apeforest commented on a change in pull request #15943: Added tests to verify
Large Vector Support for initial set of ops
URL: https://github.com/apache/incubator-mxnet/pull/15943#discussion_r317387091
##########
File path: tests/nightly/test_large_vector.py
##########
@@ -30,9 +30,159 @@
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_ndarray_zeros():
+ a = nd.zeros(shape=LARGE_X)
+ assert a[-1] == 0
+ assert a.shape == (LARGE_X,)
+ assert a.size == LARGE_X
+
+
+def test_ndarray_ones():
+ a = nd.ones(shape=LARGE_X)
+ assert a[-1] == 1
+ assert nd.sum(a).asnumpy() == LARGE_X
+
+
+@with_seed()
+def test_ndarray_random_uniform():
+ a = nd.random.uniform(shape=LARGE_X)
+ assert a[-1] != 0
+
+
+@with_seed()
+def test_ndarray_random_randint():
+ a = nd.random.randint(100, 10000, shape=LARGE_X)
+ assert a.shape == (LARGE_X,)
+ # check if randint can generate value greater than 2**32 (large)
+ low_large_value = 2**32
+ high_large_value = 2**34
+ 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)
+
+
+def test_ndarray_empty():
+ a = nd.empty(LARGE_X)
+ assert a.shape == (LARGE_X,)
+
+
+def test_elementwise():
+ a = nd.ones(shape=LARGE_X)
+ b = nd.ones(shape=LARGE_X)
+ res = a + b
+ assert res[-1].asnumpy() == 2
+ res = a + 1
+ assert res[-1].asnumpy() == 2
+ res = nd.sqrt(a + 8)
+ assert res[-1].asnumpy() == 3
+
+
+def test_reduce():
+ a = nd.ones(shape=(LARGE_X, 1))
+ assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
+
+
+def test_clip():
+ 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 = create_vector(LARGE_X)
+ assert a[0] == 0
+ idx = mx.nd.argmin(a, axis=0)
+ assert idx.shape[0] == 1
+
+
+def test_take():
+ a = nd.ones(shape=LARGE_X)
+ idx = nd.arange(LARGE_X - 1000, LARGE_X)
+ res = nd.take(a, idx)
+ assert np.sum(res.asnumpy() == 1) == res.shape[0]
+
+
+def test_slice_assign():
+ a = nd.ones(shape=LARGE_X)
+ a[LARGE_X-1:LARGE_X] = 1000
+ assert np.sum(a[-1].asnumpy() == 1000) == 1
+
+
+def test_expand_dims():
+ a = nd.ones(shape=LARGE_X)
+ res = nd.expand_dims(a, axis=0)
+ assert res[0][0] == 1
+ assert res.shape == (1, a.shape[0])
+
+
+def test_squeeze():
+ a = nd.ones(shape=LARGE_X)
+ data = nd.expand_dims(a, axis=0)
+ res = nd.squeeze(data)
+ assert a[0] == res[0]
+ assert res.shape == a.shape
+
+
+def test_broadcast_div():
+ a = nd.ones(shape=LARGE_X)
+ b = nd.ones(shape=LARGE_X) * 2
+ res = a / b
+ assert np.sum(res.asnumpy() == 0.5) == a.shape[0]
+
+
+def test_Dense(ctx=mx.cpu(0)):
+ data = mx.nd.ones(shape=LARGE_X)
+ linear = gluon.nn.Dense(2)
+ linear.initialize(ctx=ctx)
+ res = linear(data)
+ res.wait_to_read()
+ 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_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)
+ mx.nd.waitall()
+ assert (s[0].asnumpy() == (LARGE_X - 1)).all()
+
+
+def test_sort():
+ b = create_vector(size=LARGE_X)
+ s = nd.sort(b, axis=0, is_ascend=False)
+ 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)
Review comment:
would it be more consistent to rename it to ind?
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