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提交 430b4219 编写于 作者: L liaogang
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Showing with 534 addition and 338 deletion
CMakeLists.txt
浏览文件 @ 430b4219
......@@ -36,8 +36,8 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." OFF)
option(WITH_MKLML "Compile PaddlePaddle with mklml package." OFF)
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......
README.md
浏览文件 @ 430b4219
......@@ -72,7 +72,7 @@ We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
- [Deep Learning 101](http://book.paddlepaddle.org/index.html)
You might want to start from the this online interactive book that can run in Jupyter Notebook.
You might want to start from this online interactive book that can run in Jupyter Notebook.
- [Distributed Training](http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html)
......
cmake/external/mkldnn.cmake
浏览文件 @ 430b4219
......@@ -20,34 +20,30 @@ INCLUDE(ExternalProject)
SET(MKLDNN_PROJECT "extern_mkldnn")
SET(MKLDNN_SOURCES_DIR ${THIRD_PARTY_PATH}/mkldnn)
SET(MKLDNN_INSTALL_ROOT ${CMAKE_INSTALL_PREFIX})
IF(NOT "$ENV{HOME}" STREQUAL "/root")
SET(MKLDNN_INSTALL_ROOT "$ENV{HOME}")
ENDIF()
SET(MKLDNN_INSTALL_DIR "${MKLDNN_INSTALL_ROOT}/opt/paddle/third_party/mkldnn")
SET(MKLDNN_INCLUDE_DIR "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
SET(MKLDNN_INSTALL_DIR ${THIRD_PARTY_PATH}/install/mkldnn)
SET(MKLDNN_INC_DIR "${MKLDNN_INSTALL_DIR}/include" CACHE PATH "mkldnn include directory." FORCE)
IF(WIN32)
MESSAGE(WARNING "It is not supported compiling with mkldnn in windows Paddle yet."
"Force WITH_MKLDNN=OFF")
SET(WITH_MKLDNN OFF)
IF(WIN32 OR APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with MKLDNN in Paddle yet."
"Force WITH_MKLDNN=OFF")
SET(WITH_MKLDNN OFF CACHE STRING "Disable MKLDNN in Windows and MacOS" FORCE)
return()
ELSE(WIN32)
SET(MKLDNN_LIBRARY "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
#SET(CMAKE_MACOSX_RPATH 1) # hold for MacOS
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
ENDIF(WIN32)
ENDIF()
SET(MKLDNN_LIB "${MKLDNN_INSTALL_DIR}/lib/libmkldnn.so" CACHE FILEPATH "mkldnn library." FORCE)
MESSAGE(STATUS "Set ${MKLDNN_INSTALL_DIR}/lib to runtime path")
SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLDNN_INSTALL_DIR}/lib")
INCLUDE_DIRECTORIES(${MKLDNN_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
SET(MKLDNN_DEPENDS ${MKLML_PROJECT})
SET(MKLDNN_MKLROOT ${MKLML_ROOT})
SET(MKLDNN_IOMP_LIB ${MKLML_IOMP_LIB})
SET(MKLDNN_IOMP_DIR ${MKLML_LIB_DIR})
MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
ENDIF()
ExternalProject_Add(
......@@ -57,16 +53,15 @@ ExternalProject_Add(
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "v0.9"
PREFIX ${MKLDNN_SOURCES_DIR}
CONFIGURE_COMMAND mkdir -p <SOURCE_DIR>/build
BUILD_COMMAND cd <SOURCE_DIR>/build
&& cmake .. -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR} -DMKLROOT=${MKLDNN_MKLROOT}
&& $(MAKE)
INSTALL_COMMAND cd <SOURCE_DIR>/build && $(MAKE) install
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DMKLROOT=${MKLDNN_MKLROOT}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-DMKLROOT:PATH=${MKLDNN_MKLROOT}
)
ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIBRARY})
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIBRARY}")
MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
LIST(APPEND external_project_dependencies mkldnn)
cmake/external/mklml.cmake
浏览文件 @ 430b4219
......@@ -16,19 +16,23 @@ IF(NOT ${WITH_MKLML})
return()
ENDIF(NOT ${WITH_MKLML})
IF(WIN32 OR APPLE)
MESSAGE(WARNING
"Windows or Mac is not supported with MKLML in Paddle yet."
"Force WITH_MKLML=OFF")
SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in Windows and MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.20170425")
SET(MKLML_VER "mklml_lnx_2018.0.20170720")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.9/${MKLML_VER}.tgz")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "opt/paddle/third_party/mklml")
SET(MKLML_INSTALL_ROOT "${CMAKE_INSTALL_PREFIX}")
IF(NOT "$ENV{HOME}" STREQUAL "/root")
SET(MKLML_INSTALL_ROOT "$ENV{HOME}")
ENDIF()
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR}/${MKLML_VER})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
......
paddle/CMakeLists.txt
浏览文件 @ 430b4219
......@@ -15,7 +15,6 @@ if(Boost_FOUND)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
endif()
if(WITH_C_API)
......
paddle/framework/CMakeLists.txt
浏览文件 @ 430b4219
......@@ -31,8 +31,14 @@ py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
cc_library(net SRCS net.cc DEPS op_registry)
cc_test(net_op_test SRCS net_op_test.cc DEPS net)
cc_library(backward SRCS backward.cc DEPS net)
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward)
cc_library(paddle_pybind SHARED
SRCS pybind.cc
DEPS pybind python backward
fc_op
sgd_op
add_op
mean_op
cross_entropy_op
recurrent_op)
paddle/framework/backward.cc
浏览文件 @ 430b4219
......@@ -14,8 +14,8 @@
#include "paddle/framework/backward.h"
#include <list>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace framework {
......@@ -32,7 +32,7 @@ static bool AllInSet(const std::vector<std::string>& names,
}
static std::shared_ptr<OperatorBase> NOP() {
auto net_op = std::make_shared<NetOp>();
auto net_op = std::make_shared<operators::NetOp>();
net_op->type_ = "@NOP@";
net_op->CompleteAddOp();
return net_op;
......@@ -42,9 +42,9 @@ static std::shared_ptr<OperatorBase> NOP() {
//
// no_grad_names the gradient variable names without gradient calculating.
//
// uniq_id is a unique index used inside recursively calling BackwardRecursive.
// use `uid = uniq_id++;` to get the unique index, and pass `uniq_id` through
// recursive calling.
// uniq_id is a unique index used inside recursively calling
// BackwardRecursive. use `uid = uniq_id++;` to get the unique index, and
// pass `uniq_id` through recursive calling.
//
// returns The backward operator. For simple situation, it is a simple
// operator. For complex situation, it is a NetOp.
......@@ -64,8 +64,8 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
return NOP();
}
// All output gradients of forwarding operator do not need to calculate. Then
// all input gradients cannot be computed at all, and we put them into
// All output gradients of forwarding operator do not need to calculate.
// Then all input gradients cannot be computed at all, and we put them into
// `no_grad_names` set. Return an NOP.
if (AllInSet(forwardOp.outputs_, OperatorBase::GRAD_VAR_SUFFIX(),
no_grad_names)) {
......@@ -77,14 +77,14 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
}
// Returned gradient network
auto net = std::make_shared<NetOp>();
auto net = std::make_shared<operators::NetOp>();
if (forwardOp.IsNetOp()) {
// Because forwardOp is a net op, it can static_cast.
auto& forwardNet = static_cast<const NetOp&>(forwardOp);
auto& forwardNet = static_cast<const operators::NetOp&>(forwardOp);
// Map from output gradient variable name to operator's indices in backward
// net. That operator generates that variable.
// Map from output gradient variable name to operator's indices in
// backward net. That operator generates that variable.
std::unordered_map<std::string, std::vector<size_t>> dup_output_ops;
size_t local_op_id = 0;
......@@ -168,6 +168,9 @@ std::shared_ptr<OperatorBase> Backward(
std::unordered_set<std::string> no_grad_names;
no_grad_names.reserve(no_grad_vars.size());
no_grad_names.insert(OperatorBase::EMPTY_VAR_NAME() +
OperatorBase::GRAD_VAR_SUFFIX());
for (auto& name : no_grad_vars) {
no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
}
......
paddle/framework/backward_test.cc
浏览文件 @ 430b4219
......@@ -15,8 +15,9 @@
#include "paddle/framework/backward.h"
#include <gtest/gtest.h>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/type_alias.h"
namespace paddle {
namespace framework {
......@@ -70,7 +71,7 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
}
};
class FcOp : public NetOp {
class FcOp : public ops::NetOp {
public:
void Init() override {
AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
......@@ -161,8 +162,8 @@ TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(1UL, gop->inputs_.size());
ASSERT_EQ("Out" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->inputs_[0]);
ASSERT_EQ(4UL, gop->inputs_.size());
ASSERT_EQ(f::OperatorBase::EMPTY_VAR_NAME(), gop->inputs_[0]);
ASSERT_EQ("rowwise_add_grad", gop->type_);
ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->outputs_[0]);
ASSERT_EQ("b" + f::OperatorBase::GRAD_VAR_SUFFIX(), gop->outputs_[1]);
......@@ -182,7 +183,8 @@ TEST(Backward, simple_op_not_need_grad) {
auto no_input_gop = f::Backward(*fwd, {"X", "b"});
ASSERT_NE(no_input_gop, nullptr);
ASSERT_TRUE(no_input_gop->IsNetOp());
ASSERT_EQ(0UL, std::static_pointer_cast<f::NetOp>(no_input_gop)->ops_.size());
ASSERT_EQ(0UL,
std::static_pointer_cast<ops::NetOp>(no_input_gop)->ops_.size());
}
TEST(Backward, net_fc_backward_normal) {
......@@ -191,7 +193,7 @@ TEST(Backward, net_fc_backward_normal) {
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<f::NetOp *>(gop.get());
auto net = static_cast<ops::NetOp *>(gop.get());
ASSERT_NO_THROW(net->DebugString());
......@@ -214,7 +216,7 @@ TEST(Backward, net_fc_backward_not_have_b) {
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
auto net = static_cast<f::NetOp *>(gop.get());
auto net = static_cast<ops::NetOp *>(gop.get());
ASSERT_NO_THROW(net->DebugString());
......@@ -228,7 +230,7 @@ TEST(Backward, net_fc_backward_not_have_b) {
}
TEST(Backward, net_input_of_network_not_need_grad) {
f::NetOp net;
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("fc", {"X", "W1", "b1"},
{"mul_tmp_0", "add_tmp_0", "hidden0"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"hidden0", "W2", "b2"},
......@@ -236,7 +238,7 @@ TEST(Backward, net_input_of_network_not_need_grad) {
net.CompleteAddOp();
auto bwd = Backward(net, {"X"}); // X@GRAD is not need.
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<f::NetOp *>(bwd.get());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
bwd_net->outputs_.begin(), bwd_net->outputs_.end());
......@@ -253,7 +255,7 @@ TEST(Backward, net_input_of_network_not_need_grad) {
ASSERT_EQ(2UL, bwd_net->ops_.size());
ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
auto first_fc_grad = static_cast<f::NetOp *>(bwd_net->ops_[1].get());
auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
ASSERT_EQ(3UL, first_fc_grad->ops_.size());
ASSERT_EQ(
f::OperatorBase::EMPTY_VAR_NAME(),
......@@ -261,14 +263,14 @@ TEST(Backward, net_input_of_network_not_need_grad) {
}
TEST(Backward, net_shared_weight) {
f::NetOp net;
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("mul", {"X", "W"}, {"Out"}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {"Out", "W"}, {"FinalOut"}, {}));
net.CompleteAddOp();
auto bwd = f::Backward(net, {});
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<f::NetOp *>(bwd.get());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
ASSERT_EQ(3UL, bwd_net->ops_.size());
ASSERT_EQ("add", bwd_net->ops_[2]->type_);
}
......@@ -285,7 +287,7 @@ TEST(Backward, op_all_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto backward = f::Backward(*fwd, {"X", "b"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<f::NetOp *>(backward.get());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
......@@ -293,7 +295,7 @@ TEST(Backward, op_all_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto backward = f::Backward(*fwd, {"Out"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<f::NetOp *>(backward.get());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
......@@ -301,7 +303,7 @@ TEST(Backward, op_part_of_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("many_output_op", {"X"}, {"Y", "Z"}, {});
auto backward = f::Backward(*fwd, {"Z"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<f::NetOp *>(backward.get());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(net->ops_.size(), 2UL);
auto &fill_zero = *net->ops_[0];
......@@ -341,7 +343,7 @@ TEST(Backward, op_part_of_input_are_not_need) {
}
TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
f::NetOp net;
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("fc", {"x1", "w1", "b1"},
{"mul_out1", "add_out1", "out1"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"out1", "w2", "b2"},
......@@ -351,14 +353,13 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
net.CompleteAddOp();
auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
ASSERT_TRUE(backward->IsNetOp());
auto bwd_net = static_cast<f::NetOp *>(backward.get());
auto bwd_net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(bwd_net->ops_.size(), 3UL);
auto &grad_fc = *bwd_net->ops_[0];
EXPECT_EQ(grad_fc.inputs_.size(),
3UL /* external input number */
+ 1UL /* external output number*/
+ 1UL /* number of gradient of external output*/
- 1UL /*ignoreGradient varable number*/
+ 2U /* internal variable number*/);
EXPECT_EQ(grad_fc.outputs_.size(), 2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add */
......
paddle/framework/grad_op_builder.cc
浏览文件 @ 430b4219
......@@ -8,107 +8,97 @@ You may obtain a copy of the License at
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. */
WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either
express or implied. See the License for the specific language governing
permissions and limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
OperatorBase* GradOpBuilder::Build() {
BuildOpInOutArgList();
std::string grad_op_type = OpRegistry::grad_ops().at(op_.type_);
OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
grad_op->type_ = grad_op_type;
CompleteGradOp(grad_op);
return grad_op;
}
class OpRegistry;
using VarIndexMap = std::unordered_map<std::string, int>;
OpInOutArg* GradOpBuilder::BuildArg(const VarProto& var,
const VarIndexMap& var_map,
const std::vector<int>& format,
InOutType type) {
int idx = var_map.at(var.name());
int begin_idx = format.empty() ? idx : format.at(idx);
int end_idx = format.empty() ? idx + 1 : format.at(idx + 1);
return new OpInOutArg(var.name(), type, !var.ignore_gradient(), begin_idx,
end_idx);
enum class OpArgType { IN, OUT };
static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
}
void GradOpBuilder::BuildOpInOutArgList() {
const OpProto& op_proto = OpRegistry::protos().at(op_.type_);
const auto& var_map = *(OpRegistry::VarIndexMaps().at(op_.type_));
const std::vector<int>& in_format =
op_.attrs_.count("input_format")
? op_.GetAttr<std::vector<int>>("input_format")
: std::vector<int>();
const std::vector<int>& out_format =
op_.attrs_.count("output_format")
? op_.GetAttr<std::vector<int>>("output_format")
: std::vector<int>();
for (const auto& var : op_proto.inputs()) {
arg_list_.emplace_back(
std::shared_ptr<OpInOutArg>(BuildArg(var, var_map, in_format, IN)));
}
for (const auto& var : op_proto.outputs()) {
arg_list_.emplace_back(
std::shared_ptr<OpInOutArg>(BuildArg(var, var_map, out_format, OUT)));
}
static const std::vector<int>* GetOpFormat(const OperatorBase* op,
const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
}
void GradOpBuilder::AddArgIntoGradOp(const OpInOutArg* arg,
std::vector<std::string>& in_out,
std::vector<int>& format,
VarIndexMap* varmap, int& idx,
bool is_grad) const {
std::string var_name = arg->proto_name_;
if (is_grad) {
var_name += OperatorBase::GRAD_VAR_SUFFIX();
}
(*varmap)[var_name] = idx++;
size_t pre_sz = in_out.size();
auto base_it = arg->type_ == IN ? op_.inputs_.begin() : op_.outputs_.begin();
std::copy(base_it + arg->begin_idx_, base_it + arg->end_idx_,
std::back_inserter(in_out));
if (is_grad) {
for (size_t i = pre_sz; i < in_out.size(); ++i) {
in_out[i] += OperatorBase::GRAD_VAR_SUFFIX();
static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
const OpArgType& src_type, const OpArgType& dst_type,
int& idx, bool is_grad) {
const std::vector<std::string>& src_inout =
src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
const std::vector<int>* src_format = GetOpFormat(src_op, src_type);
std::vector<std::string>& dst_inout =
dst_type == OpArgType::IN ? dst_op->inputs_ : dst_op->outputs_;
std::vector<int>* dst_format = GetOpFormat(dst_op, dst_type);
const OpProto& proto = OpRegistry::protos().at(src_op->type_);
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
std::string src_name = arg.name();
std::string dst_name =
is_grad ? src_name + OperatorBase::GRAD_VAR_SUFFIX() : src_name;
(*dst_op->in_out_idxs_)[dst_name] = idx++;
int src_arg_idx = src_op->in_out_idxs_->at(src_name);
int src_begin =
src_format == nullptr ? src_arg_idx : src_format->at(src_arg_idx);
int src_end = src_format == nullptr ? src_arg_idx + 1
: src_format->at(src_arg_idx + 1);
for (int i = src_begin; i < src_end; ++i) {
std::string s = is_grad ? src_inout[i] + OperatorBase::GRAD_VAR_SUFFIX()
: arg.ignore_gradient()
? OperatorBase::EMPTY_VAR_NAME()
: src_inout[i];
dst_inout.emplace_back(s);
}
if (dst_format != nullptr) {
dst_format->push_back(dst_inout.size());
}
}
format.push_back(in_out.size());
}
void GradOpBuilder::CompleteGradOp(OperatorBase* grad_op) const {
grad_op->attrs_ = op_.attrs_;
OperatorBase* BuildGradOp(const OperatorBase* op) {
std::string grad_op_type = OpRegistry::grad_ops().at(op->type_);
OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
grad_op->type_ = grad_op_type;
grad_op->attrs_ = op->attrs_;
grad_op->attrs_.erase("input_format");
grad_op->attrs_.erase("output_format");
VarIndexMap* grad_varmap = new VarIndexMap();
if (GetOpFormat(op, OpArgType::IN) != nullptr) {
grad_op->attrs_["output_format"] = std::vector<int>({0});
}
if (GetOpFormat(op, OpArgType::IN) != nullptr ||
GetOpFormat(op, OpArgType::OUT) != nullptr) {
grad_op->attrs_["input_format"] = std::vector<int>({0});
}
grad_op->in_out_idxs_.reset(new VarIndexMap());
int in_idx = 0;
int out_idx = 0;
std::vector<int> in_format({0});
std::vector<int> out_format({0});
for (const auto& arg : arg_list_) {
// op_'s inputs_ and outputs_
if (arg->needed_in_grad_) {
AddArgIntoGradOp(arg.get(), grad_op->inputs_, in_format, grad_varmap,
in_idx, false);
}
if (arg->type_ == IN) {
// gradients of op_'s inputs_
AddArgIntoGradOp(arg.get(), grad_op->outputs_, out_format, grad_varmap,
out_idx, true);
} else {
// gradients of op_'s outputs_
AddArgIntoGradOp(arg.get(), grad_op->inputs_, in_format, grad_varmap,
in_idx, true);
}
}
grad_op->attrs_["input_format"] = in_format;
grad_op->attrs_["output_format"] = out_format;
grad_op->in_out_idxs_.reset(grad_varmap);
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::IN, in_idx, false); // I
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, false); // G
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, true); // OG
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::OUT, out_idx, true); // IG
return grad_op;
}
} // namespace framework
......
paddle/framework/grad_op_builder.h
浏览文件 @ 430b4219
......@@ -14,49 +14,12 @@ limitations under the License. */
#pragma once
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
class OpRegistry;
enum InOutType { IN, OUT };
struct OpInOutArg {
explicit OpInOutArg(const std::string& proto_name, const InOutType& type,
bool needed_in_grad, size_t begin_idx, size_t end_idx)
: proto_name_(proto_name),
type_(type),
needed_in_grad_(needed_in_grad),
begin_idx_(begin_idx),
end_idx_(end_idx) {}
std::string proto_name_;
InOutType type_;
bool needed_in_grad_;
size_t begin_idx_;
size_t end_idx_;
};
class GradOpBuilder {
using VarIndexMap = std::unordered_map<std::string, int>;
public:
explicit GradOpBuilder(const OperatorBase& op) : op_(op) {}
OperatorBase* Build();
private:
OpInOutArg* BuildArg(const VarProto& var, const VarIndexMap& var_map,
const std::vector<int>& format, InOutType type);
void BuildOpInOutArgList();
void AddArgIntoGradOp(const OpInOutArg* arg, std::vector<std::string>& in_out,
std::vector<int>& format, VarIndexMap* varmap, int& idx,
bool is_grad) const;
void CompleteGradOp(OperatorBase* grad_op) const;
const OperatorBase& op_;
std::vector<std::shared_ptr<OpInOutArg>> arg_list_;
};
OperatorBase* BuildGradOp(const OperatorBase* op);
} // namespace framework
} // namespace paddle
paddle/framework/grad_op_builder_test.cc
浏览文件 @ 430b4219
......@@ -8,10 +8,49 @@ USE_OP(add_two);
namespace paddle {
namespace framework {
class NOP : public OperatorBase {
public:
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope,
const platform::DeviceContext &dev_ctx) const override {}
};
class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
public:
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple();
AddInput("In3", "another single input");
AddOutput("Out1", "a single output");
AddOutput("Out2_mult", "a multiple output").SetMultiple();
AddComment("test op with multiple inputs and outputs");
}
};
class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
public:
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple().IgnoreGradient();
AddInput("In3_mult", "another multiple input").SetMultiple();
AddOutput("Out1_mult", "a multiple output").SetMultiple();
AddOutput("Out2", "a single output").IgnoreGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
} // namespace framework
} // namespace paddle
namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<OperatorBase> add_op(
OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
std::shared_ptr<OperatorBase> grad_add_op = OpRegistry::CreateGradOp(*add_op);
std::shared_ptr<f::OperatorBase> add_op(
f::OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(static_cast<int>(grad_add_op->inputs_.size()), 4);
EXPECT_EQ(static_cast<int>(grad_add_op->outputs_.size()), 2);
EXPECT_EQ(grad_add_op->Input("X"), "x");
......@@ -22,5 +61,85 @@ TEST(GradOpBuilder, AddTwo) {
EXPECT_EQ(grad_add_op->Output("Y@GRAD"), "y@GRAD");
}
} // namespace framework
} // namespace paddle
\ No newline at end of file
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker);
REGISTER_GRADIENT_OP(mult_io, mult_io_grad, f::NOP);
REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 4, 5}},
{"output_format", std::vector<int>{0, 1, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"mult_io", {"in1", "in2_1", "in2_2", "in2_3", "in3"},
{"out1", "out2_1", "out2_2"}, attrs));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
EXPECT_EQ(grad_test_op->Input("In3"), "in3");
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input("Out1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Inputs("Out2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>(
{"out2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"out2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_3" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Output("In3" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in3" + f::OperatorBase::GRAD_VAR_SUFFIX());
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 3, 5}},
{"output_format", std::vector<int>{0, 2, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"io_ignored", {"in1", "in2_1", "in2_2", "in3_1", "in3_2"},
{"out1_1", "out1_2", "out2"}, attrs));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({f::OperatorBase::EMPTY_VAR_NAME(),
f::OperatorBase::EMPTY_VAR_NAME()}));
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Input("Out2"), f::OperatorBase::EMPTY_VAR_NAME());
EXPECT_EQ(
grad_test_op->Inputs("Out1_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>(
{"out1_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"out1_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out2" + f::OperatorBase::GRAD_VAR_SUFFIX());
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(
grad_test_op->Outputs("In3_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in3_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in3_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
}
paddle/framework/op_registry.h
浏览文件 @ 430b4219
......@@ -306,8 +306,7 @@ class OpRegistry {
static std::shared_ptr<OperatorBase> CreateGradOp(const OperatorBase& op) {
PADDLE_ENFORCE(!op.IsNetOp(),
"Use framework::Backward to get backward ops");
GradOpBuilder builder(op);
std::shared_ptr<OperatorBase> grad_op(builder.Build());
std::shared_ptr<OperatorBase> grad_op(BuildGradOp(&op));
grad_op->Init();
return grad_op;
}
......
paddle/framework/operator.h
浏览文件 @ 430b4219
......@@ -55,6 +55,10 @@ class OperatorBase {
/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
static std::string GRAD_VAR_SUFFIX() { return "@GRAD"; }
static std::string GRAD_VAR_NAME(const std::string& name) {
return name + GRAD_VAR_SUFFIX();
}
/// Variables with this suffix are supposed to be filled up with zeros.
static std::string ZERO_VAR_SUFFIX() { return "@ZERO"; }
......
paddle/pybind/pybind.cc → paddle/framework/pybind.cc
浏览文件 @ 430b4219
......@@ -4,7 +4,7 @@ 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
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,
......@@ -17,19 +17,19 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/backward.h"
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor_py.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/type_alias.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/tensor_bind.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
namespace py = pybind11;
namespace pd = paddle::framework;
USE_OP(add_two);
USE_OP(onehot_cross_entropy);
......@@ -41,17 +41,18 @@ USE_OP(sigmoid);
USE_OP(softmax);
USE_OP(rowwise_add);
USE_OP_WITHOUT_KERNEL(recurrent_op);
namespace paddle {
namespace framework {
template <typename ClassType>
void ExposeOperator(ClassType& m) {
void ExposeOperator(ClassType &m) {
m.def("infer_shape", &ClassType::type::InferShape)
.def("run", &ClassType::type::Run)
.def("type",
[](const typename ClassType::type& op) -> std::string {
[](const typename ClassType::type &op) -> std::string {
return op.type_;
})
.def("outputs",
[](const typename ClassType::type& op) -> std::vector<std::string> {
[](const typename ClassType::type &op) -> std::vector<std::string> {
return op.outputs_;
})
.def("__str__", &ClassType::type::DebugString);
......@@ -73,80 +74,72 @@ bool IsCompileGPU() {
PYBIND11_PLUGIN(core) {
py::module m("core", "C++ core of PaddlePaddle");
py::class_<pd::Tensor>(m, "Tensor", py::buffer_protocol())
.def_buffer([](pd::Tensor& self) -> py::buffer_info {
return paddle::pybind::CastToPyBuffer(self);
})
py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
.def_buffer(
[](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
.def("get_dims",
[](const pd::Tensor& self) { return pd::vectorize(self.dims()); })
[](const Tensor &self) { return vectorize(self.dims()); })
.def("set_dims",
[](pd::Tensor& self, const std::vector<int>& dim) {
self.Resize(pd::make_ddim(dim));
[](Tensor &self, const std::vector<int> &dim) {
self.Resize(make_ddim(dim));
})
.def("alloc_float",
[](pd::Tensor& self, paddle::platform::GPUPlace& place) {
[](Tensor &self, paddle::platform::GPUPlace &place) {
self.mutable_data<float>(place);
})
.def("alloc_float",
[](pd::Tensor& self, paddle::platform::CPUPlace& place) {
[](Tensor &self, paddle::platform::CPUPlace &place) {
self.mutable_data<float>(place);
})
.def("alloc_int",
[](pd::Tensor& self, paddle::platform::CPUPlace& place) {
[](Tensor &self, paddle::platform::CPUPlace &place) {
self.mutable_data<int>(place);
})
.def("alloc_int",
[](pd::Tensor& self, paddle::platform::GPUPlace& place) {
[](Tensor &self, paddle::platform::GPUPlace &place) {
self.mutable_data<int>(place);
})
.def("set", paddle::pybind::PyCPUTensorSetFromArray<float>)
.def("set", paddle::pybind::PyCPUTensorSetFromArray<int>)
.def("set", PyCPUTensorSetFromArray<float>)
.def("set", PyCPUTensorSetFromArray<int>)
#ifndef PADDLE_ONLY_CPU
.def("set", paddle::pybind::PyCUDATensorSetFromArray<float>)
.def("set", paddle::pybind::PyCUDATensorSetFromArray<int>)
.def("set", PyCUDATensorSetFromArray<float>)
.def("set", PyCUDATensorSetFromArray<int>)
#endif
.def("shape",
[](pd::Tensor& self) { return pd::vectorize(self.dims()); });
.def("shape", [](Tensor &self) { return vectorize(self.dims()); });
py::class_<pd::Variable>(m, "Variable", R"DOC(Variable Class.
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
)DOC")
.def("is_int", [](const pd::Variable& var) { return var.IsType<int>(); })
.def("is_int", [](const Variable &var) { return var.IsType<int>(); })
.def("set_int",
[](pd::Variable& var, int val) -> void {
*var.GetMutable<int>() = val;
})
.def("get_int",
[](const pd::Variable& var) -> int { return var.Get<int>(); })
[](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
.def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
.def("get_tensor",
[](pd::Variable& self) -> pd::Tensor* {
return self.GetMutable<pd::Tensor>();
},
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
.def("get_net",
[](pd::Variable& self) -> pd::NetOp* {
return self.GetMutable<pd::NetOp>();
[](Variable &self) -> ops::NetOp * {
return self.GetMutable<ops::NetOp>();
},
py::return_value_policy::reference);
py::class_<pd::Scope>(m, "Scope", "")
py::class_<Scope>(m, "Scope", "")
.def("new_var",
[](pd::Scope& self, const std::string& name) -> pd::Variable* {
[](Scope &self, const std::string &name) -> Variable * {
return self.NewVar(name);
},
py::return_value_policy::reference)
.def("find_var", &pd::Scope::FindVar, py::return_value_policy::reference)
.def("find_var", &Scope::FindVar, py::return_value_policy::reference)
.def(py::init<>())
.def("new_scope",
[](pd::Scope& self) -> pd::Scope* { return &self.NewScope(); },
.def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
py::return_value_policy::reference)
.def("drop_kids", &pd::Scope::DropKids);
.def("drop_kids", &Scope::DropKids);
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
auto& protos = pd::OpRegistry::protos();
auto &protos = OpRegistry::protos();
std::vector<py::bytes> ret_values;
for (auto it = protos.begin(); it != protos.end(); ++it) {
PADDLE_ENFORCE(it->second.IsInitialized(),
......@@ -161,8 +154,8 @@ All parameter, weight, gradient are variables in Paddle.
m.def_submodule(
"var_names",
"The module will return special predefined variable name in Paddle")
.def("empty", pd::OperatorBase::EMPTY_VAR_NAME)
.def("temp", pd::OperatorBase::TMP_VAR_NAME);
.def("empty", OperatorBase::EMPTY_VAR_NAME)
.def("temp", OperatorBase::TMP_VAR_NAME);
// clang-format off
py::class_<paddle::platform::DeviceContext>(m, "DeviceContext")
.def_static("create",
......@@ -185,43 +178,45 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace").def(py::init<>());
py::class_<pd::OperatorBase, std::shared_ptr<pd::OperatorBase>> operator_base(
py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
m, "Operator");
operator_base.def_static("create", [](py::bytes protobin) {
pd::OpDesc desc;
OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
return pd::OpRegistry::CreateOp(desc);
return OpRegistry::CreateOp(desc);
});
operator_base.def("backward",
[](const pd::OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars) {
return pd::Backward(forwardOp, no_grad_vars);
[](const OperatorBase &forwardOp,
const std::unordered_set<std::string> &no_grad_vars) {
return Backward(forwardOp, no_grad_vars);
});
ExposeOperator(operator_base);
py::class_<pd::NetOp, std::shared_ptr<pd::NetOp>> net(m, "Net");
py::class_<ops::NetOp, std::shared_ptr<ops::NetOp>> net(m, "Net");
net.def_static("create",
[]() -> std::shared_ptr<pd::NetOp> {
auto retv = std::make_shared<pd::NetOp>();
[]() -> std::shared_ptr<ops::NetOp> {
auto retv = std::make_shared<ops::NetOp>();
retv->type_ = "plain_net";
return retv;
})
.def("add_op", &pd::NetOp::AddOp)
.def("add_op",
[](pd::NetOp& self, const std::shared_ptr<pd::NetOp>& net) -> void {
self.AddOp(std::static_pointer_cast<pd::OperatorBase>(net));
})
.def("complete_add_op", &pd::NetOp::CompleteAddOp)
.def("add_op", &ops::NetOp::AddOp)
.def(
"add_op",
[](ops::NetOp &self, const std::shared_ptr<ops::NetOp> &net) -> void {
self.AddOp(std::static_pointer_cast<OperatorBase>(net));
})
.def("complete_add_op", &ops::NetOp::CompleteAddOp)
.def("complete_add_op",
[](std::shared_ptr<pd::NetOp>& self) { self->CompleteAddOp(); });
[](std::shared_ptr<ops::NetOp> &self) { self->CompleteAddOp(); });
ExposeOperator(net);
m.def("unique_integer", UniqueIntegerGenerator);
......@@ -230,3 +225,5 @@ All parameter, weight, gradient are variables in Paddle.
return m.ptr();
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor.h
浏览文件 @ 430b4219
......@@ -26,19 +26,17 @@ limitations under the License. */
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace pybind {
namespace details { // forward declare
template <bool less, size_t i, typename... args>
struct CastToPyBufferImpl;
} // namespace details
} // namespace pybind
namespace framework {
namespace details {
template <bool less, size_t i, typename... args>
struct CastToPyBufferImpl;
}
class Tensor {
public:
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
friend struct details::CastToPyBufferImpl;
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
......
paddle/pybind/tensor_bind.h → paddle/framework/tensor_py.h
浏览文件 @ 430b4219
......@@ -23,7 +23,7 @@ namespace py = pybind11;
namespace paddle {
namespace pybind {
namespace framework {
namespace details {
......@@ -63,11 +63,8 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
}
return py::buffer_info(
dst_tensor.mutable_data<CUR_TYPE>(dst_tensor.holder_->place()),
sizeof(CUR_TYPE),
py::format_descriptor<CUR_TYPE>::format(),
(size_t)framework::arity(dst_tensor.dims()),
dims_outside,
strides);
sizeof(CUR_TYPE), py::format_descriptor<CUR_TYPE>::format(),
(size_t)framework::arity(dst_tensor.dims()), dims_outside, strides);
} else {
constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value;
return CastToPyBufferImpl<less, I + 1, ARGS...>()(tensor);
......@@ -110,8 +107,8 @@ void PyCUDATensorSetFromArray(
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<T>(place);
paddle::platform::GpuMemcpySync(
dst, array.data(), sizeof(T) * array.size(), cudaMemcpyHostToDevice);
paddle::platform::GpuMemcpySync(dst, array.data(), sizeof(T) * array.size(),
cudaMemcpyHostToDevice);
}
#endif
......
paddle/math/BaseMatrix.cu
浏览文件 @ 430b4219
......@@ -443,7 +443,7 @@ template<class T>
void BaseMatrixT<T>::clip(T p1, T p2) { applyUnary(unary::Clip<T>(p1, p2)); }
DEFINE_MATRIX_BINARY_PARAMETER_OP(ClipDerivative, TWO_PARAMETER,
a = b < p1 ? 0 : (b > p2 ? 0 : 1));
a = b < p1 ? 0 : (b > p2 ? 0 : 1));
template<class T>
void BaseMatrixT<T>::clipDerivative(BaseMatrixT& b, T p1, T p2) {
applyBinary(binary::ClipDerivative<T>(p1, p2), b);
......
paddle/operators/CMakeLists.txt
浏览文件 @ 430b4219
......@@ -41,6 +41,9 @@ function(op_library TARGET)
endif()
endfunction()
cc_library(net_op SRCS net_op.cc DEPS op_registry)
cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
op_library(add_op SRCS add_op.cc add_op.cu)
cc_test(add_op_test SRCS add_op_test.cc DEPS add_op)
......@@ -59,6 +62,6 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(fc_op
SRCS fc_op.cc
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net)
op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net)
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net_op)
cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
paddle/operators/mean_op.h
浏览文件 @ 430b4219
......@@ -47,7 +47,7 @@ public:
T ig_size = (T)framework::product(IG->dims());
EigenVector<T>::Flatten(*IG).device((context.GetEigenDevice<Place>())) =
EigenVector<T>::Flatten(*IG).device(context.GetEigenDevice<Place>()) =
EigenScalar<T>::From(*OG) / ig_size;
}
};
......
paddle/framework/net.cc → paddle/operators/net_op.cc
浏览文件 @ 430b4219
......@@ -14,11 +14,11 @@
limitations under the License.
*/
#include "paddle/framework/net.h"
#include "paddle/operators/net_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
namespace operators {
void NetOp::CompleteAddOp(bool calc) {
add_op_done_ = true;
......@@ -74,5 +74,5 @@ std::string NetOp::DebugString() const {
bool NetOp::IsNetOp() const { return true; }
} // namespace framework
} // namespace operators
} // namespace paddle
paddle/framework/net.h → paddle/operators/net_op.h
浏览文件 @ 430b4219
......@@ -14,15 +14,17 @@ limitations under the License. */
#pragma once
#include <paddle/framework/op_desc.pb.h>
#include <paddle/framework/operator.h>
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/operators/type_alias.h"
#include "paddle/platform/device_context.h"
namespace paddle {
namespace framework {
namespace operators {
/**
* @brief Network is also a type of Operator
*
......@@ -37,13 +39,13 @@ namespace framework {
* This is the base class of network, all the networks should implement the APIs
* it defines.
*/
class NetOp : public OperatorBase {
public:
class NetOp : public framework::OperatorBase {
public:
/**
* Infer all the operators' input and output variables' shapes, will be called
* before every mini-batch
*/
void InferShape(const Scope& scope) const override {
void InferShape(const framework::Scope& scope) const override {
for (auto& op : ops_) {
op->InferShape(scope);
}
......@@ -56,7 +58,7 @@ class NetOp : public OperatorBase {
* scope will be used instead. If no OpContext is provicded, default context
* will be used.
*/
void Run(const Scope& scope,
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
for (auto& op : ops_) {
op->Run(scope, dev_ctx);
......@@ -88,7 +90,7 @@ class NetOp : public OperatorBase {
std::vector<std::shared_ptr<OperatorBase>> ops_;
private:
private:
bool add_op_done_{false};
template <typename T, typename KeyType>
......@@ -97,5 +99,5 @@ class NetOp : public OperatorBase {
}
};
} // namespace framework
} // namespace operators
} // namespace paddle
paddle/framework/net_op_test.cc → paddle/operators/net_op_test.cc
浏览文件 @ 430b4219
#include "paddle/operators/net_op.h"
#include <gtest/gtest.h>
#include <paddle/framework/net.h>
#include <paddle/framework/op_registry.h>
#include <paddle/framework/operator.h>
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
namespace operators {
static int infer_shape_cnt = 0;
static int run_cnt = 0;
class TestOp : public OperatorBase {
public:
public:
void InferShape(const framework::Scope& scope) const override {
++infer_shape_cnt;
}
......@@ -21,7 +23,7 @@ class TestOp : public OperatorBase {
};
class EmptyOp : public OperatorBase {
public:
public:
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
......@@ -73,7 +75,7 @@ TEST(OpKernel, all) {
ASSERT_THROW(net->AddOp(op2), paddle::platform::EnforceNotMet);
}
TEST(Net, insert_op) {
TEST(NetOp, insert_op) {
NetOp net;
auto op1 = std::make_shared<EmptyOp>();
op1->inputs_ = {"x", "w1", "b1"};
......@@ -85,5 +87,5 @@ TEST(Net, insert_op) {
ASSERT_EQ(3UL, net.ops_.size());
}
} // namespace framework
} // namespace operators
} // namespace paddle
paddle/operators/recurrent_op.cc
浏览文件 @ 430b4219
......@@ -18,8 +18,8 @@
#include <cstring>
#include <sstream>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/platform/enforce.h"
namespace paddle {
......
paddle/operators/recurrent_op_test.cc
浏览文件 @ 430b4219
......@@ -11,14 +11,15 @@
limitations under the License.
*/
#include "paddle/operators/recurrent_op.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/recurrent_op.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
......
paddle/operators/softmax_op.cc
浏览文件 @ 430b4219
/* 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
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
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. */
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 "paddle/operators/softmax_op.h"
......@@ -20,12 +20,13 @@ namespace operators {
class SoftmaxOp : public OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Only one input is need for softmax");
PADDLE_ENFORCE(ctx.Input<Tensor>(0)->dims().size() == 2,
PADDLE_ENFORCE(ctx.InputSize() == 1UL,
"Only one input is need for softmax");
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
"The input of softmax op must be matrix");
PADDLE_ENFORCE(ctx.OutputSize() == 1,
PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
"Only one output is need for softmax");
ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......@@ -41,10 +42,19 @@ public:
class SoftmaxOpGrad : public OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "SoftmaxOpGrad";
return "";
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 3UL,
"Input of SoftmaxOpGrad should be 3, X, Y, YG");
PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
"Output of SoftmaxOpGrad should be 1");
PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
PADDLE_ENFORCE(ctx.InputVar(GRAD_VAR_NAME("Y")) != nullptr,
"Input(Y@GRAD) should not be null");
PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
ctx.Input<Tensor>(GRAD_VAR_NAME("Y"))->dims(),
"the shape of Input(0) and Input(1) should be the same");
ctx.Output<Tensor>(GRAD_VAR_NAME("X"))
->Resize(ctx.Input<Tensor>("Y")->dims());
}
};
......@@ -52,5 +62,7 @@ protected:
} // namespace paddle
REGISTER_OP(softmax, ops::SoftmaxOp, ops::SoftmaxOpMaker);
REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(softmax, ops::SoftmaxKernel<ops::CPUPlace, float>);
REGISTER_GRADIENT_OP(softmax, softmax_grad, ops::SoftmaxOpGrad);
REGISTER_OP_CPU_KERNEL(softmax_grad,
ops::SoftmaxGradKernel<ops::CPUPlace, float>);
paddle/operators/softmax_op.cu
浏览文件 @ 430b4219
......@@ -17,3 +17,4 @@
#include "paddle/operators/softmax_op.h"
REGISTER_OP_GPU_KERNEL(softmax, ops::SoftmaxKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(softmax_grad, ops::SoftmaxGradKernel<ops::GPUPlace, float>);
paddle/operators/softmax_op.h
浏览文件 @ 430b4219
/* 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
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
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. */
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. */
#pragma once
#include "paddle/framework/ddim.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/type_alias.h"
namespace paddle {
......@@ -23,8 +26,8 @@ template <typename Place, typename T>
class SoftmaxKernel : public OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
auto input = context.Input<Tensor>("X");
auto output = context.Output<Tensor>("Y");
output->mutable_data<T>(context.GetPlace());
auto logits = EigenMatrix<T>::From(*input);
......@@ -57,5 +60,38 @@ public:
.broadcast(one_by_class));
}
};
template <typename Place, typename T>
class SoftmaxGradKernel : public OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
auto Y = context.Input<Tensor>("Y");
auto dY = context.Input<Tensor>(OperatorBase::GRAD_VAR_NAME("Y"));
auto dX = context.Output<Tensor>(OperatorBase::GRAD_VAR_NAME("X"));
dX->mutable_data<T>(context.GetPlace());
const int batch_size = Y->dims()[0];
const int class_num = Y->dims()[1];
Eigen::DSizes<int, 1> along_class(1);
Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
Eigen::DSizes<int, 2> one_by_class(1, class_num);
auto Y_eigen = EigenMatrix<T>::From(*Y);
auto dY_eigen = EigenMatrix<T>::From(*dY);
auto dX_eigen = EigenMatrix<T>::From(*dX);
auto place = context.GetEigenDevice<Place>();
auto dot = (Y_eigen * dY_eigen)
.sum(along_class)
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class);
dX_eigen.device(place) = (dY_eigen - dot) * Y_eigen;
}
};
} // namespace operators
} // namespace paddle
paddle/operators/type_alias.h
浏览文件 @ 430b4219
......@@ -15,13 +15,14 @@
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
using OpKernel = framework::OpKernel;
using OperatorBase = framework::OperatorBase;
using InferShapeContext = framework::InferShapeContext;
using ExecutionContext = framework::ExecutionContext;
using Variable = framework::Variable;
......@@ -43,15 +44,16 @@ template <typename T,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Tensor = framework::Tensor;
using Scope = framework::Scope;
using OperatorWithKernel = framework::OperatorWithKernel;
using OperatorBase = framework::OperatorBase;
using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
using OpProto = framework::OpProto;
using OpAttrChecker = framework::OpAttrChecker;
using CPUPlace = platform::CPUPlace;
using GPUPlace = platform::GPUPlace;
using NetOp = framework::NetOp;
using OpRegistry = framework::OpRegistry;
using OperatorBase = framework::OperatorBase;
} // namespace operators
} // namespace paddle
......
paddle/scripts/docker/build.sh
浏览文件 @ 430b4219
......@@ -69,7 +69,7 @@ cat <<EOF
Installing ...
========================================
EOF
make install
make install -j `nproc`
pip install /usr/local/opt/paddle/share/wheels/*.whl
paddle version
......@@ -122,7 +122,7 @@ cat <<EOF
Generating .deb package ...
========================================
EOF
cpack -D CPACK_GENERATOR='DEB' ..
cpack -D CPACK_GENERATOR='DEB' -j `nproc` ..
cat <<EOF
......
paddle/scripts/travis/build_doc.sh
浏览文件 @ 430b4219
......@@ -6,14 +6,14 @@ mkdir -p $TRAVIS_BUILD_DIR/build
cd $TRAVIS_BUILD_DIR/build
# Compile paddle binaries first
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_GOLANG=ON -DWITH_STYLE_CHECK=OFF
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_GOLANG=ON -DWITH_STYLE_CHECK=OFF
mkdir output
make -j `nproc`
find .. -name '*whl' | xargs pip install # install all wheels.
rm -rf *
# Compile Documentation only.
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=ON
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
make -j `nproc` paddle_docs paddle_docs_cn
# check websites for broken links
......
python/paddle/v2/framework/tests/test_softmax_op.py
浏览文件 @ 430b4219
import unittest
from op_test_util import OpTestMeta
import numpy as np
import paddle.v2.framework.core as core
import paddle.v2.framework.create_op_creation_methods as creation
from op_test_util import OpTestMeta
def stable_softmax(x):
......@@ -19,5 +23,63 @@ class TestSoftmaxOp(unittest.TestCase):
self.Y = np.apply_along_axis(stable_softmax, 1, self.X)
class TestSoftmaxGradOp(unittest.TestCase):
def test_softmax_grad(self):
op = creation.op_creations.softmax(X="X", Y="Y")
backward_op = core.Operator.backward(op, set())
self.assertEqual(backward_op.type(), "softmax_grad")
expected = '''Op(softmax_grad), inputs:(X, Y, Y@GRAD), outputs:(X@GRAD).'''
self.assertEqual(expected, str(backward_op))
batch_size = 3
class_num = 5
# Initialize X and add 1e-2 for numerical stability
Y = np.random.rand(batch_size, class_num).astype(np.float32)
Y = Y + 1e-2
dY = np.random.rand(batch_size, class_num).astype(np.float32)
# Reference implementation of cross entropy with soft labels
def label_softmax_grad(Y, dY):
dX = Y * 0.0
for i in range(batch_size):
d = np.dot(Y[i, :], dY[i, :])
dX[i, :] = Y[i, :] * (dY[i, :] - d)
return dX
expected = label_softmax_grad(Y, dY)
scope = core.Scope()
places = []
places.append(core.CPUPlace())
if core.is_compile_gpu():
places.append(core.GPUPlace(0))
for place in places:
y = scope.new_var("Y")
y_tensor = y.get_tensor()
y_tensor.set_dims([batch_size, class_num])
y_tensor.alloc_float(place)
y_tensor.set(Y, place)
dy = scope.new_var("Y@GRAD")
dy_tensor = dy.get_tensor()
dy_tensor.set_dims([batch_size, class_num])
dy_tensor.alloc_float(place)
dy_tensor.set(dY, place)
x = scope.new_var("X")
dx = scope.new_var("X@GRAD")
tensor = scope.find_var("X@GRAD").get_tensor()
backward_op.infer_shape(scope)
self.assertEqual([batch_size, class_num], tensor.shape())
ctx = core.DeviceContext.create(place)
backward_op.run(scope, ctx)
actual = np.array(tensor)
np.testing.assert_almost_equal(actual, expected, decimal=3)
if __name__ == '__main__':
unittest.main()
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