broadcast, slice, scatter_nd ops optimizer.

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

@@ -182,30 +182,59 @@ class ArrayReduceGpuKernel : public GpuKernel {
   void InferInAndOutDesc(const std::vector<size_t> &input_shape, const std::vector<size_t> &output_shape) {
     std::vector<int> inputA;
     std::vector<size_t> outputC_shape = output_shape;
-    ShapeNdTo4d(input_shape, &inputA);
-    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(inputA_descriptor_, CUDNN_TENSOR_NCHW, data_type_, inputA[0],
-                                                           inputA[1], inputA[2], inputA[3]),
-                                "cudnnSetTensor4dDescriptor failed");
+    const int split_dim = 4;
+
+    if (input_shape.size() <= split_dim) {
+      ShapeNdTo4d(input_shape, &inputA);
+      CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(inputA_descriptor_, CUDNN_TENSOR_NCHW, data_type_,
+                                                             inputA[0], inputA[1], inputA[2], inputA[3]),
+                                  "cudnnSetTensor4dDescriptor failed");
+    } else {
+      CudnnSetTensorNdDescriptor(input_shape, inputA_descriptor_, data_type_);
+      for (auto dim : input_shape) {
+        inputA.emplace_back(SizeToInt(dim));
+      }
+    }
 
     if (axis_[0] == -1) {
-      CHECK_CUDNN_RET_WITH_EXCEPT(
-        cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_, 1, 1, 1, 1),
-        "cudnnSetTensor4dDescriptor failed");
-      if (inputA[0] == 1 && inputA[1] == 1 && inputA[2] == 1 && inputA[3] == 1) {
-        all_match_ = true;
+      outputC_shape.resize(input_shape.size(), 1);
+      if (outputC_shape.size() <= split_dim) {
+        CHECK_CUDNN_RET_WITH_EXCEPT(
+          cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_, 1, 1, 1, 1),
+          "cudnnSetTensor4dDescriptor failed");
+      } else {
+        CudnnSetTensorNdDescriptor(outputC_shape, outputC_descriptor_, data_type_);
       }
+
+      for (auto dim : inputA) {
+        if (dim != 1) {
+          return;
+        }
+      }
+
+      all_match_ = true;
       return;
     }
+
+    std::vector<int> outputC;
     if (!keep_dims_) {
       for (auto i : axis_) {
         (void)(outputC_shape.insert(outputC_shape.begin() + i, 1));
       }
     }
-    std::vector<int> outputC;
-    ShapeNdTo4d(outputC_shape, &outputC);
-    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_,
-                                                           outputC[0], outputC[1], outputC[2], outputC[3]),
-                                "cudnnSetTensor4dDescriptor failed");
+
+    if (outputC_shape.size() <= split_dim) {
+      ShapeNdTo4d(outputC_shape, &outputC);
+      CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(outputC_descriptor_, CUDNN_TENSOR_NCHW, data_type_,
+                                                             outputC[0], outputC[1], outputC[2], outputC[3]),
+                                  "cudnnSetTensor4dDescriptor failed");
+    } else {
+      CudnnSetTensorNdDescriptor(outputC_shape, outputC_descriptor_, data_type_);
+      for (auto dim : outputC_shape) {
+        outputC.emplace_back(SizeToInt(dim));
+      }
+    }
+
     if (inputA == outputC) {
       all_match_ = true;
     }

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

@@ -69,6 +69,10 @@ class ScatterNdGpuFwdKernel : public GpuKernel {
       memcpy_flag_ = true;
     }
 
+    CHECK_CUDA_RET_WITH_EXCEPT(
+      cudaMemsetAsync(output, static_cast<T>(0.0), output_size_, reinterpret_cast<cudaStream_t>(stream_ptr)),
+      "cudaMemSet failed in ScatterNdGpuFwdKernel::Launch.");
+
     const size_t input_size = input_size_ / sizeof(T);
     const size_t output_size = output_size_ / sizeof(T);
 

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

@@ -14,6 +14,7 @@
  * limitations under the License.
  */
 
+#include <vector>
 #include "backend/kernel_compiler/gpu/cuda_impl/broadcast_impl.cuh"
 #include "runtime/device/gpu/cuda_common.h"
 
@@ -107,69 +108,97 @@ __device__ __forceinline__ int Index(const int &index, const int &dim) { return
 
 template <typename T, typename S, typename Func>
 __device__ __forceinline__ void BroadcastOperator(const int &l0, const int &l1, const int &l2, const int &l3,
-                                                  const int &r0, const int &r1, const int &r2, const int &r3,
-                                                  const int &d0, const int &d1, const int &d2, const int &d3,
-                                                  const T *input0, const T *input1, S *output) {
-  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < d0 * d1 * d2 * d3; pos += blockDim.x * gridDim.x) {
-    int i = pos / (d1 * d2 * d3) % d0;
-    int j = pos / (d2 * d3) % d1;
-    int k = pos / d3 % d2;
-    int l = pos % d3;
+                                                  const int &l4, const int &l5, const int &l6, const int &r0,
+                                                  const int &r1, const int &r2, const int &r3, const int &r4,
+                                                  const int &r5, const int &r6, const int &d0, const int &d1,
+                                                  const int &d2, const int &d3, const int &d4, const int &d5,
+                                                  const int &d6, const T *input0, const T *input1, S *output) {
+  for (size_t pos = blockIdx.x * blockDim.x + threadIdx.x; pos < d0 * d1 * d2 * d3 * d4 * d5 * d6;
+       pos += blockDim.x * gridDim.x) {
+    int i = pos / (d1 * d2 * d3 * d4 * d5 * d6) % d0;
+    int j = pos / (d2 * d3 * d4 * d5 * d6) % d1;
+    int k = pos / (d3 * d4 * d5 * d6) % d2;
+    int l = pos / (d4 * d5 * d6) % d3;
+    int m = pos / (d5 * d6) % d4;
+    int n = pos / d6 % d5;
+    int o = pos % d6;
 
-    int l_index = Index(i, l0) * l1 * l2 * l3 + Index(j, l1) * l2 * l3 + Index(k, l2) * l3 + Index(l, l3);
-    int r_index = Index(i, r0) * r1 * r2 * r3 + Index(j, r1) * r2 * r3 + Index(k, r2) * r3 + Index(l, r3);
+    int l_index = Index(i, l0) * l1 * l2 * l3 * l4 * l5 * l6;
+    l_index += Index(j, l1) * l2 * l3 * l4 * l5 * l6;
+    l_index += Index(k, l2) * l3 * l4 * l5 * l6;
+    l_index += Index(l, l3) * l4 * l5 * l6;
+    l_index += Index(m, l4) * l5 * l6;
+    l_index += Index(n, l5) * l6;
+    l_index += Index(o, l6);
+    int r_index = Index(i, r0) * r1 * r2 * r3 * r4 * r5 * r6;
+    r_index += Index(j, r1) * r2 * r3 * r4 * r5 * r6;
+    r_index += Index(k, r2) * r3 * r4 * r5 * r6;
+    r_index += Index(l, r3) * r4 * r5 * r6;
+    r_index += Index(m, r4) * r5 * r6;
+    r_index += Index(n, r5) * r6;
+    r_index += Index(o, r6);
     output[pos] = Func()(input0[l_index], input1[r_index]);
   }
 }
 
 template <typename T, typename S>
-__global__ void BroadcastKernel(const int l0, const int l1, const int l2, const int l3, const int r0, const int r1,
-                                const int r2, const int r3, const int d0, const int d1, const int d2, const int d3,
-                                enum BroadcastOpType op, const T *input0, const T *input1, S *output) {
+__global__ void BroadcastKernel(const int l0, const int l1, const int l2, const int l3, const int l4, const int l5,
+                                const int l6, const int r0, const int r1, const int r2, const int r3, const int r4,
+                                const int r5, const int r6, const int d0, const int d1, const int d2, const int d3,
+                                const int d4, const int d5, const int d6, enum BroadcastOpType op, const T *input0,
+                                const T *input1, S *output) {
   switch (op) {
     case BROADCAST_TYPE_GREATER:
-      return BroadcastOperator<T, S, GreaterFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                        output);
+      return BroadcastOperator<T, S, GreaterFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                        d2, d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_LESS:
-      return BroadcastOperator<T, S, LessFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                     output);
+      return BroadcastOperator<T, S, LessFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1, d2,
+                                                     d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_MINIMUM:
-      return BroadcastOperator<T, S, MinimumFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                        output);
+      return BroadcastOperator<T, S, MinimumFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                        d2, d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_MAXIMUM:
-      return BroadcastOperator<T, S, MaximumFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                        output);
+      return BroadcastOperator<T, S, MaximumFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                        d2, d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_POWER:
-      return BroadcastOperator<T, S, PowerFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, PowerFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                      d2, d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_REALDIV:
-      return BroadcastOperator<T, S, RealDivFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, RealDivFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                        d2, d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_MUL:
-      return BroadcastOperator<T, S, MulFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, MulFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1, d2,
+                                                    d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_SUB:
-      return BroadcastOperator<T, S, SubFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, SubFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1, d2,
+                                                    d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_ADD:
-      return BroadcastOperator<T, S, AddFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, AddFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1, d2,
+                                                    d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_FLOORDIV:
-      return BroadcastOperator<T, S, FloorDivFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, FloorDivFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                         d2, d3, d4, d5, d6, input0, input1, output);
     case BROADCAST_TYPE_ABSGRAD:
-      return BroadcastOperator<T, S, AbsGradFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
-                                                      output);
+      return BroadcastOperator<T, S, AbsGradFunc<T, S>>(l0, l1, l2, l3, l4, l5, l6, r0, r1, r2, r3, r4, r5, r6, d0, d1,
+                                                        d2, d3, d4, d5, d6, input0, input1, output);
   }
 }
 
 template <typename T, typename S>
-void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1, const int &r2,
-               const int &r3, const int &d0, const int &d1, const int &d2, const int &d3, enum BroadcastOpType op,
-               const T *input0, const T *input1, S *output, cudaStream_t stream) {
-  int size = d0 * d1 * d2 * d3;
-  BroadcastKernel<<<GET_BLOCKS(size), GET_THREADS, 0, stream>>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, op,
-                                                                input0, input1, output);
+void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+               const std::vector<int> &output_shape, enum BroadcastOpType op, const T *input0, const T *input1,
+               S *output, cudaStream_t stream) {
+  int size = 1;
+  for (auto d : output_shape) {
+    size *= d;
+  }
+  BroadcastKernel<<<GET_BLOCKS(size), GET_THREADS, 0, stream>>>(lhs_shape[0], lhs_shape[1], lhs_shape[2], lhs_shape[3],
+                                                                lhs_shape[4], lhs_shape[5], lhs_shape[6], rhs_shape[0],
+                                                                rhs_shape[1], rhs_shape[2], rhs_shape[3], rhs_shape[4],
+                                                                rhs_shape[5], rhs_shape[6], output_shape[0],
+                                                                output_shape[1], output_shape[2], output_shape[3],
+                                                                output_shape[4], output_shape[5], output_shape[6],
+                                                                op, input0, input1, output);
 }
 
 template <typename T, typename S, typename Func>
@@ -236,30 +265,24 @@ void BroadcastTo(const int &i0, const int &i1, const int &i2, const int &i3, con
           output_addr);
 }
 
-template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
-                        const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
-                        enum BroadcastOpType op, const float *input0, const float *input1, bool *output,
-                        cudaStream_t stream);
-template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
-                        const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
-                        enum BroadcastOpType op, const float *input0, const float *input1, float *output,
-                        cudaStream_t stream);
-template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
-                        const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
-                        enum BroadcastOpType op, const half *input0, const half *input1, bool *output,
-                        cudaStream_t stream);
-template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
-                        const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
-                        enum BroadcastOpType op, const half *input0, const half *input1, half *output,
-                        cudaStream_t stream);
-template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
-                        const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
-                        enum BroadcastOpType op, const int *input0, const int *input1, int *output,
-                        cudaStream_t stream);
-template void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1,
-                        const int &r2, const int &r3, const int &d0, const int &d1, const int &d2, const int &d3,
-                        enum BroadcastOpType op, const int *input0, const int *input1, bool *output,
-                        cudaStream_t stream);
+template void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+                        const std::vector<int> &output_shape, enum BroadcastOpType op, const float *input0,
+                        const float *input1, bool *output, cudaStream_t stream);
+template void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+                        const std::vector<int> &output_shape, enum BroadcastOpType op, const float *input0,
+                        const float *input1, float *output, cudaStream_t stream);
+template void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+                        const std::vector<int> &output_shape, enum BroadcastOpType op, const half *input0,
+                        const half *input1, bool *output, cudaStream_t stream);
+template void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+                        const std::vector<int> &output_shape, enum BroadcastOpType op, const half *input0,
+                        const half *input1, half *output, cudaStream_t stream);
+template void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+                        const std::vector<int> &output_shape, enum BroadcastOpType op, const int *input0,
+                        const int *input1, int *output, cudaStream_t stream);
+template void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+                        const std::vector<int> &output_shape, enum BroadcastOpType op, const int *input0,
+                        const int *input1, bool *output, cudaStream_t stream);
 template void NoBroadcast(const int &nums, enum BroadcastOpType op, const float *input0, const float *input1,
                           bool *output, cudaStream_t stream);
 template void NoBroadcast(const int &nums, enum BroadcastOpType op, const float *input0, const float *input1,

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

@@ -17,6 +17,7 @@
 #ifndef MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BROADCAST_H_
 #define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_BROADCAST_H_
 
+#include <vector>
 #include "runtime/device/gpu/cuda_common.h"
 
 enum BroadcastOpType {
@@ -35,9 +36,9 @@ enum BroadcastOpType {
 };
 
 template <typename T, typename S>
-void Broadcast(const int &l0, const int &l1, const int &l2, const int &l3, const int &r0, const int &r1, const int &r2,
-               const int &r3, const int &d0, const int &d1, const int &d2, const int &d3, enum BroadcastOpType op,
-               const T *input0, const T *input1, S *output, cudaStream_t stream);
+void Broadcast(const std::vector<int> &lhs_shape, const std::vector<int> &rhs_shape,
+               const std::vector<int> &output_shape, enum BroadcastOpType op, const T *input0, const T *input1,
+               S *output, cudaStream_t stream);
 
 template <typename T, typename S>
 void NoBroadcast(const int &size, enum BroadcastOpType op, const T *input0, const T *input1, S *output,

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

@@ -25,10 +25,10 @@ __global__ void CheckValidKernel(const size_t size, const T *box, const T *img_m
     const size_t right_y = i * 4 + 3;
 
     S valid_flag = false;
-    valid_flag |= !(box[left_x] >= 0.f);
-    valid_flag |= !(box[left_y] >= 0.f);
-    valid_flag |= !(img_metas[0] * img_metas[2] - 1.f >= box[right_x]);
-    valid_flag |= !(img_metas[1] * img_metas[2] - 1.f >= box[right_y]);
+    valid_flag |= !(box[left_x] >= static_cast<T>(0.0));
+    valid_flag |= !(box[left_y] >= static_cast<T>(0.0));
+    valid_flag |= !(img_metas[1] * img_metas[2] - static_cast<T>(1.0) >= box[right_x]);
+    valid_flag |= !(img_metas[0] * img_metas[2] - static_cast<T>(1.0) >= box[right_y]);
 
     valid[i] = !valid_flag;
   }
@@ -43,3 +43,5 @@ void CheckValid(const size_t &size, const T *box, const T *img_metas, S *valid,
 
 template void CheckValid(const size_t &size, const float *box, const float *img_metas, bool *valid,
                          cudaStream_t cuda_stream);
+template void CheckValid(const size_t &size, const half *box, const half *img_metas, bool *valid,
+                         cudaStream_t cuda_stream);

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

@@ -16,27 +16,26 @@
 
 #include "backend/kernel_compiler/gpu/cuda_impl/iou_impl.cuh"
 
-template <typename T>
-__device__ T CoordinateMax(const T a, const T b) {
+__device__ float CoordinateMax(const float a, const float b) {
   return (a > b ? a : b);
 }
 
-template <typename T>
-__device__ T CoordinateMin(const T a, const T b) {
+__device__ float CoordinateMin(const float a, const float b) {
   return (a < b ? a : b);
 }
 
 template <typename T>
 __global__ void IOUKernel(const size_t size, const T *box1, const T *box2, T *iou_results, const size_t mode,
                           const size_t input_len_0) {
-  T location_coordinate[IOU_LOCATION_NUM][IOU_DIMENSION];
-  T overlaps_coordinate[IOU_DIMENSION];
-  const T epsilon = 1e-10;
+  float location_coordinate[IOU_LOCATION_NUM][IOU_DIMENSION];
+  float overlaps_coordinate[IOU_DIMENSION];
+  const float epsilon = 1e-10;
+  const float offset = 1.0;
 
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < size; i += gridDim.x * blockDim.x) {
     for (size_t j = 0; j < IOU_DIMENSION; j++) {
-      location_coordinate[0][j] = box1[(i % input_len_0) * IOU_DIMENSION + j];
-      location_coordinate[1][j] = box2[(i / input_len_0) * IOU_DIMENSION + j];
+      location_coordinate[0][j] = static_cast<float>(box1[(i % input_len_0) * IOU_DIMENSION + j]);
+      location_coordinate[1][j] = static_cast<float>(box2[(i / input_len_0) * IOU_DIMENSION + j]);
     }
 
     overlaps_coordinate[0] = CoordinateMax(location_coordinate[0][0], location_coordinate[1][0]);
@@ -44,18 +43,18 @@ __global__ void IOUKernel(const size_t size, const T *box1, const T *box2, T *io
     overlaps_coordinate[2] = CoordinateMin(location_coordinate[0][2], location_coordinate[1][2]);
     overlaps_coordinate[3] = CoordinateMin(location_coordinate[0][3], location_coordinate[1][3]);
 
-    T overlaps_w = CoordinateMax(0.f, overlaps_coordinate[2] - overlaps_coordinate[0] + 1);
-    T overlaps_h = CoordinateMax(0.f, overlaps_coordinate[3] - overlaps_coordinate[1] + 1);
-    T overlaps = overlaps_w * overlaps_h;
+    float overlaps_w = CoordinateMax(0.0, overlaps_coordinate[2] - overlaps_coordinate[0] + offset);
+    float overlaps_h = CoordinateMax(0.0, overlaps_coordinate[3] - overlaps_coordinate[1] + offset);
+    float overlaps = overlaps_w * overlaps_h;
 
-    T area1 = (location_coordinate[0][2] - location_coordinate[0][0] + 1) * (location_coordinate[0][3] -
-               location_coordinate[0][1] + 1);
-    T area2 = (location_coordinate[1][2] - location_coordinate[1][0] + 1) * (location_coordinate[1][3] -
-                                                                             location_coordinate[1][1] + 1);
+    float area1 = (location_coordinate[0][2] - location_coordinate[0][0] + offset) * (location_coordinate[0][3] -
+               location_coordinate[0][1] + offset);
+    float area2 = (location_coordinate[1][2] - location_coordinate[1][0] + offset) * (location_coordinate[1][3] -
+                                                                             location_coordinate[1][1] + offset);
     if (mode == 0) {
-      iou_results[i] = overlaps / (area1 + area2 - overlaps + epsilon);
+      iou_results[i] = static_cast<T>(overlaps / (area1 + area2 - overlaps + epsilon));
     } else {
-      iou_results[i] = overlaps / (area2 + epsilon);
+      iou_results[i] = static_cast<T>(overlaps / (area2 + epsilon));
     }
   }
 
@@ -70,3 +69,5 @@ void IOU(const size_t &size, const T *box1, const T *box2, T *iou_results, const
 
 template void IOU(const size_t &size, const float *box1, const float *box2, float *iou_results, const size_t &mode,
                   const size_t &input_len_0, cudaStream_t cuda_stream);
+template void IOU(const size_t &size, const half *box1, const half *box2, half *iou_results, const size_t &mode,
+                  const size_t &input_len_0, cudaStream_t cuda_stream);

mindspore/ccsrc/backend/kernel_compiler/gpu/gpu_kernel.h

@@ -84,6 +84,40 @@ class GpuKernel : public KernelMod {
     }
   }
 
+  // set the tensor descriptor for cudnn/cublas
+  void CudnnSetTensorNdDescriptor(const std::vector<size_t> &shape, cudnnTensorDescriptor_t descriptor,
+                                  cudnnDataType_t data_type) {
+    if (shape.size() < 3) {
+      MS_EXCEPTION(ValueError) << "cudnnSetTensorNdDescriptor don't support" << shape.size() << "D.";
+    }
+    const int nbDims = shape.size();
+    int *dim = new (std::nothrow) int[nbDims];
+    if (dim == nullptr) {
+      MS_LOG(EXCEPTION) << "malloc dim failed.";
+    }
+    int *stride = new (std::nothrow) int[nbDims];
+    if (stride == nullptr) {
+      MS_LOG(EXCEPTION) << "malloc stride failed.";
+    }
+
+    for (int i = 0; i < nbDims; i++) {
+      dim[i] = SizeToInt(shape[i]);
+      stride[i] = 1;
+    }
+
+    for (int i = nbDims - 2; i >= 0; i--) {
+      stride[i] = stride[i + 1] * SizeToInt(shape[i + 1]);
+    }
+
+    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensorNdDescriptor(descriptor, data_type, nbDims, dim, stride),
+                                "cudnnSetTensorNdDescriptor failed");
+
+    delete[] dim;
+    dim = nullptr;
+    delete[] stride;
+    stride = nullptr;
+  }
+
   // choose the suitable datatype for cudnn/cublas
   inline cudnnDataType_t GetCudnnDataType(const std::string &Type) {
     auto type = kCudnnDtypeMap.find(Type);

mindspore/ccsrc/backend/kernel_compiler/gpu/math/broadcast_gpu_kernel.h

@@ -27,6 +27,7 @@
 #include "backend/kernel_compiler/gpu/kernel_constants.h"
 namespace mindspore {
 namespace kernel {
+constexpr int MAX_DIMS = 7;
 template <typename T, typename S>
 class BroadcastOpGpuKernel : public GpuKernel {
  public:
@@ -45,9 +46,8 @@ class BroadcastOpGpuKernel : public GpuKernel {
     S *output = GetDeviceAddress<S>(outputs, 0);
 
     if (need_broadcast_) {
-      Broadcast(lhs_shape_[0], lhs_shape_[1], lhs_shape_[2], lhs_shape_[3], rhs_shape_[0], rhs_shape_[1], rhs_shape_[2],
-                rhs_shape_[3], output_shape_[0], output_shape_[1], output_shape_[2], output_shape_[3], op_type_, lhs,
-                rhs, output, reinterpret_cast<cudaStream_t>(stream_ptr));
+      Broadcast(lhs_shape_, rhs_shape_, output_shape_, op_type_, lhs, rhs, output,
+                reinterpret_cast<cudaStream_t>(stream_ptr));
     } else {
       NoBroadcast(output_num_, op_type_, lhs, rhs, output, reinterpret_cast<cudaStream_t>(stream_ptr));
     }
@@ -60,10 +60,13 @@ class BroadcastOpGpuKernel : public GpuKernel {
     auto shape2 = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
     auto shape3 = AnfAlgo::GetOutputInferShape(kernel_node, 0);
     need_broadcast_ = IsBroadcast(shape1, shape2);
-    if (need_broadcast_ && shape1.size() > 4) {
-      MS_LOG(EXCEPTION) << "Broadcast operation not support dim greater than 4";
+    if (need_broadcast_ && shape1.size() > 7) {
+      MS_LOG(EXCEPTION) << "Broadcast operation not support dim greater than 7";
     }
 
+    lhs_shape_.resize(MAX_DIMS, 1);
+    rhs_shape_.resize(MAX_DIMS, 1);
+    output_shape_.resize(MAX_DIMS, 1);
     for (size_t i = 0; i < shape3.size(); i++) {
       output_shape_[i] = shape3[i];
       output_num_ *= shape3[i];
@@ -127,9 +130,9 @@ class BroadcastOpGpuKernel : public GpuKernel {
   int input1_num_;
   int input2_num_;
   int output_num_;
-  int lhs_shape_[4] = {1, 1, 1, 1};
-  int rhs_shape_[4] = {1, 1, 1, 1};
-  int output_shape_[4] = {1, 1, 1, 1};
+  std::vector<int> lhs_shape_;
+  std::vector<int> rhs_shape_;
+  std::vector<int> output_shape_;
 
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/activation_gpu_kernel.h

@@ -83,12 +83,19 @@ class ActivationGpuFwdKernel : public GpuKernel {
       return true;
     }
     std::vector<int> shape;
-    ShapeNdTo4d(input_shape, &shape);
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetActivationDescriptor(activation_desc_, mode_, CUDNN_NOT_PROPAGATE_NAN, 0.0),
                                 "cudnnSetActivationDescriptor failed");
-    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(data_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
-                                                           shape[0], shape[1], shape[2], shape[3]),
-                                "cudnnSetTensor4dDescriptor failed");
+
+    const int split_dim = 4;
+    if (input_shape.size() <= split_dim) {
+      ShapeNdTo4d(input_shape, &shape);
+      CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(data_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
+                                                             shape[0], shape[1], shape[2], shape[3]),
+                                  "cudnnSetTensor4dDescriptor failed");
+    } else {
+      CudnnSetTensorNdDescriptor(input_shape, data_descriptor_, cudnn_data_type_);
+    }
+
     InitSizeLists();
     return true;
   }

mindspore/ccsrc/backend/kernel_compiler/gpu/nn/activation_grad_kernel.h

@@ -90,12 +90,18 @@ class ActivationGradGpuKernel : public GpuKernel {
       return true;
     }
     std::vector<int> shape;
-    ShapeNdTo4d(input_shape, &shape);
     CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetActivationDescriptor(activation_desc_, mode_, CUDNN_PROPAGATE_NAN, 0.0),
                                 "SetActivationDescriptor failed");
-    CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(data_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
-                                                           shape[0], shape[1], shape[2], shape[3]),
-                                "SetTensor4dDescriptor failed");
+
+    const int split_dim = 4;
+    if (input_shape.size() <= split_dim) {
+      ShapeNdTo4d(input_shape, &shape);
+      CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetTensor4dDescriptor(data_descriptor_, CUDNN_TENSOR_NCHW, cudnn_data_type_,
+                                                             shape[0], shape[1], shape[2], shape[3]),
+                                  "SetTensor4dDescriptor failed");
+    } else {
+      CudnnSetTensorNdDescriptor(input_shape, data_descriptor_, cudnn_data_type_);
+    }
 
     InitSizeLists();
     return true;

mindspore/ccsrc/backend/kernel_compiler/gpu/other/check_valid_gpu_kernel.cc

@@ -22,5 +22,9 @@ MS_REG_GPU_KERNEL_TWO(
   CheckValid,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
   CheckValidGpuKernel, float, bool)
+MS_REG_GPU_KERNEL_TWO(
+  CheckValid,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeBool),
+  CheckValidGpuKernel, half, bool)
 }  // namespace kernel
 }  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/gpu/other/iou_gpu_kernel.cc

@@ -21,5 +21,8 @@ namespace kernel {
 MS_REG_GPU_KERNEL_ONE(
   IOU, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   IOUGpuKernel, float)
+MS_REG_GPU_KERNEL_ONE(
+  IOU, KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  IOUGpuKernel, half)
 }  // namespace kernel
 }  // namespace mindspore

tests/st/ops/gpu/test_floordiv_op.py

@@ -37,8 +37,8 @@ def test_floor_div():
     y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
     x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
     y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
-    x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float32)
-    y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
+    x2_np = np.random.randint(1, 5, (2, 1, 1, 4, 9)).astype(np.float32)
+    y2_np = np.random.randint(1, 5, (2, 3, 4, 4, 9)).astype(np.float32)
     x3_np = np.random.randint(1, 5, 1).astype(np.float32)
     y3_np = np.random.randint(1, 5, 1).astype(np.float32)
     x4_np = np.array(768).astype(np.float32)

tests/st/ops/gpu/test_reduce_mean_op.py

@@ -70,7 +70,7 @@ x11 = np.random.rand(1, 1, 1, 1).astype(np.float32)
 axis11 = (0, 1, 2, 3)
 keep_dims11 = False
 
-x12 = np.random.rand(2, 3, 4, 4).astype(np.float32)
+x12 = np.random.rand(2, 3, 4, 4, 5, 6).astype(np.float32)
 axis12 = -2
 keep_dims12 = False