[GitHub] [incubator-mxnet] xidulu commented on a change in pull request #17415: [numpy]add op random.lognormal

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

xidulu commented on a change in pull request #17415: [numpy]add op random.lognormal 
URL: https://github.com/apache/incubator-mxnet/pull/17415#discussion_r369928236
 
 

 ##########
 File path: tests/python/unittest/test_numpy_op.py
 ##########
 @@ -3340,6 +3340,56 @@ def hybrid_forward(self, F, loc, scale):
                 assert_almost_equal(loc.grad.asnumpy().sum(), _np.ones(out_shape).sum(), rtol=1e-3, atol=1e-5)
 
 
+@with_seed()
+@use_np
+def test_np_lognormal_grad():
+    class TestLognormalGrad(HybridBlock):
+        def __init__(self, shape):
+            super(TestLognormalGrad, self).__init__()
+            self._shape = shape
+
+        def hybrid_forward(self, F, mean, sigma):
+            return F.np.random.lognormal(mean, sigma, self._shape)
+
+    param_shape = [
+        [(3, 2), (3, 2)],
+        [(3, 2, 2), (3, 2, 2)],
+        [(3, 4, 5), (4, 1)],
+    ]
+    output_shapes = [
+        (3, 2),
+        (4, 3, 2, 2),
+        (3, 4, 5)
+    ]
+    for hybridize in [False, True]:
+        for ((shape1, shape2), out_shape) in zip(param_shape, output_shapes):
+            test_lognormal_grad = TestLognormalGrad(out_shape)
+            if hybridize:
+                test_lognormal_grad.hybridize()
+            mean = np.zeros(shape1)
+            mean.attach_grad()
+            sigma = np.ones(shape2)
+            sigma.attach_grad()
+            with mx.autograd.record():
+                mx_out = test_lognormal_grad(mean, sigma)
+            np_out = _np.random.lognormal(mean = mean.asnumpy(), 
+                                            sigma = sigma.asnumpy(), size = out_shape)
+            assert_almost_equal(np_out.shape, mx_out.shape)
 
 Review comment:
   You'd better use `assert np_out.shape == mx_out.shape`

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