【Zero_dim】support zero_dim for some prim ops part2

test/legacy_test/test_assign_op.py

@@ -32,10 +32,14 @@ def setUp(self):
         self.public_python_api = paddle.assign
         self.op_type = "assign"
         self.prim_op_type = "prim"
-        x = np.random.random(size=(100, 10)).astype('float64')
+        self.init_input_configs()
+        x = np.random.random(size=self.shape).astype('float64')
         self.inputs = {'X': x}
         self.outputs = {'Out': x}
 
+    def init_input_configs(self):
+        self.shape = (100, 10)
+
     def test_forward(self):
         paddle.enable_static()
         self.check_output()
@@ -47,6 +51,11 @@ def test_backward(self):
         paddle.disable_static()
 
 
+class TestAssignOp_ZeroDim(TestAssignOp):
+    def init_input_configs(self):
+        self.shape = ()
+
+
 @unittest.skipIf(
     not paddle.is_compiled_with_cuda(), "FP16 test runs only on GPU"
 )

test/legacy_test/test_erf_op.py

@@ -30,12 +30,15 @@ def setUp(self):
         self.public_python_api = paddle.erf
         self.python_api = paddle.erf
         self.dtype = self._init_dtype()
-        self.x_shape = [11, 17]
+        self.init_shape()
         x = np.random.uniform(-1, 1, size=self.x_shape).astype(self.dtype)
         y_ref = erf(x).astype(self.dtype)
         self.inputs = {'X': x}
         self.outputs = {'Out': y_ref}
 
+    def init_shape(self):
+        self.x_shape = [11, 17]
+
     def _init_dtype(self):
         return "float64"
 
@@ -46,6 +49,11 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out', check_prim=True)
 
 
+class TestErfOp_ZeroDim(TestErfOp):
+    def init_shape(self):
+        self.x_shape = []
+
+
 class TestErfLayer(unittest.TestCase):
     def _test_case(self, place):
         x = np.random.uniform(-1, 1, size=(11, 17)).astype(np.float64)

test/legacy_test/test_expand_as_v2_op.py

@@ -54,6 +54,31 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out', check_prim=True)
 
 
+class TestExpandAs_ZeroDim1(TestExpandAsBasic):
+    def init_inputs_and_outputs(self):
+        x = np.random.random(()).astype(self.dtype)
+        target_tensor = np.random.random(1).astype(self.dtype)
+        self.inputs = {'X': x, "Y": target_tensor}
+        self.attrs = {'target_shape': target_tensor.shape}
+        bcast_dims = [1]
+        output = np.tile(self.inputs['X'], bcast_dims)
+        self.outputs = {'Out': output}
+
+
+class TestExpandAs_ZeroDim2(TestExpandAsBasic):
+    def init_inputs_and_outputs(self):
+        x = np.random.random(()).astype(self.dtype)
+        target_tensor = np.random.random(()).astype(self.dtype)
+        self.inputs = {'X': x, "Y": target_tensor}
+        self.attrs = {'target_shape': target_tensor.shape}
+        bcast_dims = []
+        output = np.tile(self.inputs['X'], bcast_dims)
+        self.outputs = {'Out': output}
+
+    def if_enable_cinn(self):
+        self.enable_cinn = False
+
+
 @unittest.skipIf(
     not core.is_compiled_with_cuda()
     or not core.is_bfloat16_supported(core.CUDAPlace(0)),

test/legacy_test/test_expand_v2_op.py

@@ -36,20 +36,43 @@ def setUp(self):
         self.attrs = {'shape': self.shape}
         output = np.tile(self.inputs['X'], self.expand_times)
         self.outputs = {'Out': output}
-        self.enable_cinn = True
+        self.if_enable_cinn()
 
     def init_data(self):
         self.ori_shape = [100]
         self.shape = [100]
         self.expand_times = [1]
 
+    def if_enable_cinn(self):
+        pass
+
     def test_check_output(self):
-        self.check_output(check_cinn=self.enable_cinn)
+        self.check_output(check_cinn=True)
 
     def test_check_grad(self):
         self.check_grad(['X'], 'Out', check_prim=True)
 
 
+class TestExpandV2OpRank1_ZeroDim1(TestExpandV2OpRank1):
+    def init_data(self):
+        self.ori_shape = []
+        self.shape = [10]
+        self.expand_times = [10]
+
+    def if_enable_cinn(self):
+        self.enable_cinn = False
+
+
+class TestExpandV2OpRank1_ZeroDim2(TestExpandV2OpRank1):
+    def init_data(self):
+        self.ori_shape = []
+        self.shape = []
+        self.expand_times = []
+
+    def if_enable_cinn(self):
+        pass
+
+
 class TestExpandV2OpRank2_DimExpanding(TestExpandV2OpRank1):
     def init_data(self):
         self.ori_shape = [120]

test/legacy_test/test_flatten2_op.py

@@ -44,6 +44,13 @@ def init_attrs(self):
         self.attrs = {"axis": self.axis}
 
 
+class TestFlattenOp_ZeroDim(TestFlattenOp):
+    def init_test_case(self):
+        self.in_shape = ()
+        self.axis = 0
+        self.new_shape = 1
+
+
 class TestFlattenOp1(TestFlattenOp):
     def init_test_case(self):
         self.in_shape = (3, 2, 5, 4)

test/legacy_test/test_full_like_op.py

@@ -142,6 +142,13 @@ def if_enable_cinn(self):
         pass
 
 
+class TestFullLikeOp1_ZeroDim(TestFullLikeOp1):
+    def init_data(self):
+        self.fill_value = 5
+        self.shape = []
+        self.dtype = np.float32
+
+
 class TestFullLikeOp2(TestFullLikeOp1):
     def init_data(self):
         self.fill_value = 1000

test/legacy_test/test_gather_nd_op.py

@@ -122,6 +122,33 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out', check_prim=True)
 
 
+class TestGatherNdOpWithIndex1_ZeroDim(TestGatherNdOpWithIndex1):
+    def setUp(self):
+        self.op_type = "gather_nd"
+        self.prim_op_type = "prim"
+        self.python_api = paddle.gather_nd
+        self.public_python_api = paddle.gather_nd
+        self.config_dtype()
+        self.if_enable_cinn()
+        if self.dtype == np.float64:
+            target_dtype = "float64"
+        elif self.dtype == np.float16:
+            target_dtype = "float16"
+        else:
+            target_dtype = "float32"
+        xnp = np.random.random((100,)).astype(target_dtype)
+        index = np.array([1]).astype("int32")
+        output = xnp[index[-1]]
+        if self.dtype == np.uint16:
+            xnp = convert_float_to_uint16(xnp)
+            output = convert_float_to_uint16(output)
+        self.inputs = {'X': xnp, 'Index': index}
+        self.outputs = {'Out': output}
+
+    def if_enable_cinn(self):
+        self.enable_cinn = False
+
+
 class TestGatherNdOpWithIndex1FP16(TestGatherNdOpWithIndex1):
     def config_dtype(self):
         self.dtype = np.float16

test/legacy_test/test_reduce_op.py

@@ -278,15 +278,18 @@ def setUp(self):
         self.python_api = paddle.max
         self.public_python_api = paddle.max
         self.if_enable_cinn()
+        self.init_inputs_and_outputs()
+
+    def if_enable_cinn(self):
+        self.enable_cinn = False
+
+    def init_inputs_and_outputs(self):
         self.inputs = {'X': np.random.random([]).astype("float64")}
         self.attrs = {'dim': []}
         self.outputs = {
             'Out': self.inputs['X'].max(axis=tuple(self.attrs['dim']))
         }
 
-    def if_enable_cinn(self):
-        self.enable_cinn = False
-
     def test_check_output(self):
         self.check_output()
 
@@ -300,6 +303,20 @@ def test_check_grad(self):
         )
 
 
+class TestMaxOp_ZeroDim1(TestMaxOp_ZeroDim):
+    def init_inputs_and_outputs(self):
+        self.inputs = {'X': np.random.random([5]).astype("float64")}
+        self.attrs = {'dim': [0]}
+        self.outputs = {'Out': self.inputs['X'].max(axis=(0,))}
+
+
+class TestMaxOp_ZeroDim2(TestMaxOp_ZeroDim1):
+    def init_inputs_and_outputs(self):
+        self.inputs = {'X': np.random.random([5, 20]).astype("float64")}
+        self.attrs = {'dim': [0, 1]}
+        self.outputs = {'Out': self.inputs['X'].max(axis=(0, 1))}
+
+
 class TestMaxFP32Op(OpTest):
     """Remove Max with subgradient from gradient check to confirm the success of CI."""