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removing tests not required for vector testing
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Rohit Kumar Srivastava committed Aug 26, 2019
1 parent 12e7736 commit fb9cdb1
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10 changes: 4 additions & 6 deletions python/mxnet/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -264,15 +264,13 @@ def assign_each2(input1, input2, function):

# For testing Large Tensors having total size > 2^32 elements
def create_2d_tensor(rows, columns, dtype=np.int64):
a = nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
b = nd.broadcast_to(a, shape=(a.shape[0], columns))
return nd.array(b, dtype=dtype)
a = mx.nd.arange(0, rows, dtype=dtype).reshape(rows, 1)
b = mx.nd.broadcast_to(a, shape=(a.shape[0], columns))
return b

# For testing Large Vectors having total size > 2^32 elements
def create_vector(size, dtype=np.int64):
a = nd.arange(0, size, dtype=dtype)
# Implicitly calling nd.waitall()
assert a[0] == 0
a = mx.nd.arange(0, size, dtype=dtype)
return a

def rand_sparse_ndarray(shape, stype, density=None, dtype=None, distribution=None,
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160 changes: 14 additions & 146 deletions tests/nightly/test_large_vector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,32 +18,22 @@
import numpy as np
import mxnet as mx

from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, create_vector
from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, create_vector
from mxnet import gluon, nd
from tests.python.unittest.common import with_seed

# dimension constants
LARGE_X = 5000000000
MEDIUM_X = 1000000000
LARGE_Y = 100000
SMALL_Y = 1


def test_slice():
a = nd.ones(LARGE_X)
res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X)
assert a[0] == 1
assert res.shape[0] == MEDIUM_X


def test_gluon_embedding():
m = gluon.nn.Embedding(1, LARGE_Y)
m.initialize()
a = nd.zeros((LARGE_Y, 1))
b = m(a)
assert b.shape == (LARGE_Y, 1, LARGE_Y)
assert b.asnumpy().size == LARGE_X*2


def test_ndarray_zeros():
a = nd.zeros(shape=LARGE_X)
assert a[-1] == 0
Expand Down Expand Up @@ -73,7 +63,7 @@ def test_ndarray_random_randint():
a = nd.random.randint(low_large_value, high_large_value, dtype=np.int64)
low = mx.nd.array([low_large_value], dtype='int64')
high = mx.nd.array([high_large_value], dtype='int64')
assert a.__gt__(low) and a.__lt__(high)
assert a > low and a < high


def test_ndarray_empty():
Expand All @@ -93,36 +83,22 @@ def test_elementwise():


def test_reduce():
a = nd.ones(shape=(LARGE_X, SMALL_Y))
a = nd.ones(shape=(LARGE_X, 1))
assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]


def test_broadcast():
a = nd.ones(shape=(LARGE_X, SMALL_Y*2))
b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
res = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y*2))
assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1]
res = mx.nd.broadcast_like(b, a)
assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1]


def test_clip():
a = nd.arange(0, LARGE_X)
a = create_vector(LARGE_X)
res = nd.clip(a, a_min=100, a_max=1000)
assert np.sum(res[-1].asnumpy() == 1000) == 1


def test_argmin():
a = nd.arange(0, LARGE_X)
a = create_vector(LARGE_X, dtype=np.float32)
assert a[0] == 0
idx = mx.nd.argmin(a, axis=0)
assert idx.shape[0] == SMALL_Y


def test_tile():
a = nd.arange(0, LARGE_X)
b = nd.tile(a, reps=(1,2))
assert b[0][LARGE_X] == b[0][0]
assert b[0][LARGE_X-1] == b[0][-1]
assert idx[0] == 0
assert idx.shape[0] == 1


def test_take():
Expand Down Expand Up @@ -169,114 +145,6 @@ def test_Dense(ctx=mx.cpu(0)):
assert res.shape == (LARGE_X, 2)


def test_pick():
a = mx.nd.ones(shape=(LARGE_X, 2))
b = mx.nd.ones(shape=LARGE_X)
res = mx.nd.pick(a, b)
assert res.shape == b.shape


def test_depthtospace():
def numpy_depth_to_space(x, blocksize):
b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w])
tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2])
y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize])
return y

shape_inp = (LARGE_X, 4, 1, 1)
data = rand_ndarray(shape_inp, 'default')
data_np = data.asnumpy()
expected = numpy_depth_to_space(data_np, 2)
output = mx.nd.depth_to_space(data, 2)
assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)


def test_spacetodepth():
def numpy_space_to_depth(x, blocksize):
b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize])
tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4])
y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize])
return y

shape_inp = (LARGE_X, 1, 2, 2)
data = rand_ndarray(shape_inp, 'default')
data_np = data.asnumpy()
expected = numpy_space_to_depth(data_np, 2)
output = mx.nd.space_to_depth(data, 2)
assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3)

@with_seed()
def test_diag():
a_np = np.random.random((LARGE_X, 2)).astype(np.float32)
a = mx.nd.array(a_np)

# k == 0
r = mx.nd.diag(a)
assert_almost_equal(r.asnumpy(), np.diag(a_np))

# k == 1
k = 1
r = mx.nd.diag(a, k=k)
assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))

# k == -1
k = -1
r = mx.nd.diag(a, k=k)
assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))


@with_seed()
def test_ravel_multi_index():
x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, SMALL_Y, 4)
x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, SMALL_Y, 3)
x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, SMALL_Y, 2)
indices_2d = [[x1, x2, x3], [y1, y2, y3]]
idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64), shape=(LARGE_X, 5))
idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, 5))
assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3


@with_seed()
def test_unravel_index():
x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, SMALL_Y, 4)
x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, SMALL_Y, 3)
x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, SMALL_Y, 2)
original_2d_indices = [[x1, x2, x3], [y1, y2, y3]]
idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, 5))
indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64), shape=(LARGE_X, 5))
assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all()


def test_transpose():
b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(1, LARGE_X)
t = b.T
assert t.shape == (LARGE_X, 1)
assert t[-1, 0].asnumpy() == (LARGE_X - 1)


def test_swapaxes():
b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(LARGE_X, 1)
t = nd.swapaxes(b, dim1=0, dim2=1)
assert t.shape == (1, LARGE_X)
assert t[0, -1].asnumpy() == (LARGE_X - 1)


def test_flip():
b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(1, LARGE_X)
t = nd.flip(b, axis=1)
assert t.shape == (1, LARGE_X)
assert t[-1, -1].asnumpy() == 0


def test_softmax():
input_data = nd.ones((2, LARGE_X))
output = nd.softmax(input_data, axis=0)
assert output[0][0] == 0.5
assert output[-1][-1] == 0.5


def test_argsort():
b = create_vector(size=LARGE_X)
s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64)
Expand All @@ -287,19 +155,19 @@ def test_argsort():
def test_sort():
b = create_vector(size=LARGE_X)
s = nd.sort(b, axis=0, is_ascend=False)
assert np.sum(s[-1][SMALL_Y//2:SMALL_Y].asnumpy() == 0).all()
assert np.sum(s[-1].asnumpy() == 0).all()
s = nd.sort(b, is_ascend=True)
assert np.sum(s[0].asnumpy() == 0).all()


def test_topk():
b = create_vector(size=LARGE_X)
k = nd.topk(b, k=10, axis=0, dtype=np.int64)
assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y
ind = nd.topk(b, k=10, axis=0, dtype=np.int64)
assert np.sum(ind.asnumpy() == (LARGE_X - 1)) == 1
ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False)
assert np.all(ind == val)
l = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value")
assert l.sum() == (LARGE_X - 1)
val = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value")
assert val.sum() == (LARGE_X - 1)


if __name__ == '__main__':
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