haojin2 commented on a change in pull request #17254: [numpy] change unary
infer type
URL: https://github.com/apache/incubator-mxnet/pull/17254#discussion_r365556490
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -1865,15 +1890,53 @@ def hybrid_forward(self, F, a, *args, **kwargs):
'arccosh' : (lambda x: 1./(x**2 - 1.)**(1./2.), 2.0, 5.0),
'arctanh' : (lambda x: -1./(x**2 - 1.), -0.99, 0.99)
}
- if has_tvm_ops():
- funcs['rad2deg'] = (lambda x: 180. / _np.pi * _np.ones(x.shape), -1.0,
1.0)
- funcs['deg2rad'] = (lambda x: _np.pi / 180. * _np.ones(x.shape), -1.0,
1.0)
+
+ dtypes = ['float16', 'float32', 'float64', 'int8', 'uint8', 'int32',
'int64', 'bool']
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():
+ i = random.choice([rand_shape_nd(ndim, dim=3), (1, 0, 2)])
+ shapes = [i for i in [rand_shape_nd(ndim, dim=3), (1, 0, 2)]];
+ for func, func_data in funcs.items():
+ for dtype, shape in itertools.product(dtypes, shapes):
+ rtol = 1e-2 if dtype == np.float16 else 1e-3
+ atol = 1e-4 if dtype == np.float16 else 1e-5
ref_grad, low, high = func_data
- check_unary_func(func, ref_grad, shape, low, high)
+ # get rid of warning: divide by zero
+ if((func=='log' or func=='log10' or func=='log2') and
+ (dtype=='int8' or dtype=='uint8' or dtype=='int32' or
+ dtype=='int64')):
+ low = 1
+ if (func=='arctanh' and dtype=='bool'):
+ continue
+ np_func = getattr(_np, func)
+ mx_func = TestUnary2(func)
+ np_test_data = _np.random.uniform(low, high, shape).astype(dtype)
+ 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_
+
+ np_out = getattr(_np, func)(np_test_data)
+ mx_out = getattr(mx.np, 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)
+
Review comment:
Please also add checks for backward computation for floating point input
cases.
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