anirudh2290 commented on a change in pull request #16476: added large tensor
support for add_n and tests for more ops
URL: https://github.com/apache/incubator-mxnet/pull/16476#discussion_r336241531
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File path: tests/nightly/test_large_vector.py
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@@ -708,6 +709,57 @@ def test_full():
assert a[-1] == 3
+def test_load_save():
+ x = create_vector(size=LARGE_X)
+ nd.save('large_vector', [x])
+ y = nd.load('large_vector')
+ y = y[0]
+ assert x[0] == y[0]
+ assert x[-1] == y[-1]
+ os.remove('large_vector')
+
+
+def test_add_n():
+ x = [nd.ones(LARGE_X)]
+ y = nd.add_n(*x)
+ assert y[0] == 1
+ assert y[-1] == 1
+
+
+def test_modulo():
+ x = mx.nd.ones(LARGE_X)*6
+ y = mx.nd.ones(LARGE_X)*4
+ z = (x%y)
+ assert z[0] == 2
+ assert z[-1] == 2
+ x = mx.nd.ones(LARGE_X)*5
+ z = nd.modulo(x,y)
+ assert z[0] == 1
+ assert z[-1] == 1
+
+
+def test_maximum():
+ x = mx.nd.ones(LARGE_X)*3
+ y = mx.nd.ones(LARGE_X)*4
+ z = nd.maximum(x, y)
+ assert z[0] == 4
+ assert z[-1] == 4
+ z = nd.maximum(x, 5)
+ assert z[0] == 5
+ assert z[-1] == 5
+
+
+def test_minimum():
+ x = mx.nd.ones(LARGE_X)*3
+ y = mx.nd.ones(LARGE_X)*2
+ z = nd.minimum(x, y)
+ assert z[0] == 2
+ assert z[-1] == 2
+ z = nd.minimum(x, 5)
+ assert z[0] == 3
+ assert z[-1] == 3
Review comment:
is there some large tensor docs in mxnet website. That should explicitly
call out that the backward pass has not been verified with large tensor.
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