Added support for quantization of fusion_gru (#27518)

cmake/external/mkldnn.cmake

@@ -20,7 +20,7 @@ SET(MKLDNN_SOURCE_DIR     ${THIRD_PARTY_PATH}/mkldnn/src/extern_mkldnn)
 SET(MKLDNN_INSTALL_DIR    ${THIRD_PARTY_PATH}/install/mkldnn)
 SET(MKLDNN_INC_DIR        "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
 SET(MKLDNN_REPOSITORY     https://github.com/oneapi-src/oneDNN.git)
-SET(MKLDNN_TAG            64a48f9565aa72f6359917b3406328075a409939)
+SET(MKLDNN_TAG            361725600224f41b7347a1c6bee9b04d1e6c14d7)
 
 # Introduce variables:
 # * CMAKE_INSTALL_LIBDIR

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1882,9 +1882,9 @@ 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",
+      std::unordered_set<std::string>(
-                                       "fc", "matmul", "pool2d", "prior_box",
+          {"concat", "conv2d", "elementwise_add", "fc", "matmul", "pool2d",
-                                       "relu", "reshape2", "transpose2"});
+           "prior_box", "relu", "reshape2", "transpose2", "fusion_gru"});
   if (!quantize_enabled_op_types.empty()) {
     supported_op_types = quantize_enabled_op_types;
   }
@@ -2280,6 +2280,23 @@ PDNode *patterns::MatmulTransposeReshapePattern::operator()() {
   return reshape_out;
 }
 
+PDNode *patterns::FusionGru::operator()() {
+  auto op = pattern->NewNode(op_repr())->assert_is_op("fusion_gru");
+  auto x = pattern->NewNode(x_repr())->AsInput()->assert_is_op_input(
+      "fusion_gru", "X");
+  auto weight_h = pattern->NewNode(weight_h_repr())
+                      ->AsInput()
+                      ->assert_is_op_input("fusion_gru", "WeightH");
+  auto weight_x = pattern->NewNode(weight_x_repr())
+                      ->AsInput()
+                      ->assert_is_op_input("fusion_gru", "WeightX");
+  auto out = pattern->NewNode(out_repr())
+                 ->AsOutput()
+                 ->assert_is_op_output("fusion_gru", "Hidden");
+  op->LinksFrom({x, weight_h, weight_x}).LinksTo({out});
+  return out;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1312,6 +1312,21 @@ struct MatmulTransposeReshapePattern : public PatternBase {
   PATTERN_DECL_NODE(reshape_out_xshape);
 };
 
+// fusion_gru op
+// Forward pass for fusion_gru.
+// fusion_gru out is a result of the operator.
+struct FusionGru : public PatternBase {
+  FusionGru(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "fusion_gru") {}
+
+  PDNode* operator()();
+  PATTERN_DECL_NODE(op);
+  PATTERN_DECL_NODE(x);
+  PATTERN_DECL_NODE(weight_h);
+  PATTERN_DECL_NODE(weight_x);
+  PATTERN_DECL_NODE(out);
+};
+
 }  // namespace patterns
 
 // Link two ir::Nodes from each other.

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

@@ -63,8 +63,9 @@ enum { U8_MAX = 255, S8_MAX = 127 };
 
 void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
                                     std::string input_name, double scale_to_one,
-                                    bool is_unsigned,
+                                    bool is_input_unsigned,
-                                    std::string scale_attr_name) const {
+                                    std::string scale_attr_name, float shift,
+                                    std::string shift_attr_name) const {
   auto inputs = op->Op()->InputNames();
   bool name_found =
       std::find(inputs.begin(), inputs.end(), input_name) != inputs.end();
@@ -72,7 +73,7 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
                     platform::errors::InvalidArgument(
                         "Var(%s) isn't the input of the %s operator.",
                         input_name, op->Op()->Type()));
-  unsigned max = is_unsigned ? U8_MAX : S8_MAX;
+  unsigned max = is_input_unsigned ? U8_MAX : S8_MAX;
   float scale = scale_to_one * max;
 
   // Create quantize output variable
@@ -86,7 +87,8 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
   q_desc.SetOutput("Output",
                    std::vector<std::string>({quantize_out_node->Name()}));
   q_desc.SetAttr("Scale", scale);
-  q_desc.SetAttr("is_negative_input", !is_unsigned);
+  q_desc.SetAttr("Shift", shift);
+  q_desc.SetAttr("is_negative_input", !is_input_unsigned);
 
   q_desc.SetAttr("output_format",
                  Has("data_layout") ? Get<std::string>("data_layout") : "NHWC");
@@ -103,11 +105,13 @@ void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
   IR_NODE_LINK_TO(quantize_out_node, op);
 
   if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale);
+  if (!shift_attr_name.empty()) op->Op()->SetAttr(shift_attr_name, shift);
 }
 
 void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
-                                     bool are_unsigned,
+                                     bool are_inputs_unsigned,
-                                     std::string scale_attr_name) const {
+                                     std::string scale_attr_name, float shift,
+                                     std::string shift_attr_name) const {
   auto inputs = op->inputs;
   auto output = op->outputs[0];
   PADDLE_ENFORCE_GE(inputs.size(), 1,
@@ -127,7 +131,7 @@ void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
   std::vector<std::string> quantize_out_node_names(inputs.size());
 
   double scale_out = GetScaleValueForNode(output);
-  unsigned max = are_unsigned ? U8_MAX : S8_MAX;
+  unsigned max = are_inputs_unsigned ? U8_MAX : S8_MAX;
   float scale = scale_out * max;
 
   for (size_t i = 0; i < inputs.size(); i++) {
@@ -137,10 +141,11 @@ void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
     quantize_out_node_names[i] = quantize_out_nodes[i]->Name();
 
     q_desc.SetAttr("Scale", scale);
+    q_desc.SetAttr("Shift", shift);
     q_desc.SetInput("Input", std::vector<std::string>({inputs[i]->Name()}));
     q_desc.SetOutput("Output",
                      std::vector<std::string>({quantize_out_node_names[i]}));
-    q_desc.SetAttr("is_negative_input", !are_unsigned);
+    q_desc.SetAttr("is_negative_input", !are_inputs_unsigned);
     auto quantize_op = g->CreateOpNode(&q_desc);  // OpDesc will be copied.
 
     // link quantize op
@@ -154,6 +159,7 @@ void CPUQuantizePass::QuantizeInputs(Graph* g, Node* op, std::string input_name,
   op->Op()->SetInput(input_name, quantize_out_node_names);
 
   if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale);
+  if (!shift_attr_name.empty()) op->Op()->SetAttr(shift_attr_name, shift);
 }
 
 void CPUQuantizePass::DequantizeOutput(Graph* g, Node* op, Node* output,
@@ -782,6 +788,62 @@ void CPUQuantizePass::QuantizeElementwiseAdd(Graph* graph) const {
                   quantize_elementwise_add_count);
 }
 
+void CPUQuantizePass::QuantizeFusionGru(Graph* graph) const {
+  GraphPatternDetector gpd;
+  patterns::FusionGru pattern{gpd.mutable_pattern(), name_scope_};
+  pattern();
+
+  int quantize_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "Quantize fusion_gru 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(out, out, pattern);
+
+    if (!AreScalesPresentForNodes(op, {x, weight_h, 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(), 1};
+    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_gru ops", quantize_count);
+}
+
 void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   VLOG(3) << "Quantizing the graph.";
   PADDLE_ENFORCE_NOT_NULL(
@@ -801,6 +863,7 @@ void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   QuantizeReshape(graph);
   QuantizeMatmul(graph);
   QuantizeElementwiseAdd(graph);
+  QuantizeFusionGru(graph);
 }
 
 }  // namespace ir

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

@@ -49,31 +49,26 @@ class CPUQuantizePass : public FusePassBase {
   void ApplyImpl(ir::Graph* graph) const override;
 
   void QuantizeConv(Graph* graph, bool with_residual_data = false) const;
-
   void QuantizeFc(Graph* graph) const;
-
   void QuantizePool(Graph* graph) const;
-
   void QuantizeConcat(Graph* graph) const;
-
   void QuantizePriorBox(Graph* graph) const;
-
   void QuantizeTranspose(Graph* graph) const;
-
   void QuantizeReshape(Graph* graph) const;
-
   void QuantizeMatmul(Graph* graph) const;
-
   void QuantizeElementwiseAdd(Graph* graph) const;
+  void QuantizeFusionGru(Graph* graph) const;
 
   void QuantizeInput(Graph* g, Node* op, Node* input, std::string input_name,
-                     double scale_to_one, bool is_unsigned,
+                     double scale_to_one, bool is_input_unsigned,
-                     std::string scale_attr_name = "") const;
+                     std::string scale_attr_name = "", float shift = 0.0,
+                     std::string shift_attr_name = "") const;
 
   // quantize all inputs of given name with the same (minimum) scale
   void QuantizeInputs(Graph* g, Node* op, std::string input_name,
-                      bool are_unsigned,
+                      bool are_inputs_unsigned,
-                      std::string scale_attr_name = "") const;
+                      std::string scale_attr_name = "", float shift = 0.0,
+                      std::string shift_attr_name = "") const;
 
   void DequantizeOutput(Graph* g, Node* op, Node* output,
                         std::string output_name, double scale_to_one,

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

@@ -91,6 +91,16 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
     op->SetAttr("Scale_x", 1.0f);
     op->SetAttr("Scale_y", 1.0f);
     op->SetAttr("Scale_out", 1.0f);
+  } else if (type == "fusion_gru") {
+    op->SetInput("X", {inputs[0]});
+    op->SetInput("Bias", {inputs[1]});
+    op->SetInput("WeightX", {inputs[2]});
+    op->SetInput("WeightH", {inputs[3]});
+    op->SetOutput("Hidden", {outputs[0]});
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+    op->SetAttr("Scale_data", 1.0f);
+    op->SetAttr("Shift_data", 0.0f);
+    op->SetAttr("Weight_scale", std::vector<float>{1.0f});
   }
 }
 
@@ -389,6 +399,77 @@ TEST(CpuQuantizePass, transpose) {
                     quant_count, dequant_count, added_nodes_count, 2.0f * 127);
 }
 
+static const std::initializer_list<std::string> variable_names_fusion_gru = {
+    "x", "wx", "wh", "b", "h"};
+
+// x->Fusion_gru->h
+ProgramDesc BuildProgramDescFusionGru() {
+  ProgramDesc prog;
+  for (auto& v : variable_names_transpose) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    if (v.find("wx") == 0 || v.find("wh") || v.find("b")) {
+      var->SetPersistable(true);
+    }
+  }
+
+  SetOp(&prog, "fusion_gru", "Fusion_gru", {"x", "wx", "wh", "b"}, {"h"}, true,
+        "int8");
+
+  return prog;
+}
+
+void MainTestFusionGru(const ProgramDesc& prog, int gru_count, int quant_count,
+                       int dequant_count, int added_nodes_count, float scale,
+                       float shift) {
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+  int original_nodes_num, current_nodes_num;
+  PreparePass(&graph, prog, variable_names_fusion_gru, &original_nodes_num,
+              &current_nodes_num);
+
+  int quantize_nodes_count = 0;
+  int dequantize_nodes_count = 0;
+  int gru_nodes_count = 0;
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp()) {
+      auto* op = node->Op();
+      if (op->Type() == "fusion_gru") {
+        gru_nodes_count++;
+
+        auto op_name = BOOST_GET_CONST(std::string, op->GetAttr("name"));
+        EXPECT_EQ(BOOST_GET_CONST(float, op->GetAttr("Scale_data")), scale)
+            << "Scale_data for node '" + op_name + "'.";
+        EXPECT_EQ(BOOST_GET_CONST(float, op->GetAttr("Shift_data")), shift)
+            << "Shift_data for node '" + op_name + "'.";
+        EXPECT_EQ(BOOST_GET_CONST(std::vector<float>,
+                                  op->GetAttr("Scale_weights"))[0],
+                  scale)
+            << "Scale_weights for node '" + op_name + "'.";
+        EXPECT_EQ(BOOST_GET_CONST(bool, op->GetAttr("force_fp32_output")), true)
+            << "force_fp32_output for node '" + op_name + "'.";
+      } else if (op->Type() == "quantize") {
+        quantize_nodes_count++;
+      } else if (op->Type() == "dequantize") {
+        dequantize_nodes_count++;
+      }
+    }
+  }
+  EXPECT_EQ(gru_nodes_count, gru_count);
+  EXPECT_EQ(quantize_nodes_count, quant_count);
+  EXPECT_EQ(dequantize_nodes_count, dequant_count);
+  EXPECT_EQ(original_nodes_num + added_nodes_count, current_nodes_num);
+}
+
+TEST(CpuQuantizePass, fusion_gru) {
+  // x->Fusion_gru->h
+  int gru_count = 1;
+  int quant_count = 1;
+  int dequant_count = 0;
+  // 1 Quant + 1 IN + 0 DeQuant + 0 OUT
+  int added_nodes_count = 1 + 1 + 0 + 0;
+  MainTestFusionGru(BuildProgramDescFusionGru(), gru_count, quant_count,
+                    dequant_count, added_nodes_count, 2. * 127, 128.);
+}
+
 static const std::initializer_list<std::string> variable_names_reshape = {
     "a", "w1", "b", "c", "d", "e", "f"};
 

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

@@ -76,6 +76,8 @@ void CPUQuantizeSquashPass::DequantQuantSquash(
         BOOST_GET_CONST(float, dequant_op->Op()->GetAttr("Scale"));
     float quant_scale =
         BOOST_GET_CONST(float, quant_op->Op()->GetAttr("Scale"));
+    float dequant_shift = dequant_op->Op()->GetAttrIfExists<float>("Shift");
+    float quant_shift = quant_op->Op()->GetAttrIfExists<float>("Shift");
     PADDLE_ENFORCE_NE(
         nodes_keep_counter->find(dequant_out), nodes_keep_counter->end(),
         platform::errors::NotFound("The dequant output node is not found."));
@@ -83,7 +85,7 @@ void CPUQuantizeSquashPass::DequantQuantSquash(
     // check if dequantize op should be kept or removed, decrease the counter
     bool keep_dequant = (*nodes_keep_counter)[dequant_out]-- > 1;
 
-    if (dequant_scale == quant_scale) {
+    if (dequant_scale == quant_scale && dequant_shift == quant_shift) {
       // squash dequantize-quantize to nothing
       auto quant_out_var_name = quant_out->Name();
       auto next_op_inputs = next_op_desc->InputNames();
@@ -110,7 +112,9 @@ void CPUQuantizeSquashPass::DequantQuantSquash(
       desc.SetInput("Input", std::vector<std::string>({dequant_in->Name()}));
       desc.SetOutput("Output", std::vector<std::string>({quant_out->Name()}));
       desc.SetAttr("Scale_in", dequant_scale);
+      desc.SetAttr("Shift_in", dequant_shift);
       desc.SetAttr("Scale_out", quant_scale);
+      desc.SetAttr("Shift_out", quant_shift);
 
       auto requant_op = g->CreateOpNode(&desc);
 
@@ -293,6 +297,7 @@ void CPUQuantizeSquashPass::MultipleQuantizeSquash(Graph* graph) const {
         }));
     auto* first_quant_out = first_quant_op->outputs[0];
     float scale = first_quant_op->Op()->GetAttrIfExists<float>("Scale");
+    float shift = first_quant_op->Op()->GetAttrIfExists<float>("Shift");
 
     PADDLE_ENFORCE_NE(scale, 0,
                       platform::errors::InvalidArgument(
@@ -302,7 +307,8 @@ void CPUQuantizeSquashPass::MultipleQuantizeSquash(Graph* graph) const {
       auto quant_op = prev_out->outputs[iter];
       if (quant_op->IsOp() && quant_op->Op()->Type() == "quantize" &&
           quant_op->id() != first_quant_op->id() &&
-          quant_op->Op()->GetAttrIfExists<float>("Scale") == scale) {
+          quant_op->Op()->GetAttrIfExists<float>("Scale") == scale &&
+          quant_op->Op()->GetAttrIfExists<float>("Shift") == shift) {
         auto quant_out = quant_op->outputs[0];
         auto last_op = quant_out->outputs[0];
 

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

@@ -95,7 +95,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
 
       // Create memory descriptors
       auto input_md = MKLDNNMemDesc({Ti, N, IC}, MKLDNNGetDataType<T>(),
-                                    MKLDNNMemoryFormat::any);
+                                    MKLDNNMemoryFormat::ntc);
       auto weight_x_md =
           MKLDNNMemDesc({L, D, IC, G, OC}, weights_dt, MKLDNNMemoryFormat::any);
       auto weight_h_md =
@@ -103,7 +103,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
       auto bias_md = MKLDNNMemDesc({L, D, G, OC}, MKLDNNGetDataType<float>(),
                                    MKLDNNMemoryFormat::ldgo);
       auto hidden_md = MKLDNNMemDesc({Ti, N, OC}, MKLDNNGetDataType<T_out>(),
-                                     MKLDNNMemoryFormat::any);
+                                     MKLDNNMemoryFormat::ntc);
       auto h0_md = MKLDNNMemDesc({L, D, N, OC}, MKLDNNGetDataType<T>(),
                                  MKLDNNMemoryFormat::ldnc);
 

python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py

@@ -66,6 +66,7 @@ class Quant2Int8MkldnnPass(object):
         self._fc_ops = ['fc']
         self._relu_ops = ['relu', 'relu6']
         self._matmul_ops = ['matmul']
+        self._gru_ops = ['fusion_gru']
         self._weight_scales = {}
         # Collect the Input and Output sclaes from Fake quant models
         self._var_quant_scales = {}
@@ -449,8 +450,43 @@ class Quant2Int8MkldnnPass(object):
                     self._var_quant_scales[weight_var_name] = (use_unsigned_int,
                                                                lod_tensor)
 
+        def _compute_gru_weight_scales(wx_name, wh_name):
+            for op in graph.all_op_nodes():
+                if op.op().type() in self._gru_ops:
+                    wx_var_name = op.input(wx_name)[0]
+                    wh_var_name = op.input(wh_name)[0]
+                    wx = np.array(self._load_param(self._scope, wx_var_name))
+                    wh = np.array(self._load_param(self._scope, wh_var_name))
+                    OC = wh.shape[0]
+                    scale_ur = 1.0 / np.max(np.abs(
+                        np.concatenate(
+                            [
+                                wx[:, :2 * OC], wh.flatten()[:2 * OC * OC]
+                                .reshape(OC, 2 * OC)
+                            ],
+                            axis=0)),
+                                            axis=0)
+                    scale_o = 1.0 / np.max(np.abs(
+                        np.concatenate(
+                            [
+                                wx[:, 2 * OC:], wh.flatten()[2 * OC * OC:]
+                                .reshape(OC, OC)
+                            ],
+                            axis=0)),
+                                           axis=0)
+
+                    gru_weights_scale = np.concatenate(
+                        [scale_ur, scale_o]).astype('float')
+
+                    lod_tensor = self._convert_scale2tensor(gru_weights_scale)
+                    use_unsigned_int = False
+                    self._var_quant_scales[wx_var_name] = (use_unsigned_int,
+                                                           lod_tensor)
+
         _compute_var_scales(self._conv_ops, "Filter", axis=1)
         _compute_var_scales(self._fc_ops, "W", axis=0)
+        _compute_var_scales(self._gru_ops, "WeightH", axis=0)
+        _compute_gru_weight_scales("WeightX", "WeightH")
         return graph
 
     def _find_avg_pooling_ids(self, graph):

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

@@ -98,18 +98,16 @@ function(download_quant_model install_dir data_file)
     endif()
 endfunction()
 
-function(save_quant_ic_model_test target quant_model_dir fp32_model_save_path int8_model_save_path)
+function(save_quant_ic_model_test target quant_model_dir int8_model_save_path)
     py_test(${target} SRCS ${CMAKE_CURRENT_SOURCE_DIR}/save_quant_model.py
             ARGS --quant_model_path ${quant_model_dir}
-	         --fp32_model_save_path ${fp32_model_save_path}
 	         --int8_model_save_path ${int8_model_save_path}
 		 --debug)
 endfunction()
 
-function(save_quant_nlp_model_test target quant_model_dir fp32_model_save_path int8_model_save_path ops_to_quantize)
+function(save_quant_nlp_model_test target quant_model_dir int8_model_save_path ops_to_quantize)
     py_test(${target} SRCS ${CMAKE_CURRENT_SOURCE_DIR}/save_quant_model.py
             ARGS --quant_model_path ${quant_model_dir}
-	         --fp32_model_save_path ${fp32_model_save_path}
 	         --int8_model_save_path ${int8_model_save_path}
 		 --ops_to_quantize ${ops_to_quantize})
 endfunction()
@@ -227,8 +225,6 @@ if(LINUX AND WITH_MKLDNN)
 	set(NLP_LABLES_PATH "${NLP_DATA_DIR}/Ernie_dataset/label.xnli.dev")
 	download_quant_data(${NLP_DATA_DIR} ${NLP_DATA_ARCHIVE})
 
-	set(QUANT2_NLP_OPS_TO_QUANTIZE "fc,reshape2,transpose2,matmul,elementwise_add")
-
 	# Quant2 Ernie
 	set(QUANT2_ERNIE_MODEL_ARCHIVE "ernie_qat.tar.gz")
 	set(QUANT2_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_quant2")
@@ -236,17 +232,25 @@ if(LINUX AND WITH_MKLDNN)
 	set(FP32_ERNIE_MODEL_ARCHIVE "ernie_fp32_model.tar.gz")
 	set(FP32_ERNIE_MODEL_DIR "${QUANT_INSTALL_DIR}/Ernie_float")
 	download_quant_fp32_model(${FP32_ERNIE_MODEL_DIR} ${FP32_ERNIE_MODEL_ARCHIVE})
-	inference_quant2_int8_nlp_test(test_quant2_int8_ernie_mkldnn ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH} ${NLP_LABLES_PATH} ${QUANT2_NLP_OPS_TO_QUANTIZE})
+	set(QUANT2_ERNIE_OPS_TO_QUANTIZE "fc,reshape2,transpose2,matmul,elementwise_add")
+	inference_quant2_int8_nlp_test(test_quant2_int8_ernie_mkldnn ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${FP32_ERNIE_MODEL_DIR}/ernie_fp32_model ${NLP_DATA_PATH} ${NLP_LABLES_PATH} ${QUANT2_ERNIE_OPS_TO_QUANTIZE})
+
+	# Quant2 GRU
+	set(QUANT2_GRU_MODEL_ARCHIVE "GRU_quant_acc.tar.gz")
+	set(QUANT2_GRU_MODEL_DIR "${QUANT_INSTALL_DIR}/GRU_quant2")
+	download_quant_model(${QUANT2_GRU_MODEL_DIR} ${QUANT2_GRU_MODEL_ARCHIVE})
+	set(QUANT2_GRU_OPS_TO_QUANTIZE "fusion_gru")
 
 	### Save FP32 model or INT8 model from Quant model
         
 	set(QUANT2_INT8_RESNET50_SAVE_PATH "${QUANT_INSTALL_DIR}/ResNet50_quant2_int8")
-	set(QUANT2_FP32_RESNET50_SAVE_PATH "${QUANT_INSTALL_DIR}/ResNet50_quant2_fp32")
+	save_quant_ic_model_test(save_quant2_model_resnet50 ${QUANT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${QUANT2_INT8_RESNET50_SAVE_PATH})
-	save_quant_ic_model_test(save_quant2_model_resnet50 ${QUANT2_RESNET50_MODEL_DIR}/ResNet50_qat_perf/float ${QUANT2_FP32_RESNET50_SAVE_PATH} ${QUANT2_INT8_RESNET50_SAVE_PATH})
 
 	set(QUANT2_INT8_ERNIE_SAVE_PATH "${QUANT_INSTALL_DIR}/Ernie_quant2_int8")
-	set(QUANT2_FP32_ERNIE_SAVE_PATH "${QUANT_INSTALL_DIR}/Ernie_quant2_fp32")
+	save_quant_nlp_model_test(save_quant2_model_ernie ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${QUANT2_INT8_ERNIE_SAVE_PATH} ${QUANT2_ERNIE_OPS_TO_QUANTIZE})
-	save_quant_nlp_model_test(save_quant2_model_ernie ${QUANT2_ERNIE_MODEL_DIR}/Ernie_qat/float ${QUANT2_FP32_ERNIE_SAVE_PATH} ${QUANT2_INT8_ERNIE_SAVE_PATH} ${QUANT2_NLP_OPS_TO_QUANTIZE})
+
+	set(QUANT2_INT8_GRU_SAVE_PATH "${QUANT_INSTALL_DIR}/GRU_quant2_int8")
+	save_quant_nlp_model_test(save_quant2_model_gru ${QUANT2_GRU_MODEL_DIR}/GRU_quant_acc ${QUANT2_INT8_GRU_SAVE_PATH} ${QUANT2_GRU_OPS_TO_QUANTIZE})
 
 	# Convert Quant2 model to dot and pdf files 
 	set(QUANT2_INT8_ERNIE_DOT_SAVE_PATH "${QUANT_INSTALL_DIR}/Ernie_quant2_int8_dot_file")

python/paddle/fluid/tests/unittests/mkldnn/test_fusion_gru_int8_mkldnn_op.py

@@ -45,9 +45,10 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
 
         # Input data
         x_f32 = np.random.rand(T, self.IC).astype('float32') * 2 - 1
-        scale_data = 63
+        scale_data = 63.0
-        shift_data = 64
+        shift_data = 64.0
-        x_u8 = (x_f32 * scale_data + shift_data).astype(np.uint8)
+        x_u8 = np.rint(x_f32 * scale_data + shift_data).astype(np.uint8)
+        #  x_u8 = (x_f32 * scale_data + shift_data).astype(np.uint8)
 
         # WeightX/WeightH data
         wx = np.random.rand(self.IC, 3 * self.OC).astype('float32') * 2 - 1
@@ -58,7 +59,8 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
         # WeightX data shape in PP: [IC, 3 * OC]
         # WeightH data shape in PP: [OC, 2 * OC] + [OC, OC]
         # Scales shape in oneDNN:   [3, OC]
-        scale_ur = 63 / np.max(np.abs(
+        s8_max = 127.0
+        scale_ur = s8_max / np.max(np.abs(
             np.concatenate(
                 [
                     wx[:, :2 * self.OC], wh.flatten()[:2 * self.OC * self.OC]
@@ -66,7 +68,7 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
                 ],
                 axis=0)),
                                    axis=0)
-        scale_o = 63 / np.max(np.abs(
+        scale_o = s8_max / np.max(np.abs(
             np.concatenate(
                 [
                     wx[:, 2 * self.OC:], wh.flatten()[2 * self.OC * self.OC:]
@@ -102,7 +104,9 @@ class TestFusionGRUINT8MKLDNNOp(OpTest):
             self.outputs = {'Hidden': (hidden_f32, self.lod)}
         else:
             self.error_margin = 1
-            hidden_u8 = (hidden_f32 * scale_data + shift_data).astype(np.uint8)
+            hidden_u8 = np.rint(hidden_f32 * scale_data + shift_data).astype(
+                np.uint8)
+            #  hidden_u8 = (hidden_f32 * scale_data + shift_data).astype(np.uint8)
             self.outputs = {'Hidden': (hidden_u8, self.lod)}
 
         self.attrs = {