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Posted to commits@mxnet.apache.org by ha...@apache.org on 2019/05/22 00:01:18 UTC
[incubator-mxnet] branch v1.5.x updated (c5265fb -> e95b551)
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haibin pushed a change to branch v1.5.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.
omit c5265fb Add primitive cache for MKL-DNN sum(elemwise_add operator (#14914)
new e95b551 Add primitive cache for MKL-DNN sum(elemwise_add operator (#14914)
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Summary of changes:
[incubator-mxnet] 01/01: Add primitive cache for MKL-DNN
sum(elemwise_add operator (#14914)
Posted by ha...@apache.org.
This is an automated email from the ASF dual-hosted git repository.
haibin pushed a commit to branch v1.5.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit e95b551a69a54aac39b7c4b8d6e8423b1a782c2a
Author: ciyong <ci...@intel.com>
AuthorDate: Thu May 16 06:30:06 2019 +0800
Add primitive cache for MKL-DNN sum(elemwise_add operator (#14914)
* Add primitive cache for mkldnn sum
* fix cpp test failure
---
src/operator/nn/mkldnn/mkldnn_sum.cc | 105 ++++++++++++++++++++----
src/operator/tensor/elemwise_binary_op_basic.cc | 8 +-
2 files changed, 94 insertions(+), 19 deletions(-)
diff --git a/src/operator/nn/mkldnn/mkldnn_sum.cc b/src/operator/nn/mkldnn/mkldnn_sum.cc
index dfb0e25..724b8a2 100644
--- a/src/operator/nn/mkldnn/mkldnn_sum.cc
+++ b/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 +59,105 @@ 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_bufs[i] = inputs[i];
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, in_bufs, data_md);
+ mxnet::mkldnn_output_t out_mem = CreateMKLDNNMem(out_data,
+ fwd.fwd_pd.dst_primitive_desc(),
+ req,
+ &in_bufs[0]);
+ fwd.SetNewMem(data_mem, *out_mem.second);
+ MKLDNNStream::Get()->RegisterPrim(fwd.GetFwd());
+ CommitOutput(out_data, out_mem);
+ MKLDNNStream::Get()->Submit();
}
} // namespace op
diff --git a/src/operator/tensor/elemwise_binary_op_basic.cc b/src/operator/tensor/elemwise_binary_op_basic.cc
index 0ff73f4..c5e30c6 100644
--- a/src/operator/tensor/elemwise_binary_op_basic.cc
+++ b/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