input should be the input of gradients-computing function.
Inputs:
- **pre_cond** (Tensor) - A precondition for starting overflow detection. It determines the executing order
of overflow state clearing and prior processions. It makes sure that the function 'start_overflow'
clears status after finishing the process of precondition.
- **compute_input** (object) - The input of subsequent process. Overflow detection should be performed on a
certain computation. Set `compute_input` as the input of the computation, to ensure overflow status is
cleared before executing the computation.
Outputs:
Tuple[object, object], the first value is False for GPU backend, while it is an instance of
NPUAllocFloatStatus for other backend. The status is used to detect overflow during overflow detection.
The second value is the same as the input of `compute_input`, but contains some information about the
execution order.
"""
status = False
if not self.gpu_target:
# init overflow buffer
status = P.NPUAllocFloatStatus()()
status = F.depend(status, pre_cond)
# clear overflow buffer
clear_status = P.NPUClearFloatStatus()(status)
compute_input = F.depend(compute_input, clear_status)
return status, compute_input
def _get_overflow_status(self, status, compute_output):
"""
Get floating-point overflow status.
Get overflow results after executing the target process for overflow detection.
Inputs:
- **status** (object) - A status instance used to detect the overflow.
- **compute_output** - Overflow detection should be performed on a certain computation. Set `compute_output`
as the output of the computation, to ensure overflow status is acquired before executing the
computation.
Outputs:
bool, whether the overflow occurs or not.
"""
if not self.gpu_target:
status = F.depend(status, compute_output)
get_status = P.NPUGetFloatStatus()(status)
status = F.depend(status, get_status)
# sum overflow buffer elements, 0:not overflow , >0:overflow
flag_sum = self.reduce_sum(status, (0,))
else:
flag_sum = self.hyper_map(F.partial(_grad_overflow), compute_output)
flag_sum = P.AddN()(flag_sum)
# convert flag_sum to scalar
flag_sum = P.Reshape()(flag_sum, (()))
if self.is_distributed:
# sum overflow flag over devices
flag_reduce = self.allreduce(flag_sum)
overflow = self.less_equal(self.base, flag_reduce)
else:
overflow = self.less_equal(self.base, flag_sum)
return overflow
def _process_loss_scale(self, overflow):
"""
Calculate loss scale according to the overflow.
Inputs:
- **overflow** (bool) - Whether the overflow occurs or not.
Outputs:
bool, overflow value.
"""
if self.loss_scaling_manager is not None:
return self.loss_scaling_manager(self.scale_sense, overflow)
return overflow