diff --git a/paddle/fluid/operators/controlflow/compare_all_op.cc b/paddle/fluid/operators/controlflow/compare_all_op.cc index adacf70f5e14548806de80e629a15f915705d749..9442c7583d98fec2d46a96be136eae9bf4d20634 100644 --- a/paddle/fluid/operators/controlflow/compare_all_op.cc +++ b/paddle/fluid/operators/controlflow/compare_all_op.cc @@ -135,15 +135,17 @@ class CompareReduceOp : public framework::OperatorWithKernel { ::paddle::framework::EmptyGradOpMaker, \ ::paddle::framework::EmptyGradOpMaker); -#define REGISTER_COMPARE_REDUCE_CPU_KERNEL(op_type, functor) \ - REGISTER_OP_CPU_KERNEL( \ - op_type, ::paddle::operators::CompareReduceOpKernel< \ - ::paddle::platform::CPUDeviceContext, functor>, \ - ::paddle::operators::CompareReduceOpKernel< \ - ::paddle::platform::CPUDeviceContext, functor>, \ - ::paddle::operators::CompareReduceOpKernel< \ - ::paddle::platform::CPUDeviceContext, functor>, \ - ::paddle::operators::CompareReduceOpKernel< \ +#define REGISTER_COMPARE_REDUCE_CPU_KERNEL(op_type, functor) \ + REGISTER_OP_CPU_KERNEL( \ + op_type, ::paddle::operators::CompareReduceOpKernel< \ + ::paddle::platform::CPUDeviceContext, functor>, \ + ::paddle::operators::CompareReduceOpKernel< \ + ::paddle::platform::CPUDeviceContext, functor>, \ + ::paddle::operators::CompareReduceOpKernel< \ + ::paddle::platform::CPUDeviceContext, functor>, \ + ::paddle::operators::CompareReduceOpKernel< \ + ::paddle::platform::CPUDeviceContext, functor>, \ + ::paddle::operators::CompareReduceOpKernel< \ ::paddle::platform::CPUDeviceContext, functor>); REGISTER_COMPARE_REDUCE_OP(equal_all, "X == Y"); diff --git a/paddle/fluid/operators/controlflow/compare_all_op.cu b/paddle/fluid/operators/controlflow/compare_all_op.cu index e3c920f78c45b4c96115b8b650f2a08f544bc788..3753ed6b15f1e369c6f8777f939ffd3d8317fba0 100644 --- a/paddle/fluid/operators/controlflow/compare_all_op.cu +++ b/paddle/fluid/operators/controlflow/compare_all_op.cu @@ -85,15 +85,18 @@ class CompareReduceOpKernel } // namespace operators } // namespace paddle -#define REGISTER_COMPARE_REDUCE_CUDA_KERNEL(op_type, functor) \ - REGISTER_OP_CUDA_KERNEL( \ - op_type, paddle::operators::CompareReduceOpKernel< \ - paddle::platform::CUDADeviceContext, functor>, \ - paddle::operators::CompareReduceOpKernel< \ - paddle::platform::CUDADeviceContext, functor>, \ - paddle::operators::CompareReduceOpKernel< \ - paddle::platform::CUDADeviceContext, functor>, \ - paddle::operators::CompareReduceOpKernel< \ +#define REGISTER_COMPARE_REDUCE_CUDA_KERNEL(op_type, functor) \ + REGISTER_OP_CUDA_KERNEL( \ + op_type, paddle::operators::CompareReduceOpKernel< \ + paddle::platform::CUDADeviceContext, functor>, \ + paddle::operators::CompareReduceOpKernel< \ + paddle::platform::CUDADeviceContext, functor>, \ + paddle::operators::CompareReduceOpKernel< \ + paddle::platform::CUDADeviceContext, functor>, \ + paddle::operators::CompareReduceOpKernel< \ + paddle::platform::CUDADeviceContext, functor>, \ + paddle::operators::CompareReduceOpKernel< \ paddle::platform::CUDADeviceContext, functor>); + REGISTER_COMPARE_REDUCE_CUDA_KERNEL(equal_all, paddle::operators::EqualReduceFunctor); diff --git a/paddle/fluid/operators/controlflow/compare_op.h b/paddle/fluid/operators/controlflow/compare_op.h index ff929ee7dfce79536a9ce7c8ae6878fb7e3871e9..36185322a96b8909c49e1a3c5a55afa47d4952bc 100644 --- a/paddle/fluid/operators/controlflow/compare_op.h +++ b/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, inverse_functor>, \ ::paddle::operators::CompareOpKernel< \ ::paddle::platform::dev##DeviceContext, \ functor, inverse_functor>, \ diff --git a/python/paddle/fluid/tests/unittests/test_compare_op.py b/python/paddle/fluid/tests/unittests/test_compare_op.py index a2dd7e49ac4ccdd6135a27d7b88f6fbdec2132b9..7a142675880220f725a083639895ae5d503cb902 100644 --- a/python/paddle/fluid/tests/unittests/test_compare_op.py +++ b/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()): diff --git a/python/paddle/fluid/tests/unittests/test_compare_reduce_op.py b/python/paddle/fluid/tests/unittests/test_compare_reduce_op.py index 67fe5c81ddc296f832f76a25c0cf76b4946f3f0b..056d1687bbf84d31873d376015437d2c4c5a3680 100644 --- a/python/paddle/fluid/tests/unittests/test_compare_reduce_op.py +++ b/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() diff --git a/python/paddle/tensor/logic.py b/python/paddle/tensor/logic.py index bdf2c477d865884bb3d10f6dd16a1f45c2284c4e..f948eeb9a48ebe723e5e22a916cdc624580b7a10 100644 --- a/python/paddle/tensor/logic.py +++ b/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