mindspore2022/tests/ut/python/parallel/test_split_grad_sens.py

# Copyright 2019 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.

import numpy as np
from mindspore import context
import mindspore.nn as nn
from mindspore.ops import operations as P
from mindspore import Tensor
from tests.ut.python.ops.test_math_ops import VirtualLoss
import mindspore as ms
from mindspore.common.api import _executor
from mindspore.ops import composite as C
import mindspore.common.dtype as mstype


class GradWrap(nn.Cell):
    def __init__(self, network):
        super(GradWrap, self).__init__()
        self.network = network

    def construct(self, x, y, b, sens):
        return C.grad_all_with_sens(self.network)(x, y, b, sens)


class GradWrap2(nn.Cell):
    def __init__(self, network):
        super(GradWrap2, self).__init__()
        self.network = network

    def construct(self, x, y, b):
        loss = self.network(x, y, b)
        sens = P.Fill()(mstype.float32, P.Shape()(loss), 1.0)
        return C.grad_all_with_sens(self.network)(x, y, b, sens)


class GradWrap3(nn.Cell):
    def __init__(self, network):
        super(GradWrap3, self).__init__()
        self.network = network

    def construct(self, x, y, bias):
        return C.grad_all(self.network)(x, y, bias)


def compile(net, x, y, b):
    net.set_auto_parallel()
    _executor.compile(net, x, y, b)


def test_no_grad():
    class Net(nn.Cell):
        def __init__(self, strategy1, strategy2):
            super().__init__()
            self.matmul1 = P.MatMul().set_strategy(strategy1)
            self.matmul2 = P.MatMul().set_strategy(strategy2)

        def construct(self, x, y, b):
            out = self.matmul1(x, y)
            out = self.matmul2(out, b)
            return out

    context.set_auto_parallel_context(device_num=8, global_rank=0)
    
    strategy1 = ((4, 2), (2, 1))
    strategy2 = ((2, 4), (4, 1))
    net = Net(strategy1, strategy2)
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")

    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
    compile(net, x, y, b)


def test_grad_sens_parameter_type():
    class Net(nn.Cell):
        def __init__(self, strategy1, strategy2):
            super().__init__()
            self.matmul1 = P.MatMul().set_strategy(strategy1)
            self.matmul2 = P.MatMul().set_strategy(strategy2)

        def construct(self, x, y, b):
            out = self.matmul1(x, y)
            out = self.matmul2(out, b)
            return out

    context.set_auto_parallel_context(device_num=8, global_rank=0)
    
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
    strategy1 = ((4, 2), (2, 1))
    strategy2 = ((2, 4), (4, 1))
    net = GradWrap(Net(strategy1, strategy2))

    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
    b = Tensor(np.ones([64, 64]), dtype=ms.float32)

    sens = Tensor(np.ones([128, 64]), dtype=ms.float32)
    # net(x, y, b, sens)
    net.set_auto_parallel()
    _executor.compile(net, x, y, b, sens)


def test_grad_sens_tensor_type():
    class Net(nn.Cell):
        def __init__(self, strategy1, strategy2):
            super().__init__()
            self.matmul1 = P.MatMul().set_strategy(strategy1)
            self.matmul2 = P.MatMul().set_strategy(strategy2)

        def construct(self, x, y, b):
            out = self.matmul1(x, y)
            out = self.matmul2(out, b)
            return out

    context.set_auto_parallel_context(device_num=8, global_rank=0)
    
    strategy1 = ((4, 2), (2, 1))
    strategy2 = ((2, 4), (4, 1))
    net = GradWrap2(Net(strategy1, strategy2))
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")

    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
    compile(net, x, y, b)


def test_grad_sens_scalar_broadcast():
    class Net(nn.Cell):
        def __init__(self, strategy0, strategy1):
            super().__init__()
            self.fc_nobias = P.MatMul(transpose_b=True).set_strategy(strategy0)
            self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy1)

        def construct(self, x, y, bias):
            out = self.fc_nobias(x, y)
            out = self.reduce_sum(out, (0,1))
            return out

    context.set_auto_parallel_context(device_num=16, global_rank=0)
    strategy0 = ((4, 1), (4, 1))
    strategy1 = ((4, 1), )
    net = GradWrap3(Net(strategy0, strategy1))
    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")

    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
    y = Tensor(np.ones([64, 32]), dtype=ms.float32)
    bias = Tensor(np.ones([64]), dtype=ms.float32)
    compile(net, x, y, bias)