[MXNET-1407] Added test to verify Large Tensor Support for pad operator

tests/nightly/test_large_array.py

@@ -319,6 +319,39 @@ def test_flip():
     assert np.sum(t[-1, :].asnumpy() == 0) == b.shape[1]
 
 
+def check_pad_with_shape(shape, pad_width, mode, dtype="float64"):
+    # bind with label
+    X = mx.symbol.Variable('X', dtype=dtype)
+    Y = mx.symbol.Pad(data=X, mode=mode, pad_width=pad_width)
+    x = mx.random.uniform(-1, 1, shape, dtype=dtype)
+    # numpy result
+    pad_grouped = list(zip(*[iter(list(pad_width))] * 2))
+    np_out = np.pad(x.asnumpy(), pad_grouped, mode)
+    # mxnet result
+    grad = mx.nd.empty(shape, dtype=dtype)
+    exec1 = Y.bind(args=[x], ctx=mx.cpu(), args_grad={'X': grad})
+    exec1.forward(is_train=True)
+    out = exec1.outputs[0].asnumpy()
+    # compare numpy + mxnet
+    assert_almost_equal(out, np_out)
+
+def test_pad():
+    shape1 = (LARGE_X, 2, 2, 2)
+    pad1 = (0, 0, 0, 0, 1, 1, 1, 1)
+    shape2 = (LARGE_X, 2, 2, 1, 1)
+    pad2 = (0, 0, 0, 0, 1, 1, 0, 0, 0, 0)
+    # note: this op doesn't support ints yet. Add tests when supported
+    dtypes = ["float16", "float32", "float64"]
+    dtypes = ["float16"]
+    for dtype in dtypes:
+        check_pad_with_shape(shape1, pad1, 'constant', dtype)
+        check_pad_with_shape(shape1, pad1, 'edge', dtype)
+        check_pad_with_shape(shape2, pad2, 'constant', dtype)
+        check_pad_with_shape(shape2, pad2, 'edge', dtype)
+        check_pad_with_shape(shape1, pad1, 'reflect', dtype)
+        check_pad_with_shape(shape2, pad2, 'reflect', dtype)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()