[Prim] reduce_as op support uint8, in8, complex64 and complex128

paddle/phi/kernels/cpu/reduce_as_grad_kernel.cc

@@ -12,12 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/phi/kernels/funcs/common_shape.h"
-#include "paddle/phi/kernels/funcs/reduce_functor.h"
 #include "paddle/phi/kernels/reduce_as_kernel.h"
 
 #include "paddle/phi/core/device_context.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/reduce_functor.h"
 #include "paddle/phi/kernels/impl/reduce_grad.h"
 
 namespace phi {
@@ -55,6 +55,8 @@ PD_REGISTER_KERNEL(reduce_as_grad,
                    int,
                    int64_t,
                    uint8_t,
-                   int8_t) {
+                   int8_t,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {
   kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
 }

paddle/phi/kernels/cpu/reduce_as_kernel.cc

@@ -13,12 +13,12 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/reduce_as_kernel.h"
-#include "paddle/phi/kernels/funcs/common_shape.h"
-#include "paddle/phi/kernels/funcs/reduce_functor.h"
 
 #include "paddle/phi/core/device_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/cpu/reduce.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
+#include "paddle/phi/kernels/funcs/reduce_functor.h"
 
 namespace phi {
 
@@ -48,4 +48,6 @@ PD_REGISTER_KERNEL(reduce_as,
                    int,
                    int64_t,
                    uint8_t,
-                   int8_t) {}
+                   int8_t,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}

paddle/phi/kernels/gpu/reduce_as_grad_kernel.cu

@@ -13,11 +13,10 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/reduce_as_grad_kernel.h"
-#include "paddle/phi/kernels/funcs/common_shape.h"
-#include "paddle/phi/kernels/funcs/reduce_functor.h"
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
 #include "paddle/phi/kernels/funcs/reduce_function.h"
 #include "paddle/phi/kernels/gpu/reduce_grad.h"
 
@@ -65,6 +64,8 @@ PD_REGISTER_KERNEL(reduce_as_grad,
                    int,
                    int64_t,
                    uint8_t,
-                   int8_t) {
+                   int8_t,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {
   kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
 }

paddle/phi/kernels/gpu/reduce_as_kernel.cu

@@ -13,11 +13,10 @@
 // limitations under the License.
 
 #include "paddle/phi/kernels/reduce_as_kernel.h"
-#include "paddle/phi/kernels/funcs/common_shape.h"
-#include "paddle/phi/kernels/funcs/reduce_functor.h"
 
 #include "paddle/phi/backends/gpu/gpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/common_shape.h"
 #include "paddle/phi/kernels/reduce_sum_kernel.h"
 
 namespace phi {
@@ -47,4 +46,6 @@ PD_REGISTER_KERNEL(reduce_as,
                    int,
                    int64_t,
                    uint8_t,
-                   int8_t) {}
+                   int8_t,
+                   phi::dtype::complex<float>,
+                   phi::dtype::complex<double>) {}

python/paddle/tensor/math.py

@@ -1583,8 +1583,8 @@ def reduce_as(x, target, name=None):
     Computes the sum of tensor elements make the shape of its result equal to the shape of target.
 
     Args:
-        x (Tensor): An N-D Tensor, the data type is bool, float16, float32, float64, int32 or int64.
-        target (Tensor): An N-D Tensor, the length of x shape must greater than or equal to the length of target shape. The data type is bool, float16, float32, float64, int32 or int64.
+        x (Tensor): An N-D Tensor, the data type is bool, float16, float32, float64, int8, uint8, int16, uint16, int32, int64, complex64 or complex128.
+        target (Tensor): An N-D Tensor, the length of x shape must greater than or equal to the length of target shape. The data type is bool, float16, float32, float64, int8, uint8, int16, uint16, int32, int64, complex64 or complex128.
 
     Returns:
         Tensor: The sum of the input tensor x along some axis has the same shape as the shape of the input tensor target, if `x.dtype='bool'`, `x.dtype='int32'`, it's data type is `'int64'`, otherwise it's data type is the same as `x`.
@@ -1617,13 +1617,17 @@ def reduce_as(x, target, name=None):
             'x',
             [
                 'bool',
-                'uint16',
                 'float16',
                 'float32',
                 'float64',
+                'int8',
+                'uint8',
                 'int16',
+                'uint16',
                 'int32',
                 'int64',
+                'complex64',
+                'complex128',
             ],
             'reduce_as',
         )
@@ -1632,13 +1636,17 @@ def reduce_as(x, target, name=None):
             'target',
             [
                 'bool',
-                'uint16',
                 'float16',
                 'float32',
                 'float64',
+                'int8',
+                'uint8',
                 'int16',
+                'uint16',
                 'int32',
                 'int64',
+                'complex64',
+                'complex128',
             ],
             'reduce_as',
         )

test/deprecated/legacy_test/test_reduce_as_op.py

@@ -39,11 +39,20 @@ def apply_to_static(net, use_cinn, input_spec=None):
     )
 
 
-class TestSumAsOp(OpTest):
+class TestReduceAsOp(OpTest):
     def setUp(self):
         self.init_dtype()
         self.init_shape()
-        self.init_input()
+        if self.dtype == np.complex64 or self.dtype == np.complex128:
+            self.x = np.random.random(self.shape_x) + 1j * np.random.random(
+                self.shape_y
+            )
+            self.y = np.random.random(self.shape_x) + 1j * np.random.random(
+                self.shape_y
+            )
+        else:
+            self.x = np.random.random(self.shape_x).astype(self.dtype)
+            self.y = np.random.random(self.shape_y).astype(self.dtype)
         self.init_attrs()
         self.calc_output()
 
@@ -60,10 +69,6 @@ def init_shape(self):
         self.shape_x = [10, 10, 6]
         self.shape_y = [10, 6]
 
-    def init_input(self):
-        self.x = np.random.random(self.shape_x).astype(self.dtype)
-        self.y = np.random.random(self.shape_y).astype(self.dtype)
-
     def init_attrs(self):
         self.attrs = {'dim': [0]}
 
@@ -84,42 +89,62 @@ def test_check_grad(self):
         )
 
 
-class TestSumAsOp2(TestSumAsOp):
+class TestReduceAsOp2(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'float32'
 
 
-class TestSumAsOp3(TestSumAsOp):
+class TestReduceAsOp3(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'float16'
 
 
-class TestSumAsOp4(TestSumAsOp):
+class TestReduceAsOp4(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'uint16'
 
 
-class TestSumAsOp5(TestSumAsOp):
+class TestReduceAsOp5(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'int16'
 
 
-class TestSumAsOp6(TestSumAsOp):
+class TestReduceAsOp6(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'int64'
 
 
-class TestSumAsOp7(TestSumAsOp):
+class TestReduceAsOp7(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'bool'
 
 
-class TestSumAsOp8(TestSumAsOp):
+class TestReduceAsOp8(TestReduceAsOp):
     def init_type(self):
         self.dtype = 'int32'
 
 
-class TestSumAsOp9(TestSumAsOp):
+class TestReduceAsOp9(TestReduceAsOp):
+    def init_type(self):
+        self.dtype = 'int8'
+
+
+class TestReduceAsOp10(TestReduceAsOp):
+    def init_type(self):
+        self.dtype = 'uint8'
+
+
+class TestReduceAs_Complex64(TestReduceAsOp):
+    def init_type(self):
+        self.dtype = np.complex64
+
+
+class TestReduceAs_Complex128(TestReduceAsOp):
+    def init_type(self):
+        self.dtype = np.complex128
+
+
+class TestReduceAsOp13(TestReduceAsOp):
     def init_shape(self):
         self.shape_x = [10, 10, 6]
         self.shape_y = [6]
@@ -128,7 +153,7 @@ def init_attrs(self):
         self.attrs = {'dim': [0, 1]}
 
 
-class TestSumAsDynamicShape(unittest.TestCase):
+class TestReduceAsDynamicShape(unittest.TestCase):
     def setUp(self):
         np.random.seed(2023)
         self.shape_x = [300, 20, 100]