From b3049a3440faf221baf094fbd3aa8303b075b7d7 Mon Sep 17 00:00:00 2001
From: zhangzhaoju <zhangzhaoju@huawei.com>
Date: Mon, 12 Dec 2022 10:57:31 +0800
Subject: [PATCH] Fix cordump problem while null input

while has null input, xdivy return null too according to tensorflow
---
 mindspore/ccsrc/kernel/kernel.h                    |  8 ++++++++
 .../plugin/device/cpu/kernel/xdivy_cpu_kernel.cc   | 14 ++++++++++----
 .../plugin/device/cpu/kernel/xdivy_cpu_kernel.h    |  1 +
 .../plugin/device/gpu/hal/device/gpu_common.h      | 13 -------------
 4 files changed, 19 insertions(+), 17 deletions(-)

diff --git a/mindspore/ccsrc/kernel/kernel.h b/mindspore/ccsrc/kernel/kernel.h
index 85eb9510d5f..ad4b0f1aa50 100644
--- a/mindspore/ccsrc/kernel/kernel.h
+++ b/mindspore/ccsrc/kernel/kernel.h
@@ -404,6 +404,14 @@ inline bool GetDynamicAttrIntValue(const std::vector<KernelTensorPtr> &inputs, c
   *attr_value = res.value();
   return true;
 }
+
+template <typename T>
+inline bool CheckNullInput(const std::vector<T> &input_shape) {
+  // If input_shape.size() == 0, it means a scalar input; If input_shape.size() != 0 and input_shape contains 0,
+  // it means a null input. Just return a null output.
+  return input_shape.size() != 0 && std::any_of(input_shape.begin(), input_shape.end(), [](T i) { return i == 0; });
+}
+#define CHECK_NULL_INPUT(input_shape) mindspore::kernel::CheckNullInput(input_shape)
 }  // namespace kernel
 }  // namespace mindspore
 
diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.cc
index a5ebfd03dc3..568d8758cc0 100644
--- a/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.cc
+++ b/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.cc
@@ -99,6 +99,9 @@ bool XdivyCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr> &inpu
                                      const std::vector<kernel::AddressPtr> &outputs) {
   CHECK_KERNEL_INPUTS_NUM(inputs.size(), INPUT_NUM, kernel_name_);
   CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), OUTPUT_NUM, kernel_name_);
+  if (has_null_input_) {
+    return true;
+  }
   auto x_addr = static_cast<T *>(inputs[0]->addr);
   auto y_addr = static_cast<T *>(inputs[1]->addr);
   auto output_addr = static_cast<T *>(outputs[0]->addr);
@@ -136,10 +139,6 @@ bool XdivyCpuKernelMod::LaunchKernel(const std::vector<kernel::AddressPtr> &inpu
 
 bool XdivyCpuKernelMod::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
                                const std::vector<AddressPtr> &outputs) {
-  const size_t kInputsNum = 2;
-  const size_t kOutputsNum = 1;
-  CHECK_KERNEL_INPUTS_NUM(inputs.size(), kInputsNum, kernel_name_);
-  CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kOutputsNum, kernel_name_);
   return kernel_func_(this, inputs, workspace, outputs);
 }
 
@@ -174,6 +173,13 @@ int XdivyCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const std::v
 
   auto x_shape = LongVecToSizeVec(inputs.at(kIndex0)->GetShapeVector());
   auto y_shape = LongVecToSizeVec(inputs.at(kIndex1)->GetShapeVector());
+
+  // while has null input, xdivy result is null too
+  has_null_input_ = CheckNullInput(x_shape) || CheckNullInput(y_shape);
+  if (has_null_input_) {
+    return 0;
+  }
+
   auto out_shape = LongVecToSizeVec(outputs.at(kIndex0)->GetShapeVector());
   if (out_shape.size() > MAX_DIMS || out_shape.size() < x_shape.size() || out_shape.size() < y_shape.size()) {
     MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input cannot be greater than " << MAX_DIMS
diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.h b/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.h
index ea197453969..32bb06dbd91 100644
--- a/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.h
+++ b/mindspore/ccsrc/plugin/device/cpu/kernel/xdivy_cpu_kernel.h
@@ -58,6 +58,7 @@ class XdivyCpuKernelMod : public NativeCpuKernelMod, public MatchKernelHelper<Xd
   std::vector<size_t> index_listx_{};
   std::vector<size_t> index_listy_{};
   bool is_need_broadcast_{false};
+  bool has_null_input_{false};
 };
 }  // namespace kernel
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_common.h b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_common.h
index 2fc80e26d5d..90432257f8f 100644
--- a/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_common.h
+++ b/mindspore/ccsrc/plugin/device/gpu/hal/device/gpu_common.h
@@ -228,19 +228,6 @@ namespace gpu {
 #define VARIABLE_NOT_USED(var) \
   { (void)(var); }
 
-template <typename T>
-inline bool CheckNullInput(const std::vector<T> &input_shape) {
-  // If input_shape.size() == 0, it means a scalar input; If input_shape.size() != 0 and input_shape contains 0,
-  // it means a null input. Just return a null output.
-  if (input_shape.size() != 0) {
-    if (std::any_of(input_shape.begin(), input_shape.end(), [](T i) { return i == 0; })) {
-      return true;
-    }
-  }
-  return false;
-}
-#define CHECK_NULL_INPUT(input_shape) mindspore::device::gpu::CheckNullInput(input_shape)
-
 inline bool CheckShapePositive(const std::vector<int64_t> &input_shape) {
   if (input_shape.size() != 0) {
     if (std::all_of(input_shape.begin(), input_shape.end(), [](int64_t i) { return i > 0; })) {