[GitHub] [incubator-mxnet] access2rohit commented on a change in pull request #19617: large tensor tests batch 6

[GitBox <gi...@apache.org>]

access2rohit commented on a change in pull request #19617:
URL: https://github.com/apache/incubator-mxnet/pull/19617#discussion_r538985565



##########
File path: tests/nightly/test_np_large_array.py
##########
@@ -2345,3 +2355,210 @@ def test_insert():
     assert out[0, 1] == 1 and out[-1, 1] == 2
     assert out2[1] == 5 and out2[2] == 6
     assertRaises(MXNetError, np.insert, arr=inp3, obj=np.array([2, 2], dtype=np.int64), values=np.array([5, 6]))
+
+
+@use_np
+def test_moveaxis():
+    inp = np.zeros((2, 1, INT_OVERFLOW))
+    inp[0, 0, -1], inp[1, 0, -1] = 1, 2
+    inp.attach_grad()
+    with mx.autograd.record():
+        out = np.moveaxis(inp, 2, 0)
+        out.backward()
+    assert out.shape == (INT_OVERFLOW, 2, 1)
+    assert out[-1, 0, 0] == 1 and out[-1, 1, 0] == 2
+    assert inp.grad.shape == inp.shape
+    assert inp.grad[-1, -1, -1] == 1
+
+
+@use_np
+def test_newaxis():
+    inp = np.zeros((2, INT_OVERFLOW))
+    inp[-1, -1] = 1
+    out1 = inp[np.newaxis, :, :]
+    assert out1.shape == (1, 2, INT_OVERFLOW)
+    assert out1[0, -1, -1] == 1
+    out1 = out1[:, :, :, np.newaxis]
+    assert out1.shape == (1, 2, INT_OVERFLOW, 1)
+    assert out1[0, -1, -1, 0] == 1
+
+
+@use_np
+def test_triu_indices():
+    N = 2**16
+    data = np.triu_indices(N, 1)
+    assert data[0].shape == (((1 + (N-1)) * (N-1) / 2), )
+    assert data[0][-1] == N - 2 and data[1][-1] == N - 1
+
+
+@use_np
+def test_triu_indices_from():
+    N = 2**16
+    arr = np.zeros((N, N))
+    data = np.triu_indices_from(arr, 1)
+    assert data[0].shape == (((1 + (N-1)) * (N-1) / 2), )
+    assert data[0][-1] == N - 2 and data[1][-1] == N - 1
+
+
+@use_np
+def test_empty():
+    data = np.empty((2, INT_OVERFLOW), dtype='float64')
+    data = data + 1
+    assert data.shape == (2, INT_OVERFLOW)
+    assert data[-1, -1] == 1
+
+
+@use_np
+def test_shape_reshape():
+    inp = np.zeros((2, INT_OVERFLOW))
+    inp[0, -1] = 1
+    npx.waitall()
+    assert np.shape(inp) == (2, INT_OVERFLOW)
+    out = np.reshape(inp, (INT_OVERFLOW, 2))
+    npx.waitall()

Review comment:
       why do we need waitall() here ?




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