access2rohit commented on a change in pull request #18932:
URL: https://github.com/apache/incubator-mxnet/pull/18932#discussion_r472437632
##########
File path: tests/nightly/test_np_large_array.py
##########
@@ -76,3 +78,459 @@ def test_softmax():
true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis]))
output = npx.softmax(input_data, axis=axis)
assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5,
atol=1e-5)
+
+'''
+ _ _ _ _ _ __ _ __ _ _
+ | ' \ || | ' \| '_ \ || |
+ |_||_\_,_|_|_|_| .__/\_, |
+ |_| |__/
+'''
+
+@use_np
+def test_ones():
+ A = np.ones((INT_OVERFLOW, 2))
+ assert A.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_zeros():
+ A = np.zeros((INT_OVERFLOW, 2))
+ assert A.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_abs():
+ A = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.abs(A)
+ print(B)
+ assert B.shape == (INT_OVERFLOW, 2)
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_absolute():
+ A = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.absolute(A)
+ print(B)
+ assert B.shape == (INT_OVERFLOW, 2)
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_add():
+ A = np.ones((INT_OVERFLOW, 2))
+ B = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ C = np.add(A, B)
+ print(C)
+ assert C.shape == (INT_OVERFLOW, 2)
+ C.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+# this will fail; broadcast needs to be fixed
+# TODO add backward test after forward is fixed
+@use_np
[email protected](reason='Does not support large tensor; to be fixed')
+def test_add_broadcast():
+ A = np.ones((INT_OVERFLOW, 2))
+ B = np.ones((INT_OVERFLOW, 1))
+ C = np.add(A, B)
+ print(C)
+ assert C.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_all():
+ A = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.all(A)
+ print(B)
+ assert B.asnumpy() == True
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
Review comment:
bkwrd value check
##########
File path: tests/nightly/test_np_large_array.py
##########
@@ -76,3 +78,459 @@ def test_softmax():
true_output = np.full((SMALL_Y, LARGE_X), (1 / input_data.shape[axis]))
output = npx.softmax(input_data, axis=axis)
assert_almost_equal(output.asnumpy(), true_output, rtol=1e-5,
atol=1e-5)
+
+'''
+ _ _ _ _ _ __ _ __ _ _
+ | ' \ || | ' \| '_ \ || |
+ |_||_\_,_|_|_|_| .__/\_, |
+ |_| |__/
+'''
+
+@use_np
+def test_ones():
+ A = np.ones((INT_OVERFLOW, 2))
+ assert A.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_zeros():
+ A = np.zeros((INT_OVERFLOW, 2))
+ assert A.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_abs():
+ A = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.abs(A)
+ print(B)
+ assert B.shape == (INT_OVERFLOW, 2)
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_absolute():
+ A = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.absolute(A)
+ print(B)
+ assert B.shape == (INT_OVERFLOW, 2)
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_add():
+ A = np.ones((INT_OVERFLOW, 2))
+ B = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ C = np.add(A, B)
+ print(C)
+ assert C.shape == (INT_OVERFLOW, 2)
+ C.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+# this will fail; broadcast needs to be fixed
+# TODO add backward test after forward is fixed
+@use_np
[email protected](reason='Does not support large tensor; to be fixed')
+def test_add_broadcast():
+ A = np.ones((INT_OVERFLOW, 2))
+ B = np.ones((INT_OVERFLOW, 1))
+ C = np.add(A, B)
+ print(C)
+ assert C.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_all():
+ A = np.ones((INT_OVERFLOW, 2))
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.all(A)
+ print(B)
+ assert B.asnumpy() == True
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
+
+@use_np
+def test_amin():
+ A = np.ones((INT_OVERFLOW, 2))
+ A[100][1] = -1
+ A.attach_grad()
+ with mx.autograd.record():
+ B = np.amin(A)
+ print(B)
+ assert B.asnumpy() == -1.0
+ B.backward()
+ assert A.grad.shape == (INT_OVERFLOW, 2)
Review comment:
bkwrd value check
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