Source code for mindspore.nn.wrap.loss_scale

# Copyright 2020 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Loss scale cell for loss scale training."""
import mindspore.context as context
from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
from mindspore.train.parallel_utils import ParallelMode
from mindspore.parallel._utils import _get_device_num, _get_parallel_mode, _get_mirror_mean
from ..cell import Cell
from ...common import Tensor, RowTensor
from ...common.parameter import Parameter
from ...ops import functional as F
from ...ops import composite as C
from ...ops import operations as P
from ...ops.operations import NPUGetFloatStatus, NPUAllocFloatStatus, NPUClearFloatStatus, ReduceSum, LessEqual, \
    ControlDepend
from ...common import dtype as mstype

_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)

[docs]class DynamicLossScaleUpdateCell(Cell): r""" Dynamic Loss scale update cell. For loss scaling training, the initial loss scaling value will be set to be `loss_scale_value`. In every training step, the loss scaling value will be updated by loss scaling value/`scale_factor` when there is overflow. And it will be increased by loss scaling value * `scale_factor` if there is no overflow for a continuous `scale_window` steps. This cell is used for Graph mode training in which all logic will be executed on device side(Another training mode is normal(non-sink) mode in which some logic will be executed on host). Args: loss_scale_value (float): Init loss scale. scale_factor (int): Coefficient of increase and decrease. scale_window (int): Maximum continuous training steps that do not have overflow. Inputs: - **inputs** (Tensor) - Tensor of shape :math:`(N, \ldots)`. - **label** (Tensor) - Tensor of shape :math:`(N, \ldots)`. Outputs: Tensor, a scalar Tensor with shape :math:`()`. Examples: >>> net_with_loss = Net() >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9) >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager) >>> train_network.set_train() >>> >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) >>> output = train_network(inputs, label, scaling_sens) """ def __init__(self, loss_scale_value, scale_factor, scale_window): super(DynamicLossScaleUpdateCell, self).__init__() self.scale_window = Tensor(scale_window, dtype=mstype.int32) self.scale_factor = Tensor(scale_factor, dtype=mstype.float32) self.loss_scale_value = loss_scale_value self.cur_iter = Parameter(Tensor(1, dtype=mstype.int32), name="current_iterator_step") self.last_overflow_iter = Parameter(Tensor(0, dtype=mstype.int32), name="last_overflow_iterator_step") self.select = P.Select() self.max = P.Maximum() self.minimum_loss_scale = Tensor(1.0, dtype=mstype.float32) self.reciprocal = P.Reciprocal() self.less_equal = P.LessEqual() self.logic_and = P.LogicalAnd() self.logic_not = P.LogicalNot() self.logic_or = P.LogicalOr() self.const_true = Tensor(True, dtype=mstype.bool_) def get_loss_scale(self): return self.loss_scale_value def construct(self, loss_scale, overflow): overflow_cond = overflow loss_scale_on_overflow = self.select(overflow_cond, self.max(loss_scale * self.reciprocal(self.scale_factor), self.minimum_loss_scale), loss_scale) should_inc = self.less_equal(self.scale_window, self.cur_iter - self.last_overflow_iter) last_iter_cond = self.logic_or(overflow_cond, should_inc) last_overflow_iter = self.select(last_iter_cond, self.cur_iter, self.last_overflow_iter) assign_last_iter = F.assign(self.last_overflow_iter, last_overflow_iter) update_scale_cond = self.logic_and(should_inc, self.logic_not(overflow_cond)) scale_mul_res = loss_scale_on_overflow * self.scale_factor scaled_loss_scale = self.select(update_scale_cond, scale_mul_res, loss_scale_on_overflow) assign_scaled_loss_scale = F.assign(loss_scale, scaled_loss_scale) inc_cur_iter = self.cur_iter + 1 assing_cur_iter = F.assign(self.cur_iter, inc_cur_iter) t = (assign_last_iter, assign_scaled_loss_scale, assing_cur_iter) F.control_depend(assign_last_iter, assing_cur_iter) return F.depend(overflow, t)
[docs]class FixedLossScaleUpdateCell(Cell): """ Static scale update cell, the loss scaling value will not be updated. For usage please refer to `DynamicLossScaleUpdateCell`. Args: loss_scale_value (float): Init loss scale. Examples: >>> net_with_loss = Net() >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9) >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager) >>> train_network.set_train() >>> >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) >>> output = train_network(inputs, label, scaling_sens) """ def __init__(self, loss_scale_value): super(FixedLossScaleUpdateCell, self).__init__() self.loss_scale_value = loss_scale_value def get_loss_scale(self): return self.loss_scale_value def construct(self, _, overflow): return overflow
[docs]class TrainOneStepWithLossScaleCell(Cell): r""" 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 TrainOneStepWithLossScaleCell will be compiled to be graph which takes `data`, `label`, `sens` as input data. The `sens` is acting as loss scaling value. If you want to update it on host side, the value should be provided. If `sens` is not given, the loss scale update logic should be provied by `scale_update_cell`. If `scale_update_cell` is not None and `sens` is provided, the `scale_update_cell` will be ignored. Args: network (Cell): The training network. optimizer (Cell): Optimizer for updating the weights. scale_update_cell(Cell): The loss scaling update logic cell. Default: None. Inputs: - **inputs** (Tensor) - Tensor of shape :math:`(N, \ldots)`. - **label** (Tensor) - Tensor of shape :math:`(N, \ldots)`. - **scaling_sens** (Tensor) - Tensor of shape :math:`()`. 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_scale** (Tensor) - Tensor with shape :math:`()`. Examples: >>> net_with_loss = Net() >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9) >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000) >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager) >>> train_network.set_train() >>> >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32)) >>> label = Tensor(np.zeros([16, 16]).astype(np.float32)) >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32) >>> output = train_network(inputs, label, scaling_sens) """ def __init__(self, network, optimizer, scale_update_cell=None): super(TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False) self.network = network self.network.set_grad() self.network.add_flags(defer_inline=True) self.weights = optimizer.parameters self.optimizer = optimizer self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True) self.hyper_map = C.HyperMap() if context.get_context("device_target") == "GPU": self.gpu_target = True self.float_status = P.FloatStatus() self.addn = P.AddN() self.reshape = P.Reshape() else: self.gpu_target = False self.alloc_status = NPUAllocFloatStatus() self.get_status = NPUGetFloatStatus() self.clear_status = NPUClearFloatStatus() self.reduce_sum = ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = LessEqual() self.depend_parameter_use = ControlDepend(depend_mode=1) self.allreduce = P.AllReduce() self.parallel_mode = _get_parallel_mode() self.grad_reducer = F.identity self.reducer_flag = self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL] if self.reducer_flag: mean = _get_mirror_mean() degree = _get_device_num() self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree) self.is_distributed = self.parallel_mode != ParallelMode.STAND_ALONE self.loss_scale = None self.loss_scaling_manager = scale_update_cell if scale_update_cell: self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32), name="loss_scale") @C.add_flags(has_effect=True) def construct(self, data, label, sens=None): weights = self.weights loss = self.network(data, label) init = False if not self.gpu_target: # init overflow buffer init = self.alloc_status() # clear overflow buffer self.clear_status(init) if sens is None: scaling_sens = self.loss_scale else: scaling_sens = sens scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss)) grads = self.grad(self.network, weights)(data, label, scaling_sens_filled) grads = self.hyper_map(F.partial(_grad_scale, scaling_sens), grads) # apply grad reducer on grads grads = self.grad_reducer(grads) # get the overflow buffer if not self.gpu_target: self.get_status(init) # sum overflow buffer elements, 0:not overflow , >0:overflow flag_sum = self.reduce_sum(init, (0,)) else: flag_sum = self.hyper_map(F.partial(_grad_overflow), grads) flag_sum = self.addn(flag_sum) # convert flag_sum to scalar flag_sum = self.reshape(flag_sum, (())) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) cond = self.less_equal(self.base, flag_reduce) else: cond = self.less_equal(self.base, flag_sum) overflow = cond if sens is None: overflow = self.loss_scaling_manager(self.loss_scale, cond) # if there is no overflow, do optimize if overflow: opt = False else: opt = self.optimizer(grads) ret = (loss, cond, scaling_sens) return F.depend(ret, opt)