add one_hot and one_like int64_t requirement

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/one_hot_gpu_kernel.h

@@ -45,36 +45,36 @@ class OneHotGpuFwdKernel : public GpuKernel {
     return true;
   }
   bool Init(const CNodePtr &kernel_node) override {
-    int axis = static_cast<int>(GetAttr<int64_t>(kernel_node, "axis"));
-    auto input = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
-    auto output = AnfAlgo::GetOutputInferShape(kernel_node, 0);
-    int input_size = SizeToInt(input.size());
-    const int default_axis = -1;
+    int64_t axis = GetAttr<int64_t>(kernel_node, "axis");
+    auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+    auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+    int64_t input_dims = static_cast<int64_t>(input_shape.size());
+    if (axis >= input_dims) {
+      MS_LOG(ERROR) << "invalid one hot axis value: " << axis << " for input dims size: " << input_shape.size();
+      return false;
+    }
+    const int64_t default_axis = -1;
 
     // Compress arbitrary tensor dimensions into three dimensions (left_dims, depth, right_dims).
-    for (int i = 0; i < input_size; i++) {
-      auto dim_size = input[IntToSize(i)];
-      if (axis == default_axis || i < axis) {
+    for (size_t i = 0; i < input_shape.size(); i++) {
+      auto dim_size = input_shape[i];
+      if (axis == default_axis || i < IntToSize(axis)) {
         left_dim_size_ *= dim_size;
       }
-      if (axis != default_axis && i >= axis) {
+      if (axis != default_axis && i >= IntToSize(axis)) {
         right_dim_size_ *= dim_size;
       }
     }
-    for (auto size : input) {
+    for (auto size : input_shape) {
       input_size_ *= size;
     }
-    for (auto size : output) {
+    for (auto size : output_shape) {
       output_size_ *= size;
     }
-    if (axis >= input_size) {
-      MS_LOG(ERROR) << "invalid one hot axis value: " << axis << " for input dims size: " << input.size();
-      return false;
-    }
     if (axis == default_axis) {
-      depth_ = output[output.size() - 1];
+      depth_ = output_shape[output_shape.size() - 1];
     } else {
-      depth_ = output[IntToSize(axis)];
+      depth_ = output_shape[IntToSize(axis)];
     }
     InitSizeLists();
     return true;

mindspore/ccsrc/backend/kernel_compiler/gpu/arrays/oneslike_gpu_kernel.h

@@ -55,11 +55,10 @@ class OnesLikeGpuKernel : public GpuKernel {
     auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
     size_t shape_size = input_shape.size();
 
-    input_size_ = 1;
+    input_size_ = sizeof(T);
     for (size_t i = 0; i < shape_size; i++) {
       input_size_ *= input_shape[i];
     }
-    input_size_ *= sizeof(T);
     output_size_ = input_size_;
     InitSizeLists();
     return true;

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/one_hot_impl.cu

@@ -21,14 +21,14 @@ __global__ void OneHotKernel(size_t size, const S *indices, size_t depth, const
                              size_t left_dim_size, size_t right_dim_size, T *output) {
   T on_v = *on_value;
   T off_v = *off_value;
-  for (int thread_idx = blockIdx.x * blockDim.x + threadIdx.x; thread_idx < size;
+  for (size_t thread_idx = blockIdx.x * blockDim.x + threadIdx.x; thread_idx < size;
        thread_idx += blockDim.x * gridDim.x) {
     if (thread_idx < size) {
-      int left_idx = (thread_idx / (depth * right_dim_size)) % left_dim_size;
-      int d_idx = thread_idx / right_dim_size % depth;
-      int right_idx = thread_idx % right_dim_size;
-      int input_idx = left_idx * right_dim_size + right_idx;
-      int output_idx = left_idx * depth * right_dim_size + d_idx * right_dim_size + right_idx;
+      size_t left_idx = (thread_idx / (depth * right_dim_size)) % left_dim_size;
+      size_t d_idx = thread_idx / right_dim_size % depth;
+      size_t right_idx = thread_idx % right_dim_size;
+      size_t input_idx = left_idx * right_dim_size + right_idx;
+      size_t output_idx = left_idx * depth * right_dim_size + d_idx * right_dim_size + right_idx;
       if (indices[input_idx] == d_idx) {
         output[output_idx] = on_v;
       } else {

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/oneslike_impl.cu

@@ -18,20 +18,20 @@
 #include "oneslike_impl.cuh"
 #include "runtime/device/gpu/cuda_common.h"
 template <typename T>
-__global__ void OnesLike(const int size, const T* input,  T* output) {
+__global__ void OnesLike(const size_t size, const T* input,  T* output) {
   int one = 1;
   T val = static_cast<T>(one);
-  for (int pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < size; pos += blockDim.x * gridDim.x) {
     output[pos] = val;
   }
   return;
 }
 template <typename T>
-void CalOnesLike(const int size, const T* input, T* output, cudaStream_t cuda_stream) {
+void CalOnesLike(const size_t size, const T* input, T* output, cudaStream_t cuda_stream) {
   OnesLike<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, output);
   return;
 }
 
-template void CalOnesLike<float>(const int size, const float* input, float* output, cudaStream_t cuda_stream);
-template void CalOnesLike<half>(const int size, const half* input, half* output, cudaStream_t cuda_stream);
-template void CalOnesLike<int>(const int size, const int* input, int* output, cudaStream_t cuda_stream);
+template void CalOnesLike<float>(const size_t size, const float* input, float* output, cudaStream_t cuda_stream);
+template void CalOnesLike<half>(const size_t size, const half* input, half* output, cudaStream_t cuda_stream);
+template void CalOnesLike<int>(const size_t size, const int* input, int* output, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/oneslike_impl.cuh

@@ -18,6 +18,6 @@
 #define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ONESLIKE_H_
 
 template <typename T>
-void CalOnesLike(const int size, const T* input, T* output, cudaStream_t cuda_stream);
+void CalOnesLike(const size_t size, const T* input, T* output, cudaStream_t cuda_stream);
 
 #endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_ONESLIKE_H_