!3521 add AbsGrad support for GPU

Merge pull request !3521 from caojian05/ms_master_dev3

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

@@ -87,6 +87,14 @@ struct FloorDivFunc<half, bool> {
   __device__ __forceinline__ half operator()(const half &lhs, const half &rhs) { return false; }
 };
 
+template <typename T, typename S>
+struct AbsGradFunc {
+  __device__ __forceinline__ S operator()(const T &lhs, const T &rhs) {
+    T zero = 0.0;
+    return lhs < zero ? -rhs : rhs;
+  }
+};
+
 
 template <>
 struct PowerFunc<half, bool> {
@@ -149,6 +157,9 @@ __global__ void BroadcastKernel(const int l0, const int l1, const int l2, const
     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);
+    case BROADCAST_TYPE_ABSGRAD:
+      return BroadcastOperator<T, S, AddFunc<T, S>>(l0, l1, l2, l3, r0, r1, r2, r3, d0, d1, d2, d3, input0, input1,
+                                                      output);
   }
 }
 
@@ -192,6 +203,8 @@ __global__ void NoBroadcastKernel(const int nums, enum BroadcastOpType op, const
       return NoBroadcastOperator<T, S, AddFunc<T, S>>(nums, input0, input1, output);
     case BROADCAST_TYPE_FLOORDIV:
       return NoBroadcastOperator<T, S, FloorDivFunc<T, S>>(nums, input0, input1, output);
+    case BROADCAST_TYPE_ABSGRAD:
+      return NoBroadcastOperator<T, S, FloorDivFunc<T, S>>(nums, input0, input1, output);
   }
 }
 

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

@@ -30,6 +30,7 @@ enum BroadcastOpType {
   BROADCAST_TYPE_SUB = 7,
   BROADCAST_TYPE_ADD = 8,
   BROADCAST_TYPE_FLOORDIV = 9,
+  BROADCAST_TYPE_ABSGRAD = 10,
   BROADCAST_TYPE_INVALID = 0xffffffff,
 };
 

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

@@ -55,6 +55,10 @@ MS_REG_GPU_KERNEL_TWO(
   FloorDiv,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   BroadcastOpGpuKernel, float, float)
+MS_REG_GPU_KERNEL_TWO(
+  AbsGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+  BroadcastOpGpuKernel, float, float)
 
 // fp16
 MS_REG_GPU_KERNEL_TWO(
@@ -93,6 +97,10 @@ MS_REG_GPU_KERNEL_TWO(
   FloorDiv,
   KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
   BroadcastOpGpuKernel, half, half)
+MS_REG_GPU_KERNEL_TWO(
+  AbsGrad,
+  KernelAttr().AddInputAttr(kNumberTypeFloat16).AddInputAttr(kNumberTypeFloat16).AddOutputAttr(kNumberTypeFloat16),
+  BroadcastOpGpuKernel, half, half)
 
 // int32
 MS_REG_GPU_KERNEL_TWO(
@@ -113,5 +121,8 @@ MS_REG_GPU_KERNEL_TWO(
 MS_REG_GPU_KERNEL_TWO(
   FloorDiv, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
   BroadcastOpGpuKernel, int, int)
+MS_REG_GPU_KERNEL_TWO(
+  AbsGrad, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  BroadcastOpGpuKernel, int, int)
 }  // namespace kernel
 }  // namespace mindspore

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

@@ -96,10 +96,10 @@ class BroadcastOpGpuKernel : public GpuKernel {
     std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
 
     static std::map<std::string, BroadcastOpType> kBroadcastTypeMap = {
-      {"Greater", BROADCAST_TYPE_GREATER},   {"Less", BROADCAST_TYPE_LESS}, {"Maximum", BROADCAST_TYPE_MAXIMUM},
-      {"Minimum", BROADCAST_TYPE_MINIMUM},   {"Pow", BROADCAST_TYPE_POWER}, {"RealDiv", BROADCAST_TYPE_REALDIV},
-      {"Mul", BROADCAST_TYPE_MUL},           {"Sub", BROADCAST_TYPE_SUB},   {"TensorAdd", BROADCAST_TYPE_ADD},
-      {"FloorDiv", BROADCAST_TYPE_FLOORDIV},
+      {"Greater", BROADCAST_TYPE_GREATER},   {"Less", BROADCAST_TYPE_LESS},       {"Maximum", BROADCAST_TYPE_MAXIMUM},
+      {"Minimum", BROADCAST_TYPE_MINIMUM},   {"Pow", BROADCAST_TYPE_POWER},       {"RealDiv", BROADCAST_TYPE_REALDIV},
+      {"Mul", BROADCAST_TYPE_MUL},           {"Sub", BROADCAST_TYPE_SUB},         {"TensorAdd", BROADCAST_TYPE_ADD},
+      {"FloorDiv", BROADCAST_TYPE_FLOORDIV}, {"AbsGrad", BROADCAST_TYPE_ABSGRAD},
     };
 
     auto iter = kBroadcastTypeMap.find(kernel_name);