Cherry-pick support the bool tensor for the compare ops (#33551)

paddle/fluid/operators/controlflow/compare_all_op.cc

@@ -138,6 +138,8 @@ class CompareReduceOp : public framework::OperatorWithKernel {
 #define REGISTER_COMPARE_REDUCE_CPU_KERNEL(op_type, functor)             \
   REGISTER_OP_CPU_KERNEL(                                                \
       op_type, ::paddle::operators::CompareReduceOpKernel<               \
+                   ::paddle::platform::CPUDeviceContext, functor<bool>>, \
+      ::paddle::operators::CompareReduceOpKernel<                        \
           ::paddle::platform::CPUDeviceContext, functor<int>>,           \
       ::paddle::operators::CompareReduceOpKernel<                        \
           ::paddle::platform::CPUDeviceContext, functor<int64_t>>,       \

paddle/fluid/operators/controlflow/compare_all_op.cu

@@ -88,6 +88,8 @@ class CompareReduceOpKernel
 #define REGISTER_COMPARE_REDUCE_CUDA_KERNEL(op_type, functor)           \
   REGISTER_OP_CUDA_KERNEL(                                              \
       op_type, paddle::operators::CompareReduceOpKernel<                \
+                   paddle::platform::CUDADeviceContext, functor<bool>>, \
+      paddle::operators::CompareReduceOpKernel<                         \
           paddle::platform::CUDADeviceContext, functor<int>>,           \
       paddle::operators::CompareReduceOpKernel<                         \
           paddle::platform::CUDADeviceContext, functor<int64_t>>,       \
@@ -95,5 +97,6 @@ class CompareReduceOpKernel
           paddle::platform::CUDADeviceContext, functor<float>>,         \
       paddle::operators::CompareReduceOpKernel<                         \
           paddle::platform::CUDADeviceContext, functor<double>>);
+
 REGISTER_COMPARE_REDUCE_CUDA_KERNEL(equal_all,
                                     paddle::operators::EqualReduceFunctor);

paddle/fluid/operators/controlflow/compare_op.h

@@ -98,6 +98,9 @@ class CompareOpKernel
 
 #define REGISTER_COMPARE_KERNEL(op_type, dev, functor, inverse_functor)       \
   REGISTER_OP_##dev##_KERNEL(op_type,                                         \
+                             ::paddle::operators::CompareOpKernel<            \
+                                 ::paddle::platform::dev##DeviceContext,      \
+                                 functor<bool>, inverse_functor<bool>>,       \
                              ::paddle::operators::CompareOpKernel<            \
                                  ::paddle::platform::dev##DeviceContext,      \
                                  functor<int>, inverse_functor<int>>,         \

python/paddle/fluid/tests/unittests/test_compare_op.py

@@ -155,6 +155,38 @@ def create_paddle_case(op_type, callback):
                                fetch_list=[out])
             self.assertEqual((res == real_result).all(), True)
 
+        def test_bool_api_4(self):
+            paddle.enable_static()
+            with program_guard(Program(), Program()):
+                x = paddle.static.data(name='x', shape=[3, 1], dtype='bool')
+                y = paddle.static.data(name='y', shape=[3, 1], dtype='bool')
+                op = eval("paddle.%s" % (self.op_type))
+                out = op(x, y)
+                exe = paddle.static.Executor(self.place)
+                input_x = np.array([True, False, True]).astype(np.bool)
+                input_y = np.array([True, True, False]).astype(np.bool)
+                real_result = callback(input_x, input_y)
+                res, = exe.run(feed={"x": input_x,
+                                     "y": input_y},
+                               fetch_list=[out])
+            self.assertEqual((res == real_result).all(), True)
+
+        def test_bool_broadcast_api_4(self):
+            paddle.enable_static()
+            with program_guard(Program(), Program()):
+                x = paddle.static.data(name='x', shape=[3, 1], dtype='bool')
+                y = paddle.static.data(name='y', shape=[1], dtype='bool')
+                op = eval("paddle.%s" % (self.op_type))
+                out = op(x, y)
+                exe = paddle.static.Executor(self.place)
+                input_x = np.array([True, False, True]).astype(np.bool)
+                input_y = np.array([True]).astype(np.bool)
+                real_result = callback(input_x, input_y)
+                res, = exe.run(feed={"x": input_x,
+                                     "y": input_y},
+                               fetch_list=[out])
+            self.assertEqual((res == real_result).all(), True)
+
         def test_attr_name(self):
             paddle.enable_static()
             with program_guard(Program(), Program()):

python/paddle/fluid/tests/unittests/test_compare_reduce_op.py

@@ -92,9 +92,28 @@ def create_test_dim1_class(op_type, typename, callback):
     globals()[cls_name] = Cls
 
 
+def create_test_dim1_class(op_type, typename, callback):
+    class Cls(op_test.OpTest):
+        def setUp(self):
+            x = y = np.random.random(size=(1)).astype(typename)
+            x = np.array([True, False, True]).astype(typename)
+            x = np.array([False, False, True]).astype(typename)
+            z = callback(x, y)
+            self.inputs = {'X': x, 'Y': y}
+            self.outputs = {'Out': z}
+            self.op_type = op_type
+
+        def test_output(self):
+            self.check_output()
+
+    cls_name = "{0}_{1}_{2}".format(op_type, typename, 'equal_all')
+    Cls.__name__ = cls_name
+    globals()[cls_name] = Cls
+
+
 np_equal = lambda _x, _y: np.array(np.array_equal(_x, _y))
 
-for _type_name in {'float32', 'float64', 'int32', 'int64'}:
+for _type_name in {'float32', 'float64', 'int32', 'int64', 'bool'}:
     create_test_not_equal_class('equal_all', _type_name, np_equal)
     create_test_equal_class('equal_all', _type_name, np_equal)
     create_test_dim1_class('equal_all', _type_name, np_equal)
@@ -107,6 +126,14 @@ class TestEqualReduceAPI(unittest.TestCase):
         out = paddle.equal_all(x, y, name='equal_res')
         assert 'equal_res' in out.name
 
+    def test_dynamic_api(self):
+        paddle.disable_static()
+        x = paddle.ones(shape=[10, 10], dtype="int32")
+        y = paddle.ones(shape=[10, 10], dtype="int32")
+        out = paddle.equal_all(x, y)
+        assert out.numpy()[0] == True
+        paddle.enable_static()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/tensor/logic.py

@@ -38,8 +38,8 @@ def equal_all(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): Tensor, data type is float32, float64, int32, int64.
-        y(Tensor): Tensor, data type is float32, float64, int32, int64.
+        x(Tensor): Tensor, data type is bool, float32, float64, int32, int64.
+        y(Tensor): Tensor, data type is bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
 
@@ -59,6 +59,8 @@ def equal_all(x, y, name=None):
           result2 = paddle.equal_all(x, z)
           print(result2) # result2 = [False ]
     """
+    if in_dygraph_mode():
+        return core.ops.equal_all(x, y)
 
     helper = LayerHelper("equal_all", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
@@ -152,8 +154,8 @@ def equal(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): Tensor, data type is float32, float64, int32, int64.
-        y(Tensor): Tensor, data type is float32, float64, int32, int64.
+        x(Tensor): Tensor, data type is bool, float32, float64, int32, int64.
+        y(Tensor): Tensor, data type is bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
 
@@ -174,10 +176,10 @@ def equal(x, y, name=None):
     if in_dygraph_mode():
         return core.ops.equal(x, y)
 
-    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
-                             "equal")
-    check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
-                             "equal")
+    check_variable_and_dtype(
+        x, "x", ["bool", "float32", "float64", "int32", "int64"], "equal")
+    check_variable_and_dtype(
+        y, "y", ["bool", "float32", "float64", "int32", "int64"], "equal")
     helper = LayerHelper("equal", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
     out.stop_gradient = True
@@ -196,8 +198,8 @@ def greater_equal(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
-        y(Tensor): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
+        x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
+        y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
     Returns:
@@ -216,9 +218,11 @@ def greater_equal(x, y, name=None):
     if in_dygraph_mode():
         return core.ops.greater_equal(x, y)
 
-    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
+    check_variable_and_dtype(x, "x",
+                             ["bool", "float32", "float64", "int32", "int64"],
                              "greater_equal")
-    check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
+    check_variable_and_dtype(y, "y",
+                             ["bool", "float32", "float64", "int32", "int64"],
                              "greater_equal")
     helper = LayerHelper("greater_equal", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
@@ -240,8 +244,8 @@ def greater_than(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
-        y(Tensor): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
+        x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
+        y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
     Returns:
@@ -260,9 +264,11 @@ def greater_than(x, y, name=None):
     if in_dygraph_mode():
         return core.ops.greater_than(x, y)
 
-    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
+    check_variable_and_dtype(x, "x",
+                             ["bool", "float32", "float64", "int32", "int64"],
                              "greater_than")
-    check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
+    check_variable_and_dtype(y, "y",
+                             ["bool", "float32", "float64", "int32", "int64"],
                              "greater_than")
     helper = LayerHelper("greater_than", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
@@ -284,8 +290,8 @@ def less_equal(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
-        y(Tensor): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
+        x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
+        y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
 
@@ -305,10 +311,10 @@ def less_equal(x, y, name=None):
     if in_dygraph_mode():
         return core.ops.less_equal(x, y)
 
-    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
-                             "less_equal")
-    check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
-                             "less_equal")
+    check_variable_and_dtype(
+        x, "x", ["bool", "float32", "float64", "int32", "int64"], "less_equal")
+    check_variable_and_dtype(
+        y, "y", ["bool", "float32", "float64", "int32", "int64"], "less_equal")
     helper = LayerHelper("less_equal", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
     out.stop_gradient = True
@@ -327,8 +333,8 @@ def less_than(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
-        y(Tensor): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
+        x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
+        y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
 
@@ -348,10 +354,10 @@ def less_than(x, y, name=None):
     if in_dygraph_mode():
         return core.ops.less_than(x, y)
 
-    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
-                             "less_than")
-    check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
-                             "less_than")
+    check_variable_and_dtype(
+        x, "x", ["bool", "float32", "float64", "int32", "int64"], "less_than")
+    check_variable_and_dtype(
+        y, "y", ["bool", "float32", "float64", "int32", "int64"], "less_than")
     helper = LayerHelper("less_than", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
     out.stop_gradient = True
@@ -370,8 +376,8 @@ def not_equal(x, y, name=None):
     **NOTICE**: The output of this OP has no gradient.
 
     Args:
-        x(Tensor): First input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
-        y(Tensor): Second input to compare which is N-D tensor. The input data type should be float32, float64, int32, int64.
+        x(Tensor): First input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
+        y(Tensor): Second input to compare which is N-D tensor. The input data type should be bool, float32, float64, int32, int64.
         name(str, optional): The default value is None.  Normally there is no need for
             user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
 
@@ -391,10 +397,10 @@ def not_equal(x, y, name=None):
     if in_dygraph_mode():
         return core.ops.not_equal(x, y)
 
-    check_variable_and_dtype(x, "x", ["float32", "float64", "int32", "int64"],
-                             "not_equal")
-    check_variable_and_dtype(y, "y", ["float32", "float64", "int32", "int64"],
-                             "not_equal")
+    check_variable_and_dtype(
+        x, "x", ["bool", "float32", "float64", "int32", "int64"], "not_equal")
+    check_variable_and_dtype(
+        y, "y", ["bool", "float32", "float64", "int32", "int64"], "not_equal")
     helper = LayerHelper("not_equal", **locals())
     out = helper.create_variable_for_type_inference(dtype='bool')
     out.stop_gradient = True