fix full and full_like when input is boolean (apache#17668)

python/mxnet/ndarray/numpy/_op.py

@@ -372,6 +372,9 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
         else:
             ret = broadcast_to(fill_value, shape).astype(dtype)
         return ret
+    if isinstance(fill_value, bool):
+        fill_value = int(fill_value)
+        dtype = _np.bool if dtype is None else dtype
     dtype = _np.float32 if dtype is None else dtype
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 # pylint: enable=too-many-arguments, redefined-outer-name
@@ -432,6 +435,8 @@ def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None): # pylin
         raise NotImplementedError
     if ctx is None:
         ctx = current_context()
+    if isinstance(fill_value, bool):
+        fill_value = int(fill_value)
     return _npi.full_like(a, fill_value=fill_value, dtype=dtype, ctx=ctx, out=out)
 
 

python/mxnet/symbol/numpy/_symbol.py

@@ -1255,6 +1255,9 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
         else:
             ret = broadcast_to(fill_value, shape).astype(dtype)
         return ret
+    if isinstance(fill_value, bool):
+        fill_value = int(fill_value)
+        dtype = _np.bool if dtype is None else dtype
     dtype = _np.float32 if dtype is None else dtype
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
@@ -1299,6 +1302,8 @@ def full_like(a, fill_value, dtype=None, order='C', ctx=None, out=None):  # pyli
         raise NotImplementedError
     if ctx is None:
         ctx = current_context()
+    if isinstance(fill_value, bool):
+        fill_value = int(fill_value)
     return _npi.full_like(a, fill_value=fill_value, ctx=ctx, dtype=dtype, out=out)
 
 

src/operator/tensor/init_op.h

@@ -95,14 +95,15 @@ struct FullLikeOpParam : public dmlc::Parameter<FullLikeOpParam> {
   dmlc::optional<int> dtype;
   DMLC_DECLARE_PARAMETER(FullLikeOpParam) {
     DMLC_DECLARE_FIELD(fill_value)
-    .describe("Value with which to fill newly created tensor");
+      .describe("Value with which to fill newly created tensor");
     DMLC_DECLARE_FIELD(ctx)
-    .set_default("")
-    .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
-              "Only used for imperative calls.");
-    DMLC_DECLARE_FIELD(dtype).set_default(dmlc::optional<int>())
-    MXNET_ADD_ALL_TYPES
-    .describe("Target data type.");
+      .set_default("")
+      .describe("Context of output, in format [cpu|gpu|cpu_pinned](n)."
+                "Only used for imperative calls.");
+    DMLC_DECLARE_FIELD(dtype)
+      .set_default(dmlc::optional<int>())
+      MXNET_ADD_ALL_TYPES_WITH_BOOL
+      .describe("Target data type.");
   }
 };
 

tests/python/unittest/test_numpy_op.py

@@ -5914,6 +5914,8 @@ def hybrid_forward(self, F, a):
     configs = [
         ((3, 4), 2.0),
         ((0, 3), 2.0),
+        ((2, 3), True),
+        ((3, 0), False),
         ((3, 4), np.array(2.0)),
         ((0, 3), np.array(2.0)),
         ((2, 3), np.array([1, 2, 3], dtype=np.float32)),
@@ -5923,7 +5925,7 @@ def hybrid_forward(self, F, a):
     ]
 
     rtol, atol = 1e-3, 1e-5
-    dtypes = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']
+    dtypes = ['float16', 'float32', 'float64', 'int8', 'int32', 'int64', 'bool']
     for shape, fill_value in configs:
         for hybridize in [True, False]:
             for dtype in dtypes:
@@ -5964,7 +5966,7 @@ def hybrid_forward(self, F, x, *args, **kwargs):
     if StrictVersion(platform.python_version()) < StrictVersion('3.0.0'):
         return
 
-    dtypes = ['float64', 'float32', 'float16', 'int64', 'int32', 'int8']
+    dtypes = ['float64', 'float32', 'float16', 'int64', 'int32', 'int8', 'bool']
     shapes = [
         (),
         (1,),
@@ -5977,11 +5979,11 @@ def hybrid_forward(self, F, x, *args, **kwargs):
         (3, 3, 1, 0),
     ]
     # numpy.full_like operator in py2 cannot handle shape like (5, 0, 3) properly
-    fill_values = [0, 1, 2, 3, 4, 5, 6]
+    fill_values = [0, 1, 2, 3, 4, 5, 6, True, False]
     flags = [True, False]
     for fill_value, dtype, shape, hybridize in itertools.product(
         fill_values, dtypes, shapes, flags):
-        param_dtype= _np.random.choice(dtypes)
+        param_dtype = _np.random.choice(dtypes)
         a = np.random.uniform(low=0, high=100, size=shape, dtype='float64').astype(dtype)
         test = TestFullLike(fill_value, param_dtype, npx.current_context())
         expected_ret = _np.full_like(a.asnumpy(), fill_value=fill_value, dtype=param_dtype)