Add primitive cache for mkldnn sum

Author: ciyongch

src/operator/nn/mkldnn/mkldnn_sum.cc

@@ -24,6 +24,7 @@
 */
 #include <iostream>
 
+#include "../../operator_common.h"
 #include "./mkldnn_ops-inl.h"
 #include "./mkldnn_base-inl.h"
 
@@ -58,37 +59,104 @@ void MKLDNNSum(const mkldnn::memory &arr1, const mkldnn::memory &arr2,
   MKLDNNStream::Get()->RegisterPrim(mkldnn::sum(sum_pd, inputs, out));
 }
 
+class MKLDNNSumFwd {
+ public:
+  mkldnn::sum::primitive_desc fwd_pd;
+
+  MKLDNNSumFwd(const std::vector<float> &scales,
+               const std::vector<mkldnn::memory::primitive_desc> &data_md)
+      : fwd_pd(scales, data_md) {
+    data_.resize(data_md.size());
+  }
+
+  void SetNewMem(const std::vector<const mkldnn::memory *> &in_data, const mkldnn::memory &output);
+
+  const mkldnn::sum &GetFwd() const { return *fwd_; }
+
+ private:
+  std::shared_ptr<mkldnn::sum> fwd_;
+  std::vector<std::shared_ptr<mkldnn::memory>> data_;
+  std::vector<mkldnn::primitive::at> data_mem_;
+  std::shared_ptr<mkldnn::memory> out_;
+};
+
+static MKLDNNSumFwd &GetSumForward(
+    const std::vector<float> &scales, const std::vector<NDArray> &in_data,
+    const std::vector<mkldnn::memory::primitive_desc> &data_md) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<OpSignature, MKLDNNSumFwd, OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<OpSignature, MKLDNNSumFwd, OpHash> fwds;
+#endif
+  OpSignature key;
+  key.AddSign(in_data);
+
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    MKLDNNSumFwd fwd(scales, data_md);
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
+void MKLDNNSumFwd::SetNewMem(const std::vector<const mkldnn::memory *> &in_data,
+                             const mkldnn::memory &output) {
+  auto num_inputs = data_.size();
+  CHECK_EQ(in_data.size(), num_inputs);
+  for (index_t i = 0; i < static_cast<index_t>(num_inputs); ++i) {
+    if (this->data_[i] == nullptr) {
+      this->data_[i] = std::shared_ptr<mkldnn::memory>(
+          new mkldnn::memory(in_data[i]->get_primitive_desc(), in_data[i]->get_data_handle()));
+      this->data_mem_.push_back(*this->data_[i]);
+    } else {
+      this->data_[i]->set_data_handle(in_data[i]->get_data_handle());
+    }
+  }
+  if (this->out_ == nullptr)
+    this->out_ = std::shared_ptr<mkldnn::memory>(
+        new mkldnn::memory(fwd_pd.dst_primitive_desc(), output.get_data_handle()));
+  else
+    this->out_->set_data_handle(output.get_data_handle());
+
+  if (this->fwd_ == nullptr)
+    this->fwd_.reset(new mkldnn::sum(fwd_pd, this->data_mem_, *this->out_));
+}
+
 void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                       const std::vector<NDArray> &inputs, const OpReqType &req,
                       const NDArray &out_data) {
-  if (req == kNullOp) {
-    return;
-  }
-
   TmpMemMgr::Get()->Init(ctx.requested[0]);
-  std::vector<mkldnn::primitive::at> in_prims;
-  std::vector<mkldnn::memory::primitive_desc> in_pds(inputs.size());
-  std::vector<float> scales(inputs.size(), 1);
-  in_prims.reserve(inputs.size());
-  std::vector<NDArray> in_bufs(inputs.size());
-  for (size_t i = 0; i < inputs.size(); i++) {
+  auto num_inputs = inputs.size();
+  std::vector<mkldnn::memory::primitive_desc> data_md;
+  std::vector<const mkldnn::memory *> data_mem;
+  std::vector<float> scales(num_inputs, 1);
+  std::vector<NDArray> in_bufs(num_inputs);
+
+  data_md.reserve(num_inputs);
+  data_mem.reserve(num_inputs);
+
+  for (index_t i = 0; i < static_cast<index_t>(num_inputs); ++i) {
     const mkldnn::memory *in_mem;
     if (inputs[i].IsMKLDNNData() && inputs[i].IsView()) {
       in_bufs[i] = inputs[i].Reorder2Default();
       in_mem = in_bufs[i].GetMKLDNNData();
     } else {
       in_mem = inputs[i].GetMKLDNNData();
     }
-    in_prims.push_back(*in_mem);
-    in_pds[i] = in_mem->get_primitive_desc();
+    mkldnn::memory::primitive_desc tmp_pd = in_mem->get_primitive_desc();
+    data_md.push_back(tmp_pd);
+    data_mem.push_back(in_mem);
   }
 
-  mkldnn::sum::primitive_desc pdesc(scales, in_pds);
-  auto mem = CreateMKLDNNMem(out_data, pdesc.dst_primitive_desc(), req, &inputs[0]);
-  MKLDNNStream *stream = MKLDNNStream::Get();
-  stream->RegisterPrim(mkldnn::sum(pdesc, in_prims, *mem.second));
-  CommitOutput(out_data, mem);
-  stream->Submit();
+  MKLDNNSumFwd &fwd = GetSumForward(scales, inputs, data_md);
+  mxnet::mkldnn_output_t out_mem = CreateMKLDNNMem(out_data,
+                                                   fwd.fwd_pd.dst_primitive_desc(),
+                                                   req,
+                                                   &inputs[0]);
+  fwd.SetNewMem(data_mem, *out_mem.second);
+  MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+  CommitOutput(out_data, out_mem);
+  MKLDNNStream::Get()->Submit();
 }
 
 }  // namespace op

src/operator/tensor/elemwise_binary_op_basic.cc

@@ -30,6 +30,12 @@
 namespace mxnet {
 namespace op {
 
+bool SupportMKLDNNSum(const NDArray& input) {
+  int ndim = input.shape().ndim();
+  return input.dtype() == mshadow::kFloat32 && (ndim >= 1 && ndim <= 4) &&
+         input.storage_type() == kDefaultStorage;
+}
+
 static void ElemwiseAddEx(const nnvm::NodeAttrs& attrs,
                           const OpContext& ctx,
                           const std::vector<NDArray>& inputs,
@@ -38,7 +44,7 @@ static void ElemwiseAddEx(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(inputs.size(), 2U);
   CHECK_EQ(outputs.size(), 1U);
 #if MXNET_USE_MKLDNN == 1
-  if (SupportMKLDNN(inputs[0]) && SupportMKLDNN(inputs[1])) {
+  if (SupportMKLDNNSum(inputs[0]) && SupportMKLDNNSum(inputs[1])) {
     MKLDNNSumForward(attrs, ctx, inputs, req[0], outputs[0]);
     return;
   } else if (inputs[0].storage_type() == kDefaultStorage