diff --git a/CMakeLists.txt b/CMakeLists.txt index b2481912232cbca95999994417d7f30e98cd4f26..ed3c390066dfac2322d802c6039bc7155a36e38a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -49,6 +49,7 @@ option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF) option(ON_TRAVIS "Exclude special unit test on Travis CI" OFF) option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF) option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF) +option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF) # CMAKE_BUILD_TYPE if(NOT CMAKE_BUILD_TYPE) @@ -129,6 +130,10 @@ if(WITH_GPU) endif(NOT WITH_DSO) endif(WITH_GPU) +if(USE_NNPACK) + list(APPEND EXTERNAL_LIBS ${NNPACK_LIB} ${PTHREADPOOL_LIB} "rt") +endif(USE_NNPACK) + add_subdirectory(proto) # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be diff --git a/cmake/generic.cmake b/cmake/generic.cmake index 03dabe72832cccbf90d6c4ca809fdbc7b068bcb4..88be13b2ac95172d5d9099d62a40449c6a01e98a 100644 --- a/cmake/generic.cmake +++ b/cmake/generic.cmake @@ -101,23 +101,16 @@ function(merge_static_libs TARGET_NAME) # First get the file names of the libraries to be merged foreach(lib ${libs}) - get_target_property(libtype ${lib} TYPE) - if(NOT libtype STREQUAL "STATIC_LIBRARY") - message(FATAL_ERROR "merge_static_libs can only process static libraries") - endif() set(libfiles ${libfiles} $) endforeach() if(APPLE) # Use OSX's libtool to merge archives - add_custom_target(${TARGET_NAME}_archive - COMMAND libtool -static -o "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a" ${libfiles} - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - DEPENDS ${libs} - ) - add_library(${TARGET_NAME} STATIC IMPORTED GLOBAL) - set_property(TARGET ${TARGET_NAME} PROPERTY - IMPORTED_LOCATION "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a") - add_dependencies(${TARGET_NAME} ${TARGET_NAME}_archive) + set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/${TARGET_NAME}_dummy.c) + file(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";") + add_library(${TARGET_NAME} STATIC ${dummyfile}) + add_custom_command(TARGET ${TARGET_NAME} POST_BUILD + COMMAND rm "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a" + COMMAND /usr/bin/libtool -static -o "${CMAKE_CURRENT_BINARY_DIR}/lib${TARGET_NAME}.a" ${libfiles}) else() # general UNIX: use "ar" to extract objects and re-add to a common lib foreach(lib ${libs}) set(objlistfile ${lib}.objlist) # list of objects in the input library diff --git a/doc/design/build_system/README.md b/doc/design/build_system/README.md index 310739f37ae48934afe1d042e87efef85b98f1fc..bf0e4dddc1b640ecbce489f65820aaf8a4b3b1e7 100644 --- a/doc/design/build_system/README.md +++ b/doc/design/build_system/README.md @@ -105,3 +105,48 @@ shared_library(api ### Implementation As above example CMakeLists.txt executes, each function invocation adds "nodes" to a dependency graph. It also use this graph to generate CMake commands including `add_executable`, `add_dependencies`, `target_link_libraries`, and `add_test`. + +### Using Package Manager For Go + +Building Go binaries and libraries need to satisfy their dependencies, generally +we can do `go get ./...` to download and compile all external dependencies. The +problems are: + +1. `go get` will always get the latest code from the default branch of the + remote repo, so changes of dependents might break the build. This is very + different with what we already have in `cmake/external` which download a + specific version or commit id of the dependency. +1. Some locations can not access external dependencies through the internet, as mentioned + in https://github.com/PaddlePaddle/Paddle/issues/2605. Using package management + tools can package the dependencies as a "vendor" package, which can be mirrored + at many cloud file hosting, so users what to compile paddle by themselves can + download this "vendor" package from a mirror site. + +#### Choose A Suitable Tool + +As mentioned by @wangkuiyi, [Here](https://github.com/golang/go/wiki/PackageManagementTools) +list dozens of Go package managers. We choose the tool using following principles: + +- Most "active" projects with more stars, more pull requests or commits +- Widely used project + +After comparing all these projects, we shall choose between the most popular +tools: Godep and Glide. + +Here's a brief comparison between Godep and Glide +: https://github.com/Masterminds/glide/wiki/Go-Package-Manager-Comparison. There are +also many complaints about using `Godep`. There's also a new "official" pakcage +management tool has been started at: https://github.com/golang/dep to resolve +such problems, but it's currently at Alpha stage. So the best choice now is +glide obviously. + +#### Manage Go Packages + +- Dependencies: `go/glide.yaml` will store the dependencies and their versions which + is directly imported by paddle. `go/glide.lock` will store all dependencies recursively + with their commit id. Builds will "lock" to these packages if we don't `glide up` + them +- Vendor package: `go/vendor` directory will generated when running `cmake` command. `cmake` + will download the code corresponding to `go/glide.lock`. If we put a vendor folder + under `go/`, cmake will just check the commit id to the packages under the folder, + if commit id matches, there will be no download at all. diff --git a/doc/design/cluster_train/save_model.md b/doc/design/cluster_train/save_model.md new file mode 100644 index 0000000000000000000000000000000000000000..b70f00176b6701ef487ef88ac0933b9b227037ea --- /dev/null +++ b/doc/design/cluster_train/save_model.md @@ -0,0 +1,110 @@ +# Design Doc: Save Model + +## Overview + +The model is the output of the training process. There are two +ways from which user can obtain a model: + +- Save model triggered by user code: user code asks PaddlePaddle to + save a model. +- Convert model from the checkpoint: model being converted from + pservers' periodic checkpoint. In this way, the user can cancel a + job at any time, and still have a relatively fresh model (we + checkpoint around every 5 minutes). + +### Trainer Saving Model vs. Pservers Saving Model + +Both trainers and pservers have access to the model. So the model can +be saved from a trainer or pservers. We need to decide where the model +is saved from. + +#### Dense Update vs. Sparse Update + +There are two types of model update methods: dense update and sparse +update (when the model parameter is configured to be sparse). + +- Dense update + + Every trainer has it's own full copy of the model. Every model + update will update the entire model. + +- Sparse update + + The training input is sparse, and the trainer does not have the + entire model. It will only download the sub-model necessary related + to the input. When updating the model, only the sub-model related to + the training input is updated. + + +#### Pservers Saving Model + +The benefit of letting pservers save model is they have the entire +model all the time. However, since pservers are on different nodes, it +requires a merging process to merge model shards into the same +model. Thus requires the pservers to write models to a distributed +filesystem, making the checkpoint shards visible to the merge program. + +#### Trainer Saving Model + +The benefit of letting one trainer to save the model is it does not +require a distributed filesystem. And it's reusing the same save model +logic when training locally - except when doing sparse update, the +trainer needs to download the entire model during the saving process. + +#### Conclusion + +Given trainer saving model does not require a distributed filesystem, +and is an intuitive extension to trainer saving model when training +locally, we decide to let the trainer save the model when doing +distributed training. + + +### Convert Model from Checkpoint + +TODO + + +## Timeline + +We first implement trainer save the model. Converting the latest +snapshot to a model will be a TODO for future. + + +## Trainer Save Model + +### Trainer Election + +One trainer will be elected as the one to save the model. When using +etcd, trainer ID is a randomly generated UUID, we will utilize etcd to +elect one trainer. When not using etcd, unique trainer IDs will be +given by the administrator, the trainer whose ID is "0" is elected to +save the model. + +### Model Save Path + +Each trainer will be given the directory to save the model. The +elected trainer will save the model to +`given-directory/trainerID`. Since the trainer ID is unique, this +would prevent concurrent save to the same file when multiple trainers +are elected to save the model when split-brain problem happens. + +### What Happens When Model Is Saving + +It takes some time to save model, we need to define what will happen +when save model is taking place. + +When doing dense update, the trainer uses the local model. Pservers +does not need to pause model update. + +When doing sparse update. The trainer needs to download the entire +model while saving. To get the most accurate model, the model update +needs to be paused before the download starts and resumed after the +download finishes. Otherwise, the trainer gets a model that is +"polluted": some part of the model is old, some part of the model is +new. + +It's unclear that the "polluted" model will be inferior due to the +stochastic nature of deep learning, and pausing the model update will +add more complexity to the system. Since supporting sparse update is a +TODO item. We defer the evaluation of pause the model update or not +during saving model to the future. diff --git a/doc/getstarted/concepts/src/train.py b/doc/getstarted/concepts/src/train.py index 679d0a931a7d650108ea89a04080a55d2976f72e..7e604f23de38543a00f305d508af0791193f78ba 100644 --- a/doc/getstarted/concepts/src/train.py +++ b/doc/getstarted/concepts/src/train.py @@ -31,7 +31,7 @@ def event_handler(event): # define training dataset reader def train_reader(): train_x = np.array([[1, 1], [1, 2], [3, 4], [5, 2]]) - train_y = np.array([-2, -3, -7, -7]) + train_y = np.array([[-2], [-3], [-7], [-7]]) def reader(): for i in xrange(train_y.shape[0]): diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 1c39ced3c9e3da4079a66e29c00be9cc18411b68..1518a8a654cfb54376a49760dc5873733c916937 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -10,6 +10,14 @@ if(WITH_GPU) cuda_compile(cu_objs ${cu_files}) endif() +if(USE_NNPACK) + include(nnpack/nnpack.cmake) + list(APPEND cpp_files nnpack/NNPACKConvOp.cpp) + if(WITH_TESTING) + add_unittest(NNPACKConvOpTest nnpack/NNPACKConvOpTest.cpp) + endif() +endif() + add_library(paddle_function STATIC ${cpp_files} ${cu_objs}) add_dependencies(paddle_function ${external_project_dependencies}) add_dependencies(paddle_function paddle_proto) diff --git a/paddle/function/nnpack/NNPACKConvOp.cpp b/paddle/function/nnpack/NNPACKConvOp.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e8080c3d714b324f072a380f738b9764477dfe04 --- /dev/null +++ b/paddle/function/nnpack/NNPACKConvOp.cpp @@ -0,0 +1,238 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "nnpack.h" +#include "paddle/function/ConvOp.h" + +DEFINE_bool(nnpack_allocate_outside, + false, + "Allocate and free workspace memory outside the NNPACK interface."); +DEFINE_int32(nnpack_num_threads, + 0, + "The number of nnpack threads" + "default: 0; 0 to disable threadpool."); + +namespace paddle { + +nnp_convolution_algorithm get_nnp_convolution_algorithm( + const std::string& algorithm) { + if (algorithm == "auto") { + return nnp_convolution_algorithm_auto; + } else if (algorithm == "ft8x8") { + return nnp_convolution_algorithm_ft8x8; + } else if (algorithm == "ft16x16") { + return nnp_convolution_algorithm_ft16x16; + } else if (algorithm == "wt8x8") { + return nnp_convolution_algorithm_wt8x8; + } else if (algorithm == "implicit-gemm") { + return nnp_convolution_algorithm_implicit_gemm; + } else if (algorithm == "direct") { + return nnp_convolution_algorithm_direct; + } else { + return nnp_convolution_algorithm_auto; + } +} + +template +class NNPACKConvFunction : public ConvFunctionBase { +public: + void init(const FuncConfig& config) override { + ConvFunctionBase::init(config); + CHECK_EQ(groups_, (size_t)1); + algorithm_ = get_nnp_convolution_algorithm(config.get("algo")); + // algorithm_ = nnp_convolution_algorithm_auto; + transform_strategy_ = nnp_convolution_transform_strategy_compute; + nnp_status status = nnp_initialize(); + CHECK_EQ(status, nnp_status_success); + workspaceBuffer_ = nullptr; + workspaceSize_ = 0; + + threadpool_ = nullptr; + if (FLAGS_nnpack_num_threads) { + threadpool_ = pthreadpool_create(FLAGS_nnpack_num_threads); + VLOG(3) << "Number of threads " + << pthreadpool_get_threads_count(threadpool_); + } + } + + ~NNPACKConvFunction() { + if (threadpool_) { + pthreadpool_destroy(threadpool_); + } + if (workspaceBuffer_) { + free(workspaceBuffer_); + } + } + + virtual void check(const BufferArgs& inputs, + const BufferArgs& outputs) override { + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + checkShape(input, filter, output); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(numInputs_, inputs.size()); + CHECK_EQ(numOutputs_, outputs.size()); + CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO); + check(inputs, outputs); + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + + size_t batchSize = input[0]; + size_t inputChannels = input[1]; + size_t inputHeight = input[2]; + size_t inputWidth = input[3]; + size_t filterHeight = getFilterHeight(filter); + size_t filterWidth = getFilterWidth(filter); + size_t outputChannels = output[1]; + // size_t outputHeight = output[2]; + // size_t outputWidth = output[3]; + + nnp_size inputSize = {.width = inputWidth, .height = inputHeight}; + nnp_padding padding = {.top = (size_t)paddingH(), + .right = (size_t)paddingW(), + .bottom = (size_t)paddingH(), + .left = (size_t)paddingW()}; + nnp_size kernelSize = {.width = filterWidth, .height = filterHeight}; + nnp_size outputSubsampling = {.width = (size_t)strideW(), + .height = (size_t)strideH()}; + + float* inputData = inputs[0].data(); + float* filterData = inputs[1].data(); + float* outputData = outputs[0].data(); + + void* bufferPtr = nullptr; + size_t* sizePtr = nullptr; + size_t needSize; + if (FLAGS_nnpack_allocate_outside) { + if (batchSize == 1) { + nnp_status status = nnp_convolution_inference(algorithm_, + transform_strategy_, + inputChannels, + outputChannels, + inputSize, + padding, + kernelSize, + outputSubsampling, + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + &needSize, + nnp_activation_identity, + nullptr, + nullptr, + nullptr); + CHECK_EQ(status, nnp_status_success); + } else { + // only supports stride = 1 + CHECK_EQ(strideH(), 1); + CHECK_EQ(strideW(), 1); + nnp_status status = nnp_convolution_output(algorithm_, + batchSize, + inputChannels, + outputChannels, + inputSize, + padding, + kernelSize, + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + &needSize, + nnp_activation_identity, + nullptr, + nullptr, + nullptr); + CHECK_EQ(status, nnp_status_success); + } + + VLOG(3) << "workspace size is " << needSize; + if (needSize > workspaceSize_) { + workspaceSize_ = needSize; + if (workspaceBuffer_) { + free(workspaceBuffer_); + } else { + posix_memalign(&workspaceBuffer_, 64, needSize); + } + } + + if (needSize) { + bufferPtr = workspaceBuffer_; + sizePtr = &needSize; + } + } + + if (batchSize == 1) { + nnp_status status = + nnp_convolution_inference(algorithm_, + transform_strategy_, + inputChannels, + outputChannels, + inputSize, + padding, + kernelSize, + outputSubsampling, + inputData, + filterData, + nullptr, /* bias */ + outputData, + bufferPtr, + sizePtr, + nnp_activation_identity, + nullptr, + threadpool_, /* threadpool */ + nullptr); + CHECK_EQ(status, nnp_status_success); + } else { + // only supports stride = 1 + CHECK_EQ(strideH(), 1); + CHECK_EQ(strideW(), 1); + nnp_status status = nnp_convolution_output(algorithm_, + batchSize, + inputChannels, + outputChannels, + inputSize, + padding, + kernelSize, + inputData, + filterData, + nullptr, /* bias */ + outputData, + bufferPtr, + sizePtr, + nnp_activation_identity, + nullptr, + threadpool_, /* threadpool */ + nullptr); + CHECK_EQ(status, nnp_status_success); + } + } + +private: + nnp_convolution_algorithm algorithm_; + nnp_convolution_transform_strategy transform_strategy_; + void* workspaceBuffer_; + size_t workspaceSize_; + pthreadpool_t threadpool_; +}; + +REGISTER_TYPED_FUNC(NNPACKConv, CPU, NNPACKConvFunction); + +} // namespace paddle diff --git a/paddle/function/nnpack/NNPACKConvOpTest.cpp b/paddle/function/nnpack/NNPACKConvOpTest.cpp new file mode 100644 index 0000000000000000000000000000000000000000..48180112111c67f36ddd425008187201655089c9 --- /dev/null +++ b/paddle/function/nnpack/NNPACKConvOpTest.cpp @@ -0,0 +1,99 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include "paddle/function/Function.h" +#include "paddle/function/FunctionTest.h" + +DEFINE_string(algo, + "auto", + "The algorithm (auto, ft8x8, ft16x16, wt8x8, " + "implicit-gemm, or direct) for computing convolution of NNPACK."); + +namespace paddle { + +#define IS_NNPACK_SUPPORT(algo, filterSize, stride) \ + if (algo == "direct" && filterSize != 1) continue; \ + if (algo == "direct" && batchSize != 1) continue; \ + if (algo == "wt8x8" && filterSize != 3) continue; \ + if (algo == "implicit-gemm" && batchSize != 1) continue; \ + if (algo != "auto" && algo != "implicit-gemm" && stride > 1) continue; + +class ConvolutionTest { +public: + ConvolutionTest(const std::string& conv1, + const std::string& conv2, + std::string algo = "auto") { + for (size_t batchSize : {1, 32}) { + for (size_t inputSize : {7, 14, 54}) { + for (size_t filterSize : {1, 3, 5}) { + for (size_t inputChannels : {3, 64}) { + for (size_t outputChannels : {3, 64, 128}) { + if (inputChannels < outputChannels) break; + for (size_t stride : {1, 2}) { + // if batchSize > 1 NNPACKConv only supports stride = 1 + if (batchSize > 1 && stride > 1) break; + for (size_t padding : {0, 1}) { + if (padding >= filterSize) break; + size_t outputSize = + (inputSize - filterSize + 2 * padding + stride) / stride; + IS_NNPACK_SUPPORT(algo, filterSize, stride); + LOG(INFO) << " batchSize=" << batchSize + << " inputChannels=" << inputChannels + << " inputHeight=" << inputSize + << " inputWidth=" << inputSize + << " outputChannels=" << outputChannels + << " filterHeight=" << filterSize + << " filterWidth=" << filterSize + << " outputHeight=" << outputSize + << " outputWidth=" << outputSize + << " stride=" << stride << " padding=" << padding; + + std::vector paddings = {padding, padding}; + std::vector strides = {stride, stride}; + Compare2Function test( + conv1, + conv2, + FuncConfig() + .set("paddings", paddings) + .set("strides", strides) + .set("groups", (size_t)1) + .set("algo", algo)); + + TensorShape shape0{ + batchSize, inputChannels, inputSize, inputSize}; + TensorShape shape1{ + outputChannels, inputChannels, filterSize, filterSize}; + TensorShape shape2{ + batchSize, outputChannels, outputSize, outputSize}; + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape0)); + test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape1)); + test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape2)); + test.run(); + } + } + } + } + } + } + } + } +}; + +TEST(Convolution, NNPACK) { + // NNPACK only supports stride = 1 + ConvolutionTest test("GemmConv-CPU", "NNPACKConv-CPU", FLAGS_algo); +} + +} // namespace paddle diff --git a/paddle/function/nnpack/nnpack.cmake b/paddle/function/nnpack/nnpack.cmake new file mode 100644 index 0000000000000000000000000000000000000000..7182730ae8f133bdc4f73bfc46fa8acbe5f3b603 --- /dev/null +++ b/paddle/function/nnpack/nnpack.cmake @@ -0,0 +1,16 @@ +# Find the NNPACK library +# NNPACK_ROOT - where to find NNPACK include and library. +# + +set(NNPACK_FOUND OFF) +set(NNPACK_ROOT $ENV{NNPACK_ROOT} CACHE PATH "Folder contains NNPACK") +find_path(NNPACK_INC_DIR nnpack.h PATHS ${NNPACK_ROOT}/include) +find_library(NNPACK_LIB NAMES nnpack PATHS ${NNPACK_ROOT}/lib) +find_library(PTHREADPOOL_LIB NAMES pthreadpool PATHS ${NNPACK_ROOT}/lib) + +if(NNPACK_INC_DIR AND NNPACK_LIB AND PTHREADPOOL_LIB) + set(NNPACK_FOUND ON) + INCLUDE_DIRECTORIES(${NNPACK_INC_DIR}) +else() + message(FATAL_ERROR "Cannot find NNPACK in (${NNPACK_ROOT})") +endif() diff --git a/paddle/gserver/layers/ExpandConvLayer.cpp b/paddle/gserver/layers/ExpandConvLayer.cpp index 914689e66cdb8947e886e17e75829183c1af1a42..af79e65a7c09e5a1b55febf1df1e8f5bb61bdcb8 100644 --- a/paddle/gserver/layers/ExpandConvLayer.cpp +++ b/paddle/gserver/layers/ExpandConvLayer.cpp @@ -16,6 +16,10 @@ limitations under the License. */ #include "paddle/utils/Logging.h" #include "paddle/utils/Stat.h" +DEFINE_bool(use_nnpack, + false, + "Whether to use nnpack for convolution calculation."); + namespace paddle { /* @@ -37,26 +41,38 @@ bool ExpandConvLayer::init(const LayerMap &layerMap, for (int i = 0; i < config_.inputs_size(); i++) { std::vector paddings = {(size_t)paddingY_[i], (size_t)padding_[i]}; std::vector strides = {(size_t)strideY_[i], (size_t)stride_[i]}; - createFunction(forward_, - !isDeconv_ ? "GemmConv" : "GemmConvGradInput", - FuncConfig() - .set("paddings", paddings) - .set("strides", strides) - .set("groups", (size_t)groups_[i])); - - createFunction(backward_, - !isDeconv_ ? "GemmConvGradInput" : "GemmConv", - FuncConfig() - .set("paddings", paddings) - .set("strides", strides) - .set("groups", (size_t)groups_[i])); - - createFunction(backward_, - "GemmConvGradFilter", - FuncConfig() - .set("paddings", paddings) - .set("strides", strides) - .set("groups", (size_t)groups_[i])); + + if (FLAGS_use_nnpack) { + CHECK_EQ(isDeconv_, false); + createFunction(forward_, + "NNPACKConv", + FuncConfig() + .set("paddings", paddings) + .set("strides", strides) + .set("groups", (size_t)groups_[i]) + .set("algo", std::string("auto"))); + } else { + createFunction(forward_, + !isDeconv_ ? "GemmConv" : "GemmConvGradInput", + FuncConfig() + .set("paddings", paddings) + .set("strides", strides) + .set("groups", (size_t)groups_[i])); + + createFunction(backward_, + !isDeconv_ ? "GemmConvGradInput" : "GemmConv", + FuncConfig() + .set("paddings", paddings) + .set("strides", strides) + .set("groups", (size_t)groups_[i])); + + createFunction(backward_, + "GemmConvGradFilter", + FuncConfig() + .set("paddings", paddings) + .set("strides", strides) + .set("groups", (size_t)groups_[i])); + } } return true; } diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index a9842152c8640aa4748967cf43dd26ed2c14606b..361e764e25ba1801bd22f785bc282e51f058aae6 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -13,8 +13,11 @@ set(PY_FILES paddle/__init__.py ${V2_PY_FILES}) add_custom_target(copy_paddle_master) + +SET(COPY_PADDLE_MASTER "") if(WITH_GOLANG) - add_custom_command(TARGET copy_paddle_master + SET(COPY_PADDLE_MASTER "copy_paddle_master") + add_custom_command(TARGET ${COPY_PADDLE_MASTER} COMMAND cp ${paddle_master_LIB_PATH} ${PROJ_ROOT}/python/paddle/v2/master/ ) add_dependencies(copy_paddle_master paddle_master) @@ -26,7 +29,7 @@ configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in add_custom_command(OUTPUT ${OUTPUT_DIR}/.timestamp COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel COMMAND ${CMAKE_COMMAND} -E touch ${OUTPUT_DIR}/.timestamp - DEPENDS gen_proto_py ${PY_FILES} ${external_project_dependencies} copy_paddle_master) + DEPENDS gen_proto_py ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER}) add_custom_target(paddle_python ALL DEPENDS ${OUTPUT_DIR}/.timestamp) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 58e4902f57aa8018b820f48f6cbf659f1e5f5183..b7418101d83fde1b91781d3a42b056cc7708cba9 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2082,10 +2082,10 @@ class MaxOutLayer(LayerBase): class RowConvLayer(LayerBase): def __init__(self, name, inputs, context_length, **xargs): super(RowConvLayer, self).__init__( - name, 'maxout', 0, inputs=inputs, **xargs) + name, 'row_conv', 0, inputs=inputs, **xargs) config_assert( len(self.inputs) == 1, - 'TransLayer must have one and only one input') + 'row convolution layer must have one and only one input.') input_layer = self.get_input_layer(0) row_conv_conf = self.config.inputs[0].row_conv_conf row_conv_conf.context_length = context_length diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr index 9ec15d2a19ec50a1729f9eeaa6dce8b1153c776b..19c9f16574ca6fb3a9e9dbfb2d1f52024e604239 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_conv.protostr @@ -7,7 +7,7 @@ layers { } layers { name: "__row_conv_layer_0__" - type: "maxout" + type: "row_conv" size: 2560 active_type: "relu" inputs { diff --git a/python/paddle/v2/dataset/flowers.py b/python/paddle/v2/dataset/flowers.py index 158cfe158c4f1c8d82d157301adcfbe0351c55df..e2a21e6e3e04e79fdfc225ce1b4496b6b69d1e89 100644 --- a/python/paddle/v2/dataset/flowers.py +++ b/python/paddle/v2/dataset/flowers.py @@ -30,6 +30,7 @@ http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}. """ import cPickle import itertools +import functools from common import download import tarfile import scipy.io as scio @@ -54,21 +55,26 @@ TEST_FLAG = 'trnid' VALID_FLAG = 'valid' -def default_mapper(sample): +def default_mapper(is_train, sample): ''' map image bytes data to type needed by model input layer ''' img, label = sample img = load_image_bytes(img) - img = simple_transform(img, 256, 224, True) + img = simple_transform( + img, 256, 224, is_train, mean=[103.94, 116.78, 123.68]) return img.flatten().astype('float32'), label +train_mapper = functools.partial(default_mapper, True) +test_mapper = functools.partial(default_mapper, False) + + def reader_creator(data_file, label_file, setid_file, dataset_name, - mapper=default_mapper, + mapper, buffered_size=1024, use_xmap=True): ''' @@ -118,7 +124,7 @@ def reader_creator(data_file, return map_readers(mapper, reader) -def train(mapper=default_mapper, buffered_size=1024, use_xmap=True): +def train(mapper=train_mapper, buffered_size=1024, use_xmap=True): ''' Create flowers training set reader. It returns a reader, each sample in the reader is @@ -141,7 +147,7 @@ def train(mapper=default_mapper, buffered_size=1024, use_xmap=True): buffered_size, use_xmap) -def test(mapper=default_mapper, buffered_size=1024, use_xmap=True): +def test(mapper=test_mapper, buffered_size=1024, use_xmap=True): ''' Create flowers test set reader. It returns a reader, each sample in the reader is @@ -164,7 +170,7 @@ def test(mapper=default_mapper, buffered_size=1024, use_xmap=True): buffered_size, use_xmap) -def valid(mapper=default_mapper, buffered_size=1024, use_xmap=True): +def valid(mapper=test_mapper, buffered_size=1024, use_xmap=True): ''' Create flowers validation set reader. It returns a reader, each sample in the reader is diff --git a/python/paddle/v2/image.py b/python/paddle/v2/image.py index 0d648e9ae697ff0373c6cdc166608d395a8d8086..965d965335a56a97448bd8c738b03eceaee550e2 100644 --- a/python/paddle/v2/image.py +++ b/python/paddle/v2/image.py @@ -262,7 +262,12 @@ def left_right_flip(im): return im[:, ::-1, :] -def simple_transform(im, resize_size, crop_size, is_train, is_color=True): +def simple_transform(im, + resize_size, + crop_size, + is_train, + is_color=True, + mean=None): """ Simply data argumentation for training. These operations include resizing, croping and flipping. @@ -288,7 +293,19 @@ def simple_transform(im, resize_size, crop_size, is_train, is_color=True): im = left_right_flip(im) else: im = center_crop(im, crop_size) - im = to_chw(im) + if len(im.shape) == 3: + im = to_chw(im) + + im = im.astype('float32') + if mean is not None: + mean = np.array(mean, dtype=np.float32) + # mean value, may be one value per channel + if mean.ndim == 1: + mean = mean[:, np.newaxis, np.newaxis] + else: + # elementwise mean + assert len(mean.shape) == len(im) + im -= mean return im @@ -297,7 +314,8 @@ def load_and_transform(filename, resize_size, crop_size, is_train, - is_color=True): + is_color=True, + mean=None): """ Load image from the input file `filename` and transform image for data argumentation. Please refer to the `simple_transform` interface @@ -318,5 +336,5 @@ def load_and_transform(filename, :type is_train: bool """ im = load_image(filename) - im = simple_transform(im, resize_size, crop_size, is_train, is_color) + im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean) return im