Merge remote-tracking branch 'gitlab/develop' into incubate/lite

fix conflicts

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

@@ -58,6 +58,111 @@ void scale<float>(const float* din, float* dout, int num, float scale,
   }
 }
 
+template <>
+void scale<float>(const float* din, float* dout, int outer_dim, int scale_dim,
+                  int inner_dim, const float* scale_data,
+                  const float* bias_data) {
+  int cnt = inner_dim >> 4;
+  int remain = inner_dim % 16;
+  int size = inner_dim * scale_dim;
+  for (int n = 0; n < outer_dim; n++) {
+    const float* din_ptr_n = din + n * size;
+    float* dout_ptr_n = dout + n * size;
+#pragma omp parallel for
+    for (int i = 0; i < scale_dim; i++) {
+      const float* din_ptr = din_ptr_n + i * inner_dim;
+      float* dout_ptr = dout_ptr_n + i * inner_dim;
+      float scale = scale_data[i];
+      float32x4_t vscale = vdupq_n_f32(scale);
+      float bias = bias_data[i];
+      float32x4_t vbias = vdupq_n_f32(bias);
+      for (int j = 0; j < cnt; j++) {
+        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);
+
+        float32x4_t vsum1 = vmlaq_f32(vbias, din0, vscale);
+        float32x4_t vsum2 = vmlaq_f32(vbias, din1, vscale);
+        float32x4_t vsum3 = vmlaq_f32(vbias, din2, vscale);
+        float32x4_t vsum4 = vmlaq_f32(vbias, din3, vscale);
+
+        din_ptr += 16;
+        vst1q_f32(dout_ptr, vsum1);
+        vst1q_f32(dout_ptr + 4, vsum2);
+        vst1q_f32(dout_ptr + 8, vsum3);
+        vst1q_f32(dout_ptr + 12, vsum4);
+
+        dout_ptr += 16;
+      }
+      for (int j = 0; j < remain; j++) {
+        *dout_ptr = *din_ptr * scale + bias;
+        dout_ptr++;
+        din_ptr++;
+      }
+    }
+  }
+}
+
+template <>
+void scale<float>(const float* din, float* dout, int outer_dim, int scale_dim,
+                  const float* scale_data, const float* bias_data) {
+  int cnt = scale_dim >> 4;
+  int remain = scale_dim % 16;
+  for (int n = 0; n < outer_dim; n++) {
+    const float* din_ptr_n = din + n * scale_dim;
+    float* dout_ptr_n = dout + n * scale_dim;
+#pragma omp parallel for
+    for (int i = 0; i < cnt; i++) {
+      int idx = i << 4;
+      const float* din_ptr = din_ptr_n + idx;
+      const float* scale_ptr = scale_data + idx;
+      const float* bias_ptr = bias_data + idx;
+      float* dout_ptr = dout_ptr_n + idx;
+
+      float32x4_t din0 = vld1q_f32(din_ptr);
+      float32x4_t vscale0 = vld1q_f32(scale_ptr);
+      float32x4_t vbias0 = vld1q_f32(bias_ptr);
+
+      float32x4_t din1 = vld1q_f32(din_ptr + 4);
+      float32x4_t vscale1 = vld1q_f32(scale_ptr + 4);
+      float32x4_t vbias1 = vld1q_f32(bias_ptr + 4);
+
+      float32x4_t din2 = vld1q_f32(din_ptr + 8);
+      float32x4_t vscale2 = vld1q_f32(scale_ptr + 8);
+      float32x4_t vbias2 = vld1q_f32(bias_ptr + 8);
+
+      float32x4_t vsum1 = vmlaq_f32(vbias0, din0, vscale0);
+      float32x4_t vsum2 = vmlaq_f32(vbias1, din1, vscale1);
+
+      float32x4_t din3 = vld1q_f32(din_ptr + 12);
+      float32x4_t vscale3 = vld1q_f32(scale_ptr + 12);
+      float32x4_t vbias3 = vld1q_f32(bias_ptr + 12);
+
+      vst1q_f32(dout_ptr, vsum1);
+      vst1q_f32(dout_ptr + 4, vsum2);
+
+      float32x4_t vsum3 = vmlaq_f32(vbias2, din2, vscale2);
+      float32x4_t vsum4 = vmlaq_f32(vbias3, din3, vscale3);
+
+      vst1q_f32(dout_ptr + 8, vsum3);
+      vst1q_f32(dout_ptr + 12, vsum4);
+    }
+    int idx = cnt << 4;
+    const float* din_ptr = din_ptr_n + idx;
+    float* dout_ptr = dout_ptr_n + idx;
+    const float* scale_ptr = scale_data + idx;
+    const float* bias_ptr = bias_data + idx;
+    for (int j = 0; j < remain; j++) {
+      *dout_ptr = *din_ptr * (*scale_ptr) + (*bias_ptr);
+      dout_ptr++;
+      din_ptr++;
+      scale_ptr++;
+      bias_ptr++;
+    }
+  }
+}
+
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

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

@@ -22,6 +22,14 @@ namespace math {
 template <typename T>
 void scale(const T* din, T* dout, int num, float scale, float bias);
 
+template <typename T>
+void scale(const T* din, T* dout, int outer_dim, int scale_dim, int inner_dim,
+           const float* scale_data, const float* bias_data);
+
+template <typename T>
+void scale(const T* din, T* dout, int outer_dim, int scale_dim,
+           const float* scale_data, const float* bias_data);
+
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

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

@@ -52,10 +52,10 @@ void split_cpy<float>(const float* din, float* dout, int num) {
 }
 
 template <>
-void split<float>(const float* din, std::vector<lite::Tensor*>* dout,
+void split<float>(const float* din, const std::vector<lite::Tensor*>& dout,
                   const int axis, const std::vector<int>& in_strides) {
   int input_offset = 0;
-  for (auto out : *dout) {
+  for (auto out : dout) {
     auto out_dim = out->dims();
     std::vector<int> out_strides(out_dim.size());
     out_strides[out_dim.size() - 1] = out_dim[out_dim.size() - 1];

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

@@ -26,7 +26,7 @@ template <typename T>
 void split_cpy(const T* din, T* dout, int num);
 
 template <typename T>
-void split(const T* din, std::vector<lite::Tensor*>* dout, const int axis,
+void split(const T* din, const std::vector<lite::Tensor*>& dout, const int axis,
            const std::vector<int>& in_strides);
 
 }  // namespace math

paddle/fluid/lite/core/cpu_info.cc

@@ -54,15 +54,15 @@ void DeviceInfo::InitInternal(DeviceInfo* dev) {
               << ", cluster ID: " << dev->cluster_ids_[dev->core_ids_[i]]
               << ", CPU ARCH: A" << dev->archs_[i];
   }
-  LOG(INFO) << "L1 DataCache size is: ";
+  VLOG(1) << "L1 DataCache size is: ";
   for (int i = 0; i < dev->compute_core_num_; ++i) {
-    LOG(INFO) << dev->L1_cache_[i] / 1024 << " KB";
+    VLOG(1) << dev->L1_cache_[i] / 1024 << " KB";
   }
-  LOG(INFO) << "L2 Cache size is: ";
+  VLOG(1) << "L2 Cache size is: ";
   for (int i = 0; i < dev->compute_core_num_; ++i) {
-    LOG(INFO) << dev->L2_cache_[i] / 1024 << " KB";
+    VLOG(1) << dev->L2_cache_[i] / 1024 << " KB";
   }
-  LOG(INFO) << "Total memory: " << dev->max_memory_ << "KB";
+  VLOG(1) << "Total memory: " << dev->max_memory_ << "KB";
 
   dev->max_freq_ = max_freq[0];
   for (int j = 1; j < dev->compute_core_num_; ++j) {

paddle/fluid/lite/kernels/arm/CMakeLists.txt

@@ -6,10 +6,11 @@ message(STATUS "compile with lite ARM kernels")
 
 cc_library(fc_compute_arm SRCS fc_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(relu_compute_arm SRCS relu_compute.cc DEPS ${lite_kernel_deps})
-cc_library(mul_compute_arm SRCS mul_compute.cc DEPS ${lite_kernel_deps} eigen3)
+cc_library(mul_compute_arm SRCS mul_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(scale_compute_arm SRCS scale_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(softmax_compute_arm SRCS softmax_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(conv_compute_arm SRCS conv_compute.cc DEPS ${lite_kernel_deps} math_arm)
+cc_library(batch_norm_compute_arm SRCS batch_norm_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(elementwise_add_compute_arm SRCS elementwise_add_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(pool_compute_arm SRCS pool_compute.cc DEPS ${lite_kernel_deps} math_arm)
 cc_library(split_compute_arm SRCS split_compute.cc DEPS ${lite_kernel_deps} math_arm)
@@ -18,8 +19,10 @@ lite_cc_test(test_fc_compute_arm SRCS fc_compute_test.cc DEPS fc_compute_arm mat
 lite_cc_test(test_scale_compute_arm SRCS scale_compute_test.cc DEPS scale_compute_arm)
 lite_cc_test(test_softmax_compute_arm SRCS softmax_compute_test.cc DEPS softmax_compute_arm)
 lite_cc_test(test_conv_compute_arm SRCS conv_compute_test.cc DEPS conv_compute_arm)
+lite_cc_test(test_batch_norm_compute_arm SRCS batch_norm_compute_test.cc DEPS batch_norm_compute_arm)
 lite_cc_test(test_elementwise_add_compute_arm SRCS elementwise_add_compute_test.cc DEPS elementwise_add_compute_arm)
 lite_cc_test(test_pool_compute_arm SRCS pool_compute_test.cc DEPS pool_compute_arm)
+lite_cc_test(test_mul_compute_arm SRCS mul_compute_test.cc DEPS mul_compute_arm)
 lite_cc_test(test_split_compute_arm SRCS split_compute_test.cc DEPS split_compute_arm)
 
 set(arm_kernels
@@ -29,6 +32,7 @@ set(arm_kernels
     scale_compute_arm
     softmax_compute_arm
     conv_compute_arm
+    batch_norm_compute_arm
     elementwise_add_compute_arm
     pool_compute_arm
     split_compute_arm

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/lite/kernels/arm/batch_norm_compute.h"
+#include "paddle/fluid/lite/arm/math/funcs.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/type_system.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+void BatchNormCompute::PrepareForRun() {
+  auto& param = this->Param<param_t>();
+  auto x_dims = param.x->dims();
+  bool global_stats = param.is_test || param.use_global_stats;
+  if (global_stats) {
+    int64_t channel_size = 0;
+    switch (param.data_layout) {
+      case DATALAYOUT(kNCHW):
+        channel_size = x_dims[1];
+        break;
+      // case DATALAYOUT(kNHWC):
+      //   channel_size = x_dims[x_dims.size() - 1];
+      //   break;
+      default:
+        LOG(FATAL) << "Unknown storage order: "
+                   << DataLayoutToStr(param.data_layout);
+        break;
+    }
+    new_scale.Resize({channel_size});
+    new_bias.Resize({channel_size});
+    auto* scale_data = param.scale->mutable_data<float>();
+    auto* bias_data = param.bias->mutable_data<float>();
+    auto* mean_data = param.mean->mutable_data<float>();
+    auto* variance_data = param.variance->mutable_data<float>();
+    auto* new_scale_data = new_scale.mutable_data<float>();
+    auto* new_bias_data = new_bias.mutable_data<float>();
+    for (int c = 0; c < channel_size; c++) {
+      float inv_scale = 1.f / (std::sqrt(variance_data[c] + param.epsilon));
+      new_bias_data[c] =
+          bias_data[c] - inv_scale * scale_data[c] * mean_data[c];
+      new_scale_data[c] = inv_scale * scale_data[c];
+    }
+  }
+}
+
+void BatchNormCompute::Run() {
+  auto& param = this->Param<param_t>();
+  auto x_dims = param.x->dims();
+  auto x_data = param.x->mutable_data<float>();
+  auto y_data = param.y->mutable_data<float>();
+  bool global_stats = param.is_test || param.use_global_stats;
+  if (global_stats) {
+    auto* new_scale_data = new_scale.mutable_data<float>();
+    auto* new_bias_data = new_bias.mutable_data<float>();
+    int64_t outer_size = 0;
+    int64_t channel_size = 0;
+    int64_t inner_size = 0;
+    switch (param.data_layout) {
+      case DATALAYOUT(kNCHW):
+        outer_size = x_dims[0];
+        channel_size = x_dims[1];
+        inner_size = x_dims.Slice(2, x_dims.size()).production();
+        lite::arm::math::scale(x_data, y_data, outer_size, channel_size,
+                               inner_size, new_scale_data, new_bias_data);
+        break;
+      // case DATALAYOUT(kNHWC):
+      //   outer_size = x_dims.Slice(0, x_dims.size() - 1).production();
+      //   channel_size = x_dims[x_dims.size() - 1];
+      //   lite::arm::math::scale(x_data, y_data, outer_size, channel_size,
+      //                          new_scale_data, new_bias_data);
+      //   break;
+      default:
+        LOG(FATAL) << "Unknown storage order: "
+                   << DataLayoutToStr(param.data_layout);
+        break;
+    }
+  } else {
+    // TODO(hong19860320) calculate mean_out, variance_out, saved_mean and
+    // saved_variance
+  }
+}
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_KERNEL(batch_norm, kARM, kFloat, kNCHW,
+                     paddle::lite::kernels::arm::BatchNormCompute, def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("Scale", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("Mean", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindInput("Variance", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("Y", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("MeanOut", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("VarianceOut", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("SavedMean", {LiteType::GetTensorTy(TARGET(kARM))})
+    .BindOutput("SavedVariance", {LiteType::GetTensorTy(TARGET(kARM))})
+    .Finalize();

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+class BatchNormCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::BatchNormParam;
+
+  void PrepareForRun() override;
+
+  void Run() override;
+
+  virtual ~BatchNormCompute() = default;
+
+ private:
+  Tensor new_scale;
+  Tensor new_bias;
+};
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/lite/kernels/arm/batch_norm_compute.h"
+#include <gtest/gtest.h>
+#include <memory>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+template <typename dtype>
+void batch_norm_compute_ref(const operators::BatchNormParam& param) {
+  DDim x_dims = param.x->dims();
+  auto x_data = param.x->mutable_data<dtype>();
+  auto scale_data = param.scale->mutable_data<dtype>();
+  auto bias_data = param.bias->mutable_data<dtype>();
+  auto mean_data = param.mean->mutable_data<dtype>();
+  auto variance_data = param.variance->mutable_data<dtype>();
+  auto y_data = param.y->mutable_data<dtype>();
+  float epsilon = param.epsilon;
+  float momentum = param.momentum;
+  DataLayoutType data_layout = param.data_layout;
+
+  bool global_stats = param.is_test || param.use_global_stats;
+  if (global_stats) {
+    int64_t outer_size = 0;
+    int64_t channel_size = 0;
+    int64_t inner_size = 0;
+    switch (data_layout) {
+      case DATALAYOUT(kNCHW):
+        outer_size = x_dims[0];
+        channel_size = x_dims[1];
+        inner_size = x_dims.Slice(2, x_dims.size()).production();
+        break;
+      // case DATALAYOUT(kNHWC):
+      //   outer_size = x_dims.Slice(0, x_dims.size() - 1).production();
+      //   channel_size = x_dims[x_dims.size() - 1];
+      //   inner_size = 1;
+      //   break;
+      default:
+        LOG(FATAL) << "Unknown storage order: " << DataLayoutToStr(data_layout);
+        break;
+    }
+    auto x_ptr = x_data;
+    auto y_ptr = y_data;
+    for (int o = 0; o < outer_size; o++) {
+      for (int c = 0; c < channel_size; c++) {
+        for (int i = 0; i < inner_size; i++) {
+          dtype norm_x =
+              (*x_ptr - mean_data[c]) / std::sqrt(variance_data[c] + epsilon);
+          *y_ptr = norm_x * scale_data[c] + bias_data[c];
+          x_ptr++;
+          y_ptr++;
+        }
+      }
+    }
+  } else {
+    // TODO(hong19860320) calculate mean_out, variance_out, saved_mean and
+    // saved_variance
+  }
+}
+
+TEST(batch_norm_arm, retrive_op) {
+  auto batch_norm =
+      KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>(
+          "batch_norm");
+  ASSERT_FALSE(batch_norm.empty());
+  ASSERT_TRUE(batch_norm.front());
+}
+
+TEST(batch_norm_arm, init) {
+  BatchNormCompute batch_norm;
+  ASSERT_EQ(batch_norm.precision(), PRECISION(kFloat));
+  ASSERT_EQ(batch_norm.target(), TARGET(kARM));
+}
+
+TEST(batch_norm_arm, compute) {
+  DeviceInfo::Init();
+  for (auto n : {1, 2}) {
+    for (auto c : {6, 32 /*, 128*/}) {
+      for (auto h : {9, 18 /*, 56 , 112, 224, 512*/}) {
+        for (auto w : {9, 18 /*, 56, 112, 224, 512*/}) {
+          for (auto is_test : {/*false, */ true}) {
+            for (auto use_global_stats : {false, true}) {
+              for (auto epsilon : {1e-4f, 1e-5f}) {
+                for (auto momentum : {0.9f, 0.99f}) {
+                  for (auto data_layout :
+                       {DATALAYOUT(kNCHW) /*, DATALAYOUT(kNHWC)*/}) {
+                    Tensor x;
+                    Tensor scale;
+                    Tensor bias;
+                    Tensor mean;
+                    Tensor variance;
+                    Tensor y;
+                    Tensor mean_out;
+                    Tensor variance_out;
+                    Tensor saved_mean;
+                    Tensor saved_variance;
+                    Tensor y_ref;
+                    Tensor mean_out_ref;
+                    Tensor variance_out_ref;
+                    Tensor saved_mean_ref;
+                    Tensor saved_variance_ref;
+                    // set the dims of input, output, ref output tensors
+                    std::vector<int64_t> in_out_shape;
+                    switch (data_layout) {
+                      case DATALAYOUT(kNCHW):
+                        in_out_shape = {n, c, h, w};
+                        break;
+                      // case DATALAYOUT(kNHWC):
+                      //   in_out_shape = {n, h, w, c};
+                      //   break;
+                      default:
+                        LOG(FATAL) << "Unknown storage order: "
+                                   << DataLayoutToStr(data_layout);
+                        break;
+                    }
+                    x.Resize(in_out_shape);
+                    scale.Resize({c});
+                    bias.Resize({c});
+                    mean.Resize({c});
+                    variance.Resize({c});
+                    y.Resize(in_out_shape);
+                    mean_out.Resize({c});
+                    variance_out.Resize({c});
+                    saved_mean.Resize({c});
+                    saved_variance.Resize({c});
+                    y_ref.Resize(in_out_shape);
+                    mean_out_ref.Resize({c});
+                    variance_out_ref.Resize({c});
+                    saved_mean_ref.Resize({c});
+                    saved_variance_ref.Resize({c});
+                    // initialize the data of input tensors
+                    auto* x_data = x.mutable_data<float>();
+                    auto* scale_data = scale.mutable_data<float>();
+                    auto* bias_data = bias.mutable_data<float>();
+                    auto* mean_data = mean.mutable_data<float>();
+                    auto* variance_data = variance.mutable_data<float>();
+                    auto* y_data = y.mutable_data<float>();
+                    for (int i = 0; i < x.dims().production(); i++) {
+                      x_data[i] = static_cast<float>(i % 64);
+                    }
+                    for (int i = 0; i < scale.dims().production(); i++) {
+                      scale_data[i] = static_cast<float>(i) * 0.01f + 0.03f;
+                    }
+                    for (int i = 0; i < bias.dims().production(); i++) {
+                      bias_data[i] = static_cast<float>(i) * 0.065f + 0.1f;
+                    }
+                    for (int i = 0; i < mean.dims().production(); i++) {
+                      mean_data[i] = static_cast<float>(i) * 0.0565f;
+                    }
+                    for (int i = 0; i < variance.dims().production(); i++) {
+                      variance_data[i] = static_cast<float>(i) * 2.08f + 1.5f;
+                    }
+                    // prepare kernel params and run
+                    BatchNormCompute batch_norm;
+                    std::unique_ptr<KernelContext> ctx(new KernelContext);
+                    ctx->As<ARMContext>();
+                    batch_norm.SetContext(std::move(ctx));
+                    operators::BatchNormParam param;
+                    param.x = &x;
+                    param.scale = &scale;
+                    param.bias = &bias;
+                    param.mean = &mean;
+                    param.variance = &variance;
+                    param.is_test = is_test;
+                    param.use_global_stats = use_global_stats;
+                    param.epsilon = epsilon;
+                    param.momentum = momentum;
+                    param.data_layout = data_layout;
+                    param.y = &y;
+                    param.mean_out = &mean_out;
+                    param.variance_out = &variance_out;
+                    param.saved_mean = &saved_mean;
+                    param.saved_variance = &saved_variance;
+                    batch_norm.SetParam(param);
+                    batch_norm.Launch();
+                    // invoking ref implementation and compare results
+                    param.y = &y_ref;
+                    param.mean_out = &mean_out_ref;
+                    param.variance_out = &variance_out_ref;
+                    param.saved_mean = &saved_mean_ref;
+                    param.saved_variance = &saved_variance_ref;
+                    batch_norm_compute_ref<float>(param);
+                    auto* y_ref_data = y_ref.mutable_data<float>();
+                    for (int i = 0; i < y.dims().production(); i++) {
+                      EXPECT_NEAR(y_data[i], y_ref_data[i], 1e-5);
+                    }
+                  }
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(batch_norm, kARM, kFloat, kNCHW, def);

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

@@ -124,7 +124,20 @@ TEST(conv_arm, init) {
 
 TEST(conv_arm, compute) {
   DeviceInfo::Init();
-#if 0
+#if 1
+  for (auto n : {2}) {
+    for (auto ic : {6}) {
+      for (auto oc : {6}) {
+        for (auto ih : {9}) {
+          for (auto iw : {9}) {
+            for (auto flag_bias : {false, true}) {
+              for (auto flag_relu : {false, true}) {
+                for (auto depthwise : {false, true}) {
+                  for (auto dilation : {1}) {
+                    for (auto stride : {1, 2}) {
+                      for (auto padding : {0, 1, 2}) {
+                        for (auto ks : {1, 3, 5}) {
+#else
   for (auto n : {1, 2}) {
     for (auto ic : {6, 32 /*, 128*/}) {
       for (auto oc : {6, 32 /*, 128*/}) {
@@ -137,19 +150,6 @@ TEST(conv_arm, compute) {
                     for (auto stride : {1, 2}) {
                       for (auto padding : {0, 1, 2}) {
                         for (auto ks : {1, 3, 5}) {
-#else
-  for (auto n : {1}) {
-    for (auto ic : {6}) {
-      for (auto oc : {6}) {
-        for (auto ih : {9}) {
-          for (auto iw : {9}) {
-            for (auto flag_bias : {false, true}) {
-              for (auto flag_relu : {false, true}) {
-                for (auto depthwise : {false, true}) {
-                  for (auto dilation : {1}) {
-                    for (auto stride : {1}) {
-                      for (auto padding : {0, 1}) {
-                        for (auto ks : {1, 3, 5}) {
 #endif
                           int group = 1;
                           if (depthwise) {  // depthwise convolution ?

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

@@ -22,6 +22,10 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
+void FcCompute::PrepareForRun() {
+  // TODO(TJ): transpose weight
+}
+
 void FcCompute::Run() {
   auto& param = this->Param<operators::FcParam>();
   auto x_dims = param.input->dims();
@@ -48,22 +52,16 @@ void FcCompute::Run() {
                               &ctx);
     lite::arm::math::sgemm_prepack(packed_in, w_data, b_data, o_data, x_h, n,
                                    x_w, false, false, false, &ctx);
-
     if (param.bias) {
       CHECK_EQ(param.bias->numel(), n);
       lite::arm::math::fill_bias_fc(o_data, b_data, x_h, n);
     }
   } else {
-    // use sgemmv
-    // sgemv((const float*)weights, (const float*)din, (float*)dout,
-    //       false, n, x_w, _param->_flag_bias, (float*)bias, false);
+    lite::arm::math::sgemv(w_data, i_data, o_data, false, n, x_w,
+                           b_data != nullptr, b_data, false);
   }
 }
 
-TargetType FcCompute::target() const { return TARGET(kARM); }
-
-PrecisionType FcCompute::precision() const { return PRECISION(kFloat); }
-
 }  // namespace arm
 }  // namespace kernels
 }  // namespace lite

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

@@ -25,10 +25,9 @@ class FcCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
  public:
   using param_t = operators::FcParam;
 
-  void Run() override;
+  void PrepareForRun() override;
 
-  TargetType target() const override;
-  PrecisionType precision() const override;
+  void Run() override;
 
   virtual ~FcCompute() = default;
 };

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

@@ -12,57 +12,57 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <Eigen/Core>
-#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/kernels/arm/mul_compute.h"
+#include "paddle/fluid/lite/arm/math/funcs.h"
 #include "paddle/fluid/lite/core/op_registry.h"
-#include "paddle/fluid/lite/core/types.h"
+#include "paddle/fluid/lite/core/type_system.h"
 
 namespace paddle {
 namespace lite {
 namespace kernels {
 namespace arm {
 
-template <typename T>
-void mul_compute_eigen(const T* x, int x_h, int x_w, const T* y, int y_h,
-                       int y_w, T* out) {
-  using matrix_t =
-      Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+void MulCompute::PrepareForRun() {
+  // TODO(TJ): transpose x or y if necessary
+}
 
-  Eigen::Map<const matrix_t> X(x, x_h, x_w);
-  Eigen::Map<const matrix_t> Y(y, y_h, y_w);
-  Eigen::Map<matrix_t> Out(out, x_h, y_w);
+void MulCompute::Run() {
+  auto& param = Param<param_t>();
 
-  Out = X * Y;
-}
+  const auto* x_data = param.x->data<float>();
+  const auto* y_data = param.y->data<float>();
+  auto* o_data = param.output->mutable_data<float>();
 
-class MulCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
- public:
-  using param_t = operators::MulParam;
+  int m = static_cast<int>(
+      param.x->dims().Slice(0, param.x_num_col_dims).production());
+  int x_w =
+      static_cast<int>(param.x->dims()
+                           .Slice(param.x_num_col_dims, param.x->dims().size())
+                           .production());
+  int y_h = static_cast<int>(
+      param.y->dims().Slice(0, param.y_num_col_dims).production());
+  int n =
+      static_cast<int>(param.y->dims()
+                           .Slice(param.y_num_col_dims, param.y->dims().size())
+                           .production());
 
-  void Run() override {
-    auto& param = Param<operators::MulParam>();
-    core::dim2 x_shape(
-        {static_cast<int>(
-             param.x->dims().Slice(0, param.x_num_col_dims).production()),
-         static_cast<int>(
-             param.x->dims()
-                 .Slice(param.x_num_col_dims, param.x->dims().size())
-                 .production())});
-    core::dim2 y_shape(
-        {static_cast<int>(
-             param.y->dims().Slice(0, param.y_num_col_dims).production()),
-         static_cast<int>(
-             param.y->dims()
-                 .Slice(param.y_num_col_dims, param.y->dims().size())
-                 .production())});
+  CHECK_EQ(x_w, y_h) << "x_w must be equal with y_h";
+  auto k = x_w;
+  if (n == 1) {
+    lite::arm::math::sgemv(x_data, y_data, o_data, false, m, k, false, nullptr,
+                           false);
 
-    mul_compute_eigen(param.x->data<float>(), x_shape.x, x_shape.y,  //
-                      param.y->data<float>(), y_shape.x, y_shape.y,  //
-                      param.output->mutable_data<float>());
-  }
+  } else {
+    constexpr bool is_tranposed_y = false;
+    auto& ctx = this->ctx_->template As<ARMContext>();
 
-  virtual ~MulCompute() = default;
-};
+    float* packed_x = static_cast<float*>(ctx.workspace_data<float>()) +
+                      ctx.l2_cache_size() / sizeof(float);
+    lite::arm::math::prepackA(packed_x, x_data, k, 0, m, 0, k, false, &ctx);
+    lite::arm::math::sgemm_prepack(packed_x, y_data, nullptr, o_data, m, n, k,
+                                   false, false, is_tranposed_y, &ctx);
+  }
+}
 
 }  // namespace arm
 }  // namespace kernels

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle/fluid/lite/core/kernel.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+#include "paddle/fluid/lite/core/types.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+class MulCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::MulParam;
+
+  void PrepareForRun() override;
+
+  void Run() override;
+
+  virtual ~MulCompute() = default;
+};
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle

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

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/lite/kernels/arm/mul_compute.h"
+#include <gtest/gtest.h>
+#include <algorithm>
+#include <iostream>
+#include <memory>
+#include <random>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/lite/arm/math/funcs.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace arm {
+
+template <typename T>
+void FillData(T* a, const int n, const T lower = static_cast<T>(-2.f),
+              const T upper = static_cast<T>(2.f)) {
+  static unsigned int seed = 100;
+  std::mt19937 rng(seed++);
+  std::uniform_real_distribution<double> uniform_dist(0, 1);
+  for (int i = 0; i < n; ++i) {
+    a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
+  }
+}
+
+TEST(mul_arm, retrive_op) {
+  auto mul =
+      KernelRegistry::Global().Create<TARGET(kARM), PRECISION(kFloat)>("mul");
+  ASSERT_FALSE(mul.empty());
+  ASSERT_TRUE(mul.front());
+}
+
+TEST(mul_arm, init) {
+  MulCompute mul;
+  ASSERT_EQ(mul.precision(), PRECISION(kFloat));
+  ASSERT_EQ(mul.target(), TARGET(kARM));
+}
+
+TEST(mul_arm, compare_test) {
+  using T = float;
+
+  for (int m : {1, 2, 3, 4}) {
+    for (int n : {1, 2, 3, 4}) {
+      for (int k : {1, 2, 3, 4}) {
+        VLOG(3) << "m: " << m << ", n: " << n << ", k: " << k;
+        lite::Tensor x, y, out, ref;
+        x.Resize({m, k});
+        y.Resize({k, n});
+        out.Resize({m, n});
+        ref.Resize({m, n});
+
+        auto* x_data = x.mutable_data<T>();
+        auto* y_data = y.mutable_data<T>();
+        auto* out_data = out.mutable_data<T>();
+        auto* ref_data = ref.mutable_data<T>();
+
+        FillData<T>(x_data, x.dims().production());
+        FillData<T>(y_data, y.dims().production());
+        FillData<T>(out_data, out.dims().production(), 0, 0);
+        FillData<T>(ref_data, ref.dims().production(), 0, 0);
+
+        MulCompute mul;
+        operators::MulParam param;
+
+        param.x = &x;
+        param.y = &y;
+        param.output = &out;
+
+        DeviceInfo::Init();
+        std::unique_ptr<KernelContext> ctx(new KernelContext);
+        ctx->As<ARMContext>();
+        mul.SetParam(param);
+        mul.SetContext(std::move(ctx));
+        mul.PrepareForRun();
+
+        mul.Run();
+
+        lite::arm::math::mul_compute_eigen(x_data, m, k, y_data, k, n,
+                                           ref_data);
+        for (int i = 0; i < out.dims().production(); i++) {
+          EXPECT_NEAR(out_data[i], ref_data[i], 1e-3);
+        }
+      }
+    }
+  }
+}
+
+TEST(mul_arm, num_col_dims) {
+  using T = float;
+
+  lite::Tensor x, y, out, ref;
+  x.Resize({2, 3, 4});
+  y.Resize({3, 4, 5});
+  out.Resize({2, 5});
+  ref.Resize({2, 5});
+
+  auto* x_data = x.mutable_data<T>();
+  auto* y_data = y.mutable_data<T>();
+  auto* out_data = out.mutable_data<T>();
+  auto* ref_data = ref.mutable_data<T>();
+
+  FillData<T>(x_data, x.dims().production());
+  FillData<T>(y_data, y.dims().production());
+  FillData<T>(out_data, out.dims().production());
+  FillData<T>(ref_data, out.dims().production());
+
+  MulCompute mul;
+  operators::MulParam param;
+
+  param.x = &x;
+  param.y = &y;
+  param.output = &out;
+  param.x_num_col_dims = 1;
+  param.y_num_col_dims = 2;
+
+  DeviceInfo::Init();
+  std::unique_ptr<KernelContext> ctx(new KernelContext);
+  ctx->As<ARMContext>();
+  mul.SetParam(param);
+  mul.SetContext(std::move(ctx));
+  mul.PrepareForRun();
+
+  mul.Run();
+
+  lite::arm::math::mul_compute_eigen(x_data, 2, 12, y_data, 12, 5, ref_data);
+  for (int i = 0; i < out.dims().production(); i++) {
+    EXPECT_NEAR(out_data[i], ref_data[i], 1e-3);
+  }
+}
+
+}  // namespace arm
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+USE_LITE_KERNEL(mul, kARM, kFloat, kNCHW, def);

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

@@ -182,7 +182,7 @@ TEST(pool_arm, compute) {
       for (auto stride : {2}) {
         for (auto pad : {0}) {
           for (auto n : {1, 3, 4, 11}) {
-            for (auto c : {1, 3, 11, 4, 1024}) {
+            for (auto c : {1, 3, 11 /* ,1024 */}) {  // speedup for ci
               for (auto h : {3, 1, 11, 4, 1}) {
                 for (auto w : {1, 3, 4, 12, 1}) {
                   VLOG(3) << "n:" << n << " c:" << c << " h:" << h << " w:" << w

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

@@ -54,6 +54,15 @@ TEST(scale_arm, compute) {
   lite::Tensor output;
   lite::Tensor output_ref;
 
+#if 1  // for ci speedup
+  for (auto n : {1, 3}) {
+    for (auto c : {1, 3}) {
+      for (auto h : {3, 4}) {
+        for (auto w : {4, 3}) {
+          for (auto bias_after_scale : {true, false}) {
+            for (auto s : {-1.0f, 0.13f}) {
+              for (auto b : {-15.f, 0.11234f}) {
+#else
   for (auto n : {1, 3, 4, 11}) {
     for (auto c : {1, 3, 11, 4}) {
       for (auto h : {3, 1, 11, 4}) {
@@ -61,6 +70,8 @@ TEST(scale_arm, compute) {
           for (auto bias_after_scale : {true, false}) {
             for (auto s : {-100.25f, -1.0f, 0.13f, 3840.975f}) {
               for (auto b : {-3075.495f, -15.f, 0.11234f, 128.15f}) {
+#endif
+
                 x.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
                 output.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
                 output_ref.Resize(DDim(std::vector<int64_t>({n, c, h, w})));

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

@@ -24,7 +24,7 @@ namespace arm {
 void SplitCompute::Run() {
   auto& param = Param<operators::SplitParam>();
   const float* din = param.x->data<float>();
-  auto* dout = param.output;
+  auto& dout = param.output;
   auto in_dim = param.x->dims();
   std::vector<int> in_strides(in_dim.size());
   in_strides[in_dim.size() - 1] = in_dim[in_dim.size() - 1];

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

@@ -24,20 +24,10 @@ namespace kernels {
 namespace arm {
 
 void splite_resize_out(const lite::Tensor* din,
-                       std::vector<lite::Tensor*>* dout, int axis, int num,
-                       const std::vector<int>& sections) {
-  for (auto out : *dout) delete out;
-  dout->clear();
+                       const std::vector<lite::Tensor*>& dout, int axis,
+                       int num, const std::vector<int>& sections) {
   auto in_dims = din->dims();
-  int outs_number;
-  if (num > 0) {
-    outs_number = num;
-  } else {
-    outs_number = sections.size();
-  }
-  for (int i = 0; i < outs_number; i++) {
-    dout->push_back(new lite::Tensor);
-  }
+  int outs_number = dout.size();
 
   std::vector<lite::DDimLite> outs_dims;
   outs_dims.reserve(outs_number);
@@ -58,7 +48,7 @@ void splite_resize_out(const lite::Tensor* din,
   }
 
   for (int j = 0; j < outs_dims.size(); ++j) {
-    (*dout)[j]->Resize(outs_dims[j]);
+    dout[j]->Resize(outs_dims[j]);
   }
 }
 
@@ -75,7 +65,7 @@ void split_compute_ref(const operators::SplitParam& param) {
   }
 
   int input_offset = 0;
-  for (auto out : *dout) {
+  for (auto out : dout) {
     auto out_dim = out->dims();
     std::vector<int> out_strides(out_dim.size());
     out_strides[out_dim.size() - 1] = out_dim[out_dim.size() - 1];
@@ -128,16 +118,31 @@ TEST(split_arm, compute) {
                 for (int i = 0; i < x.dims().production(); i++) {
                   x_data[i] = i;
                 }
-                splite_resize_out(&x, &output, axis, num, sections);
-                splite_resize_out(&x, &output_ref, axis, num, sections);
+                for (auto out : output) delete out;
+                for (auto out : output_ref) delete out;
+                output.clear();
+                output_ref.clear();
+
+                int outs_number;
+                if (num > 0) {
+                  outs_number = num;
+                } else {
+                  outs_number = sections.size();
+                }
+                for (int i = 0; i < outs_number; i++) {
+                  output.push_back(new lite::Tensor);
+                  output_ref.push_back(new lite::Tensor);
+                }
+                splite_resize_out(&x, output, axis, num, sections);
+                splite_resize_out(&x, output_ref, axis, num, sections);
                 param.x = &x;
                 param.axis = axis;
                 param.num = num;
-                param.sections = &sections;
-                param.output = &output;
+                param.sections = sections;
+                param.output = output;
                 split.SetParam(param);
                 split.Run();
-                param.output = &output_ref;
+                param.output = output_ref;
                 split_compute_ref<float>(param);
                 for (int i = 0; i < output.size(); i++) {
                   float* output_data = output[i]->mutable_data<float>();

paddle/fluid/lite/operators/CMakeLists.txt

@@ -8,6 +8,7 @@ cc_library(mul_op_lite SRCS mul_op.cc DEPS ${op_DEPS})
 cc_library(scale_op_lite SRCS scale_op.cc DEPS ${op_DEPS})
 cc_library(softmax_op_lite SRCS softmax_op.cc DEPS ${op_DEPS})
 cc_library(reshape_op_lite SRCS reshape_op.cc DEPS ${op_DEPS} )
+cc_library(batch_norm_op_lite SRCS batch_norm_op.cc DEPS ${op_DEPS})
 cc_library(feed_op_lite SRCS feed_op.cc DEPS ${op_DEPS})
 cc_library(fetch_op_lite SRCS fetch_op.cc DEPS ${op_DEPS})
 cc_library(io_copy_op_lite SRCS io_copy_op.cc DEPS ${op_DEPS})
@@ -30,6 +31,7 @@ set(ops_lite
         scale_op_lite
         softmax_op_lite
         reshape_op_lite
+        batch_norm_op_lite
         feed_op_lite
         fetch_op_lite
         io_copy_op_lite
@@ -52,4 +54,5 @@ lite_cc_test(test_pool_op_lite SRCS pool_op_test.cc
 lite_cc_test(test_scale_op_lite SRCS scale_op_test.cc DEPS scale_op_lite memory_lite)
 lite_cc_test(test_softmax_op_lite SRCS softmax_op_test.cc DEPS softmax_op_lite memory_lite)
 lite_cc_test(test_reshape_op_lite SRCS reshape_op_test.cc DEPS reshape_op_lite memory_lite)
+lite_cc_test(test_batch_norm_op_lite SRCS batch_norm_op_test.cc DEPS batch_norm_op_lite memory_lite)
 lite_cc_test(test_concat_op_lite SRCS concat_op_test.cc DEPS concat_op_lite memory_lite)

paddle/fluid/lite/operators/batch_norm_op.cc

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/lite/operators/batch_norm_op.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+namespace paddle {
+namespace lite {
+namespace operators {
+
+bool BatchNormOp::CheckShape() const {
+  CHECK_OR_FALSE(param_.x);
+  CHECK_OR_FALSE(param_.bias);
+  CHECK_OR_FALSE(param_.scale);
+  CHECK_OR_FALSE(param_.mean);
+  CHECK_OR_FALSE(param_.variance);
+  CHECK_OR_FALSE(param_.y);
+  if (!param_.is_test) {
+    CHECK_OR_FALSE(param_.mean_out);
+    CHECK_OR_FALSE(param_.variance_out);
+    CHECK_OR_FALSE(param_.saved_mean);
+    CHECK_OR_FALSE(param_.saved_variance);
+  }
+  auto x_dims = param_.x->dims();
+  auto scale_dims = param_.scale->dims();
+  auto bias_dims = param_.bias->dims();
+  auto mean_dims = param_.mean->dims();
+  auto variance_dims = param_.variance->dims();
+  CHECK(x_dims.size() >= 2 && x_dims.size() <= 5)
+      << "Input X must have 2 to 5 dimensions.";
+  CHECK_EQ(scale_dims.size(), 1UL) << "Input Scale must have 1 dimensions.";
+  CHECK_EQ(bias_dims.size(), 1UL) << "Input Bias must have 1 dimensions.";
+  CHECK_EQ(mean_dims.size(), 1UL) << "Input Mean must have 1 dimensions.";
+  CHECK_EQ(variance_dims.size(), 1UL)
+      << "Input Variance must have 1 dimensions.";
+  return true;
+}
+
+bool BatchNormOp::InferShape() const {
+  auto x_dims = param_.x->dims();
+  int64_t channel_size = 0;
+  switch (param_.data_layout) {
+    case DATALAYOUT(kNCHW):
+      channel_size = x_dims[1];
+      break;
+    // case DATALAYOUT(kNHWC):
+    //   channel_size = x_dims[x_dims.size() - 1];
+    //   break;
+    default:
+      LOG(FATAL) << "Unknown storage order: "
+                 << DataLayoutToStr(param_.data_layout);
+      break;
+  }
+  if (!param_.is_test) {
+    param_.mean_out->Resize({channel_size});
+    param_.variance_out->Resize({channel_size});
+    param_.saved_mean->Resize({channel_size});
+    param_.saved_variance->Resize({channel_size});
+  }
+  param_.y->Resize(x_dims);
+  return true;
+}
+
+bool BatchNormOp::AttachImpl(const cpp::OpDesc &op_desc, lite::Scope *scope) {
+  param_.x = scope->FindVar(op_desc.Input("X").front())->GetMutable<Tensor>();
+  param_.bias =
+      scope->FindVar(op_desc.Input("Bias").front())->GetMutable<Tensor>();
+  param_.scale =
+      scope->FindVar(op_desc.Input("Scale").front())->GetMutable<Tensor>();
+  param_.mean =
+      scope->FindVar(op_desc.Input("Mean").front())->GetMutable<Tensor>();
+  param_.variance =
+      scope->FindVar(op_desc.Input("Variance").front())->GetMutable<Tensor>();
+  param_.y = scope->FindVar(op_desc.Output("Y").front())->GetMutable<Tensor>();
+  param_.is_test = op_desc.GetAttr<bool>("is_test");
+  param_.use_global_stats = op_desc.GetAttr<bool>("use_global_stats");
+  if (!param_.is_test) {
+    param_.mean_out =
+        scope->FindVar(op_desc.Output("MeanOut").front())->GetMutable<Tensor>();
+    param_.variance_out = scope->FindVar(op_desc.Output("VarianceOut").front())
+                              ->GetMutable<Tensor>();
+    param_.saved_mean = scope->FindVar(op_desc.Output("SavedMean").front())
+                            ->GetMutable<Tensor>();
+    param_.saved_variance =
+        scope->FindVar(op_desc.Output("SavedVariance").front())
+            ->GetMutable<Tensor>();
+  }
+  param_.epsilon = op_desc.GetAttr<float>("epsilon");
+  param_.momentum = op_desc.GetAttr<float>("momentum");
+  std::string data_layout = op_desc.GetAttr<std::string>("data_layout");
+  CHECK_EQ(data_layout, "NCHW") << "TODO(hong19860320): Only support NCHW.";
+  // param_.data_layout = StringToDataLayout(data_layout);
+  return true;
+}
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_OP(batch_norm, paddle::lite::operators::BatchNormOp);

paddle/fluid/lite/operators/batch_norm_op.h

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include <string>
+#include <vector>
+#include "paddle/fluid/lite/core/op_lite.h"
+#include "paddle/fluid/lite/core/scope.h"
+#include "paddle/fluid/lite/utils/all.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+class BatchNormOp : public OpLite {
+ public:
+  BatchNormOp() {}
+  explicit BatchNormOp(const std::string &op_type) : OpLite(op_type) {}
+
+  bool CheckShape() const override;
+
+  bool InferShape() const override;
+
+  bool AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) override;
+
+  void AttachKernel(KernelBase *kernel) override { kernel->SetParam(param_); }
+  std::string DebugString() const override { return "batch_norm"; }
+
+ private:
+  mutable BatchNormParam param_;
+};
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/operators/batch_norm_op_test.cc

+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/lite/operators/batch_norm_op.h"
+#include <gtest/gtest.h>
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace operators {
+
+TEST(batch_norm_op_lite, test) {
+  // prepare variables
+  Scope scope;
+  auto* x = scope.Var("x")->GetMutable<Tensor>();
+  auto* scale = scope.Var("scale")->GetMutable<Tensor>();
+  auto* bias = scope.Var("bias")->GetMutable<Tensor>();
+  auto* mean = scope.Var("mean")->GetMutable<Tensor>();
+  auto* variance = scope.Var("variance")->GetMutable<Tensor>();
+  auto* y = scope.Var("y")->GetMutable<Tensor>();
+  x->Resize({2, 32, 10, 20});
+  auto x_dims = x->dims();
+  const int64_t channel_size = x_dims[1];  // NCHW
+  scale->Resize({channel_size});
+  bias->Resize({channel_size});
+  mean->Resize({channel_size});
+  variance->Resize(DDim({channel_size}));
+
+  // prepare op desc
+  cpp::OpDesc desc;
+  desc.SetType("batch_norm");
+  desc.SetInput("X", {"x"});
+  desc.SetInput("Scale", {"scale"});
+  desc.SetInput("Bias", {"bias"});
+  desc.SetInput("Mean", {"mean"});
+  desc.SetInput("Variance", {"variance"});
+  desc.SetOutput("Y", {"y"});
+  desc.SetAttr("is_test", true);
+  desc.SetAttr("use_global_stats", false);
+  desc.SetAttr("epsilon", 1e-5f);
+  desc.SetAttr("momentum", 0.9f);
+  desc.SetAttr("data_layout", std::string("NCHW"));
+
+  BatchNormOp batch_norm("batch_norm");
+
+  batch_norm.SetValidPlaces({Place{TARGET(kHost), PRECISION(kFloat)}});
+  batch_norm.Attach(desc, &scope);
+  batch_norm.CheckShape();
+  batch_norm.InferShape();
+
+  // check output dims
+  auto y_dims = y->dims();
+  CHECK_EQ(y_dims.size(), x_dims.size());
+  for (size_t i = 0; i < y_dims.size(); i++) {
+    CHECK_EQ(y_dims[i], x_dims[i]);
+  }
+}
+
+TEST(batch_norm_op_lite, test_enable_is_test) {
+  // prepare variables
+  Scope scope;
+  auto* x = scope.Var("x")->GetMutable<Tensor>();
+  auto* scale = scope.Var("scale")->GetMutable<Tensor>();
+  auto* bias = scope.Var("bias")->GetMutable<Tensor>();
+  auto* mean = scope.Var("mean")->GetMutable<Tensor>();
+  auto* variance = scope.Var("variance")->GetMutable<Tensor>();
+  auto* y = scope.Var("y")->GetMutable<Tensor>();
+  auto* mean_out = scope.Var("mean_out")->GetMutable<Tensor>();
+  auto* variance_out = scope.Var("variance_out")->GetMutable<Tensor>();
+  auto* saved_mean = scope.Var("saved_mean")->GetMutable<Tensor>();
+  auto* saved_variance = scope.Var("saved_variance")->GetMutable<Tensor>();
+  x->Resize({2, 32, 10, 20});
+  auto x_dims = x->dims();
+  const int64_t channel_size = x_dims[1];  // NCHW
+  scale->Resize({channel_size});
+  bias->Resize({channel_size});
+  mean->Resize({channel_size});
+  variance->Resize({channel_size});
+
+  // prepare op desc
+  cpp::OpDesc desc;
+  desc.SetType("batch_norm");
+  desc.SetInput("X", {"x"});
+  desc.SetInput("Scale", {"scale"});
+  desc.SetInput("Bias", {"bias"});
+  desc.SetInput("Mean", {"mean"});
+  desc.SetInput("Variance", {"variance"});
+  desc.SetOutput("Y", {"y"});
+  desc.SetOutput("MeanOut", {"mean_out"});
+  desc.SetOutput("VarianceOut", {"variance_out"});
+  desc.SetOutput("SavedMean", {"saved_mean"});
+  desc.SetOutput("SavedVariance", {"saved_variance"});
+  desc.SetAttr("is_test", false);
+  desc.SetAttr("use_global_stats", false);
+  desc.SetAttr("epsilon", 1e-5f);
+  desc.SetAttr("momentum", 0.9f);
+  desc.SetAttr("data_layout", std::string("NCHW"));
+
+  BatchNormOp batch_norm("batch_norm");
+
+  batch_norm.SetValidPlaces({Place{TARGET(kHost), PRECISION(kFloat)}});
+  batch_norm.Attach(desc, &scope);
+  batch_norm.CheckShape();
+  batch_norm.InferShape();
+
+  // check output dims
+  auto y_dims = y->dims();
+  CHECK_EQ(y_dims.size(), x_dims.size());
+  for (size_t i = 0; i < y_dims.size(); i++) {
+    CHECK_EQ(y_dims[i], x_dims[i]);
+  }
+  auto mean_out_dims = mean_out->dims();
+  auto variance_out_dims = variance_out->dims();
+  auto saved_mean_dims = saved_mean->dims();
+  auto saved_variance_dims = saved_variance->dims();
+  CHECK_EQ(mean_out_dims.size(), 1UL);
+  CHECK_EQ(variance_out_dims.size(), 1UL);
+  CHECK_EQ(saved_mean_dims.size(), 1UL);
+  CHECK_EQ(saved_variance_dims.size(), 1UL);
+  CHECK_EQ(mean_out_dims[0], channel_size);
+  CHECK_EQ(variance_out_dims[0], channel_size);
+  CHECK_EQ(saved_mean_dims[0], channel_size);
+  CHECK_EQ(saved_variance_dims[0], channel_size);
+}
+
+}  // namespace operators
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/operators/op_params.h

@@ -57,6 +57,7 @@ struct FcParam {
   lite::Tensor* output{};
   lite::DDim in_mat_dims;
   int in_num_col_dims{1};
+  bool weight_transposed{false};
 };
 
 struct ReluParam {
@@ -145,6 +146,25 @@ struct ConvParam {
   std::string data_format{"Anylayout"};
 };
 
+// For BatchNorm op
+struct BatchNormParam {
+  lite::Tensor* x{};
+  lite::Tensor* bias{};
+  lite::Tensor* scale{};
+  lite::Tensor* mean{};
+  lite::Tensor* variance{};
+  lite::Tensor* y{};
+  lite::Tensor* mean_out{};
+  lite::Tensor* variance_out{};
+  lite::Tensor* saved_mean{};
+  lite::Tensor* saved_variance{};
+  bool is_test{true};
+  bool use_global_stats{false};
+  float epsilon;
+  float momentum;
+  DataLayoutType data_layout{DATALAYOUT(kNCHW)};
+};
+
 // For Pooling op
 struct PoolParam {
   lite::Tensor* x{};
@@ -177,10 +197,10 @@ struct DropoutParam {
 // For Split op
 struct SplitParam {
   lite::Tensor* x{};
-  std::vector<lite::Tensor*>* output{};
+  std::vector<lite::Tensor*> output{};
   int axis{-1};
   int num{0};
-  std::vector<int>* sections;
+  std::vector<int> sections;
 };
 
 /// ----------------------- element wise operators ----------------------

paddle/fluid/lite/operators/split_op.cc

@@ -21,7 +21,7 @@ namespace operators {
 
 bool SplitOp::CheckShape() const {
   CHECK_OR_FALSE(param_.x);
-  CHECK_OR_FALSE(param_.output);
+  CHECK_GT_OR_FALSE(param_.output.size(), 1UL);
   auto x_dims = param_.x->dims();
   auto x_rank = x_dims.size();
   CHECK_OR_FALSE(param_.axis >= -static_cast<int>(x_rank) &&
@@ -31,7 +31,7 @@ bool SplitOp::CheckShape() const {
 
 bool SplitOp::InferShape() const {
   const auto &outs = param_.output;
-  auto in_dims = param_.x.dims();
+  auto in_dims = param_.x->dims();
   int axis = param_.axis;
   int num = param_.num;
   const auto &sections = param_.sections;
@@ -68,7 +68,7 @@ bool SplitOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
   param_.sections = opdesc.GetAttr<std::vector<int>>("sections");
   param_.x = const_cast<lite::Tensor *>(
       &scope->FindVar(opdesc.Input("X").front())->Get<lite::Tensor>());
-  auto outs = op_desc.Output("Out");
+  auto outs = opdesc.Output("Out");
   for (auto var : outs) {
     param_.output.push_back(scope->FindVar(var)->GetMutable<lite::Tensor>());
   }
@@ -79,4 +79,4 @@ bool SplitOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
 }  // namespace lite
 }  // namespace paddle
 
-REGISTER_LITE_OP(softmax, paddle::lite::operators::SoftmaxOp);
+REGISTER_LITE_OP(split, paddle::lite::operators::SplitOp);

paddle/fluid/lite/operators/split_op.h

@@ -23,7 +23,7 @@ namespace paddle {
 namespace lite {
 namespace operators {
 
-class SoftmaxOp : public OpLite {
+class SplitOp : public OpLite {
  public:
   SplitOp() {}
   explicit SplitOp(const std::string &op_type) : OpLite(op_type) {}

paddle/fluid/lite/tools/build.sh

@@ -59,11 +59,15 @@ function cmake_arm {
         -DARM_TARGET_OS=$1 -DARM_TARGET_ARCH_ABI=$2
 }
 
+function build_single {
+    #make $1 -j$(expr $(nproc) - 2)
+    make $1 -j8
+}
+
 function build {
     file=$1
     for _test in $(cat $file); do
-        #make $_test -j$(expr $(nproc) - 2)
-        make $_test -j8
+        build_single $_test
     done
 }
 
@@ -81,39 +85,6 @@ function test_lite {
     done
 }
 
-port_armv8=5554
-port_armv7=5556
-
-# Run test on android
-function test_lite_android {
-    local file=$1
-    local adb_abi=$2
-    local port=
-    if [[ ${adb_abi} == "armeabi-v7a" ]]; then
-        port=${port_armv7}
-    fi
-
-    if [[ ${adb_abi} == "arm64-v8a" ]]; then
-        port=${port_armv8}
-    fi
-    if [[ "${port}x" == "x" ]]; then
-        echo "Port can not be empty"
-        exit 1
-    fi
-
-    echo "file: ${file}"
-    # push all to adb and test
-    adb_work_dir="/data/local/tmp"
-    skip_list="test_model_parser_lite"
-    for _test in $(cat $file); do
-        [[ $skip_list =~ (^|[[:space:]])$_test($|[[:space:]]) ]] && continue || echo 'skip $_test'
-        testpath=$(find ./paddle/fluid -name ${_test})
-        adb -s emulator-${port} push ${testpath} ${adb_work_dir}
-        adb -s emulator-${port} shell chmod +x "${adb_work_dir}/${_test}"
-        adb -s emulator-${port} shell "./${adb_work_dir}/${_test}"
-    done
-}
-
 # Build the code and run lite server tests. This is executed in the CI system.
 function build_test_server {
     mkdir -p ./build
@@ -126,8 +97,34 @@ function build_test_server {
     build $LIBS_FILE
 }
 
-# Build the code and run lite server tests. This is executed in the CI system.
+# test_arm_android <some_test_name> <adb_port_number>
+function test_arm_android {
+    test_name=$1
+    port=$2
+    if [[ "${test_name}x" == "x" ]]; then
+        echo "test_name can not be empty"
+        exit 1
+    fi
+    if [[ "${port}x" == "x" ]]; then
+        echo "Port can not be empty"
+        exit 1
+    fi
+
+    echo "test name: ${test_name}"
+    adb_work_dir="/data/local/tmp"
+    skip_list="test_model_parser_lite" # add more with space
+    [[ $skip_list =~ (^|[[:space:]])$test_name($|[[:space:]]) ]] && continue || echo 'skip $test_name'
+    testpath=$(find ./paddle/fluid -name ${test_name})
+    adb -s emulator-${port} push ${testpath} ${adb_work_dir}
+    adb -s emulator-${port} shell chmod +x "${adb_work_dir}/${test_name}"
+    adb -s emulator-${port} shell "./${adb_work_dir}/${test_name}"
+}
+
+# Build the code and run lite arm tests. This is executed in the CI system.
 function build_test_arm {
+    port_armv8=5554
+    port_armv7=5556
+
     adb kill-server
     adb devices | grep emulator | cut -f1 | while read line; do adb -s $line emu kill; done
     # start android arm64-v8a armeabi-v7a emulators first
@@ -140,6 +137,7 @@ function build_test_arm {
 
     for os in "android" "armlinux" ; do
         for abi in "arm64-v8a" "armeabi-v7a" "armeabi-v7a-hf" ; do
+            # TODO(TJ): enable compile on v7-hf on andorid and all v7 on armlinux
             if [[ ${abi} == "armeabi-v7a-hf" ]]; then
                 echo "armeabi-v7a-hf is not supported on both android and armlinux"
                 continue
@@ -156,17 +154,30 @@ function build_test_arm {
             cmake_arm ${os} ${abi}
             build $TESTS_FILE
 
+            # armlinux need in another docker
+            # TODO(TJ): enable test with armlinux
             if [[ ${os} == "android" ]]; then
                 adb_abi=${abi}
                 if [[ ${adb_abi} == "armeabi-v7a-hf" ]]; then
                     adb_abi="armeabi-v7a"
                 fi
                 if [[ ${adb_abi} == "armeabi-v7a" ]]; then
-                    # skip v7 tests
+                    # skip all armv7 tests
+                    # TODO(TJ): enable test with armv7
                     continue
                 fi
-                test_lite_android $TESTS_FILE ${adb_abi}
-                # armlinux need in another docker
+                local port=
+                if [[ ${adb_abi} == "armeabi-v7a" ]]; then
+                    port=${port_armv7}
+                fi
+
+                if [[ ${adb_abi} == "arm64-v8a" ]]; then
+                    port=${port_armv8}
+                fi
+                echo "test file: ${TESTS_FILE}"
+                for _test in $(cat $TESTS_FILE); do
+                    test_arm_android $_test $port
+                done
             fi
             cd -
         done
@@ -182,12 +193,13 @@ function print_usage {
     echo "----------------------------------------"
     echo -e "cmake_x86: run cmake with X86 mode"
     echo -e "cmake_cuda: run cmake with CUDA mode"
-    echo -e "cmake_arm: run cmake with ARM mode"
+    echo -e "--arm_os=<os> --arm_abi=<abi> cmake_arm: run cmake with ARM mode"
     echo
     echo -e "build: compile the tests"
+    echo -e "--test_name=<test_name> build_single: compile single test"
     echo
     echo -e "test_server: run server tests"
-    echo -e "test_mobile: run mobile tests"
+    echo -e "--test_name=<test_name> --adb_port_number=<adb_port_number> test_arm_android: run arm test"
     echo "----------------------------------------"
     echo
 }
@@ -200,11 +212,31 @@ function main {
                 TESTS_FILE="${i#*=}"
                 shift
                 ;;
+            --test_name=*)
+                TEST_NAME="${i#*=}"
+                shift
+                ;;
+            --arm_os=*)
+                ARM_OS="${i#*=}"
+                shift
+                ;;
+            --arm_abi=*)
+                ARM_ABI="${i#*=}"
+                shift
+                ;;
+            --arm_port=*)
+                ARM_PORT="${i#*=}"
+                shift
+                ;;
             build)
                 build $TESTS_FILE
                 build $LIBS_FILE
                 shift
                 ;;
+            build_single)
+                build_single $TEST_NAME
+                shift
+                ;;
             cmake_x86)
                 cmake_x86
                 shift
@@ -214,15 +246,15 @@ function main {
                 shift
                 ;;
             cmake_arm)
-                cmake_arm $2 $3
+                cmake_arm $ARM_OS $ARM_ABI
                 shift
                 ;;
             test_server)
                 test_lite $TESTS_FILE
                 shift
                 ;;
-            test_mobile)
-                test_lite $TESTS_FILE
+            test_arm_android)
+                test_arm_android $TEST_NAME $ARM_PORT
                 shift
                 ;;
             build_test_server)
@@ -250,6 +282,4 @@ function main {
     done
 }
 
-print_usage
-
 main $@