dangqingqing
7 years ago
commit
a1bf4c2833
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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IF(MOBILE_INFERENCE)
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return()
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ENDIF()
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include (ExternalProject)
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# NOTE: c-ares is needed when linking with grpc.
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SET(CARES_SOURCES_DIR ${THIRD_PARTY_PATH}/cares)
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SET(CARES_INSTALL_DIR ${THIRD_PARTY_PATH}/install/cares)
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SET(CARES_INCLUDE_DIR "${CARES_INSTALL_DIR}/include/" CACHE PATH "cares include directory." FORCE)
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ExternalProject_Add(
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extern_cares
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GIT_REPOSITORY "https://github.com/c-ares/c-ares.git"
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GIT_TAG "cares-1_13_0"
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PREFIX ${CARES_SOURCES_DIR}
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UPDATE_COMMAND ""
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CONFIGURE_COMMAND ./buildconf && ./configure --disable-shared --prefix=${CARES_INSTALL_DIR}
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BUILD_IN_SOURCE 1
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BUILD_COMMAND make
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INSTALL_COMMAND make install
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)
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ADD_LIBRARY(cares STATIC IMPORTED GLOBAL)
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SET_PROPERTY(TARGET cares PROPERTY IMPORTED_LOCATION
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"${CARES_INSTALL_DIR}/lib/libcares.a")
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include_directories(${CARES_INCLUDE_DIR})
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ADD_DEPENDENCIES(cares extern_cares)
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@ -0,0 +1,66 @@
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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IF(MOBILE_INFERENCE)
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return()
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ENDIF()
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include (ExternalProject)
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SET(GRPC_SOURCES_DIR ${THIRD_PARTY_PATH}/grpc)
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SET(GRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/grpc)
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SET(GRPC_INCLUDE_DIR "${GRPC_INSTALL_DIR}/include/" CACHE PATH "grpc include directory." FORCE)
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SET(GRPC_CPP_PLUGIN "${GRPC_INSTALL_DIR}/bin/grpc_cpp_plugin" CACHE FILEPATH "GRPC_CPP_PLUGIN" FORCE)
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IF(APPLE)
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SET(BUILD_CMD make -n | sed "s/-Werror//g" | sh)
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ELSE()
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SET(BUILD_CMD make)
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ENDIF()
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ExternalProject_Add(
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extern_grpc
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DEPENDS protobuf zlib
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GIT_REPOSITORY "https://github.com/grpc/grpc.git"
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GIT_TAG "v1.7.x"
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PREFIX ${GRPC_SOURCES_DIR}
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UPDATE_COMMAND ""
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CONFIGURE_COMMAND ""
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BUILD_IN_SOURCE 1
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# NOTE(yuyang18):
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# Disable -Werror, otherwise the compile will fail in MacOS.
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# It seems that we cannot configure that by make command.
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# Just dry run make command and remove `-Werror`, then use a shell to run make commands
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BUILD_COMMAND ${BUILD_CMD}
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INSTALL_COMMAND make prefix=${GRPC_INSTALL_DIR} install
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)
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# FIXME(typhoonzero): hack to get static lib path, try a better way like merge them.
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ADD_LIBRARY(grpc++_unsecure STATIC IMPORTED GLOBAL)
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SET_PROPERTY(TARGET grpc++_unsecure PROPERTY IMPORTED_LOCATION
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"${GRPC_INSTALL_DIR}/lib/libgrpc++_unsecure.a")
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ADD_LIBRARY(grpc++ STATIC IMPORTED GLOBAL)
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SET_PROPERTY(TARGET grpc++ PROPERTY IMPORTED_LOCATION
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"${GRPC_INSTALL_DIR}/lib/libgrpc++.a")
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ADD_LIBRARY(gpr STATIC IMPORTED GLOBAL)
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SET_PROPERTY(TARGET gpr PROPERTY IMPORTED_LOCATION
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"${GRPC_INSTALL_DIR}/lib/libgpr.a")
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ADD_LIBRARY(grpc_unsecure STATIC IMPORTED GLOBAL)
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SET_PROPERTY(TARGET grpc_unsecure PROPERTY IMPORTED_LOCATION
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"${GRPC_INSTALL_DIR}/lib/libgrpc_unsecure.a")
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include_directories(${GRPC_INCLUDE_DIR})
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ADD_DEPENDENCIES(grpc++_unsecure extern_grpc)
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,158 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "FactorizationMachineLayer.h"
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#include <algorithm>
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#include <vector>
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#include "paddle/math/SparseMatrix.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(factorization_machine, FactorizationMachineLayer);
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bool FactorizationMachineLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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factorSize_ = config_.factor_size();
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/* initialize the latentVectors_ */
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CHECK_EQ(inputLayers_.size(), 1UL);
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size_t inputSize = inputLayers_[0]->getSize();
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CHECK_EQ(parameters_[0]->getSize(), inputSize * factorSize_);
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latentVectors_ = std::unique_ptr<Weight>(
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new Weight(inputSize, factorSize_, parameters_[0]));
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return true;
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}
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void FactorizationMachineLayer::forward(PassType passType) {
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Layer::forward(passType);
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const MatrixPtr& inputV = getInputValue(0);
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size_t batchSize = inputV->getHeight();
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size_t outputSize = getSize();
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size_t inputSize = inputLayers_[0]->getSize();
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reserveOutput(batchSize, outputSize);
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MatrixPtr outV = getOutputValue();
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Matrix::resizeOrCreate(
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latentVectorsSquare_, inputSize, factorSize_, false, useGpu_);
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Matrix::resizeOrCreate(
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inputMulFactor_, batchSize, factorSize_, false, useGpu_);
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Matrix::resizeOrCreate(tmpOut_, batchSize, factorSize_, false, useGpu_);
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REGISTER_TIMER_INFO("FmInputMulFactorTimer", getName().c_str());
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inputMulFactor_->mul(*inputV, *latentVectors_->getW());
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inputMulFactor_->square2(*tmpOut_);
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outV->sumRows(*tmpOut_, 0.5, 0);
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if (dynamic_cast<CpuSparseMatrix*>(inputV.get())) {
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Matrix::resizeOrCreateSparseMatrix(inputSquare_,
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inputV->getHeight(),
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inputV->getWidth(),
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inputV->getElementCnt(),
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inputV->getValueType());
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inputSquare_->copyFrom(*inputV);
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(dynamic_cast<CpuSparseMatrix*>(inputSquare_.get()))->square2();
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} else {
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Matrix::resizeOrCreate(
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inputSquare_, inputV->getHeight(), inputV->getWidth(), false, useGpu_);
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inputV->square2(*inputSquare_);
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}
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latentVectors_->getW()->square2(*latentVectorsSquare_);
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tmpOut_->mul(*inputSquare_, *latentVectorsSquare_);
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outV->sumRows(*tmpOut_, -0.5, 1.0);
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/* activation */ {
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REGISTER_TIMER_INFO("FmFwAtvTimer", getName().c_str());
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forwardActivation();
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}
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}
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void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
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/* Do derivation */ { backwardActivation(); }
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const MatrixPtr& inputV = getInputValue(0);
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const MatrixPtr& oGrad = getOutputGrad();
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Matrix::resizeOrCreate(
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tmpSum_, 1, latentVectors_->getW()->getHeight(), false, useGpu_);
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MatrixPtr tmpSumTrans = Matrix::create(tmpSum_->getRowBuf(0),
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latentVectors_->getW()->getHeight(),
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1,
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false,
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useGpu_);
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/* Calculate the gradients of the latentVectors_ matrix */
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if (latentVectors_->getWGrad()) {
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if (dynamic_cast<CpuSparseMatrix*>(inputV.get())) {
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Matrix::resizeOrCreateSparseMatrix(tmpInput_,
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inputV->getHeight(),
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inputV->getWidth(),
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inputV->getElementCnt());
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CpuSparseMatrix* sparseInputV =
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dynamic_cast<CpuSparseMatrix*>(inputV.get());
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CpuSparseMatrix* sparseInputSquare =
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dynamic_cast<CpuSparseMatrix*>(inputSquare_.get());
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CpuSparseMatrix* sparseTmpInput =
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dynamic_cast<CpuSparseMatrix*>(tmpInput_.get());
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sparseTmpInput->copyFrom(*sparseInputV);
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sparseTmpInput->rowScale(0, *sparseInputV, *oGrad);
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latentVectors_->getWGrad()->mul(
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*sparseTmpInput->getTranspose(), *inputMulFactor_, 1, 1);
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sparseTmpInput->rowScale(0, *sparseInputSquare, *oGrad);
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Matrix::resizeOrCreate(negOnes_, 1, inputV->getHeight(), false, useGpu_);
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negOnes_->zeroMem();
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negOnes_->add(-1);
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tmpSum_->mul(*negOnes_, *sparseTmpInput, 1, 0);
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} else {
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Matrix::resizeOrCreate(
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tmpInput_, inputV->getHeight(), inputV->getWidth(), false, useGpu_);
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tmpInput_->rowScale(0, *inputV, *oGrad);
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latentVectors_->getWGrad()->mul(
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*tmpInput_->getTranspose(), *inputMulFactor_, 1, 1);
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tmpInput_->rowScale(0, *inputSquare_, *oGrad);
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tmpSum_->sumCols(*tmpInput_, -1, 0);
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}
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latentVectors_->getWGrad()->addRowScale(
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0, *latentVectors_->getW(), *tmpSumTrans);
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/* Increasing the number of gradient */
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latentVectors_->getParameterPtr()->incUpdate(callback);
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}
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/* Calculate the input layers gradient */
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MatrixPtr inGrad = getInputGrad(0);
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if (inGrad != NULL) {
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inGrad->mul(
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*inputMulFactor_, *latentVectors_->getW()->getTranspose(), 1, 1);
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tmpSumTrans->sumRows(*latentVectorsSquare_, -1, 0);
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inGrad->addColScale(0, *inputV, *tmpSum_);
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inGrad->rowScale(0, *inGrad, *oGrad);
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}
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}
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} // namespace paddle
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@ -0,0 +1,80 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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||||
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/utils/ThreadLocal.h"
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namespace paddle {
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/**
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* @brief The Factorization Machine models pairwise (order-2) feature
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* interactions as inner product of the learned latent vectors corresponding
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* to each input feature.
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*
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* The Factorization Machine can effectively capture feature interactions
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* especially when the input is sparse. While in principle FM can model higher
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* order feature interaction, in practice usually only order-2 feature
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* interactions are considered. The Factorization Machine Layer here only
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* computes the order-2 interations with the formula:
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*
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* \f[
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* y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j
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* \f]
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*
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* The detailed calculation for forward and backward can be found at this paper:
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*
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* Factorization machines.
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*
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* The config file api is factorization_machine.
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*/
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class FactorizationMachineLayer : public Layer {
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protected:
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// The latent vectors, shape: (size, factorSize_)
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// Each row of the latentVectors_ matrix is the latent vector
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// corresponding to one input feature dimension
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std::unique_ptr<Weight> latentVectors_;
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// The hyperparameter that defines the dimensionality of the factorization
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size_t factorSize_;
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private:
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// Store the square values of the letent vectors matrix
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MatrixPtr latentVectorsSquare_;
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// Store the square values of input matrix
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MatrixPtr inputSquare_;
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// The result of input matrix * latent vector matrix that will be used in
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// both forward and backward step
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MatrixPtr inputMulFactor_;
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// Store temporary calculation result
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MatrixPtr tmpOut_;
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MatrixPtr tmpSum_;
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MatrixPtr tmpInput_;
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// Negative identity matrix
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MatrixPtr negOnes_;
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public:
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explicit FactorizationMachineLayer(const LayerConfig& config)
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: Layer(config) {}
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~FactorizationMachineLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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} // namespace paddle
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