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[mkldnn-v1.0] Add MKL-DNN softmax_output #16222

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Oct 1, 2019
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[mkldnn-v1.0] Add MKL-DNN softmax_output #16222

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13 changes: 7 additions & 6 deletions src/operator/nn/mkldnn/mkldnn_ops-inl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -44,12 +44,6 @@ namespace mxnet {
namespace op {

#if MXNET_USE_MKLDNN == 1
/* For softmax_output */
void MKLDNNSoftmaxOutputForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
const std::vector<NDArray> &in_data,
const std::vector<OpReqType> &req,
const std::vector<NDArray> &out_data);

/* For sum */
void MKLDNNSumForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
const std::vector<NDArray> &inputs, const OpReqType &req,
Expand Down Expand Up @@ -121,6 +115,7 @@ void MKLDNNActivationForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
void MKLDNNActivationBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
const NDArray &out_grad, const NDArray &in_data,
const OpReqType &req, const NDArray &in_grad);

void MKLDNNLeakyReluForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
const NDArray &in_data, const OpReqType &req,
const NDArray &out_data);
Expand All @@ -133,6 +128,12 @@ void MKLDNNSoftmaxForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
const NDArray &in_data, const OpReqType &req,
const NDArray &out_data);

/* For softmax_output */
void MKLDNNSoftmaxOutputForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
const std::vector<NDArray> &in_data,
const std::vector<OpReqType> &req,
const std::vector<NDArray> &out_data);

void MKLDNNSum(const mkldnn::memory &arr1, const mkldnn::memory &arr2,
const mkldnn::memory &out);

Expand Down
39 changes: 8 additions & 31 deletions src/operator/nn/mkldnn/mkldnn_softmax_output.cc
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Expand Up @@ -23,19 +23,17 @@
* \author Zhang Rong A
*/

#if MXNET_USE_MKLDNN == 1
#if MXNET_USE_MKLDNN == 100
#include "../../softmax_output-inl.h"
#include "./mkldnn_ops-inl.h"
#include "./mkldnn_base-inl.h"

namespace mxnet {
namespace op {

static mkldnn::softmax_forward::primitive_desc GetSoftmaxOutputFwdDescImpl(
const SoftmaxOutputParam& param, bool is_train,
const int axis, const mkldnn::memory &input_mem) {
mkldnn::memory::primitive_desc data_mpd = input_mem.get_primitive_desc();
mkldnn::memory::desc data_md = data_mpd.desc();
mkldnn::memory::desc data_md = input_mem.get_desc();
auto cpu_engine = CpuEngine::Get()->get_engine();
auto prop = is_train ? mkldnn::prop_kind::forward_training
: mkldnn::prop_kind::forward_scoring;
Expand All @@ -47,38 +45,17 @@ typedef ParamOpSign<SoftmaxOutputParam> MKLDNNSoftmaxOuputSignature;

class MKLDNNSoftmaxOutputFwd {
std::shared_ptr<mkldnn::softmax_forward> fwd_;
std::shared_ptr<mkldnn::memory> data_;
std::shared_ptr<mkldnn::memory> out_;

public:
const mkldnn::softmax_forward::primitive_desc fwd_pd;

MKLDNNSoftmaxOutputFwd(const SoftmaxOutputParam& param, bool is_train,
const int axis, const mkldnn::memory &mem): fwd_pd(
GetSoftmaxOutputFwdDescImpl(param, is_train, axis, mem)) {
fwd_ = std::make_shared<mkldnn::softmax_forward>(fwd_pd);
}

void SetNewMem(const mkldnn::memory &data, const mkldnn::memory &output) {
if (this->data_ == nullptr)
this->data_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
data.get_primitive_desc(), data.get_data_handle()));
else
this->data_->set_data_handle(data.get_data_handle());

if (this->out_ == nullptr)
this->out_ = std::shared_ptr<mkldnn::memory>(new mkldnn::memory(
output.get_primitive_desc(), output.get_data_handle()));
else
this->out_->set_data_handle(output.get_data_handle());

if (this->fwd_ == nullptr) {
this->fwd_ = std::shared_ptr<mkldnn::softmax_forward>(
new mkldnn::softmax_forward(fwd_pd, mkldnn::primitive::at(*this->data_),
*this->out_));
}
}

const mkldnn::softmax_forward &GetFwd() const {
const inline mkldnn::softmax_forward &GetFwd() const {
return *fwd_;
}
};
Expand Down Expand Up @@ -129,17 +106,17 @@ void MKLDNNSoftmaxOutputForward(const nnvm::NodeAttrs& attrs,

auto input_mem = idata.GetMKLDNNData();
auto out_mem = CreateMKLDNNMem(out_data[softmaxout_enum::kOut],
input_mem->get_primitive_desc(), req[softmaxout_enum::kOut]);
input_mem->get_desc(), req[softmaxout_enum::kOut]);

MKLDNNSoftmaxOutputFwd &fwd = GetSoftmaxOutputForward(param, ctx, idata);
fwd.SetNewMem(*input_mem, *out_mem.second);

MKLDNNStream *stream = MKLDNNStream::Get();
stream->RegisterPrim(fwd.GetFwd());

stream->RegisterPrimArgs(fwd.GetFwd(),
{{MKLDNN_ARG_SRC, *input_mem}, {MKLDNN_ARG_DST, *out_mem.second}});
CommitOutput(out_data[softmaxout_enum::kOut], out_mem);
stream->Submit();
}
} // namespace op
} // namespace mxnet
#endif

7 changes: 4 additions & 3 deletions src/operator/softmax_output.cc
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Expand Up @@ -24,8 +24,9 @@
* \author Bing Xu, Zhang Rong A
*/
#include "./softmax_output-inl.h"
#if MXNET_USE_MKLDNN == 1
#if MXNET_USE_MKLDNN == 100
#include "./nn/mkldnn/mkldnn_ops-inl.h"
#include "./nn/mkldnn/mkldnn_base-inl.h"
#endif
namespace mxnet {
namespace op {
Expand Down Expand Up @@ -121,7 +122,7 @@ static bool SoftmaxOutputShape(const nnvm::NodeAttrs& attrs,
return true;
}

#if MXNET_USE_MKLDNN == 1
#if MXNET_USE_MKLDNN == 100
inline static bool SoftmaxOutputStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
Expand Down Expand Up @@ -231,7 +232,7 @@ NNVM_REGISTER_OP(SoftmaxOutput)
.set_num_inputs(2)
.set_num_outputs(1)
.set_attr_parser(ParamParser<SoftmaxOutputParam>)
#if MXNET_USE_MKLDNN == 1
#if MXNET_USE_MKLDNN == 100
.set_attr<FInferStorageType>("FInferStorageType", SoftmaxOutputStorageType)
.set_attr<bool>("TIsMKLDNN", true)
.set_attr<FComputeEx>("FComputeEx<cpu>", SoftmaxOutputComputeExCPU)
Expand Down