【Hackathon 5th No.13】【关联 PR】Added int support for sign -Part (PaddleP…

…addle#58255)

* ♻️ Refactor: added sign int type support

* ✏️ Refactor: update typo

paddle/phi/kernels/cpu/sign_kernel.cc

@@ -21,4 +21,13 @@ limitations under the License. */
 // See Note [ Why still include the fluid headers? ]
 #include "paddle/phi/common/bfloat16.h"
 
-PD_REGISTER_KERNEL(sign, CPU, ALL_LAYOUT, phi::SignKernel, float, double) {}
+PD_REGISTER_KERNEL(sign,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::SignKernel,
+                   int8_t,
+                   int16_t,
+                   int32_t,
+                   int64_t,
+                   float,
+                   double) {}

paddle/phi/kernels/funcs/eigen/sign.cc

@@ -29,6 +29,10 @@ struct EigenSign<Eigen::DefaultDevice, T> {
   }
 };
 
+template struct EigenSign<Eigen::DefaultDevice, int8_t>;
+template struct EigenSign<Eigen::DefaultDevice, int16_t>;
+template struct EigenSign<Eigen::DefaultDevice, int32_t>;
+template struct EigenSign<Eigen::DefaultDevice, int64_t>;
 template struct EigenSign<Eigen::DefaultDevice, float>;
 template struct EigenSign<Eigen::DefaultDevice, double>;
 

paddle/phi/kernels/funcs/eigen/sign.cu

@@ -29,6 +29,10 @@ struct EigenSign<Eigen::GpuDevice, T> {
   }
 };
 
+template struct EigenSign<Eigen::GpuDevice, int8_t>;
+template struct EigenSign<Eigen::GpuDevice, int16_t>;
+template struct EigenSign<Eigen::GpuDevice, int32_t>;
+template struct EigenSign<Eigen::GpuDevice, int64_t>;
 template struct EigenSign<Eigen::GpuDevice, float>;
 template struct EigenSign<Eigen::GpuDevice, double>;
 template struct EigenSign<Eigen::GpuDevice, dtype::float16>;

paddle/phi/kernels/gpu/sign_kernel.cu.cc

@@ -25,6 +25,10 @@ PD_REGISTER_KERNEL(sign,
                    GPU,
                    ALL_LAYOUT,
                    phi::SignKernel,
+                   int8_t,
+                   int16_t,
+                   int32_t,
+                   int64_t,
                    float,
                    double,
                    phi::dtype::float16,

python/paddle/tensor/math.py

@@ -4565,7 +4565,7 @@ def sign(x, name=None):
     Returns sign of every element in `x`: 1 for positive, -1 for negative and 0 for zero.
 
     Args:
-        x (Tensor): The input tensor. The data type can be float16, float32 or float64.
+        x (Tensor): The input tensor. The data type can be int8, int16, int32, int64, float16, float32 or float64.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -4586,7 +4586,19 @@ def sign(x, name=None):
         return _C_ops.sign(x)
     else:
         check_variable_and_dtype(
-            x, 'x', ['float16', 'float32', 'float64', 'uint16'], 'sign'
+            x,
+            'x',
+            [
+                'int8',
+                'int16',
+                'int32',
+                'int64',
+                'float16',
+                'float32',
+                'float64',
+                'uint16',
+            ],
+            'sign',
         )
         helper = LayerHelper("sign", **locals())
         out = helper.create_variable_for_type_inference(dtype=x.dtype)

test/legacy_test/test_sign_op.py

@@ -76,28 +76,12 @@ def test_check_grad(self):
         self.check_grad_with_place(self.place, ['X'], 'Out')
 
 
-class TestSignOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # The input type of sign_op must be Variable or numpy.ndarray.
-            input1 = 12
-            self.assertRaises(TypeError, paddle.sign, input1)
-            # The input dtype of sign_op must be float16, float32, float64.
-            input2 = paddle.static.data(
-                name='input2', shape=[-1, 12, 10], dtype="int32"
-            )
-            input3 = paddle.static.data(
-                name='input3', shape=[-1, 12, 10], dtype="int64"
-            )
-            self.assertRaises(TypeError, paddle.sign, input2)
-            self.assertRaises(TypeError, paddle.sign, input3)
-            input4 = paddle.static.data(
-                name='input4', shape=[-1, 4], dtype="float16"
-            )
-            paddle.sign(input4)
-
-
 class TestSignAPI(unittest.TestCase):
+    def setUp(self):
+        self.place = [base.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            self.place.append(base.CUDAPlace(0))
+
     def test_dygraph(self):
         with base.dygraph.guard():
             np_x = np.array([-1.0, 0.0, -0.0, 1.2, 1.5], dtype='float64')
@@ -108,23 +92,51 @@ def test_dygraph(self):
             self.assertEqual((np_z == z_expected).all(), True)
 
     def test_static(self):
-        with program_guard(Program(), Program()):
-            # The input type of sign_op must be Variable or numpy.ndarray.
-            input1 = 12
-            self.assertRaises(TypeError, paddle.tensor.math.sign, input1)
-            # The input dtype of sign_op must be float16, float32, float64.
-            input2 = paddle.static.data(
-                name='input2', shape=[-1, 12, 10], dtype="int32"
-            )
-            input3 = paddle.static.data(
-                name='input3', shape=[-1, 12, 10], dtype="int64"
-            )
-            self.assertRaises(TypeError, paddle.tensor.math.sign, input2)
-            self.assertRaises(TypeError, paddle.tensor.math.sign, input3)
-            input4 = paddle.static.data(
-                name='input4', shape=[-1, 4], dtype="float16"
-            )
-            paddle.sign(input4)
+        np_input2 = np.random.uniform(-10, 10, (12, 10)).astype("int16")
+        np_input3 = np.random.uniform(-10, 10, (12, 10)).astype("int32")
+        np_input4 = np.random.uniform(-10, 10, (12, 10)).astype("int64")
+        np_out2 = np.sign(np_input2)
+        np_out3 = np.sign(np_input3)
+        np_out4 = np.sign(np_input4)
+
+        def run(place):
+            with program_guard(Program(), Program()):
+                # The input type of sign_op must be Variable or numpy.ndarray.
+                input1 = 12
+                self.assertRaises(TypeError, paddle.tensor.math.sign, input1)
+                # The result of sign_op must correct.
+                input2 = paddle.static.data(
+                    name='input2', shape=[12, 10], dtype="int16"
+                )
+                input3 = paddle.static.data(
+                    name='input3', shape=[12, 10], dtype="int32"
+                )
+                input4 = paddle.static.data(
+                    name='input4', shape=[12, 10], dtype="int64"
+                )
+                out2 = paddle.sign(input2)
+                out3 = paddle.sign(input3)
+                out4 = paddle.sign(input4)
+                exe = paddle.static.Executor(place)
+                res2, res3, res4 = exe.run(
+                    paddle.static.default_main_program(),
+                    feed={
+                        "input2": np_input2,
+                        "input3": np_input3,
+                        "input4": np_input4,
+                    },
+                    fetch_list=[out2, out3, out4],
+                )
+                self.assertEqual((res2 == np_out2).all(), True)
+                self.assertEqual((res3 == np_out3).all(), True)
+                self.assertEqual((res4 == np_out4).all(), True)
+                input5 = paddle.static.data(
+                    name='input5', shape=[-1, 4], dtype="float16"
+                )
+                paddle.sign(input5)
+
+        for place in self.place:
+            run(place)
 
 
 class TestSignDoubleGradCheck(unittest.TestCase):