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@ -62,7 +62,7 @@ static DDim GetDims(const Scope& scope, const std::string& name,
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if (var->IsType<LoDTensor>()) {
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const LoDTensor& tensor = var->Get<LoDTensor>();
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if (!tensor.IsInitialized()) {
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if (UNLIKELY(!tensor.IsInitialized())) {
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return DDim({-1});
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}
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return tensor.dims();
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@ -91,13 +91,13 @@ static std::string GetDtype(const Scope& scope, const std::string& name) {
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if (var->IsType<LoDTensor>()) {
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const LoDTensor& tensor = var->Get<LoDTensor>();
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if (!tensor.IsInitialized()) {
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if (UNLIKELY(!tensor.IsInitialized())) {
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return "";
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}
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return DataTypeToString(ToDataType(tensor.type()));
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} else if (var->IsType<SelectedRows>()) {
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auto tensor = var->Get<SelectedRows>().value();
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if (!tensor.IsInitialized()) {
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if (UNLIKELY(!tensor.IsInitialized())) {
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return "uninited";
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} else {
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return DataTypeToString(ToDataType(tensor.type()));
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@ -130,7 +130,7 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
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if (var->IsType<LoDTensor>()) {
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const LoDTensor& tensor = var->Get<LoDTensor>();
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if (!tensor.IsInitialized()) {
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if (UNLIKELY(!tensor.IsInitialized())) {
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return default_lod;
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}
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return tensor.lod();
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@ -149,11 +149,13 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
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platform::SetDeviceId(dev_id);
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#endif
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}
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VLOG(3) << "start pool";
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platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
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platform::RecordEvent record_event(Type(), pool.Get(place));
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VLOG(3) << "start RunImpl";
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// The profile has a process-wide mutex, results in serious performance issue
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// in concurrency scenerio. Here use an `if` to fix this issue.
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// Please not remove the `if`, ask @Superjomn if there are any concern.
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RunImpl(scope, place);
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VLOG(3) << place << " " << DebugStringEx(&scope);
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}
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@ -206,7 +208,6 @@ const std::vector<std::string>& OperatorBase::Outputs(
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}
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std::string OperatorBase::DebugStringEx(const Scope* scope) const {
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VLOG(3) << this->Type() << " scope ptr " << scope;
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std::stringstream ss;
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ss << "Op(" << type_ << "), inputs:{";
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for (auto it = inputs_.begin(); it != inputs_.end();) {
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@ -470,35 +471,35 @@ class RuntimeInferShapeContext : public InferShapeContext {
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: op_(op), scope_(scope) {}
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bool HasInput(const std::string& name) const override {
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if (!op_.HasInputs(name)) {
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// has only one input
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const auto& ins = op_.Inputs();
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auto it = ins.find(name);
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if (it == ins.end()) {
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return false;
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}
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auto& ins = Inputs(name);
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size_t length = ins.size();
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if (length == 0) {
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const auto& in = it->second;
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if (in.size() == 0 || in[0] == kEmptyVarName) {
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return false;
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}
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PADDLE_ENFORCE_EQ(length, 1UL,
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PADDLE_ENFORCE_EQ(in.size(), 1UL,
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"Input %s should not have more than one inputs", name);
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auto ipt = ins[0];
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auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
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return var != nullptr;
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return scope_.FindVar(in[0]) != nullptr;
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}
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bool HasOutput(const std::string& name) const override {
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if (!op_.HasOutputs(name)) {
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// has only one output
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const auto& outs = op_.Outputs();
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auto it = outs.find(name);
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if (it == outs.end()) {
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return false;
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}
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auto& outs = Outputs(name);
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size_t length = outs.size();
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if (length == 0) {
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const auto& out = it->second;
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if (out.size() == 0 || out[0] == kEmptyVarName) {
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return false;
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}
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PADDLE_ENFORCE_EQ(length, 1UL,
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"Output %s should not have more than one inputs", name);
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auto ipt = outs[0];
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auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
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return var != nullptr;
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PADDLE_ENFORCE_EQ(out.size(), 1UL,
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"Output %s should not have more than one outputs", name);
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return scope_.FindVar(out[0]) != nullptr;
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}
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bool HasInputs(const std::string& name) const override {
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@ -545,13 +546,45 @@ class RuntimeInferShapeContext : public InferShapeContext {
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return op_.Outputs(name);
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}
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void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
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size_t j = 0) const override {
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void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
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size_t j = 0) override {
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PADDLE_ENFORCE_LT(i, Inputs(in).size());
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PADDLE_ENFORCE_LT(j, Outputs(out).size());
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Variable* in_var = scope_.FindVar(Inputs(in)[i]);
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Variable* out_var = scope_.FindVar(Outputs(out)[j]);
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const std::string& input_n = Inputs(in)[i];
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const std::string& output_n = Outputs(out)[j];
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Variable* in_var = scope_.FindVar(input_n);
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Variable* out_var = scope_.FindVar(output_n);
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PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
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"The type of %s and %s is not the same.", output_n,
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GetDim(input_n));
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if (in_var->IsType<framework::SelectedRows>()) {
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auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
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auto out_sele_rows = out_var->GetMutable<framework::SelectedRows>();
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out_sele_rows->mutable_value()->Resize(in_sele_rows.value().dims());
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out_sele_rows->set_rows(in_sele_rows.rows());
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out_sele_rows->set_height(in_sele_rows.height());
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} else if (in_var->IsType<framework::LoDTensor>()) {
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auto& in_lod_tensor = in_var->Get<framework::LoDTensor>();
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auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
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out_lod_tensor->Resize(in_lod_tensor.dims());
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} else {
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PADDLE_THROW(
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"Currently, the input type of ShareDim only can be LoDTensor "
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"or SelectedRows.");
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}
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}
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void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
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size_t j = 0) const override {
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const std::vector<std::string>& inputs = Inputs(in);
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const std::vector<std::string>& outputs = Outputs(out);
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PADDLE_ENFORCE_LT(i, inputs.size());
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PADDLE_ENFORCE_LT(j, outputs.size());
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Variable* in_var = scope_.FindVar(inputs.at(i));
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if (!in_var->IsType<LoDTensor>()) return;
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Variable* out_var = scope_.FindVar(outputs.at(j));
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PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
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"The %d-th output of Output(%s) must be LoDTensor.", j, out);
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auto in_tensor = in_var->Get<LoDTensor>();
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@ -579,20 +612,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
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out_tensor->set_layout(in_tensor.layout());
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}
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void ShareLayout(const std::string& in, const std::string& out, size_t i = 0,
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size_t j = 0) const {
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PADDLE_ENFORCE_LT(i, Inputs(in).size());
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PADDLE_ENFORCE_LT(j, Outputs(out).size());
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Variable* in_var = scope_.FindVar(Inputs(in)[i]);
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Variable* out_var = scope_.FindVar(Outputs(out)[j]);
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if (!in_var->IsType<LoDTensor>()) return;
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PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
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"The %d-th output of Output(%s) must be LoDTensor.", j, out);
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auto in_tensor = in_var->Get<LoDTensor>();
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auto* out_tensor = out_var->GetMutable<LoDTensor>();
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out_tensor->set_layout(in_tensor.layout());
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}
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bool IsRuntime() const override { return true; }
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protected:
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@ -663,16 +682,12 @@ static void CheckTensorNANOrInf(const std::string& name,
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void OperatorWithKernel::RunImpl(const Scope& scope,
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const platform::Place& place) const {
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RuntimeInferShapeContext infer_shape_ctx(*this, scope);
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VLOG(3) << "start Infershape";
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this->InferShape(&infer_shape_ctx);
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VLOG(3) << "Infershape Pass";
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platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
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auto* dev_ctx = pool.Get(place);
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// check if op[type] has kernel registered.
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VLOG(3) << "Start Kernels";
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auto& all_op_kernels = AllOpKernels();
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VLOG(3) << "Kernel map finish";
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auto kernels_iter = all_op_kernels.find(type_);
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if (kernels_iter == all_op_kernels.end()) {
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PADDLE_THROW(
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@ -690,7 +705,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
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auto expected_kernel_key =
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this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
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VLOG(3) << "expected_kernel_key: " << expected_kernel_key;
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VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
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auto kernel_iter = kernels.find(expected_kernel_key);
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#ifdef PADDLE_WITH_MKLDNN
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dzhwinter
6 years ago