diff --git a/cmake/cpplint.cmake b/cmake/cpplint.cmake index e50530411cc74392091c8026fa012ec7631f7f6b..5184f0815faac005b3dff1015395235f4e19d65b 100644 --- a/cmake/cpplint.cmake +++ b/cmake/cpplint.cmake @@ -42,29 +42,21 @@ macro(add_style_check_target TARGET_NAME) if(WITH_STYLE_CHECK) set(SOURCES_LIST ${ARGN}) list(REMOVE_DUPLICATES SOURCES_LIST) - list(SORT SOURCES_LIST) - foreach(filename ${SOURCES_LIST}) - set(LINT ON) foreach(pattern ${IGNORE_PATTERN}) if(filename MATCHES ${pattern}) - message(STATUS "DROP LINT ${filename}") - set(LINT OFF) + list(REMOVE_ITEM SOURCES_LIST ${filename}) endif() endforeach() - if(LINT MATCHES ON) - # cpplint code style - get_filename_component(base_filename ${filename} NAME) - set(CUR_GEN ${CMAKE_CURRENT_BINARY_DIR}/${base_filename}.cpplint) - add_custom_command(OUTPUT ${CUR_GEN} PRE_BUILD - COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py" - "--filter=${STYLE_FILTER}" - "--write-success=${CUR_GEN}" ${filename} - DEPENDS ${filename} ${PROJ_ROOT}/paddle/scripts/cpplint.py - WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}) - add_custom_target(${base_filename}.cpplint DEPENDS ${CUR_GEN}) - add_dependencies(${TARGET_NAME} ${base_filename}.cpplint) - endif() endforeach() + + if(SOURCES_LIST) + add_custom_command(TARGET ${TARGET_NAME} POST_BUILD + COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py" + "--filter=${STYLE_FILTER}" + ${SOURCES_LIST} + COMMENT "cpplint: Checking source code style" + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}) + endif() endif() endmacro() diff --git a/cmake/flags.cmake b/cmake/flags.cmake index d00a9bb3a30cfb16623e073414088059481c3e1a..e26d8d9df386e65137aa83cc60a43bfeabf7a4a6 100644 --- a/cmake/flags.cmake +++ b/cmake/flags.cmake @@ -115,7 +115,7 @@ set(COMMON_FLAGS -Wno-error=literal-suffix -Wno-error=sign-compare -Wno-error=unused-local-typedefs - -Wno-error=parentheses-equality # Warnings in Pybind11 + -Wno-error=parentheses-equality # Warnings in pybind11 ) set(GPU_COMMON_FLAGS @@ -195,6 +195,7 @@ endif() # Modern gpu architectures: Pascal if (CUDA_VERSION VERSION_GREATER "8.0" OR CUDA_VERSION VERSION_EQUAL "8.0") list(APPEND __arch_flags " -gencode arch=compute_60,code=sm_60") + list(APPEND CUDA_NVCC_FLAGS --expt-relaxed-constexpr) endif() # Custom gpu architecture diff --git a/doc/design/mkldnn/README.MD b/doc/design/mkldnn/README.MD new file mode 100644 index 0000000000000000000000000000000000000000..e956994431fbb43438c56dcd96ad8313cf516090 --- /dev/null +++ b/doc/design/mkldnn/README.MD @@ -0,0 +1,110 @@ +# Intel® MKL-DNN on PaddlePaddle: Design Doc + +我们计划将Intel深度神经网络数学库(**MKL-DNN**\[[1](#references)\])集成到PaddlePaddle，充分展现英特尔平台的优势，有效提升PaddlePaddle在英特尔架构上的性能。 + +我们短期内的基本目标是： + +- 完成常用layer的MKL-DNN实现。 +- 完成常见深度神经网络VGG，GoogLeNet 和 ResNet的MKL-DNN实现。 + + +## Contents + +- [Overview](#overview) +- [Actions](#actions) + - [CMake](#cmake) + - [Layers](#layers) + - [Activations](#activations) + - [Unit Tests](#unit-tests) + - [Protobuf Messages](#protobuf-messages) + - [Python API](#python-api) + - [Demos](#demos) + - [Benchmarking](#benchmarking) + - [Others](#others) +- [Design Concerns](#design-concerns) + +## Overview + +我们会把MKL-DNN作为第三方库集成进PaddlePaddle，整体框架图 +
+
+Figure 1. PaddlePaddle on IA. +
+ +## Actions +我们把集成方案大致分为了如下几个方面。 + +### CMake +我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。 + +同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。 + +所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。 + +**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。 + +### Layers +所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在 +`paddle/gserver/layers`中，并且文件名都会一以*Mkldnn*开头。 + +所有MKL-DNN的layers都会继承于一个叫做`MkldnnLayer`的父类，该父类继承于PaddlePaddle的基类`Layer`。 + +### Activations +由于在PaddlePaddle中，激活函数是独立于layer概念的，所以会在`paddle/gserver/activations`目录下添加一个`MkldnnActivation.h`文件定义一些用于MKL-DNN的接口，实现方法还是会在`ActivationFunction.cpp`文件。 + +### Unit Tests +会在`paddle/gserver/test`目录下添加`test_Mkldnn.cpp`和`MkldnnTester.*`用于MKL-DNN的测试。 + +Activation的测试，计划在PaddlePaddle原有的测试文件上直接添加新的测试type。 + +### Protobuf Messages +根据具体layer的需求可能会在`proto/ModelConfig.proto`里面添加必要的选项。 + +### Python API +目前只考虑**v1 API**。 + +计划在`python/paddle/trainer/config_parser.py`里面添加`use_mkldnn`这个选择，方便用户选择使用MKL-DNN的layers。 + +具体实现方式比如： + +```python +use_mkldnn = bool(int(g_command_config_args.get("use_mkldnn", 0))) +if use_mkldnn + self.layer_type = mkldnn_* +``` + +所有MKL-DNN的layer type会以*mkldnn_*开头，以示区分。 + +并且可能在`python/paddle/trainer_config_helper`目录下的`activations.py `和`layers.py`里面添加必要的MKL-DNN的接口。 + +### Demos + +会在`v1_api_demo`目录下添加一个`mkldnn`的文件夹，里面放入一些用于MKL-DNN测试的demo脚本。 + +### Benchmarking +会考虑添加部分逻辑在`benchmark/paddle/image/run.sh`，添加使用MKL-DNN的测试。 + +### Others +1. 如果在使用MKL-DNN的情况下，会把CPU的Buffer对齐为64。 +2. 深入PaddlePaddle，寻找有没有其他可以优化的可能，进一步优化。比如可能会用OpenMP改进SGD的更新性能。 + +## Design Concerns + +为了更好的符合PaddlePaddle的代码风格\[[2](#references)\]，同时又尽可能少的牺牲MKL-DNN的性能\[[3](#references)\]。 + +我们总结出一些特别需要注意的点： + +1. 使用**deviceId_**。为了尽可能少的在父类Layer中添加变量或者函数，我们决定使用已有的`deviceId_`变量来区分layer的属性，定义`-2`为`MkldnnLayer`特有的设备ID。 +2. 重写父类Layer的**init**函数，修改`deviceId_`为`-2`，代表这个layer是用于跑在MKL-DNN的环境下。 +3. 创建`MkldnnMatrix`，用于管理MKL-DNN会用到的相关memory函数、接口以及会用的到格式信息。 +4. 创建`MkldnnBase`，定义一些除了layer和memory相关的类和函数。包括MKL-DNN会用到`MkldnnStream`和`CpuEngine`，和未来可能还会用到`FPGAEngine`等。 +5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。 +6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。 +7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。 + +## References + +1. [Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN)](https://github.com/01org/mkl-dnn "Intel MKL-DNN") +2. [原来的方案](https://github.com/PaddlePaddle/Paddle/pull/3096)会引入**nextLayer**的信息。但是在PaddlePaddle中，无论是重构前的layer还是重构后的op，都不会想要知道next layer/op的信息。 +3. MKL-DNN的高性能格式与PaddlePaddle原有的`NCHW`不同(PaddlePaddle中的CUDNN部分使用的也是`NCHW`，所以不存在这个问题)，所以需要引入一个转换方法，并且只需要在必要的时候转换这种格式，才能更好的发挥MKL-DNN的性能。 + diff --git a/doc/design/mkldnn/image/overview.png b/doc/design/mkldnn/image/overview.png new file mode 100644 index 0000000000000000000000000000000000000000..84b455c28230703599a2529f014cfbb222138fef Binary files /dev/null and b/doc/design/mkldnn/image/overview.png differ diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index f8a88cf317aee6c5dd25e4cc25d588c6c50fcbce..cf61a243e9df2fd4a580e41f07cb0a22dcc72083 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -22,7 +22,5 @@ if(WITH_C_API) endif() if(WITH_SWIG_PY) - configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in - ${CMAKE_CURRENT_SOURCE_DIR}/setup.py) add_subdirectory(api) endif() diff --git a/paddle/api/CMakeLists.txt b/paddle/api/CMakeLists.txt index 84da89a1422b6095b995744cebb6a3af98a071c6..7a1e8b8b26ac6330c3799b7dfeb4447e171fe0f1 100644 --- a/paddle/api/CMakeLists.txt +++ b/paddle/api/CMakeLists.txt @@ -82,9 +82,7 @@ SWIG_LINK_LIBRARIES(swig_paddle add_custom_command(OUTPUT ${PROJ_ROOT}/paddle/py_paddle/_swig_paddle.so COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/swig_paddle.py ${PROJ_ROOT}/paddle/py_paddle COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/_swig_paddle.so ${PROJ_ROOT}/paddle/py_paddle - COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel - COMMAND ${CMAKE_COMMAND} -E touch dist/.timestamp - COMMAND rm -rf py_paddle.egg-info build + COMMAND ${CMAKE_COMMAND} -E touch .timestamp WORKING_DIRECTORY ${PROJ_ROOT}/paddle DEPENDS _swig_paddle ) @@ -92,10 +90,6 @@ add_custom_command(OUTPUT ${PROJ_ROOT}/paddle/py_paddle/_swig_paddle.so # TODO(yuyang18) : make wheel name calculated by cmake add_custom_target(python_api_wheel ALL DEPENDS ${PROJ_ROOT}/paddle/py_paddle/_swig_paddle.so) -install(DIRECTORY ${CMAKE_SOURCE_DIR}/paddle/dist/ - DESTINATION opt/paddle/share/wheels -) - if(WITH_TESTING) IF(NOT PY_PIP_FOUND) SET(PIP_SOURCES_DIR ${PYTHON_SOURCES_DIR}/pip) @@ -108,7 +102,7 @@ if(WITH_TESTING) BUILD_COMMAND "" INSTALL_COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py install BUILD_IN_SOURCE 1 - DEPENDS python setuptools python_api_wheel + #DEPENDS python setuptools python_api_wheel ) ENDIF() add_subdirectory(test) diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 6c26183818a9d6996e3d3ce2af74ba36f4711eca..b2813da83d9e4c525e66bb1f79b28769627eaec2 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -260,6 +260,12 @@ class OpRegistry { return CreateOp(op_desc.type(), inputs, outputs, attrs); } + static bool SupportGPU(const std::string& op_type) { + OperatorWithKernel::OpKernelKey key; + key.place_ = platform::GPUPlace(); + return OperatorWithKernel::AllOpKernels().at(op_type).count(key) != 0; + } + static std::shared_ptr CreateGradOp(const OperatorBase& op) { PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index b25362fef336fd84934e901108b6c8358463fe03..9672492d1c2c6a82c37e0a840a4ca9c111de06d8 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -174,7 +174,11 @@ class OperatorContext { template T* Output(const size_t index) const { auto var = OutputVar(index); - PADDLE_ENFORCE(var != nullptr, "Output(%d) should not be nullptr", index); + PADDLE_ENFORCE( + var != nullptr, + "Output(%d) not be nullptr, which means variable [%s] does not " + "exist in scope", + index, op_.outputs_[index]); return var->GetMutable(); } diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index 7c450d4f5c25ee182ba90b1edceaaa69525b546e..cefdd88e8984733b3a5ea551de2052270a7281c2 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -32,7 +32,7 @@ limitations under the License. */ namespace py = pybind11; USE_OP(add_two); -USE_OP(onehot_cross_entropy); +USE_OP_CPU(onehot_cross_entropy); USE_OP_WITHOUT_KERNEL(fc); USE_OP(sgd); USE_OP(mul); @@ -201,6 +201,8 @@ All parameter, weight, gradient are variables in Paddle. return OpRegistry::CreateOp(desc); }); + operator_base.def_static("support_gpu", &OpRegistry::SupportGPU); + operator_base.def("backward", [](const OperatorBase &forwardOp, const std::unordered_set &no_grad_vars) { diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index 4546d12a903084e7a746b967c39d67a0ade4c0cd..5511ab6b8bb05108e76cc0913264d864d2fecf5b 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -1,10 +1,5 @@ # gserver pacakge unittests -file(GLOB_RECURSE GSERVER_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.h") -file(GLOB_RECURSE GSERVER_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cpp") -add_style_check_target(paddle_gserver ${GSERVER_SOURCES}) -add_style_check_target(paddle_gserver ${GSERVER_HEADER}) - ################### test_ProtoDataProvider ############ add_unittest_without_exec(test_ProtoDataProvider test_ProtoDataProvider.cpp) diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_op.cc index 7fbdd84a391c7d0048fca473f7318561df50daa2..d4c05ed483ca56a31dd8ee4d81b54551ae6da0d1 100644 --- a/paddle/operators/add_op.cc +++ b/paddle/operators/add_op.cc @@ -20,8 +20,8 @@ namespace operators { class AddOp : public OperatorWithKernel { protected: void InferShape(const InferShapeContext &ctx) const override { - PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of AddOp must be two"); - PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one"); + PADDLE_ENFORCE_EQ(ctx.InputSize(), 2); + PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1); PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.InputVar(1) != nullptr, "Inputs of AddOp must all be set"); PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr, diff --git a/paddle/operators/cross_entropy_op.cu b/paddle/operators/cross_entropy_op.cu index 2f453f8379ca7ce0612fed757719acb2d2cf0ad8..ec73721a810fa86d65409f643401eb77248ad5de 100644 --- a/paddle/operators/cross_entropy_op.cu +++ b/paddle/operators/cross_entropy_op.cu @@ -14,6 +14,3 @@ #define EIGEN_USE_GPU #include "paddle/operators/cross_entropy_op.h" - -REGISTER_OP_GPU_KERNEL(onehot_cross_entropy, - ops::OnehotCrossEntropyOpKernel); diff --git a/paddle/operators/cross_entropy_op.h b/paddle/operators/cross_entropy_op.h index 88d06e13469f8e6fc9e634d804c1fe0bed5e2d75..e02e3e2945af13fe283f95f7faa03b2a76d06125 100644 --- a/paddle/operators/cross_entropy_op.h +++ b/paddle/operators/cross_entropy_op.h @@ -18,7 +18,24 @@ limitations under the License. */ namespace paddle { namespace operators { -static const float kCrossEntropyLogThreshold{1e-20}; +template +T tolerable_value(T x) { + static_assert(std::is_floating_point::value, + "tolerable_value works only on float, " + "double and double double."); + + const T kApproInf = 1e20; + + if (x == INFINITY) { + return kApproInf; + } + + if (x == -INFINITY) { + return -kApproInf; + } + + return x; +} template class OnehotCrossEntropyOpKernel : public OpKernel { @@ -36,10 +53,9 @@ class OnehotCrossEntropyOpKernel : public OpKernel { int batch_size = X->dims()[0]; int class_num = X->dims()[1]; - // Y[i] = -log(X[i][j]) for (int i = 0; i < batch_size; ++i) { - Ydata[i] = -std::log(std::max(Xdata[i * class_num + label_data[i]], - kCrossEntropyLogThreshold)); + int index = i * class_num + label_data[i]; + Ydata[i] = -tolerable_value(std::log(Xdata[index])); } } }; @@ -62,9 +78,8 @@ class OnehotCrossEntropyGradientOpKernel : public OpKernel { const int class_num = X->dims()[1]; for (int i = 0; i < batch_size; ++i) { - dXdata[i * class_num + label_data[i]] = - -dYdata[i] / std::max(Xdata[i * class_num + label_data[i]], - kCrossEntropyLogThreshold); + int index = i * class_num + label_data[i]; + dXdata[index] = -tolerable_value(dYdata[i] / Xdata[index]); } } }; diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index f41e95e9db494109925fb600ec6bbd47edf6cc74..ccab9a994cc7aa9e389bd259e4c7365a06e93aa1 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -23,12 +23,16 @@ class MulOp : public OperatorWithKernel { PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs"); auto dim0 = ctx.Input(0)->dims(); auto dim1 = ctx.Input(1)->dims(); - PADDLE_ENFORCE(dim0.size() == 2 && dim1.size() == 2, - "The input of mul op must be matrix"); - PADDLE_ENFORCE( - dim0[1] == dim1[0], + PADDLE_ENFORCE_EQ(dim0.size(), 2, + "input X(%s) should be a tensor with 2 dims, a matrix", + ctx.op_.Input("X")); + PADDLE_ENFORCE_EQ(dim1.size(), 2, + "input Y(%s) should be a tensor with 2 dims, a matrix", + ctx.op_.Input("Y")); + PADDLE_ENFORCE_EQ( + dim0[1], dim1[0], "First matrix's width must be equal with second matrix's height."); - PADDLE_ENFORCE(ctx.OutputSize() == 1, "The mul op must take one output"); + PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "The mul op takes only one output"); ctx.Output(0)->Resize({dim0[0], dim1[1]}); } }; diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index 389d4323950269b81912a7213ff64872aafb410f..5e9c15ca0e6a7c56611a0fadda6c3c0839f309e6 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -36,6 +36,7 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const { InitMemories(step_scopes[0], true /*infer_shape_mode*/); Variable* net = scope.FindVar(arg_->step_net); PADDLE_ENFORCE(net != nullptr, "failed to get step net"); + for (size_t i = 0; i < seq_len_; i++) { if (i > 0) { rnn::LinkMemories(step_scopes, arg_->memories, i, -1, @@ -56,6 +57,7 @@ void RecurrentAlgorithm::Run(const Scope& scope, Variable* net = scope.FindVar(arg_->step_net); for (size_t step_id = 0; step_id < seq_len_; step_id++) { + // create output alias variables if (step_id > 0) { rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/); @@ -67,22 +69,31 @@ void RecurrentAlgorithm::Run(const Scope& scope, } void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { - // TODO(xxx) Only two scopes are needed for inference, this case will be + // TODO(superjom) Only two scopes are needed for inference, this case will be // supported later. - auto step_scopes = - scope.FindVar(arg_->step_scopes)->GetMutable>(); + auto step_scopes_var = scope.FindVar(arg_->step_scopes); + PADDLE_ENFORCE(step_scopes_var != nullptr, ""); + auto step_scopes = step_scopes_var->GetMutable>(); + + // Now all variables in scope must be created outside of op. + auto net_var = scope.FindVar(arg_->step_net); + PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope", + arg_->step_net); + auto net_op = net_var->GetMutable(); + PADDLE_ENFORCE(!net_op->outputs_.empty(), "net_op has no outputs"); if (seq_len_ > step_scopes->size()) { for (size_t i = step_scopes->size(); i < seq_len_; ++i) { auto& step_scope = scope.NewScope(); - // Now all variables in scope must be created outside of op. - auto net_op = scope.FindVar(arg_->step_net)->GetMutable(); + // create step net's temp inputs for (auto& input : net_op->inputs_) { // the weight are located in parent scope - if (!step_scope.FindVar(input)) step_scope.NewVar(input); + if (!step_scope.FindVar(input)) + step_scope.NewVar(input)->GetMutable(); } - for (auto& output : net_op->outputs_) { + // create stepnet's outputs + for (const auto& output : net_op->outputs_) { step_scope.NewVar(output); } step_scopes->emplace_back(&step_scope); @@ -100,6 +111,7 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope, Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable(); if (infer_shape_mode) { pre_mem->Resize(boot_mem->dims()); + PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2); } else { pre_mem->ShareDataWith(*boot_mem); } diff --git a/paddle/operators/rnn/recurrent_op_utils.cc b/paddle/operators/rnn/recurrent_op_utils.cc index 43c97ba29f637828d717ac82516769deff52c7da..32c6c2dd4efa85359b4e95471e8ba09e56afec57 100644 --- a/paddle/operators/rnn/recurrent_op_utils.cc +++ b/paddle/operators/rnn/recurrent_op_utils.cc @@ -53,11 +53,13 @@ void ConcatOutputs(const std::vector& step_scopes, PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.", outlinks[i].external); Tensor* output = output_var->GetMutable(); + if (infer_shape_mode) { - fmw::DDim step_dims = step_scopes[0] - ->FindVar(outlinks[i].internal) - ->GetMutable() - ->dims(); + auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal); + PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope", + outlinks[i].internal); + fmw::DDim step_dims = + step_scope_var->template GetMutable()->dims(); std::vector dims_vec = vectorize(step_dims); dims_vec.insert(dims_vec.begin(), seq_len); output->Resize(fmw::make_ddim(dims_vec)); @@ -79,14 +81,15 @@ void LinkMemories(const std::vector& scopes, const std::vector& memories, const size_t step_id, const int offset, bool infer_shape_mode) { - PADDLE_ENFORCE(step_id < scopes.size(), - "step [%d] is out of range of step scopes' size [%d]", step_id, - scopes.size()); - PADDLE_ENFORCE(static_cast(step_id) + offset >= 0, - "offset [%d] must be large than -[%d]", offset, step_id); - PADDLE_ENFORCE(step_id + offset < scopes.size(), - "offset [%d] is out of range, it must be less than (%d - %d)", - offset, scopes.size(), step_id); + PADDLE_ENFORCE_LT(step_id, scopes.size(), + "step [%d] is out of range of step scopes' size [%d]", + step_id, scopes.size()); + PADDLE_ENFORCE_GE(static_cast(step_id) + offset, 0, + "offset [%d] must be large than -[%d]", offset, step_id); + PADDLE_ENFORCE_LT( + step_id + offset, scopes.size(), + "offset [%d] is out of range, it must be less than (%d - %d)", offset, + scopes.size(), step_id); auto scope = scopes[step_id]; auto linked_scope = scopes[step_id + offset]; for (auto& attr : memories) { diff --git a/paddle/operators/sigmoid_op.cc b/paddle/operators/sigmoid_op.cc index 9d201eb93a2c0e34dd8e6869e97b43c4e278596e..1eb795faa858796f7a34aa495b43d043fdb5dd43 100644 --- a/paddle/operators/sigmoid_op.cc +++ b/paddle/operators/sigmoid_op.cc @@ -37,10 +37,8 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker { class SigmoidOpGrad : public OperatorWithKernel { protected: - void InferShape(const InferShapeContext &ctx) const override {} - std::string DebugString() const override { - LOG(INFO) << "SigmoidGrad"; - return ""; + void InferShape(const InferShapeContext &ctx) const override { + ctx.Output(0)->Resize(ctx.Input(0)->dims()); } }; @@ -51,3 +49,5 @@ REGISTER_OP(sigmoid, ops::SigmoidOp, ops::SigmoidOpMaker); REGISTER_GRADIENT_OP(sigmoid, sigmoid_grad, ops::SigmoidOpGrad); REGISTER_OP_CPU_KERNEL(sigmoid, ops::SigmoidKernel); +REGISTER_OP_CPU_KERNEL(sigmoid_grad, + ops::SigmoidGradKernel); diff --git a/paddle/operators/sigmoid_op.cu b/paddle/operators/sigmoid_op.cu index 2123b17e4b5e90c22c2d6e9177f2a8956f8a4ac9..e80ba081f2ff805664cf92f3cb47e9ad51889058 100644 --- a/paddle/operators/sigmoid_op.cu +++ b/paddle/operators/sigmoid_op.cu @@ -16,3 +16,5 @@ #include "paddle/operators/sigmoid_op.h" REGISTER_OP_GPU_KERNEL(sigmoid, ops::SigmoidKernel); +REGISTER_OP_GPU_KERNEL(sigmoid_grad, + ops::SigmoidGradKernel); diff --git a/paddle/operators/sigmoid_op.h b/paddle/operators/sigmoid_op.h index eb473920a5f866825b52ecb946653ccead7000ea..d513261e74423ce93a50eaaaec1c7d5fadb8f4a8 100644 --- a/paddle/operators/sigmoid_op.h +++ b/paddle/operators/sigmoid_op.h @@ -27,6 +27,7 @@ class SigmoidKernel : public OpKernel { auto output = context.Output(0); output->mutable_data(context.GetPlace()); + // The clipping is used in Paddle's raw implenmention auto X = EigenVector::Flatten(*input); auto Y = EigenVector::Flatten(*output); auto place = context.GetEigenDevice(); @@ -34,5 +35,23 @@ class SigmoidKernel : public OpKernel { Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp()); } }; + +template +class SigmoidGradKernel : public OpKernel { + public: + void Compute(const ExecutionContext& context) const override { + auto Y_t = context.Input("Y"); + auto dY_t = context.Input(framework::GradVarName("Y")); + auto dX_t = context.Output(framework::GradVarName("X")); + + dX_t->mutable_data(context.GetPlace()); + + auto dX = EigenVector::Flatten(*dX_t); + auto Y = EigenVector::Flatten(*Y_t); + auto dY = EigenVector::Flatten(*dY_t); + dX.device(context.GetEigenDevice()) = dY * Y * (1. - Y); + } +}; + } // namespace operators } // namespace paddle diff --git a/paddle/scripts/CMakeLists.txt b/paddle/scripts/CMakeLists.txt index 66a46e1883a49d491f0cb3056a7039407d72e337..a52f06fe497dac467e4ef2543ebda7a423ca326d 100644 --- a/paddle/scripts/CMakeLists.txt +++ b/paddle/scripts/CMakeLists.txt @@ -1,17 +1,15 @@ configure_file(submit_local.sh.in - submit_local.sh + paddle @ONLY) -install(FILES ${CMAKE_CURRENT_BINARY_DIR}/submit_local.sh DESTINATION bin +install(FILES ${CMAKE_CURRENT_BINARY_DIR}/paddle DESTINATION bin PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ - GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ - RENAME paddle) + GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ) configure_file(tools/usage_stat/usage.sh - usage.sh + paddle_usage @ONLY) -install(FILES ${CMAKE_CURRENT_BINARY_DIR}/usage.sh DESTINATION opt/paddle/bin +install(FILES ${CMAKE_CURRENT_BINARY_DIR}/paddle_usage DESTINATION opt/paddle/bin PERMISSIONS OWNER_EXECUTE OWNER_WRITE OWNER_READ - GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ - RENAME paddle_usage) + GROUP_EXECUTE GROUP_READ WORLD_EXECUTE WORLD_READ) diff --git a/paddle/scripts/docker/build_android.sh b/paddle/scripts/docker/build_android.sh index 56d290be4ab04a9f6974023159aa8571d27f8dd5..5584e29e2a155a8062f7d4f2016bd389bd9803f3 100644 --- a/paddle/scripts/docker/build_android.sh +++ b/paddle/scripts/docker/build_android.sh @@ -20,4 +20,4 @@ cmake -DCMAKE_SYSTEM_NAME=Android \ -DWITH_SWIG_PY=OFF \ .. make -j `nproc` -make install +make install -j `nproc` diff --git a/paddle/scripts/submit_local.sh.in b/paddle/scripts/submit_local.sh.in old mode 100644 new mode 100755 diff --git a/paddle/setup.py.in b/paddle/setup.py.in deleted file mode 100644 index af107e76723135124e56db52a76e4f8aff5c4acf..0000000000000000000000000000000000000000 --- a/paddle/setup.py.in +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from setuptools import setup, Extension - -setup(name="py_paddle", - version="${PADDLE_VERSION}", - packages=['py_paddle'], - include_package_data=True, - package_data={'py_paddle':['*.py','_swig_paddle.so']}, - install_requires = [ - 'nltk>=3.2.2', - # We use `numpy.flip` in `test_image.py`. - # `numpy.flip` is introduced in `1.12.0` - 'numpy>=1.12.0', # The numpy is required. - 'protobuf==${PROTOBUF_VERSION}' # The paddle protobuf version - ], - url='http://www.paddlepaddle.org/', - license='Apache 2.0', -) diff --git a/paddle/trainer/tests/simple_sparse_neural_network.py b/paddle/trainer/tests/simple_sparse_neural_network.py index 9604e1b9b45e571130c2f1bdc6d6a5fbd9c177c4..30346ef299d0bc8585ccff7f2fc4885b0d9f9dfc 100644 --- a/paddle/trainer/tests/simple_sparse_neural_network.py +++ b/paddle/trainer/tests/simple_sparse_neural_network.py @@ -1,6 +1,6 @@ from paddle.trainer_config_helpers import * -settings(batch_size=128, learning_method=AdaGradOptimizer(), learning_rate=1e-4) +settings(batch_size=17, learning_method=AdaGradOptimizer(), learning_rate=1e-4) file_list = 'trainer/tests/fake_file_list.list' @@ -12,7 +12,7 @@ define_py_data_sources2( embedding = embedding_layer( input=data_layer( - name="word_ids", size=65536), + name="word_ids", size=8191), size=128, param_attr=ParamAttr(sparse_update=True)) prediction = fc_layer(input=embedding, size=10, act=SoftmaxActivation()) diff --git a/paddle/trainer/tests/simple_sparse_neural_network_dp.py b/paddle/trainer/tests/simple_sparse_neural_network_dp.py index 8bfd1f37e7114f2dcd0798ff1e8180b111ad988f..86b272edfe1bbb23c45cffe282f6475ceaa0cc41 100644 --- a/paddle/trainer/tests/simple_sparse_neural_network_dp.py +++ b/paddle/trainer/tests/simple_sparse_neural_network_dp.py @@ -7,15 +7,15 @@ def init_hook(settings, is_train, **kwargs): @provider( - input_types={'word_ids': integer_value(65536), + input_types={'word_ids': integer_value(8191), 'label': integer_value(10)}, min_pool_size=0, init_hook=init_hook) def process(settings, filename): if settings.is_train: - data_size = 2**20 - else: data_size = 2**10 + else: + data_size = 2**5 for _ in xrange(data_size): - yield random.randint(0, 65535), random.randint(0, 9) + yield random.randint(0, 8190), random.randint(0, 9) diff --git a/paddle/trainer/tests/test_TrainerOnePass.cpp b/paddle/trainer/tests/test_TrainerOnePass.cpp index 4d0174f784a0dc7314977d586c3ad1f0f9c69f6d..00ba61377aeff17d82e03f7560c0d71b3570d14f 100644 --- a/paddle/trainer/tests/test_TrainerOnePass.cpp +++ b/paddle/trainer/tests/test_TrainerOnePass.cpp @@ -100,25 +100,25 @@ TEST(average_window, gpu) { } TEST(average_window, gpu2) { - FLAGS_num_passes = 100; + FLAGS_num_passes = 20; trainerOnePassTest(configFile1, true, false, 2, 0.01); FLAGS_num_passes = 1; } TEST(average_window, gpu4) { - FLAGS_num_passes = 100; + FLAGS_num_passes = 20; trainerOnePassTest(configFile1, true, false, 4, 0.01); FLAGS_num_passes = 1; } TEST(average_window_cpu, gpu2) { - FLAGS_num_passes = 100; + FLAGS_num_passes = 20; trainerOnePassTest(configFile1, true, false, 2, 0.01, true); FLAGS_num_passes = 1; } TEST(average_window_cpu, gpu4) { - FLAGS_num_passes = 100; + FLAGS_num_passes = 20; trainerOnePassTest(configFile1, true, false, 4, 0.01, true); FLAGS_num_passes = 1; } diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 0171f9d8ccd6045cb876d57684269a2a49e77f96..b5030da8e75eb94e857ae4effc6adb6d19dc0e93 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -39,7 +39,7 @@ add_custom_command(OUTPUT ${OUTPUT_DIR}/.timestamp DEPENDS gen_proto_py copy_paddle_pybind framework_py_proto ${PY_FILES} ${external_project_dependencies} ${COPY_PADDLE_MASTER}) add_custom_target(paddle_python ALL DEPENDS - ${OUTPUT_DIR}/.timestamp) + ${OUTPUT_DIR}/.timestamp paddle_pserver_main paddle_trainer paddle_merge_model python_api_wheel) set(PADDLE_PYTHON_PACKAGE_DIR ${CMAKE_CURRENT_BINARY_DIR}/dist/) diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py index 034df88ed841ca0d0e42a58cf1768a4b24d33ffd..da6bed0fcd690d5a7f53f44d0181c75f12e5d074 100644 --- a/python/paddle/v2/framework/tests/op_test_util.py +++ b/python/paddle/v2/framework/tests/op_test_util.py @@ -24,7 +24,7 @@ class OpTestMeta(type): scope = core.Scope() kwargs = dict() places = [core.CPUPlace()] - if core.is_compile_gpu(): + if core.is_compile_gpu() and core.Operator.support_gpu(self.type): places.append(core.GPUPlace(0)) for place in places: @@ -62,7 +62,9 @@ class OpTestMeta(type): for out_name in Operator.get_op_output_names(self.type): actual = numpy.array(scope.find_var(out_name).get_tensor()) expect = self.outputs[out_name] - numpy.isclose(actual, expect) + self.assertTrue( + numpy.allclose(actual, expect), + "output name: " + out_name + "has diff") obj.test_all = test_all return obj diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index 0457e3f16a709140180ce433c1d56d146f0b6974..5c77c477b347f4713e4af2a8cb462b243d7a779c 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -1,3 +1,4 @@ +import logging import paddle.v2.framework.core as core import unittest import numpy as np @@ -7,10 +8,9 @@ ops = creation.op_creations def create_tensor(scope, name, shape): - tensor = scope.create_var(name).get_tensor() + tensor = scope.new_var(name).get_tensor() tensor.set_dims(shape) - tensor.alloc_float() - tensor.set(np.random.random(shape)) + tensor.set(np.random.random(shape), core.CPUPlace()) return tensor @@ -31,40 +31,36 @@ class TestRNN(unittest.TestCase): - h ''' + input_dim = 30 + batch_size = 50 + weight_dim = 15 + sent_len = 11 + def init(self): - input_dim = 30 - batch_size = 50 - weight_dim = 15 - - self.scope = core.Scope(None) - - # create vars - create_tensor(self.scope, "x", [batch_size, input_dim]) - create_tensor(self.scope, "W", [input_dim, weight_dim]) - create_tensor(self.scope, "U", [weight_dim, weight_dim]) - create_tensor(self.scope, "h_boot", [batch_size, weight_dim]) - - x_alias = "x@alias" - y_alias = "y@alias" - memory = "h@alias" - prememory = "h@pre" - output = "rnn_out" - output_alias = "rnn_out@alias" - - # create step net - stepnet_var = self.scope.create_var("stepnet") - stepnet = stepnet_var.get_net() - # stepnet = core.Net.create() - x_fc_op = ops.fc(X=x_alias, W="W", Y="Wx") - h_fc_op = ops.fc(X=prememory, W="U", Y="Uh") - sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum") - sig_op = ops.sigmoid(X="sum", Y=memory) - stepnet.add_op(x_fc_op) - stepnet.add_op(h_fc_op) - stepnet.add_op(sum_op) - stepnet.add_op(sig_op) - stepnet.complete_add_op(True) + self.scope = core.Scope() + + self.create_global_variables() + self.create_step_net() + rnn_op = self.create_rnn_op() + ctx = core.DeviceContext.create(core.CPUPlace()) + print 'infer_shape' + rnn_op.infer_shape(self.scope) + + rnn_op.run(self.scope, ctx) + + def create_global_variables(self): + # create inlink + create_tensor(self.scope, "x", + [self.sent_len, self.batch_size, self.input_dim]) + create_tensor(self.scope, "W", [self.input_dim, self.input_dim]) + create_tensor(self.scope, "U", [self.input_dim, self.input_dim]) + create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim]) + self.scope.new_var("step_scopes") + self.scope.new_var("h@alias") + self.scope.new_var("h") + + def create_rnn_op(self): # create RNNOp rnnop = ops.recurrent_op( # inputs @@ -72,17 +68,27 @@ class TestRNN(unittest.TestCase): boot_memories=["h_boot"], step_net="stepnet", # outputs - outlinks=[output], + outlinks=["h"], step_scopes="step_scopes", # attributes inlink_alias=["x@alias"], - outlink_alias=[output_alias], - pre_memories=[prememory], - memories=[memory]) + outlink_alias=["h@alias"], + pre_memories=["h@pre"], + memories=["h@alias"]) + return rnnop + + def create_step_net(self): + var = self.scope.new_var("stepnet") + stepnet = var.get_net() - ctx = core.DeviceContext.cpu_context() - rnnop.infer_shape(self.scope) - rnnop.run(self.scope, ctx) + x_fc_op = ops.fc(X="x@alias", W="W", Y="Wx") + h_fc_op = ops.fc(X="h@pre", W="U", Y="Uh") + sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum") + sig_op = ops.sigmoid(X="sum", Y="h@alias") + + for op in [x_fc_op, h_fc_op, sum_op, sig_op]: + stepnet.add_op(op) + stepnet.complete_add_op(True) def test_recurrent(self): self.init() diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py index 2610bcf16303d492dce3ce63c93b54b0c88f6bba..2a57a41ed8b718fd420062ba68e853a4861b7359 100644 --- a/python/paddle/v2/framework/tests/test_sigmoid_op.py +++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py @@ -12,5 +12,8 @@ class TestSigmoidOp(unittest.TestCase): self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))} +#class TestSigmoidGradOp(unittest.TestCase): +#TODO(qingqing) add unit test + if __name__ == '__main__': unittest.main() diff --git a/python/setup.py.in b/python/setup.py.in index 7808238aa6ba5ca5c13292638f1c513f87cc2af2..38f0a503bee3eb29ae3c893c96d6e333be54b96e 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -1,4 +1,8 @@ -from setuptools import setup +from setuptools import setup, Distribution + +class BinaryDistribution(Distribution): + def has_ext_modules(foo): + return True packages=['paddle', 'paddle.proto', @@ -11,7 +15,8 @@ packages=['paddle', 'paddle.v2.master', 'paddle.v2.plot', 'paddle.v2.framework', - 'paddle.v2.framework.proto'] + 'paddle.v2.framework.proto', + 'py_paddle'] setup_requires=["requests", "numpy>=1.12", @@ -21,23 +26,33 @@ setup_requires=["requests", "rarfile", "scipy>=0.19.0", "Pillow", - "nltk"] + "nltk>=3.2.2"] if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']: setup_requires+=["opencv-python"] -setup(name='paddle', +setup(name='paddlepaddle', version='${PADDLE_VERSION}', description='Parallel Distributed Deep Learning', install_requires=setup_requires, packages=packages, - package_data={'paddle.v2.master': ['libpaddle_master.so'], - 'paddle.v2.framework': ['core.so'] + package_data={ + 'paddle.v2.master': ['libpaddle_master.so'], + 'paddle.v2.framework': ['core.so'], + 'py_paddle':['*.py','_swig_paddle.so'] }, package_dir={ '': '${CMAKE_CURRENT_SOURCE_DIR}', # The paddle.v2.framework.proto will be generated while compiling. # So that package points to other directory. - 'paddle.v2.framework.proto': '${PROJ_BINARY_ROOT}/paddle/framework' + 'paddle.v2.framework.proto': '${PROJ_BINARY_ROOT}/paddle/framework', + 'py_paddle': '${PROJ_ROOT}/paddle/py_paddle' }, + scripts=['${PROJ_BINARY_ROOT}/paddle/scripts/paddle'], + distclass=BinaryDistribution, + data_files=[('/usr/local/opt/paddle/bin', + ['${PROJ_BINARY_ROOT}/paddle/scripts/paddle_usage', + '${PROJ_BINARY_ROOT}/paddle/trainer/paddle_trainer', + '${PROJ_BINARY_ROOT}/paddle/trainer/paddle_merge_model', + '${PROJ_BINARY_ROOT}/paddle/pserver/paddle_pserver_main'])] )