all changes

fix sanity problem

change is_op_runnable's location

Fix

Author: JiangZhaoh

python/mxnet/numpy_dispatch_protocol.py

@@ -117,6 +117,8 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'transpose',
     'unique',
     'var',
+    'vdot',
+    'vstack',
     'zeros_like',
     'linalg.norm',
     'trace',
@@ -214,6 +216,12 @@ def _register_array_function():
     'trunc',
     'floor',
     'logical_not',
+    'equal',
+    'not_equal',
+    'less',
+    'less_equal',
+    'greater',
+    'greater_equal'
 ]
 
 

tests/python/unittest/test_numpy_interoperability.py

@@ -32,6 +32,14 @@
 _INT_DTYPES = [np.int8, np.int32, np.int64, np.uint8]
 _FLOAT_DTYPES = [np.float16, np.float32, np.float64]
 _DTYPES = _INT_DTYPES + _FLOAT_DTYPES
+_TVM_OPS = [
+    'equal',
+    'not_equal',
+    'less',
+    'less_equal',
+    'greater',
+    'greater_equal'
+]
 
 
 class OpArgMngr(object):
@@ -535,6 +543,13 @@ def _add_workload_roll():
 
 def _add_workload_stack(array_pool):
     OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2)
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, 1)
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, -1)
+    OpArgMngr.add_workload('stack', [array_pool['4x1']] * 2, -2)
+    OpArgMngr.add_workload('stack', np.random.normal(size=(2, 4, 3)), 2)
+    OpArgMngr.add_workload('stack', np.random.normal(size=(2, 4, 3)), -3)
+    OpArgMngr.add_workload('stack', np.array([[], [], []]), 1)
+    OpArgMngr.add_workload('stack', np.array([[], [], []]))
 
 
 def _add_workload_sum():
@@ -590,10 +605,22 @@ def _add_workload_unique():
 
 def _add_workload_var(array_pool):
     OpArgMngr.add_workload('var', array_pool['4x1'])
+    OpArgMngr.add_workload('var', np.array([np.float16(1.)]))
+    OpArgMngr.add_workload('var', np.array([1]))
+    OpArgMngr.add_workload('var', np.array([1.]))
+    OpArgMngr.add_workload('var', np.array([[1, 2, 3], [4, 5, 6]]))
+    OpArgMngr.add_workload('var', np.array([[1, 2, 3], [4, 5, 6]]), 0)
+    OpArgMngr.add_workload('var', np.array([[1, 2, 3], [4, 5, 6]]), 1)
+    OpArgMngr.add_workload('var', np.array([np.nan]))
+    OpArgMngr.add_workload('var', np.array([1, -1, 1, -1]))
+    OpArgMngr.add_workload('var', np.array([1,2,3,4], dtype='f8'))
 
 
 def _add_workload_zeros_like(array_pool):
     OpArgMngr.add_workload('zeros_like', array_pool['4x1'])
+    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float64))
+    OpArgMngr.add_workload('zeros_like', np.random.uniform(size=(3, 3)).astype(np.float32))
+    OpArgMngr.add_workload('zeros_like', np.random.randint(2, size = (3, 3)))
 
 
 def _add_workload_outer():
@@ -933,6 +960,53 @@ def _add_workload_logical_not(array_pool):
     OpArgMngr.add_workload('logical_not', np.array([True, False, True, False], dtype=np.bool))
 
 
+def _add_workload_vdot():
+    OpArgMngr.add_workload('vdot', np.random.normal(size=(2, 4)), np.random.normal(size=(4, 2)))
+    OpArgMngr.add_workload('vdot', np.random.normal(size=(2, 4)).astype(np.float64), np.random.normal(size=(2, 4)).astype(np.float64))
+
+
+def _add_workload_vstack(array_pool):
+    OpArgMngr.add_workload('vstack', (array_pool['4x1'], np.random.uniform(size=(5, 1))))
+    OpArgMngr.add_workload('vstack', array_pool['4x1'])
+    OpArgMngr.add_workload('vstack', array_pool['1x1x0'])
+
+
+def _add_workload_equal(array_pool):
+    OpArgMngr.add_workload('equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('equal', np.array([np.nan]), np.array([np.nan]))
+    OpArgMngr.add_workload('equal', array_pool['4x1'], array_pool['1x2'])
+
+
+def _add_workload_not_equal(array_pool):
+    OpArgMngr.add_workload('not_equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('not_equal', np.array([np.nan]), np.array([np.nan]))
+    OpArgMngr.add_workload('not_equal', array_pool['4x1'], array_pool['1x2'])
+
+
+def _add_workload_greater(array_pool):
+    OpArgMngr.add_workload('greater', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('greater', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('greater', np.array([np.nan]), np.array([np.nan]))
+
+
+def _add_workload_greater_equal(array_pool):
+    OpArgMngr.add_workload('greater_equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('greater_equal', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('greater_equal', np.array([np.nan]), np.array([np.nan]))
+
+
+def _add_workload_less(array_pool):
+    OpArgMngr.add_workload('less', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('less', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('less', np.array([np.nan]), np.array([np.nan]))
+
+
+def _add_workload_less_equal(array_pool):
+    OpArgMngr.add_workload('less_equal', np.array([0, 1, 2, 4, 2], dtype=np.float16), np.array([-2, 5, 1, 4, 3], dtype=np.float16))
+    OpArgMngr.add_workload('less_equal', array_pool['4x1'], array_pool['1x2'])
+    OpArgMngr.add_workload('less_equal', np.array([np.nan]), np.array([np.nan]))
+
+
 @use_np
 def _prepare_workloads():
     array_pool = {
@@ -1028,6 +1102,14 @@ def _prepare_workloads():
     _add_workload_turnc(array_pool)
     _add_workload_floor(array_pool)
     _add_workload_logical_not(array_pool)
+    _add_workload_vdot()
+    _add_workload_vstack(array_pool)
+    _add_workload_equal(array_pool)
+    _add_workload_not_equal(array_pool)
+    _add_workload_greater(array_pool)
+    _add_workload_greater_equal(array_pool)
+    _add_workload_less(array_pool)
+    _add_workload_less_equal(array_pool)
 
 
 _prepare_workloads()
@@ -1070,6 +1152,8 @@ def _check_interoperability_helper(op_name, *args, **kwargs):
 
 def check_interoperability(op_list):
     for name in op_list:
+        if name in _TVM_OPS and not is_op_runnable():
+            continue
         print('Dispatch test:', name)
         workloads = OpArgMngr.get_workloads(name)
         assert workloads is not None, 'Workloads for operator `{}` has not been ' \