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_r316442809
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File path: tests/nightly/test_large_vector.py
##########
@@ -33,6 +35,293 @@ def test_slice():
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
+ 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 np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+ res = a + 1
+ assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+ res = nd.sqrt(a + 3)
+ assert np.sum(res[-1].asnumpy() == 2) == a.shape[1]
+
+
+def test_reduce():
+ a = nd.ones(shape=(LARGE_X, SMALL_Y))
+ assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1]
+
+
+def test_FullyConnected():
+ a = nd.ones(shape=(LARGE_X, SMALL_Y))
+ b = nd.ones(shape=(SMALL_Y, SMALL_Y))
+ res = nd.FullyConnected(a, b, num_hidden=b.shape[1], no_bias=True)
+ assert np.sum(res[-1].asnumpy() == SMALL_Y) == b.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)
+ res = nd.clip(a, a_min=100, a_max=1000)
+ assert np.sum(res[-1].asnumpy() == 1000) == 101
Review comment:
Have you tested this? Isn't np.sum(res[-1].asnumpy() == 1000) == 1?
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