mindspore2022/tests/ut/python/parallel/test_auto_star_elimination.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.
from mindspore.nn.loss.loss import _Loss
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore import Tensor, Parameter
from mindspore.common import dtype as mstype
from mindspore.common.initializer import initializer
import mindspore.nn as nn
import math
import numpy as np
import os
from tests.ut.python.ops.test_math_ops import VirtualLoss
from mindspore.ops import composite as C
from mindspore import context
from mindspore.common.api import _executor
import mindspore as ms

class NetWithLoss(nn.Cell):
    def __init__(self, network):
        super(NetWithLoss, self).__init__()
        self.loss = VirtualLoss()
        self.network = network

    def construct(self, x, y):
        predict = self.network(x, y)
        return self.loss(predict)

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

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


class CustomMatMul(nn.Cell):
    def __init__(self, transpose_a=False, transpose_b=False):
        super(CustomMatMul, self).__init__()
        self.fc = P.MatMul(transpose_a=transpose_a, transpose_b=transpose_b)

    def construct(self, x1, x2):
        out = self.fc(x1, x2)
        return out


class MarginCE(_Loss):
    def __init__(self):
        super(MarginCE, self).__init__()
        self.fc = CustomMatMul(transpose_b=True)
        self.fc1 = CustomMatMul(transpose_b=True)
        self.fc2 = CustomMatMul(transpose_b=True)
        self.fc3 = CustomMatMul(transpose_b=True)
        self.fc4 = CustomMatMul(transpose_b=True)
        self.param = Parameter(Tensor(np.ones([512, 512]), dtype=mstype.float32), name="param", requires_grad=False)
        self.param2 = Parameter(Tensor(np.ones([512, 512]), dtype=mstype.float32), name="param", requires_grad=False)

    def construct(self, feature, label):
        fc_out = self.fc(feature, label)

        fc1_out = self.fc1(self.param2, self.param)
        fc2_out = self.fc2(fc1_out, fc_out)
        fc3_out = self.fc3(fc1_out, fc_out)
        fc4_out = self.fc4(fc2_out, fc3_out)
        return fc4_out


def test_marin_loss():
    context.set_auto_parallel_context(device_num=4, global_rank=0)
    
    x = Tensor(np.ones([512, 512]), dtype=ms.float32)
    y = Tensor(np.ones([512, 512]), dtype=ms.float32)

    net = GradWrap(NetWithLoss(MarginCE()))
    context.set_auto_parallel_context(parallel_mode="auto_parallel")
    _executor.compile(net, x, y)