Merge branch 'hongming/arm-fix' of http://10.87.145.36/inference/paddlelite into xzl/incubate/lite

.gitlab-ci.yml

@@ -9,6 +9,8 @@ stages:
   - build_mobile
 
 check:prebuilt:
+    tags:
+        - lite
     stage: ci
     script:
         #- pip3 install pre-commit
@@ -24,17 +26,21 @@ check:prebuilt:
             - /root/.cache
 
 build:server:
+    tags:
+        - lite
     image: $SERVER_LITE_DOCKER_IMAGE
     stage: build_server
     cache:
         key: server_thirdparty
         paths:
             - build/third_party
+            - /root/.ccache
     script:
-        #- export http_proxy=http://172.19.57.45:3128
-        #- export https_proxy=http://172.19.57.45:3128
-        - export http_proxy=http://agent.baidu.com:8118
-        - export https_proxy=http://agent.baidu.com:8118
+        - apt install ccache
+        - export http_proxy=http://172.19.57.45:3128
+        - export https_proxy=http://172.19.57.45:3128
+        #- export http_proxy=http://agent.baidu.com:8118
+        #- export https_proxy=http://agent.baidu.com:8118
         - mkdir -p build
         - cd build
         - ../paddle/fluid/lite/tools/build.sh cmake_x86
@@ -49,6 +55,8 @@ build:server:
         - check:prebuilt
 
 build:mobile:
+    tags:
+        - lite
     stage: build_mobile
     image: $MOBILE_LITE_DOCKER_IMAGE
     cache:
@@ -56,7 +64,9 @@ build:mobile:
         paths:
             - $MOBILE_LITE_CACHE0
             - $MOBILE_LITE_CACHE1
+            - /root/.ccache
     script:
+        - apt install ccache
         - export http_proxy=http://172.19.57.45:3128
         - export https_proxy=http://172.19.57.45:3128
         - ./paddle/fluid/lite/tools/build.sh build_test_arm

CMakeLists.txt

@@ -166,6 +166,7 @@ if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
     #include(external/zlib)     # download, build, install gtest
     include(external/protobuf)  # download, build, install protobuf
     include(external/eigen)     # download eigen3
+    include(ccache)             # set ccache for compilation
 
     include(generic)            # simplify cmake module
     include(configure)          # add paddle env configuration

paddle/fluid/lite/api/cxx_api_bin.cc

@@ -13,17 +13,25 @@
 // limitations under the License.
 
 #include "paddle/fluid/lite/api/cxx_api.h"
-
-#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+#include <chrono>
 #include "paddle/fluid/lite/core/mir/passes.h"
-#endif
-
 #include "paddle/fluid/lite/core/op_registry.h"
-
 namespace paddle {
 namespace lite {
 
-void Run(const char* model_dir) {
+using Time = decltype(std::chrono::high_resolution_clock::now());
+Time time() { return std::chrono::high_resolution_clock::now(); }
+double time_diff(Time t1, Time t2) {
+  typedef std::chrono::microseconds ms;
+  auto diff = t2 - t1;
+  ms counter = std::chrono::duration_cast<ms>(diff);
+  return counter.count() / 1000.0;
+}
+
+void Run(const char* model_dir, int repeat) {
+#ifdef LITE_WITH_ARM
+  DeviceInfo::Init();
+#endif
   lite::ExecutorLite predictor;
   std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
                                    Place{TARGET(kARM), PRECISION(kFloat)}});
@@ -32,13 +40,19 @@ void Run(const char* model_dir) {
                   valid_places);
 
   auto* input_tensor = predictor.GetInput(0);
-  input_tensor->Resize(DDim(std::vector<DDim::value_type>({3, 224, 224})));
+  input_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 3, 224, 224})));
   auto* data = input_tensor->mutable_data<float>();
-  for (int i = 0; i < 3 * 224 * 224; i++) {
-    data[i] = i;
+  for (int i = 0; i < input_tensor->dims().production(); i++) {
+    data[i] = 1;
   }
 
-  predictor.Run();
+  for (int i = 0; i < 10; i++) predictor.Run();
+
+  auto time1 = time();
+  for (int i = 0; i < repeat; i++) predictor.Run();
+  auto time2 = time();
+  std::cout << " predict cost: " << time_diff(time1, time2) / repeat << "ms"
+            << std::endl;
 
   auto* out = predictor.GetOutput(0);
   LOG(INFO) << out << " memory size " << out->data_size();
@@ -53,7 +67,7 @@ void Run(const char* model_dir) {
 
 int main(int argc, char** argv) {
   CHECK_EQ(argc, 2) << "usage: ./cmd <model_dir>";
-  paddle::lite::Run(argv[1]);
+  paddle::lite::Run(argv[1], 1);
 
   return 0;
 }
@@ -66,7 +80,7 @@ USE_LITE_OP(fetch);
 USE_LITE_OP(io_copy);
 
 USE_LITE_OP(conv2d);
-// USE_LITE_OP(batch_norm);
+USE_LITE_OP(batch_norm);
 USE_LITE_OP(relu);
 USE_LITE_OP(depthwise_conv2d);
 USE_LITE_OP(pool2d);
@@ -85,7 +99,7 @@ USE_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(batch_norm, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW, def);
-// USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(elementwise_add, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(softmax, kARM, kFloat, kNCHW, def);
 

paddle/fluid/lite/arm/math/elementwise.cc

@@ -41,15 +41,15 @@ void elementwise_add<float>(const float* dinx, const float* diny, float* dout,
     float32x4_t diny2 = vld1q_f32(diny_ptr + 8);
     float32x4_t diny3 = vld1q_f32(diny_ptr + 12);
 
-    float32x4_t vsum0 = vaddq_f32(dinx0, diny0);
-    float32x4_t vsum1 = vaddq_f32(dinx1, diny1);
-    float32x4_t vsum2 = vaddq_f32(dinx2, diny2);
-    float32x4_t vsum3 = vaddq_f32(dinx3, diny3);
+    dinx0 = vaddq_f32(dinx0, diny0);
+    dinx1 = vaddq_f32(dinx1, diny1);
+    dinx2 = vaddq_f32(dinx2, diny2);
+    dinx3 = vaddq_f32(dinx3, diny3);
 
-    vst1q_f32(dout_ptr, vsum0);
-    vst1q_f32(dout_ptr + 4, vsum1);
-    vst1q_f32(dout_ptr + 8, vsum2);
-    vst1q_f32(dout_ptr + 12, vsum3);
+    vst1q_f32(dout_ptr, dinx0);
+    vst1q_f32(dout_ptr + 4, dinx1);
+    vst1q_f32(dout_ptr + 8, dinx2);
+    vst1q_f32(dout_ptr + 12, dinx3);
   }
   if (remain > 0) {
     const float* dinx_ptr = dinx + (cnt << 4);
@@ -64,6 +64,69 @@ void elementwise_add<float>(const float* dinx, const float* diny, float* dout,
   }
 }
 
+template <>
+void elementwise_add_axis<float>(const float* dinx, const float* diny,
+                                 float* dout, int batch, int channels,
+                                 int num) {
+#pragma omp parallel for collapse(2)
+  for (int i = 0; i < batch; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      int offset = (i * channels + j) * num;
+      const float* din_ptr = dinx + offset;
+      const float diny_data = diny[j];
+      float* dout_ptr = dout + offset;
+
+      int cnt = num >> 4;
+      int remain = num % 16;
+      float32x4_t rb = vdupq_n_f32(diny_data);
+      for (int k = 0; k < cnt; ++k) {
+        float32x4_t din0 = vld1q_f32(din_ptr);
+        float32x4_t din1 = vld1q_f32(din_ptr + 4);
+        float32x4_t din2 = vld1q_f32(din_ptr + 8);
+        float32x4_t din3 = vld1q_f32(din_ptr + 12);
+
+        din0 = vaddq_f32(din0, rb);
+        din1 = vaddq_f32(din1, rb);
+        din2 = vaddq_f32(din2, rb);
+        din3 = vaddq_f32(din3, rb);
+
+        vst1q_f32(dout_ptr, din0);
+        vst1q_f32(dout_ptr + 4, din1);
+        vst1q_f32(dout_ptr + 8, din2);
+        vst1q_f32(dout_ptr + 12, din3);
+        din_ptr += 16;
+        dout_ptr += 16;
+      }
+      if (remain >= 8) {
+        float32x4_t din0 = vld1q_f32(din_ptr);
+        float32x4_t din1 = vld1q_f32(din_ptr + 4);
+        din0 = vaddq_f32(din0, rb);
+        din1 = vaddq_f32(din1, rb);
+        vst1q_f32(dout_ptr, din0);
+        vst1q_f32(dout_ptr + 4, din1);
+        din_ptr += 8;
+        dout_ptr += 8;
+        remain -= 8;
+      }
+      if (remain >= 4) {
+        float32x4_t din0 = vld1q_f32(din_ptr);
+        din0 = vaddq_f32(din0, rb);
+        vst1q_f32(dout_ptr, din0);
+        din_ptr += 4;
+        dout_ptr += 4;
+        remain -= 4;
+      }
+      if (remain > 0) {
+        for (int p = 0; p < remain; p++) {
+          *dout_ptr = *din_ptr + diny_data;
+          dout_ptr++;
+          din_ptr++;
+        }
+      }
+    }
+  }
+}
+
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

paddle/fluid/lite/arm/math/elementwise.h

@@ -22,6 +22,10 @@ namespace math {
 template <typename T>
 void elementwise_add(const T* dinx, const T* diny, T* dout, int num);
 
+template <typename T>
+void elementwise_add_axis(const T* dinx, const T* diny, T* dout, int batch,
+                          int channels, int num);
+
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

paddle/fluid/lite/core/mir/passes.h

@@ -21,6 +21,7 @@ namespace mir {}  // namespace mir
 }  // namespace lite
 }  // namespace paddle
 
+#ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 USE_MIR_PASS(demo);
 USE_MIR_PASS(lite_fc_fuse_pass);
 USE_MIR_PASS(lite_conv_elementwise_add_act_fuse_pass);
@@ -30,6 +31,7 @@ USE_MIR_PASS(type_target_transform_pass);
 USE_MIR_PASS(generate_program_pass);
 USE_MIR_PASS(io_copy_kernel_pick_pass);
 USE_MIR_PASS(argument_type_display_pass);
+#endif
 USE_MIR_PASS(runtime_context_assign_pass);
 USE_MIR_PASS(lite_conv_bn_fuse_pass);
 USE_MIR_PASS(graph_visualze);

paddle/fluid/lite/core/naive_test_model.py

@@ -18,10 +18,10 @@ import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.backward import append_backward
 
-a = fluid.layers.data(name="a", shape=[100], dtype='float32')
-label = fluid.layers.data(name="label", shape=[100], dtype='float32')
+a = fluid.layers.data(name="a", shape=[2], dtype='float32')
+label = fluid.layers.data(name="label", shape=[10], dtype='float32')
 
-a1 = fluid.layers.fc(input=a, size=500, act=None, bias_attr=False)
+a1 = fluid.layers.fc(input=a, size=3, act=None, bias_attr=False)
 
 cost = fluid.layers.square_error_cost(a1, label)
 avg_cost = fluid.layers.mean(cost)
@@ -36,7 +36,7 @@ exe.run(fluid.default_startup_program())
 with open('startup_program.pb', 'wb') as f:
     f.write(fluid.default_startup_program().desc.serialize_to_string())
 
-data_1 = np.array(numpy.random.random([100, 100]), dtype='float32')
+#data_1 = np.array(numpy.random.random([100, 100]), dtype='float32')
 
 #fluid.default_main_program().desc.
 
@@ -50,7 +50,7 @@ with open('main_program.pb', 'wb') as f:
 
 #outs = exe.run(program=prog, feed={'a':data_1, }, fetch_list=[cost])
 
-sys.exit(0)
+#sys.exit(0)
 fluid.io.save_inference_model("./model2", [a.name], [a1], exe)
 
-print(numpy.array(outs))
+#print(numpy.array(outs))

paddle/fluid/lite/core/optimizer.h

@@ -51,8 +51,8 @@ class Optimizer {
           "lite_conv_bn_fuse_pass",                   //
           "lite_conv_elementwise_add_act_fuse_pass",  //
           "lite_fc_fuse_pass",                        //
-          "static_kernel_pick_pass",                  //
 #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
+          "static_kernel_pick_pass",        //
           "variable_place_inference_pass",  //
           "argument_type_display_pass",     //
           "type_target_transform_pass",     //

paddle/fluid/lite/kernels/arm/conv_compute.cc

@@ -100,15 +100,15 @@ void ConvCompute::Run() {
 REGISTER_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW,
                      paddle::lite::kernels::arm::ConvCompute, def)
     .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
+    //  .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
 
 REGISTER_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW,
                      paddle::lite::kernels::arm::ConvCompute, def)
     .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
+    //    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindInput("Filter", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("Output", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();

paddle/fluid/lite/kernels/arm/conv_compute_test.cc

@@ -45,7 +45,7 @@ void conv_compute_ref(const operators::ConvParam& param) {
     bias_data = param.bias->mutable_data<float>();
   }
   bool flag_bias = bias_data != nullptr;
-  bool flag_relu = false;  // TODO(hong19860320) param.relu
+  bool flag_relu = param.fuse_relu;
 
   int num = input_dims[0];
   int chout = output_dims[1];
@@ -183,7 +183,8 @@ TEST(conv_arm, compute) {
                           auto* filter_data = filter.mutable_data<float>();
                           auto* output_data = output.mutable_data<float>();
                           for (int i = 0; i < input.dims().production(); i++) {
-                            input_data[i] = static_cast<float>(i % 128);
+                            float sign = i % 3 == 0 ? -1.0f : 1.0f;
+                            input_data[i] = sign * static_cast<float>(i % 128);
                           }
                           for (int i = 0; i < filter.dims().production(); i++) {
                             filter_data[i] =
@@ -208,7 +209,7 @@ TEST(conv_arm, compute) {
                             }
                             param.bias = &bias;
                           }
-                          // TODO(hong19860320) param.relu = flag_relu;
+                          param.fuse_relu = flag_relu;
                           param.paddings = std::vector<int>({padding, padding});
                           param.strides = std::vector<int>({stride, stride});
                           param.dilations =

paddle/fluid/lite/kernels/arm/elementwise_add_compute.cc

@@ -25,8 +25,31 @@ void ElementwiseAddCompute::Run() {
   const float* x_data = param.X->data<float>();
   const float* y_data = param.Y->data<float>();
   float* out_data = param.Out->mutable_data<float>();
-  int n = param.X->dims().production();
-  lite::arm::math::elementwise_add(x_data, y_data, out_data, n);
+  int axis = param.axis;
+  auto x_dims = param.X->dims();
+  auto y_dims = param.Y->dims();
+  if (axis < 0) {
+    axis = x_dims.size() - y_dims.size();
+  }
+  if (x_dims.size() == y_dims.size()) {
+    lite::arm::math::elementwise_add(x_data, y_data, out_data,
+                                     x_dims.production());
+  } else {
+    int batch = 1;
+    int channels = 1;
+    int num = 1;
+    for (int i = 0; i < axis; ++i) {
+      batch *= x_dims[i];
+    }
+    for (int i = 0; i < y_dims.size(); ++i) {
+      channels *= y_dims[i];
+    }
+    for (int i = y_dims.size() + axis; i < x_dims.size(); ++i) {
+      num *= x_dims[i];
+    }
+    lite::arm::math::elementwise_add_axis(x_data, y_data, out_data, batch,
+                                          channels, num);
+  }
 }
 
 }  // namespace arm

paddle/fluid/lite/kernels/arm/elementwise_add_compute_test.cc

@@ -41,40 +41,97 @@ void elementwise_add_compute_ref(const operators::ElementwiseParam& param) {
   const dtype* x_data = param.X->data<const dtype>();
   const dtype* y_data = param.Y->data<const dtype>();
   dtype* out_data = param.Out->mutable_data<dtype>();
-  DDim dim = param.X->dims();
-  ASSERT_EQ(dim.data(), param.Out->dims().data());
-  for (int i = 0; i < dim.production(); i++) {
-    out_data[i] = x_data[i] + y_data[i];
+  auto x_dims = param.X->dims();
+  auto y_dims = param.Y->dims();
+  int axis = param.axis;
+  if (axis < 0) {
+    axis = x_dims.size() - y_dims.size();
+  }
+  int batch = 1;
+  int channels = 1;
+  int num = 1;
+  for (int i = 0; i < axis; ++i) {
+    batch *= x_dims[i];
+  }
+  for (int i = 0; i < y_dims.size(); ++i) {
+    channels *= y_dims[i];
+  }
+  for (int i = y_dims.size() + axis; i < x_dims.size(); ++i) {
+    num *= x_dims[i];
+  }
+  for (int i = 0; i < batch; ++i) {
+    for (int j = 0; j < channels; ++j) {
+      int offset = (i * channels + j) * num;
+      const dtype* din_ptr = x_data + offset;
+      const dtype diny_data = y_data[j];
+      dtype* dout_ptr = out_data + offset;
+      for (int k = 0; k < num; ++k) {
+        *dout_ptr = *din_ptr + diny_data;
+        dout_ptr++;
+        din_ptr++;
+      }
+    }
   }
 }
 
 TEST(elementwise_add, compute) {
   ElementwiseAddCompute elementwise_add;
   operators::ElementwiseParam param;
+  lite::Tensor x, y, output, output_ref;
+
+  for (auto n : {1, 3, 4, 11}) {
+    for (auto c : {1, 3, 4, 11}) {
+      for (auto h : {1, 3, 4, 11}) {
+        for (auto w : {1, 3, 4, 11}) {
+          for (auto axis : {-1, 0, 1, 2, 3}) {
+            for (auto yd :
+                 {std::vector<int64_t>({n}), std::vector<int64_t>({c}),
+                  std::vector<int64_t>({h}), std::vector<int64_t>({w}),
+                  std::vector<int64_t>({n, c}), std::vector<int64_t>({c, h}),
+                  std::vector<int64_t>({h, w}), std::vector<int64_t>({n, c, h}),
+                  std::vector<int64_t>({c, h, w}),
+                  std::vector<int64_t>({n, c, h, w})}) {
+              auto x_dim = DDim(std::vector<int64_t>({n, c, h, w}));
+              auto y_dim = DDim(yd);
+              int axis_t = axis < 0 ? x_dim.size() - y_dim.size() : axis;
+
+              if (axis_t + y_dim.size() > 4) continue;
+              bool flag = false;
+              for (int i = 0; i < y_dim.size(); i++) {
+                if (x_dim[i + axis_t] != y_dim[i]) flag = true;
+              }
+              if (flag) continue;
 
-  lite::Tensor x, y, out, out_ref;
-  x.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  y.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  out.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
-  out_ref.Resize(DDim(std::vector<int64_t>({2, 3, 4, 5})));
+              x.Resize(x_dim);
+              y.Resize(y_dim);
+              output.Resize(x_dim);
+              output_ref.Resize(x_dim);
               auto* x_data = x.mutable_data<float>();
               auto* y_data = y.mutable_data<float>();
-  auto* out_data = out.mutable_data<float>();
-  auto* out_ref_data = out_ref.mutable_data<float>();
-  for (int i = 0; i < x.dims().production(); i++) {
-    x_data[i] = y_data[i] = i;
+              auto* output_data = output.mutable_data<float>();
+              auto* output_ref_data = output_ref.mutable_data<float>();
+              for (int i = 0; i < x_dim.production(); i++) {
+                x_data[i] = i;
+              }
+              for (int i = 0; i < y_dim.production(); i++) {
+                y_data[i] = i;
               }
-
               param.X = &x;
               param.Y = &y;
-  param.Out = &out;
+              param.axis = axis;
+              param.Out = &output;
               elementwise_add.SetParam(param);
               elementwise_add.Run();
-
-  param.Out = &out_ref;
+              param.Out = &output_ref;
               elementwise_add_compute_ref<float>(param);
-  for (int i = 0; i < out.dims().production(); i++) {
-    EXPECT_NEAR(out_data[i], out_ref_data[i], 1e-5);
+              for (int i = 0; i < output.dims().production(); i++) {
+                EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
+              }
+            }
+          }
+        }
+      }
+    }
   }
 }
 

paddle/fluid/lite/kernels/arm/pool_compute.cc

@@ -163,7 +163,7 @@ PrecisionType PoolCompute::precision() const { return PRECISION(kFloat); }
 }  // namespace lite
 }  // namespace paddle
 
-REGISTER_LITE_KERNEL(pool, kARM, kFloat, kNCHW,
+REGISTER_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW,
                      paddle::lite::kernels::arm::PoolCompute, def)
     .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})

paddle/fluid/lite/kernels/arm/pool_compute_test.cc

@@ -272,4 +272,4 @@ TEST(pool, retrive_op) {
 }  // namespace lite
 }  // namespace paddle
 
-USE_LITE_KERNEL(pool, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);

paddle/fluid/lite/kernels/arm/relu_compute.h

@@ -45,4 +45,6 @@ class ReluCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
 
 REGISTER_LITE_KERNEL(relu, kARM, kFloat, kNCHW,
                      paddle::lite::kernels::arm::ReluCompute, def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();

paddle/fluid/lite/operators/batch_norm_op_test.cc

@@ -46,7 +46,7 @@ TEST(batch_norm_op_lite, test) {
   desc.SetInput("Mean", {"mean"});
   desc.SetInput("Variance", {"variance"});
   desc.SetOutput("Y", {"y"});
-  desc.SetAttr("is_test", true);
+  desc.SetAttr("is_test", static_cast<int>(1));
   desc.SetAttr("use_global_stats", false);
   desc.SetAttr("epsilon", 1e-5f);
   desc.SetAttr("momentum", 0.9f);
@@ -101,7 +101,7 @@ TEST(batch_norm_op_lite, test_enable_is_test) {
   desc.SetOutput("VarianceOut", {"variance_out"});
   desc.SetOutput("SavedMean", {"saved_mean"});
   desc.SetOutput("SavedVariance", {"saved_variance"});
-  desc.SetAttr("is_test", false);
+  desc.SetAttr("is_test", static_cast<int>(0));
   desc.SetAttr("use_global_stats", false);
   desc.SetAttr("epsilon", 1e-5f);
   desc.SetAttr("momentum", 0.9f);

paddle/fluid/lite/operators/conv_op.h

@@ -56,23 +56,26 @@ class ConvOpLite : public OpLite {
     if (std::find(input_arg_names.begin(), input_arg_names.end(), "Bias") !=
         input_arg_names.end()) {
       auto bias_arguments = op_desc.Input("Bias");
-      if (bias_arguments.size() != 0) {
+      if (bias_arguments.size() > 0) {
         auto bias_var = scope->FindVar(bias_arguments.front());
         if (bias_var != nullptr) {
-          param_.bias = bias_var->GetMutable<lite::Tensor>();
+          param_.bias =
+              const_cast<lite::Tensor*>(&(bias_var->Get<lite::Tensor>()));
         }
       }
     }
     if (std::find(input_arg_names.begin(), input_arg_names.end(),
                   "ResidualData") != input_arg_names.end()) {
-      auto res_argument = op_desc.Input("ResidualData");
-      if (res_argument.size() != 0) {
-        auto residual_data_var = scope->FindVar(res_argument.front());
+      auto res_data_arguments = op_desc.Input("ResidualData");
+      if (res_data_arguments.size() > 0) {
+        auto residual_data_var = scope->FindVar(res_data_arguments.front());
         if (residual_data_var != nullptr) {
-          param_.residualData = residual_data_var->GetMutable<lite::Tensor>();
+          param_.residualData = const_cast<lite::Tensor*>(
+              &(residual_data_var->Get<lite::Tensor>()));
         }
       }
     }
+    param_.fuse_relu = op_desc.GetAttr<bool>("fuse_relu");
     return true;
   }
 

paddle/fluid/lite/operators/pool_op.h

@@ -53,17 +53,25 @@ class PoolOpLite : public OpLite {
     param_.strides = op_desc.GetAttr<std::vector<int>>("strides");
     param_.paddings = op_desc.GetAttr<std::vector<int>>("paddings");
 
+    if (op_desc.HasAttr("exclusive")) {
       param_.exclusive = op_desc.GetAttr<bool>("exclusive");
+    }
+    if (op_desc.HasAttr("adaptive")) {
       param_.adaptive = op_desc.GetAttr<bool>("adaptive");
+    }
+    if (op_desc.HasAttr("ceil_mode")) {
       param_.ceil_mode = op_desc.GetAttr<bool>("ceil_mode");
+    }
+    if (op_desc.HasAttr("use_quantizer")) {
       param_.use_quantizer = op_desc.GetAttr<bool>("use_quantizer");
+    }
     // param_.data_format = op_desc.GetAttr<bool>("data_format");
     return true;
   }
 
   void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
 
-  std::string DebugString() const override { return "pool"; }
+  std::string DebugString() const override { return "pool2d"; }
 
  private:
   mutable PoolParam param_;

paddle/fluid/lite/operators/pool_op_test.cc

@@ -38,7 +38,7 @@ TEST(pool_op_lite, test) {
 
   // prepare op desc
   cpp::OpDesc desc;
-  desc.SetType("pool");
+  desc.SetType("pool2d");
   desc.SetInput("X", {"x"});
   desc.SetOutput("Out", {"output"});
 
@@ -69,7 +69,7 @@ TEST(pool_op_lite, test) {
   bool use_quantizer{false};
   desc.SetAttr("use_quantizer", use_quantizer);
 
-  PoolOpLite pool("pool");
+  PoolOpLite pool("pool2d");
   pool.SetValidPlaces({Place{TARGET(kARM), PRECISION(kFloat)}});
   pool.Attach(desc, &scope);
   auto kernels = pool.CreateKernels({Place{TARGET(kARM), PRECISION(kFloat)}});
@@ -86,5 +86,5 @@ TEST(pool_op_lite, test) {
 }  // namespace paddle
 
 #ifdef LITE_WITH_ARM
-USE_LITE_KERNEL(pool, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);
 #endif

paddle/fluid/lite/operators/split_op.cc

@@ -37,7 +37,7 @@ bool SplitOp::InferShape() const {
   const auto &sections = param_.sections;
 
   const int outs_number = outs.size();
-  std::vector<lite::DDimHvy> outs_dims;
+  std::vector<lite::DDim> outs_dims;
   outs_dims.reserve(outs_number);
 
   if (num > 0) {