Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle-Lite into dwh_dev

cmake/external/flatbuffers.cmake

@@ -97,7 +97,7 @@ function(compile_flatbuffers_schema_to_cpp_opt TARGET SRC_FBS OPT)
     OUTPUT ${GEN_HEADER}
     COMMAND "${FLATBUFFERS_FLATC_EXECUTABLE}"
             --cpp --gen-mutable --gen-object-api --reflect-names
-            --cpp-ptr-type flatbuffers::unique_ptr # Used to test with C++98 STLs
+            --force-empty --force-empty-vectors
             ${OPT}
             -I "${CMAKE_CURRENT_SOURCE_DIR}/tests/include_test"
             -o "${CMAKE_CURRENT_SOURCE_DIR}/${SRC_FBS_DIR}"

lite/api/test_yolov3_lite_bm.cc

@@ -59,9 +59,9 @@ void TestModel(const std::vector<Place>& valid_places) {
   }
   auto* image_tensor = predictor.GetInput(1);
   image_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 2})));
-  data = image_tensor->mutable_data<float>();
-  data[0] = FLAGS_im_height;
-  data[1] = FLAGS_im_width;
+  auto* data_1 = image_tensor->mutable_data<int>();
+  data_1[0] = FLAGS_im_height;
+  data_1[1] = FLAGS_im_width;
 
   for (int i = 0; i < FLAGS_warmup; ++i) {
     predictor.Run();

lite/backends/arm/math/activation.cc

@@ -763,24 +763,6 @@ void act_thresholded_relu<float>(
   }
 }
 
-#ifdef LITE_WITH_TRAIN
-template <>
-void act_square_grad(const float* din,
-                     const float* dout_grad,
-                     float* din_grad,
-                     int size,
-                     int threads) {
-  const float* ptr_out_grad = dout_grad;
-  float* ptr_in_grad = din_grad;
-  for (int i = 0; i < size; ++i) {
-    ptr_in_grad[0] = ptr_out_grad[0] * 2.0 * din[0];
-    ptr_out_grad++;
-    ptr_in_grad++;
-    din++;
-  }
-}
-#endif
-
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

lite/backends/arm/math/activation.h

@@ -90,12 +90,6 @@ template <typename T>
 void act_thresholded_relu(
     const T* din, T* dout, int size, float threshold, int threads);
 
-#ifdef LITE_WITH_TRAIN
-template <typename T>
-void act_square_grad(
-    const T* din, const T* dout_grad, T* din_grad, int size, int threads);
-#endif
-
 }  // namespace math
 }  // namespace arm
 }  // namespace lite

lite/core/mir/fusion/conv_bn_fuser.cc

@@ -192,7 +192,8 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
   } else if (is_weight_quantization) {
     std::string scale_name = conv_weight_name + "_quant_scale";
     if (conv_op_desc->HasAttr(scale_name)) {
-      auto scale = conv_op_desc->GetAttr<std::vector<float>>(scale_name);
+      std::vector<float> scale =
+          conv_op_desc->GetAttr<std::vector<float>>(scale_name);
       CHECK_EQ(scale.size(), alpha_tensor.numel());
       for (size_t i = 0; i < scale.size(); i++) {
         scale[i] *= alpha_data[i];

lite/core/mir/fusion/transpose_softmax_transpose_fuser.cc

@@ -84,11 +84,12 @@ cpp::OpDesc TransposeSoftmaxTransposeFuser::GenOpDesc(
   op_desc.SetInput("X", {matched.at("x1")->arg()->name});
   op_desc.SetOutput("Out", {matched.at("out")->arg()->name});
   op_desc.SetAttr("axis",
-                  matched.at("transpose1")
+                  *(matched.at("transpose1")
                         ->stmt()
                         ->op_info()
                         ->GetAttr<std::vector<int>>("axis")
-                      .back());
+                        .end() -
+                    1));
 
   return op_desc;
 }

lite/core/mir/graph_visualize_pass.cc

@@ -62,15 +62,17 @@ std::string Visualize(mir::SSAGraph* graph) {
            << string_trunc(op_info->GetAttr<std::string>(attr_name)) << "\"";
         break;
       case AttrType::FLOATS: {
-        auto vals = op_info->GetAttr<std::vector<float>>(attr_name);
+        std::vector<float> vals =
+            op_info->GetAttr<std::vector<float>>(attr_name);
         os << ":floats: {" + Join(vals, ",") << "}";
       } break;
       case AttrType::INTS: {
-        auto vals = op_info->GetAttr<std::vector<int>>(attr_name);
+        std::vector<int> vals = op_info->GetAttr<std::vector<int>>(attr_name);
         os << ":ints: {" + Join(vals, ",") + "}";
       } break;
       case AttrType::STRINGS: {
-        auto vals = op_info->GetAttr<std::vector<std::string>>(attr_name);
+        std::vector<std::string> vals =
+            op_info->GetAttr<std::vector<std::string>>(attr_name);
         os << ":strings: {" + string_trunc(Join(vals, ",")) << "}";
       } break;
       default:

lite/core/program.cc

@@ -195,7 +195,7 @@ void Program::Build(const cpp::ProgramDesc& prog) {
   CHECK(ops_.empty()) << "Executor duplicate Build found";
 
   // Create operators.
-  auto program = prog;
+  auto& program = prog;
   CHECK(program.BlocksSize());
   auto& main_block = *program.GetBlock<cpp::BlockDesc>(0);
   for (size_t i = 0; i < main_block.OpsSize(); ++i) {
@@ -262,7 +262,7 @@ void Program::PrepareWorkspace(const cpp::ProgramDesc& prog,
     }
   };
 
-  auto program = prog;
+  auto& program = prog;
   CHECK(program.BlocksSize());
   for (size_t b = 0; b < program.BlocksSize(); ++b) {
     auto& main_block = *program.GetBlock<cpp::BlockDesc>(b);

lite/core/program.h

@@ -46,7 +46,8 @@ struct Program {
           const std::shared_ptr<Scope>& root,
           const std::vector<Place>& valid_places,
           const std::vector<std::string>& var_names = {})
-      : scope_(root), valid_places_(valid_places), desc_(desc) {
+      : scope_(root), valid_places_(valid_places) {
+    desc_.CopyFrom(desc);
     CHECK(scope_) << "scope should be init first";
     VLOG(4) << "prepare work";
     PrepareWorkspace(desc, var_names);

lite/kernels/arm/CMakeLists.txt

@@ -103,7 +103,6 @@ add_kernel(deformable_conv_compute_arm ARM extra SRCS deformable_conv_compute.cc
 add_kernel(mean_compute_arm ARM extra SRCS mean_compute.cc DEPS ${lite_kernel_deps} math_arm)
 
 add_kernel(mean_grad_compute_arm ARM train SRCS mean_grad_compute.cc DEPS ${lite_kernel_deps} math_arm)
-add_kernel(activation_grad_compute_arm ARM train SRCS activation_grad_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(elementwise_grad_compute_arm ARM train SRCS elementwise_grad_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(mul_grad_compute_arm ARM train SRCS mul_grad_compute.cc DEPS ${lite_kernel_deps} math_arm)
 add_kernel(sgd_compute_arm ARM train SRCS sgd_compute.cc DEPS ${lite_kernel_deps} math_arm)

lite/kernels/bm/bridges/batch_norm_op.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include <bmcompiler_if.h>
+#include <math.h>
 #include "lite/kernels/bm/bridges/graph.h"
 #include "lite/kernels/bm/bridges/utility.h"
 #include "lite/kernels/npu/bridges/registry.h"
@@ -64,10 +65,16 @@ int BatchNormConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   auto* bias_data = bias->mutable_data<float>();
   auto* mean_data = mean->mutable_data<float>();
   auto* variance_data = variance->mutable_data<float>();
+
+  float* new_bias = static_cast<float*>(malloc(bias->memory_size()));
+  float* new_scale = static_cast<float*>(malloc(scale->memory_size()));
+  CHECK(new_bias != nullptr);
+  CHECK(new_scale != nullptr);
+
   for (int c = 0; c < channel_size; c++) {
     float inv_scale = 1.f / (std::sqrt(variance_data[c] + epsilon));
-    bias_data[c] = bias_data[c] - inv_scale * scale_data[c] * mean_data[c];
-    scale_data[c] = inv_scale * scale_data[c];
+    new_bias[c] = bias_data[c] - inv_scale * scale_data[c] * mean_data[c];
+    new_scale[c] = inv_scale * scale_data[c];
   }
 
   const int input_num = 1;
@@ -86,11 +93,13 @@ int BatchNormConverter(void* ctx, OpLite* op, KernelBase* kernel) {
                   output_dims.size(),
                   static_cast<const char*>(output_var_name.c_str()),
                   static_cast<const char*>(unique_op_name.c_str()),
-                  static_cast<const float*>(scale->mutable_data<float>()),
-                  static_cast<const float*>(bias->mutable_data<float>()),
+                  static_cast<const float*>(new_scale),
+                  static_cast<const float*>(new_bias),
                   1,
                   1,
                   1);
+  free(new_scale);
+  free(new_bias);
   delete[] shape;
   delete[] name;
   delete[] dim;

lite/kernels/bm/bridges/density_prior_box_op.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include <bmcompiler_if.h>
+#include <math.h>
 #include "lite/kernels/bm/bridges/graph.h"
 #include "lite/kernels/bm/bridges/utility.h"
 #include "lite/kernels/npu/bridges/registry.h"

lite/kernels/bm/bridges/interpolate_op.cc

@@ -76,6 +76,8 @@ int InterpolateConverter(void* ctx, OpLite* op, KernelBase* kernel) {
                         static_cast<const char*>(output_var_name.c_str()),
                         0,
                         0,
+                        0,
+                        0,
                         type);
   }
   graph->AddNode(output_var_name);

lite/kernels/bm/subgraph_compute.cc

@@ -76,9 +76,10 @@ bool SubgraphEngine::BuildDeviceProgram() {
       return false;
     }
   }
-  std::string net_name = "bmnetc_f32umodel";
+  std::string net_name = "bmnet_f32bmodel";
+  auto unique_net_name = lite::subgraph::bm::UniqueName(net_name);
   __bmcompile_opt(
-      graph.GetCompilerHandle(), const_cast<char*>(net_name.c_str()), 1);
+      graph.GetCompilerHandle(), const_cast<char*>(unique_net_name.c_str()), 2);
   void* bmodel_data = nullptr;
   unsigned int data_size = 0;
   bm_hd_ = static_cast<bm_handle_t>(ctx.GetHandle());

lite/kernels/host/CMakeLists.txt

@@ -19,6 +19,7 @@ add_kernel(read_from_array_compute_host Host extra SRCS read_from_array_compute.
 add_kernel(assign_compute_host Host extra SRCS assign_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(retinanet_detection_output_compute_host Host extra SRCS retinanet_detection_output_compute.cc DEPS ${lite_kernel_deps})
 add_kernel(where_index_compute_host Host extra SRCS where_index_compute.cc DEPS ${lite_kernel_deps})
+add_kernel(activation_grad_compute_host Host train SRCS activation_grad_compute.cc DEPS ${lite_kernel_deps})
 
 if(LITE_BUILD_EXTRA)
   lite_cc_test(test_where_index_compute_host SRCS where_index_compute.cc DEPS where_index_compute_host)

lite/kernels/host/activation_grad_compute.cc

+// Copyright (c) 2020 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 "lite/kernels/host/activation_grad_compute.h"
+
+namespace paddle {
+namespace lite {
+namespace kernels {
+namespace host {
+
+void SquareGradCompute::Run() {
+  auto& param = this->Param<param_t>();
+  CHECK(param.X);
+  auto out_grad_dims = param.Out_grad->dims();
+  auto out_grad_data = param.Out_grad->data<float>();
+
+  auto x_data = param.X->data<float>();
+  auto x_grad_data = param.X_grad->mutable_data<float>();
+  for (int i = 0; i < out_grad_dims.production(); i++) {
+    x_grad_data[i] = out_grad_data[i] * 2.0 * x_data[i];
+  }
+}
+
+void ReluGradCompute::Run() {
+  auto& param = this->Param<param_t>();
+  CHECK(param.X);
+  auto out_grad_dims = param.Out_grad->dims();
+  auto out_grad_data = param.Out_grad->data<float>();
+
+  auto x_data = param.X->data<float>();
+  auto x_grad_data = param.X_grad->mutable_data<float>();
+  for (int i = 0; i < out_grad_dims.production(); i++) {
+    x_grad_data[i] = x_data[i] > 0 ? out_grad_data[i] : 0.0;
+  }
+}
+
+void TanhGradCompute::Run() {
+  auto& param = this->Param<param_t>();
+  CHECK(param.Out);
+  auto out_grad_dims = param.Out_grad->dims();
+  auto out_grad_data = param.Out_grad->data<float>();
+
+  auto out_data = param.Out->data<float>();
+  auto x_grad_data = param.X_grad->mutable_data<float>();
+  for (int i = 0; i < out_grad_dims.production(); i++) {
+    x_grad_data[i] = out_grad_data[i] *
+                     (static_cast<float>(1.0) - out_data[i] * out_data[i]);
+  }
+}
+
+}  // namespace host
+}  // namespace kernels
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_LITE_KERNEL(square_grad,
+                     kHost,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::host::SquareGradCompute,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Out@GRAD", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindOutput("X@GRAD", {LiteType::GetTensorTy(TARGET(kHost))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(relu_grad,
+                     kHost,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::host::SquareGradCompute,
+                     def)
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Out@GRAD", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindOutput("X@GRAD", {LiteType::GetTensorTy(TARGET(kHost))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(tanh_grad,
+                     kHost,
+                     kFloat,
+                     kNCHW,
+                     paddle::lite::kernels::host::SquareGradCompute,
+                     def)
+    .BindInput("Out", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindInput("Out@GRAD", {LiteType::GetTensorTy(TARGET(kHost))})
+    .BindOutput("X@GRAD", {LiteType::GetTensorTy(TARGET(kHost))})
+    .Finalize();

lite/kernels/arm/activation_grad_compute.h → lite/kernels/host/activation_grad_compute.h

@@ -20,9 +20,9 @@
 namespace paddle {
 namespace lite {
 namespace kernels {
-namespace arm {
+namespace host {
 
-class SquareGradCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
+class SquareGradCompute : public KernelLite<TARGET(kHost), PRECISION(kFloat)> {
  public:
   using param_t = operators::ActivationGradParam;
 
@@ -31,7 +31,25 @@ class SquareGradCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
   virtual ~SquareGradCompute() = default;
 };
 
-}  // namespace arm
+class ReluGradCompute : public KernelLite<TARGET(kHost), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ActivationGradParam;
+
+  void Run() override;
+
+  virtual ~ReluGradCompute() = default;
+};
+
+class TanhGradCompute : public KernelLite<TARGET(kHost), PRECISION(kFloat)> {
+ public:
+  using param_t = operators::ActivationGradParam;
+
+  void Run() override;
+
+  virtual ~TanhGradCompute() = default;
+};
+
+}  // namespace host
 }  // namespace kernels
 }  // namespace lite
 }  // namespace paddle

lite/kernels/mlu/bridges/conv_op.cc

@@ -107,8 +107,7 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
                                             CNML_FILTER,
                                             CNML_NCHW,
                                             graph->FPType());
-  const auto weight_scale =
-      op_info->GetAttr<std::vector<float>>("weight_scale");
+  const auto weight_scale = op_info->GetInputScale(filter_var_name);
 
   if (filter->precision() == PrecisionType::kUnk ||
       filter->precision() == PrecisionType::kInt8) {
@@ -162,7 +161,7 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
     graph->BindConstData(bias_var_name, bias);
   }
 
-  const auto input_scale = op_info->GetAttr<float>("input_scale");
+  const auto input_scale = op_info->GetInputScale(input_var_name)[0];
 
   bool use_first_conv = false;
   if (lite::TargetWrapperMlu::UseFirstConv() && input_dims[1] == 3) {

lite/kernels/mlu/bridges/conv_op_test.cc

@@ -224,8 +224,10 @@ void test_conv(int bs,
   opdesc_mlu.SetAttr("groups", groups);
   opdesc_mlu.SetAttr("fuse_relu", static_cast<bool>(fuse_relu));
 
-  opdesc_mlu.SetAttr("weight_scale", std::vector<float>(oc, filter_scale));
-  opdesc_mlu.SetAttr("input_scale", input_scale);
+  OpInfo op_info(opdesc_mlu);
+  op_info.SetInputScale(filter_int_var_name,
+                        std::vector<float>(oc, filter_scale));
+  op_info.SetInputScale(input_var_name, {input_scale});
 
   if (has_bias) {
     if (is_channel_bias) {
@@ -234,7 +236,7 @@ void test_conv(int bs,
       bias->Resize({output_shape});
     }
     FillTensor<float>(bias);
-    opdesc_mlu.SetInput("Bias", {bias_var_name});
+    op_info.SetInput("Bias", {bias_var_name});
   }
 
   for (int i = 0; i < bs; i++) {
@@ -248,7 +250,7 @@ void test_conv(int bs,
   }
 
   // create and convert op to MLU model, then run it on MLU
-  auto op = CreateOp<operators::ConvOpLite>(opdesc_mlu, &scope);
+  auto op = CreateOp<operators::ConvOpLite>(op_info, &scope);
   LaunchOp(op, {input_var_name}, {output_var_name});
   // compare results
   auto* output_data = output->mutable_data<float>();

lite/kernels/mlu/bridges/fc_op.cc

@@ -68,7 +68,7 @@ int FCConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   auto w_tensor = graph->AddNode(
       w_var_name, cnml_w_shape, CNML_FILTER, CNML_NCHW, graph->FPType());
 
-  auto input_scale = op_info->GetAttr<float>("input_scale");
+  auto input_scale = op_info->GetInputScale(x_var_name)[0];
 
   auto output_tensor = graph->AddNode(output_var_name,
                                       output->dims().Vectorize(),
@@ -101,7 +101,7 @@ int FCConverter(void* ctx, OpLite* op, KernelBase* kernel) {
                             bias_tensor ? bias_tensor->mlu_tensor() : nullptr));
   graph->SetComputingDataType(
       fc_op, graph->GetNode(x_var_name)->mlu_tensor(), 1 / input_scale);
-  auto weight_scale = op_info->GetAttr<std::vector<float>>("weight_scale");
+  auto weight_scale = op_info->GetInputScale(w_var_name);
 
   // LOG(INFO) << "W precision " << int(w->precision());
   if (w->precision() == PrecisionType::kUnk ||

lite/kernels/mlu/bridges/fc_op_test.cc

@@ -131,14 +131,15 @@ void test_fc(const std::vector<int64_t>& input_shape,
   fc_op_desc_mlu.SetOutput("Out", {out_var_name});
   fc_op_desc_mlu.SetAttr("in_num_col_dims", static_cast<int>(in_num_col_dims));
 
-  fc_op_desc_mlu.SetAttr("weight_scale",
+  OpInfo op_info(fc_op_desc_mlu);
+  op_info.SetInputScale(w_int_var_name,
                         std::vector<float>(w_shape[1], w_scale));
-  fc_op_desc_mlu.SetAttr("input_scale", input_scale);
+  op_info.SetInputScale(input_var_name, {input_scale});
   if (has_bias) {
-    fc_op_desc_mlu.SetInput("Bias", {bias_var_name});
+    op_info.SetInput("Bias", {bias_var_name});
   }
 
-  auto fc_op_mlu = CreateOp<operators::FcOpLite>(fc_op_desc_mlu, &scope);
+  auto fc_op_mlu = CreateOp<operators::FcOpLite>(op_info, &scope);
 
   Tensor input_tmp, out_tmp;
   input_tmp.Resize(input_shape);

lite/kernels/mlu/bridges/lrn_op.cc

@@ -49,8 +49,7 @@ int LrnConverter(void* ctx, OpLite* op, KernelBase* kernel) {
         << "Unsuport WithinChannel";
   }
   auto local_size = op_info->GetAttr<int>("n");
-  CHECK(op_info->HasAttr("input_scale"));
-  auto input_scale = op_info->GetAttr<float>("input_scale");
+  auto input_scale = op_info->GetInputScale(x_var_name)[0];
   VLOG(5) << "lrn input scale: " << input_scale;
 
   cnmlLrnOpParam_t param;

lite/kernels/mlu/bridges/lrn_op_test.cc

@@ -178,9 +178,10 @@ void test_lrn(float alpha,
   opdesc.SetAttr("k", k);
   opdesc.SetAttr("n", local_size);
   opdesc.SetAttr("norm_region", norm_region);
-  opdesc.SetAttr<float>("input_scale", (*dmax - *dmin) / 255.f);
+  OpInfo op_info(opdesc);
+  op_info.SetInputScale(x_var_name, {(*dmax - *dmin) / 255.f});
 
-  auto op = CreateOp<operators::LrnOpLite>(opdesc, &scope);
+  auto op = CreateOp<operators::LrnOpLite>(op_info, &scope);
 
   // baseline
   lrn_compute_ref(op);
@@ -213,7 +214,7 @@ void test_lrn(float alpha,
   auto output_data = output_trans.mutable_data<float>();
   auto* output_ref_data = out_ref->mutable_data<float>();
   for (size_t i = 0; i < out->data_size(); i++) {
-    EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-4);
+    EXPECT_NEAR(output_data[i], output_ref_data[i], 5e-4);
   }
 }
 

lite/kernels/mlu/subgraph_compute.h

@@ -54,10 +54,11 @@ class SubgraphEngine : public subgraph::Engine {
     VLOG(4) << "[MLU] PADDLE_LITE_MLU_SAVE_OFFLINE_MODEL is "
             << GetBoolFromEnv("PADDLE_LITE_MLU_SAVE_OFFLINE_MODEL");
     VLOG(4) << "[MLU] PADDLE_LITE_MLU_DISABLE_BATCH_SIZE_CHANGEABLE is "
-            << GetBoolFromEnv("PADDLE_LITE_MLU_DISABLE_BATCH_SIZE_CHANGEABLE");
+            << GetBoolFromEnv("PADDLE_LITE_MLU_DISABLE_BATCH_SIZE_CHANGEABLE",
+                              true);
     VLOG(4) << "[MLU] LITE_DISABLE_MLU_CAST is "
             << GetBoolFromEnv("LITE_DISABLE_MLU_CAST");
-    if (GetBoolFromEnv("PADDLE_LITE_MLU_DISABLE_BATCH_SIZE_CHANGEABLE")) {
+    if (GetBoolFromEnv("PADDLE_LITE_MLU_DISABLE_BATCH_SIZE_CHANGEABLE", true)) {
       disable_batch_size_changeable_ = true;
     }
   }

lite/model_parser/base/block_desc.h

@@ -54,10 +54,16 @@ class BlockDescWriteAPI {
   virtual void SetForwardBlockIdx(int32_t idx) { NotImplemented(); }
 
   template <typename T>
-  T* AddVar();
+  T* AddVar() {
+    NotImplemented();
+    return nullptr;
+  }
 
   template <typename T>
-  T* AddOp();
+  T* AddOp() {
+    NotImplemented();
+    return nullptr;
+  }
 
   virtual ~BlockDescWriteAPI() = default;
 

lite/model_parser/base/op_desc.h

@@ -73,7 +73,9 @@ class OpDescWriteAPI {
   }
 
   template <typename T>
-  void SetAttr(const std::string& name, const T& v);
+  void SetAttr(const std::string& name, const T& v) {
+    NotImplemented();
+  }
 
   virtual ~OpDescWriteAPI() = default;
 

lite/model_parser/base/program_desc.h

@@ -40,7 +40,10 @@ class ProgramDescWriteAPI {
   virtual void SetVersion(int64_t version) { NotImplemented(); }
 
   template <typename T>
-  T* AddBlock();
+  T* AddBlock() {
+    NotImplemented();
+    return nullptr;
+  }
 
   virtual ~ProgramDescWriteAPI() = default;
 

lite/model_parser/base/vector_view.h

@@ -57,6 +57,7 @@ class VectorView {
  public:
   typedef vector_view::VectorTraits<T, U> Traits;
   explicit VectorView(typename Traits::vector_type const* cvec) {
+    CHECK(cvec);
     cvec_ = cvec;
   }
   typename Traits::subscript_return_type operator[](size_t i) const {

lite/model_parser/compatible_pb.cc

@@ -277,7 +277,7 @@ void OpAttrsCppToAny(const cpp::OpDesc &cpp_desc, OpDescType *any_desc) {
   template <>                                                            \
   void TransformProgramDescCppToAny<NT::T>(const cpp::T &cpp_desc,       \
                                            NT::T *any_desc) {            \
-    auto desc = cpp_desc;                                                \
+    auto &desc = cpp_desc;                                               \
     if (desc.HasVersion()) {                                             \
       any_desc->SetVersion(desc.Version());                              \
     }                                                                    \

lite/model_parser/flatbuffers/CMakeLists.txt

@@ -8,9 +8,6 @@ endfunction()
 lite_fbs_library(fbs_op_desc SRCS op_desc.cc FBS_DEPS framework_fbs_header)
 lite_fbs_library(fbs_var_desc SRCS var_desc.cc FBS_DEPS framework_fbs_header)
 lite_fbs_library(fbs_block_desc SRCS block_desc.cc FBS_DEPS framework_fbs_header)
-lite_fbs_library(fbs_program_desc SRCS program_desc.cc FBS_DEPS framework_fbs_header)
-
-lite_cc_test(test_vector_view SRCS vector_view_test.cc)
-if (TARGET test_vector_view)
-  add_dependencies(test_vector_view framework_fbs_header)
-endif()
+lite_cc_library(fbs_program_desc SRCS program_desc.cc DEPS fbs_op_desc fbs_var_desc fbs_block_desc)
+lite_cc_library(fbs_io SRCS io.cc DEPS fbs_program_desc)
+lite_cc_test(test_vector_view SRCS vector_view_test.cc DEPS fbs_program_desc)

lite/model_parser/flatbuffers/block_desc.cc

@@ -19,15 +19,27 @@ namespace lite {
 namespace fbs {
 
 template <>
-proto::VarDesc* BlockDesc::GetVar<proto::VarDesc>(int32_t idx) {
+proto::VarDesc const* BlockDesc::GetVar<proto::VarDesc>(int32_t idx) const {
   CHECK_LT(idx, VarsSize()) << "idx >= vars.size()";
-  return const_cast<proto::VarDesc*>(desc_->vars()->Get(idx));
+  return desc_->vars()->Get(idx);
 }
 
 template <>
-proto::OpDesc* BlockDesc::GetOp<proto::OpDesc>(int32_t idx) {
+proto::OpDesc const* BlockDesc::GetOp<proto::OpDesc>(int32_t idx) const {
   CHECK_LT(idx, OpsSize()) << "idx >= ops.size()";
-  return const_cast<proto::OpDesc*>(desc_->ops()->Get(idx));
+  return desc_->ops()->Get(idx);
+}
+
+template <>
+VarDesc const* BlockDesc::GetVar<VarDesc>(int32_t idx) const {
+  CHECK_LT(idx, VarsSize()) << "idx >= vars.size()";
+  return &vars_[idx];
+}
+
+template <>
+OpDesc const* BlockDesc::GetOp<OpDesc>(int32_t idx) const {
+  CHECK_LT(idx, OpsSize()) << "idx >= ops.size()";
+  return &ops_[idx];
 }
 
 }  // namespace fbs

lite/model_parser/flatbuffers/block_desc.h

@@ -14,8 +14,11 @@
 
 #pragma once
 
+#include <vector>
 #include "lite/model_parser/base/block_desc.h"
 #include "lite/model_parser/flatbuffers/framework_generated.h"
+#include "lite/model_parser/flatbuffers/op_desc.h"
+#include "lite/model_parser/flatbuffers/var_desc.h"
 #include "lite/utils/all.h"
 
 namespace paddle {
@@ -24,7 +27,17 @@ namespace fbs {
 
 class BlockDesc : public BlockDescAPI {
  public:
-  explicit BlockDesc(proto::BlockDesc* desc) : desc_(desc) { CHECK(desc_); }
+  explicit BlockDesc(proto::BlockDesc const* desc) : desc_(desc) {
+    CHECK(desc_);
+    vars_.reserve(VarsSize());
+    ops_.reserve(OpsSize());
+    for (size_t idx = 0; idx < VarsSize(); ++idx) {
+      vars_.push_back(VarDesc(desc_->vars()->Get(idx)));
+    }
+    for (size_t idx = 0; idx < OpsSize(); ++idx) {
+      ops_.push_back(OpDesc(desc_->ops()->Get(idx)));
+    }
+  }
 
   int32_t Idx() const override { return desc_->idx(); }
 
@@ -33,11 +46,12 @@ class BlockDesc : public BlockDescAPI {
   size_t VarsSize() const override { return desc_->vars()->size(); }
 
   template <typename T>
-  T* GetVar(int32_t idx);
+  T const* GetVar(int32_t idx) const;
 
   template <typename T>
-  T const* GetVar(int32_t idx) const {
-    return GetVar<T>(idx);
+  T* GetVar(int32_t idx) {
+    NotImplemented();
+    return nullptr;
   }
 
   size_t OpsSize() const override {
@@ -47,21 +61,32 @@ class BlockDesc : public BlockDescAPI {
   }
 
   template <typename T>
-  T* GetOp(int32_t idx);
+  T const* GetOp(int32_t idx) const;
 
   template <typename T>
-  T const* GetOp(int32_t idx) const {
-    return GetOp<T>(idx);
+  T* GetOp(int32_t idx) {
+    NotImplemented();
+    return nullptr;
   }
 
+  const std::vector<VarDesc>& GetVars() const { return vars_; }
+
   int32_t ForwardBlockIdx() const override {
     return desc_->forward_block_idx();
   }
 
-  BlockDesc() = delete;
+  BlockDesc() { NotImplemented(); }
+
+ private:
+  proto::BlockDesc const* desc_;  // not_own
+  std::vector<VarDesc> vars_;
+  std::vector<OpDesc> ops_;
 
  private:
-  proto::BlockDesc* desc_;  // not_own
+  void NotImplemented() const {
+    LOG(FATAL) << "The additional interfaces of BlockDesc is temporarily "
+                  "unavailable in read-only mode.";
+  }
 };
 
 }  // namespace fbs

lite/kernels/arm/activation_grad_compute.cc → lite/model_parser/flatbuffers/io.cc

@@ -12,41 +12,26 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "lite/kernels/arm/activation_grad_compute.h"
-#include "lite/backends/arm/math/funcs.h"
+#include "lite/model_parser/flatbuffers/io.h"
+#include <memory>
+#include <utility>
 
 namespace paddle {
 namespace lite {
-namespace kernels {
-namespace arm {
+namespace fbs {
 
-void SquareGradCompute::Run() {
-  auto& param = this->Param<param_t>();
-  auto& ctx = this->ctx_->template As<ARMContext>();
-  auto out_grad_dims = param.Out_grad->dims();
-  auto out_grad_data = param.Out_grad->data<float>();
-
-  auto x_data = param.X->data<float>();
-  auto x_grad_data = param.X_grad->mutable_data<float>();
-  lite::arm::math::act_square_grad<float>(x_data,
-                                          out_grad_data,
-                                          x_grad_data,
-                                          out_grad_dims.production(),
-                                          ctx.threads());
+void LoadModel(const std::string& path, ProgramDesc* prog) {
+  FILE* file = fopen(path.c_str(), "rb");
+  fseek(file, 0, SEEK_END);
+  int64_t size = ftell(file);
+  rewind(file);
+  char* data = new char[size];
+  size = fread(data, 1, size, file);
+  fclose(file);
+  std::unique_ptr<char[]> buf(data);
+  prog->Init(std::move(buf));
 }
 
-}  // namespace arm
-}  // namespace kernels
+}  // namespace fbs
 }  // namespace lite
 }  // namespace paddle
-
-REGISTER_LITE_KERNEL(square_grad,
-                     kARM,
-                     kFloat,
-                     kNCHW,
-                     paddle::lite::kernels::arm::SquareGradCompute,
-                     def)
-    .BindInput("X", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindInput("Out@GRAD", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindOutput("X@GRAD", {LiteType::GetTensorTy(TARGET(kARM))})
-    .Finalize();

lite/model_parser/flatbuffers/io.h

+// Copyright (c) 2020 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 "lite/model_parser/flatbuffers/program_desc.h"
+
+namespace paddle {
+namespace lite {
+namespace fbs {
+
+void LoadModel(const std::string& path, ProgramDesc* prog);
+
+}  // namespace fbs
+}  // namespace lite
+}  // namespace paddle

lite/model_parser/flatbuffers/op_desc.h

@@ -30,7 +30,7 @@ namespace fbs {
 
 class OpDesc : public OpDescAPI {
  public:
-  explicit OpDesc(proto::OpDesc* desc) : desc_(desc) { CHECK(desc_); }
+  explicit OpDesc(proto::OpDesc const* desc) : desc_(desc) { CHECK(desc_); }
 
   std::string Type() const override { return desc_->type()->str(); }
 
@@ -95,7 +95,7 @@ class OpDesc : public OpDescAPI {
 
   OpDescAPI::AttrType GetAttrType(const std::string& name) const override {
     const auto& attr = desc_->attrs()->LookupByKey(name.c_str());
-    CHECK(attr);
+    CHECK(attr) << "Can not find attr: " << name;
     return static_cast<OpDescAPI::AttrType>(attr->type());
   }
 
@@ -124,10 +124,8 @@ class OpDesc : public OpDescAPI {
   template <typename T>
   typename lite::OpDataTypeTrait<T, Flatbuffers>::RT GetAttr(size_t idx) const;
 
-  OpDesc() = delete;
-
  private:
-  proto::OpDesc* desc_;
+  proto::OpDesc const* desc_;
 
   // To reduce overhead, we expect to use namespace aliasing to make cpp::Desc
   // and flatbuffers::Desc replace each other. However, there is no direct
@@ -138,6 +136,7 @@ class OpDesc : public OpDescAPI {
   // caused by different building options.
 
  public:
+  OpDesc() { NotImplemented(); }
   bool HasInput(const std::string& param) const {
     return desc_->inputs()->LookupByKey(param.c_str()) != nullptr;
   }

lite/model_parser/flatbuffers/program_desc.cc

@@ -19,9 +19,16 @@ namespace lite {
 namespace fbs {
 
 template <>
-proto::BlockDesc* ProgramDesc::GetBlock<proto::BlockDesc>(int32_t idx) {
+proto::BlockDesc const* ProgramDesc::GetBlock<proto::BlockDesc>(
+    int32_t idx) const {
   CHECK_LT(idx, BlocksSize()) << "idx >= blocks.size()";
-  return const_cast<proto::BlockDesc*>(desc_->blocks()->Get(idx));
+  return desc_->blocks()->Get(idx);
+}
+
+template <>
+BlockDesc const* ProgramDesc::GetBlock<BlockDesc>(int32_t idx) const {
+  CHECK_LT(idx, BlocksSize()) << "idx >= blocks.size()";
+  return &blocks_[idx];
 }
 
 }  // namespace fbs

lite/model_parser/flatbuffers/program_desc.h

@@ -15,7 +15,10 @@
 #pragma once
 
 #include <memory>
+#include <utility>
+#include <vector>
 #include "lite/model_parser/base/program_desc.h"
+#include "lite/model_parser/flatbuffers/block_desc.h"
 #include "lite/model_parser/flatbuffers/framework_generated.h"
 #include "lite/utils/all.h"
 
@@ -26,18 +29,40 @@ namespace fbs {
 class ProgramDesc : public ProgramDescAPI {
  public:
   ProgramDesc() = default;
-  explicit ProgramDesc(proto::ProgramDesc *desc) : desc_(desc) { CHECK(desc); }
+  explicit ProgramDesc(std::unique_ptr<const char[]> buf) {
+    Init(std::move(buf));
+  }
 
   size_t BlocksSize() const override { return desc_->blocks()->size(); }
 
+  void Init(std::unique_ptr<const char[]> buf) {
+    CHECK(buf.get() != nullptr);
+    buf_ = std::move(buf);
+    desc_ = proto::GetProgramDesc(buf_.get());
+    blocks_.reserve(BlocksSize());
+    for (size_t idx = 0; idx < BlocksSize(); ++idx) {
+      blocks_.push_back(BlockDesc(desc_->blocks()->Get(idx)));
+    }
+  }
+
+  void CopyFrom(const ProgramDesc& other) {
+    size_t length = strlen(static_cast<const char*>(other.raw_buf()));
+    std::unique_ptr<char[]> buf(new char[length]);
+    memcpy(buf.get(), other.raw_buf(), length);
+    Init(std::move(buf));
+  }
+
   template <typename T>
-  T *GetBlock(int32_t idx);
+  T const* GetBlock(int32_t idx) const;
 
   template <typename T>
-  T const *GetBlock(int32_t idx) const {
-    return GetBlock<T>(idx);
+  T* GetBlock(int32_t idx) {
+    NotImplemented();
+    return nullptr;
   }
 
+  const std::vector<BlockDesc>& GetBlocks() const { return blocks_; }
+
   bool HasVersion() const override { return desc_->version() != nullptr; }
 
   int64_t Version() const override {
@@ -45,8 +70,22 @@ class ProgramDesc : public ProgramDescAPI {
     return desc_->version()->version();
   }
 
+  proto::ProgramDesc const* raw_desc() const { return desc_; }
+
+  const void* raw_buf() const { return buf_.get(); }
+
  private:
-  proto::ProgramDesc *desc_;  // not_own
+  proto::ProgramDesc const* desc_;
+  std::unique_ptr<const char[]> buf_;
+  std::vector<BlockDesc> blocks_;
+
+ private:
+  ProgramDesc& operator=(const ProgramDesc&) = delete;
+  ProgramDesc(const ProgramDesc&) = delete;
+  void NotImplemented() const {
+    LOG(FATAL) << "The additional interfaces of ProgramDesc is temporarily "
+                  "unavailable in read-only mode.";
+  }
 };
 
 }  // namespace fbs

lite/model_parser/flatbuffers/var_desc.h

@@ -27,7 +27,7 @@ namespace fbs {
 
 class VarDesc : public VarDescAPI {
  public:
-  explicit VarDesc(proto::VarDesc* desc) : desc_(desc) {}
+  explicit VarDesc(proto::VarDesc const* desc) : desc_(desc) {}
 
   std::string Name() const override { return desc_->name()->str(); }
 
@@ -48,10 +48,14 @@ class VarDesc : public VarDescAPI {
     return dims_vec;
   }
 
-  VarDesc() = delete;
+  VarDescAPI::Type GetDataType() const {
+    CHECK(GetType() == VarDescAPI::Type::LOD_TENSOR);
+    return static_cast<VarDescAPI::Type>(
+        desc_->type()->lod_tensor()->tensor()->data_type());
+  }
 
  private:
-  proto::VarDesc* desc_;
+  proto::VarDesc const* desc_;
 
   // To reduce overhead, we expect to use namespace aliasing to make cpp::Desc
   // and flatbuffers::Desc replace each other. However, there is no direct
@@ -62,10 +66,7 @@ class VarDesc : public VarDescAPI {
   // caused by different building options.
 
  public:
-  VarDescAPI::Type GetDataType() const {
-    NotImplemented();
-    return data_type_;
-  }
+  VarDesc() { NotImplemented(); }
   void SetDataType(Type data_type) { NotImplemented(); }
   void SetShape(const std::vector<int64_t>& dims) { NotImplemented(); }
 
@@ -74,7 +75,6 @@ class VarDesc : public VarDescAPI {
     LOG(FATAL) << "The additional interfaces of VarDesc is temporarily "
                   "unavailable in read-only mode.";
   }
-  Type data_type_;
   std::vector<int64_t> shape_;
 };
 

lite/model_parser/flatbuffers/vector_view.h

@@ -104,21 +104,33 @@ class VectorView<std::string, Flatbuffers> {
   explicit VectorView(typename Traits::vector_type const* cvec) {
     cvec_ = cvec;
   }
-  std::string operator[](size_t i) const { return cvec_->operator[](i)->str(); }
+  std::string operator[](size_t i) const {
+    CHECK(cvec_);
+    return cvec_->operator[](i)->str();
+  }
   vector_view::FBSStrIterator begin() const {
+    CHECK(cvec_);
     return vector_view::FBSStrIterator(cvec_->begin());
   }
   vector_view::FBSStrIterator end() const {
+    CHECK(cvec_);
     return vector_view::FBSStrIterator(cvec_->end());
   }
-  size_t size() const { return cvec_->size(); }
+  size_t size() const {
+    if (cvec_ == nullptr) {
+      return 0;
+    }
+    return cvec_->size();
+  }
   operator std::vector<std::string>() const {
     VLOG(5) << "Copying elements out of VectorView will damage performance.";
     std::vector<std::string> tmp;
-    tmp.reserve(cvec_->size());
+    tmp.reserve(size());
+    if (cvec_ != nullptr) {
       for (auto val : *cvec_) {
         tmp.push_back(val->str());
       }
+    }
     return tmp;
   }
   ~VectorView() = default;

lite/model_parser/general/block_desc.cc

@@ -24,6 +24,12 @@ VarDesc* BlockDesc::GetVar<VarDesc>(int32_t idx) {
   return &vars_[idx];
 }
 
+template <>
+VarDesc const* BlockDesc::GetVar<VarDesc>(int32_t idx) const {
+  CHECK_LT(idx, VarsSize()) << "idx >= vars.size()";
+  return &vars_[idx];
+}
+
 template <>
 VarDesc* BlockDesc::AddVar<VarDesc>() {
   vars_.emplace_back();
@@ -36,6 +42,12 @@ OpDesc* BlockDesc::GetOp<OpDesc>(int32_t idx) {
   return &ops_[idx];
 }
 
+template <>
+OpDesc const* BlockDesc::GetOp<OpDesc>(int32_t idx) const {
+  CHECK_LT(idx, OpsSize()) << "idx >= ops.size()";
+  return &ops_[idx];
+}
+
 template <>
 OpDesc* BlockDesc::AddOp<OpDesc>() {
   ops_.emplace_back();

lite/model_parser/general/block_desc.h

@@ -46,12 +46,10 @@ class BlockDesc : public BlockDescAPI {
   template <typename T>
   T* GetVar(int32_t idx);
 
-  std::vector<VarDesc>& GetVars() { return vars_; }
-
   template <typename T>
-  T const* GetVar(int32_t idx) const {
-    return GetVar<T>(idx);
-  }
+  T const* GetVar(int32_t idx) const;
+
+  std::vector<VarDesc>& GetVars() { return vars_; }
 
   template <typename T>
   T* AddVar();
@@ -64,9 +62,7 @@ class BlockDesc : public BlockDescAPI {
   T* GetOp(int32_t idx);
 
   template <typename T>
-  T const* GetOp(int32_t idx) const {
-    return GetOp<T>(idx);
-  }
+  T const* GetOp(int32_t idx) const;
 
   template <typename T>
   T* AddOp();

lite/model_parser/general/program_desc.cc

@@ -24,6 +24,12 @@ BlockDesc* ProgramDesc::GetBlock<BlockDesc>(int32_t idx) {
   return &blocks_[idx];
 }
 
+template <>
+BlockDesc const* ProgramDesc::GetBlock<BlockDesc>(int32_t idx) const {
+  CHECK_LT(idx, BlocksSize()) << "idx >= blocks.size()";
+  return &blocks_[idx];
+}
+
 template <>
 BlockDesc* ProgramDesc::AddBlock<BlockDesc>() {
   blocks_.emplace_back();

lite/model_parser/general/program_desc.h

@@ -30,6 +30,13 @@ class ProgramDesc : public ProgramDescAPI {
  public:
   ProgramDesc() = default;
 
+  void CopyFrom(const ProgramDesc& other) {
+    version_ = other.Version();
+    blocks_ = other.blocks();
+  }
+
+  const std::vector<BlockDesc>& blocks() const { return blocks_; }
+
   size_t BlocksSize() const override { return blocks_.size(); }
 
   void ClearBlocks() override { blocks_.clear(); }
@@ -37,12 +44,10 @@ class ProgramDesc : public ProgramDescAPI {
   template <typename T>
   T* GetBlock(int32_t idx);
 
-  std::vector<BlockDesc>& GetBlocks() { return blocks_; }
-
   template <typename T>
-  T const* GetBlock(int32_t idx) const {
-    return GetBlock<T>(idx);
-  }
+  T const* GetBlock(int32_t idx) const;
+
+  std::vector<BlockDesc>& GetBlocks() { return blocks_; }
 
   template <typename T>
   T* AddBlock();

lite/model_parser/model_parser.cc

@@ -176,7 +176,7 @@ void LoadCombinedParamsPb(const std::string &path,
                           const cpp::ProgramDesc &cpp_prog,
                           bool params_from_memory) {
   CHECK(scope);
-  auto prog = cpp_prog;
+  auto &prog = cpp_prog;
   auto &main_block_desc = *prog.GetBlock<cpp::BlockDesc>(0);
 
   // Get vars
@@ -310,7 +310,7 @@ void SaveModelPb(const std::string &model_dir,
 void SaveCombinedParamsPb(const std::string &path,
                           const lite::Scope &exec_scope,
                           const cpp::ProgramDesc &cpp_prog) {
-  auto prog = cpp_prog;
+  auto &prog = cpp_prog;
   auto &main_block_desc = *prog.GetBlock<cpp::BlockDesc>(0);
 
   // Get vars
@@ -526,7 +526,7 @@ void SaveCombinedParamsNaive(const std::string &path,
   naive_buffer::proto::CombinedParamsDesc pt_desc(&table);
   naive_buffer::CombinedParamsDesc desc(&pt_desc);
 
-  auto prog = cpp_prog;
+  auto &prog = cpp_prog;
   auto &main_block_desc = *prog.GetBlock<cpp::BlockDesc>(0);
   // set unique_var_names to avoid saving shared params repeatedly
   std::set<std::string> unique_var_names;
@@ -681,7 +681,7 @@ void LoadCombinedParamsNaive(const std::string &path,
   }
 
   // Check all params loaded
-  auto prog = cpp_prog;
+  auto &prog = cpp_prog;
   auto &main_block_desc = *prog.GetBlock<cpp::BlockDesc>(0);
   for (size_t i = 0; i < main_block_desc.VarsSize(); ++i) {
     auto &var = *main_block_desc.GetVar<cpp::VarDesc>(i);

lite/model_parser/naive_buffer/block_desc.h

@@ -55,11 +55,6 @@ class BlockDesc : public BlockDescAPI {
   template <typename T>
   T* GetVar(int32_t idx);
 
-  template <typename T>
-  T const* GetVar(int32_t idx) const {
-    return GetVar<T>(idx);
-  }
-
   template <typename T>
   T* AddVar();
 
@@ -70,11 +65,6 @@ class BlockDesc : public BlockDescAPI {
   template <typename T>
   T* GetOp(int32_t idx);
 
-  template <typename T>
-  T const* GetOp(int32_t idx) const {
-    return GetOp<T>(idx);
-  }
-
   template <typename T>
   T* AddOp();
 

lite/model_parser/naive_buffer/program_desc.h

@@ -45,11 +45,6 @@ class ProgramDesc : public ProgramDescAPI {
   template <typename T>
   T *GetBlock(int32_t idx);
 
-  template <typename T>
-  T const *GetBlock(int32_t idx) const {
-    return GetBlock<T>(idx);
-  }
-
   template <typename T>
   T *AddBlock();
 

lite/operators/activation_grad_ops.cc

@@ -41,15 +41,11 @@ bool ActivationGradOp::AttachImpl(const cpp::OpDesc& opdesc,
   if (opdesc.HasInput("X")) {
     auto X_name = opdesc.Input("X").front();
     param_.X = GetVar<lite::Tensor>(scope, X_name);
-  } else {
-    param_.X = param_.X_grad;
   }
 
   if (opdesc.HasInput("Out")) {
     auto Out_name = opdesc.Input("Out").front();
     param_.Out = GetVar<lite::Tensor>(scope, Out_name);
-  } else {
-    param_.Out = param_.Out_grad;
   }
 
   return true;
@@ -60,3 +56,5 @@ bool ActivationGradOp::AttachImpl(const cpp::OpDesc& opdesc,
 }  // namespace paddle
 
 REGISTER_LITE_OP(square_grad, paddle::lite::operators::ActivationGradOp);
+REGISTER_LITE_OP(relu_grad, paddle::lite::operators::ActivationGradOp);
+REGISTER_LITE_OP(tanh_grad, paddle::lite::operators::ActivationGradOp);

lite/operators/deformable_conv_op.h

@@ -83,7 +83,7 @@ class DeformableConvOpLite : public OpLite {
     param_.conv_param.filter =
         scope->FindVar(Filter)->GetMutable<lite::Tensor>();
     param_.conv_param.strides = op_desc.GetAttr<std::vector<int>>("strides");
-    auto paddings = op_desc.GetAttr<std::vector<int>>("paddings");
+    std::vector<int> paddings = op_desc.GetAttr<std::vector<int>>("paddings");
     auto dilations = op_desc.GetAttr<std::vector<int>>("dilations");
     param_.conv_param.groups = op_desc.GetAttr<int>("groups");
     param_.conv_param.dilations = std::make_shared<std::vector<int>>(dilations);

lite/operators/max_pool_with_index_op.h

@@ -54,7 +54,7 @@ class MaxPoolWithIndexOpLite : public OpLite {
     param_.ksize = op_desc.GetAttr<std::vector<int>>("ksize");
     param_.global_pooling = op_desc.GetAttr<bool>("global_pooling");
     param_.strides = op_desc.GetAttr<std::vector<int>>("strides");
-    auto paddings = op_desc.GetAttr<std::vector<int>>("paddings");
+    std::vector<int> paddings = op_desc.GetAttr<std::vector<int>>("paddings");
     if (op_desc.HasAttr("adaptive")) {
       param_.adaptive = op_desc.GetAttr<bool>("adaptive");
     }

lite/tests/kernels/activation_grad_compute_test.cc

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "lite/kernels/arm/activation_grad_compute.h"
+#include "lite/kernels/host/activation_grad_compute.h"
 #include <gtest/gtest.h>
 #include "lite/core/op_registry.h"
 #include "lite/kernels/arm/activation_compute.h"
@@ -20,13 +20,11 @@
 namespace paddle {
 namespace lite {
 namespace kernels {
-namespace arm {
 
 using param_t = operators::ActivationParam;
 using grad_param_t = operators::ActivationGradParam;
-using kernel_t = SquareCompute;
-using grad_kernel_t = SquareGradCompute;
 
+template <class kernel_t, class grad_kernel_t>
 class ActivationGradTester {
  public:
   explicit ActivationGradTester(DDim dims) : dims_(dims) {}
@@ -71,22 +69,28 @@ class ActivationGradTester {
   void run_backward(grad_param_t* param,
                     grad_kernel_t* kernel,
                     const std::vector<float>& in_vec,
+                    const std::vector<float>& out_vec,
                     const std::vector<float>& out_grad_vec,
                     float* in_grad_vec) {
     Tensor x;
+    Tensor out;
     Tensor x_grad;
     Tensor out_grad;
     x.Resize(dims_);
+    out.Resize(dims_);
     x_grad.Resize(dims_);
     out_grad.Resize(dims_);
     auto* x_data = x.mutable_data<float>();
+    auto* out_data = out.mutable_data<float>();
     auto* out_grad_data = out_grad.mutable_data<float>();
 
     for (int i = 0; i < dims_.production(); i++) {
       x_data[i] = in_vec[i];
+      out_data[i] = out_vec[i];
       out_grad_data[i] = out_grad_vec[i];
     }
     param->X = &x;
+    param->Out = &out;
     param->X_grad = &x_grad;
     param->Out_grad = &out_grad;
     kernel->SetParam(*param);
@@ -102,7 +106,9 @@ class ActivationGradTester {
     std::vector<float> x(dims_.production());
     std::vector<float> out(dims_.production());
     for (int i = 0; i < dims_.production(); i++) {
-      x[i] = 1.0 * static_cast<float>(i % 128) * 0.3f - 1.1;
+      x[i] = static_cast<float>(i % 3 - 2.0) / 2.0 * 0.333 +
+             static_cast<float>(i % 19 - 10.0) / 10.0 * 0.333 +
+             static_cast<float>(i % 39 - 20.0) / 20.0 * 0.333 + 0.001213;
     }
     this->run_forward(&param_, &kernel_, x, out.data());
 
@@ -120,7 +126,8 @@ class ActivationGradTester {
     for (int i = 0; i < dims_.production(); i++) {
       out_grad[i] = 1.0;
     }
-    this->run_backward(&grad_param_, &grad_kernel_, x, out_grad, x_grad.data());
+    this->run_backward(
+        &grad_param_, &grad_kernel_, x, out, out_grad, x_grad.data());
 
     for (int i = 0; i < dims_.production(); i++) {
       EXPECT_NEAR(x_grad[i], (out_delta[i] - out[i]) / delta, max_grad_delta);
@@ -137,31 +144,58 @@ class ActivationGradTester {
   grad_param_t grad_param_;
 };
 
-void TestNormalCase(DDim dims) {
-  std::unique_ptr<ActivationGradTester> tester(new ActivationGradTester(dims));
+void TestSquareGrad(DDim dims) {
+  LOG(INFO) << "Test Square grad";
+  std::unique_ptr<
+      ActivationGradTester<arm::SquareCompute, host::SquareGradCompute>>
+      tester(
+          new ActivationGradTester<arm::SquareCompute, host::SquareGradCompute>(
+              dims));
   tester->prepare_kernel();
   float delta = 0.001;
   float max_grad_delta = 0.005;
   tester->check_grad(delta, max_grad_delta);
 }
 
-TEST(activation_grad_arm, compute) {
-  LOG(INFO) << "Test Square grad";
+void TestReluGrad(DDim dims) {
+  LOG(INFO) << "Test Relu grad";
+  std::unique_ptr<ActivationGradTester<arm::ReluCompute, host::ReluGradCompute>>
+      tester(new ActivationGradTester<arm::ReluCompute, host::ReluGradCompute>(
+          dims));
+  tester->prepare_kernel();
+  float delta = 0.001;
+  float max_grad_delta = 0.005;
+  tester->check_grad(delta, max_grad_delta);
+}
+
+void TestTanhGrad(DDim dims) {
+  LOG(INFO) << "Test Tanh grad";
+  std::unique_ptr<ActivationGradTester<arm::TanhCompute, host::TanhGradCompute>>
+      tester(new ActivationGradTester<arm::TanhCompute, host::TanhGradCompute>(
+          dims));
+  tester->prepare_kernel();
+  float delta = 0.001;
+  float max_grad_delta = 0.005;
+  tester->check_grad(delta, max_grad_delta);
+}
+
+TEST(activation_grad_host, compute) {
   DeviceInfo::Init();
-  for (auto n : {2}) {
-    for (auto c : {2}) {
-      for (auto h : {2}) {
-        for (auto w : {2}) {
-          TestNormalCase(DDim(std::vector<int64_t>({n, c, h, w})));
+  for (auto n : {2, 1}) {
+    for (auto c : {2, 9}) {
+      for (auto h : {2, 1}) {
+        for (auto w : {2, 10}) {
+          TestSquareGrad(DDim(std::vector<int64_t>({n, c, h, w})));
+          TestReluGrad(DDim(std::vector<int64_t>({n, c, h, w})));
+          TestTanhGrad(DDim(std::vector<int64_t>({n, c, h, w})));
         }
       }
     }
   }
 }
 
-}  // namespace arm
 }  // namespace kernels
 }  // namespace lite
 }  // namespace paddle
 USE_LITE_KERNEL(square, kARM, kFloat, kNCHW, def);
-USE_LITE_KERNEL(square_grad, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(square_grad, kHost, kFloat, kNCHW, def);

lite/tests/kernels/elementwise_grad_compute_test.cc

@@ -215,18 +215,6 @@ class ElementwiseAddGradTester {
     fill_data_rand(y.data(), -1.f, 1.f, y_dims_.production());
     this->run_forward(&param_, &kernel_, x, y, out.data());
 
-    for (int i = 0; i < x_dims_.production(); i++) {
-      LOG(INFO) << "x_" << i << ": " << x[i];
-    }
-
-    for (int i = 0; i < y_dims_.production(); i++) {
-      LOG(INFO) << "y_" << i << ": " << y[i];
-    }
-
-    for (int i = 0; i < out_dims_.production(); i++) {
-      LOG(INFO) << "out_" << i << ": " << out[i];
-    }
-
     // backward
     std::vector<float> out_grad(out_dims_.production());
     std::vector<float> x_grad(x_dims_.production());
@@ -242,14 +230,6 @@ class ElementwiseAddGradTester {
                        x_grad.data(),
                        y_grad.data());
 
-    for (int i = 0; i < x_grad.size(); i++) {
-      LOG(INFO) << "x_grad_" << i << ": " << x_grad[i];
-    }
-
-    for (int i = 0; i < y_grad.size(); i++) {
-      LOG(INFO) << "y_grad_" << i << ": " << y_grad[i];
-    }
-
     // get numeric gradient
     std::vector<float> x_delta(x_dims_.production());
     std::vector<float> y_delta(y_dims_.production());
@@ -443,18 +423,6 @@ class ElementwiseSubGradTester {
     fill_data_rand(y.data(), -1.f, 1.f, y_dims_.production());
     this->run_forward(&param_, &kernel_, x, y, out.data());
 
-    for (int i = 0; i < x_dims_.production(); i++) {
-      LOG(INFO) << "x_" << i << ": " << x[i];
-    }
-
-    for (int i = 0; i < y_dims_.production(); i++) {
-      LOG(INFO) << "y_" << i << ": " << y[i];
-    }
-
-    for (int i = 0; i < out_dims_.production(); i++) {
-      LOG(INFO) << "out_" << i << ": " << out[i];
-    }
-
     // backward
     std::vector<float> out_grad(out_dims_.production());
     std::vector<float> x_grad(x_dims_.production());
@@ -470,14 +438,6 @@ class ElementwiseSubGradTester {
                        x_grad.data(),
                        y_grad.data());
 
-    for (int i = 0; i < x_grad.size(); i++) {
-      LOG(INFO) << "x_grad_" << i << ": " << x_grad[i];
-    }
-
-    for (int i = 0; i < y_grad.size(); i++) {
-      LOG(INFO) << "y_grad_" << i << ": " << y_grad[i];
-    }
-
     // get numeric gradient
     std::vector<float> x_delta(x_dims_.production());
     std::vector<float> y_delta(y_dims_.production());

lite/tests/kernels/sequence_conv_compute_test.cc

@@ -85,21 +85,31 @@ class SequenceConvComputeTester : public arena::TestCase {
     auto output_dims = output->dims();
     auto output_data = output->mutable_data<float>();
     std::vector<std::vector<float>> res;
-    if (contextStart_ == -2) {
+
+    if (contextStart_ == -2 && lod_.size() == 1 &&
+        lod_[0] == std::vector<uint64_t>({0, 4})) {
       res = {{-0.08867277f, -0.17257819f, -0.2564836f},
              {0.194508f, 0.05720823f, -0.08009153f},
              {0.73512584f, 0.5749428f, 0.41475973f},
              {0.5635012f, 0.49485126f, 0.42620137f}};
-    } else if (contextStart_ == -1) {
+    } else if (contextStart_ == -1 && lod_.size() == 1 &&
+               lod_[0] == std::vector<uint64_t>({0, 4})) {
       res = {{0.194508f, 0.05720823f, -0.08009153f},
              {0.73512584f, 0.5749428f, 0.41475973f},
              {0.5635012f, 0.49485126f, 0.42620137f},
              {0.2517162f, 0.23646072f, 0.22120519f}};
-    } else if (contextStart_ == 0) {
+    } else if (contextStart_ == 0 && lod_.size() == 1 &&
+               lod_[0] == std::vector<uint64_t>({0, 4})) {
       res = {{0.73512584f, 0.5749428f, 0.41475973f},
              {0.5635012f, 0.49485126f, 0.42620137f},
              {0.2517162f, 0.23646072f, 0.22120519f},
              {0.02574372f, 0.03337148f, 0.04099924f}};
+    } else if (contextStart_ == -1 && lod_.size() == 1 &&
+               lod_[0] == std::vector<uint64_t>({0, 2, 4})) {
+      res = {{0.194508, 0.05720823, -0.08009153},
+             {0.7093821, 0.57208234, 0.43478262},
+             {0.19450802, 0.17925248, 0.16399695},
+             {0.2517162, 0.23646072, 0.22120519}};
     } else {
       fprintf(stderr, "not supported contextStart_\n");
       exit(-1);
@@ -136,12 +146,25 @@ void TestNormalCase(Place place, float abs_error = 2e-5) {
   }
 }
 
+void TestBatchCase(Place place, float abs_error = 2e-5) {
+  std::vector<std::vector<uint64_t>> lod{{0, 2, 4}};
+  std::vector<int64_t> dims{4, 5};
+  std::vector<int> candidate_pad_idx{-1};
+  for (int pad_idx : candidate_pad_idx) {
+    std::unique_ptr<arena::TestCase> tester(new SequenceConvComputeTester(
+        place, "def", lod, DDim(dims), pad_idx, 1, 3, 3));
+    arena::Arena arena(std::move(tester), place, abs_error);
+    arena.TestPrecision();
+  }
+}
+
 TEST(sequence_conv, precision) {
 #ifdef LITE_WITH_ARM
   float abs_error = 2e-5;
   Place place(TARGET(kARM));
 
   TestNormalCase(place, abs_error);
+  TestBatchCase(place, abs_error);
 #endif
 }
 

lite/tools/build_android.sh

@@ -269,6 +269,7 @@ function main {
     if [ -z "$1" ]; then
         # compiling result contains light_api lib only, recommanded.
         make_tiny_publish_so $ARCH $TOOLCHAIN $ANDROID_STL
+        exit 0
     fi
 
     # Parse command line.
@@ -358,6 +359,7 @@ function main {
     done
     # compiling result contains light_api lib only, recommanded.
     make_tiny_publish_so
+    exit 0
 }
 
 main $@

lite/tools/build_bm.sh

@@ -70,6 +70,13 @@ function build_bm {
     mkdir -p $build_dir
     cd $build_dir
 
+    if [ $TARGET_NAME == "BM1684" ]; then
+      BM_SDK_ROOT="$workspace/third-party/bmlibs/bm_sc5_libs"
+    else
+      BM_SDK_ROOT="$workspace/third-party/bmlibs/bm_sc3_libs"
+    fi
+    echo $BM_SDK_ROOT
+
     prepare_workspace
     cmake .. \
         ${CMAKE_COMMON_OPTIONS} \
@@ -95,17 +102,7 @@ function main {
         case $i in
             --target_name=*)
                 TARGET_NAME="${i#*=}"
-                shift
-                ;;
-            #--bm_sdk_root=*)
-            #    BM_SDK_ROOT="${i#*=}"
-            #    shift
-            #    ;;
-            bm)
                 build_bm
-                shift
-                ;;
-            *)
                 # unknown option
                 print_usage
                 exit 1

lite/tools/build_ios.sh

@@ -152,6 +152,7 @@ function main {
         esac
     done
     make_ios $ARCH
+    exit 0
 }
 
 main $@

lite/tools/check_api_approvals.sh

@@ -71,7 +71,7 @@ function CheckLibSizeDiff() {
     if [ $diff_size -gt 10485 ]; then
         echo_line="Your PR has increased basic inference lib for $diff_size Byte, exceeding maximum requirement of  10485 Byte (0.01M). You need Superjomn's (Yunchunwei) approval or you can contact DannyIsFunny(HuZhiqiang).\n"
         echo "****************"
-        echo -e "${echo_list[@]}"
+        echo -e "${echo_line[@]}"
         echo "There is an approved errors."
         echo "****************"
         exit 1