[PIR] A-1、A-3 Adapt fill_constant and mean test_errors

python/paddle/tensor/stat.py

@@ -93,7 +93,7 @@ def mean(x, axis=None, keepdim=False, name=None):
         check_variable_and_dtype(
             x,
             'x/input',
-            ['uint16', 'float16', 'float32', 'float64'],
+            ['uint16', "int32", 'float16', 'float32', 'float64'],
             'mean/reduce_mean',
         )
         check_type(

test/legacy_test/test_fill_constant_op.py

@@ -20,7 +20,7 @@
 
 import paddle
 from paddle import base
-from paddle.base import Program, core, program_guard
+from paddle.base import core
 from paddle.pir_utils import test_with_pir_api
 
 
@@ -419,8 +419,11 @@ def test_ninf(self):
 
 
 class TestFillConstantOpError(unittest.TestCase):
-    def test_errors(self):
-        with paddle_static_guard(), program_guard(Program(), Program()):
+    @test_with_pir_api
+    def test_errors1(self):
+        with paddle_static_guard(), paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
             # for ci coverage
             x1 = paddle.static.data(name='x1', shape=[-1, 1], dtype="int16")
             self.assertRaises(
@@ -440,27 +443,30 @@ def test_errors(self):
                 out=x1,
             )
 
-            # The argument dtype of fill_constant_op must be one of bool, float16,
-            # float32, float64, uint8, int16, int32 or int64
-            x2 = paddle.static.data(name='x2', shape=[-1, 1], dtype="int32")
-
+            x3 = np.random.randn(100, 100).astype('int32')
             self.assertRaises(
                 TypeError,
                 paddle.tensor.fill_constant,
-                shape=[1],
+                shape=[100, 100],
                 value=5,
                 dtype='float64',
-                out=x2,
+                out=x3,
             )
 
-            x3 = np.random.randn(100, 100).astype('int32')
+    def test_errors2(self):
+        with paddle_static_guard(), paddle.static.program_guard(
+            paddle.static.Program(), paddle.static.Program()
+        ):
+            # The argument dtype of fill_constant_op must be one of bool, float16,
+            # float32, float64, uint8, int16, int32 or int64
+            x2 = paddle.static.data(name='x2', shape=[-1, 1], dtype="int32")
             self.assertRaises(
                 TypeError,
                 paddle.tensor.fill_constant,
-                shape=[100, 100],
+                shape=[1],
                 value=5,
                 dtype='float64',
-                out=x3,
+                out=x2,
             )
 
             # The argument shape's type of fill_constant_op must be list, tuple or Variable.
@@ -490,57 +496,26 @@ def test_shape_tensor_list_dtype():
 
             self.assertRaises(TypeError, test_shape_tensor_list_dtype)
 
-        with paddle.pir_utils.IrGuard(), program_guard(Program()):
-            x1 = paddle.static.data(name='x1', shape=[-1, 1], dtype="int16")
-            self.assertRaises(
-                TypeError,
-                paddle.tensor.fill_constant,
-                shape=[1],
-                value=5,
-                dtype='uint4',
-            )
-
-            self.assertRaises(
-                TypeError,
-                paddle.tensor.fill_constant,
-                shape=[1.1],
-                value=5,
-                dtype='float32',
-                out=x1,
-            )
-
-            x3 = np.random.randn(100, 100).astype('int32')
-            self.assertRaises(
-                TypeError,
-                paddle.tensor.fill_constant,
-                shape=[100, 100],
-                value=5,
-                dtype='float64',
-                out=x3,
-            )
-
     def test_pir_errors(self):
         def test_shape_type():
             # The shape dtype of fill_constant_op must be int32 or int64.
             # test_shape_tensor_dtype:
-            with paddle.pir_utils.IrGuard():
-                pir_program = paddle.static.Program()
-                with paddle.static.program_guard(pir_program):
-                    shape = paddle.static.data(
-                        name="shape_tensor", shape=[2], dtype="int32"
-                    )
-                    out = paddle.tensor.fill_constant(
-                        shape=shape, dtype="float32", value=1
-                    )
-                    exe = base.Executor(place=base.CPUPlace())
-                    exe.run(
-                        feed={
-                            "shape_tensor": np.array([1, 2]).astype("float32")
-                        },
-                        fetch_list=[out],
-                    )
-
-        self.assertRaises(ValueError, test_shape_type)
+            shape = paddle.static.data(
+                name="shape_tensor", shape=[2], dtype="int32"
+            )
+            out = paddle.tensor.fill_constant(
+                shape=shape, dtype="float32", value=1
+            )
+            exe = base.Executor(place=base.CPUPlace())
+            exe.run(
+                feed={"shape_tensor": np.array([1, 2]).astype("float32")},
+                fetch_list=[out],
+            )
+
+        with paddle.pir_utils.IrGuard():
+            pir_program = paddle.static.Program()
+            with paddle.static.program_guard(pir_program):
+                self.assertRaises(ValueError, test_shape_type)
 
 
 class TestFillConstantOp_ValueTensorBf16(OpTest):

test/legacy_test/test_mean_op.py

@@ -84,34 +84,19 @@ def setUp(self):
             else paddle.CPUPlace()
         )
 
+    @test_with_pir_api
     def test_errors(self):
         paddle.enable_static()
         with program_guard(Program(), Program()):
             # The input type of mean_op must be Variable.
             input1 = 12
             self.assertRaises(TypeError, paddle.mean, input1)
-            # The input dtype of mean_op must be float16, float32, float64.
-            input2 = paddle.static.data(
-                name='input2', shape=[-1, 12, 10], dtype="int32"
-            )
-            self.assertRaises(TypeError, paddle.mean, input2)
-            input3 = paddle.static.data(
-                name='input3', shape=[-1, 4], dtype="float16"
-            )
-            paddle.nn.functional.softmax(input3)
-
-        with paddle.pir_utils.IrGuard(), program_guard(Program(), Program()):
-            input1 = 12
-            self.assertRaises(TypeError, paddle.mean, input1)
 
-            input2 = paddle.static.data(
-                name='input2', shape=[2, 3, 4, 5], dtype="int32"
-            )
-
-            out = paddle.mean(input2)
-
-            exe = paddle.static.Executor(self.place)
-            res = exe.run(feed={'input2': self.x}, fetch_list=[out])
+            if paddle.is_compiled_with_cuda():
+                input3 = paddle.static.data(
+                    name='input3', shape=[-1, 4], dtype="float16"
+                )
+                paddle.nn.functional.softmax(input3)
 
         paddle.disable_static()
 
@@ -536,10 +521,6 @@ def test_errors(self):
         x = paddle.to_tensor(x)
         self.assertRaises(Exception, paddle.mean, x, -3)
         self.assertRaises(Exception, paddle.mean, x, 2)
-        paddle.enable_static()
-        with paddle.static.program_guard(paddle.static.Program()):
-            x = paddle.static.data('X', [10, 12], 'int32')
-            self.assertRaises(TypeError, paddle.mean, x)
 
 
 class TestMeanWithTensorAxis1(TestReduceOPTensorAxisBase):

test/xpu/test_mean_op_xpu.py

@@ -87,10 +87,6 @@ def test_errors(self):
             input1 = 12
             self.assertRaises(TypeError, paddle.mean, input1)
             # The input dtype of mean_op must be float16, float32, float64.
-            input2 = paddle.static.data(
-                name='input2', shape=[-1, 12, 10], dtype="int32"
-            )
-            self.assertRaises(TypeError, paddle.mean, input2)
             input3 = paddle.static.data(
                 name='input3', shape=[-1, 4], dtype="float16"
             )