mindspore/tests/st/nccl/test_nccl_all_reduce_op.py

# Copyright 2019-2021 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.
# ============================================================================
import numpy as np

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
from mindspore.communication.management import init, NCCL_WORLD_COMM_GROUP, get_rank, get_group_size
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner

context.set_context(mode=context.GRAPH_MODE, device_target='GPU')

init()
rank = get_rank()
size = get_group_size()
x = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
y = np.ones([3, 4, 6, 3]).astype(np.float32) * 0.01 * (rank + 1)

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1')
        self.x2 = Parameter(initializer(Tensor(x), x.shape), name='x2')
        self.x3 = Parameter(initializer(Tensor(x), x.shape), name='x3')

        self.op0 = "sum"
        self.op1 = "sum"
        self.op2 = "sum"

        self.all_reduce1 = P.AllReduce(self.op0, group=NCCL_WORLD_COMM_GROUP)
        self.all_reduce2 = P.AllReduce(self.op1, group=NCCL_WORLD_COMM_GROUP)
        self.all_reduce3 = P.AllReduce(self.op2, group=NCCL_WORLD_COMM_GROUP)

    def construct(self):
        return (self.all_reduce1(self.x1),
                self.all_reduce2(self.x2),
                self.all_reduce3(self.x3))


def test_AllReduce():
    all_reduce = Net()
    output = all_reduce()

    expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 0
    for i in range(size):
        part = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
        expect0 += part
    diff0 = output[0].asnumpy() - expect0
    error0 = np.ones(shape=expect0.shape) * 1.0e-5
    assert np.all(diff0 < error0)
    assert output[0].shape == expect0.shape

    expect1 = expect0
    diff1 = output[1].asnumpy() - expect1
    error1 = np.ones(shape=expect1.shape) * 1.0e-5
    assert np.all(diff1 < error1)
    assert output[1].shape == expect1.shape

    expect2 = expect1
    diff2 = output[2].asnumpy() - expect2
    error2 = np.ones(shape=expect2.shape) * 1.0e-5
    assert np.all(diff2 < error2)
    assert output[2].shape == expect2.shape


class Net2(nn.Cell):
    def __init__(self):
        super(Net2, self).__init__()
        self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1')

        self.op0 = "sum"
        self.op1 = "sum"
        self.op2 = "sum"

        self.all_reduce1 = P.AllReduce(self.op0, group=NCCL_WORLD_COMM_GROUP)
        self.all_reduce2 = P.AllReduce(self.op1, group=NCCL_WORLD_COMM_GROUP)
        self.all_reduce3 = P.AllReduce(self.op2, group=NCCL_WORLD_COMM_GROUP)

    def construct(self):
        x_ = self.all_reduce1(self.x1)
        y_ = self.all_reduce2(x_)
        z_ = self.all_reduce3(y_)
        return (x_, y_, z_)


def test_AllReduce2():
    all_reduce = Net2()
    output = all_reduce()

    expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 0
    for i in range(size):
        part = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
        expect0 += part
    diff0 = abs(output[0].asnumpy() - expect0)
    error0 = np.ones(shape=expect0.shape) * 1.0e-5
    assert np.all(diff0 < error0)
    assert output[0].shape == expect0.shape

    expect1 = expect0 * size
    diff1 = abs(output[1].asnumpy() - expect1)
    error1 = np.ones(shape=expect1.shape) * 1.0e-5
    assert np.all(diff1 < error1)
    assert output[1].shape == expect1.shape

    expect2 = expect1 * size
    diff2 = abs(output[2].asnumpy() - expect2)
    error2 = np.ones(shape=expect2.shape) * 1.0e-5
    assert np.all(diff2 < error2)
    assert output[2].shape == expect2.shape


class DynamicAllReduceNet(nn.Cell):
    def __init__(self):
        super(DynamicAllReduceNet, self).__init__()
        self.op = "sum"
        self.all_reduce = P.AllReduce(self.op, group=NCCL_WORLD_COMM_GROUP)
        self.d = inner.GpuConvertToDynamicShape()

    def construct(self, input_x):
        out = self.d(input_x)
        out = self.all_reduce(out)
        return out


def test_all_reduce_dynamic():
    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
    input1 = Tensor(x)
    input2 = Tensor(y)
    net = DynamicAllReduceNet()

    output1 = net(input1)
    expect1 = np.ones([3, 1, 3, 3]).astype(np.float32) * 0
    for i in range(size):
        part = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (i + 1)
        expect1 += part
    diff1 = abs(output1.asnumpy() - expect1)
    error1 = np.ones(shape=expect1.shape) * 1.0e-5
    assert np.all(diff1 < error1)
    assert output1.shape == expect1.shape

    output2 = net(input2)
    expect2 = np.ones([3, 4, 6, 3]).astype(np.float32) * 0
    for i in range(size):
        part = np.ones([3, 4, 6, 3]).astype(np.float32) * 0.01 * (i + 1)
        expect2 += part
    diff2 = abs(output2.asnumpy() - expect2)
    error2 = np.ones(shape=expect2.shape) * 1.0e-5
    assert np.all(diff2 < error2)
    assert output2.shape == expect2.shape