[X86][XPU] add reduce_max; fix xpu fill_any_like; test=develop

Author: zhupengyang

lite/kernels/x86/reduce_compute.cc

@@ -33,3 +33,13 @@ REGISTER_LITE_KERNEL(reduce_mean,
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(reduce_max,
+                     kX86,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::x86::ReduceMaxCompute<float>,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
+    .Finalize();

lite/kernels/x86/reduce_compute.h

@@ -38,6 +38,13 @@ struct MeanFunctor {
   }
 };
 
+struct MaxFunctor {
+  template <typename X, typename Y, typename Dim>
+  void operator()(X* x, Y* y, const Dim& dim) {
+    y->device(lite::fluid::EigenDeviceType<TARGET(kX86)>()) = x->maximum(dim);
+  }
+};
+
 #define HANDLE_DIM(NDIM, RDIM, FUNCTOR)                                \
   if (ndim == NDIM && rdim == RDIM) {                                  \
     paddle::lite::kernels::x86::                                       \
@@ -120,6 +127,44 @@ class ReduceMeanCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
   virtual ~ReduceMeanCompute() = default;
 };
 
+template <typename T>
+class ReduceMaxCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ReduceParam;
+
+  void Run() override {
+    auto& param = *param_.get_mutable<operators::ReduceParam>();
+    auto* input = param.X;
+    auto* Out = param.Out;
+    param.Out->template mutable_data<T>();
+
+    const auto& dims = param.dim;
+    bool keep_dim = param.keep_dim;
+
+    if (dims.size() == 0) {
+      // Flatten and reduce 1-D tensor
+      auto x = lite::fluid::EigenVector<T>::Flatten(*input);
+      auto out = lite::fluid::EigenScalar<T>::From(Out);
+      auto reduce_dim = Eigen::array<int, 1>({{0}});
+      MaxFunctor functor;
+      functor(&x, &out, reduce_dim);
+    } else {
+      int ndim = input->dims().size();
+      int rdim = dims.size();
+      HANDLE_DIM(4, 3, MaxFunctor);
+      HANDLE_DIM(4, 2, MaxFunctor);
+      HANDLE_DIM(4, 1, MaxFunctor);
+      HANDLE_DIM(3, 2, MaxFunctor);
+      HANDLE_DIM(3, 1, MaxFunctor);
+      HANDLE_DIM(2, 2, MaxFunctor);
+      HANDLE_DIM(2, 1, MaxFunctor);
+      HANDLE_DIM(1, 1, MaxFunctor);
+    }
+  }
+
+  virtual ~ReduceMaxCompute() = default;
+};
+
 }  // namespace x86
 }  // namespace kernels
 }  // namespace lite

lite/kernels/xpu/fill_any_like_compute.cc

@@ -52,6 +52,7 @@ void FillAnyLikeCompute::Run() {
                                   static_cast<int64_t>(param.value));
       break;
     }
+    case -1:
     case 5: {
       auto data = param.Out->mutable_data<float>(TARGET(kXPU));
       r = xdnn::constant<float>(ctx.GetRawContext(),

lite/tests/kernels/reduce_max_compute_test.cc

@@ -398,7 +398,7 @@ class ReduceMaxComputeTester : public arena::TestCase {
   }
 };
 
-void test_reduce_max(Place place) {
+void test_reduce_max_4d(Place place) {
   std::vector<std::vector<int>> reduce_dim{
       {0}, {1}, {2}, {3}, {0, 1}, {1, 2}, {2, 3}, {-2, -1}};
   for (auto n : {1, 3}) {
@@ -421,7 +421,7 @@ void test_reduce_max(Place place) {
   }
 }
 
-void test_reduce_max_for_three(Place place) {
+void test_reduce_max_3d(Place place) {
   std::vector<std::vector<int>> reduce_dim{{0}, {1}, {2}};
   for (bool keep_dim : {false, true}) {
     for (auto dim : reduce_dim) {
@@ -435,14 +435,15 @@ void test_reduce_max_for_three(Place place) {
 }
 
 TEST(ReduceMax, precision) {
-// #ifdef LITE_WITH_X86
-//   Place place(TARGET(kX86));
-// #endif
-#ifdef LITE_WITH_ARM
-  Place place(TARGET(kARM));
-  test_reduce_max(place);
-  test_reduce_max_for_three(place);
+  Place place;
+#if defined(LITE_WITH_ARM)
+  place = TARGET(kARM);
+#elif defined(LITE_WITH_X86)
+  place = TARGET(kX86);
 #endif
+
+  test_reduce_max_4d(place);
+  test_reduce_max_3d(place);
 }
 
 }  // namespace lite