[Numpy] Add gammaln, erf, erfinv to npx namespace

src/operator/tensor/elemwise_unary_op_basic.cc

@@ -877,6 +877,7 @@ The storage type of ``fix`` output depends upon the input storage type:
 
 // erf
 MXNET_OPERATOR_REGISTER_UNARY(erf)
+.add_alias("_npx_erf")
 .describe(R"code(Returns element-wise gauss error function of the input.
 
 Example::
@@ -898,6 +899,7 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_erf)
 
 // erfinv
 MXNET_OPERATOR_REGISTER_UNARY(erfinv)
+.add_alias("_npx_erfinv")
 .describe(R"code(Returns element-wise inverse gauss error function of the input.
 
 Example::
@@ -929,6 +931,7 @@ MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR(_backward_gamma,
 
 // gammaln
 MXNET_OPERATOR_REGISTER_UNARY_WITH_SPARSE_DR(gammaln, cpu, mshadow_op::gammaln)
+.add_alias("_npx_gammaln")
 MXNET_ADD_SPARSE_OP_ALIAS(gammaln)
 .describe(R"code(Returns element-wise log of the absolute value of the gamma function \
 of the input.

tests/python/unittest/test_numpy_op.py

@@ -24,6 +24,7 @@
 import platform
 import mxnet as mx
 import scipy.stats as ss
+import scipy.special as scipy_special
 from nose.tools import assert_raises
 from mxnet import np, npx
 from mxnet.gluon import HybridBlock
@@ -2550,6 +2551,59 @@ def _test_bernoulli_exception(prob, logit):
                 assertRaises(MXNetError, _test_bernoulli_exception, scaled_prob, None)
 
 
+@with_seed()
+@use_np
+def test_npx_special_unary_func():
+    def check_unary_func(func, ref_grad, shape, low, high):
+        class TestUnary(HybridBlock):
+            def __init__(self, func):
+                super(TestUnary, self).__init__()
+                self._func = func
+
+            def hybrid_forward(self, F, a, *args, **kwargs):
+                return getattr(F.npx, self._func)(a)
+
+        np_func = getattr(scipy_special, func)
+        mx_func = TestUnary(func)
+        np_test_data = _np.random.uniform(low, high, shape).astype(_np.float32)
+        mx_test_data = mx.numpy.array(np_test_data)
+        for hybridize in [True, False]:
+            if hybridize:
+                mx_func.hybridize()
+            if ref_grad:
+                mx_test_data.attach_grad()
+            np_out = np_func(np_test_data)
+            with mx.autograd.record():
+                y = mx_func(mx_test_data)
+            assert y.shape == np_out.shape
+            assert_almost_equal(y.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+            if np_out.dtype == np.bool_:
+                assert y.dtype == np.bool_
+
+            if ref_grad:
+                y.backward()
+                assert_almost_equal(mx_test_data.grad.asnumpy(), ref_grad(np_test_data), rtol=1e-1, atol=1e-2, equal_nan=True)
+
+        np_out = getattr(scipy_special, func)(np_test_data)
+        mx_out = getattr(mx.npx, func)(mx_test_data)
+        assert mx_out.shape == np_out.shape
+        assert_almost_equal(mx_out.asnumpy(), np_out, rtol=1e-3, atol=1e-5)
+
+    import math
+    funcs = {
+        'erf' : (lambda x: 2.0 / math.sqrt(math.pi) * _np.exp(-(x ** 2)), 0.5, 0.5),
+        'erfinv' : (lambda x: 0.5 * math.sqrt(math.pi) * _np.exp(scipy_special.erfinv(x) ** 2), 0.5, 0.5),
+        'gamma' : (lambda x: scipy_special.gamma(x) * scipy_special.psi(x), 0.5, 0.5),
+        'gammaln' : (lambda x: scipy_special.psi(x), 0.5, 0.5)
+    }
+    ndim = random.choice([2, 3, 4])
+    shape = random.choice([rand_shape_nd(ndim, dim=3), (1, 0, 2)])
+    for shape in [rand_shape_nd(ndim, dim=3), (1, 0, 2)]:
+        for func, func_data in funcs.items():
+            ref_grad, low, high = func_data
+            check_unary_func(func, ref_grad, shape, low, high)
+
+
 @with_seed()
 @use_np
 def test_np_random():