fixed decorators in cpu tests

mindspore/ccsrc/backend/kernel_compiler/cpu/mirror_pad_cpu_kernel.cc

@@ -20,6 +20,7 @@
 namespace mindspore {
 namespace kernel {
 void MirrorPadCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  CheckParam(kernel_node);
   std::string mode = AnfAlgo::GetNodeAttr<std::string>(kernel_node, "mode");
   dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
   if (mode == "REFLECT") {
@@ -47,7 +48,6 @@ void MirrorPadCPUKernel::InitKernel(const CNodePtr &kernel_node) {
     tensor_size_ *= input_shape[i];
     input_shape_.push_back(SizeToLong(input_shape[i]));
   }
-
   std::vector<size_t> padding_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
   num_paddings_ = SizeToLong(padding_shape[0]);
 
@@ -59,7 +59,6 @@ void MirrorPadCPUKernel::InitKernel(const CNodePtr &kernel_node) {
 
   int64_t max_width = input_shape_[3];
   int64_t max_height = input_shape_[2];
-
   if (mode_ == 1) {  // symmetric
     max_width = max_width + (2 * max_width);
     max_height = max_height + (2 * max_height);
@@ -110,7 +109,6 @@ void MirrorPadCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, con
   const int64_t padded_height = output_shape_[dim_offset];
   const int64_t padded_width = output_shape_[dim_offset + 1];
   const int64_t padd_dim = num_paddings_;
-
   const int64_t mode = mode_;
 
   int64_t paddings[MAX_PADDINGS * PADDING_SIZE];  // local and fixed size to keep in registers

mindspore/ccsrc/backend/kernel_compiler/cpu/mirror_pad_cpu_kernel.h

@@ -77,6 +77,20 @@ MS_REG_CPU_KERNEL(
 MS_REG_CPU_KERNEL(
   MirrorPad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt32),
   MirrorPadCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  MirrorPad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
+  MirrorPadCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  MirrorPad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
+  MirrorPadCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  MirrorPad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  MirrorPadCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MIRROR_PAD_CPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/cpu/mirror_pad_grad_cpu_kernel.h

@@ -94,6 +94,21 @@ MS_REG_CPU_KERNEL(
   MirrorPadGrad,
   KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt32),
   MirrorPadGradCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  MirrorPadGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat16),
+  MirrorPadGradCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  MirrorPadGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
+  MirrorPadGradCPUKernel);
+
+MS_REG_CPU_KERNEL(
+  MirrorPadGrad,
+  KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  MirrorPadGradCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 #endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_MIRROR_PAD_CPU_KERNEL_H_

mindspore/nn/layer/basic.py

@@ -801,7 +801,7 @@ class Pad(Cell):
         if mode == "CONSTANT":
             self.pad = P.Pad(self.paddings)
         else:
-            self.paddings = Tensor(np.array(self.paddings))
+            self.paddings = Tensor(np.array(self.paddings), dtype=mstype.int64)
             self.pad = P.MirrorPad(mode=mode)
 
     def construct(self, x):

mindspore/ops/_op_impl/cpu/mirror_pad.py

@@ -22,6 +22,9 @@ mirror_pad_op_info = CpuRegOp("MirrorPad") \
     .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
     .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .get_op_info()
 
 

mindspore/ops/_op_impl/cpu/mirror_pad_grad.py

@@ -22,6 +22,9 @@ mirror_pad_grad_op_info = CpuRegOp("MirrorPadGrad") \
     .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
     .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .get_op_info()
 
 

tests/st/ops/cpu/test_arithmetic_op.py

@@ -82,7 +82,7 @@ def test_sub():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_div():
     prop = 1 if np.random.random() < 0.5 else -1
@@ -175,7 +175,7 @@ def test_div():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_floor_div():
     prop = 1 if np.random.random() < 0.5 else -1
@@ -240,7 +240,7 @@ def test_floor_div():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_mod():
     prop = 1 if np.random.random() < 0.5 else -1
@@ -334,7 +334,7 @@ def test_mod():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_floor_mod():
     prop = 1 if np.random.random() < 0.5 else -1

tests/st/ops/cpu/test_broadcast_to_op.py

@@ -22,7 +22,7 @@ from mindspore.ops import operations as P
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_broadcast():
     context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
@@ -76,7 +76,7 @@ def test_broadcast():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_broadcast_dyn_init():
     """
@@ -105,7 +105,7 @@ def test_broadcast_dyn_init():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_broadcast_dyn_invalid_init():
     """

tests/st/ops/cpu/test_depthtospace_op.py

@@ -55,61 +55,61 @@ def DepthToSpace(nptype, block_size=2, input_shape=(1, 12, 1, 1)):
     assert (output.asnumpy() == expect).all()
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_float32():
     DepthToSpace(np.float32)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_float16():
     DepthToSpace(np.float16)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_int32():
     DepthToSpace(np.int32)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_int64():
     DepthToSpace(np.int64)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_int8():
     DepthToSpace(np.int8)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_int16():
     DepthToSpace(np.int16)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_uint8():
     DepthToSpace(np.uint8)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_uint16():
     DepthToSpace(np.uint16)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_uint32():
     DepthToSpace(np.uint32)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_depthtospace_graph_uint64():
     DepthToSpace(np.uint64)

tests/st/ops/cpu/test_isfinite_op.py

@@ -34,7 +34,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net():
     x0 = Tensor(np.array([np.log(-1), 0.4, np.log(0)]).astype(np.float16))

tests/st/ops/cpu/test_isnan_op.py

@@ -39,7 +39,7 @@ x3 = np.array([[1, 2], [3, 4], [5.0, 88.0]]).astype(np.float32)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_nan():
     ms_isnan = Netnan()

tests/st/ops/cpu/test_l2normalize_grad_op.py

@@ -33,7 +33,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net01():
     context.set_context(mode=context.GRAPH_MODE, device_target='CPU')

tests/st/ops/cpu/test_l2normalize_op.py

@@ -34,7 +34,7 @@ class Net(Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_l2normalize_float32():
     x = np.arange(20*20*20*20).astype(np.float32).reshape(20, 20, 20, 20)
@@ -50,7 +50,7 @@ def test_l2normalize_float32():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_l2normalize_float16():
     x = np.arange(96).astype(np.float16).reshape(2, 3, 4, 4)
@@ -66,7 +66,7 @@ def test_l2normalize_float16():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_l2normalize_axis():
     axis = -2
@@ -83,7 +83,7 @@ def test_l2normalize_axis():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_l2normalize_epsilon():
     axis = -1

tests/st/ops/cpu/test_less_equal_op.py

@@ -30,7 +30,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net_fp32():
     x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
@@ -84,7 +84,7 @@ def test_net_fp32():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net_fp16():
     x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
@@ -138,7 +138,7 @@ def test_net_fp16():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net_int32():
     x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int32)
@@ -156,7 +156,7 @@ def test_net_int32():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net_int64():
     x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int64)
@@ -174,7 +174,7 @@ def test_net_int64():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net_float64():
     x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float64)
@@ -192,7 +192,7 @@ def test_net_float64():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net_int16():
     x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int16)

tests/st/ops/cpu/test_less_op.py

@@ -30,7 +30,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net():
     x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)

tests/st/ops/cpu/test_maximum_grad.py

@@ -33,7 +33,7 @@ class MaxmumGradNet(Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_grad_random():
     np.random.seed(0)
@@ -49,7 +49,7 @@ def test_maximum_grad_random():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_broadcast_grad_cpu():
     x = np.array([[[[0.659578],

tests/st/ops/cpu/test_maximum_op.py

@@ -42,7 +42,7 @@ class TwoTensorsMaximum(Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_constScalar_tensor_int():
     x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
@@ -58,7 +58,7 @@ def test_maximum_constScalar_tensor_int():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_two_tensors_Not_Broadcast_int():
     x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
@@ -75,7 +75,7 @@ def test_maximum_two_tensors_Not_Broadcast_int():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_two_tensors_Broadcast_int():
     x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
@@ -92,7 +92,7 @@ def test_maximum_two_tensors_Broadcast_int():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_two_tensors_Broadcast_oneDimension_int():
     x = Tensor(np.array([[2, 3, 4], [100, 200, 300]]).astype(np.int32))
@@ -109,7 +109,7 @@ def test_maximum_two_tensors_Broadcast_oneDimension_int():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_two_tensors_notBroadcast_all_oneDimension_int():
     x = Tensor(np.array([[2]]).astype(np.int32))
@@ -126,7 +126,7 @@ def test_maximum_two_tensors_notBroadcast_all_oneDimension_int():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_two_tensors_notBroadcast_float32():
     x = Tensor(np.array([[2.0, 2.0], [-1, 100]]).astype(np.float32))
@@ -143,7 +143,7 @@ def test_maximum_two_tensors_notBroadcast_float32():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_maximum_two_tensors_notBroadcast_float64():
     x = Tensor(np.array([[2.0, 2.0], [-1, 100]]).astype(np.float64))

tests/st/ops/cpu/test_mirror_pad.py

@@ -24,7 +24,7 @@ from mindspore import Tensor
 from mindspore.ops.composite import GradOperation
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_mirror_pad():
     context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
@@ -72,7 +72,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_mirror_pad_backprop():
     context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
@@ -88,7 +88,7 @@ def test_mirror_pad_backprop():
     np.testing.assert_array_almost_equal(dx, expected_dx)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_mirror_pad_fwd_back_4d_int32_reflect():
     context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
@@ -129,7 +129,7 @@ def test_mirror_pad_fwd_back_4d_int32_reflect():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_mirror_pad_fwd_back_4d_int32_symm():
     context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

tests/st/ops/cpu/test_norm_op.py

@@ -41,7 +41,7 @@ class NetNorm(nn.Cell):
                 self.norm_4(indices))
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_norm():
     norm = NetNorm()

tests/st/ops/cpu/test_one_hot_op.py

@@ -45,7 +45,7 @@ class NetOneHot(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_one_hot():
     one_hot = NetOneHot()

tests/st/ops/cpu/test_oneslike_op.py

@@ -32,7 +32,7 @@ class NetOnesLike(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_OnesLike():
     x0_np = np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32)

tests/st/ops/cpu/test_pad.py

@@ -25,7 +25,7 @@ from mindspore.ops.composite import GradOperation
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_pad_basic():
     """
@@ -53,7 +53,7 @@ def test_pad_basic():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_pad_row():
     """
@@ -84,7 +84,7 @@ def test_pad_row():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_pad_column():
     """
@@ -115,7 +115,7 @@ def test_pad_column():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_pad_3d_pad():
     """
@@ -173,7 +173,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_pad_3d_backprop():
     """
@@ -212,7 +212,7 @@ def test_pad_3d_backprop():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_pad_error_cases():
     context.set_context(mode=context.GRAPH_MODE, device_target="CPU")

tests/st/ops/cpu/test_pow_op.py

@@ -32,7 +32,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net():
     x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)

tests/st/ops/cpu/test_realdiv_op.py

@@ -32,7 +32,7 @@ class NetRealDiv(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_real_div():
     x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)

tests/st/ops/cpu/test_rmsprop.py

@@ -82,7 +82,7 @@ def rmspropcented_numpy(variable, gradients, mean_gradients, mean_square, moment
 
 
 @pytest.mark.level0
-@pytest.mark.platform_cpu_training
+@pytest.mark.platform_cpu
 @pytest.mark.env_onecard
 def test_rmsprop():
     learning_rate, decay, momentum, epsilon, centered = [0.5, 0.8, 0.9, 1e-3, True]
@@ -143,7 +143,7 @@ def test_rmsprop():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_cpu_training
+@pytest.mark.platform_cpu
 @pytest.mark.env_onecard
 def test_rmspropcenter():
     learning_rate, decay, momentum, epsilon, centered = [0.1, 0.3, 0.9, 1.0, False]

tests/st/ops/cpu/test_spacetodepth_op.py

@@ -49,61 +49,61 @@ def SpaceToDepth(nptype):
     assert (output.asnumpy() == expect).all()
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_float32():
     SpaceToDepth(np.float32)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_float16():
     SpaceToDepth(np.float16)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_int32():
     SpaceToDepth(np.int32)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_int64():
     SpaceToDepth(np.int64)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_int8():
     SpaceToDepth(np.int8)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_int16():
     SpaceToDepth(np.int16)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_uint8():
     SpaceToDepth(np.uint8)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_uint16():
     SpaceToDepth(np.uint16)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_uint32():
     SpaceToDepth(np.uint32)
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_spacetodepth_graph_uint64():
     SpaceToDepth(np.uint64)

tests/st/ops/cpu/test_squared_difference_op.py

@@ -35,7 +35,7 @@ class Net(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net01():
     net = Net()
@@ -65,7 +65,7 @@ def test_net01():
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_net02():
     net = Net()

tests/st/ops/cpu/test_unpack_op.py

@@ -132,84 +132,84 @@ def unpack_pynative(nptype):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_graph_float32():
     unpack(np.float32)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_graph_float16():
     unpack(np.float16)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_graph_int32():
     unpack(np.int32)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_graph_int16():
     unpack(np.int16)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_graph_uint8():
     unpack(np.uint8)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_graph_bool():
     unpack(np.bool)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_pynative_float32():
     unpack_pynative(np.float32)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_pynative_float16():
     unpack_pynative(np.float16)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_pynative_int32():
     unpack_pynative(np.int32)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_pynative_int16():
     unpack_pynative(np.int16)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_pynative_uint8():
     unpack_pynative(np.uint8)
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unpack_pynative_bool():
     unpack_pynative(np.bool)

tests/st/ops/cpu/test_zeroslike_op.py

@@ -32,7 +32,7 @@ class NetZerosLike(nn.Cell):
 
 
 @pytest.mark.level0
-@pytest.mark.platform_x86_cpu_training
+@pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_ZerosLike():
     x0_np = np.random.uniform(-2, 2, (2, 3, 4, 4)).astype(np.float32)