test/add text-classification test (#13081)

paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc

@@ -86,15 +86,24 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
       }
       op_desc.SetInput("Bias", {new_bias_var});
     }
-
 #undef GET_NODE
 
+    // Create temp variables.
+    scope->Var(name_scope + "/BatchedInput.new")
+        ->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/BatchCellPreAct.new")
+        ->GetMutable<framework::LoDTensor>();
+    scope->Var(name_scope + "/BatchedGate.new")
+        ->GetMutable<framework::LoDTensor>();
+
     op_desc.SetInput("H0", {});
     op_desc.SetInput("C0", {});
     op_desc.SetOutput("Hidden", {hidden_n->Name()});
     op_desc.SetOutput("Cell", {cell_n->Name()});
     op_desc.SetOutput("XX", {xx_n->Name()});
-    op_desc.SetOutput("BatchedInput", {"blstm_0.tmp_2"});
+    op_desc.SetOutput("BatchedGate", {name_scope + "/BatchedGate.new"});
+    op_desc.SetOutput("BatchCellPreAct", {name_scope + "/BatchCellPreAct.new"});
+    op_desc.SetOutput("BatchedInput", {name_scope + "/BatchedInput.new"});
     op_desc.SetAttr("is_reverse", lstm_n->Op()->GetAttr("is_reverse"));
     op_desc.SetAttr("use_peepholes", lstm_n->Op()->GetAttr("use_peepholes"));
     // TODO(TJ): get from attr
@@ -130,8 +139,8 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
 
   int fusion_count{0};
 
-  auto fc_no_bias_handler = [&](
-      const GraphPatternDetector::subgraph_t& subgraph, Graph* g) {
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
 #define GET_NODE(name__)                                \
   std::string name__##key = name_scope + "/" + #name__; \
   auto* name__##n = pattern->RetrieveNode(name__##key); \
@@ -152,21 +161,24 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
 
     if (with_fc_bias) {
       GET_NODE(fc_bias);
+      GET_NODE(elementwise_add);
       lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, fc_bias);
+      // Remove unneeded nodes.
+      std::unordered_set<const Node*> marked_nodes(
+          {mul_n, lstm_n, elementwise_add_n});
+      GraphSafeRemoveNodes(graph, marked_nodes);
     } else {
       lstm_creator(lstm, x, w, Weight, Bias, Hidden, Cell, fc_out, -1);
+      // Remove unneeded nodes.
+      std::unordered_set<const Node*> marked_nodes({mul_n, lstm_n});
+      GraphSafeRemoveNodes(graph, marked_nodes);
     }
 #undef GET_NODE
 
-    // Remove unneeded nodes.
-    std::unordered_set<const Node*> marked_nodes({mul_n, lstm_n});
-
-    GraphSafeRemoveNodes(graph, marked_nodes);
-
     ++fusion_count;
   };
 
-  gpd(graph, fc_no_bias_handler);
+  gpd(graph, handler);
 
   return fusion_count;
 }

paddle/fluid/framework/ir/fc_lstm_fuse_pass.h

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#pragma once
+
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -73,7 +73,6 @@ void PDPattern::AddEdge(PDNode* a, PDNode* b) {
 void GraphPatternDetector::operator()(Graph* graph,
                                       GraphPatternDetector::handle_t handler) {
   if (!MarkPDNodesInGraph(*graph)) {
-    LOG(INFO) << "Mark failed";
     return;
   }
 

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -19,6 +19,9 @@
 #endif
 
 #include <numeric>
+#include <string>
+#include <utility>
+#include <vector>
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/inference/analysis/dot.h"

paddle/fluid/inference/analysis/CMakeLists.txt

@@ -58,7 +58,7 @@ endif()
 inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
     ARGS --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
-        --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
+         --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
 
 inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
 inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
@@ -74,7 +74,7 @@ inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)
 set(CHINESE_NER_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner_model.tar.gz")
 set(CHINESE_NER_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/chinese_ner-data.txt.tar.gz")
 set(CHINESE_NER_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/chinese_ner" CACHE PATH "Chinese ner model and data root." FORCE)
-if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING)
+if (NOT EXISTS ${CHINESE_NER_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
   inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_MODEL_URL} "chinese_ner_model.tar.gz")
   inference_download_and_uncompress(${CHINESE_NER_INSTALL_DIR} ${CHINESE_NER_DATA_URL} "chinese_ner-data.txt.tar.gz")
 endif()
@@ -87,7 +87,7 @@ inference_analysis_test(test_analyzer_ner SRCS analyzer_ner_tester.cc
 set(LAC_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/lac_model.tar.gz")
 set(LAC_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/lac_data.txt.tar.gz")
 set(LAC_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/lac" CACHE PATH "LAC model and data root." FORCE)
-if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING)
+if (NOT EXISTS ${LAC_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
     inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_MODEL_URL} "lac_model.tar.gz")
     inference_download_and_uncompress(${LAC_INSTALL_DIR} ${LAC_DATA_URL} "lac_data.txt.tar.gz")
 endif()
@@ -96,3 +96,15 @@ inference_analysis_test(test_analyzer_lac SRCS analyzer_lac_tester.cc
     EXTRA_DEPS paddle_inference_api paddle_fluid_api
     ARGS --infer_model=${LAC_INSTALL_DIR}/model
         --infer_data=${LAC_INSTALL_DIR}/data.txt)
+
+
+set(TEXT_CLASSIFICATION_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/text-classification-Senta.tar.gz")
+set(TEXT_CLASSIFICATION_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo/text_classification" CACHE PATH "Text Classification model and data root." FORCE)
+
+if (NOT EXISTS ${TEXT_CLASSIFICATION_INSTALL_DIR} AND WITH_TESTING AND WITH_INFERENCE)
+  inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
+endif()
+
+inference_analysis_test(test_text_classification SRCS test_text_classification.cc
+    EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
+    ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)

paddle/fluid/inference/analysis/analyzer.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include <string>
+#include <vector>
 #include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
 #include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
 #include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
@@ -41,20 +42,16 @@ class DfgPassManagerImpl final : public DfgPassManager {
  public:
   DfgPassManagerImpl() {
     // TODO(Superjomn) set the key with pass reprs.
-    LOG(INFO)
-        << "-----------------------------------------------------------------";
-    if (FLAGS_IA_enable_ir) {
-      AddPass("fluid-to-ir-pass", new FluidToIrPass);
-    } else {
+    if (!FLAGS_IA_enable_ir) {
       AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
+    } else {
+      AddPass("fluid-to-ir-pass", new FluidToIrPass);
     }
     TryAddTensorRtPass();
     AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
     if (!FLAGS_IA_output_storage_path.empty()) {
       AddPass("model-store-pass", new ModelStorePass);
     }
-    LOG(INFO)
-        << "-----------------------------------------------------------------";
   }
 
   std::string repr() const override { return "dfg-pass-manager"; }
@@ -101,19 +98,16 @@ class DfgPassManagerImpl final : public DfgPassManager {
 Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
 
 void Analyzer::Run(Argument* argument) {
+  std::vector<std::string> passes;
+  for (auto& pass : all_ir_passes_) {
+    if (!disabled_ir_passes_.count(pass)) {
+      passes.push_back(pass);
+      passes.push_back("graph_viz_pass");  // add graphviz for debug.
+    }
+  }
+  passes.push_back("graph_viz_pass");
   // Ugly support fluid-to-ir-pass
-  argument->Set(kFluidToIrPassesAttr,
-                new std::vector<std::string>({
-                    // Manual update the passes here.
-                    "graph_viz_pass",                              //
-                    "infer_clean_graph_pass", "graph_viz_pass",    //
-                    "attention_lstm_fuse_pass", "graph_viz_pass",  //
-                    "fc_lstm_fuse_pass", "graph_viz_pass",         //
-                    "mul_lstm_fuse_pass", "graph_viz_pass",        //
-                    "seq_concat_fc_fuse_pass", "graph_viz_pass",   //
-                    "fc_fuse_pass", "graph_viz_pass"               //
-
-                }));
+  argument->Set(kFluidToIrPassesAttr, new std::vector<std::string>(passes));
 
   for (auto& x : data_) {
     PADDLE_ENFORCE(x->Initialize(argument));
@@ -122,6 +116,11 @@ void Analyzer::Run(Argument* argument) {
   }
 }
 
+Analyzer& Analyzer::DisableIrPasses(const std::vector<std::string>& passes) {
+  disabled_ir_passes_.insert(passes.begin(), passes.end());
+  return *this;
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/analyzer.h

@@ -36,16 +36,10 @@ limitations under the License. */
  */
 
 #include <gflags/gflags.h>
+#include "paddle/fluid/inference/analysis/flags.h"
 #include "paddle/fluid/inference/analysis/pass.h"
 #include "paddle/fluid/inference/analysis/pass_manager.h"
 
-// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
-// flag if not available.
-DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
-DECLARE_string(IA_graphviz_log_root);
-DECLARE_string(IA_output_storage_path);
-DECLARE_bool(IA_enable_ir);
-
 namespace paddle {
 namespace inference {
 namespace analysis {
@@ -57,7 +51,26 @@ class Analyzer : public OrderedRegistry<PassManager> {
 
   void Run(Argument* argument);
 
+  Analyzer& DisableIrPasses(const std::vector<std::string>& passes);
+
   DISABLE_COPY_AND_ASSIGN(Analyzer);
+
+ private:
+  // All avaiable IR passes.
+  // The bigger fuse comes first, so that the small operators prefer to be
+  // merged in a larger fuse op. The small fusion will not break the pattern of
+  // larger fusion.
+  const std::vector<std::string> all_ir_passes_{{
+      // Manual update the passes here.
+      "infer_clean_graph_pass",    //
+      "attention_lstm_fuse_pass",  //
+      "fc_lstm_fuse_pass",         //
+      "mul_lstm_fuse_pass",        //
+      "seq_concat_fc_fuse_pass",   //
+      "fc_fuse_pass",              //
+  }};
+
+  std::unordered_set<std::string> disabled_ir_passes_;
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -271,17 +271,22 @@ void TestDituRNNPrediction(const std::string &model_path,
                            const std::string &data_path, int batch_size,
                            bool use_analysis, bool activate_ir,
                            int num_times = 1) {
-  NativeConfig config;
+  AnalysisConfig config;
   config.prog_file = FLAGS_infer_ditu_rnn_model + "/__model__";
   config.param_file = FLAGS_infer_ditu_rnn_model + "/param";
   config.use_gpu = false;
   config.device = 0;
   config.specify_input_name = true;
+  config.enable_ir_optim = activate_ir;
+  PADDLE_ENFORCE(config.ir_mode ==
+                 AnalysisConfig::IrPassMode::kExclude);  // default
+  config.ir_passes.clear();  // Do not exclude any pass.
 
   auto base_predictor =
       CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
   auto predictor =
-      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kAnalysis>(config);
+      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+          config);
   std::vector<PaddleTensor> input_slots;
   DataRecord data(data_path, batch_size);
   // Prepare inputs.

paddle/fluid/inference/analysis/flags.h

+// Copyright (c) 2018 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 <gflags/gflags.h>
+
+// TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
+// flag if not available.
+DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
+DECLARE_string(IA_graphviz_log_root);
+DECLARE_string(IA_output_storage_path);
+DECLARE_bool(IA_enable_ir);

paddle/fluid/inference/analysis/fluid_to_ir_pass.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/inference/analysis/flags.h"
 #include "paddle/fluid/inference/analysis/ir_pass_manager.h"
 #include "paddle/fluid/inference/analysis/pass.h"
 
@@ -85,9 +86,11 @@ class FluidToIrPass final : public DataFlowGraphPass {
           new Scope *(&argument_->Get<Scope>(ir::kParamScopeAttr)));
     }
 
-    const auto &ir_passes_to_apply =
-        argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
-    ir_passes.Apply(ir_passes_to_apply);
+    if (FLAGS_IA_enable_ir) {
+      const auto &ir_passes_to_apply =
+          argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
+      ir_passes.Apply(ir_passes_to_apply);
+    }
 
     PADDLE_ENFORCE(argument_->main_dfg.get());
     argument_->main_dfg->Build(ir_passes.graph());

paddle/fluid/inference/analysis/test_text_classification.cc

+// Copyright (c) 2018 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 <gflags/gflags.h>
+#include <glog/logging.h>  // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/inference/analysis/analyzer.h"
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/timer.h"
+
+DEFINE_string(infer_model, "", "Directory of the inference model.");
+DEFINE_string(infer_data, "", "Path of the dataset.");
+DEFINE_int32(batch_size, 1, "batch size.");
+DEFINE_int32(repeat, 1, "How many times to repeat run.");
+
+namespace paddle {
+
+template <typename T>
+std::string to_string(const std::vector<T> &vec) {
+  std::stringstream ss;
+  for (const auto &c : vec) {
+    ss << c << " ";
+  }
+  return ss.str();
+}
+
+void PrintTime(const double latency, const int bs, const int repeat) {
+  LOG(INFO) << "===========profile result===========";
+  LOG(INFO) << "batch_size: " << bs << ", repeat: " << repeat
+            << ", avg latency: " << latency / repeat << "ms";
+  LOG(INFO) << "=====================================";
+}
+
+void Main(int batch_size) {
+  // Three sequence inputs.
+  std::vector<PaddleTensor> input_slots(1);
+  // one batch starts
+  // data --
+  int64_t data0[] = {0, 1, 2};
+  for (auto &input : input_slots) {
+    input.data.Reset(data0, sizeof(data0));
+    input.shape = std::vector<int>({3, 1});
+    // dtype --
+    input.dtype = PaddleDType::INT64;
+    // LoD --
+    input.lod = std::vector<std::vector<size_t>>({{0, 3}});
+  }
+
+  // shape --
+  // Create Predictor --
+  AnalysisConfig config;
+  config.model_dir = FLAGS_infer_model;
+  config.use_gpu = false;
+  config.enable_ir_optim = true;
+  config.ir_passes.push_back("fc_lstm_fuse_pass");
+  auto predictor =
+      CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
+          config);
+
+  inference::Timer timer;
+  double sum = 0;
+  std::vector<PaddleTensor> output_slots;
+  for (int i = 0; i < FLAGS_repeat; i++) {
+    timer.tic();
+    CHECK(predictor->Run(input_slots, &output_slots));
+    sum += timer.toc();
+  }
+  PrintTime(sum, batch_size, FLAGS_repeat);
+
+  // Get output
+  LOG(INFO) << "get outputs " << output_slots.size();
+
+  for (auto &output : output_slots) {
+    LOG(INFO) << "output.shape: " << to_string(output.shape);
+    // no lod ?
+    CHECK_EQ(output.lod.size(), 0UL);
+    LOG(INFO) << "output.dtype: " << output.dtype;
+    std::stringstream ss;
+    for (int i = 0; i < 5; i++) {
+      ss << static_cast<float *>(output.data.data())[i] << " ";
+    }
+    LOG(INFO) << "output.data summary: " << ss.str();
+    // one batch ends
+  }
+}
+
+TEST(text_classification, basic) { Main(FLAGS_batch_size); }
+
+}  // namespace paddle
+
+USE_PASS(fc_fuse_pass);
+USE_PASS(seq_concat_fc_fuse_pass);
+USE_PASS(fc_lstm_fuse_pass);
+USE_PASS(graph_viz_pass);
+USE_PASS(infer_clean_graph_pass);
+USE_PASS(attention_lstm_fuse_pass);

paddle/fluid/inference/api/CMakeLists.txt

@@ -44,7 +44,19 @@ function(inference_api_test TARGET_NAME)
 endfunction(inference_api_test)
 
 cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
-cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api)
+cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api
+          analysis
+          ir_pass_manager
+          pass
+          fc_fuse_pass
+          fc_lstm_fuse_pass
+          seq_concat_fc_fuse_pass
+          graph_viz_pass
+          infer_clean_graph_pass
+          graph_pattern_detector
+          infer_clean_graph_pass
+          attention_lstm_fuse_pass
+  )
 
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc

paddle/fluid/inference/api/analysis_predictor.cc

@@ -14,6 +14,8 @@
 
 #include "paddle/fluid/inference/api/analysis_predictor.h"
 #include <memory>
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/framework/scope.h"
@@ -28,6 +30,8 @@ bool AnalysisPredictor::Init(
   VLOG(3) << "Predictor::init()";
   if (config_.use_gpu) {
     place_ = paddle::platform::CUDAPlace(config_.device);
+    LOG(WARNING) << "ir optimize only supports CPU currently";
+    config_.enable_ir_optim = false;
   } else {
     place_ = paddle::platform::CPUPlace();
   }
@@ -73,7 +77,7 @@ bool AnalysisPredictor::Init(
 
 void AnalysisPredictor::OptimizeInferenceProgram() {
   LOG(INFO) << "optimize begin";
-  FLAGS_IA_enable_ir = true;
+  FLAGS_IA_enable_ir = config_.enable_ir_optim;
   FLAGS_IA_enable_tensorrt_subgraph_engine = false;
   FLAGS_IA_output_storage_path = "";  // Don't output the model.
   // Analyze inference_program
@@ -90,24 +94,26 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
   }
   argument_.origin_program_desc.reset(
       new ProgramDesc(*inference_program_->Proto()));
-  Analyzer().Run(&argument_);
+  PADDLE_ENFORCE(config_.ir_mode == AnalysisConfig::IrPassMode::kExclude,
+                 "Only kExclude is supported yet.");
+  Analyzer().DisableIrPasses(config_.ir_passes).Run(&argument_);
+
   CHECK(argument_.transformed_program_desc);
   VLOG(5) << "to prepare executor";
-  // LOG(INFO) << "transformed_parogram_desc " <<
-  // argument.transformed_program_desc->DebugString();
   inference_program_.reset(
       new framework::ProgramDesc(*argument_.transformed_program_desc));
-  PADDLE_ENFORCE(argument_.Has(framework::ir::kParamScopeAttr));
-  // Update scope.
-  scope_.reset(
-      argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
-  LOG(INFO) << "optimize end ==";
+  if (argument_.Has(framework::ir::kParamScopeAttr)) {
+    // Update scope.
+    scope_.reset(
+        argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
+  }
+  LOG(INFO) << "== optimize end ==";
 }
 
 template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
-    NativeConfig, PaddleEngineKind::kAnalysis>(const NativeConfig& config) {
-  VLOG(3) << "create NativePredictor";
+    AnalysisConfig, PaddleEngineKind::kAnalysis>(const AnalysisConfig& config) {
+  VLOG(3) << "create AnalysisConfig";
   if (config.use_gpu) {
     // 1. GPU memeroy
     PADDLE_ENFORCE_GT(

paddle/fluid/inference/api/analysis_predictor.h

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <string>
+#include <vector>
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/api/api_impl.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
@@ -28,7 +30,7 @@ using framework::proto::ProgramDesc;
  */
 class AnalysisPredictor : public NativePaddlePredictor {
  public:
-  explicit AnalysisPredictor(const NativeConfig& config)
+  explicit AnalysisPredictor(const AnalysisConfig& config)
       : NativePaddlePredictor(config), config_(config) {}
 
   bool Init(const std::shared_ptr<framework::Scope>& parent_scope);
@@ -44,7 +46,7 @@ class AnalysisPredictor : public NativePaddlePredictor {
   Argument& analysis_argument() { return argument_; }
 
  private:
-  NativeConfig config_;
+  AnalysisConfig config_;
   Argument argument_;
 };
 

paddle/fluid/inference/api/api_impl.cc

@@ -176,7 +176,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
                                     framework::Scope *scope) {
   VLOG(3) << "Predictor::set_feed";
   if (inputs.size() != feeds_.size()) {
-    LOG(ERROR) << "wrong feed input size.";
+    LOG(ERROR) << "wrong feed input size, need " << feeds_.size() << " but get "
+               << inputs.size();
     return false;
   }
   for (size_t i = 0; i < inputs.size(); ++i) {

paddle/fluid/inference/api/paddle_inference_api.h

@@ -150,6 +150,21 @@ struct TensorRTConfig : public NativeConfig {
   int workspace_size{1 << 30};
 };
 
+// NOTE WIP, not stable yet.
+struct AnalysisConfig : public NativeConfig {
+  //
+  enum class IrPassMode {
+    kSystem,   // Use system default passes, not customize.
+    kInclude,  // Specify the passes in `ir_passes`.
+    kExclude   // Specify the disabled passes in `ir_passes`.
+  };
+
+  bool enable_ir_optim = true;
+  IrPassMode ir_mode{IrPassMode::kExclude};
+  // attention lstm fuse works only on some specific models, disable as default.
+  std::vector<std::string> ir_passes{"attention_lstm_fuse_pass"};
+};
+
 // A factory to help create different predictors.
 //
 // FOR EXTENSION DEVELOPER:

paddle/fluid/operators/lookup_table_op.h

@@ -57,7 +57,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
           memset(output + i * row_width, 0, row_width * sizeof(T));
         } else {
           PADDLE_ENFORCE_LT(ids[i], row_number);
-          PADDLE_ENFORCE_GE(ids[i], 0);
+          PADDLE_ENFORCE_GE(ids[i], 0, "ids %d", i);
           memcpy(output + i * row_width, table + ids[i] * row_width,
                  row_width * sizeof(T));
         }