diff --git a/mindspore/ccsrc/kernel/gpu/arrays/argmaxwithvalue_gpu_kernel.h b/mindspore/ccsrc/kernel/gpu/arrays/argmaxwithvalue_gpu_kernel.h
index fb7796b022..304f0ab161 100644
--- a/mindspore/ccsrc/kernel/gpu/arrays/argmaxwithvalue_gpu_kernel.h
+++ b/mindspore/ccsrc/kernel/gpu/arrays/argmaxwithvalue_gpu_kernel.h
@@ -38,7 +38,7 @@ class ArgmaxWithValueGpuKernel : public GpuKernel {
     T *input = GetDeviceAddress<T>(inputs, 0);
     T *output = GetDeviceAddress<T>(outputs, 1);
     S *index = GetDeviceAddress<S>(outputs, 0);
-    CalArgmaxWithValue(input_size_ / sizeof(T), input, bound_, outerSize_, innerSize_, index, output,
+    CalArgmaxWithValue(input, bound_, outerSize_, innerSize_, index, output,
                        reinterpret_cast<cudaStream_t>(stream_ptr));
     return true;
   }
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cu
index a0687a2768..3313fc6853 100644
--- a/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cu
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cu
@@ -18,41 +18,39 @@
 #include "device/gpu/cuda_common.h"
 #include "include/cuda_fp16.h"
 template <typename T, typename S>
-__global__ void ArgmaxWithValue(size_t size, const T* input, const int bound, int outerSize, int innerSize,
-                                S* index, T* output) {
-  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (size); pos += blockDim.x * gridDim.x) {
-      for (int i = 0; i < outerSize; i++)   {
-        int inputOutterOffset = i * innerSize * bound;
-        int outputOutterOffset = i * innerSize;
-        for (int j = 0; j < innerSize; j++) {
-            auto outputInnerOffset = outputOutterOffset + j;
-            S idx = 0;
-            T maxData = input[j + inputOutterOffset];
-            for (S c = 0; c < bound; c++) {
-                int offset = j + c * innerSize;
-                auto inputData = input[inputOutterOffset + offset];
-                idx = inputData > maxData ? c : idx;
-                maxData = inputData > maxData ? inputData : maxData;
-            }
-            output[outputInnerOffset] = maxData;
-            index[outputInnerOffset] = idx;
+__global__ void ArgmaxWithValue(const T* input, const int bound, int outerSize, int innerSize, S* index,
+                                T* output) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < (outerSize); pos += blockDim.x * gridDim.x) {
+      int inputOutterOffset = pos * innerSize * bound;
+      int outputOutterOffset = pos * innerSize;
+      for (int j = 0; j < innerSize; j++) {
+          auto outputInnerOffset = outputOutterOffset + j;
+          S idx = 0;
+          T maxData = input[j + inputOutterOffset];
+          for (S c = 0; c < bound; c++) {
+              int offset = j + c * innerSize;
+              auto inputData = input[inputOutterOffset + offset];
+              idx = inputData > maxData ? c : idx;
+              maxData = inputData > maxData ? inputData : maxData;
+          }
+          output[outputInnerOffset] = maxData;
+          index[outputInnerOffset] = idx;
       }
-    }
   }
   return;
 }
 
 template <typename T, typename S>
-void CalArgmaxWithValue(size_t size, const T* input, const int bound_, const int outerSize_, const int innerSize_,
+void CalArgmaxWithValue(const T* input, const int bound_, const int outerSize_, const int innerSize_,
                         S* index, T* output, cudaStream_t cuda_stream) {
-  ArgmaxWithValue<<<GET_BLOCKS(size), GET_THREADS, 0, cuda_stream>>>(size, input, bound_, outerSize_, innerSize_,
-                                                                     index, output);
+  ArgmaxWithValue<<<GET_BLOCKS(outerSize_), GET_THREADS, 0, cuda_stream>>>(input, bound_, outerSize_, innerSize_,
+                                                                           index, output);
   return;
 }
 
-template void CalArgmaxWithValue<float, int>(size_t size, const float* input, const int bound_, const int outerSize_,
+template void CalArgmaxWithValue<float, int>(const float* input, const int bound_, const int outerSize_,
                                   const int innerSize_, int* index, float* output,
                                   cudaStream_t cuda_stream);
-template void CalArgmaxWithValue<half, int>(size_t size, const half* input, const int bound_, const int outerSize_,
+template void CalArgmaxWithValue<half, int>(const half* input, const int bound_, const int outerSize_,
                                   const int innerSize_, int* index, half* output,
                                   cudaStream_t cuda_stream);
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cuh b/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cuh
index 0d4f4b62a3..67c061a966 100644
--- a/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cuh
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/argmaxwithvalue_impl.cuh
@@ -17,6 +17,6 @@
 #ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ARGMAXWITHVALUE_H_
 #define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ARGMAXWITHVALUE_H_
 template <typename T, typename S>
-void CalArgmaxWithValue(size_t size, const T* input, const int bound_, const int outerSize_, const int innerSize_,
-                        S* index, T* output, cudaStream_t cuda_stream);
+void CalArgmaxWithValue(const T *input, const int bound_, const int outerSize_, const int innerSize_, S *index,
+                        T *output, cudaStream_t cuda_stream);
 #endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMP_ARGMAXWITHVALUE_H_
diff --git a/mindspore/ccsrc/kernel/gpu/cuda_impl/unary_op_impl.cu b/mindspore/ccsrc/kernel/gpu/cuda_impl/unary_op_impl.cu
index 4a8af83aa4..09b347e3d5 100755
--- a/mindspore/ccsrc/kernel/gpu/cuda_impl/unary_op_impl.cu
+++ b/mindspore/ccsrc/kernel/gpu/cuda_impl/unary_op_impl.cu
@@ -36,6 +36,13 @@ __global__ void LogarithmKernel(T *input, T *output, size_t count) {
   }
   return;
 }
+template <>
+__global__ void LogarithmKernel(half *input, half *output, size_t count) {
+  for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (count); i += blockDim.x * gridDim.x) {
+    output[i] = hlog(input[i]);
+  }
+  return;
+}
 template <typename T>
 __global__ void NegativeKernel(T *input, T *output, size_t count) {
   T neg_one = -1;