Huawei_Technology
/
mindspore

# 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.
# ============================================================================
"""momentum"""
import mindspore.common.dtype as mstype
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.common.parameter import ParameterTuple
from mindspore.common.tensor import Tensor
from mindspore.nn.optim.optimizer import Optimizer
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.parallel._utils import _get_device_num, _get_mirror_mean
from model.grad_reducer_thor import DistributedGradReducerThor

momentum_opt = C.MultitypeFuncGraph("momentum_opt")


@momentum_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor")
def _tensor_run_opt_ext(opt, learning_rate, momentum, gradient, weight, moment):
    """Apply momentum optimizer to the weight parameter using Tensor."""
    success = True
    success = F.depend(success, opt(weight, moment, learning_rate, gradient, momentum))
    return success


op_add = P.AddN()
apply_decay = C.MultitypeFuncGraph("apply_decay")


@apply_decay.register("Number", "Bool", "Tensor", "Tensor")
def _tensor_apply_decay(weight_decay, if_apply, weight, gradient):
    """Get grad with weight_decay."""
    if if_apply:
        return op_add((weight * weight_decay, gradient))
    return gradient


class THOR(Optimizer):
    """THOR"""
    def __init__(self, params, learning_rate, momentum, matrix_A, matrix_G, A_inv_max, G_inv_max, weight_decay=0.0,
                 loss_scale=1.0,
                 decay_filter=lambda x: x.name not in []):
        super(THOR, self).__init__(learning_rate, params, weight_decay, loss_scale)
        if isinstance(momentum, float) and momentum < 0.0:
            raise ValueError("momentum should be at least 0.0, but got momentum {}".format(momentum))
        self.momentum = Parameter(Tensor(momentum, mstype.float32), name="momentum")
        self.params = self.parameters
        self.moments = self.params.clone(prefix="moments", init='zeros')
        self.hyper_map = C.HyperMap()
        self.opt = P.ApplyMomentum()
        self.matrix_A = ParameterTuple(matrix_A)
        self.matrix_G = ParameterTuple(matrix_G)
        self.A_inv_max = ParameterTuple(A_inv_max)
        self.G_inv_max = ParameterTuple(G_inv_max)
        self.cube_matmul_left = P.CusMatMulCubeFraczLeftCast()
        self.cube_matmul_left_fc = P.CusMatMulCubeDenseLeft()
        self.cube_matmul_right_fc = P.CusMatMulCubeDenseRight()
        self.cube_matmul_right_mul = P.CusMatMulCubeFraczRightMul()
        self.transpose = P.Transpose()
        self.shape = P.Shape()
        self.reshape = P.Reshape()
        self.mul = P.Mul()
        self.weight_idx = []
        for i in range(len(self.params)):
            if "conv" in self.params[i].name or "end_point" in self.params[i].name:
                self.weight_idx.append(i)
        self.weight_idx.append(len(self.params))
        self.feature_map = [1.0 / 12544, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136,
                            1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136, 1.0 / 3136,
                            1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784,
                            1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784, 1.0 / 784,
                            1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196,
                            1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196, 1.0 / 196,
                            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_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)
        self.matrix_A_inv = ()
        self.matrix_G_inv = ()
        self.matrix_max_inv = ()

        for i in range(54):
            self.matrix_max_inv = self.matrix_max_inv + (
                Parameter(initializer(1, [1], mstype.float32), name="matrix_max" + str(i), requires_grad=False),)
        self.log = P.Log()
        self.exp = P.Exp()
        self.sqrt = P.Sqrt()
        self.matrix_max_inv = ParameterTuple(self.matrix_max_inv)
        self.assign = P.Assign()
        self.cast = P.Cast()
        self.thor = True
        self.weight_decay = weight_decay * loss_scale
        self.decay_flags = tuple(decay_filter(x) for x in self.parameters)

    def construct(self, gradients):
        params = self.params
        moments = self.moments
        if self.thor:
            matrix_A_allreduce = ()
            matrix_G_allreduce = ()
            matrix_A_max_allreduce = ()
            matrix_G_max_allreduce = ()
            for i in range(54):
                g = gradients[i * 3]
                matrix_A = self.matrix_A[i]
                matrix_G = self.matrix_G[i]
                A_max = self.A_inv_max[i]
                G_max = self.G_inv_max[i]
                matrix_A = F.depend(matrix_A, g)
                matrix_G = F.depend(matrix_G, g)
                A_max = F.depend(A_max, g)
                G_max = F.depend(G_max, g)
                matrix_A_allreduce = matrix_A_allreduce + (matrix_A,)
                matrix_G_allreduce = matrix_G_allreduce + (matrix_G,)
                matrix_A_max_allreduce = matrix_A_max_allreduce + (A_max,)
                matrix_G_max_allreduce = matrix_G_max_allreduce + (G_max,)
            matrix_A_allreduce = self.grad_reducer_A(matrix_A_allreduce)
            matrix_G_allreduce = self.grad_reducer_G(matrix_G_allreduce)
            matrix_A_max_allreduce = self.grad_reducer_Amax(matrix_A_max_allreduce)
            matrix_G_max_allreduce = self.grad_reducer_Gmax(matrix_G_max_allreduce)
            new_grads = ()
            for i in range(54):
                g = gradients[i * 3]
                temp_a = matrix_A_allreduce[i]
                temp_g = matrix_G_allreduce[i]
                temp_a = self.cast(temp_a, mstype.float32)
                temp_g = self.cast(temp_g, mstype.float32)
                matrix_A_inv_max = self.log(matrix_A_max_allreduce[i])
                matrix_A_inv_max = self.mul(matrix_A_inv_max, -1)
                matrix_A_inv_max = self.exp(matrix_A_inv_max)
                temp_a = self.mul(temp_a, matrix_A_inv_max)
                matrix_G_inv_max = self.log(matrix_G_max_allreduce[i])
                matrix_G_inv_max = self.mul(matrix_G_inv_max, -1)
                matrix_G_inv_max = self.exp(matrix_G_inv_max)
                temp_g = self.mul(temp_g, matrix_G_inv_max)
                temp_max = self.mul(matrix_A_max_allreduce[i], matrix_G_max_allreduce[i])
                temp_max = self.mul(temp_max, self.feature_map[i])
                if i == 53:
                    g = self.cube_matmul_left_fc(temp_g, g)
                    g = self.cube_matmul_right_fc(g, temp_a, temp_max)
                else:
                    g = self.cube_matmul_left(temp_g, g)
                    g = self.cube_matmul_right_mul(g, temp_a, temp_max)
                fake_A = self.assign(self.matrix_A[i], temp_a)
                fake_G = self.assign(self.matrix_G[i], temp_g)
                fake_max = self.assign(self.matrix_max_inv[i], temp_max)
                g = F.depend(g, fake_A)
                g = F.depend(g, fake_G)
                g = F.depend(g, fake_max)
                if i == 53:
                    new_grads = new_grads + (g,)
                else:
                    new_grads = new_grads + (g, gradients[i * 3 + 1], gradients[i * 3 + 2])
            gradients = new_grads
        else:
            new_grads = ()
            for i in range(54):
                g = gradients[i * 3]
                matrix_A = self.matrix_A[i]
                matrix_G = self.matrix_G[i]
                matrix_max = self.matrix_max_inv[i]
                matrix_A = F.depend(matrix_A, g)
                matrix_G = F.depend(matrix_G, g)
                matrix_max = F.depend(matrix_max, g)
                if i == 53:
                    g = self.cube_matmul_left_fc(matrix_G, g)
                    g = self.cube_matmul_right_fc(g, matrix_A, matrix_max)
                    new_grads = new_grads + (g,)
                else:
                    g = self.cube_matmul_left(matrix_G, g)
                    g = self.cube_matmul_right_mul(g, matrix_A, matrix_max)
                    new_grads = new_grads + (g, gradients[i * 3 + 1], gradients[i * 3 + 2])
            gradients = new_grads

        if self.weight_decay > 0:
            gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_flags,
                                       params, gradients)
        gradients = self.scale_grad(gradients)
        lr = self.get_lr()
        success = self.hyper_map(F.partial(momentum_opt, self.opt, lr, self.momentum), gradients, params, moments)
        return success