!8278 fix cpu kernel select type and raise exception for unsupported type

From: @huaweib
Reviewed-by: @kisnwang,@jjfeing
Signed-off-by: @jjfeing

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

@@ -132,14 +132,14 @@ void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
 bool ArithmeticCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
                                  const std::vector<kernel::AddressPtr> & /*workspace*/,
                                  const std::vector<kernel::AddressPtr> &outputs) {
-  if (dtype_ == kNumberTypeInt32) {
+  if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt16) {
     LaunchKernel<int>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeFloat32) {
+  } else if (dtype_ == kNumberTypeFloat32 || dtype_ == kNumberTypeFloat16 || dtype_ == kNumberTypeFloat64) {
     LaunchKernel<float>(inputs, outputs);
   } else if (dtype_ == kNumberTypeInt64) {
     LaunchKernel<int64_t>(inputs, outputs);
   } else {
-    MS_LOG(EXCEPTION) << "Only support int32, float32, but actual data type is " << TypeIdLabel(dtype_);
+    MS_LOG(EXCEPTION) << "Data type is " << TypeIdLabel(dtype_) << "is not support.";
   }
   return true;
 }

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

@@ -56,7 +56,7 @@ bool ArithmeticSelfCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inpu
   } else if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt16 || dtype_ == kNumberTypeInt64) {
     LaunchKernel<int>(inputs, outputs);
   } else {
-    MS_LOG(EXCEPTION) << "Only support float32, int32, but actual data type is " << TypeIdLabel(dtype_);
+    MS_LOG(EXCEPTION) << "Data type is " << TypeIdLabel(dtype_) << "is not support.";
   }
   return true;
 }

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

@@ -20,8 +20,9 @@ namespace kernel {
 void CPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
-  size_t type_size = sizeof(float);
   for (size_t input_index = 0; input_index < input_num; ++input_index) {
+    TypeId type_id = AnfAlgo::GetInputDeviceDataType(kernel_node, input_index);
+    size_t type_size = GetTypeByte(TypeIdToType(type_id));
     std::vector<size_t> shape = AnfAlgo::GetInputDeviceShape(kernel_node, input_index);
     size_t tensor_size =
       shape.empty() ? type_size : std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies<size_t>());
@@ -29,6 +30,8 @@ void CPUKernel::InitInputOutputSize(const CNodePtr &kernel_node) {
   }
   size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
   for (size_t output_index = 0; output_index < output_num; ++output_index) {
+    TypeId type_id = AnfAlgo::GetOutputDeviceDataType(kernel_node, output_index);
+    size_t type_size = GetTypeByte(TypeIdToType(type_id));
     std::vector<size_t> shape = AnfAlgo::GetOutputDeviceShape(kernel_node, output_index);
     size_t tensor_size =
       shape.empty() ? type_size : std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies<size_t>());

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

@@ -123,14 +123,14 @@ void EltWiseGradCPUKernel::InitKernel(const CNodePtr &kernel_node) {
 bool EltWiseGradCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
                                   const std::vector<kernel::AddressPtr> & /*workspace*/,
                                   const std::vector<kernel::AddressPtr> &outputs) {
-  if (dtype_ == kNumberTypeInt32) {
+  if (dtype_ == kNumberTypeInt32 || dtype_ == kNumberTypeInt16) {
     LaunchKernel<int>(inputs, outputs);
-  } else if (dtype_ == kNumberTypeFloat32) {
+  } else if (dtype_ == kNumberTypeFloat32 || dtype_ == kNumberTypeFloat16 || dtype_ == kNumberTypeFloat64) {
     LaunchKernel<float>(inputs, outputs);
   } else if (dtype_ == kNumberTypeInt64) {
     LaunchKernel<int64_t>(inputs, outputs);
   } else {
-    MS_LOG(EXCEPTION) << "Only support int32, float32, but actual data type is " << TypeIdLabel(dtype_);
+    MS_LOG(EXCEPTION) << "Data type is " << TypeIdLabel(dtype_) << "is not support.";
   }
   return true;
 }

mindspore/ccsrc/runtime/device/cpu/cpu_kernel_runtime.cc

@@ -54,7 +54,10 @@ void CPUKernelRuntime::AssignValueNodeAddress(session::KernelGraph *kernel_graph
       }
       auto tensor = node_value->cast<TensorPtr>();
       MS_EXCEPTION_IF_NULL(tensor);
-      TypeId output_type_id = AnfAlgo::GetOutputInferDataType(item_node, 0);
+      TypeId output_type_id = AnfAlgo::GetOutputDeviceDataType(item_node, 0);
+      if (output_type_id == kTypeUnknown) {
+        output_type_id = AnfAlgo::GetOutputInferDataType(item_node, 0);
+      }
       size_t type_size = GetTypeByte(TypeIdToType(output_type_id));
       ShapeVector data_shape = tensor->shape();
       size_t tensor_size = std::accumulate(data_shape.begin(), data_shape.end(), type_size, std::multiplies<size_t>());

mindspore/ccsrc/runtime/device/cpu/kernel_select_cpu.cc

@@ -205,6 +205,37 @@ void SetKernelBuildInfo(const std::vector<std::string> &input_formats, const std
   builder->SetOutputsDeviceType(output_types);
   AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), kernel_node);
 }
+
+void KernelNotSupportException(const AnfNodePtr &kernel_node, const std::vector<TypeId> &input_types,
+                               const std::vector<TypeId> &infer_output_types) {
+  std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
+  std::stringstream operator_info;
+  operator_info << "Operator[" << kernel_name << "] ";
+  size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
+  if (input_num > 0) {
+    operator_info << " input(";
+    for (size_t i = 0; i < input_num; ++i) {
+      operator_info << TypeIdLabel(input_types[i]);
+      if (i != input_num - 1) {
+        operator_info << ",";
+      }
+    }
+    operator_info << ") ";
+  }
+  size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
+  if (output_num > 0) {
+    operator_info << "output(";
+    for (size_t i = 0; i < output_num; ++i) {
+      operator_info << TypeIdLabel(infer_output_types[i]);
+      if (i != output_num - 1) {
+        operator_info << ",";
+      }
+    }
+    operator_info << ") ";
+  }
+  operator_info << "is not support.";
+  MS_LOG(EXCEPTION) << operator_info.str();
+}
 }  // namespace
 bool SelectKernel(const CNodePtr &kernel_node, KernelAttr *selected_kernel_attr,
                   const std::vector<KernelAttr> &kernel_attrs, const std::vector<std::string> &input_formats,
@@ -275,10 +306,17 @@ void SetKernelInfo(const CNodePtr &kernel_node) {
   std::pair<bool, bool> matched = std::make_pair(false, false);
   if (!SelectKernel(kernel_node, &selected_kernel_attr, kernel_attrs, input_formats, input_types,
                     input_not_cnode_indexes, infer_output_formats, infer_output_types, &matched, true)) {
+    if (AnfAlgo::GetCNodeName(kernel_node) == "Cast") {
+      KernelNotSupportException(kernel_node, input_types, infer_output_types);
+    }
     matched = std::make_pair(false, false);
     SelectKernel(kernel_node, &selected_kernel_attr, kernel_attrs, input_formats, input_types, input_not_cnode_indexes,
                  infer_output_formats, infer_output_types, &matched, false);
+    if (!matched.first) {
+      KernelNotSupportException(kernel_node, input_types, infer_output_types);
+    }
   }
+
   if (selected_kernel_attr.GetInputSize() > 0 &&
       (matched.first || input_types.size() == input_not_cnode_indexes.size())) {
     MS_LOG(INFO) << "Input format and dtype is matched";