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PaddleDetection
提交 927adb4a 编写于 作者: C caoying03
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Merge branch 'develop' into kmax_score_layer

上级 855ae59d 78a772ea
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Showing with 1266 addition and 666 deletion
README.md
浏览文件 @ 927adb4a
......@@ -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 @ 927adb4a
......@@ -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 @ 927adb4a
......@@ -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 ${MKLML_ROOT}/include)
......
cmake/generic.cmake
浏览文件 @ 927adb4a
......@@ -187,7 +187,13 @@ function(cc_library TARGET_NAME)
endif()
# cpplint code style
add_style_check_target(${TARGET_NAME} ${cc_library_SRCS})
foreach(source_file ${cc_library_SRCS})
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND cc_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
endforeach()
add_style_check_target(${TARGET_NAME} ${cc_library_SRCS} ${cc_library_HEADERS})
else(cc_library_SRCS)
if (cc_library_DEPS)
......@@ -239,6 +245,14 @@ function(nv_library TARGET_NAME)
add_dependencies(${TARGET_NAME} ${nv_library_DEPS})
target_link_libraries(${TARGET_NAME} ${nv_library_DEPS})
endif()
# cpplint code style
foreach(source_file ${nv_library_SRCS})
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND cc_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
endforeach()
add_style_check_target(${TARGET_NAME} ${nv_library_SRCS} ${nv_library_HEADERS})
else(nv_library_SRCS)
if (nv_library_DEPS)
merge_static_libs(${TARGET_NAME} ${nv_library_DEPS})
......
paddle/CMakeLists.txt
浏览文件 @ 927adb4a
......@@ -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/cuda/src/hl_cuda_cudnn.cc
浏览文件 @ 927adb4a
......@@ -1022,6 +1022,15 @@ void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
real alpha = 1.0f;
real beta = 1.0f;
cudnnBatchNormMode_t mode = CUDNN_BATCHNORM_SPATIAL;
int batch_size = ((cudnn_tensor_descriptor)inputDesc)->batch_size;
if (batch_size > 1024 && g_cudnn_lib_version < 6000) {
LOG(INFO) << " To process current batch data with size " << batch_size
<< " (>1024), cudnnBatchNorm requires cuDNN version >= 6000."
<< " If there is an error complaining CUDNN_STATUS_NOT_SUPPORTED,"
<< " just recompile PaddlePaddle with cuDNN >= 6000, replacing"
<< " current version " << g_cudnn_lib_version;
}
CHECK_CUDNN(
dynload::cudnnBatchNormalizationForwardInference(t_resource.cudnn_handle,
mode,
......
paddle/framework/CMakeLists.txt
浏览文件 @ 927adb4a
......@@ -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 @ 927adb4a
......@@ -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 @ 927adb4a
......@@ -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/ddim.h
浏览文件 @ 927adb4a
......@@ -25,18 +25,15 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace {
typedef boost::variant<Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>, Dim<7>,
Dim<8>, Dim<9>>
DDimVar;
}
/**
* \brief A dynamically sized dimension.
*
* The number of dimensions must be between [1, 9].
*/
struct DDim {
typedef boost::variant<Dim<1>, Dim<2>, Dim<3>, Dim<4>, Dim<5>, Dim<6>, Dim<7>,
Dim<8>, Dim<9>>
DDimVar;
DDimVar var;
DDim() : var(Dim<1>()) {}
......
paddle/framework/grad_op_builder.cc
浏览文件 @ 927adb4a
......@@ -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 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#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 {
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:
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 @ 927adb4a
......@@ -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 @ 927adb4a
......@@ -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;
}
......@@ -315,7 +314,7 @@ class OpRegistry {
static std::unordered_map<std::string, OpProto>& protos() {
static std::unordered_map<std::string, OpProto> protos_;
return protos_;
};
}
static std::unordered_map<std::string, std::string>& grad_ops() {
static std::unordered_map<std::string, std::string> grad_ops_;
......@@ -337,7 +336,7 @@ class OpRegistry {
static std::unordered_map<std::string, OpAttrChecker>& op_checkers() {
static std::unordered_map<std::string, OpAttrChecker> op_checkers_;
return op_checkers_;
};
}
static void GenerateTempVariableName(OperatorBase* op) {
static std::atomic<size_t> gUniqId(0UL);
......@@ -354,7 +353,7 @@ class OpRegistry {
template <typename OpType, typename ProtoMakerType>
class OpRegisterHelper {
public:
OpRegisterHelper(const char* op_type) {
explicit OpRegisterHelper(const char* op_type) {
OpRegistry::RegisterOp<OpType, ProtoMakerType>(op_type);
}
};
......@@ -400,6 +399,14 @@ class GradOpRegisterHelper {
return 0; \
}
/**
* Macro to Forbid user register Gradient Operator.
*/
#define NO_GRADIENT(__op_type) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_gradient_op__##__op_type##__op_type##_grad, \
"NO_GRADIENT must be in global namespace")
/**
* Macro to Register OperatorKernel.
*/
......
paddle/framework/operator.h
浏览文件 @ 927adb4a
......@@ -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"; }
......@@ -280,7 +284,7 @@ class OperatorWithKernel : public OperatorBase {
platform::Place place_;
OpKernelKey() = default;
OpKernelKey(const platform::DeviceContext& dev_ctx) {
explicit OpKernelKey(const platform::DeviceContext& dev_ctx) {
place_ = dev_ctx.GetPlace();
}
......
paddle/pybind/pybind.cc → paddle/framework/pybind.cc
浏览文件 @ 927adb4a
......@@ -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 +74,81 @@ 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()); })
.def("set_float_element",
[](Tensor &self, size_t offset, float f) {
// TODO(yuyang18): Only support GPU now.
self.data<float>()[offset] = f;
})
.def("get_float_element", [](Tensor &self, size_t offset) -> float {
// TODO(yuyang18): Only support GPU now.
return self.data<float>()[offset];
});
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 +163,8 @@ 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 +187,45 @@ 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 +234,5 @@ All parameter, weight, gradient are variables in Paddle.
return m.ptr();
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor.h
浏览文件 @ 927adb4a
......@@ -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;
......@@ -167,4 +165,4 @@ class Tensor {
} // namespace framework
} // namespace paddle
#include "paddle/framework/detail/tensor-inl.h"
#include "paddle/framework/tensor_impl.h"
paddle/pybind/tensor_bind.h → paddle/framework/tensor_py.h
浏览文件 @ 927adb4a
......@@ -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/function/ConvOp.h
浏览文件 @ 927adb4a
......@@ -109,6 +109,13 @@ protected:
return filter[filter.ndims() - 1];
}
// determine whether im2col needs to be performed
inline bool isNeedIm2col(const TensorShape& filter) const {
return !(getFilterHeight(filter) == 1 && getFilterWidth(filter) == 1 &&
strideH() == 1 && strideW() == 1 && paddingH() == 0 &&
paddingW() == 0);
}
std::vector<size_t> strides_;
std::vector<size_t> paddings_;
......
paddle/function/GemmConvOp.cpp
浏览文件 @ 927adb4a
......@@ -66,16 +66,23 @@ public:
real* inputData = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* outputData = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf());
TensorShape colShape;
real* colData = NULL;
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm;
......@@ -86,15 +93,18 @@ public:
for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW());
if (needIm2col) {
im2col(inputData + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW());
} else {
colData = inputData + g * inputOffset;
}
int M = outputChannels / groups_;
int N = outputHeight * outputWidth;
int K = inputChannels / groups_ * filterHeight * filterWidth;
......@@ -159,19 +169,27 @@ public:
real* outputGrad = inputs[0].data<real>();
real* filterData = inputs[1].data<real>();
real* inputGrad = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf());
TensorShape colShape;
real* colData = NULL;
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Col2ImFunctor<kCFO, Device, real> col2im;
GemmFunctor<Device, real> gemm;
size_t inputOffset = imShape.getElements();
size_t outputOffset =
(outputChannels / groups_) * outputHeight * outputWidth;
......@@ -182,6 +200,11 @@ public:
int K = outputChannels / groups_;
int N = outputHeight * outputWidth;
int M = inputChannels / groups_ * filterHeight * filterWidth;
real scale = 0.0f;
if (!needIm2col) {
colData = inputGrad + g * inputOffset;
scale = 1.0f;
}
gemm(CblasTrans,
CblasNoTrans,
M,
......@@ -192,17 +215,19 @@ public:
M,
outputGrad + g * outputOffset,
N,
0.0f,
scale,
colData,
N);
col2im(inputGrad + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW());
if (needIm2col) {
col2im(inputGrad + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW());
}
}
inputGrad += inputChannels * inputHeight * inputWidth;
outputGrad += outputChannels * outputHeight * outputWidth;
......@@ -255,16 +280,23 @@ public:
real* outputGrad = inputs[0].data<real>();
real* inputData = inputs[1].data<real>();
real* filterGrad = outputs[0].data<real>();
bool needIm2col = isNeedIm2col(filter);
TensorShape imShape =
TensorShape({inputChannels / groups_, inputHeight, inputWidth});
TensorShape colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
real* colData = reinterpret_cast<real*>(memory_->getBuf());
TensorShape colShape;
real* colData = NULL;
if (needIm2col) {
colShape = TensorShape({inputChannels / groups_,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
resizeBuffer<Device>(colShape.getElements());
colData = reinterpret_cast<real*>(memory_->getBuf());
}
Im2ColFunctor<kCFO, Device, real> im2col;
GemmFunctor<Device, real> gemm;
......@@ -274,15 +306,18 @@ public:
size_t filterOffset = filter.getElements() / groups_;
for (size_t i = 0; i < batchSize; i++) {
for (size_t g = 0; g < groups_; g++) {
im2col(inputData + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW());
if (needIm2col) {
im2col(inputData + g * inputOffset,
imShape,
colData,
colShape,
strideH(),
strideW(),
paddingH(),
paddingW());
} else {
colData = inputData + g * inputOffset;
}
int M = outputChannels / groups_;
int K = outputHeight * outputWidth;
int N = inputChannels / groups_ * filterHeight * filterWidth;
......
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @ 927adb4a
......@@ -967,8 +967,9 @@ void RecurrentGradientMachine::generateSequence() {
size_t numSequences = getGenBatchSize();
resizeBootFrame(numSequences);
// We create only two sub-network in generation for alternate use.
// Thus, we can reduce total memory of output_ in layer forward.
// We create only two sub-network in generation, one stores states of all
// layers in previous time step and the other storing the states at current
// time step.
resizeOrCreateFrames(2);
// outFrameLines_.size() > 1UL
......@@ -1001,10 +1002,9 @@ void RecurrentGradientMachine::generateSequence() {
// init outArg
size_t resultNum = generator_.config.num_results_per_sample();
IVector::resizeOrCreate(
generator_.outArg.ids,
generator_.config.max_num_frames() * numSequences * resultNum,
false);
size_t maxGenWordCount =
generator_.config.max_num_frames() * numSequences * resultNum;
IVector::resizeOrCreate(generator_.outArg.ids, maxGenWordCount, false);
if (resultNum > 1) {
CHECK_LE(resultNum, static_cast<size_t>(generator_.config.beam_size()));
Matrix::resizeOrCreate(generator_.outArg.in,
......@@ -1012,6 +1012,11 @@ void RecurrentGradientMachine::generateSequence() {
/* width */ resultNum,
false,
/* useGpu */ false);
Matrix::resizeOrCreate(generator_.outArg.value,
/* height */ maxGenWordCount,
/* width */ 1,
false,
/* useGpu */ false);
}
ICpuGpuVector::resizeOrCreate(generator_.outArg.sequenceStartPositions,
numSequences + 1,
......@@ -1313,13 +1318,20 @@ void RecurrentGradientMachine::fillGenOutputs() {
starts[0] = 0;
if (numResults > 1) {
real* probs = generator_.outArg.in->getData();
real* idsProb = generator_.outArg.value->getData();
size_t curPos = 0;
for (size_t i = 0; i < finalPaths_.size(); ++i) {
for (size_t j = 0; j < finalPaths_[i].size(); ++j) {
Path& path = finalPaths_[i][j];
generator_.ids.push_back(path.ids.size()); // sequence size
size_t genLen = path.ids.size();
generator_.ids.push_back(genLen); // sequence size
generator_.ids.insert(
generator_.ids.end(), path.ids.begin(), path.ids.end());
generator_.ids.push_back(-1); // end of sequence
memcpy(idsProb + curPos, path.idsProb.data(), sizeof(real) * genLen);
curPos += genLen;
idsProb[curPos++] = -1.0;
probs[i * numResults + j] = path.logProb;
if (!j && dataArgsSize_) {
......
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
浏览文件 @ 927adb4a
......@@ -189,6 +189,11 @@ public:
*/
std::vector<int> ids;
/**
* @brief idsProb, log probability of each generated words.
*/
std::vector<real> idsProb;
/**
* @brief logProb, current probability of path.
*/
......@@ -228,11 +233,13 @@ public:
*/
Path(Path& old, int newId, real logProb, int machineId, int topIndex)
: ids(old.ids),
idsProb(old.idsProb),
logProb(old.logProb + logProb),
machineId(machineId),
topIndex(topIndex),
seqId(old.seqId) {
ids.push_back(newId);
idsProb.push_back(logProb);
if (!old.probHistory.empty()) {
this->probHistory = old.probHistory;
// probHistory store current prob, not sum
......@@ -411,8 +418,9 @@ protected:
struct Generator {
GeneratorConfig config;
std::vector<int> ids; // store generated sequences
Argument outArg; // final output argument
std::vector<int> ids; // store generated sequences
std::vector<real> idsProb; // log probability of each generated word
Argument outArg; // final output argument
};
bool generating_;
Generator generator_;
......
paddle/gserver/layers/KmaxSeqScoreLayer.cpp
浏览文件 @ 927adb4a
......@@ -39,12 +39,13 @@ REGISTER_LAYER(kmax_seq_score, KmaxSeqScoreLayer);
bool KmaxSeqScoreLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
bool ret = Layer::init(layerMap, parameterMap);
CHECK_EQ(1UL, inputLayers_.size());
CHECK_EQ(1U, inputLayers_.size());
beamSize_ = config_.beam_size();
CHECK_GE(beamSize_, 1LU);
CHECK_GE(beamSize_, 1U);
setNeedSequenceInfo(false);
setNeedGradient(false);
return ret;
}
......@@ -96,18 +97,19 @@ void KmaxSeqScoreLayer::forward(PassType passType) {
scores_ = inputScore;
}
MatrixPtr outputValue = getOutputValue();
Matrix::resizeOrCreate(
outputValue,
output_.value,
input.hasSubseq() ? input.getNumSubSequences() : input.getNumSequences(),
beamSize_);
outputValue->one();
outputValue->mulScalar(-1.);
beamSize_,
false,
false);
output_.value->one();
output_.value->mulScalar(-1.);
kmaxScorePerSeq(scores_->getData(),
output_.value->getData(),
input.hasSeq() ? input.subSequenceStartPositions
: input.sequenceStartPositions);
input.hasSubseq() ? input.subSequenceStartPositions
: input.sequenceStartPositions);
}
void KmaxSeqScoreLayer::backward(const UpdateCallback& callback) {}
......
paddle/gserver/tests/LayerGradUtil.cpp
浏览文件 @ 927adb4a
......@@ -400,7 +400,6 @@ void initDataLayer(TestConfig testConf,
const std::vector<int>& labelSeqStartPositions =
testConf.inputDefs[i].labelSeqStartPositions;
if (labelSeqStartPositions.size() != 0) {
CHECK(!sequenceStartPositions);
CHECK_GE(static_cast<int>(labelSeqStartPositions.size()), 2);
sequenceStartPositions =
......@@ -410,6 +409,19 @@ void initDataLayer(TestConfig testConf,
useGpu);
data.sequenceStartPositions = sequenceStartPositions;
}
const std::vector<int>& labelSubSeqStartPositions =
testConf.inputDefs[i].labelSubSeqStartPositions;
if (labelSubSeqStartPositions.size() != 0) {
CHECK_GE(static_cast<int>(labelSubSeqStartPositions.size()), 2);
subSequenceStartPositions =
ICpuGpuVector::create(labelSubSeqStartPositions.size(), useGpu);
subSequenceStartPositions->copyFrom(labelSubSeqStartPositions.data(),
labelSubSeqStartPositions.size(),
useGpu);
data.subSequenceStartPositions = subSequenceStartPositions;
}
break;
}
default:
......
paddle/gserver/tests/LayerGradUtil.h
浏览文件 @ 927adb4a
......@@ -67,6 +67,7 @@ struct InputDef {
bool isStatic;
std::vector<int> labelInitValue;
std::vector<int> labelSeqStartPositions;
std::vector<int> labelSubSeqStartPositions;
MatrixPtr selfDefinedData;
InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) {
......@@ -81,8 +82,10 @@ struct InputDef {
InputDef(InputType type,
string nameIn,
MatrixPtr selfDefinedData,
std::vector<int> selfDefinedSeqStartPos = {})
std::vector<int> selfDefinedSeqStartPos = {},
std::vector<int> selfDefinedSubSeqStartPos = {})
: labelSeqStartPositions(selfDefinedSeqStartPos),
labelSubSeqStartPositions(selfDefinedSubSeqStartPos),
selfDefinedData(selfDefinedData) {
inputType = type;
name = nameIn;
......
paddle/gserver/tests/test_KmaxSeqScore.cpp
浏览文件 @ 927adb4a
......@@ -32,7 +32,6 @@ DECLARE_int32(gpu_id);
DECLARE_bool(thread_local_rand_use_global_seed);
vector<int> randSampling(int range, int n) {
srand(1);
CHECK_GE(range, n);
vector<int> num(range);
iota(begin(num), end(num), 0);
......@@ -45,7 +44,7 @@ vector<int> randSampling(int range, int n) {
void genRandomSeqInfo(vector<int>& seqStartPosition,
vector<int>& subSeqStartPosition) {
const int maxSeqNum = 5;
const int maxSeqNum = 100;
// generate random start position information
int seqNum = 1 + (rand() % maxSeqNum);
seqStartPosition.resize(seqNum + 1, 0);
......@@ -61,78 +60,93 @@ void genRandomSeqInfo(vector<int>& seqStartPosition,
void genRandomGroundTruth(real* values,
vector<vector<int>>& groundTruth,
vector<int>& seqStartPosition,
vector<int>& subSeqStartPosition,
bool useSubseqInfo,
vector<int>& startPos,
size_t beamSize) {
auto genData = [&](real* values, vector<int>& startPos, size_t beamSize) {
groundTruth.resize(startPos.size() - 1, vector<int>(beamSize, -1));
for (size_t i = 0; i < startPos.size() - 1; ++i) {
int seqLen = startPos[i + 1] - startPos[i];
vector<int> pos =
randSampling(seqLen, min(static_cast<int>(beamSize), seqLen));
for (size_t j = 0; j < pos.size(); ++j) {
groundTruth[i][j] = pos[j];
values[subSeqStartPosition[i] + pos[j]] = 1.;
}
groundTruth.resize(startPos.size() - 1, vector<int>(beamSize, -1));
for (size_t i = 0; i < startPos.size() - 1; ++i) {
int seqLen = startPos[i + 1] - startPos[i];
vector<int> pos =
randSampling(seqLen, min(static_cast<int>(beamSize), seqLen));
for (size_t j = 0; j < pos.size(); ++j) {
groundTruth[i][j] = pos[j];
values[startPos[i] + pos[j]] = 1.;
}
};
}
}
if (useSubseqInfo)
genData(values, subSeqStartPosition, beamSize);
else
genData(values, seqStartPosition, beamSize);
void checkLayerOut(vector<vector<int>> groundTruth,
real* layerOut,
size_t beamSize) {
for (size_t i = 0; i < groundTruth.size(); ++i) {
int begPos = i * beamSize;
vector<real> tmp(layerOut + begPos, layerOut + begPos + beamSize);
sort(begin(tmp), end(tmp));
sort(begin(groundTruth[i]), end(groundTruth[i]));
for (size_t j = 0; j < beamSize; ++j) CHECK_EQ(tmp[j], groundTruth[i][j]);
}
}
// Test that the batchNormLayer can be followed by a ConvLayer
TEST(Layer, kmaxSeqScoreLayer) {
const size_t beamSize = 5;
const size_t maxBeamSize = 100;
int beamSize = 1 + (rand() % maxBeamSize);
vector<int> seqStartPosition;
vector<int> subSeqStartPosition;
genRandomSeqInfo(seqStartPosition, subSeqStartPosition);
MatrixPtr inValue =
Matrix::create(subSeqStartPosition.back(), 1, false, false);
inValue->randomizeUniform();
for (auto hasSubseq : {false, true}) {
vector<vector<int>> groundTruth;
inValue->randomizeUniform();
genRandomGroundTruth(inValue->getData(),
groundTruth,
seqStartPosition,
subSeqStartPosition,
hasSubseq,
hasSubseq ? subSeqStartPosition : seqStartPosition,
beamSize);
for (auto useGpu : {false, true}) {
TestConfig config;
config.layerConfig.set_type("kmax_seq_score");
config.layerConfig.set_beam_size(beamSize);
config.inputDefs.push_back(
{hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
"layer_0",
1,
0});
if (hasSubseq) {
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA,
"scores",
inValue,
seqStartPosition,
subSeqStartPosition});
} else {
config.inputDefs.push_back(
{INPUT_SELF_DEFINE_DATA, "scores", inValue, seqStartPosition});
}
config.layerConfig.add_inputs();
// data layer initialize
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(config,
&dataLayers,
&datas,
&layerMap,
"kmax_seq_score",
100,
false,
useGpu);
initDataLayer(
config,
&dataLayers,
&datas,
&layerMap,
"kmax_seq_score",
100 /* actually this parameter is unused in self-defined input*/,
false,
useGpu);
// test layer initialize
std::vector<ParameterPtr> parameters;
LayerPtr kmaxSeqScoreLayer;
FLAGS_use_gpu = useGpu;
initTestLayer(config, &layerMap, &parameters, &kmaxSeqScoreLayer);
kmaxSeqScoreLayer->forward(PASS_TRAIN);
const MatrixPtr outValue = kmaxSeqScoreLayer->getOutputValue();
CHECK_EQ(outValue->getHeight(),
hasSubseq ? subSeqStartPosition.size() - 1
: seqStartPosition.size() - 1);
CHECK_EQ(outValue->getWidth(), beamSize);
checkLayerOut(groundTruth, outValue->getData(), beamSize);
}
}
}
......@@ -141,6 +155,6 @@ int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
FLAGS_thread_local_rand_use_global_seed = true;
srand(1);
srand((size_t)(time(NULL)));
return RUN_ALL_TESTS();
}
paddle/math/BaseMatrix.cu
浏览文件 @ 927adb4a
......@@ -442,7 +442,8 @@ DEFINE_MATRIX_UNARY_PARAMETER_OP(Clip, TWO_PARAMETER,
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));
DEFINE_MATRIX_BINARY_PARAMETER_OP(ClipDerivative, TWO_PARAMETER,
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/memory/detail/buddy_allocator.h
浏览文件 @ 927adb4a
......@@ -39,7 +39,7 @@ class BuddyAllocator {
public:
void* Alloc(size_t unaligned_size);
void Free(void*);
void Free(void* ptr);
size_t Used();
public:
......
paddle/memory/detail/meta_cache.h
浏览文件 @ 927adb4a
......@@ -33,17 +33,17 @@ namespace detail {
*/
class MetadataCache {
public:
MetadataCache(bool uses_gpu);
explicit MetadataCache(bool uses_gpu);
public:
/*! \brief Load the associated metadata for the specified memory block. */
Metadata load(const MemoryBlock*);
Metadata load(const MemoryBlock* memory_block);
/*! \brief Store the associated metadata for the specified memory block. */
void store(MemoryBlock*, const Metadata&);
void store(MemoryBlock* memory_block, const Metadata& meta_data);
/*! \brief Indicate that the specified metadata will no longer be used. */
void invalidate(MemoryBlock*);
void invalidate(MemoryBlock* memory_block);
public:
MetadataCache(const MetadataCache&) = delete;
......
paddle/memory/memory.h
浏览文件 @ 927adb4a
......@@ -68,7 +68,7 @@ class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
public:
PODDeleter(Place place) : place_(place) {}
explicit PODDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr)); }
private:
......
paddle/operators/CMakeLists.txt
浏览文件 @ 927adb4a
......@@ -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)
......@@ -59,11 +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_network_op
SRCS recurrent_network_op.cc
DEPS op_desc tensor net)
cc_test(recurrent_network_op_test
SRCS recurrent_network_op_test.cc
DEPS recurrent_network_op mul_op add_op)
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/add_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/add_op.h"
......
paddle/operators/add_op.h
浏览文件 @ 927adb4a
......@@ -28,9 +28,13 @@ public:
output->mutable_data<T>(context.GetPlace());
EigenVector<T>::Flatten(*output).device(context.GetEigenDevice<Place>()) =
framework::EigenVector<T>::Flatten(*input0) +
framework::EigenVector<T>::Flatten(*input1);
auto X = EigenVector<T>::Flatten(*input0);
auto Y = EigenVector<T>::Flatten(*input1);
auto Z = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
Z.device(place) = X + Y;
}
};
......
paddle/operators/cross_entropy_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/cross_entropy_op.h"
REGISTER_OP_GPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<ops::GPUPlace, float>);
\ No newline at end of file
ops::OnehotCrossEntropyOpKernel<ops::GPUPlace, float>);
paddle/operators/fill_zeros_like_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/fill_zeros_like_op.h"
REGISTER_OP_GPU_KERNEL(
fill_zeros_like,
paddle::operators::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
\ No newline at end of file
paddle::operators::FillZerosLikeKernel<paddle::platform::GPUPlace, float>);
paddle/operators/mean_op.cc
浏览文件 @ 927adb4a
......@@ -33,13 +33,23 @@ public:
MeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op");
AddOutput("Out", "The output of mean op").IgnoreGradient();
AddComment("Mean Operator");
}
};
class MeanGradOp : public OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
ctx.Output<Tensor>("X" + GRAD_VAR_SUFFIX())
->Resize(ctx.Input<Tensor>("X")->dims());
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker);
REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel<ops::CPUPlace, float>);
REGISTER_GRADIENT_OP(mean, mean_grad, ops::MeanGradOp);
REGISTER_OP_CPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::CPUPlace, float>);
paddle/operators/mean_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/mean_op.h"
REGISTER_OP_GPU_KERNEL(mean, ops::MeanKernel<ops::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::GPUPlace, float>);
paddle/operators/mean_op.h
浏览文件 @ 927adb4a
......@@ -27,8 +27,28 @@ public:
output->mutable_data<T>(context.GetPlace());
EigenScalar<T>::From(*output).device(context.GetEigenDevice<Place>()) =
EigenVector<T>::Flatten(*input).mean();
auto X = EigenVector<T>::Flatten(*input);
auto y = EigenScalar<T>::From(*output);
auto place = context.GetEigenDevice<Place>();
y.device(place) = X.mean();
}
};
template <typename Place, typename T>
class MeanGradKernel : public OpKernel {
public:
void Compute(const ExecutionContext& context) const override {
auto OG = context.Input<Tensor>("Out" + OperatorBase::GRAD_VAR_SUFFIX());
PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
"Mean Gradient should be scalar");
auto IG = context.Output<Tensor>("X" + OperatorBase::GRAD_VAR_SUFFIX());
IG->mutable_data<T>(context.GetPlace());
T ig_size = (T)framework::product(IG->dims());
EigenVector<T>::Flatten(*IG).device(context.GetEigenDevice<Place>()) =
EigenScalar<T>::From(*OG) / ig_size;
}
};
......
paddle/operators/mul_op.cu
浏览文件 @ 927adb4a
......@@ -15,4 +15,4 @@
#define EIGEN_USE_GPU
#include "paddle/operators/mul_op.h"
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<ops::GPUPlace, float>);
\ No newline at end of file
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<ops::GPUPlace, float>);
paddle/operators/mul_op.h
浏览文件 @ 927adb4a
......@@ -26,13 +26,18 @@ public:
Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
{Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
auto input0 = context.Input<Tensor>("X");
auto input1 = context.Input<Tensor>("Y");
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
EigenMatrix<T>::From(*output).device(context.GetEigenDevice<Place>()) =
EigenMatrix<T>::From(*context.Input<Tensor>("X"))
.contract(EigenMatrix<T>::From(*context.Input<Tensor>("Y")),
dim_pair);
auto X = EigenMatrix<T>::From(*input0);
auto Y = EigenMatrix<T>::From(*input1);
auto Z = EigenMatrix<T>::From(*output);
auto place = context.GetEigenDevice<Place>();
Z.device(place) = X.contract(Y, dim_pair);
}
};
} // namespace operators
......
paddle/framework/net.cc → paddle/operators/net_op.cc
浏览文件 @ 927adb4a
......@@ -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 @ 927adb4a
......@@ -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 @ 927adb4a
#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_network_op.cc → paddle/operators/recurrent_op.cc
浏览文件 @ 927adb4a
......@@ -12,14 +12,14 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/recurrent_network_op.h"
#include "paddle/operators/recurrent_op.h"
#include <glog/logging.h>
#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 {
......@@ -29,11 +29,15 @@ namespace rnn {
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len) {
const size_t seq_len,
bool infer_shape_mode) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
Tensor* input =
step_scopes[0]->FindVar(inlinks[i].external)->GetMutable<Tensor>();
auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
PADDLE_ENFORCE(input_var != nullptr,
"input link [%s] is not in scope.",
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
......@@ -41,7 +45,9 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
*step_input = input->Slice<float>(j, j + 1);
if (!infer_shape_mode) {
*step_input = input->Slice<float>(j, j + 1);
}
step_input->Resize(step_dims);
}
}
......@@ -49,36 +55,41 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len) {
const size_t seq_len,
bool infer_shape_mode) {
for (size_t i = 0; i < outlinks.size(); i++) {
Tensor* output =
step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
// TODO(qingiqng) remove following code after adding
// InferShape in RecurrentGradientOp
DDim step_dims = step_scopes[0]
->FindVar(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->mutable_data<float>(make_ddim(dims_vec), platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_output =
step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(output->Slice<float>(j, j + 1))
.CopyFrom<float>(*step_output, platform::CPUPlace());
auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
PADDLE_ENFORCE(output_var != nullptr,
"output link [%s] is not in scope.",
outlinks[i].external);
Tensor* output = output_var->GetMutable<Tensor>();
if (infer_shape_mode) {
DDim step_dims = step_scopes[0]
->FindVar(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_output =
step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(output->Slice<float>(j, j + 1))
.CopyFrom<float>(*step_output, platform::CPUPlace());
}
}
}
}
void LinkMemories(const std::vector<Scope*>& scopes,
const std::vector<rnn::MemoryAttr>& memories,
size_t step_id,
int offset) {
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,
......@@ -95,18 +106,13 @@ void LinkMemories(const std::vector<Scope*>& scopes,
auto scope = scopes[step_id];
auto linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
auto mem = scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
// maybe share variable is better?
auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
mem->ShareDataWith<float>(*linked_mem);
// TODO(qingqing) remove following code
// the memory of current step should be allocated in step net
auto m = scope->NewVar(attr.var)->GetMutable<Tensor>();
// for unit test, as addOp and mulOp are null currently, if not
// mutable_data, mem.data() in output will be error. We will
// remove this line after merge the correct addOp and mulOp.
m->mutable_data<float>(mem->dims(), platform::CPUPlace());
if (infer_shape_mode) {
mem->Resize(linked_mem->dims());
} else {
mem->ShareDataWith<float>(*linked_mem);
}
}
}
......@@ -175,60 +181,39 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
->dims()[0];
CreateScopes(scope);
auto step_scopes = GetStepScopes(scope);
// SegmentInputs is called in InferShape. The input must hold memory in
// SegmentInputs. But the other op only set dimension for the output in
// InferShape. That's a problem. Wether the RNN op needs InferShape or not?
// Wether the following functions (SegmentInputs, InitMemories, ...) need
// to rewrite for RNN op?
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
InitMemories(step_scopes[0]);
PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
"stepnet [%s] is not in scope.",
arg_->step_net);
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
InitMemories(step_scopes[0], true /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
// If the InferShape is called in OperatorBase's run function,
// the rnn op only needs to do InferShape for the first time step
for (size_t i = 0; i < seq_len_; i++) {
if (i > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, i, -1);
rnn::LinkMemories(
step_scopes, arg_->memories, i, -1, true /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
}
auto outlinks = arg_->outlinks;
for (size_t i = 0; i < outlinks.size(); i++) {
DDim step_dims = step_scopes[0]
->FindVar(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
// now only support fixed length
dims_vec.insert(dims_vec.begin(), seq_len_);
Tensor* output =
step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
output->Resize(make_ddim(dims_vec));
}
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
}
void RecurrentAlgorithm::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
InitMemories(step_scopes[0], false /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
for (size_t step_id = 0; step_id < seq_len_; step_id++) {
// the link memory is done in InferShape
// maybe remove following code after testing
if (step_id > 0) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1);
rnn::LinkMemories(
step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
}
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
}
void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
......@@ -244,18 +229,19 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// Now all variables in scope must be created outside of op.
auto net_op = scope.FindVar(arg_->step_net)->GetMutable<NetOp>();
for (auto& input : net_op->inputs_) {
// the weight are located in parent scope
if (!step_scope.FindVar(input)) step_scope.NewVar(input);
}
for (auto& output : net_op->outputs_) {
step_scope.NewVar(output);
}
step_scopes->emplace_back(&step_scope);
}
}
}
void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
void RecurrentAlgorithm::InitMemories(Scope* step_scope,
bool infer_shape_mode) const {
for (auto& attr : arg_->memories) {
Tensor* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
......@@ -263,13 +249,11 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
attr.var,
attr.boot_var);
Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
pre_mem->ShareDataWith<float>(*boot_mem);
// TODO(qingqing) remove following code
// the memory of current step should be allocated in step net
// here for unit test
auto cur_step_mem = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
cur_step_mem->mutable_data<float>(boot_mem->dims(), platform::CPUPlace());
if (infer_shape_mode) {
pre_mem->Resize(boot_mem->dims());
} else {
pre_mem->ShareDataWith<float>(*boot_mem);
}
}
}
......@@ -307,13 +291,14 @@ public:
: OpProtoAndCheckerMaker(proto, op_checker) {
const auto& name = RecurrentOp::kArgName;
// inputs and outputs stored in proto
AddInput(name.inlinks, "the input that need to be segmented for each step.")
AddInput(name.inlinks,
"the inputs that need to be segmented for each step.")
.SetMultiple();
AddInput(name.boot_memories, "variables to initialize memories.")
.SetMultiple();
AddInput(name.step_net, "network shared by all steps.");
AddOutput(name.outlinks, "the output that need to concated for all steps.")
AddOutput(name.outlinks, "the outputs that need to concated for all steps.")
.SetMultiple();
AddOutput(name.step_scopes, "step scopes");
......@@ -331,34 +316,39 @@ public:
void RecurrentGradientAlgorithm::Run(
const Scope& scope, const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
"step net is not in scope.");
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
rnn::LinkMemories(
step_scopes, arg_->memories, step_id, 1, false /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
}
LinkBootMemoryGradients(step_scopes[0]);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
LinkBootMemoryGradients(step_scopes[0], false);
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
}
void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
Scope* step_scope) const {
Scope* step_scope, bool infer_shape_mode) const {
for (auto& attr : arg_->memories) {
Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
PADDLE_ENFORCE(mem_grad != nullptr,
"boot_tensor should be retrieved before");
PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr,
"memory variable [%s] does not exists",
attr.var);
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
"memory [%s]'s boot variable [%s] not exists",
attr.var,
"boot variable [%s] does not exists",
attr.boot_var);
Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
Tensor* boot_mem_grad =
step_scope->NewVar(attr.boot_var)->GetMutable<Tensor>();
boot_mem_grad->ShareDataWith<float>(*mem_grad);
if (infer_shape_mode) {
boot_mem_grad->Resize(mem_grad->dims());
} else {
boot_mem_grad->ShareDataWith<float>(*mem_grad);
}
}
}
......@@ -367,34 +357,20 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
->GetMutable<Tensor>()
->dims()[0];
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
PADDLE_ENFORCE(scope.FindVar(arg_->step_net) != nullptr,
"step net is not in scope.");
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
rnn::LinkMemories(
step_scopes, arg_->memories, step_id, 1, true /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
}
auto outlinks = arg_->outlinks;
for (size_t i = 0; i < outlinks.size(); i++) {
DDim step_dims = step_scopes[0]
->FindVar(outlinks[i].internal)
->GetMutable<Tensor>()
->dims();
std::vector<int> dims_vec = vectorize(step_dims);
// now only support fixed length
dims_vec.insert(dims_vec.begin(), seq_len_);
Tensor* output =
step_scopes[0]->FindVar(outlinks[i].external)->GetMutable<Tensor>();
output->Resize(make_ddim(dims_vec));
}
LinkBootMemoryGradients(step_scopes[0]);
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
}
void RecurrentGradientOp::Init() {
......
paddle/operators/recurrent_network_op.h → paddle/operators/recurrent_op.h
浏览文件 @ 927adb4a
......@@ -19,7 +19,7 @@
namespace paddle {
namespace operators {
using namespace paddle::framework;
using namespace paddle::framework; // NOLINT
namespace rnn {
......@@ -72,26 +72,29 @@ struct ArgumentName {
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len);
const size_t seq_len,
bool infer_shape_mode);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len);
const size_t seq_len,
bool infer_shape_mode);
void LinkMemories(const std::vector<Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories,
size_t step_id,
int offset);
const size_t step_id,
const int offset,
bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg);
}; // namespace rnn
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO:
// TODO(Yan Chunwei):
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
......@@ -122,7 +125,7 @@ protected:
return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
}
void InitMemories(Scope* step_scopes) const;
void InitMemories(Scope* step_scopes, bool infer_shape_mode) const;
private:
std::unique_ptr<rnn::Argument> arg_;
......@@ -145,7 +148,7 @@ public:
void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
void LinkBootMemoryGradients(Scope* step_scopes) const;
void LinkBootMemoryGradients(Scope* step_scopes, bool infer_shape_mode) const;
/**
* InferShape must be called before Run.
......@@ -169,12 +172,10 @@ public:
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const Scope& scope) const override {
alg_.InferShape(scope);
}
void InferShape(const Scope& scope) const override { alg_.InferShape(scope); }
virtual void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
......@@ -191,12 +192,10 @@ public:
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const Scope& scope) const override {
alg_.InferShape(scope);
}
void InferShape(const Scope& scope) const override { alg_.InferShape(scope); }
virtual void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
......
paddle/operators/recurrent_network_op_test.cc → paddle/operators/recurrent_op_test.cc
浏览文件 @ 927adb4a
......@@ -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_network_op.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
......@@ -55,7 +56,7 @@ protected:
w->GetMutable<Tensor>()->mutable_data<float>(
make_ddim(std::vector<int>{30, 30}), platform::CPUPlace());
for (auto boot : std::vector<std::string>{"x_boot", "h_boot"}) {
for (auto boot : std::vector<std::string>{"h_boot"}) {
LOG(INFO) << "create global variable " << boot;
Variable* h_boot = scope_.NewVar(boot);
h_boot->GetMutable<Tensor>()->mutable_data<float>(
......@@ -79,7 +80,6 @@ protected:
op_desc.add_inputs("x0");
op_desc.add_inputs("x1");
// boot_memories 3
op_desc.add_inputs("x_boot");
op_desc.add_inputs("h_boot");
// step net 5
op_desc.add_inputs("step_net");
......@@ -91,7 +91,7 @@ protected:
auto _input_format = std::vector<int>{
0, // in_link
3, // memories
5 // step_net
4 // step_net
};
auto input_format = op_desc.add_attrs();
input_format->set_name("input_format");
......@@ -129,12 +129,11 @@ protected:
inlink_alias->add_strings(item);
}
// pre memories
for (const auto& item :
std::vector<std::string>{"rnn/x@pre", "rnn/h@pre"}) {
for (const auto& item : std::vector<std::string>{"rnn/h@pre"}) {
pre_memories->add_strings(item);
}
// memories
for (const auto& item : std::vector<std::string>{"rnn/x", "rnn/h"}) {
for (const auto& item : std::vector<std::string>{"rnn/h"}) {
memories->add_strings(item);
}
// output alias
......@@ -151,14 +150,11 @@ protected:
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
// rnn/s is net's input or output?
net->inputs_ = {"rnn/h@pre", "rnn/w", "rnn/x"};
net->inputs_ = {"rnn/s", "rnn/h"};
net->AddOp(
OpRegistry::CreateOp("mul", {"rnn/h@pre", "rnn/w"}, {"rnn/s"}, {}));
net->AddOp(
OpRegistry::CreateOp("add_two", {"rnn/x", "rnn/s"}, {"rnn/h"}, {}));
OpRegistry::CreateOp("add_two", {"x@alias", "rnn/s"}, {"rnn/h"}, {}));
net->CompleteAddOp();
}
......@@ -297,7 +293,10 @@ protected:
inlink.internal = "rnn/x";
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10);
rnn::SegmentInputs(*step_scopes,
std::vector<rnn::Link>{inlink},
10,
true /*infer_shape_mode*/);
}
void LinkeMemories() {
......@@ -311,7 +310,8 @@ protected:
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 1; i < 10; ++i) {
rnn::LinkMemories(*step_scopes, memories, i, -1);
rnn::LinkMemories(
*step_scopes, memories, i, -1, true /*infer_shape_mode*/);
}
}
......@@ -333,14 +333,14 @@ TEST(RecurrentOp, LinkMemories) {
using namespace paddle::operators;
// create and init step scopes
int len = 10;
size_t len = 10;
std::vector<Scope*> step_scopes;
for (int i = 0; i < len; ++i) {
for (size_t i = 0; i < len; ++i) {
auto scope = new Scope();
scope->NewVar("pre_h");
auto tensor = scope->NewVar("h")->GetMutable<Tensor>();
float* data = tensor->mutable_data<float>({15, 20}, CPUPlace());
for (int j = 0; j < 15 * 20; ++j) {
for (size_t j = 0; j < 15 * 20; ++j) {
data[j] = rand() * (1. / (double)RAND_MAX);
}
step_scopes.push_back(scope);
......@@ -354,24 +354,24 @@ TEST(RecurrentOp, LinkMemories) {
std::vector<rnn::MemoryAttr> memories;
memories.push_back(mem_attr);
for (int i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1);
for (size_t i = 1; i < len; ++i) {
rnn::LinkMemories(step_scopes, memories, i, -1, false /*infer_shape_mode*/);
}
// check
for (int i = 0; i < len - 1; ++i) {
for (size_t i = 0; i < len - 1; ++i) {
const float* a =
step_scopes[i]->FindVar("h")->GetMutable<Tensor>()->data<float>();
const float* b = step_scopes[i + 1]
->FindVar("pre_h")
->GetMutable<Tensor>()
->data<float>();
for (size_t i = 0; i < 15 * 20; ++i) {
ASSERT_FLOAT_EQ(a[i], b[i]);
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
for (int i = len - 2; i >= 0; --i) {
rnn::LinkMemories(step_scopes, memories, i, 1);
rnn::LinkMemories(step_scopes, memories, i, 1, false /*infer_shape_mode*/);
}
// check
for (int i = len - 2; i >= 0; --i) {
......@@ -379,8 +379,8 @@ TEST(RecurrentOp, LinkMemories) {
step_scopes[i]->FindVar("pre_h")->GetMutable<Tensor>()->data<float>();
const float* b =
step_scopes[i + 1]->FindVar("h")->GetMutable<Tensor>()->data<float>();
for (size_t i = 0; i < 15 * 20; ++i) {
ASSERT_FLOAT_EQ(a[i], b[i]);
for (size_t j = 0; j < 15 * 20; ++j) {
ASSERT_FLOAT_EQ(a[j], b[j]);
}
}
......@@ -391,9 +391,3 @@ TEST(RecurrentOp, LinkMemories) {
USE_OP(add_two);
USE_OP(mul);
// int main() {
// //! TODO(yuyang18): Temporary disable this unit-test because implementation
// //! error.
// return 0;
//}
\ No newline at end of file
paddle/operators/rowwise_add_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/rowwise_add_op.h"
......
paddle/operators/sgd_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sgd_op.h"
REGISTER_OP_GPU_KERNEL(sgd, ops::SGDOpKernel<ops::GPUPlace, float>);
\ No newline at end of file
REGISTER_OP_GPU_KERNEL(sgd, ops::SGDOpKernel<ops::GPUPlace, float>);
paddle/operators/sgd_op.h
浏览文件 @ 927adb4a
......@@ -29,8 +29,12 @@ public:
param_out->mutable_data<T>(ctx.GetPlace());
EigenVector<T>::Flatten(*param_out).device(ctx.GetEigenDevice<Place>()) =
EigenVector<T>::Flatten(*param) - lr * EigenVector<T>::Flatten(*grad);
auto p = EigenVector<T>::Flatten(*param);
auto g = EigenVector<T>::Flatten(*grad);
auto o = EigenVector<T>::Flatten(*param_out);
auto place = ctx.GetEigenDevice<Place>();
o.device(place) = p - lr * g;
}
};
......
paddle/operators/sigmoid_op.cu
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sigmoid_op.h"
......
paddle/operators/sigmoid_op.h
浏览文件 @ 927adb4a
......@@ -27,8 +27,11 @@ public:
auto output = context.Output<Tensor>(0);
output->mutable_data<T>(context.GetPlace());
EigenVector<T>::Flatten(*output).device(context.GetEigenDevice<Place>()) =
1.0 / (1.0 + (-1.0 * EigenVector<T>::Flatten(*input)).exp());
auto X = EigenVector<T>::Flatten(*input);
auto Y = EigenVector<T>::Flatten(*output);
auto place = context.GetEigenDevice<Place>();
Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp());
}
};
} // namespace operators
......
paddle/operators/softmax_op.cc
浏览文件 @ 927adb4a
/* 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"
namespace paddle {
......@@ -19,12 +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());
}
};
......@@ -40,10 +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());
}
};
......@@ -51,5 +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 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#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 @ 927adb4a
/* 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 @ 927adb4a
......@@ -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;
......@@ -43,14 +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;
} // namespace operators
} // namespace paddle
......
paddle/platform/device_context.h
浏览文件 @ 927adb4a
......@@ -40,7 +40,7 @@ class DeviceContext {
class CPUDeviceContext : public DeviceContext {
public:
CPUDeviceContext();
CPUDeviceContext(CPUPlace);
explicit CPUDeviceContext(CPUPlace);
virtual ~CPUDeviceContext() {}
Eigen::DefaultDevice* eigen_device() const;
......@@ -55,7 +55,7 @@ class CPUDeviceContext : public DeviceContext {
class CUDADeviceContext : public DeviceContext {
public:
explicit CUDADeviceContext(GPUPlace);
CUDADeviceContext(GPUPlace); // NOLINT
virtual ~CUDADeviceContext();
/*! \brief Wait for all operations completion in the stream. */
......@@ -69,10 +69,10 @@ class CUDADeviceContext : public DeviceContext {
// clang-format off
/*! \brief Return cublas handle in the device context. */
cublasHandle_t cublas_handle ();
cublasHandle_t cublas_handle();
/*! \brief Return cudnn handle in the device context. */
cudnnHandle_t cudnn_handle ();
cudnnHandle_t cudnn_handle();
/*! \brief Return curand handle in the device context. */
curandGenerator_t curand_generator();
......
paddle/platform/dynload/cublas.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/platform/dynload/cublas.h>
namespace paddle {
......
paddle/platform/dynload/cudnn.cc
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/platform/dynload/cudnn.h>
namespace paddle {
......@@ -25,4 +39,4 @@ CUDNN_DNN_ROUTINE_EACH_R5(DEFINE_WRAP);
} // namespace dynload
} // namespace platform
} // namespace paddle
\ No newline at end of file
} // namespace paddle
paddle/platform/dynload/curand.cc
浏览文件 @ 927adb4a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/platform/dynload/curand.h>
namespace paddle {
......@@ -10,6 +24,7 @@ void *curand_dso_handle;
#define DEFINE_WRAP(__name) DynLoad__##__name __name
CURAND_RAND_ROUTINE_EACH(DEFINE_WRAP);
}
}
}
\ No newline at end of file
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/platform/place.h
浏览文件 @ 927adb4a
......@@ -32,7 +32,7 @@ struct CPUPlace {
struct GPUPlace {
GPUPlace() : GPUPlace(0) {}
GPUPlace(int d) : device(d) {}
GPUPlace(int d) : device(d) {} // NOLINT
// needed for variant equality comparison
inline bool operator==(const GPUPlace &o) const { return device == o.device; }
......
paddle/pybind/CMakeLists.txt
浏览文件 @ 927adb4a
......@@ -6,4 +6,4 @@ cc_library(paddle_pybind SHARED
add_op
mean_op
cross_entropy_op
recurrent_network_op)
recurrent_op)
paddle/scripts/docker/build.sh
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......@@ -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
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......@@ -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
......
paddle/string/piece.h
浏览文件 @ 927adb4a
......@@ -39,8 +39,8 @@ public:
// size_ is 0.
Piece();
Piece(const char* d, size_t n);
Piece(const char* d);
Piece(const std::string& s);
Piece(const char* d); // NOLINT
Piece(const std::string& s); // NOLINT
const char* data() const { return data_; }
size_t len() const { return size_; }
......
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @ 927adb4a
......@@ -7,7 +7,7 @@ test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer,
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer)
export whole_configs=(test_split_datasource)
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ 927adb4a
......@@ -13,4 +13,5 @@ add_python_test(test_framework
test_sigmoid_op.py
test_softmax_op.py
test_rowwise_add_op.py
test_network.py)
test_network.py
gradient_checker.py)
python/paddle/v2/framework/tests/gradient_checker.py 0 → 100644
浏览文件 @ 927adb4a
import paddle.v2.framework.core as core
from paddle.v2.framework.create_op_creation_methods import op_creations
import numpy
import unittest
__all__ = ['get_numeric_gradient']
def get_numeric_gradient(op,
input_values,
output_name,
input_to_check,
delta=1e-2,
local_scope=None):
"""
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
:return: The gradient array in numpy format.
"""
if local_scope is None:
local_scope = core.Scope()
# Create all input variable in local_scope
for var_name in input_values:
var = local_scope.new_var(var_name)
tensor = var.get_tensor()
tensor.set_dims(input_values[var_name].shape)
tensor.alloc_float(core.CPUPlace())
tensor.set(input_values[var_name], core.CPUPlace())
# Create all output variable in local_scope
for output in op.outputs():
if local_scope.find_var(output) is None:
local_scope.new_var(output).get_tensor()
op.infer_shape(local_scope)
# allocate output memory
for output in op.outputs():
local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace())
# TODO(yuyang18): Only CPU is support now.
cpu_ctx = core.DeviceContext.create(core.CPUPlace())
def get_output():
op.run(local_scope, cpu_ctx)
return numpy.array(local_scope.find_var(output_name).get_tensor()).sum()
def product(dim):
return reduce(lambda a, b: a * b, dim, 1)
tensor_to_check = local_scope.find_var(input_to_check).get_tensor()
tensor_size = product(tensor_to_check.get_dims())
gradient_flat = numpy.zeros(shape=(tensor_size, ), dtype='float32')
for i in xrange(tensor_size):
origin = tensor_to_check.get_float_element(i)
x_pos = origin + delta
tensor_to_check.set_float_element(i, x_pos)
y_pos = get_output()
x_neg = origin - delta
tensor_to_check.set_float_element(i, x_neg)
y_neg = get_output()
tensor_to_check.set_float_element(i, origin) # restore old value
gradient_flat[i] = (y_pos - y_neg) / delta / 2
return gradient_flat.reshape(tensor_to_check.get_dims())
if __name__ == '__main__':
class GetNumericGradientTest(unittest.TestCase):
def test_add_op(self):
add_op = op_creations.add_two(X="X", Y="Y", Out="Z")
x = numpy.random.random((10, 1)).astype("float32")
y = numpy.random.random((10, 1)).astype("float32")
arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-2)
unittest.main()
python/paddle/v2/framework/tests/test_softmax_op.py
浏览文件 @ 927adb4a
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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