Add int8 GRU kernel (#27220)

* Add int8 GRU kernel with UTs

* Lint fixes

* More lint fixes

cmake/external/mkldnn.cmake

@@ -19,8 +19,8 @@ SET(MKLDNN_PREFIX_DIR     ${THIRD_PARTY_PATH}/mkldnn)
 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/intel/mkl-dnn.git)
-SET(MKLDNN_TAG            4c05c181b40cf7132f8943411fb3fab1786df0f7)
+SET(MKLDNN_REPOSITORY     https://github.com/oneapi-src/oneDNN.git)
+SET(MKLDNN_TAG            64a48f9565aa72f6359917b3406328075a409939)
 
 # Introduce variables:
 # * CMAKE_INSTALL_LIBDIR

paddle/fluid/operators/fused/fusion_gru_op.cc

@@ -206,6 +206,27 @@ void FusionGRUOpMaker::Make() {
   AddAttr<bool>("use_mkldnn",
                 "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
+  AddAttr<std::string>(
+      "mkldnn_data_type",
+      "(string, default \"float32\"). Data type of mkldnn kernel")
+      .SetDefault("float32")
+      .InEnum({"float32", "int8", "bfloat16"});
+  AddAttr<float>("Scale_data",
+                 "Scale to be used for int8 input/output data."
+                 "Only used with MKL-DNN INT8.")
+      .SetDefault(1.0f);
+  AddAttr<float>("Shift_data",
+                 "Shift to be used for int8 input/output data."
+                 "Only used with MKL-DNN INT8.")
+      .SetDefault(0.0f);
+  AddAttr<std::vector<float>>("Scale_weights",
+                              "Scale_weights to be used for int8 weights data."
+                              "Only used with MKL-DNN INT8.")
+      .SetDefault({1.0f});
+  AddAttr<bool>("force_fp32_output",
+                "(bool, default false) Force INT8 kernel output FP32, only "
+                "used in MKL-DNN INT8")
+      .SetDefault(false);
   AddComment(R"DOC(
 The Fusion complete GRU Operator.
 This operator fuse the fully-connected operator into GRU, 

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

@@ -21,11 +21,12 @@ namespace operators {
 using paddle::framework::LoDTensor;
 using paddle::framework::Tensor;
 using paddle::platform::CPUDeviceContext;
+using paddle::platform::CreateKey;
 using paddle::platform::MKLDNNGetDataType;
 using paddle::platform::MKLDNNMemDesc;
 using platform::to_void_cast;
 
-template <typename T>
+template <typename T, typename T_out = T>
 class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
  public:
   GRUMKLDNNHandler(const paddle::framework::ExecutionContext& ctx,
@@ -38,7 +39,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
                    const std::string& unique_name)
       : platform::MKLDNNHandlerT<T, dnnl::gru_forward>(
             dev_ctx, dev_ctx.GetEngine(), cpu_place,
-            platform::CreateKey(unique_name, Ti)),
+            CreateKey(unique_name, MKLDNNGetDataType<T>(), Ti)),
         N(N),
         Ti(Ti),
         IC(IC),
@@ -47,9 +48,29 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
     // do not depend on Ti size but primitive and input/output memory do
     if (platform::MKLDNNDeviceContext::tls().get_cur_mkldnn_session_id() !=
         platform::MKLDNNDeviceContextThreadLocals::kMKLDNNSessionID_Default) {
-      memory_key_ = unique_name;
+      memory_key_ = CreateKey(unique_name, MKLDNNGetDataType<T>());
     } else {
-      memory_key_ = unique_name + "-t:" + platform::ThreadIDasStr();
+      memory_key_ = CreateKey(unique_name, MKLDNNGetDataType<T>(), "-t:",
+                              platform::ThreadIDasStr());
+    }
+
+    // Is it int8 kernel
+    const bool is_INT8 = std::is_same<T, uint8_t>::value;
+
+    if (is_INT8) {
+      // Int8 attributes
+      const float scale_data = ctx.Attr<float>("Scale_data");
+      const float shift_data = ctx.Attr<float>("Shift_data");
+      const auto scale_weights = ctx.Attr<std::vector<float>>("Scale_weights");
+
+      const int weights_scale_mask =
+          0 +
+          (1 << 3)  // bit, indicating the unique scales for `g` dim in `ldigo`
+          +
+          (1 << 4);  // bit, indicating the unique scales for `o` dim in `ldigo`
+
+      attr_.set_rnn_data_qparams(scale_data, shift_data);
+      attr_.set_rnn_weights_qparams(weights_scale_mask, scale_weights);
     }
 
     if (!this->isCached()) {
@@ -63,6 +84,10 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
           platform::errors::Unimplemented(
               "oneDNN fusion_gru supports only tanh as an activation."));
 
+      // Weights for int8 kernel are of a type s8
+      const auto weights_dt =
+          is_INT8 ? dnnl::memory::data_type::s8 : dnnl::memory::data_type::f32;
+
       // oneDNN RNN dimensions
       const int64_t D = 1;  // Directions
       const int64_t L = 1;  // Layers (PP supports only 1 stacked layer)
@@ -71,19 +96,16 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
       // Create memory descriptors
       auto input_md = MKLDNNMemDesc({Ti, N, IC}, MKLDNNGetDataType<T>(),
                                     MKLDNNMemoryFormat::any);
-      auto weight_x_md = MKLDNNMemDesc(
-          {L, D, IC, G, OC}, MKLDNNGetDataType<T>(), MKLDNNMemoryFormat::any);
-      auto weight_h_md = MKLDNNMemDesc(
-          {L, D, OC, G, OC}, MKLDNNGetDataType<T>(), MKLDNNMemoryFormat::any);
+      auto weight_x_md =
+          MKLDNNMemDesc({L, D, IC, G, OC}, weights_dt, MKLDNNMemoryFormat::any);
+      auto weight_h_md =
+          MKLDNNMemDesc({L, D, OC, G, OC}, weights_dt, MKLDNNMemoryFormat::any);
       auto bias_md = MKLDNNMemDesc({L, D, G, OC}, MKLDNNGetDataType<float>(),
                                    MKLDNNMemoryFormat::ldgo);
-      auto hidden_md = MKLDNNMemDesc({Ti, N, OC}, MKLDNNGetDataType<T>(),
+      auto hidden_md = MKLDNNMemDesc({Ti, N, OC}, MKLDNNGetDataType<T_out>(),
                                      MKLDNNMemoryFormat::any);
-      auto h0_md = dnnl::memory::desc();
-      if (h0) {
-        h0_md = MKLDNNMemDesc({L, D, N, OC}, MKLDNNGetDataType<T>(),
-                              MKLDNNMemoryFormat::ldnc);
-      }
+      auto h0_md = MKLDNNMemDesc({L, D, N, OC}, MKLDNNGetDataType<T>(),
+                                 MKLDNNMemoryFormat::ldnc);
 
       // Create GRU oneDNN primitive
       const auto direction =
@@ -91,7 +113,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
                      : dnnl::rnn_direction::unidirectional_left2right;
 
       this->AcquireForwardPrimitiveDescriptor(
-          dnnl::prop_kind::forward_inference, direction, input_md, h0_md,
+          attr_, dnnl::prop_kind::forward_inference, direction, input_md, h0_md,
           weight_x_md, weight_h_md, bias_md, hidden_md, dnnl::memory::desc());
     }
   }
@@ -101,29 +123,31 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
             dnnl::memory::format_tag::ntc);
   }
 
-  void reorderRNNdata(const T* input_data, T* output_data,
+  void reorderRNNdata(void* input_data, void* output_data,
                       std::vector<size_t> lod, const bool is_reverse,
                       platform::RNNReorderType reorder_type) {
     switch (reorder_type) {
       // Reorder input memory [WORDS, C] + LoD -> [N, T, C]
       case platform::RNNReorderType::PP_NTC: {
-        auto* input_data_iter = input_data;
+        auto* input_data_iter = reinterpret_cast<T*>(input_data);
+        auto* output_data_iter = reinterpret_cast<T*>(output_data);
         for (int n = 0; n < N; ++n) {
           const auto num_elements = (lod[n + 1] - lod[n]) * IC;
           const auto offset = is_reverse ? (Ti * IC - num_elements) : 0;
-          memcpy(output_data + n * Ti * IC + offset, input_data_iter,
+          memcpy(output_data_iter + n * Ti * IC + offset, input_data_iter,
                  sizeof(T) * num_elements);
           input_data_iter += num_elements;
         }
       } break;
       // Reorder input memory [WORDS, C] + LoD -> [T, N, C]
       case platform::RNNReorderType::PP_TNC: {
-        auto* input_data_iter = input_data;
+        auto* input_data_iter = reinterpret_cast<T*>(input_data);
+        auto* output_data_iter = reinterpret_cast<T*>(output_data);
         for (int n = 0; n < N; ++n) {
           const auto num_elements = (lod[n + 1] - lod[n]);
           const auto offset = is_reverse ? (Ti - num_elements) : 0;
           for (size_t t = 0; t < num_elements; ++t) {
-            memcpy(output_data + (t + offset) * N * IC + n * IC,
+            memcpy(output_data_iter + (t + offset) * N * IC + n * IC,
                    input_data_iter, sizeof(T) * IC);
             input_data_iter += IC;
           }
@@ -131,24 +155,27 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
       } break;
       // Reorder output values to PP format [N, T, C] -> [WORDS, C]
       case platform::RNNReorderType::NTC_PP: {
-        auto* output_data_iter = output_data;
+        auto* input_data_iter = reinterpret_cast<T_out*>(input_data);
+        auto* output_data_iter = reinterpret_cast<T_out*>(output_data);
         for (int n = 0; n < N; ++n) {
           const auto num_elements = (lod[n + 1] - lod[n]) * OC;
           const auto offset = is_reverse ? (Ti * OC - num_elements) : 0;
-          memcpy(output_data_iter, input_data + n * Ti * OC + offset,
-                 sizeof(T) * num_elements);
+          memcpy(output_data_iter, input_data_iter + n * Ti * OC + offset,
+                 sizeof(T_out) * num_elements);
           output_data_iter += num_elements;
         }
       } break;
       // Reorder output values to PP format [T, N, C] -> [WORDS, C]
       case platform::RNNReorderType::TNC_PP: {
-        auto* output_data_iter = output_data;
+        auto* input_data_iter = reinterpret_cast<T_out*>(input_data);
+        auto* output_data_iter = reinterpret_cast<T_out*>(output_data);
         for (int n = 0; n < N; ++n) {
           const auto num_elements = lod[n + 1] - lod[n];
           const auto offset = is_reverse ? (Ti - num_elements) : 0;
           for (size_t t = 0; t < num_elements; ++t) {
             memcpy(output_data_iter,
-                   input_data + (t + offset) * N * OC + n * OC, sizeof(T) * OC);
+                   input_data_iter + (t + offset) * N * OC + n * OC,
+                   sizeof(T_out) * OC);
             output_data_iter += OC;
           }
         }
@@ -169,9 +196,9 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
     }
 
     const auto& input_lod = input->lod()[0];
-    auto* x_data = input->data<T>();
+    auto* x_data = to_void_cast(input->data<T>());
 
-    auto* x_onednn_data = reinterpret_cast<T*>(memory_p->get_data_handle());
+    auto* x_onednn_data = memory_p->get_data_handle();
     memset(x_onednn_data, 0, sizeof(T) * N * Ti * IC);
 
     if (platform::GetMKLDNNFormat(this->fwd_pd_->src_desc()) ==
@@ -198,19 +225,35 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
     return memory_p;
   }
 
+  // TODO(grygielski) H0 is for now persistable
   std::shared_ptr<dnnl::memory> AcquireH0Memory(const Tensor* h0) {
     const std::string h0_key = memory_key_ + "@h0";
     auto memory_p =
         std::static_pointer_cast<dnnl::memory>(this->dev_ctx_.GetBlob(h0_key));
 
-    auto* h0_data = to_void_cast(h0->data<T>());
-
     if (!memory_p) {
-      memory_p = std::make_shared<dnnl::memory>(
-          this->fwd_pd_->weights_layer_desc(), this->engine_, h0_data);
+      auto user_h0_memory = dnnl::memory();
+      if (h0) {
+        user_h0_memory =
+            dnnl::memory({{1, 1, N, OC},
+                          MKLDNNGetDataType<float>(),
+                          MKLDNNMemoryFormat::ldnc},
+                         this->engine_, to_void_cast(h0->data<float>()));
+      } else {
+        user_h0_memory = dnnl::memory({{1, 1, N, OC},
+                                       MKLDNNGetDataType<float>(),
+                                       MKLDNNMemoryFormat::ldnc},
+                                      this->engine_);
+        memset(user_h0_memory.get_data_handle(), 0, sizeof(float) * N * OC);
+      }
+      memory_p = std::make_shared<dnnl::memory>(this->fwd_pd_->src_iter_desc(),
+                                                this->engine_);
+
+      dnnl::stream astream(this->engine_);
+      dnnl::reorder(user_h0_memory, *memory_p, attr_)
+          .execute(astream, user_h0_memory, *memory_p);
+
       this->dev_ctx_.SetBlob(h0_key, memory_p);
-    } else {
-      memory_p->set_data_handle(h0_data);
     }
     return memory_p;
   }
@@ -245,7 +288,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
           this->fwd_pd_->weights_layer_desc(), this->engine_);
 
       dnnl::stream astream(this->engine_);
-      dnnl::reorder(user_memory, *memory_p)
+      dnnl::reorder(user_memory, *memory_p, attr_)
           .execute(astream, user_memory, *memory_p);
 
       this->dev_ctx_.SetBlob(wx_key, memory_p);
@@ -298,7 +341,7 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
           this->fwd_pd_->weights_iter_desc(), this->engine_);
 
       dnnl::stream astream(this->engine_);
-      dnnl::reorder(user_memory, *memory_p)
+      dnnl::reorder(user_memory, *memory_p, attr_)
           .execute(astream, user_memory, *memory_p);
 
       this->dev_ctx_.SetBlob(wh_key, memory_p);
@@ -347,12 +390,26 @@ class GRUMKLDNNHandler : public platform::MKLDNNHandlerT<T, dnnl::gru_forward> {
   // Memory size of weights, bias and h0 does not depend
   // on Ti size, thus we need another key to cache them
   std::string memory_key_;
+  dnnl::primitive_attr attr_;
 };
 
 template <typename T>
 class FusionGRUMKLDNNKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const bool is_INT8 = std::is_same<T, uint8_t>::value;
+    const bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
+
+    // TODO(grygielski) Add option for bfloat
+    if (!is_INT8 || force_fp32_output) {
+      RunKernel<float>(ctx);
+    } else {
+      RunKernel<uint8_t>(ctx);
+    }
+  }
+
+  template <typename Tout = T>
+  void RunKernel(const framework::ExecutionContext& ctx) const {
     auto& dev_ctx =
         ctx.template device_context<platform::MKLDNNDeviceContext>();
     const auto& mkldnn_engine = dev_ctx.GetEngine();
@@ -390,12 +447,14 @@ class FusionGRUMKLDNNKernel : public framework::OpKernel<T> {
     const int64_t IC = x_mat_dims_vec[1];  // Input channels
     const int64_t OC = weight_h_dims[0];   // Output channels
 
-    GRUMKLDNNHandler<T> handler(ctx, dev_ctx, mkldnn_engine, ctx.GetPlace(),
-                                input, weight_h, h0, is_reverse, N, Ti, IC, OC,
-                                ctx.InputName("X") + ctx.InputName("WeightH"));
+    GRUMKLDNNHandler<T, Tout> handler(
+        ctx, dev_ctx, mkldnn_engine, ctx.GetPlace(), input, weight_h, h0,
+        is_reverse, N, Ti, IC, OC,
+        ctx.InputName("X") + ctx.InputName("WeightH"));
 
     auto input_memory_p =
         handler.AcquireInputMemoryWithReorder(input, is_reverse);
+    auto h0_memory_p = handler.AcquireH0Memory(h0);
     auto weight_x_memory_p =
         handler.AcquireWeightXMemory(weight_x, origin_mode);
     auto weight_h_memory_p =
@@ -405,25 +464,21 @@ class FusionGRUMKLDNNKernel : public framework::OpKernel<T> {
 
     std::unordered_map<int, dnnl::memory> gru_args = {
         {DNNL_ARG_SRC_LAYER, *input_memory_p},
+        {DNNL_ARG_SRC_ITER, *h0_memory_p},
         {DNNL_ARG_WEIGHTS_LAYER, *weight_x_memory_p},
         {DNNL_ARG_WEIGHTS_ITER, *weight_h_memory_p},
         {DNNL_ARG_BIAS, *bias_memory_p},
         {DNNL_ARG_DST_LAYER, *hidden_onednn_memory_p}};
 
-    if (h0) {
-      auto h0_memory_p = handler.AcquireH0Memory(h0);
-      gru_args.insert({DNNL_ARG_SRC_ITER, *h0_memory_p});
-    }
-
     auto gru_forward_p = handler.AcquireForwardPrimitive();
 
     dnnl::stream astream(mkldnn_engine);
     gru_forward_p->execute(astream, gru_args);
     astream.wait();
 
-    auto* hidden_onednn_data =
-        reinterpret_cast<T*>(hidden_onednn_memory_p->get_data_handle());
-    auto* hidden_data = hidden->mutable_data<T>(ctx.GetPlace());
+    auto* hidden_onednn_data = hidden_onednn_memory_p->get_data_handle();
+    auto* hidden_data =
+        to_void_cast(hidden->mutable_data<Tout>(ctx.GetPlace()));
     if (handler.is_NTC()) {
       handler.reorderRNNdata(hidden_onednn_data, hidden_data, input_lod,
                              is_reverse, platform::RNNReorderType::NTC_PP);
@@ -439,4 +494,5 @@ class FusionGRUMKLDNNKernel : public framework::OpKernel<T> {
 
 namespace ops = paddle::operators;
 REGISTER_OP_KERNEL(fusion_gru, MKLDNN, paddle::platform::CPUPlace,
-                   ops::FusionGRUMKLDNNKernel<float>);
+                   ops::FusionGRUMKLDNNKernel<float>,
+                   ops::FusionGRUMKLDNNKernel<uint8_t>);

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

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.test_fusion_gru_op import fusion_gru
+from paddle.fluid.tests.unittests.test_fusion_lstm_op import fc, ACTIVATION
+
+
+class TestFusionGRUINT8MKLDNNOp(OpTest):
+    def set_confs(self):
+        pass
+
+    def setUp(self):
+        self.op_type = "fusion_gru"
+        self.lod = [[2, 4, 3]]
+        self.IC = 3
+        self.OC = 5
+        self.is_reverse = False
+        self.with_h0 = False
+        self.with_bias = True
+        self.act_state = 'tanh'
+        self.act_gate = 'sigmoid'
+        self.origin_mode = True
+        self.use_mkldnn = True
+        self.force_fp32_output = True
+        self.error_margin = 1e-5
+        self.set_confs()
+
+        # RNN dimensions
+        T = sum(self.lod[0])
+        N = len(self.lod[0])
+
+        # Input data
+        x_f32 = np.random.rand(T, self.IC).astype('float32') * 2 - 1
+        scale_data = 63
+        shift_data = 64
+        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
+        wh = np.random.rand(self.OC, 3 * self.OC).astype('float32') * 2 - 1
+
+        # Calculating weight scales
+        # scales = 63 / max(abs(channel_wise(weightsX + weightsH)))
+        # 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(
+            np.concatenate(
+                [
+                    wx[:, :2 * self.OC], wh.flatten()[:2 * self.OC * self.OC]
+                    .reshape(self.OC, 2 * self.OC)
+                ],
+                axis=0)),
+                               axis=0)
+        scale_o = 63 / np.max(np.abs(
+            np.concatenate(
+                [
+                    wx[:, 2 * self.OC:], wh.flatten()[2 * self.OC * self.OC:]
+                    .reshape(self.OC, self.OC)
+                ],
+                axis=0)),
+                              axis=0)
+
+        scale_weights = np.concatenate([scale_ur, scale_o]).astype('float')
+
+        bias = np.random.rand(
+            1, 3 * self.OC).astype('float32') if self.with_bias else np.zeros(
+                (1, 3 * self.OC), dtype='float32')
+        h0 = np.random.rand(
+            N, self.OC).astype('float32') if self.with_h0 else np.zeros(
+                (N, self.OC), dtype='float32')
+
+        _, _, _, hidden_f32 = fusion_gru(x_f32, self.lod, h0, wx, wh, bias,
+                                         self.is_reverse, self.origin_mode,
+                                         ACTIVATION[self.act_state],
+                                         ACTIVATION[self.act_gate])
+
+        self.inputs = {'X': (x_u8, self.lod), 'WeightX': wx, 'WeightH': wh}
+
+        if self.with_bias:
+            self.inputs['Bias'] = bias
+
+        if self.with_h0:
+            self.inputs['H0'] = h0
+
+        if self.force_fp32_output:
+            self.error_margin = 1e-1
+            self.outputs = {'Hidden': (hidden_f32, self.lod)}
+        else:
+            self.error_margin = 1
+            hidden_u8 = (hidden_f32 * scale_data + shift_data).astype(np.uint8)
+            self.outputs = {'Hidden': (hidden_u8, self.lod)}
+
+        self.attrs = {
+            'activation': self.act_state,
+            'gate_activation': self.act_gate,
+            'is_reverse': self.is_reverse,
+            'origin_mode': self.origin_mode,
+            'use_mkldnn': self.use_mkldnn,
+            'force_fp32_output': self.force_fp32_output,
+            'Scale_data': scale_data,
+            'Shift_data': shift_data,
+            'Scale_weights': scale_weights
+        }
+
+    def test_check_output(self):
+        self.check_output(check_dygraph=False, atol=self.error_margin)
+
+
+class TestFusionGRUINT8MKLDNNOp2(TestFusionGRUINT8MKLDNNOp):
+    def set_confs(self):
+        self.force_fp32_output = False
+
+
+class TestFusionGRUINT8MKLDNNOp3(TestFusionGRUINT8MKLDNNOp):
+    def set_confs(self):
+        self.origin_mode = False
+
+
+class TestFusionGRUINT8MKLDNNOp4(TestFusionGRUINT8MKLDNNOp):
+    def set_confs(self):
+        self.with_bias = False
+
+
+class TestFusionGRUINT8MKLDNNOp5(TestFusionGRUINT8MKLDNNOp):
+    def set_confs(self):
+        self.with_h0 = False
+
+
+if __name__ == "__main__":
+    unittest.main()