test_ndarray.py

import os
import mxnet as mx
import numpy as np
import pickle as pkl
from mxnet.test_utils import *
from numpy.testing import assert_allclose

def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]):
    """check function consistency with uniform random numbers"""
    if isinstance(arg_shapes, int):
        assert dim
        shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
        arg_shapes = [shape] * arg_shapes
    for dtype in type_list:
        ndarray_arg = []
        numpy_arg = []
        for s in arg_shapes:
            npy = np.random.uniform(rmin, 10, s).astype(dtype)
            narr = mx.nd.array(npy, dtype=dtype)
            ndarray_arg.append(narr)
            numpy_arg.append(npy)
        out1 = uf(*ndarray_arg)
        if npuf is None:
            out2 = uf(*numpy_arg).astype(dtype)
        else:
            out2 = npuf(*numpy_arg).astype(dtype)

        assert out1.shape == out2.shape
        if isinstance(out1, mx.nd.NDArray):
            out1 = out1.asnumpy()
        if dtype == np.float16:
            assert reldiff(out1, out2) < 2e-3
        else:
            assert reldiff(out1, out2) < 1e-6


def random_ndarray(dim):
    shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim))
    data = mx.nd.array(np.random.uniform(-10, 10, shape))
    return data


def test_ndarray_setitem():
    shape = (3, 4, 2)

    # scalar assignment
    x = mx.nd.zeros(shape)
    x[:] = 1
    x_np = np.ones(shape, dtype=x.dtype)
    assert same(x.asnumpy(), x_np)

    # ndarray assignment
    x = mx.nd.zeros(shape)
    x[:] = mx.nd.ones(shape)
    x_np = np.ones(shape, dtype=x.dtype)
    assert same(x.asnumpy(), x_np)

    # numpy assignment
    x = mx.nd.zeros(shape)
    x[:] = np.ones(shape)
    x_np = np.ones(shape, dtype=x.dtype)
    assert same(x.asnumpy(), x_np)

    # indexing sub-arrays
    x = mx.nd.zeros(shape)
    x[1] = 1
    x_np = np.zeros(shape, dtype=x.dtype)
    x_np[1] = 1
    assert same(x.asnumpy(), x_np)

    # all-dim indexing
    x = mx.nd.zeros(shape)
    val = mx.nd.ones((3, 2, 1))
    x[:, 1:3, 1] = val
    x_np = np.zeros(shape, dtype=x.dtype)
    x_np[:, 1:3, 1:2] = val.asnumpy()
    assert same(x.asnumpy(), x_np)

    x = mx.nd.zeros(shape)
    x[:, 1:3, 1] = 1
    x_np = np.zeros(shape, dtype=x.dtype)
    x_np[:, 1:3, 1:2] = 1
    assert same(x.asnumpy(), x_np)


def test_ndarray_elementwise():
    np.random.seed(0)
    nrepeat = 10
    maxdim = 4
    all_type = [np.float32, np.float64, np.float16, np.uint8, np.int32]
    real_type = [np.float32, np.float64, np.float16]
    for repeat in range(nrepeat):
        for dim in range(1, maxdim):
            check_with_uniform(lambda x, y: x + y, 2, dim, type_list=all_type)
            check_with_uniform(lambda x, y: x - y, 2, dim, type_list=all_type)
            check_with_uniform(lambda x, y: x * y, 2, dim, type_list=all_type)
            check_with_uniform(lambda x, y: x / y, 2, dim, type_list=real_type)
            check_with_uniform(lambda x, y: x / y, 2, dim, rmin=1, type_list=all_type)
            check_with_uniform(mx.nd.sqrt, 1, dim, np.sqrt, rmin=0)
            check_with_uniform(mx.nd.square, 1, dim, np.square, rmin=0)
            check_with_uniform(lambda x: mx.nd.norm(x).asscalar(), 1, dim, np.linalg.norm)

def test_ndarray_elementwisesum():
    ones = mx.nd.ones((10,), dtype=np.int32)
    res = mx.nd.ElementWiseSum(ones, ones*2, ones*4, ones*8)
    assert same(res.asnumpy(), ones.asnumpy()*15)

def test_ndarray_negate():
    npy = np.random.uniform(-10, 10, (2,3,4))
    arr = mx.nd.array(npy)
    assert reldiff(npy, arr.asnumpy()) < 1e-6
    assert reldiff(-npy, (-arr).asnumpy()) < 1e-6

    # a final check to make sure the negation (-) is not implemented
    # as inplace operation, so the contents of arr does not change after
    # we compute (-arr)
    assert reldiff(npy, arr.asnumpy()) < 1e-6


def test_ndarray_choose():
    shape = (100, 20)
    npy = np.arange(np.prod(shape)).reshape(shape)
    arr = mx.nd.array(npy)
    nrepeat = 3
    for repeat in range(nrepeat):
        indices = np.random.randint(shape[1], size=shape[0])
        assert same(npy[np.arange(shape[0]), indices],
                    mx.nd.choose_element_0index(arr, mx.nd.array(indices)).asnumpy())


def test_ndarray_fill():
    shape = (100, 20)
    npy = np.arange(np.prod(shape)).reshape(shape)
    arr = mx.nd.array(npy)
    new_npy = npy.copy()
    nrepeat = 3
    for repeat in range(nrepeat):
        indices = np.random.randint(shape[1], size=shape[0])
        val = np.random.randint(shape[1], size=shape[0])
        new_npy[:] = npy
        new_npy[np.arange(shape[0]), indices] = val
        assert same(new_npy,
                    mx.nd.fill_element_0index(arr, mx.nd.array(val), mx.nd.array(indices)).asnumpy())


def test_ndarray_onehot():
    shape = (100, 20)
    npy = np.arange(np.prod(shape)).reshape(shape)
    arr = mx.nd.array(npy)
    nrepeat = 3
    for repeat in range(nrepeat):
        indices = np.random.randint(shape[1], size=shape[0])
        npy[:] = 0.0
        npy[np.arange(shape[0]), indices] = 1.0
        mx.nd.onehot_encode(mx.nd.array(indices), out=arr)
        assert same(npy, arr.asnumpy())


def test_ndarray_copy():
    c = mx.nd.array(np.random.uniform(-10, 10, (10, 10)))
    d = c.copyto(mx.Context('cpu', 0))
    assert np.sum(np.abs(c.asnumpy() != d.asnumpy())) == 0.0


def test_ndarray_scalar():
    c = mx.nd.empty((10,10))
    d = mx.nd.empty((10,10))
    c[:] = 0.5
    d[:] = 1.0
    d -= c * 2 / 3 * 6.0
    c += 0.5
    assert(np.sum(c.asnumpy()) - 100 < 1e-5)
    assert(np.sum(d.asnumpy()) + 100 < 1e-5)
    c[:] = 2
    assert(np.sum(c.asnumpy()) - 200 < 1e-5)
    d = -c + 2
    assert(np.sum(d.asnumpy()) < 1e-5)

def test_ndarray_pickle():
    np.random.seed(0)
    maxdim = 5
    nrepeat = 10
    for repeat in range(nrepeat):
        for dim in range(1, maxdim):
            a = random_ndarray(dim)
            b = mx.nd.empty(a.shape)
            a[:] = np.random.uniform(-10, 10, a.shape)
            b[:] = np.random.uniform(-10, 10, a.shape)
            a = a + b
            data = pkl.dumps(a)
            a2 = pkl.loads(data)
            assert np.sum(a.asnumpy() != a2.asnumpy()) == 0


def test_ndarray_saveload():
    np.random.seed(0)
    maxdim = 5
    nrepeat = 10
    fname = 'tmp_list.bin'
    for repeat in range(nrepeat):
        data = []
        for i in range(10):
            data.append(random_ndarray(np.random.randint(1, 5)))
        mx.nd.save(fname, data)
        data2 = mx.nd.load(fname)
        assert len(data) == len(data2)
        for x, y in zip(data, data2):
            assert np.sum(x.asnumpy() != y.asnumpy()) == 0
        dmap = {'ndarray xx %s' % i : x for i, x in enumerate(data)}
        mx.nd.save(fname, dmap)
        dmap2 = mx.nd.load(fname)
        assert len(dmap2) == len(dmap)
        for k, x in dmap.items():
            y = dmap2[k]
            assert np.sum(x.asnumpy() != y.asnumpy()) == 0
    os.remove(fname)


def test_ndarray_slice():
    shape = (10,)
    A = mx.nd.array(np.random.uniform(-10, 10, shape))
    A2 = A.asnumpy()
    assert same(A[3:8].asnumpy(), A2[3:8])
    A2[3:8] *= 10;
    A[3:8] = A2[3:8]
    assert same(A[3:8].asnumpy(), A2[3:8])


def test_ndarray_crop():
    # get crop
    x = mx.nd.ones((2, 3, 4))
    y = mx.nd.crop(x, begin=(0, 0, 0), end=(2, 1, 3))
    assert same(y.asnumpy(), np.ones((2, 1, 3), dtype=y.dtype))

    # crop assign
    z = mx.nd.zeros((2, 1, 3))
    mx.nd._internal._crop_assign(x, z, begin=(0, 0, 0),
                                 end=(2, 1, 3), out=x)
    np_x = np.ones(x.shape, dtype=x.dtype)
    np_x[0:2, 0:1, 0:3] = 0
    assert same(x.asnumpy(), np_x)

    # crop assign with scalar
    x = mx.nd.ones((2, 3, 4))
    mx.nd._internal._crop_assign_scalar(x, scalar=5,
                                        begin=(0, 0, 0),
                                        end=(2, 1, 3), out=x)
    np_x = np.ones(x.shape, dtype=x.dtype)
    np_x[0:2, 0:1, 0:3] = 5
    assert same(x.asnumpy(), np_x)


def test_ndarray_concatenate():
    axis = 1
    shapes = [(2, 3, 4, 2), (2, 2, 4, 2), (2, 1, 4, 2)]
    arrays_np = [np.random.uniform(-10, 10, s).astype(np.float32) for s in shapes]
    arrays_nd = [mx.nd.array(x) for x in arrays_np]

    array_nd = mx.nd.concatenate(arrays_nd, axis=axis)
    array_np = np.concatenate(arrays_np, axis=axis)

    assert same(array_np, array_nd.asnumpy())


def test_clip():
    shape = (10,)
    A = mx.random.uniform(-10, 10, shape)
    B = mx.nd.clip(A, -2, 2)
    B1 = B.asnumpy()
    for i in range(shape[0]):
        assert B1[i] >= -2
        assert B1[i] <= 2

def test_dot():
    # Test normal dot
    a = np.random.uniform(-3, 3, (3, 4))
    b = np.random.uniform(-3, 3, (4, 5))
    c = np.dot(a, b)
    A = mx.nd.array(a)
    B = mx.nd.array(b)
    C = mx.nd.dot(A, B)
    assert reldiff(c, C.asnumpy()) < 1e-5
    # Test dot with transpose kargs
    a = np.random.uniform(-3, 3, (3, 4))
    b = np.random.uniform(-3, 3, (3, 5))
    c = np.dot(a.T, b)
    A = mx.nd.array(a)
    B = mx.nd.array(b)
    C = mx.nd.dot(A, B, transpose_a=True)
    assert reldiff(c, C.asnumpy()) < 1e-5
    # Test dot with transpose kargs
    a = np.random.uniform(-3, 3, (3, 4))
    b = np.random.uniform(-3, 3, (5, 4))
    c = np.dot(a, b.T)
    A = mx.nd.array(a)
    B = mx.nd.array(b)
    C = mx.nd.dot(A, B, transpose_b=True)
    assert reldiff(c, C.asnumpy()) < 1e-5
    # Test dot with transpose kargs
    a = np.random.uniform(-3, 3, (4, 3))
    b = np.random.uniform(-3, 3, (5, 4))
    c = np.dot(a.T, b.T)
    A = mx.nd.array(a)
    B = mx.nd.array(b)
    C = mx.nd.dot(A, B, transpose_a=True, transpose_b=True)
    assert reldiff(c, C.asnumpy()) < 1e-5


def test_reduce():
    sample_num = 200
    def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes):
        for i in range(sample_num):
            ndim = np.random.randint(1, 6)
            shape = np.random.randint(1, 11, size=ndim)
            dat = np.random.rand(*shape) - 0.5
            keepdims = np.random.randint(0, 2)
            if multi_axes:
                axis_flags = np.random.randint(0, 2, size=ndim)
                axes = []
                for (axis, flag) in enumerate(axis_flags):
                    if flag:
                        axes.append(axis)
                if 0 == len(axes):
                    axes = tuple(range(ndim))
                else:
                    axes = tuple(axes)
            else:
                axes = np.random.randint(0, ndim)
            numpy_ret = numpy_reduce_func(dat, axis=axes, keepdims=keepdims)

            ndarray_ret = nd_reduce_func(mx.nd.array(dat), axis=axes, keepdims=keepdims)
            if type(ndarray_ret) is mx.ndarray.NDArray:
                ndarray_ret = ndarray_ret.asnumpy()
            assert (ndarray_ret.shape == numpy_ret.shape) or \
                   (ndarray_ret.shape == (1,) and numpy_ret.shape == ()), "nd:%s, numpy:%s" \
                                                         %(ndarray_ret.shape, numpy_ret.shape)
            err = np.square(ndarray_ret - numpy_ret).mean()
            assert err < 1E-4
    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.sum),
                      mx.nd.sum, True)
    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.max),
                      mx.nd.max, True)
    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.min),
                      mx.nd.min, True)
    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.argmax),
                      mx.nd.argmax, False)
    test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.argmin),
                      mx.nd.argmin, False)

def test_broadcast():
    sample_num = 1000
    def test_broadcast_to():
        for i in range(sample_num):
            ndim = np.random.randint(1, 6)
            target_shape = np.random.randint(1, 11, size=ndim)
            shape = target_shape.copy()
            axis_flags = np.random.randint(0, 2, size=ndim)
            axes = []
            for (axis, flag) in enumerate(axis_flags):
                if flag:
                    shape[axis] = 1
            dat = np.random.rand(*shape) - 0.5
            numpy_ret = dat
            ndarray_ret = mx.nd.array(dat).broadcast_to(shape=target_shape)
            if type(ndarray_ret) is mx.ndarray.NDArray:
                ndarray_ret = ndarray_ret.asnumpy()
            assert (ndarray_ret.shape == target_shape).all()
            err = np.square(ndarray_ret - numpy_ret).mean()
            assert err < 1E-8
    test_broadcast_to()

def test_broadcast_binary():
    N = 100
    def check_broadcast_binary(fn):
        for _ in range(N):
            ndim = np.random.randint(1, 6)
            oshape = np.random.randint(1, 6, size=(ndim,))
            bdim = np.random.randint(1, ndim+1)
            lshape = list(oshape)
            rshape = list(oshape[ndim-bdim:])
            for i in range(bdim):
                sep = np.random.uniform(0, 1)
                if sep < 0.33:
                    lshape[ndim-i-1] = 1
                elif sep < 0.66:
                    rshape[bdim-i-1] = 1
            lhs = np.random.normal(0, 1, size=lshape)
            rhs = np.random.normal(0, 1, size=rshape)
            assert_allclose(fn(lhs, rhs),
                            fn(mx.nd.array(lhs), mx.nd.array(rhs)).asnumpy(),
                            rtol=1e-4, atol=1e-4)

    check_broadcast_binary(lambda x, y: x + y)
    check_broadcast_binary(lambda x, y: x - y)
    check_broadcast_binary(lambda x, y: x * y)
    check_broadcast_binary(lambda x, y: x / y)
    check_broadcast_binary(lambda x, y: x > y)
    check_broadcast_binary(lambda x, y: x < y)
    check_broadcast_binary(lambda x, y: x >= y)
    check_broadcast_binary(lambda x, y: x <= y)
    check_broadcast_binary(lambda x, y: x == y)

def test_arange():
    for i in range(5):
        start = np.random.rand() * 10
        stop = start + np.random.rand() * 100
        step = np.random.rand() * 4
        repeat = int(np.random.rand() * 5) + 1
        gt = np.arange(start=start, stop=stop, step=step)
        gt = np.broadcast_to(gt.reshape((gt.shape[0], 1)), shape=(gt.shape[0], repeat)).ravel()
        pred = mx.nd.arange(start=start, stop=stop, step=step, repeat=repeat).asnumpy()
        assert_almost_equal(pred, gt, default_numerical_threshold())

def test_order(ctx=default_context()):
    def gt_topk(dat, axis, ret_typ, k, is_ascend):
        if ret_typ == "indices":
            if is_ascend:
                indices = np.arange(k)
            else:
                indices = np.arange(-1, -k-1, -1)
            ret = np.take(dat.argsort(axis=axis), axis=axis, indices=indices, mode='wrap')
        elif ret_typ == "value":
            if is_ascend:
                indices = np.arange(k)
            else:
                indices = np.arange(-1, -k-1, -1)
            ret = np.take(np.sort(dat, axis=axis), axis=axis, indices=indices, mode='wrap')
        else:
            assert dat.shape == (5, 5, 5, 5)
            assert axis is None or axis ==1
            ret = np.zeros(dat.shape)
            if is_ascend:
                indices = np.arange(k)
            else:
                indices = np.arange(-1, -k-1, -1)
            gt_argsort = np.take(dat.argsort(axis=axis), axis=axis, indices=indices, mode='wrap')
            if axis is None:
                ret.ravel()[gt_argsort] = 1
            else:
                for i in range(5):
                    for j in range(5):
                        for k in range(5):
                            ret[i, gt_argsort[i, :, j, k], j, k] = 1
        return ret
    a_npy = np.random.normal(size=(5, 5, 5, 5))
    a_nd = mx.nd.array(a_npy, ctx=ctx)

    # test for ret_typ=indices
    nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="indices", k=3, is_ascend=True).asnumpy()
    gt = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
    assert_almost_equal(nd_ret_topk, gt)
    nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="indices", k=2, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=3, ret_typ="indices", k=2, is_ascend=False)
    assert_almost_equal(nd_ret_topk, gt)
    nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="indices", k=21, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=None, ret_typ="indices", k=21, is_ascend=False)
    assert_almost_equal(nd_ret_topk, gt)

    # test for ret_typ=value
    nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="value", k=3, is_ascend=True).asnumpy()
    gt = gt_topk(a_npy, axis=1, ret_typ="value", k=3, is_ascend=True)
    assert_almost_equal(nd_ret_topk, gt)
    nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="value", k=2, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=3, ret_typ="value", k=2, is_ascend=False)
    assert_almost_equal(nd_ret_topk, gt)
    nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="value", k=21, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=None, ret_typ="value", k=21, is_ascend=False)
    assert_almost_equal(nd_ret_topk, gt)

    # test for ret_typ=mask
    nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="mask", k=3, is_ascend=True).asnumpy()
    gt = gt_topk(a_npy, axis=1, ret_typ="mask", k=3, is_ascend=True)
    assert_almost_equal(nd_ret_topk, gt)
    nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="mask", k=2, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=1, ret_typ="mask", k=2, is_ascend=False)
    assert_almost_equal(nd_ret_topk, gt)
    nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="mask", k=21, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=None, ret_typ="mask", k=21, is_ascend=False)
    assert_almost_equal(nd_ret_topk, gt)

    # test for ret_typ=both
    nd_ret_topk_val, nd_ret_topk_ind = mx.nd.topk(a_nd, axis=1, ret_typ="both", k=3, is_ascend=True)
    nd_ret_topk_val = nd_ret_topk_val.asnumpy()
    nd_ret_topk_ind = nd_ret_topk_ind.asnumpy()
    gt_val = gt_topk(a_npy, axis=1, ret_typ="value", k=3, is_ascend=True)
    gt_ind = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
    assert_almost_equal(nd_ret_topk_val, gt_val)
    assert_almost_equal(nd_ret_topk_ind, gt_ind)

    # test for sort
    nd_ret_sort = mx.nd.sort(a_nd, axis=1, is_ascend=True).asnumpy()
    gt = gt_topk(a_npy, axis=1, ret_typ="value", k=5, is_ascend=True)
    assert_almost_equal(nd_ret_sort, gt)
    nd_ret_sort = mx.nd.sort(a_nd, axis=None, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=None, ret_typ="value", k=5*5*5*5, is_ascend=False)
    assert_almost_equal(nd_ret_sort, gt)

    # test for argsort
    nd_ret_argsort = mx.nd.argsort(a_nd, axis=3, is_ascend=True).asnumpy()
    gt = gt_topk(a_npy, axis=3, ret_typ="indices", k=5, is_ascend=True)
    assert_almost_equal(nd_ret_argsort, gt)
    nd_ret_argsort = mx.nd.argsort(a_nd, axis=None, is_ascend=False).asnumpy()
    gt = gt_topk(a_npy, axis=None, ret_typ="indices", k=5*5*5*5, is_ascend=False)
    assert_almost_equal(nd_ret_argsort, gt)

def test_ndarray_equal():
    x = mx.nd.zeros((2, 3))
    y = mx.nd.ones((2, 3))
    z = x == y
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = 0 == x
    assert (z.asnumpy() == np.ones((2, 3))).all()

def test_ndarray_not_equal():
    x = mx.nd.zeros((2, 3))
    y = mx.nd.ones((2, 3))
    z = x != y
    assert (z.asnumpy() == np.ones((2, 3))).all()
    z = 0 != x
    assert (z.asnumpy() == np.zeros((2, 3))).all()

def test_ndarray_greater():
    x = mx.nd.zeros((2, 3))
    y = mx.nd.ones((2, 3))
    z = x > y
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = y > 0
    assert (z.asnumpy() == np.ones((2, 3))).all()
    z = 0 > y
    assert (z.asnumpy() == np.zeros((2, 3))).all()

def test_ndarray_greater_equal():
    x = mx.nd.zeros((2, 3))
    y = mx.nd.ones((2, 3))
    z = x >= y
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = y >= 0
    assert (z.asnumpy() == np.ones((2, 3))).all()
    z = 0 >= y
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = y >= 1
    assert (z.asnumpy() == np.ones((2, 3))).all()

def test_ndarray_lesser():
    x = mx.nd.zeros((2, 3))
    y = mx.nd.ones((2, 3))
    z = y < x
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = 0 < y
    assert (z.asnumpy() == np.ones((2, 3))).all()
    z = y < 0
    assert (z.asnumpy() == np.zeros((2, 3))).all()

def test_ndarray_lesser_equal():
    x = mx.nd.zeros((2, 3))
    y = mx.nd.ones((2, 3))
    z = y <= x
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = 0 <= y
    assert (z.asnumpy() == np.ones((2, 3))).all()
    z = y <= 0
    assert (z.asnumpy() == np.zeros((2, 3))).all()
    z = 1 <= y
    assert (z.asnumpy() == np.ones((2, 3))).all()

def test_take():
    for data_ndim in range(2, 5):
        for idx_ndim in range(1, 4):
            data_shape = ()
            for _ in range(data_ndim):
                data_shape += (np.random.randint(low=3, high=6), )
            data_real = np.random.normal(size=data_shape).astype('float32')
            idx_shape = ()
            for _ in range(idx_ndim):
                idx_shape += (np.random.randint(low=3, high=5), )
            idx_real = np.random.randint(low=0, high=data_shape[0], size=idx_shape)
            data_real_mx = mx.nd.array(data_real)
            idx_real_mx = mx.nd.array(idx_real)
            result = mx.nd.take(data_real_mx, idx_real_mx)
            assert reldiff(result.asnumpy(), data_real[idx_real]) < 1e-6

if __name__ == '__main__':
    test_broadcast_binary()
    test_ndarray_setitem()
    test_ndarray_crop()
    test_ndarray_concatenate()
    test_broadcast()
    test_ndarray_elementwise()
    test_ndarray_elementwisesum()
    test_ndarray_slice()
    test_ndarray_pickle()
    test_ndarray_saveload()
    test_ndarray_copy()
    test_ndarray_negate()
    test_ndarray_scalar()
    test_clip()
    test_dot()
    test_ndarray_choose()
    test_ndarray_onehot()
    test_ndarray_fill()
    test_reduce()
    test_arange()
    test_order()
    test_ndarray_equal()
    test_take()