haojin2 commented on a change in pull request #15893: Numpy kron operator
URL: https://github.com/apache/incubator-mxnet/pull/15893#discussion_r314178597
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -829,6 +829,57 @@ def get_indices(axis_size):
assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3,
atol=1e-5)
+@with_seed()
+@use_np
+def test_np_kron():
+ class TestKron(HybridBlock):
+ def __init__(self):
+ super(TestKron, self).__init__()
+
+ def hybrid_forward(self, F, a, b):
+ return F.np.kron(a, b)
+
+ # test input
+ tensor_shapes = [
+ ((3,), (3,)),
+ ((2, 3), (3,)),
+ ((2, 3, 4), (2,)),
+ ((3, 2), ())
+ ]
+
+ for hybridize in [True, False]:
+ for a_shape, b_shape in tensor_shapes:
+ for dtype in [_np.float32, _np.float64]:
+ test_kron = TestKron()
+ if hybridize:
+ test_kron.hybridize()
+ a = rand_ndarray(shape=a_shape, dtype=dtype).as_np_ndarray()
+ b = rand_ndarray(shape=b_shape, dtype=dtype).as_np_ndarray()
+ a.attach_grad()
+ b.attach_grad()
+
+ np_out = _np.kron(a.asnumpy(), b.asnumpy())
+ with mx.autograd.record():
+ mx_out = test_kron(a, b)
+ assert mx_out.shape == np_out.shape
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3,
atol=1e-5)
+ mx_out.backward()
+
+ # Test imperative once again
+ mx_out = np.kron(a, b)
+ np_out = _np.kron(a.asnumpy(), b.asnumpy())
+ assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3,
atol=1e-5)
+
+ # test numeric gradient
+ if (len(a_shape) > 0 and len(b_shape) > 0 and
_np.prod(a_shape) > 0
+ and _np.prod(b_shape) > 0):
+ a_sym = mx.sym.Variable("a").as_np_ndarray()
+ b_sym = mx.sym.Variable("b").as_np_ndarray()
+ mx_sym = mx.sym.np.kron(a_sym, b_sym).as_nd_ndarray()
+ check_numeric_gradient(mx_sym, [a.as_nd_ndarray(),
b.as_nd_ndarray()],
+ rtol=1e-1, atol=1e-1, dtype=dtype)
Review comment:
Have you tried smaller numbers for `atol` and `rtol` ? Something like
`rtol=1e-2` and `atol=1e-4`?
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