Add fusion_lstm INT8 PTQ (#35334)

* Add fusion_lstm INT8 PTQ

* Correct mkldnn_cache_capacity and enable fc_lstm_fuse_pass only for this test

* Change mkldnn_cache_capacity

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -2249,9 +2249,10 @@ PDNode *patterns::MultipleQuantize::operator()() {
 PDNode *patterns::QuantizePlacement::operator()(
     const std::unordered_set<std::string> &quantize_enabled_op_types) {
   std::unordered_set<std::string> supported_op_types =
-      std::unordered_set<std::string>(
-          {"concat", "conv2d", "elementwise_add", "fc", "matmul", "pool2d",
-           "prior_box", "reshape2", "transpose2", "fusion_gru", "multi_gru"});
+      std::unordered_set<std::string>({"concat", "conv2d", "elementwise_add",
+                                       "fc", "matmul", "pool2d", "prior_box",
+                                       "reshape2", "transpose2", "fusion_gru",
+                                       "fusion_lstm", "multi_gru"});
   if (!quantize_enabled_op_types.empty()) {
     supported_op_types = quantize_enabled_op_types;
   }
@@ -2723,6 +2724,26 @@ PDNode *patterns::FusionGru::operator()() {
   return out;
 }
 
+PDNode *patterns::FusionLSTM::operator()() {
+  auto op = pattern->NewNode(op_repr())->assert_is_op("fusion_lstm");
+  auto x = pattern->NewNode(x_repr())->AsInput()->assert_is_op_input(
+      "fusion_lstm", "X");
+  auto weight_h = pattern->NewNode(weight_h_repr())
+                      ->AsInput()
+                      ->assert_is_op_input("fusion_lstm", "WeightH");
+  auto weight_x = pattern->NewNode(weight_x_repr())
+                      ->AsInput()
+                      ->assert_is_op_input("fusion_lstm", "WeightX");
+  auto hidden = pattern->NewNode(hidden_repr())
+                    ->AsOutput()
+                    ->assert_is_op_output("fusion_lstm", "Hidden");
+  auto cell = pattern->NewNode(cell_repr())
+                  ->AsOutput()
+                  ->assert_is_op_output("fusion_lstm", "Cell");
+  op->LinksFrom({x, weight_h, weight_x}).LinksTo({hidden, cell});
+  return hidden;
+}
+
 PDNode *patterns::TwoFusionGruConcat::operator()() {
   auto x = pattern->NewNode(x_repr())->AsInput()->assert_is_op_input(
       "fusion_gru", "X");

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1562,6 +1562,28 @@ struct FusionGru : public PatternBase {
   PATTERN_DECL_NODE(out);
 };
 
+// fusion_lstm op
+// Forward pass for fusion_lstm.
+// fusion_lstm out is a result of the operator.
+struct FusionLSTM : public PatternBase {
+  FusionLSTM(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "fusion_lstm") {}
+  // TODO(lidanqing): Is it enough to detect fusion_lstm with these things
+  PDNode* operator()();
+
+  // declare op
+  PATTERN_DECL_NODE(op);
+
+  // declate inputs
+  PATTERN_DECL_NODE(x);
+  PATTERN_DECL_NODE(weight_h);
+  PATTERN_DECL_NODE(weight_x);
+
+  // decalre outputs
+  PATTERN_DECL_NODE(hidden);
+  PATTERN_DECL_NODE(cell);
+};
+
 // two concatenated fusion_gru ops
 // Forward pass for fusion of two concatenated fusion_gru ops.
 // concat_out is a result of the operator().

paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc

@@ -944,6 +944,64 @@ void CPUQuantizePass::QuantizeMultiGru(Graph* graph) const {
   PrettyLogDetail("---    quantized %d multi_gru ops", quantize_count);
 }
 
+void CPUQuantizePass::QuantizeFusionLSTM(Graph* graph) const {
+  GraphPatternDetector gpd;
+  patterns::FusionLSTM pattern{gpd.mutable_pattern(), name_scope_};
+  pattern();
+
+  int quantize_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "Quantize fusion_lstm op";
+    GET_IR_NODE_FROM_SUBGRAPH(op, op, pattern);
+
+    // skip if should not be quantized
+    if (!platform::HasOpINT8DataType(op->Op())) {
+      LogQuantizationDisabled(op);
+      return;
+    }
+
+    GET_IR_NODE_FROM_SUBGRAPH(x, x, pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(weight_h, weight_h, pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(weight_x, weight_x, pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(hidden, hidden, pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(cell, cell, pattern);
+
+    // Starting from here there maybe issues
+    if (!AreScalesPresentForNodes({x, weight_x})) {
+      LogCannotQuantizeOp(op);
+      return;
+    }
+
+    bool is_x_unsigned{false};
+    auto input_x_scale = GetScaleValueForNode(x, &is_x_unsigned);
+
+    double input_x_shift{128.};
+    if (is_x_unsigned) input_x_shift = 0.;
+
+    QuantizeInput(g, op, x, "X", input_x_scale, is_x_unsigned, "Scale_data",
+                  input_x_shift, "Shift_data");
+
+    auto weight_scale_tensor = GetScaleTensorForNode(weight_x);
+    EigenVectorArrayMap eigen_tensor{weight_scale_tensor.data<double>(),
+                                     weight_scale_tensor.numel()};
+    eigen_tensor *= static_cast<double>(S8_MAX);
+    std::vector<float> scale_weights{
+        weight_scale_tensor.data<double>(),
+        weight_scale_tensor.data<double>() + weight_scale_tensor.numel()};
+
+    op->Op()->SetAttr("Scale_weights", scale_weights);
+    // return fp32 data
+    op->Op()->SetAttr("force_fp32_output", true);
+
+    ++quantize_count;
+  };
+  gpd(graph, handler);
+  AddStatis(quantize_count);
+
+  PrettyLogDetail("---    quantized %d fusion_lstm ops", quantize_count);
+}
+
 void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   VLOG(3) << "Quantizing the graph.";
   PADDLE_ENFORCE_NOT_NULL(
@@ -965,6 +1023,7 @@ void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   QuantizeElementwiseAdd(graph);
   QuantizeFusionGru(graph);
   QuantizeMultiGru(graph);
+  QuantizeFusionLSTM(graph);
 }
 
 }  // namespace ir

paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h

@@ -60,6 +60,7 @@ class CPUQuantizePass : public FusePassBase {
   void QuantizeElementwiseAdd(Graph* graph) const;
   void QuantizeFusionGru(Graph* graph) const;
   void QuantizeMultiGru(Graph* graph) const;
+  void QuantizeFusionLSTM(Graph* graph) const;
 
   void QuantizeInput(Graph* g, Node* op, Node* input, std::string input_name,
                      double scale_to_one, bool is_input_unsigned,

paddle/fluid/inference/api/mkldnn_quantizer.cc

@@ -61,8 +61,8 @@ static void check_tensor(const LoDTensor& tensor) {
                                                  "Tensor dimension is empty."));
 }
 
-void AnalysisPredictor::MkldnnQuantizer::CalculateScalesForGRUWeights(
-    const paddle::framework::OpDesc* op) {
+void AnalysisPredictor::MkldnnQuantizer::CalculateScalesForRNNWeights(
+    const paddle::framework::OpDesc* op, bool gru) {
   const auto& wx_names = op->Input("WeightX");
   const auto& wh_names = op->Input("WeightH");
   for (size_t i = 0; i < wx_names.size(); ++i) {
@@ -74,14 +74,20 @@ void AnalysisPredictor::MkldnnQuantizer::CalculateScalesForGRUWeights(
     check_var(wh_var, wh_name);
     LoDTensor* wx_tensor = wx_var->GetMutable<LoDTensor>();
     LoDTensor* wh_tensor = wh_var->GetMutable<LoDTensor>();
+    if (gru) {
       scales_[wx_name] = GetMaxChGRUScalingFactor(*wx_tensor, *wh_tensor);
+    } else {
+      scales_[wx_name] = GetMaxChLSTMScalingFactor(*wx_tensor, *wh_tensor);
+    }
   }
 }
 
 void AnalysisPredictor::MkldnnQuantizer::CalculateScalesForOpInputs(
     const paddle::framework::OpDesc* op) {
   if (op->Type() == "fusion_gru" || op->Type() == "multi_gru") {
-    CalculateScalesForGRUWeights(op);
+    CalculateScalesForRNNWeights(op, true);
+  } else if (op->Type() == "fusion_lstm") {
+    CalculateScalesForRNNWeights(op, false);
   }
   for (auto const& input : op->Inputs()) {
     for (const auto& var_name : input.second) {
@@ -464,6 +470,41 @@ AnalysisPredictor::MkldnnQuantizer::GetMaxChGRUScalingFactor(
   return std::make_pair(is_unsigned, scale_tensor);
 }
 
+std::pair<bool, LoDTensor>
+AnalysisPredictor::MkldnnQuantizer::GetMaxChLSTMScalingFactor(
+    const LoDTensor& wx_tensor, const LoDTensor& wh_tensor) const {
+  check_tensor(wx_tensor);
+  check_tensor(wh_tensor);
+
+  std::vector<float> scale(wx_tensor.dims()[1]);
+
+  for (int row_id = 0; row_id < wx_tensor.dims()[0]; row_id++) {
+    for (int col_id = 0; col_id < wx_tensor.dims()[1]; col_id++) {
+      int idx = (row_id * wx_tensor.dims()[1]) + col_id;
+      auto abs_value = std::abs(wx_tensor.data<float>()[idx]);
+      if (row_id == 0) {
+        scale[col_id] = abs_value;
+      } else {
+        if (abs_value > scale[col_id]) scale[col_id] = abs_value;
+      }
+    }
+  }
+  for (int row_id = 0; row_id < wh_tensor.dims()[0]; row_id++) {
+    for (int col_id = 0; col_id < wh_tensor.dims()[1]; col_id++) {
+      int idx = (row_id * wh_tensor.dims()[1]) + col_id;
+      auto abs_value = std::abs(wh_tensor.data<float>()[idx]);
+      if (abs_value > scale[col_id]) scale[col_id] = abs_value;
+    }
+  }
+  transform(scale.begin(), scale.end(), scale.begin(),
+            [](float& c) { return 1 / c; });
+  LoDTensor scale_tensor = CreateScaleTensor(scale.size());
+  auto* scale_ptr = scale_tensor.mutable_data<double>(CPUPlace());
+  std::copy(scale.begin(), scale.end(), scale_ptr);
+  bool is_unsigned = false;
+  return std::make_pair(is_unsigned, scale_tensor);
+}
+
 std::pair<std::vector<int>, float>
 AnalysisPredictor::MkldnnQuantizer::Histogram(
     const framework::LoDTensor& var_tensor, float min_val, float max_val,

paddle/fluid/inference/api/mkldnn_quantizer.h

@@ -69,7 +69,8 @@ class AnalysisPredictor::MkldnnQuantizer {
                             bool is_unsigned);
   void CalculateSingleGRUWeightsScale(const std::string& var_name,
                                       const framework::LoDTensor& var_tensor);
-  void CalculateScalesForGRUWeights(const paddle::framework::OpDesc* op);
+  void CalculateScalesForRNNWeights(const paddle::framework::OpDesc* op,
+                                    bool gru);
   void CalculateScalesForOpOutputs(const paddle::framework::OpDesc* op);
   void CalculateScalesForOpInputs(const paddle::framework::OpDesc* op);
   void PrepareArgument() const;
@@ -91,6 +92,10 @@ class AnalysisPredictor::MkldnnQuantizer {
       const framework::LoDTensor& wx_tensor,
       const framework::LoDTensor& wh_tensor) const;
 
+  std::pair<bool, framework::LoDTensor> GetMaxChLSTMScalingFactor(
+      const framework::LoDTensor& wx_tensor,
+      const framework::LoDTensor& wh_tensor) const;
+
   std::pair<bool, framework::LoDTensor> GetMaxScalingFactor(
       const framework::LoDTensor& var_tensor, bool is_unsigned) const;
 

paddle/fluid/inference/api/mkldnn_quantizer_config.cc

@@ -85,6 +85,25 @@ MkldnnQuantizerConfig::MkldnnQuantizerConfig() {
   rules_["multi_gru"]["WeightH"] = ScaleAlgo::NONE;  // separately
   rules_["multi_gru"]["Scale_weights"] = ScaleAlgo::NONE;
   rules_["multi_gru"]["Hidden"] = ScaleAlgo::KL;
+
+  rules_["fusion_lstm"]["X"] = ScaleAlgo::KL;
+  rules_["fusion_lstm"]["H0"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["C0"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["Bias"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["WeightX"] =
+      ScaleAlgo::NONE;  // Weights will be handled separately
+  rules_["fusion_lstm"]["WeightH"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["XX"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["Cell"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["BatchedInput"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["BatchedHidden"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["BatchedCell"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["BatchedGate"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["BatchedCellPreAct"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["ReorderedH0"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["ReorderedC0"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["CheckedCell"] = ScaleAlgo::NONE;
+  rules_["fusion_lstm"]["Hidden"] = ScaleAlgo::KL;
 }
 
 ScaleAlgo MkldnnQuantizerConfig::scale_algo(

paddle/fluid/inference/api/mkldnn_quantizer_tester.cc

@@ -62,6 +62,12 @@ class MkldnnQuantizerTest : public testing::Test {
     return mkldnn_quantizer->GetMaxChGRUScalingFactor(wx_tensor, wh_tensor);
   }
 
+  std::pair<bool, framework::LoDTensor> GetMaxChLSTMScalingFactor(
+      const framework::LoDTensor& wx_tensor,
+      const framework::LoDTensor& wh_tensor) const {
+    return mkldnn_quantizer->GetMaxChLSTMScalingFactor(wx_tensor, wh_tensor);
+  }
+
  protected:
   std::unique_ptr<PaddlePredictor> predictor;
   std::unique_ptr<AnalysisPredictor::MkldnnQuantizer> mkldnn_quantizer;
@@ -297,4 +303,33 @@ TEST_F(MkldnnQuantizerTest, max_ch_gru_scaling_factor) {
     ASSERT_NEAR(lod_tensor.data<double>()[i], scales[i], abs_error);
   }
 }
+
+TEST_F(MkldnnQuantizerTest, max_ch_lstm_scaling_factor) {
+  framework::LoDTensor wx_tensor, wh_tensor, lod_tensor;
+
+  wx_tensor.Resize(framework::make_dim(wx.size(), wx[0].size()));
+  for (size_t i = 0; i < wx.size(); i++)
+    std::copy(
+        begin(wx[i]), end(wx[i]),
+        wx_tensor.mutable_data<float>(platform::CPUPlace()) + i * wx[0].size());
+
+  wh_tensor.Resize(framework::make_dim(wh.size(), wh[0].size()));
+  for (size_t i = 0; i < wh.size(); i++)
+    std::copy(
+        begin(wh[i]), end(wh[i]),
+        wh_tensor.mutable_data<float>(platform::CPUPlace()) + i * wh[0].size());
+
+  bool is_unsigned;
+  std::tie(is_unsigned, lod_tensor) =
+      GetMaxChLSTMScalingFactor(wx_tensor, wh_tensor);
+
+  std::vector<double> scales = {2.35381475, 1.10797026, 1.00151656,
+                                1.19001095, 1.09045166, 1.01785819};
+  ASSERT_EQ(is_unsigned, false);
+  ASSERT_EQ(lod_tensor.numel(), static_cast<int64_t>(scales.size()));
+  for (int64_t i = 0; i < lod_tensor.numel(); i++) {
+    ASSERT_NEAR(lod_tensor.data<double>()[i], scales[i], abs_error);
+  }
+}
+
 }  // namespace paddle

paddle/fluid/operators/fused/mkldnn/fusion_lstm_mkldnn_op.cc

@@ -78,12 +78,12 @@ class LSTMMKLDNNHandler
       auto bias_md = MKLDNNMemDesc({L, D, G, OC}, MKLDNNGetDataType<float>(),
                                    MKLDNNMemoryFormat::ldgo);
       auto hidden_md = MKLDNNMemDesc({Ti, N, OC}, MKLDNNGetDataType<T_out>(),
-                                     MKLDNNMemoryFormat::tnc);
+                                     MKLDNNMemoryFormat::any);
 
       auto h0_md = MKLDNNMemDesc({L, D, N, OC}, MKLDNNGetDataType<T>(),
-                                 MKLDNNMemoryFormat::ldnc);
+                                 MKLDNNMemoryFormat::any);
       auto c0_md = MKLDNNMemDesc({L, D, N, OC}, MKLDNNGetDataType<float>(),
-                                 MKLDNNMemoryFormat::ldnc);
+                                 MKLDNNMemoryFormat::any);
 
       // Create LSTM oneDNN primitive
       const auto direction =

python/paddle/fluid/contrib/slim/tests/CMakeLists.txt

@@ -43,6 +43,12 @@ function(download_quant_fp32_model install_dir data_file check_sum)
     endif()
 endfunction()
 
+function(download_lstm_model install_dir data_file check_sum)
+    if (NOT EXISTS ${install_dir}/${data_file})
+	    inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/lstm ${data_file} ${check_sum})
+    endif()
+endfunction()
+
 function(inference_quant_int8_image_classification_test target quant_model_dir dataset_path)
     py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant_int8_image_classification_comparison.py"
             ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
@@ -86,6 +92,20 @@ function(inference_quant2_int8_nlp_test target quant_model_dir fp32_model_dir da
 		 --ops_to_quantize ${ops_to_quantize})
 endfunction()
 
+function(inference_quant2_int8_lstm_model_test target fp32_model dataset_path)
+    py_test(${target} SRCS "${CMAKE_CURRENT_SOURCE_DIR}/quant2_int8_lstm_model.py"
+            ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
+                 OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
+                 FLAGS_use_mkldnn=true
+            ARGS --fp32_model ${fp32_model}
+                 --infer_data ${dataset_path}
+                 --num_threads 4
+                 --mkldnn_cache_capacity 100
+                 --warmup_iter 100
+                 --warmup_batch_size 1
+                 --acc_diff_threshold 0.11)
+endfunction()
+
 function(download_quant_data install_dir data_file check_sum)
     if (NOT EXISTS ${install_dir}/${data_file})
            inference_download_and_uncompress(${install_dir} ${INFERENCE_URL}/int8 ${data_file} ${check_sum})
@@ -260,6 +280,16 @@ if(LINUX AND WITH_MKLDNN)
 	set(QUANT2_INT8_ERNIE_DOT_SAVE_PATH "${QUANT_INSTALL_DIR}/Ernie_quant2_int8_dot_file")
 	convert_model2dot_test(convert_model2dot_ernie ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${QUANT2_INT8_ERNIE_DOT_SAVE_PATH} "Ernie_quant2_int8")
 
+	### PTQ INT8
+
+	# PTQ int8 lstm model
+	set(LSTM_DATA_ARCHIVE "unittest_model_data/quant_lstm_input_data.tar.gz")
+	set(QUANT2_INT8_LSTM_SAVE_PATH "${QUANT_INSTALL_DIR}/lstm_quant2")
+	download_quant_data(${QUANT2_INT8_LSTM_SAVE_PATH} ${LSTM_DATA_ARCHIVE} add84c754e9b792fea1fbd728d134ab7)
+	set(QUANT2_FP32_LSTM_MODEL_ARCHIVE "lstm_fp32_model.tar.gz")
+	download_lstm_model(${QUANT2_INT8_LSTM_SAVE_PATH} ${QUANT2_FP32_LSTM_MODEL_ARCHIVE} eecd9f44d69a84acc1cf2235c4b8b743)
+	inference_quant2_int8_lstm_model_test(test_quant2_int8_lstm_mkldnn ${QUANT2_INT8_LSTM_SAVE_PATH}/lstm_fp32_model ${QUANT2_INT8_LSTM_SAVE_PATH}/quant_lstm_input_data)
+
 endif()
 
 # Since the tests for Quant & INT8 comparison support only testing on Linux 
@@ -323,4 +353,5 @@ if(LINUX AND WITH_MKLDNN)
     set_tests_properties(test_quant2_int8_ernie_mkldnn PROPERTIES TIMEOUT 120)
     set_tests_properties(test_quant_int8_googlenet_mkldnn PROPERTIES TIMEOUT 120)
     set_tests_properties(test_quant2_int8_resnet50_mkldnn PROPERTIES TIMEOUT 120)
+	set_tests_properties(test_quant2_int8_lstm_mkldnn PROPERTIES TIMEOUT 120)
 endif()

python/paddle/fluid/contrib/slim/tests/quant2_int8_lstm_model.py

+# Copyright (c) 2021 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.
+
+import argparse
+import numpy as np
+import struct
+import sys
+import time
+import unittest
+from paddle import fluid
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--fp32_model', type=str, default='', help='A path to a FP32 model.')
+    parser.add_argument('--infer_data', type=str, default='', help='Data file.')
+    parser.add_argument(
+        '--num_threads', type=int, default=1, help='Number of threads.')
+    parser.add_argument(
+        '--warmup_iter',
+        type=int,
+        default=1,
+        help='Number of the first iterations to skip in performance statistics.')
+    parser.add_argument(
+        '--warmup_batch_size',
+        type=int,
+        default=1,
+        help='Number of batches to use in PTQ warmup. Default: 1.')
+    parser.add_argument(
+        '--acc_diff_threshold',
+        type=float,
+        default=0.01,
+        help='Accepted accuracy difference threshold.')
+    parser.add_argument(
+        '--mkldnn_cache_capacity',
+        type=int,
+        default=0,
+        help='Mkldnn cache capacity. The default value in Python API is 15, which can slow down int8 models. Default 0 means unlimited cache.'
+    )
+
+    test_args, args = parser.parse_known_args(namespace=unittest)
+    return test_args, sys.argv[:1] + args
+
+
+class TestLstmModelPTQ(unittest.TestCase):
+    def get_warmup_tensor(self, data_path, place, warmup_batch_size):
+        data = []
+        with open(data_path, 'rb') as in_f:
+            while True:
+                plen = in_f.read(4)
+                if plen is None or len(plen) != 4:
+                    break
+
+                alllen = struct.unpack('i', plen)[0]
+                label_len = alllen & 0xFFFF
+                seq_len = (alllen >> 16) & 0xFFFF
+
+                label = in_f.read(4 * label_len)
+                label = np.frombuffer(
+                    label, dtype=np.int32).reshape([len(label) // 4])
+                feat = in_f.read(4 * seq_len * 8)
+                feat = np.frombuffer(
+                    feat, dtype=np.float32).reshape([len(feat) // 4 // 8, 8])
+                lod_feat = [feat.shape[0]]
+                minputs = fluid.create_lod_tensor(feat, [lod_feat], place)
+
+                infer_data = fluid.core.PaddleTensor()
+                infer_data.lod = minputs.lod()
+                infer_data.data = fluid.core.PaddleBuf(np.array(minputs))
+                infer_data.shape = minputs.shape()
+                infer_data.dtype = fluid.core.PaddleDType.FLOAT32
+                infer_label = fluid.core.PaddleTensor()
+                infer_label.data = fluid.core.PaddleBuf(np.array(label))
+                infer_label.shape = label.shape
+                infer_label.dtype = fluid.core.PaddleDType.INT32
+                data.append([infer_data, infer_label])
+        warmup_data = data[:warmup_batch_size]
+        inputs = data[warmup_batch_size:]
+        return warmup_data, inputs
+
+    def set_config(self,
+                   model_path,
+                   num_threads,
+                   mkldnn_cache_capacity,
+                   warmup_batch_size,
+                   warmup_data=None,
+                   enable_int8=False):
+        config = AnalysisConfig(model_path)
+        config.disable_gpu()
+        config.switch_use_feed_fetch_ops(True)
+        config.switch_ir_optim(True)
+        config.set_cpu_math_library_num_threads(num_threads)
+        # This pass to work properly, must be added before fc_fuse_pass
+        config.pass_builder().insert_pass(5, "fc_lstm_fuse_pass")
+        config.enable_mkldnn()
+        config.set_mkldnn_cache_capacity(mkldnn_cache_capacity)
+        if enable_int8:
+            config.enable_quantizer()
+            config.quantizer_config().set_quant_data(warmup_data)
+            config.quantizer_config().set_quant_batch_size(warmup_batch_size)
+        return config
+
+    def run_program(self,
+                    model_path,
+                    data_path,
+                    num_threads,
+                    mkldnn_cache_capacity,
+                    warmup_iter,
+                    warmup_batch_size,
+                    enable_ptq_int8=False):
+        place = fluid.CPUPlace()
+        warmup_data, inputs = self.get_warmup_tensor(data_path, place,
+                                                     warmup_batch_size)
+        warmup_data = [item[0] for item in warmup_data]
+        config = self.set_config(model_path, num_threads, mkldnn_cache_capacity,
+                                 warmup_batch_size, warmup_data,
+                                 enable_ptq_int8)
+
+        predictor = create_paddle_predictor(config)
+        data = [item[0] for item in inputs]
+        label = np.array([item[1] for item in inputs])
+
+        all_hz_num = 0
+        ok_hz_num = 0
+        all_ctc_num = 0
+        ok_ctc_num = 0
+
+        dataset_size = len(data)
+        start = time.time()
+        for i in range(dataset_size):
+            if i == warmup_iter:
+                start = time.time()
+            hz_out, ctc_out = predictor.run([data[i]])
+            np_hz_out = np.array(hz_out.data.float_data()).reshape(-1)
+            np_ctc_out = np.array(ctc_out.data.int64_data()).reshape(-1)
+
+            out_hz_label = np.argmax(np_hz_out)
+
+            this_label = label[i]
+            this_label_data = np.array(this_label.data.int32_data()).reshape(-1)
+            if this_label.shape[0] == 1:
+                all_hz_num += 1
+                best = this_label_data[0]
+                if out_hz_label == best:
+                    ok_hz_num += 1
+
+                if this_label_data[0] <= 6350:
+                    all_ctc_num += 1
+                    if np_ctc_out.shape[0] == 1 and np_ctc_out.all(
+                    ) == this_label_data.all():
+                        ok_ctc_num += 1
+            else:
+                all_ctc_num += 1
+                if np_ctc_out.shape[0] == this_label.shape[
+                        0] and np_ctc_out.all() == this_label_data.all():
+                    ok_ctc_num += 1
+
+            if all_ctc_num > 1000 or all_hz_num > 1000:
+                break
+
+        end = time.time()
+        fps = (dataset_size - warmup_iter) / (end - start)
+        hx_acc = ok_hz_num / all_hz_num
+        ctc_acc = ok_ctc_num / all_ctc_num
+        return hx_acc, ctc_acc, fps
+
+    def test_lstm_model(self):
+        if not fluid.core.is_compiled_with_mkldnn():
+            return
+
+        fp32_model = test_case_args.fp32_model
+        assert fp32_model, 'The FP32 model path cannot be empty. Please, use the --fp32_model option.'
+        infer_data = test_case_args.infer_data
+        assert infer_data, 'The dataset path cannot be empty. Please, use the --infer_data option.'
+        num_threads = test_case_args.num_threads
+        mkldnn_cache_capacity = test_case_args.mkldnn_cache_capacity
+        warmup_iter = test_case_args.warmup_iter
+        warmup_batch_size = test_case_args.warmup_batch_size
+        acc_diff_threshold = test_case_args.acc_diff_threshold
+
+        (fp32_hx_acc, fp32_ctc_acc, fp32_fps) = self.run_program(
+            fp32_model, infer_data, num_threads, mkldnn_cache_capacity,
+            warmup_iter, warmup_batch_size, False)
+
+        (int8_hx_acc, int8_ctc_acc, int8_fps) = self.run_program(
+            fp32_model, infer_data, num_threads, mkldnn_cache_capacity,
+            warmup_iter, warmup_batch_size, True)
+
+        print("FP32: fps {0}, hx_acc {1}, ctc_acc {2}.".format(
+            fp32_fps, fp32_hx_acc, fp32_ctc_acc))
+
+        print("PTQ INT8: fps {0}, hx_acc {1}, ctc_acc {2}.".format(
+            int8_fps, int8_hx_acc, int8_ctc_acc))
+
+        sys.stdout.flush()
+
+        hx_delta_value = fp32_hx_acc - int8_hx_acc
+        ctc_delta_value = fp32_ctc_acc - int8_ctc_acc
+        self.assertLess(hx_delta_value, acc_diff_threshold)
+        self.assertLess(ctc_delta_value, acc_diff_threshold)
+
+
+if __name__ == "__main__":
+    global test_case_args
+    test_case_args, remaining_args = parse_args()
+    unittest.main(argv=remaining_args)