[Numpy] More numpy dispatch tests (#16426)

* tests added

* remove not equal

* fix tiny bug

* remove meshgrid test

* modify meshgrid return type, add test

python/mxnet/numpy/stride_tricks.py

@@ -51,6 +51,6 @@ def broadcast_arrays(*args):
 
     if all(array.shape == shape for array in args):
         # Common case where nothing needs to be broadcasted.
-        return args
+        return list(args)
 
     return [_mx_np_op.broadcast_to(array, shape) for array in args]

python/mxnet/numpy_dispatch_protocol.py

@@ -113,6 +113,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'transpose',
     'var',
     'zeros_like',
+    'meshgrid',
+    'outer'
 ]
 
 
@@ -196,6 +198,7 @@ def _register_array_function():
     'ceil',
     'trunc',
     'floor',
+    'logical_not',
 ]
 
 

tests/python/unittest/test_numpy_interoperability.py

@@ -77,6 +77,9 @@ def _prepare_workloads():
     OpArgMngr.add_workload('min', array_pool['4x1'])
     OpArgMngr.add_workload('mean', array_pool['4x1'])
     OpArgMngr.add_workload('mean', array_pool['4x1'], axis=0, keepdims=True)
+    OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]))
+    OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=0)
+    OpArgMngr.add_workload('mean', np.array([[1, 2, 3], [4, 5, 6]]), axis=1)
     OpArgMngr.add_workload('ones_like', array_pool['4x1'])
     OpArgMngr.add_workload('prod', array_pool['4x1'])
 
@@ -157,6 +160,10 @@ def _prepare_workloads():
     OpArgMngr.add_workload('transpose', array_pool['4x1'])
     OpArgMngr.add_workload('var', array_pool['4x1'])
     OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
+    OpArgMngr.add_workload('outer', np.ones((5)), np.ones((2)))
+    OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]))
+    OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]))
+    OpArgMngr.add_workload('meshgrid', np.array([1, 2, 3]), np.array([4, 5, 6, 7]), indexing='ij')
 
     # workloads for array ufunc protocol
     OpArgMngr.add_workload('add', array_pool['4x1'], array_pool['1x2'])
@@ -175,6 +182,9 @@ def _prepare_workloads():
     OpArgMngr.add_workload('power', array_pool['4x1'], 2)
     OpArgMngr.add_workload('power', 2, array_pool['4x1'])
     OpArgMngr.add_workload('power', array_pool['4x1'], array_pool['1x1x0'])
+    OpArgMngr.add_workload('power', np.array([1, 2, 3], np.int32), 2.00001)
+    OpArgMngr.add_workload('power', np.array([15, 15], np.int64), np.array([15, 15], np.int64))
+    OpArgMngr.add_workload('power', 0, np.arange(1, 10))
     OpArgMngr.add_workload('mod', array_pool['4x1'], array_pool['1x2'])
     OpArgMngr.add_workload('mod', array_pool['4x1'], 2)
     OpArgMngr.add_workload('mod', 2, array_pool['4x1'])
@@ -256,6 +266,12 @@ def _signs(dt):
     OpArgMngr.add_workload('exp', array_pool['4x1'])
     OpArgMngr.add_workload('log', array_pool['4x1'])
     OpArgMngr.add_workload('log2', array_pool['4x1'])
+    OpArgMngr.add_workload('log2', np.array(2.**65))
+    OpArgMngr.add_workload('log2', np.array(np.inf))
+    OpArgMngr.add_workload('log2', np.array(1.))
+    OpArgMngr.add_workload('log1p', np.array(-1.))
+    OpArgMngr.add_workload('log1p', np.array(np.inf))
+    OpArgMngr.add_workload('log1p', np.array(1e-6))
     OpArgMngr.add_workload('log10', array_pool['4x1'])
     OpArgMngr.add_workload('expm1', array_pool['4x1'])
     OpArgMngr.add_workload('sqrt', array_pool['4x1'])
@@ -282,6 +298,11 @@ def _signs(dt):
     OpArgMngr.add_workload('ceil', array_pool['4x1'])
     OpArgMngr.add_workload('trunc', array_pool['4x1'])
     OpArgMngr.add_workload('floor', array_pool['4x1'])
+    OpArgMngr.add_workload('logical_not', np.ones(10, dtype=np.int32))
+    OpArgMngr.add_workload('logical_not', array_pool['4x1'])
+    OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))
+
+
 
 
 _prepare_workloads()