Merge branch 'xzl/incubate/lite' into 'incubate/lite'

INT8 ARM MobilenetV1 union test.

See merge request inference/paddlelite!67

paddle/fluid/inference/analysis/dot.h

@@ -25,7 +25,7 @@
 #include <vector>
 // #include "paddle/fluid/lite/utils/logging.h"
 // #ifndef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
-#include <glog/logging.h>
+#include <glog/logging.h>  // NOLINT
 // #endif
 
 namespace paddle {

paddle/fluid/lite/api/CMakeLists.txt

@@ -114,9 +114,17 @@ if (WITH_TESTING)
     add_dependencies(test_paddle_api_lite extern_lite_download_lite_naive_model_tar_gz)
 endif()
 
+#lite_cc_binary(cxx_api_lite_bin SRCS cxx_api_bin.cc
+    #X86_DEPS operator
+    #DEPS light_api_lite model_parser_lite target_wrapper_host mir_passes
+    #ARM_DEPS ${arm_kernels})
+
+lite_cc_binary(cxx_api_lite_bin SRCS cxx_api_bin_int8.cc
+    DEPS
+    cxx_api_lite
+    model_parser_lite
+    target_wrapper_host
+    mir_passes
+    ${ops_lite} ${host_kernels}
+    ARM_DEPS ${arm_kernels})
 lite_cc_binary(model_optimize_tool SRCS model_optimize_tool.cc DEPS paddle_api_full)
-
-# lite_cc_binary(cxx_api_lite_bin SRCS cxx_api_bin.cc
-#     X86_DEPS operator
-#     DEPS light_api_lite model_parser_lite target_wrapper_host mir_passes
-#     ARM_DEPS ${arm_kernels})

paddle/fluid/lite/api/cxx_api_bin.cc

@@ -29,16 +29,18 @@ double time_diff(Time t1, Time t2) {
   return counter.count() / 1000.0;
 }
 
-void Run(const char* model_dir, int repeat, int thread_num) {
+void Run(const char* model_dir, int repeat) {
 #ifdef LITE_WITH_ARM
   DeviceInfo::Init();
-  DeviceInfo::Global().SetRunMode(LITE_POWER_HIGH, thread_num);
 #endif
   lite::Predictor predictor;
-  std::vector<Place> valid_places({Place{TARGET(kHost), PRECISION(kFloat)},
-                                   Place{TARGET(kARM), PRECISION(kFloat)}});
+  std::vector<Place> valid_places({
+      Place{TARGET(kHost), PRECISION(kFloat)},
+      Place{TARGET(kARM), PRECISION(kFloat)},
+      Place{TARGET(kARM), PRECISION(kInt8)},
+  });
 
-  predictor.Build(model_dir, Place{TARGET(kARM), PRECISION(kFloat)},
+  predictor.Build(model_dir, Place{TARGET(kARM), PRECISION(kInt8)},
                   valid_places);
 
   auto* input_tensor = predictor.GetInput(0);
@@ -48,8 +50,6 @@ void Run(const char* model_dir, int repeat, int thread_num) {
     data[i] = 1;
   }
 
-  for (int i = 0; i < 10; i++) predictor.Run();
-
   auto time1 = time();
   for (int i = 0; i < repeat; i++) predictor.Run();
   auto time2 = time();
@@ -68,8 +68,8 @@ void Run(const char* model_dir, int repeat, int thread_num) {
 }  // namespace paddle
 
 int main(int argc, char** argv) {
-  CHECK_EQ(argc, 4) << "usage: ./cmd <model_dir> <repeat> <thread_num>";
-  paddle::lite::Run(argv[1], std::stoi(argv[2]), std::stoi(argv[3]));
+  CHECK_EQ(argc, 3) << "usage: ./cmd <model_dir> <repeat>";
+  paddle::lite::Run(argv[1], std::stoi(argv[2]));
 
   return 0;
 }
@@ -93,13 +93,18 @@ USE_LITE_OP(fake_dequantize_max_abs);
 
 USE_LITE_KERNEL(feed, kHost, kAny, kAny, def);
 USE_LITE_KERNEL(fetch, kHost, kAny, kAny, def);
+USE_LITE_OP(calib);
 
 #ifdef LITE_WITH_ARM
 USE_LITE_KERNEL(fc, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(fc, kARM, kInt8, kNCHW, int8out);
+USE_LITE_KERNEL(fc, kARM, kInt8, kNCHW, fp32out);
 USE_LITE_KERNEL(mul, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(scale, kARM, kFloat, kNCHW, def);
 
 USE_LITE_KERNEL(conv2d, kARM, kFloat, kNCHW, def);
+USE_LITE_KERNEL(conv2d, kARM, kInt8, kNCHW, int8_out);
+USE_LITE_KERNEL(conv2d, kARM, kInt8, kNCHW, fp32_out);
 USE_LITE_KERNEL(batch_norm, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(depthwise_conv2d, kARM, kFloat, kNCHW, def);
@@ -107,6 +112,9 @@ USE_LITE_KERNEL(pool2d, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(elementwise_add, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(softmax, kARM, kFloat, kNCHW, def);
 
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, fp32_to_int8);
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, int8_to_fp32);
+
 // USE_LITE_KERNEL(feed, kARM, kAny, kAny, def);
 // USE_LITE_KERNEL(fetch, kARM, kAny, kAny, def);
 #endif  // LITE_WITH_ARM

paddle/fluid/lite/api/cxx_api_bin_int8.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/api/cxx_api.h"
+#include <chrono>  // NOLINT
+#include "paddle/fluid/lite/api/paddle_use_kernels.h"
+#include "paddle/fluid/lite/api/paddle_use_ops.h"
+#include "paddle/fluid/lite/api/paddle_use_passes.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+
+using Time = decltype(std::chrono::high_resolution_clock::now());
+Time time() { return std::chrono::high_resolution_clock::now(); }
+double time_diff(Time t1, Time t2) {
+  typedef std::chrono::microseconds ms;
+  auto diff = t2 - t1;
+  ms counter = std::chrono::duration_cast<ms>(diff);
+  return counter.count() / 1000.0;
+}
+
+void Run(const char* model_dir, int repeat) {
+#ifdef LITE_WITH_ARM
+  DeviceInfo::Init();
+#endif
+  lite::Predictor predictor;
+  std::vector<Place> valid_places({
+      Place{TARGET(kHost), PRECISION(kFloat)},
+      Place{TARGET(kARM), PRECISION(kFloat)},
+      Place{TARGET(kARM), PRECISION(kInt8)},
+  });
+
+  predictor.Build(model_dir, Place{TARGET(kARM), PRECISION(kInt8)},
+                  valid_places);
+
+  auto* input_tensor = predictor.GetInput(0);
+  input_tensor->Resize(DDim(std::vector<DDim::value_type>({1, 3, 224, 224})));
+  auto* data = input_tensor->mutable_data<float>();
+  for (int i = 0; i < input_tensor->dims().production(); i++) {
+    data[i] = 1;
+  }
+
+  auto time1 = time();
+  for (int i = 0; i < repeat; i++) predictor.Run();
+  auto time2 = time();
+  std::cout << " predict cost: " << time_diff(time1, time2) / repeat << "ms"
+            << std::endl;
+
+  auto* out = predictor.GetOutput(0);
+  LOG(INFO) << out << " memory size " << out->data_size();
+  LOG(INFO) << "out " << out->data<float>()[0];
+  LOG(INFO) << "out " << out->data<float>()[1];
+  LOG(INFO) << "dims " << out->dims();
+  LOG(INFO) << "out data size: " << out->data_size();
+}
+
+}  // namespace lite
+}  // namespace paddle
+
+int main(int argc, char** argv) {
+  CHECK_EQ(argc, 3) << "usage: ./cmd <model_dir> <repeat>";
+  paddle::lite::Run(argv[1], std::stoi(argv[2]));
+
+  return 0;
+}

paddle/fluid/lite/api/paddle_use_kernels.h

@@ -38,6 +38,13 @@ USE_LITE_KERNEL(relu, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(transpose, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(transpose2, kARM, kFloat, kNCHW, def);
 USE_LITE_KERNEL(batch_norm, kARM, kFloat, kNCHW, def);
+
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, fp32_to_int8);
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, int8_to_fp32);
+USE_LITE_KERNEL(conv2d, kARM, kInt8, kNCHW, int8_out);
+USE_LITE_KERNEL(conv2d, kARM, kInt8, kNCHW, fp32_out);
+USE_LITE_KERNEL(fc, kARM, kInt8, kNCHW, int8out);
+USE_LITE_KERNEL(fc, kARM, kInt8, kNCHW, fp32out);
 #endif
 
 #ifdef LITE_WITH_X86

paddle/fluid/lite/api/paddle_use_ops.h

@@ -38,3 +38,7 @@ USE_LITE_OP(batch_norm)
 USE_LITE_OP(fusion_elementwise_sub_activation)
 USE_LITE_OP(transpose)
 USE_LITE_OP(transpose2)
+
+USE_LITE_OP(fake_quantize_moving_average_abs_max);
+USE_LITE_OP(fake_dequantize_max_abs);
+USE_LITE_OP(calib);

paddle/fluid/lite/api/paddle_use_passes.h

@@ -31,3 +31,5 @@ USE_MIR_PASS(identity_scale_eliminate_pass);
 USE_MIR_PASS(lite_conv_elementwise_add_activation_fuse_pass);
 USE_MIR_PASS(lite_elementwise_add_activation_fuse_pass);
 USE_MIR_PASS(lite_quant_dequant_fuse_pass);
+USE_MIR_PASS(precision_cast_transform_pass);
+USE_MIR_PASS(trans_weight_pass);

paddle/fluid/lite/core/mir/CMakeLists.txt

@@ -18,10 +18,12 @@ cc_library(mir_passes
       static_kernel_pick_pass.cc
       variable_place_inference_pass.cc
       type_target_transform_pass.cc
+      precision_cast_transform_pass.cc
       io_copy_kernel_pick_pass.cc
       graph_visualize_pass.cc
       generate_program_pass.cc
       argument_type_display_pass.cc
+      trans_weigths_pass.cc
       demo_pass.cc
       runtime_context_assign_pass.cc
   DEPS mir_pass types_lite context_lite ${mir_fusers})

paddle/fluid/lite/core/mir/fusion/fc_fuser.cc

@@ -60,7 +60,7 @@ void FcFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
 }
 
 cpp::OpDesc FcFuser::GenOpDesc(const key2nodes_t& matched) {
-  cpp::OpDesc op_desc;
+  cpp::OpDesc op_desc = *matched.at("mul")->stmt()->op_info();
   op_desc.SetType("fc");
   op_desc.SetInput("Input", {matched.at("x")->arg()->name});
   op_desc.SetInput("W", {matched.at("W")->arg()->name});

paddle/fluid/lite/core/mir/precision_cast_transform_pass.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/core/mir/precision_cast_transform_pass.h"
+#include <list>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/lite/core/mir/graph_visualize_pass.h"
+#include "paddle/fluid/lite/core/mir/pass_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+void PrecisionCastPass::Apply(const std::unique_ptr<SSAGraph>& graph) {
+  // Start from inputs of the graph, those should have place set.
+  std::list<Node*> nodes;
+  for (auto& node : graph->mutable_nodes()) {
+    nodes.push_back(&node);
+  }
+
+  for (auto& node : nodes) {
+    if (!node->IsStmt()) continue;
+    auto inlinks = node->inlinks;
+    for (auto* in : inlinks) {
+      ComplementInputs(graph.get(), node, in);
+    }
+  }
+  VLOG(3) << "\n" << Visualize(graph.get());
+}
+
+void PrecisionCastPass::ComplementInputs(SSAGraph* graph, Node* inst_node,
+                                         Node* in) {
+  // If this input is out of date.
+  if (inst_node->inlinks.end() ==
+      std::find(inst_node->inlinks.begin(), inst_node->inlinks.end(), in))
+    return;
+
+  CHECK(inst_node->IsStmt());
+  auto& inst = inst_node->AsStmt();
+  CHECK(in->IsRoleSet());
+  CHECK(in->IsArg());
+  auto in_arg_name = in->AsArg().name;
+  std::string tmp;
+  CHECK(inst.op_info()->GetInputArgname(in_arg_name, &tmp));
+  auto decl_arg_type = inst.picked_kernel().GetInputDeclType(tmp);
+  CHECK(in->AsArg().type);
+  LOG(INFO) << inst.picked_kernel().name();
+  // if (!in->AsArg().is_weight && !PrecisionCompatibleTo(*in->AsArg().type,
+  // *decl_arg_type)) {
+  if (!PrecisionCompatibleTo(*in->AsArg().type, *decl_arg_type)) {
+    LOG(INFO) << "found Target unmatched tensor: " << in->AsArg().name
+              << " for kernel " << inst.op()->DebugString() << " "
+              << *in->AsArg().type << " -> " << *decl_arg_type;
+    // Add an Cast instruction to make the input compatible with other dist.
+    AddCastInst(*in->AsArg().type, *decl_arg_type, in, graph, inst_node,
+                graph->valid_places());
+  }
+}
+
+void PrecisionCastPass::AddCastInst(const Type& from, const Type& to, Node* in,
+                                    SSAGraph* graph, Node* inst_node,
+                                    const std::vector<Place>& valid_places) {
+  CHECK(!valid_places.empty()) << "valid_place should be set";
+
+  // var -> new_transform_op -> new_var -> inst
+  // So there will be a new Argument node and a new Cast Statement Node.
+  CHECK(in->IsArg());
+  auto node_id = [&] { return graph->nodes().size(); };
+  auto cast_op_output_name =
+      in->AsArg().name + "/trans/" + std::to_string(node_id());
+  auto* cast_op_output_arg = graph->NewArgumentNode(cast_op_output_name);
+  auto* cast_inst = graph->NewInstructNode();
+
+  // create Op and kernels.
+  auto cast_op = LiteOpRegistry::Global().Create("calib");
+  CHECK(cast_op) << "create op [" << cast_op << "] failed";
+
+  // Create the new var manually.
+  inst_node->AsStmt().op()->scope()->Var(cast_op_output_name);
+
+  // Create Calib Instruction.
+  cpp::OpDesc op_desc;
+  op_desc.SetType("calib");
+  op_desc.SetInput("Input", {in->AsArg().name});
+  op_desc.SetOutput("Out", {cast_op_output_name});
+  CHECK(inst_node->AsStmt().op_info()->HasAttr("input_scale"));
+  op_desc.SetAttr("scale",
+                  inst_node->AsStmt().op_info()->GetAttr<float>("input_scale"));
+
+  cast_op->Attach(op_desc, inst_node->AsStmt().op()->scope());
+  auto kernels = cast_op->CreateKernels(valid_places);
+  std::vector<std::unique_ptr<KernelBase>> selected_kernels;
+  bool is_found = false;
+  for (auto& kernel : kernels) {
+    const Type* in_arg_ty = kernel->GetInputDeclType("Input");
+    const Type* out_arg_ty = kernel->GetOutputDeclType("Out");
+    if (in_arg_ty->precision() == from.precision() &&
+        out_arg_ty->precision() == to.precision()) {
+      is_found = true;
+      selected_kernels.emplace_back(std::move(kernel));
+      // we pick the kernel
+      cast_inst->AsStmt("calib", std::move(selected_kernels), cast_op);
+      break;
+    }
+  }
+
+  CHECK(is_found) << "Can't find a Cast kernel for Cast op: " << from << ":"
+                  << in->AsArg().name << "->" << to << ":"
+                  << inst_node->AsStmt().op_info()->Type();
+
+  // Remove the old link
+  RemoveDirectedLink(in, inst_node);
+
+  // Update the original instruction OpDesc.
+  // Update its input to the io_copy_output_name
+
+  // Add new link, var -> new_inst, new_inst->newarg, newarg->inst
+  DirectedLink(in, cast_inst);
+  DirectedLink(cast_inst, cast_op_output_arg);
+  DirectedLink(cast_op_output_arg, inst_node);
+
+  // reset opdesc and update kernel information
+  UpdateInputTo(inst_node->AsStmt().op()->mutable_op_info(), in->AsArg().name,
+                cast_op_output_name);
+
+  // recreate the op
+  auto original_selected_kernel =
+      std::move(inst_node->AsStmt().kernels().front());
+  auto updated_op_info = *inst_node->AsStmt().mutable_op_info();
+
+  inst_node->AsStmt().ResetOp(updated_op_info, graph->valid_places());
+  inst_node->AsStmt().kernels().clear();
+  inst_node->AsStmt().kernels().emplace_back(
+      std::move(original_selected_kernel));
+  for (auto& kernel : inst_node->AsStmt().kernels()) {
+    LOG(INFO) << "kernel info: " << kernel->name();
+    inst_node->AsStmt().op()->AttachKernel(kernel.get());
+  }
+  graph->CheckValid();
+}
+
+void PrecisionCastPass::SetValidPlaces(const std::vector<Place>& valid_places) {
+  CHECK(!valid_places.empty());
+  valid_places_ = valid_places;
+}
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_MIR_PASS(precision_cast_transform_pass,
+                  paddle::lite::mir::PrecisionCastPass);

paddle/fluid/lite/core/mir/precision_cast_transform_pass.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 <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/lite/core/mir/pass.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+static void UpdateInputTo(cpp::OpDesc* desc, const std::string& from,
+                          const std::string& to) {
+  for (auto& item : *desc->mutable_inputs()) {
+    for (auto& input : item.second) {
+      if (input == from) {
+        input = to;
+      }
+    }
+  }
+}
+
+/*
+ * The pass complement the necessary instruction to make data
+ * transferring or transformation between different places.
+ */
+class PrecisionCastPass : public ProgramPass {
+ public:
+  void Apply(const std::unique_ptr<SSAGraph>& graph) override;
+
+  void ComplementInputs(SSAGraph* graph, Node* inst_node, Node* in);
+
+  void AddCastInst(const Type& from, const Type& to, Node* in, SSAGraph* graph,
+                   Node* inst_node, const std::vector<Place>& valid_places);
+
+  void SetValidPlaces(const std::vector<Place>& valid_places);
+
+  const std::vector<Place>& valid_places() const { return valid_places_; }
+
+ private:
+  std::vector<Place> valid_places_;
+};
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/core/mir/static_kernel_pick_pass.cc

@@ -33,9 +33,12 @@ void StaticKernelPickPass::Apply(const std::unique_ptr<SSAGraph>& graph) {
       << "kernel_pick_factors should be specified first";
   CHECK(graph) << "graph not valid";
   // sort kernels by the factors.
+
   for (auto& node : graph->mutable_nodes()) {
     if (!node.IsStmt()) continue;
     auto& instruct = node.AsStmt();
+
+    // Get candidate kernels
     std::vector<std::pair<size_t, std::unique_ptr<KernelBase>>> scored;
     CHECK(!instruct.kernels().empty()) << "No kernels found for "
                                        << instruct.op_type();
@@ -43,15 +46,56 @@ void StaticKernelPickPass::Apply(const std::unique_ptr<SSAGraph>& graph) {
       size_t score = KernelGrade(*kernel);
       scored.emplace_back(score, std::move(kernel));
     }
-
     std::sort(scored.begin(), scored.end(), KernelScoreCmp);
-
-    // Move kernel back
-    // Just keep a single best kernel.
-    // TODO(Superjomn) reconsider this.
     instruct.kernels().clear();
-    instruct.kernels().emplace_back(std::move(scored.front().second));
-    VLOG(2) << "pick " << instruct.kernels().front()->name();
+
+    if (!instruct.op_info()->HasAttr("enable_int8")) {
+      // Move kernel back
+      // Just keep a single best kernel.
+      // TODO(Superjomn) reconsider this.
+      instruct.kernels().emplace_back(std::move(scored.front().second));
+      VLOG(2) << "pick " << instruct.kernels().front()->name();
+
+    } else {
+      bool out_type_int8 = true;
+      // Only if all ops linked to this op output has enable_int8 attr,
+      // then the op output type is int8, or fp32.
+      for (auto* out_n : node.outlinks) {
+        CHECK(out_n->IsArg());
+        for (auto* tmp_op : out_n->outlinks) {
+          CHECK(tmp_op->IsStmt());
+          if (!tmp_op->AsStmt().op_info()->HasAttr("enable_int8")) {
+            out_type_int8 = false;
+            break;
+          }
+        }
+        if (!out_type_int8) break;
+      }
+
+      // According to the out type, we pick the kernel.
+      auto output_arguments = instruct.op_info()->OutputArgumentNames();
+      for (auto& candidate : scored) {
+        bool all_output_type_match = true;
+        auto expect_output_type =
+            out_type_int8 ? PRECISION(kInt8) : PRECISION(kFloat);
+
+        for (auto& arg_name : output_arguments) {
+          const Type* out_arg_ty =
+              candidate.second->GetOutputDeclType(arg_name);
+          if (out_arg_ty->precision() != expect_output_type) {
+            all_output_type_match = false;
+          }
+        }
+
+        if (all_output_type_match) {
+          instruct.kernels().emplace_back(std::move(candidate.second));
+          VLOG(2) << "pick " << instruct.kernels().front()->name();
+          break;
+        }
+      }
+      CHECK(!instruct.kernels().empty()) << "No kernels found for "
+                                         << instruct.op_type();
+    }
   }
 }
 

paddle/fluid/lite/core/mir/trans_weigths_pass.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/core/mir/trans_weigths_pass.h"
+#include <list>
+#include <string>
+#include <utility>
+#include <vector>
+#include "paddle/fluid/lite/core/mir/graph_visualize_pass.h"
+#include "paddle/fluid/lite/core/mir/pass_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+void TransWeightPass::Apply(const std::unique_ptr<SSAGraph>& graph) {
+  // Start from inputs of the graph, those should have place set.
+  std::list<Node*> nodes;
+  for (auto& node : graph->mutable_nodes()) {
+    nodes.push_back(&node);
+  }
+
+  for (auto& node : nodes) {
+    if (!node->IsStmt()) continue;
+    auto& instruct = node->AsStmt();
+    if (!instruct.op_info()->HasAttr("enable_int8")) {
+      continue;
+    }
+    std::vector<std::string> output_arg_names =
+        instruct.op_info()->output_argnames();
+
+    CHECK(output_arg_names.size() == 1)
+        << "Currently, the op that supports int8 supports only one output";
+    // After static kernel select pass, there is only one kernel here.
+    const Type* out_arg_ty =
+        instruct.kernels()[0]->GetOutputDeclType(output_arg_names[0]);
+    auto out_precision = out_arg_ty->precision();
+    bool out_type_int8 = out_precision == PRECISION(kInt8) ? true : false;
+    float in_scale, out_scale;
+
+    in_scale = instruct.op_info()->GetAttr<float>("input_scale");
+
+    // Get next input op's input_scale
+    if (out_type_int8) {
+      LOG(INFO) << "output_type_int8";
+      auto out_node = node->outlinks.front();
+      CHECK(out_node->IsArg());
+      auto one_adj_op_node = out_node->outlinks.front();
+      CHECK(one_adj_op_node->IsStmt());
+      auto& one_adj_instruct = one_adj_op_node->AsStmt();
+      CHECK(one_adj_instruct.op_info()->HasAttr("enable_int8"));
+      CHECK(one_adj_instruct.op_info()->HasAttr("input_scale"));
+      out_scale = one_adj_instruct.op_info()->GetAttr<float>("input_scale");
+      instruct.mutable_op_info()->SetAttr("output_scale", out_scale);
+    } else {
+      LOG(INFO) << "output_type_fp32";
+    }
+
+    std::string op_type = instruct.op_info()->Type();
+    std::vector<float> weight_scale;
+    auto* scope = instruct.op()->scope();
+
+    if (op_type == "depthwise_conv2d" || op_type == "conv2d") {
+      std::string weight_var_name = instruct.op_info()->Input("Filter").front();
+      auto conv_weight_t =
+          scope->FindVar(weight_var_name)->GetMutable<lite::Tensor>();
+      // till now, all the weight should be float32 type
+      float* conv_weight_d = conv_weight_t->mutable_data<float>();
+      int64_t axis_size = conv_weight_t->dims()[0];
+      int64_t inner_size = conv_weight_t->data_size() / axis_size;
+      weight_scale =
+          GetWeightScale(conv_weight_d, axis_size, inner_size, 127.0);
+
+      Tensor temp_tensor;
+      temp_tensor.Resize(conv_weight_t->dims());
+      int8_t* temp_data = temp_tensor.mutable_data<int8_t>();
+      FP32ToInt8(conv_weight_d, temp_data, weight_scale.data(), axis_size, 1,
+                 inner_size);
+      conv_weight_t->CopyDataFrom(temp_tensor);
+    } else if (op_type == "fc" || op_type == "mul") {
+      std::string weight_arg_name = "W";
+      if (op_type == "mul") weight_arg_name = "Y";
+      std::string weight_var_name =
+          instruct.op_info()->Input(weight_arg_name).front();
+
+      auto fc_weight_t =
+          scope->FindVar(weight_var_name)->GetMutable<lite::Tensor>();
+      // till now, all the weight should be float32 type
+      float* fc_weight_d = fc_weight_t->mutable_data<float>();
+
+      CHECK_EQ(fc_weight_t->dims().size(), 2UL);
+
+      int64_t h = fc_weight_t->dims()[0];
+      int64_t w = fc_weight_t->data_size() / h;
+      Tensor trans_w_t, int8_temp_t;
+      trans_w_t.CopyDataFrom(*fc_weight_t);
+      float* trans_w_data = trans_w_t.mutable_data<float>();
+      int8_temp_t.Resize(fc_weight_t->dims());
+      int8_t* int8_temp_data = int8_temp_t.mutable_data<int8_t>();
+      // trans weight for calc the weight scale.
+      for (int i = 0; i < h; i++) {
+        for (int j = 0; j < w; j++) {
+          trans_w_data[i * w + j] = fc_weight_d[j * h + i];
+        }
+      }
+      weight_scale = GetWeightScale(trans_w_data, w, h, 127.0);
+
+      int8_t* fc_weight_int8_d = fc_weight_t->mutable_data<int8_t>();
+      FP32ToInt8(trans_w_data, int8_temp_data, weight_scale.data(), w, 1, h);
+      // Retrans back
+      for (int i = 0; i < w; i++) {
+        for (int j = 0; j < h; j++) {
+          fc_weight_int8_d[i * h + j] = int8_temp_data[j * w + i];
+        }
+      }
+    }
+
+    // Convert fp32 bias to int8 bias
+    std::vector<std::string> input_arg_names =
+        instruct.op_info()->InputArgumentNames();
+    if (std::find(input_arg_names.begin(), input_arg_names.end(), "Bias") !=
+            input_arg_names.end() &&
+        instruct.op_info()->Input("Bias").size() > 0) {
+      std::string bias_var_name = instruct.op_info()->Input("Bias").front();
+      auto bias_weight_t =
+          scope->FindVar(bias_var_name)->GetMutable<lite::Tensor>();
+      float* bias_weight_d = bias_weight_t->mutable_data<float>();
+
+      Tensor temp_bias;
+      temp_bias.Resize(bias_weight_t->dims());
+      int* temp_bias_data = temp_bias.mutable_data<int>();
+      TransFP32BiasToInt32(bias_weight_d, temp_bias_data, temp_bias.data_size(),
+                           in_scale, weight_scale);
+      bias_weight_t->CopyDataFrom(temp_bias);
+    }
+
+    instruct.mutable_op_info()->SetAttr("weight_scale", weight_scale);
+
+    auto original_selected_kernel = std::move(instruct.kernels().front());
+    auto updated_op_info = *instruct.mutable_op_info();
+    instruct.ResetOp(updated_op_info, graph->valid_places());
+    instruct.kernels().clear();
+    instruct.kernels().emplace_back(std::move(original_selected_kernel));
+    for (auto& kernel : instruct.kernels()) {
+      LOG(INFO) << "kernel info: " << kernel->name();
+      instruct.op()->AttachKernel(kernel.get());
+    }
+  }
+}
+
+void TransWeightPass::SetValidPlaces(const std::vector<Place>& valid_places) {
+  CHECK(!valid_places.empty());
+  valid_places_ = valid_places;
+}
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle
+
+REGISTER_MIR_PASS(trans_weight_pass, paddle::lite::mir::TransWeightPass);

paddle/fluid/lite/core/mir/trans_weigths_pass.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 <cmath>
+#include <memory>
+#include <string>
+#include <vector>
+#include "paddle/fluid/lite/arm/math/saturate.h"
+#include "paddle/fluid/lite/core/mir/pass.h"
+#include "paddle/fluid/lite/core/op_registry.h"
+
+namespace paddle {
+namespace lite {
+namespace mir {
+
+/*
+ * IoComplementPass complement the necessary instruction to make data
+ * transferring or transformation between different places.
+ */
+class TransWeightPass : public ProgramPass {
+ public:
+  void Apply(const std::unique_ptr<SSAGraph>& graph) override;
+  std::vector<float> GetWeightScale(float* in_data, int64_t axis_size,
+                                    int64_t inner_size, float scale_factor) {
+    std::vector<float> scale_out(axis_size);
+    auto calc_abs_max = [&](float* in, size_t data_size) -> float {
+      float max_data = 0.0;
+      for (size_t i = 0; i < data_size; i++) {
+        if (max_data < std::abs(in[i])) max_data = std::abs(in[i]);
+      }
+      return max_data;
+    };
+    for (int c = 0; c < axis_size; c++) {
+      float* part_in = in_data + c * inner_size;
+      scale_out[c] = calc_abs_max(part_in, inner_size) / scale_factor;
+    }
+    return scale_out;
+  }
+  void FP32ToInt8(const float* din, int8_t* dout, const float* scale,
+                  int axis_size, int64_t outer_size, int64_t inner_size) {
+    int loop_size = axis_size * outer_size;
+    for (int i = 0; i < loop_size; ++i) {
+      float inv_scale = 1.f / scale[i % axis_size];
+      for (int j = 0; j < inner_size; ++j) {
+        dout[j] = static_cast<int8_t>(std::roundf(din[j] * inv_scale));
+      }
+      dout += inner_size;
+      din += inner_size;
+    }
+  }
+
+  void TransFP32BiasToInt32(const float* din, int* dout, size_t data_size,
+                            float in_scale, std::vector<float> weight_scale) {
+    CHECK(data_size == weight_scale.size())
+        << "Bias data size should be equal toe the weight scale data size.";
+    for (size_t i = 0; i < data_size; i++) {
+      dout[i] =
+          static_cast<int>(std::roundf(din[i] / in_scale / weight_scale[i]));
+    }
+  }
+
+  void SetValidPlaces(const std::vector<Place>& valid_places);
+
+  const std::vector<Place>& valid_places() const { return valid_places_; }
+
+ private:
+  std::vector<Place> valid_places_;
+};
+
+}  // namespace mir
+}  // namespace lite
+}  // namespace paddle

paddle/fluid/lite/core/optimizer.h

@@ -49,34 +49,37 @@ class Optimizer {
     InitTargetTypeTransformPass();
 
     if (passes.empty()) {
-      RunPasses(std::vector<std::string>{{
-          "lite_quant_dequant_fuse_pass",  //
-          "lite_conv_bn_fuse_pass",        //
+      RunPasses(std::vector<std::string>{
+          {"lite_quant_dequant_fuse_pass",  //
+           "lite_conv_bn_fuse_pass",        //
 // This pass is disabled to force some opencl kernels selected for final
 // running, otherwise, they will be fused to ARM fusion kernels, and the OpenCL
 // devices will be discarded.
 // TODO(Superjomn) Refine the fusion related design to select fusion kernels for
 // devices automatically.
 #ifndef LITE_WITH_OPENCL
-          "lite_conv_elementwise_add_activation_fuse_pass",  //
+           "lite_conv_elementwise_add_activation_fuse_pass",  //
 #endif
-          "lite_fc_fuse_pass",              //
-          "identity_scale_eliminate_pass",  //
+           "lite_fc_fuse_pass",              //
+           "identity_scale_eliminate_pass",  //
 #ifdef LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
 #ifndef LITE_WITH_OPENCL
-          "lite_elementwise_add_activation_fuse_pass",  //
+           "lite_elementwise_add_activation_fuse_pass",  //
 #endif
 #endif
-          "static_kernel_pick_pass",        //
-          "variable_place_inference_pass",  //
-          "argument_type_display_pass",     //
-          "type_target_transform_pass",     //
-          "variable_place_inference_pass",  //
-          "argument_type_display_pass",     //
-          "io_copy_kernel_pick_pass",       //
-          "variable_place_inference_pass",  //
-          "runtime_context_assign_pass",    //
-      }});
+           "static_kernel_pick_pass",        //
+           "variable_place_inference_pass",  //
+           "argument_type_display_pass",     //
+           "type_target_transform_pass",     //
+           "variable_place_inference_pass",  //
+           "argument_type_display_pass",     //
+           "io_copy_kernel_pick_pass",       //
+           "variable_place_inference_pass",  //
+           "precision_cast_transform_pass",  //
+           "argument_type_display_pass",     //
+           "trans_weight_pass",              //
+           "runtime_context_assign_pass",    //
+           "graph_visualze"}});
     } else {
       RunPasses(passes);
     }
@@ -134,7 +137,7 @@ class Optimizer {
     for (auto& x : passes) {
       LOG(INFO) << "== Running pass " << x;
       auto* pass = mir::PassManager::Global().LookUp(x);
-      CHECK(pass);
+      CHECK(pass) << "Can not find pass: " << x;
       pass->Apply(graph_);
     }
   }

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

@@ -31,7 +31,7 @@ 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)
 lite_cc_test(test_concat_compute_arm SRCS concat_compute_test.cc DEPS concat_compute_arm)
 lite_cc_test(test_dropout_compute_arm SRCS dropout_compute_test.cc DEPS dropout_compute_arm)
-lite_cc_test(test_calib_compute_arm SRCS calib_compute_test.cc DEPS calib_compute_arm)
+# lite_cc_test(test_calib_compute_arm SRCS calib_compute_test.cc DEPS calib_compute_arm)
 lite_cc_test(test_transpose_compute_arm SRCS transpose_compute_test.cc DEPS transpose_compute_arm)
 
 set(arm_kernels
@@ -48,6 +48,7 @@ set(arm_kernels
     concat_compute_arm
     dropout_compute_arm
     transpose_compute_arm
+    calib_compute_arm
     )
 
 set(arm_kernels "${arm_kernels}" CACHE INTERNAL "arm kernels")

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

@@ -23,26 +23,24 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
-void CalibCompute::Run() {
+void CalibComputeFp32ToInt8::Run() {
   auto& param = this->Param<operators::CalibParam>();
-  std::vector<float> scale = {param.in_scale};
-  if (param.in_dtype == PRECISION(kFloat) &&
-      param.out_dtype == PRECISION(kInt8)) {
-    const auto* din = param.input->data<float>();
-    auto* dout = param.output->mutable_data<signed char>();
-    lite::arm::math::fp32_to_int8(din, dout, scale.data(), 1, 1,
-                                  param.input->numel());
-    return;
-  }
-  if (param.in_dtype == PRECISION(kInt8) &&
-      param.out_dtype == PRECISION(kFloat)) {
-    const auto* din = param.input->data<signed char>();
-    auto* dout = param.output->mutable_data<float>();
-    lite::arm::math::int8_to_fp32(din, dout, scale.data(), 1, 1,
-                                  param.input->numel());
-    return;
-  }
-  LOG(FATAL) << "Unsupport Dtype.";
+  std::vector<float> scale = {param.scale};
+  const auto* din = param.input->data<float>();
+  auto* dout = param.output->mutable_data<signed char>();
+  lite::arm::math::fp32_to_int8(din, dout, scale.data(), 1, 1,
+                                param.input->numel());
+  return;
+}
+
+void CalibComputeInt8ToFp32::Run() {
+  auto& param = this->Param<operators::CalibParam>();
+  const auto* din = param.input->data<signed char>();
+  std::vector<float> scale = {param.scale};
+  auto* dout = param.output->mutable_data<float>();
+  lite::arm::math::int8_to_fp32(din, dout, scale.data(), 1, 1,
+                                param.input->numel());
+  return;
 }
 
 }  // namespace arm
@@ -51,7 +49,16 @@ void CalibCompute::Run() {
 }  // namespace paddle
 
 REGISTER_LITE_KERNEL(calib, kARM, kInt8, kNCHW,
-                     paddle::lite::kernels::arm::CalibCompute, def)
-    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM))})
-    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
+                     paddle::lite::kernels::arm::CalibComputeFp32ToInt8,
+                     fp32_to_int8)
+    .BindInput("Input",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(calib, kARM, kInt8, kNCHW,
+                     paddle::lite::kernels::arm::CalibComputeInt8ToFp32,
+                     int8_to_fp32)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))})
     .Finalize();

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

@@ -21,13 +21,26 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
-class CalibCompute : public KernelLite<TARGET(kARM), PRECISION(kInt8)> {
+class CalibComputeFp32ToInt8
+    : public KernelLite<TARGET(kARM), PRECISION(kInt8)> {
  public:
   using param_t = operators::CalibParam;
 
   void Run() override;
 
-  ~CalibCompute() override{};
+  ~CalibComputeFp32ToInt8() override{};
+
+ private:
+};
+
+class CalibComputeInt8ToFp32
+    : public KernelLite<TARGET(kARM), PRECISION(kInt8)> {
+ public:
+  using param_t = operators::CalibParam;
+
+  void Run() override;
+
+  ~CalibComputeInt8ToFp32() override{};
 
  private:
 };

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

@@ -146,4 +146,5 @@ TEST(calib_arm, int8_to_fp32) {
 }  // namespace lite
 }  // namespace paddle
 
-USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, def);
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, int8_to_fp32);
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, fp32_to_int8);

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

@@ -123,13 +123,16 @@ void ConvComputeInt8<Ptype_out>::PrepareForRun() {
 
   // weigth is int8 and bias is int32 so do not need trans
   if (param.groups == ic && ic == oc && kps_equal && no_dilation && flag_dw) {
-    impl_ = new lite::arm::math::DepthwiseConvInt8<Ptype_out>;
-    VLOG(3) << "DepthwiseConv Int8";
+    // impl_ = new lite::arm::math::DepthwiseConvInt8<Ptype_out>;
+    impl_ = new lite::arm::math::GemmLikeConvInt8<Ptype_out>;
+    VLOG(3) << "Run DepthwiseConv Int8";
   } else if (param.groups == 1 && kw == 3 && (sw == 1 || sw == 2) &&
              kps_equal && no_dilation) {
-    impl_ = new lite::arm::math::DirectConvInt8<Ptype_out>;
+    VLOG(3) << "Run DirectConv Int8";
+    impl_ = new lite::arm::math::GemmLikeConvInt8<Ptype_out>;
+    // impl_ = new lite::arm::math::DirectConvInt8<Ptype_out>;
   } else {
-    VLOG(3) << "GemmLikeConvInt8";
+    VLOG(3) << "Run GemmLikeConvInt8";
     impl_ = new lite::arm::math::GemmLikeConvInt8<Ptype_out>;
   }
 
@@ -189,3 +192,25 @@ REGISTER_LITE_KERNEL(
     .BindOutput("Output",
                 {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(
+    depthwise_conv2d, kARM, kInt8, kNCHW,
+    paddle::lite::kernels::arm::ConvComputeInt8<PRECISION(kInt8)>, int8_out)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindInput("Filter",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Output",
+                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(
+    depthwise_conv2d, kARM, kInt8, kNCHW,
+    paddle::lite::kernels::arm::ConvComputeInt8<PRECISION(kFloat)>, fp32_out)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindInput("Filter",
+               {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Output",
+                {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))})
+    .Finalize();

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

@@ -14,9 +14,13 @@
 
 #include "paddle/fluid/lite/kernels/arm/fc_compute.h"
 #include <vector>
+#include "paddle/fluid/lite/api/paddle_place.h"
 #include "paddle/fluid/lite/arm/math/funcs.h"
+#include "paddle/fluid/lite/arm/math/gemm_prepacked_int8.h"
+#include "paddle/fluid/lite/arm/math/gemv_arm_int8.h"
 #include "paddle/fluid/lite/core/op_registry.h"
 #include "paddle/fluid/lite/core/type_system.h"
+
 namespace paddle {
 namespace lite {
 namespace kernels {
@@ -71,8 +75,8 @@ void FcCompute::Run() {
 
   auto& ctx = this->ctx_->template As<ARMContext>();
   if (m_ > 1) {
-    float* packed_in = static_cast<float*>(ctx.workspace_data<float>()) +
-                       ctx.l2_cache_size() / sizeof(float);
+    float* packed_in =
+        ctx.workspace_data<float>() + ctx.l2_cache_size() / sizeof(float);
     lite::arm::math::prepackA(packed_in, i_data, k_, 0, m_, 0, k_, false, &ctx);
     lite::arm::math::sgemm_prepack(packed_in, w_data, b_data, o_data, m_, n_,
                                    k_, false, false, false, &ctx);
@@ -89,6 +93,97 @@ void FcCompute::Run() {
   }
 }
 
+template <PrecisionType Ptype_out>
+void FcComputeInt8<Ptype_out>::PrepareForRun() {
+  auto& param = this->Param<operators::FcParam>();
+  auto x_dims = param.input->dims();
+  auto w_dims = param.w->dims();
+
+  auto& ctx = this->ctx_->template As<ARMContext>();
+  if (!tmp_int32_out_) {
+    tmp_int32_out_ = new Tensor;
+    tmp_int32_out_->Resize(param.output->dims());
+  }
+
+  CHECK_GE(x_dims.size(), 2UL);
+  CHECK_EQ(w_dims.size(), 2UL);
+  CHECK_EQ(param.output->dims().size(), 2UL);
+
+  this->m_ = x_dims.Slice(0, param.in_num_col_dims).production();
+  this->k_ = x_dims.Slice(param.in_num_col_dims, x_dims.size()).production();
+  this->n_ = w_dims[1];
+  CHECK_EQ(k_, static_cast<int>(w_dims[0]));
+
+  if (this->m_ == 1) {
+    if (!this->transed_weight_) {
+      this->transed_weight_ = new Tensor;
+    }
+    this->transed_weight_->Resize({this->n_, this->k_});
+    const auto* w_data = param.w->template data<int8_t>();
+    auto* t_data = this->transed_weight_->template mutable_data<int8_t>();
+    int i = 0;
+
+    for (int nn = 0; nn < this->n_; ++nn) {
+      for (int kk = 0; kk < this->k_; ++kk) {
+        t_data[i++] = w_data[kk * this->n_ + nn];
+      }
+    }
+  }
+
+  if (this->m_ > 1) {
+    int hblock = lite::arm::math::get_hblock(ctx.arch());
+    int m_round = hblock * ((this->m_ + hblock - 1) / hblock);
+    ctx.ExtendWorkspace(DDimLite(std::vector<int64_t>({m_round * this->k_})));
+  }
+}
+
+template <PrecisionType Ptype_out>
+void FcComputeInt8<Ptype_out>::Run() {
+  auto& param = this->Param<operators::FcParam>();
+
+  const auto* i_data = param.input->template data<int8_t>();
+  const auto* w_data = param.w->template data<int8_t>();
+  const auto* b_data = param.bias ? param.bias->template data<int>() : nullptr;
+  int* o_data = nullptr;
+
+  auto& ctx = this->ctx_->template As<ARMContext>();
+
+  o_data = this->tmp_int32_out_->template mutable_data<int>();
+  if (m_ > 1) {
+    int8_t* packed_in =
+        static_cast<int8_t*>(ctx.template workspace_data<int8_t>()) +
+        ctx.l2_cache_size() / sizeof(int8_t);
+    lite::arm::math::prepackA_int8(packed_in, i_data, k_, 0, m_, 0, k_, false);
+    lite::arm::math::gemm_prepack_int8(packed_in, w_data, b_data, o_data, m_,
+                                       n_, k_, false, false, false, nullptr,
+                                       &ctx);
+    if (param.bias) {
+      CHECK_EQ(param.bias->numel(), n_);
+      lite::arm::math::fill_bias_fc(o_data, b_data, m_, n_);
+    }
+  } else {
+    CHECK(transed_weight_);
+    const auto* t_data = transed_weight_->template data<int8_t>();
+    lite::arm::math::gemv_int8(t_data, i_data, o_data, false, n_, k_, nullptr,
+                               b_data != nullptr, b_data, false);
+  }
+
+  float i_scale = param.input_scale;
+  std::vector<float> weight_scale = param.weight_scale;
+  if (Ptype_out == PRECISION(kInt8)) {
+    float o_scale = param.output_scale;
+    param.output->template mutable_data<int8_t>();
+    lite::arm::math::trans_tensor_dtype<PRECISION(kInt32), PRECISION(kInt8)>(
+        tmp_int32_out_, param.output, i_scale, o_scale, weight_scale);
+  } else if (Ptype_out == PRECISION(kFloat)) {
+    param.output->template mutable_data<float>();
+    lite::arm::math::trans_tensor_dtype<PRECISION(kInt32), PRECISION(kFloat)>(
+        tmp_int32_out_, param.output, i_scale, 1.f, weight_scale);
+  } else {
+    LOG(ERROR) << "unsupported precision type!!";
+  }
+}
+
 }  // namespace arm
 }  // namespace kernels
 }  // namespace lite
@@ -101,3 +196,21 @@ REGISTER_LITE_KERNEL(fc, kARM, kFloat, kNCHW,
     .BindInput("W", {LiteType::GetTensorTy(TARGET(kARM))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM))})
     .Finalize();
+
+REGISTER_LITE_KERNEL(
+    fc, kARM, kInt8, kNCHW,
+    paddle::lite::kernels::arm::FcComputeInt8<PRECISION(kInt8)>, int8out)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindInput("W", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .Finalize();
+
+REGISTER_LITE_KERNEL(
+    fc, kARM, kInt8, kNCHW,
+    paddle::lite::kernels::arm::FcComputeInt8<PRECISION(kFloat)>, fp32out)
+    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindInput("Bias", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt32))})
+    .BindInput("W", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kInt8))})
+    .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kARM), PRECISION(kFloat))})
+    .Finalize();

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

@@ -13,6 +13,8 @@
 // limitations under the License.
 
 #pragma once
+#include <stdint.h>
+#include "paddle/fluid/lite/arm/math/type_trans.h"
 #include "paddle/fluid/lite/core/kernel.h"
 #include "paddle/fluid/lite/operators/fc_op.h"
 
@@ -40,6 +42,27 @@ class FcCompute : public KernelLite<TARGET(kARM), PRECISION(kFloat)> {
   int m_, n_, k_;
 };
 
+template <PrecisionType Ptype_out>
+class FcComputeInt8 : public KernelLite<TARGET(kARM), PRECISION(kInt8)> {
+ public:
+  using param_t = operators::FcParam;
+
+  void PrepareForRun() override;
+
+  void Run() override;
+
+  ~FcComputeInt8() override {
+    if (transed_weight_) {
+      delete transed_weight_;
+    }
+  };
+
+ private:
+  lite::Tensor* transed_weight_{nullptr};
+  Tensor* tmp_int32_out_{nullptr};
+  int m_, n_, k_;
+};
+
 }  // namespace arm
 }  // namespace kernels
 }  // namespace lite

paddle/fluid/lite/operators/calib_op.cc

@@ -37,12 +37,8 @@ bool CalibOpLite::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
   param_.input = const_cast<lite::Tensor *>(&(x_var->Get<lite::Tensor>()));
   param_.output = output_var->GetMutable<lite::Tensor>();
   std::vector<std::string> input_arg_names = opdesc.InputArgumentNames();
-  param_.in_dtype =
-      static_cast<lite::PrecisionType>(opdesc.GetAttr<int>("in_dtype"));
-  param_.out_dtype =
-      static_cast<lite::PrecisionType>(opdesc.GetAttr<int>("out_dtype"));
-  if (opdesc.HasAttr("in_scale")) {
-    param_.in_scale = opdesc.GetAttr<float>("in_scale");
+  if (opdesc.HasAttr("scale")) {
+    param_.scale = opdesc.GetAttr<float>("scale");
   }
   CHECK(param_.input) << "Input(X) of CalibOp should not be null.";
   CHECK(param_.output) << "Output(Out) of CalibOp should not be null.";

paddle/fluid/lite/operators/calib_op_test.cc

@@ -11,7 +11,6 @@
 // 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/calib_op.h"
 #include <gtest/gtest.h>
 #include "paddle/fluid/lite/core/op_registry.h"
@@ -42,9 +41,7 @@ TEST(calib_op_lite, TestARM) {
   desc.SetType("calib");
   desc.SetInput("Input", {"Input"});
   desc.SetOutput("Out", {"output"});
-  desc.SetAttr("in_dtype", static_cast<int>(PRECISION(kInt8)));
-  desc.SetAttr("out_dtype", static_cast<int>(PRECISION(kFloat)));
-  desc.SetAttr("in_scale", 10.0f);
+  desc.SetAttr("scale", 10.0f);
 
   CalibOpLite calib("calib");
 
@@ -60,5 +57,6 @@ TEST(calib_op_lite, TestARM) {
 }  // namespace paddle
 
 #ifdef LITE_WITH_ARM
-USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, def);
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, fp32_to_int8);
+USE_LITE_KERNEL(calib, kARM, kInt8, kNCHW, int8_to_fp32);
 #endif

paddle/fluid/lite/operators/conv_op.h

@@ -76,6 +76,17 @@ class ConvOpLite : public OpLite {
       }
     }
     param_.fuse_relu = op_desc.GetAttr<bool>("fuse_relu");
+    // For Int8
+    if (op_desc.HasAttr("enable_int8")) {
+      param_.enable_int8 = op_desc.GetAttr<bool>("enable_int8");
+      if (op_desc.HasAttr("input_scale"))
+        param_.input_scale = op_desc.GetAttr<float>("input_scale");
+      if (op_desc.HasAttr("weight_scale"))
+        param_.weight_scale =
+            op_desc.GetAttr<std::vector<float>>("weight_scale");
+      if (op_desc.HasAttr("output_scale"))
+        param_.output_scale = op_desc.GetAttr<float>("output_scale");
+    }
     return true;
   }
 

paddle/fluid/lite/operators/fc_op.h

@@ -59,6 +59,17 @@ class FcOpLite : public OpLite {
     param_.output = scope->FindVar(out)->GetMutable<lite::Tensor>();
     param_.in_num_col_dims = op_desc.GetAttr<int>("in_num_col_dims");
 
+    // For Int8
+    if (op_desc.HasAttr("enable_int8")) {
+      param_.enable_int8 = op_desc.GetAttr<bool>("enable_int8");
+      if (op_desc.HasAttr("input_scale"))
+        param_.input_scale = op_desc.GetAttr<float>("input_scale");
+      if (op_desc.HasAttr("weight_scale"))
+        param_.weight_scale =
+            op_desc.GetAttr<std::vector<float>>("weight_scale");
+      if (op_desc.HasAttr("output_scale"))
+        param_.output_scale = op_desc.GetAttr<float>("output_scale");
+    }
     return true;
   }
 

paddle/fluid/lite/operators/op_params.h

@@ -19,11 +19,6 @@
 #include "paddle/fluid/lite/core/framework.pb.h"
 #include "paddle/fluid/lite/utils/all.h"
 
-#define WITH_INT8_CONFIG             \
-  bool enable_int8;                  \
-  float input_scale;                 \
-  std::vector<float> weight_scale{}; \
-  float output_scale;
 /*
  * This file contains all the argument parameter data structure for operators.
  */
@@ -33,6 +28,11 @@ namespace lite {
 namespace operators {
 
 using param_t = Any;
+#define WITH_INT8_CONFIG             \
+  bool enable_int8{false};           \
+  float input_scale{1.0};            \
+  std::vector<float> weight_scale{}; \
+  float output_scale{1.0};
 
 /// ----------------------- Functional operators ------------------------------
 struct FeedParam {
@@ -56,9 +56,7 @@ struct IoCopyParam {
 struct CalibParam {
   const lite::Tensor* input{};
   lite::Tensor* output{};
-  float in_scale;
-  PrecisionType in_dtype;
-  PrecisionType out_dtype;
+  float scale;
 };
 
 /// -------------------------- NN operators ------------------------------------
@@ -71,6 +69,8 @@ struct FcParam {
   lite::DDim in_mat_dims;
   int in_num_col_dims{1};
   bool weight_transposed{false};
+  // for int8
+  WITH_INT8_CONFIG
 };
 
 // For Mul Op
@@ -81,6 +81,8 @@ struct MulParam {
 
   int x_num_col_dims{1};
   int y_num_col_dims{1};
+  // for int8
+  WITH_INT8_CONFIG
 };
 
 struct MulGradParam {
@@ -152,6 +154,7 @@ struct ConvParam {
   float scale_weights{1.0f};      // only used with mkl-dnn int8
   bool force_fp32_output{false};  // only used in mkl-dnn int8
   std::string data_format{"Anylayout"};
+  // for int8
   WITH_INT8_CONFIG
 };