Enable MKL-DNN INT8 Concat Kernel. (#16156)

* Enable INT8 Concat Kernel to improve the performance of MobileNet-SSD.
test=develop

* Optimize UT format.
test=develop

* Fix UT file address issue.
test=develop

* Refine the license year.
test=develop

* Optimize code for new API.
test=develop

* Restructure INT8 Concat kernel.
test=develop

paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include <memory>
 #include "paddle/fluid/operators/concat_op.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
+#include "paddle/fluid/platform/mkldnn_reuse.h"
 
 namespace paddle {
 namespace operators {
@@ -38,15 +39,20 @@ static void EnforceLayouts(const std::vector<const Tensor*> inputs) {
 }
 
 static memory::primitive_desc CreateMemPrimDesc(const Tensor& input,
-                                                const mkldnn::engine& engine) {
-  constexpr auto data_type = mkldnn::memory::f32;
+                                                const mkldnn::engine& engine,
+                                                const memory::data_type& dt) {
   const auto dims = paddle::framework::vectorize2int(input.dims());
   const auto format = input.format();
-  auto description = memory::desc(dims, data_type, format);
+  auto description = memory::desc(dims, dt, format);
   auto mem_prim_desc = memory::primitive_desc(description, engine);
   return mem_prim_desc;
 }
 
+static mkldnn::memory::format GetDstMemFormat(
+    const concat::primitive_desc& concat_pd) {
+  return (memory::format)concat_pd.dst_primitive_desc().desc().data.format;
+}
+
 static platform::CPUPlace GetCpuPlace(
     const paddle::framework::ExecutionContext& ctx) {
   auto place = ctx.GetPlace();
@@ -61,14 +67,30 @@ static const mkldnn::engine& GetMKLDNNEngine(
   return dev_ctx.GetEngine();
 }
 
+std::string CreateKey(const paddle::framework::ExecutionContext& ctx,
+                      const std::vector<const Tensor*> multi_input,
+                      const int64_t& concat_axis, const memory::data_type& dt) {
+  std::string key;
+  key.reserve(platform::MKLDNNHandler::MaxKeyLength);
+  for (size_t i = 0; i < multi_input.size(); i++) {
+    platform::MKLDNNHandler::AppendKeyDims(
+        &key, paddle::framework::vectorize2int(multi_input[i]->dims()));
+  }
+  platform::MKLDNNHandler::AppendKey(&key, std::to_string(concat_axis));
+  platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Out"));
+  platform::MKLDNNHandler::AppendKey(&key, std::to_string(dt));
+  return key;
+}
+
 template <typename T>
 class ConcatPrimitiveFactory {
  public:
   concat::primitive_desc CreateConcatPrimDescriptor(
       const std::vector<const Tensor*> multi_input, Tensor* output,
-      int concat_axis, const mkldnn::engine& mkldnn_engine) {
-    CreateSourcesDescriptors(multi_input, mkldnn_engine);
-    auto dst_desc = CreateDstMemDescriptor(output);
+      int concat_axis, const mkldnn::engine& mkldnn_engine,
+      const memory::data_type& dt = memory::data_type::f32) {
+    CreateSourcesDescriptors(multi_input, mkldnn_engine, dt);
+    auto dst_desc = CreateDstMemDescriptor(output, dt);
     return concat::primitive_desc(dst_desc, concat_axis, srcs_pd);
   }
 
@@ -79,23 +101,39 @@ class ConcatPrimitiveFactory {
     return concat(concat_pd, inputs, dst_mem.get());
   }
 
+  void SetSrcDataHandleByIndex(const std::vector<memory>& srcs, const size_t& i,
+                               void* handler) {
+    srcs[i].set_data_handle(handler);
+  }
+
+  void SetDstDataHandle(const memory& dst_mem, void* handler) {
+    dst_mem.set_data_handle(handler);
+  }
+
+  std::vector<memory> GetSrcs() { return srcs; }
+
+  memory GetDst() { return dst_mem.get(); }
+
  private:
-  memory::desc CreateDstMemDescriptor(Tensor* output) {
+  memory::desc CreateDstMemDescriptor(Tensor* output,
+                                      const memory::data_type& dt) {
     auto dst_dims = paddle::framework::vectorize2int(output->dims());
-    return memory::desc(dst_dims, platform::MKLDNNGetDataType<T>(),
-                        memory::format::any);
+    return memory::desc(dst_dims, dt, memory::format::any);
   }
 
   mkldnn::memory CreateDstMemory(const concat::primitive_desc& concat_pd,
-                                 Tensor* output, platform::CPUPlace place) {
+                                 Tensor* output,
+                                 const platform::CPUPlace& place) {
     return memory(concat_pd.dst_primitive_desc(),
                   output->mutable_data<T>(place));
   }
 
   void CreateSourcesDescriptors(const std::vector<const Tensor*> multi_input,
-                                const mkldnn::engine& mkldnn_engine) {
+                                const mkldnn::engine& mkldnn_engine,
+                                const memory::data_type& dt) {
     for (size_t i = 0; i < multi_input.size(); i++) {
-      auto mem_prim_desc = CreateMemPrimDesc(*multi_input[i], mkldnn_engine);
+      auto mem_prim_desc =
+          CreateMemPrimDesc(*multi_input[i], mkldnn_engine, dt);
       srcs_pd.push_back(mem_prim_desc);
       srcs.push_back(
           memory(mem_prim_desc, to_void_cast(multi_input[i]->data<T>())));
@@ -120,21 +158,59 @@ template <typename T>
 class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
  public:
   void Compute(const paddle::framework::ExecutionContext& ctx) const override {
-    auto place = GetCpuPlace(ctx);
-    const auto& mkldnn_engine = GetMKLDNNEngine(ctx);
-
     auto multi_input = ctx.MultiInput<Tensor>("X");
     EnforceLayouts(multi_input);
     Tensor* output = ctx.Output<Tensor>("Out");
     int64_t concat_axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
+    auto place = GetCpuPlace(ctx);
+
+    memory::data_type dt =
+        paddle::framework::ToMKLDNNDataType(multi_input[0]->type());
 
     ConcatPrimitiveFactory<T> prim_creator;
-    auto concat_pd = prim_creator.CreateConcatPrimDescriptor(
-        multi_input, output, static_cast<int>(concat_axis), mkldnn_engine);
-    auto concat = prim_creator.CreateConcatPrimitive(concat_pd, output, place);
-    stream(stream::kind::eager).submit({concat}).wait();
+    std::string key = CreateKey(ctx, multi_input, concat_axis, dt);
+    const std::string key_prim = key + "@concat_p";
+    const std::string key_concat_pd = key + "@concat_pd";
+    const std::string key_srcs = key + "@concat_srcs";
+    const std::string key_dst = key + "@concat_dst";
+
+    std::shared_ptr<concat::primitive_desc> concat_pd;
+    std::shared_ptr<std::vector<memory>> srcs;
+    std::shared_ptr<memory> dst_mem;
+    auto concat_p = std::static_pointer_cast<concat>(dev_ctx.GetBlob(key_prim));
+
+    if (concat_p == nullptr) {
+      const auto& mkldnn_engine = dev_ctx.GetEngine();
+      concat_pd = std::make_shared<concat::primitive_desc>(
+          prim_creator.CreateConcatPrimDescriptor(multi_input, output,
+                                                  static_cast<int>(concat_axis),
+                                                  mkldnn_engine, dt));
+      concat_p = std::make_shared<concat>(
+          prim_creator.CreateConcatPrimitive(*concat_pd, output, place));
+      srcs = std::make_shared<std::vector<memory>>(prim_creator.GetSrcs());
+      dst_mem = std::make_shared<memory>(prim_creator.GetDst());
+      dev_ctx.SetBlob(key_prim, concat_p);
+      dev_ctx.SetBlob(key_concat_pd, concat_pd);
+      dev_ctx.SetBlob(key_srcs, srcs);
+      dev_ctx.SetBlob(key_dst, dst_mem);
+    } else {
+      srcs = std::static_pointer_cast<std::vector<memory>>(
+          dev_ctx.GetBlob(key_srcs));
+      dst_mem = std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_dst));
+      concat_pd = std::static_pointer_cast<concat::primitive_desc>(
+          dev_ctx.GetBlob(key_concat_pd));
+      for (size_t i = 0; i < multi_input.size(); i++) {
+        prim_creator.SetSrcDataHandleByIndex(
+            *srcs, i, to_void_cast<T>(multi_input[i]->data<T>()));
+      }
+      prim_creator.SetDstDataHandle(*dst_mem, output->mutable_data<T>(place));
+    }
+
+    stream(stream::kind::eager).submit({*concat_p}).wait();
 
-    output->set_mkldnn_prim_desc(concat_pd.dst_primitive_desc());
+    output->set_mkldnn_prim_desc(concat_pd->dst_primitive_desc());
   }
 };
 }  // namespace operators
@@ -143,4 +219,6 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 namespace ops = paddle::operators;
 
 REGISTER_OP_KERNEL(concat, MKLDNN, ::paddle::platform::CPUPlace,
-                   ops::ConcatMKLDNNOpKernel<float>)
+                   ops::ConcatMKLDNNOpKernel<float>,
+                   ops::ConcatMKLDNNOpKernel<int8_t>,
+                   ops::ConcatMKLDNNOpKernel<uint8_t>);

python/paddle/fluid/tests/unittests/mkldnn/test_concat_int8_mkldnn_op.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from paddle.fluid.tests.unittests.op_test import OpTest
+
+
+class TestConcatOp(OpTest):
+    def setUp(self):
+        self.op_type = "concat"
+        self.use_mkldnn = True
+        self._cpu_only = True
+        self.init_axis()
+        self.init_shape()
+        self.init_test_data()
+        self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
+        self.attrs = {'axis': self.axis, 'use_mkldnn': True}
+
+        self.output = np.concatenate(
+            (self.x0, self.x1, self.x2), axis=self.axis).astype('int')
+
+        self.outputs = {'Out': self.output}
+
+    def test_check_output(self):
+        self.check_output()
+
+#--------------------test concat s8 in with axis 0--------------------
+
+    def init_test_data(self):
+        self.x0 = (np.random.randint(0, 100, self.x0_shape) - 50).astype('int8')
+        self.x1 = (np.random.randint(0, 80, self.x1_shape) - 30).astype('int8')
+        self.x2 = (np.random.randint(0, 110, self.x2_shape) - 80).astype('int8')
+
+    def init_axis(self):
+        self.axis = 0
+
+    def init_shape(self):
+        self.x0_shape = [2, 2, 1, 2]
+        self.x1_shape = [1, 2, 1, 2]
+        self.x2_shape = [3, 2, 1, 2]
+
+
+#--------------------test concat u8 in with axis 0--------------------
+
+
+class TestConcatOp2(TestConcatOp):
+    def init_test_data(self):
+        self.x0 = (np.random.randint(0, 100, self.x0_shape)).astype('uint8')
+        self.x1 = (np.random.randint(0, 50, self.x1_shape)).astype('uint8')
+        self.x2 = (np.random.randint(0, 80, self.x2_shape)).astype('uint8')
+
+    def init_axis(self):
+        self.axis = 0
+
+    def init_shape(self):
+        self.x0_shape = [2, 1, 5, 5]
+        self.x1_shape = [1, 1, 5, 5]
+        self.x2_shape = [3, 1, 5, 5]
+
+
+def create_test_int8_class(parent):
+
+    #--------------------test concat s8/u8 in with axis 1--------------------
+
+    class TestAxis1Case(parent):
+        def init_axis(self):
+            self.axis = 1
+
+        def init_shape(self):
+            self.x0_shape = [1, 1, 5, 5]
+            self.x1_shape = [1, 2, 5, 5]
+            self.x2_shape = [1, 3, 5, 5]
+
+#--------------------test concat s8/u8 in with axis 2--------------------
+
+    class TestAxis2Case(parent):
+        def init_axis(self):
+            self.axis = 2
+
+        def init_shape(self):
+            self.x0_shape = [2, 3, 4, 5]
+            self.x1_shape = [2, 3, 5, 5]
+            self.x2_shape = [2, 3, 6, 5]
+
+#--------------------test concat s8/u8 in with axis 3--------------------
+
+    class TestAxis3Case(parent):
+        def init_axis(self):
+            self.axis = 3
+
+        def init_shape(self):
+            self.x0_shape = [2, 3, 5, 5]
+            self.x1_shape = [2, 3, 5, 6]
+            self.x2_shape = [2, 3, 5, 7]
+
+    cls_name_1 = "{0}_axis_{1}".format(parent.__name__, "1")
+    cls_name_2 = "{0}_axis_{1}".format(parent.__name__, "2")
+    cls_name_3 = "{0}_axis_{1}".format(parent.__name__, "3")
+    TestAxis1Case.__name__ = cls_name_1
+    TestAxis2Case.__name__ = cls_name_2
+    TestAxis3Case.__name__ = cls_name_3
+    globals()[cls_name_1] = TestAxis1Case
+    globals()[cls_name_2] = TestAxis2Case
+    globals()[cls_name_3] = TestAxis3Case
+
+create_test_int8_class(TestConcatOp)
+create_test_int8_class(TestConcatOp2)
+
+if __name__ == '__main__':
+    unittest.main()