Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Integrate MKL-DNN leakyrelu #16075

Merged
merged 9 commits into from
Sep 24, 2019
Merged

Integrate MKL-DNN leakyrelu #16075

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
36c8163
add mkldnn leakyrelu support
xinyu-intel Sep 2, 2019
194e33a
Merge remote-tracking branch 'upstream/master' into leakyrelu_only
xinyu-intel Sep 2, 2019
03f13c6
improve mkldnn act param
xinyu-intel Sep 3, 2019
65f4d20
register gpu path
xinyu-intel Sep 3, 2019
03ba2b7
remove old code
xinyu-intel Sep 3, 2019
596e884
trigger
xinyu-intel Sep 4, 2019
d4fa980
fix lint and improve backward function
xinyu-intel Sep 6, 2019
dbfca67
Merge branch 'leakyrelu_only' of https://github.com/xinyu-intel/incub…
xinyu-intel Sep 6, 2019
862a9d8
Merge remote-tracking branch 'upstream/master' into leakyrelu_only
xinyu-intel Sep 16, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
192 changes: 38 additions & 154 deletions src/operator/leaky_relu-inl.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -332,166 +332,50 @@ class LeakyReLUOp : public Operator {
}; // class LeakyReLUOp

template<typename xpu>
Operator* CreateOp(LeakyReLUParam type, int dtype);
void LeakyReLUCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx, const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const LeakyReLUParam &param = nnvm::get<LeakyReLUParam>(attrs.parsed);
const std::vector<TBlob> no_use_but_adapt_origin_api;
size_t expected = param.act_type == leakyrelu::kPReLU ? 2 : 1;
CHECK_EQ(inputs.size(), expected);

#if DMLC_USE_CXX11
class LeakyReLUProp : public OperatorProperty {
public:
void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override {
param_.Init(kwargs);
}

std::map<std::string, std::string> GetParams() const override {
return param_.__DICT__();
}

bool InferShape(mxnet::ShapeVector *in_shape,
mxnet::ShapeVector *out_shape,
mxnet::ShapeVector *aux_shape) const override {
using namespace mshadow;
if (param_.act_type == leakyrelu::kPReLU) {
CHECK_EQ(in_shape->size(), 2U) << "Input:[data, gamma]";
} else {
CHECK_EQ(in_shape->size(), 1U) << "Input:[data]";
}
const mxnet::TShape &dshape = in_shape->at(leakyrelu::kData);
if (!mxnet::ndim_is_known(dshape)) return false;
if (param_.act_type == leakyrelu::kPReLU) {
const mxnet::TShape &gshape = in_shape->at(leakyrelu::kGamma);
if (!mxnet::ndim_is_known(gshape)) {
in_shape->at(leakyrelu::kGamma) = mxnet::TShape(Shape1(dshape[1]));
}
if (dshape == gshape) {
SHAPE_ASSIGN_CHECK(*out_shape, 0, dshape);
}
}
out_shape->clear();
out_shape->push_back(dshape);
if (param_.act_type == leakyrelu::kRReLU) {
out_shape->push_back(dshape);
}
return true;
}

bool InferType(std::vector<int> *in_type,
std::vector<int> *out_type,
std::vector<int> *aux_type) const override {
int dtype = -1;
for (const int& type : *in_type) {
type_assign(&dtype, type);
}
for (const int& type : *out_type) {
type_assign(&dtype, type);
}

for (size_t i = 0; i < in_type->size(); ++i) {
TYPE_ASSIGN_CHECK(*in_type, i, dtype);
}
for (size_t i = 0; i < out_type->size(); ++i) {
TYPE_ASSIGN_CHECK(*out_type, i, dtype);
}
return dtype != -1;
}

OperatorProperty* Copy() const override {
auto ptr = new LeakyReLUProp();
ptr->param_ = param_;
return ptr;
}

std::string TypeString() const override {
return "LeakyReLU";
}

// decalre dependency and inplace optimization options
std::vector<int> DeclareBackwardDependency(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data) const override {
if (param_.act_type == leakyrelu::kPReLU) {
return {out_grad[leakyrelu::kOut],
out_data[leakyrelu::kOut],
in_data[leakyrelu::kData],
in_data[leakyrelu::kGamma]};
} else if (param_.act_type == leakyrelu::kRReLU) {
return {out_grad[leakyrelu::kOut], out_data[leakyrelu::kMask], out_data[leakyrelu::kOut]};
} else {
return {out_grad[leakyrelu::kOut], out_data[leakyrelu::kData]};
}
}
MSHADOW_REAL_TYPE_SWITCH(inputs[leakyrelu::kData].type_flag_, DType, {
LeakyReLUOp<xpu, DType> op(param);
op.Forward(ctx, inputs, req, outputs, no_use_but_adapt_origin_api);
});
}

std::vector<std::pair<int, void*> > BackwardInplaceOption(
const std::vector<int> &out_grad,
const std::vector<int> &in_data,
const std::vector<int> &out_data,
const std::vector<void*> &in_grad) const override {
return {{out_grad[leakyrelu::kOut], in_grad[leakyrelu::kData]}};
}

std::vector<std::pair<int, void*> > ForwardInplaceOption(
const std::vector<int> &in_data,
const std::vector<void*> &out_data) const override {
if (param_.act_type == leakyrelu::kPReLU) {
return {};
} else {
return {{in_data[leakyrelu::kData], out_data[leakyrelu::kOut]}};
}
}

std::vector<std::string> ListArguments() const override {
if (param_.act_type == leakyrelu::kPReLU) {
return {"data", "gamma"};
} else {
return {"data"};
}
}

std::vector<std::string> ListOutputs() const override {
if (param_.act_type == leakyrelu::kRReLU) {
return {"output", "mask"};
} else {
return {"output"};
}
}

int NumOutputs() const override {
if (param_.act_type == leakyrelu::kRReLU) {
return 2;
} else {
return 1;
}
}

int NumVisibleOutputs() const override {
return 1;
}

std::vector<ResourceRequest> ForwardResource(
const mxnet::ShapeVector &in_shape) const override {
if (param_.act_type == leakyrelu::kRReLU) {
return {ResourceRequest::kRandom};
} else {
return std::vector<ResourceRequest>();
}
}
template<typename xpu>
void LeakyReLUGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const LeakyReLUParam& param = nnvm::get<LeakyReLUParam>(attrs.parsed);
const std::vector<TBlob> no_use_but_adapt_origin_api;
// inputs: out_grad, input_data, input_gamma, output, output_mask
size_t expected_in = param.act_type == leakyrelu::kPReLU ? 2 : 1;
size_t expected_out = param.act_type == leakyrelu::kRReLU ? 2 : 1;

std::vector<ResourceRequest> BackwardResource(
const mxnet::ShapeVector &in_shape) const override {
return {ResourceRequest::kTempSpace};
}
CHECK_GE(inputs.size(), 1 + expected_in + expected_out);
std::vector<TBlob> out_grad{inputs[0]};
xinyu-intel marked this conversation as resolved.
Show resolved Hide resolved
std::vector<TBlob> in_data(inputs.begin() + 1,
inputs.begin() + 1 + expected_in);
std::vector<TBlob> out_data(inputs.begin() + 1 + expected_in,
inputs.begin() + 1 + expected_in + expected_out);

Operator* CreateOperator(Context ctx) const override {
LOG(FATAL) << "Not Implemented.";
return NULL;
}
CHECK_EQ(req.size(), outputs.size());
int dtype = inputs[0].type_flag_;
const std::vector<TBlob> &in_grad = outputs;

Operator* CreateOperatorEx(Context ctx, mxnet::ShapeVector *in_shape,
std::vector<int> *in_type) const override;
MSHADOW_REAL_TYPE_SWITCH(dtype, DType, {
LeakyReLUOp<xpu, DType> op(param);
op.Backward(ctx, out_grad, in_data, out_data, req, in_grad, no_use_but_adapt_origin_api);
});
}

private:
LeakyReLUParam param_;
};
#endif // DMLC_USE_CXX11
} // namespace op
} // namespace mxnet

Expand Down
Loading