diff --git a/paddle/capi/gradient_machine.cpp b/paddle/capi/gradient_machine.cpp index b3287552db87d25edbf6e7f3d5e68121df49e9d6..629449bbd497a7444144c533ad079b3ae6b51438 100644 --- a/paddle/capi/gradient_machine.cpp +++ b/paddle/capi/gradient_machine.cpp @@ -146,3 +146,19 @@ paddle_error paddle_gradient_machine_randomize_param( m->machine->randParameters(); return kPD_NO_ERROR; } + +paddle_error paddle_gradient_machine_get_layer_output( + paddle_gradient_machine machine, + const char* layerName, + paddle_arguments args) { + auto m = cast(machine); + auto out = paddle::capi::cast(args); + if (m == nullptr || layerName == nullptr || out == nullptr || + m->machine == nullptr) { + return kPD_NULLPTR; + } + + auto layerOutput = m->machine->getLayerOutput(layerName); + out->args.push_back(layerOutput); + return kPD_NO_ERROR; +} diff --git a/paddle/capi/gradient_machine.h b/paddle/capi/gradient_machine.h index c613ade5b24efbbf52f21c7ee86dd3189981c5ef..28eeb23e3bbdd4cc22a25c14170bf56c294f8cd7 100644 --- a/paddle/capi/gradient_machine.h +++ b/paddle/capi/gradient_machine.h @@ -39,7 +39,11 @@ PD_API paddle_error paddle_gradient_machine_create_for_inference( /** * @brief Create a gradient machine used for model inference, using config with * parameters which is generated by `paddle merge_model`. - * @param [out] machine that used for model inference. + * Example: + * paddle merge_model \ + * --model_dir="pass-00000" \ + * --model_file="merged_model.paddle" + * @param [out] machine that used for model inference * @param [in] mergedModel * @param [in] size * @return paddle_error @@ -97,6 +101,18 @@ paddle_gradient_machine_randomize_param(paddle_gradient_machine machine); PD_API paddle_error paddle_gradient_machine_destroy(paddle_gradient_machine machine); +/** + * @brief Get the output of the layer named `layerName`. + * @param [in] gradient machine that have run a inference + * @param [in] layerName name of specified layer + * @param [out] args output of the specified layer + * @return paddle_error + */ +PD_API paddle_error +paddle_gradient_machine_get_layer_output(paddle_gradient_machine machine, + const char* layerName, + paddle_arguments args); + #ifdef __cplusplus } #endif diff --git a/paddle/framework/op_registry.cc b/paddle/framework/op_registry.cc index 1caa02a2a1d046778f875d04eeaef957be741302..8eae86e9605da74cdc37caeb9569e7500aac2a63 100644 --- a/paddle/framework/op_registry.cc +++ b/paddle/framework/op_registry.cc @@ -17,5 +17,48 @@ limitations under the License. */ #include namespace paddle { -namespace framework {} // namespace framework +namespace framework { + +std::unique_ptr OpRegistry::CreateOp(const std::string& type, + const VarNameMap& inputs, + const VarNameMap& outputs, + AttributeMap attrs) { + auto it = op_info_map().find(type); + PADDLE_ENFORCE(it != op_info_map().end(), + "Operator '%s' has not been registered.", type); + it->second.checker_->Check(attrs); + auto op = it->second.creator_(type, inputs, outputs, attrs); + return std::unique_ptr(op); +} + +std::unique_ptr OpRegistry::CreateOp(const OpDesc& op_desc) { + VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); + VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); + AttributeMap attrs; + for (auto& attr : op_desc.attrs()) { + attrs[attr.name()] = GetAttrValue(attr); + } + + return CreateOp(op_desc.type(), inputs, outputs, attrs); +} + +OperatorBase::VarNameMap OpRegistry::ConvertOpDescVarsToVarNameMap( + const google::protobuf::RepeatedPtrField& op_desc_vars) { + VarNameMap ret_val; + for (auto& var : op_desc_vars) { + auto& var_names = ret_val[var.parameter()]; + auto& var_names_in_proto = var.arguments(); + var_names.reserve(static_cast(var_names_in_proto.size())); + std::copy(var_names_in_proto.begin(), var_names_in_proto.end(), + std::back_inserter(var_names)); + } + return ret_val; +} + +std::unique_ptr OpRegistry::CreateGradOp(const OperatorBase& op) { + PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); + return std::unique_ptr(BuildGradOp(&op)); +} + +} // namespace framework } // namespace paddle diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index f0cc0012e11def9bcdd98a6c72a29ec181a6580e..4c2d13d639005d2d2710c19f63988333d89bce13 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -29,103 +29,6 @@ limitations under the License. */ namespace paddle { namespace framework { -// this class not only make proto but also init attribute checkers. -class OpProtoAndCheckerMaker { - public: - OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker) - : proto_(proto), op_checker_(op_checker) {} - - ~OpProtoAndCheckerMaker() { - PADDLE_ENFORCE(validated_, "should call Validate after build"); - } - - void Validate() { - validated_ = true; - CheckNoDuplicatedInOutAttrs(); - } - - protected: - struct VariableBuilder { - OpProto::Var* var_; - - VariableBuilder& AsDuplicable() { - var_->set_duplicable(true); - return *this; - } - - VariableBuilder& AsIntermediate() { - var_->set_intermediate(true); - return *this; - } - - // TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it - // means that input/output is not needed when calculate gradient. It does - // not mean no gradient when backward. It should be changed soon. - VariableBuilder& AsNoGradient() { - var_->set_no_gradient(true); - return *this; - } - }; - - VariableBuilder AddInput(const std::string& name, - const std::string& comment) { - auto* input = proto_->add_inputs(); - input->set_name(name); - input->set_comment(comment); - return VariableBuilder{input}; - } - - VariableBuilder AddOutput(const std::string& name, - const std::string& comment) { - auto* output = proto_->add_outputs(); - output->set_name(name); - output->set_comment(comment); - return VariableBuilder{output}; - } - - template - TypedAttrChecker& AddAttr(const std::string& name, - const std::string& comment, - bool generated = false) { - auto* attr = proto_->add_attrs(); - attr->set_name(name); - attr->set_comment(comment); - attr->set_generated(generated); - attr->set_type(AttrTypeID()); - return op_checker_->AddAttrChecker(name); - } - - void AddComment(const std::string& comment) { proto_->set_comment(comment); } - - private: - void CheckNoDuplicatedInOutAttrs() { - std::unordered_set names; - auto checker = [&](const std::string& name) { - PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name); - names.insert(name); - }; - for (auto& attr : proto_->attrs()) { - checker(attr.name()); - } - for (auto& input : proto_->inputs()) { - checker(input.name()); - } - for (auto& output : proto_->outputs()) { - checker(output.name()); - } - } - - OpProto* proto_; - OpAttrChecker* op_checker_; - bool validated_{false}; -}; - -class NOPMaker : public OpProtoAndCheckerMaker { - public: - NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) {} -}; - class OpRegistry { using VarNameMap = OperatorBase::VarNameMap; using OpCreator = std::function CreateOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, - AttributeMap attrs) { - auto it = op_info_map().find(type); - PADDLE_ENFORCE(it != op_info_map().end(), - "Operator '%s' has not been registered.", type); - it->second.checker_->Check(attrs); - auto op = it->second.creator_(type, inputs, outputs, attrs); - return std::unique_ptr(op); - } - - static VarNameMap ConvertOpDescVarsToVarNameMap( - const google::protobuf::RepeatedPtrField& op_desc_vars) { - VarNameMap ret_val; - for (auto& var : op_desc_vars) { - auto& var_names = ret_val[var.parameter()]; - auto& var_names_in_proto = var.arguments(); - var_names.reserve(static_cast(var_names_in_proto.size())); - std::copy(var_names_in_proto.begin(), var_names_in_proto.end(), - std::back_inserter(var_names)); - } - return ret_val; - } + AttributeMap attrs); - static std::unique_ptr CreateOp(const OpDesc& op_desc) { - VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); - VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); - AttributeMap attrs; - for (auto& attr : op_desc.attrs()) { - attrs[attr.name()] = GetAttrValue(attr); - } + static std::unique_ptr CreateOp(const OpDesc& op_desc); - return CreateOp(op_desc.type(), inputs, outputs, attrs); - } + static VarNameMap ConvertOpDescVarsToVarNameMap( + const google::protobuf::RepeatedPtrField& op_desc_vars); - static std::unique_ptr CreateGradOp(const OperatorBase& op) { - PADDLE_ENFORCE(!op.IsNetOp(), - "Use framework::Backward to get backward ops"); - return std::unique_ptr(BuildGradOp(&op)); - } + static std::unique_ptr CreateGradOp(const OperatorBase& op); static std::unordered_map& op_info_map() { static std::unordered_map op_info_map_; diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index 0daf12e7f5f3539d460ce67d39ca1c06f5aa2237..eadd8f3316ff1ebffb94a56b2e62d661e4e0b38f 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -164,5 +164,43 @@ std::vector OperatorBase::OutputVars(bool has_intermediate) const { return ret_val; } +void OpProtoAndCheckerMaker::Validate() { + validated_ = true; + CheckNoDuplicatedInOutAttrs(); +} + +OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput( + const std::string& name, const std::string& comment) { + auto* input = proto_->add_inputs(); + input->set_name(name); + input->set_comment(comment); + return OpProtoAndCheckerMaker::VariableBuilder{input}; +} + +OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput( + const std::string& name, const std::string& comment) { + auto* output = proto_->add_outputs(); + output->set_name(name); + output->set_comment(comment); + return OpProtoAndCheckerMaker::VariableBuilder{output}; +} + +void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() { + std::unordered_set names; + auto checker = [&](const std::string& name) { + PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name); + names.insert(name); + }; + for (auto& attr : proto_->attrs()) { + checker(attr.name()); + } + for (auto& input : proto_->inputs()) { + checker(input.name()); + } + for (auto& output : proto_->outputs()) { + checker(output.name()); + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 64481706528c03abd1f474a7f766cd4725a5d10b..5efb945e81c70a519b73f450b97c553804dfcb3f 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -158,6 +158,74 @@ class NOP : public OperatorBase { } }; +// this class not only make proto but also init attribute checkers. +class OpProtoAndCheckerMaker { + public: + OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker) + : proto_(proto), op_checker_(op_checker) {} + + ~OpProtoAndCheckerMaker() { + PADDLE_ENFORCE(validated_, "should call Validate after build"); + } + + void Validate(); + + protected: + struct VariableBuilder { + OpProto::Var* var_; + + VariableBuilder& AsDuplicable() { + var_->set_duplicable(true); + return *this; + } + + VariableBuilder& AsIntermediate() { + var_->set_intermediate(true); + return *this; + } + + // TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it + // means that input/output is not needed when calculate gradient. It does + // not mean no gradient when backward. It should be changed soon. + VariableBuilder& AsNoGradient() { + var_->set_no_gradient(true); + return *this; + } + }; + + VariableBuilder AddInput(const std::string& name, const std::string& comment); + + VariableBuilder AddOutput(const std::string& name, + const std::string& comment); + + template + TypedAttrChecker& AddAttr(const std::string& name, + const std::string& comment, + bool generated = false) { + auto* attr = proto_->add_attrs(); + attr->set_name(name); + attr->set_comment(comment); + attr->set_generated(generated); + attr->set_type(AttrTypeID()); + return op_checker_->AddAttrChecker(name); + } + + void AddComment(const std::string& comment) { proto_->set_comment(comment); } + + private: + void CheckNoDuplicatedInOutAttrs(); + + OpProto* proto_; + OpAttrChecker* op_checker_; + bool validated_{false}; +}; + +class NOPMaker : public OpProtoAndCheckerMaker { + public: + NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) {} +}; + class InferShapeContext { public: InferShapeContext(const OperatorBase& op, const Scope& scope) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 9c4dd5f25083d210bbd218a85d8dbb3cce2c3d0e..0654a301049dcb347b79879076a869a0c14a07ae 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -27,16 +27,24 @@ class SGD(object): SGD Trainer combines data reader, network topolopy and update_equation together to train/test a neural network. - :param update_equation: The optimizer object. - :type update_equation: paddle.v2.optimizer.Optimizer :param cost: Target cost that neural network should be optimized. :type cost: paddle.v2.config_base.Layer :param parameters: The parameters dictionary. :type parameters: paddle.v2.parameters.Parameters + :param update_equation: The optimizer object. + :type update_equation: paddle.v2.optimizer.Optimizer :param extra_layers: Some layers in the neural network graph are not in the path of cost layer. - :param pserver_spec: pserver location, eg: localhost:3000 :type extra_layers: paddle.v2.config_base.Layer + :param is_local: Whether trainning locally + :type is_local: bool + :param pserver_spec: comma string for pserver location, + eg:127.10.0.10:3000,127.10.0.11:3000, + and this parameter is only used for fault + tolerant mode cluster training. + :type pserver_spec: string + :param use_etcd: Whether using etcd pserver. + :param use_etcd: bool """ def __init__(self,