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提交 329370e8 编写于 作者: Y Yu Yang
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Merge branch 'develop' of github.com:baidu/Paddle into feature/fast_python_unittest

上级 aa57f0fc dc21a58b
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Showing with 1565 addition and 648 deletion
CMakeLists.txt
浏览文件 @ 329370e8
......@@ -36,8 +36,8 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." OFF)
option(WITH_MKLML "Compile PaddlePaddle with mklml package." OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......
cmake/cpplint.cmake
浏览文件 @ 329370e8
......@@ -56,11 +56,14 @@ macro(add_style_check_target TARGET_NAME)
# cpplint code style
get_filename_component(base_filename ${filename} NAME)
set(CUR_GEN ${CMAKE_CURRENT_BINARY_DIR}/${base_filename}.cpplint)
add_custom_command(TARGET ${TARGET_NAME} PRE_BUILD
add_custom_command(OUTPUT ${CUR_GEN} PRE_BUILD
COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
"--filter=${STYLE_FILTER}"
"--write-success=${CUR_GEN}" ${filename}
DEPENDS ${filename} ${PROJ_ROOT}/paddle/scripts/cpplint.py
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
add_custom_target(${base_filename}.cpplint DEPENDS ${CUR_GEN})
add_dependencies(${TARGET_NAME} ${base_filename}.cpplint)
endif()
endforeach()
endif()
......
cmake/generic.cmake
浏览文件 @ 329370e8
......@@ -187,7 +187,13 @@ function(cc_library TARGET_NAME)
endif()
# cpplint code style
add_style_check_target(${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} ${nv_library_DEPS})
......
cmake/util.cmake
浏览文件 @ 329370e8
......@@ -118,7 +118,6 @@ endfunction()
macro(add_unittest_without_exec TARGET_NAME)
add_executable(${TARGET_NAME} ${ARGN})
link_paddle_test(${TARGET_NAME})
add_style_check_target(${TARGET_NAME} ${ARGN})
endmacro()
# add_unittest
......
paddle/CMakeLists.txt
浏览文件 @ 329370e8
......@@ -15,7 +15,6 @@ if(Boost_FOUND)
add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(pybind)
endif()
if(WITH_C_API)
......
paddle/framework/CMakeLists.txt
浏览文件 @ 329370e8
......@@ -12,13 +12,15 @@ cc_test(variable_test SRCS variable_test.cc)
cc_library(scope SRCS scope.cc)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
proto_library(attr_type SRCS attr_type.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attr_type)
proto_library(op_desc SRCS op_desc.proto DEPS attr_type)
proto_library(attribute_proto SRCS attribute.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attribute_proto)
proto_library(op_desc SRCS op_desc.proto DEPS attribute_proto)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope)
cc_library(attribute SRCS attribute.cc DEPS op_desc op_proto)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope attribute)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)
......@@ -26,13 +28,19 @@ cc_library(op_registry SRCS op_registry.cc DEPS op_desc grad_op_builder)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
py_proto_compile(framework_py_proto SRCS attr_type.proto op_proto.proto op_desc.proto)
py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
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/attribute.cc 0 → 100644
浏览文件 @ 329370e8
/* 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/attribute.h"
#include <vector>
namespace paddle {
namespace framework {
template <>
AttrType AttrTypeID<int>() {
return INT;
}
template <>
AttrType AttrTypeID<float>() {
return FLOAT;
}
template <>
AttrType AttrTypeID<std::string>() {
return STRING;
}
template <>
AttrType AttrTypeID<std::vector<int>>() {
return INTS;
}
template <>
AttrType AttrTypeID<std::vector<float>>() {
return FLOATS;
}
template <>
AttrType AttrTypeID<std::vector<std::string>>() {
return STRINGS;
}
Attribute GetAttrValue(const AttrDesc& attr_desc) {
switch (attr_desc.type()) {
case paddle::framework::AttrType::INT: {
return attr_desc.i();
}
case paddle::framework::AttrType::FLOAT: {
return attr_desc.f();
}
case paddle::framework::AttrType::STRING: {
return attr_desc.s();
}
case paddle::framework::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
return val;
}
case paddle::framework::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
return val;
}
case paddle::framework::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
return val;
}
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
}
} // namespace framework
} // namespace paddle
paddle/framework/attr_checker.h → paddle/framework/attribute.h
浏览文件 @ 329370e8
/* 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 <boost/variant.hpp>
......@@ -6,6 +20,9 @@
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/framework/attribute.pb.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/platform/enforce.h"
namespace paddle {
......@@ -14,13 +31,19 @@ namespace framework {
typedef boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>>
Attribute;
typedef std::unordered_map<std::string, Attribute> AttributeMap;
template <typename T>
AttrType AttrTypeID();
Attribute GetAttrValue(const AttrDesc& attr_desc);
// check whether a value(attribute) fit a certain limit
template <typename T>
class LargerThanChecker {
public:
LargerThanChecker(T lower_bound) : lower_bound_(lower_bound) {}
explicit LargerThanChecker(T lower_bound) : lower_bound_(lower_bound) {}
void operator()(T& value) const {
PADDLE_ENFORCE(value > lower_bound_, "larger_than check fail");
}
......@@ -35,7 +58,8 @@ class LargerThanChecker {
template <typename T>
class DefaultValueSetter {
public:
DefaultValueSetter(T default_value) : default_value_(default_value) {}
explicit DefaultValueSetter(T default_value)
: default_value_(default_value) {}
void operator()(T& value) const { value = default_value_; }
private:
......@@ -78,7 +102,8 @@ class TypedAttrChecker {
typedef std::function<void(T&)> ValueChecker;
public:
TypedAttrChecker(const std::string& attr_name) : attr_name_(attr_name) {}
explicit TypedAttrChecker(const std::string& attr_name)
: attr_name_(attr_name) {}
TypedAttrChecker& InEnum(const std::unordered_set<T>& range) {
value_checkers_.push_back(EnumInContainer<T>(range));
......
paddle/framework/backward.cc
浏览文件 @ 329370e8
......@@ -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.
......@@ -59,32 +59,30 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
// If all input gradients of forwarding operator do not need to calculate,
// just return an NOP. Not return null ptr because NOP does not take
// too much time for calculation, but it is useful for simplifying logic.
if (AllInSet(forwardOp.inputs_, OperatorBase::GRAD_VAR_SUFFIX(),
no_grad_names)) {
if (AllInSet(forwardOp.inputs_, kGradVarSuffix, no_grad_names)) {
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)) {
if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
for (auto& name : forwardOp.inputs_) {
// Mark all input is not need
no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
no_grad_names.insert(name + kGradVarSuffix);
}
return NOP();
}
// 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;
......@@ -134,9 +132,9 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
for (std::string& grad_input : grad_op->inputs_) {
if (no_grad_names.count(grad_input)) {
std::string prefix = grad_input.substr(
0, grad_input.size() - OperatorBase::GRAD_VAR_SUFFIX().size());
grad_input = prefix + OperatorBase::ZERO_VAR_SUFFIX();
std::string prefix =
grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
grad_input = prefix + kZeroVarSuffix;
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
......@@ -147,7 +145,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
for (std::string& grad_output : grad_op->outputs_) {
if (no_grad_names.count(grad_output)) {
grad_output = OperatorBase::EMPTY_VAR_NAME();
grad_output = kEmptyVarName;
}
}
......@@ -168,14 +166,14 @@ 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());
no_grad_names.insert(kEmptyVarName + kGradVarSuffix);
for (auto& name : no_grad_vars) {
no_grad_names.insert(name + OperatorBase::GRAD_VAR_SUFFIX());
no_grad_names.insert(name + kGradVarSuffix);
}
size_t uid = 0;
return BackwardRecursive(forwardOp, no_grad_names, uid);
}
} // namespace framework
} // namespace paddle
paddle/framework/backward_test.cc
浏览文件 @ 329370e8
......@@ -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 {
......@@ -70,21 +71,21 @@ 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")},
{Output("mul_result")}, {}));
auto b_name = Input("b");
std::string before_act = "mul_result";
if (b_name != EMPTY_VAR_NAME()) {
if (b_name != kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
{Output("add_result")}, {}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
if (out_varname != EMPTY_VAR_NAME()) {
this->Rename(out_varname, EMPTY_VAR_NAME());
if (out_varname != kEmptyVarName) {
this->Rename(out_varname, kEmptyVarName);
}
}
......@@ -161,14 +162,13 @@ 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::kEmptyVarName, 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]);
ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(),
gop->Output("X" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
}
TEST(Backward, simple_op_not_need_grad) {
......@@ -176,13 +176,14 @@ TEST(Backward, simple_op_not_need_grad) {
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"X"});
ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
"X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"X" + f::kGradVarSuffix),
gop->outputs_.end());
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 +192,7 @@ 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());
......@@ -208,13 +209,13 @@ TEST(Backward, net_fc_backward_normal) {
}
TEST(Backward, net_fc_backward_not_have_b) {
std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
"fc", {"X", "w", f::OperatorBase::EMPTY_VAR_NAME()},
{"mul_result", "add_result", "tmp"}, {});
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
{"mul_result", "add_result", "tmp"}, {});
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 +229,7 @@ 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"},
......@@ -236,39 +237,36 @@ 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());
all_output.erase(f::OperatorBase::EMPTY_VAR_NAME());
all_output.erase(f::kEmptyVarName);
for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
ASSERT_NE(all_output.find(out + f::OperatorBase::GRAD_VAR_SUFFIX()),
all_output.end());
ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
}
// Not Generated X
ASSERT_EQ(all_output.find("X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
all_output.end());
ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.end());
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(),
first_fc_grad->ops_[2]->Output("A" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ(f::kEmptyVarName,
first_fc_grad->ops_[2]->Output("A" + f::kGradVarSuffix));
}
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 +283,7 @@ 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 +291,7 @@ 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 +299,7 @@ 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];
......@@ -309,17 +307,15 @@ TEST(Backward, op_part_of_output_are_not_need) {
ASSERT_EQ(1UL, fill_zero.inputs_.size());
ASSERT_EQ("Z", fill_zero.inputs_[0]);
ASSERT_EQ(1UL, fill_zero.outputs_.size());
ASSERT_EQ("Z" + f::OperatorBase::ZERO_VAR_SUFFIX(), fill_zero.outputs_[0]);
ASSERT_EQ("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.type_);
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size()); // I/O/OG
ASSERT_EQ("Z" + f::OperatorBase::ZERO_VAR_SUFFIX(),
d_many_out.Input("z" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("Y" + f::OperatorBase::GRAD_VAR_SUFFIX(),
d_many_out.Input("y" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("X" + f::OperatorBase::GRAD_VAR_SUFFIX(),
d_many_out.Output("x" + f::OperatorBase::GRAD_VAR_SUFFIX()));
ASSERT_EQ("Z" + f::kZeroVarSuffix, d_many_out.Input("z" + f::kGradVarSuffix));
ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" + f::kGradVarSuffix));
ASSERT_EQ("X" + f::kGradVarSuffix,
d_many_out.Output("x" + f::kGradVarSuffix));
}
TEST(Backward, op_part_of_input_are_not_need) {
......@@ -329,19 +325,17 @@ TEST(Backward, op_part_of_input_are_not_need) {
ASSERT_EQ(grad_mul.type_, "mul_grad");
ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
ASSERT_EQ(grad_mul.Output("A" + f::OperatorBase::GRAD_VAR_SUFFIX()),
f::OperatorBase::EMPTY_VAR_NAME());
ASSERT_EQ(grad_mul.Output("B" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"b" + f::OperatorBase::GRAD_VAR_SUFFIX());
ASSERT_EQ(grad_mul.Input("Out" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out" + f::OperatorBase::GRAD_VAR_SUFFIX());
ASSERT_EQ(grad_mul.Output("A" + f::kGradVarSuffix), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
"out" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("A"), "a");
ASSERT_EQ(grad_mul.Input("B"), "b");
ASSERT_EQ(grad_mul.Input("Out"), "out");
}
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 +345,13 @@ 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 */
......@@ -367,23 +360,4 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
/*
EXPECT_EQ(grad_fc.Output("X" + f::OperatorBase::GRAD_VAR_SUFFIX()),
f::OperatorBase::EMPTY_VAR_NAME());
EXPECT_EQ(grad_fc.Output("W" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"w3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Output("b" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"b3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Output("mul_result" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"mul_out3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Input("Out" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out3" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(grad_fc.Input("X"), "out2");
EXPECT_EQ(grad_fc.Input("W"), "w3");
EXPECT_EQ(grad_fc.Input("mul_result"), "mul_out3");
EXPECT_EQ(grad_fc.Input("add_result"), "tmp_out3");
EXPECT_EQ(grad_fc.Input("Out"), "out3");
*/
}
paddle/framework/ddim.h
浏览文件 @ 329370e8
......@@ -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 @ 329370e8
......@@ -8,107 +8,95 @@ 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 + kGradVarSuffix : 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] + kGradVarSuffix
: (arg.ignore_gradient() ? kEmptyVarName : 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 @ 329370e8
/* 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 @ 329370e8
......@@ -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 +61,77 @@ 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::kGradVarSuffix),
"out1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Inputs("Out2_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"out2_1" + f::kGradVarSuffix, "out2_2" + f::kGradVarSuffix}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
"in1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
std::vector<std::string>({"in2_1" + f::kGradVarSuffix,
"in2_2" + f::kGradVarSuffix,
"in2_3" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Output("In3" + f::kGradVarSuffix),
"in3" + f::kGradVarSuffix);
}
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::kEmptyVarName, f::kEmptyVarName}));
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::kEmptyVarName);
EXPECT_EQ(grad_test_op->Inputs("Out1_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"out1_1" + f::kGradVarSuffix, "out1_2" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::kGradVarSuffix),
"out2" + f::kGradVarSuffix);
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
"in1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"in2_1" + f::kGradVarSuffix, "in2_2" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Outputs("In3_mult" + f::kGradVarSuffix),
std::vector<std::string>(
{"in3_1" + f::kGradVarSuffix, "in3_2" + f::kGradVarSuffix}));
}
paddle/framework/op_desc.proto
浏览文件 @ 329370e8
......@@ -15,7 +15,7 @@ limitations under the License. */
syntax="proto2";
package paddle.framework;
import "attr_type.proto";
import "attribute.proto";
// AttrDesc is used to describe Attributes of an Operator. It contain's
// name, type, and value of Attribute.
......
paddle/framework/op_proto.proto
浏览文件 @ 329370e8
......@@ -21,7 +21,7 @@ limitations under the License. */
syntax="proto2";
package paddle.framework;
import "attr_type.proto";
import "attribute.proto";
// Attribute protocol message for 3rd-party language binding.
// It will store the Op support what attribute and what type.
......
paddle/framework/op_registry.cc
浏览文件 @ 329370e8
......@@ -14,37 +14,8 @@ limitations under the License. */
#include <paddle/framework/op_registry.h>
namespace paddle {
namespace framework {
template <>
void AttrTypeHelper::SetAttrType<int>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INT);
}
template <>
void AttrTypeHelper::SetAttrType<float>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOAT);
}
template <>
void AttrTypeHelper::SetAttrType<std::string>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRING);
}
#include <vector>
template <>
void AttrTypeHelper::SetAttrType<std::vector<int>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::INTS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<float>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::FLOATS);
}
template <>
void AttrTypeHelper::SetAttrType<std::vector<std::string>>(AttrProto* attr) {
attr->set_type(paddle::framework::AttrType::STRINGS);
}
} // namespace framework
namespace paddle {
namespace framework {} // namespace framework
} // namespace paddle
paddle/framework/op_registry.h
浏览文件 @ 329370e8
......@@ -19,7 +19,7 @@ limitations under the License. */
#include <type_traits>
#include <unordered_map>
#include <unordered_set>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/scope.h"
......@@ -27,49 +27,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
// helper class to set attribute type
struct AttrTypeHelper {
template <typename T>
static void SetAttrType(AttrProto* attr);
static Attribute GetAttrValue(const AttrDesc& attr_desc) {
switch (attr_desc.type()) {
case paddle::framework::AttrType::INT: {
return attr_desc.i();
}
case paddle::framework::AttrType::FLOAT: {
return attr_desc.f();
}
case paddle::framework::AttrType::STRING: {
return attr_desc.s();
}
case paddle::framework::AttrType::INTS: {
std::vector<int> val(attr_desc.ints_size());
for (int i = 0; i < attr_desc.ints_size(); ++i) {
val[i] = attr_desc.ints(i);
}
return val;
}
case paddle::framework::AttrType::FLOATS: {
std::vector<float> val(attr_desc.floats_size());
for (int i = 0; i < attr_desc.floats_size(); ++i) {
val[i] = attr_desc.floats(i);
}
return val;
}
case paddle::framework::AttrType::STRINGS: {
std::vector<std::string> val(attr_desc.strings_size());
for (int i = 0; i < attr_desc.strings_size(); ++i) {
val[i] = attr_desc.strings(i);
}
return val;
}
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
}
};
// this class not only make proto but also init attribute checkers.
class OpProtoAndCheckerMaker {
public:
......@@ -136,7 +93,7 @@ class OpProtoAndCheckerMaker {
*attr->mutable_name() = name;
*attr->mutable_comment() = comment;
attr->set_generated(generated);
AttrTypeHelper::SetAttrType<T>(attr);
attr->set_type(AttrTypeID<T>());
return op_checker_->AddAttrChecker<T>(name);
}
......@@ -297,7 +254,7 @@ class OpRegistry {
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
attrs[attr.name()] = GetAttrValue(attr);
}
return CreateOp(op_desc.type(), inputs, outputs, attrs);
......@@ -306,8 +263,7 @@ 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 +271,7 @@ 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_;
......@@ -337,12 +293,12 @@ 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);
for (auto& outname : op->outputs_) {
if (outname == OperatorBase::TMP_VAR_NAME()) {
if (outname == kTempVarName) {
outname += op->type_;
outname += "@";
outname += std::to_string(gUniqId.fetch_add(1));
......@@ -354,7 +310,7 @@ 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);
}
};
......
paddle/framework/operator.h
浏览文件 @ 329370e8
......@@ -20,7 +20,7 @@ limitations under the License. */
#include <unordered_map>
#include <vector>
#include "paddle/framework/attr_checker.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/scope.h"
......@@ -32,9 +32,29 @@ limitations under the License. */
namespace paddle {
namespace framework {
/// If a variable is a empty variable, that name will be used.
const std::string kEmptyVarName = "@EMPTY@";
/// If a variable is a temporary variable, that name will be set in Python,
/// but it will be convert to a unique name in scope after OpCreator.
const std::string kTempVarName = "@TEMP@";
/// If a variable's name has a certain suffix, it means that the
/// variable is the gradient of another varibale.
/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
const std::string kGradVarSuffix = "@GRAD";
/// Variables with this suffix are supposed to be filled up with zeros.
const std::string kZeroVarSuffix = "@ZERO";
inline std::string GradVarName(const std::string& var_name) {
return var_name + kGradVarSuffix;
}
class OperatorBase;
class InferShapeContext;
class ExecutionContext;
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -43,21 +63,6 @@ class ExecutionContext;
*/
class OperatorBase {
public:
/// If a variable is a empty variable, that name will be used.
static std::string EMPTY_VAR_NAME() { return "@EMPTY@"; }
/// If a variable is a temporary variable, that name will be set in Python,
/// but it will be convert to a unique name in scope after OpCreator.
static std::string TMP_VAR_NAME() { return "@TEMP@"; }
/// If a variable's name has a certain suffix, it means that the
/// variable is the gradient of another varibale.
/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
static std::string GRAD_VAR_SUFFIX() { return "@GRAD"; }
/// Variables with this suffix are supposed to be filled up with zeros.
static std::string ZERO_VAR_SUFFIX() { return "@ZERO"; }
virtual ~OperatorBase() {}
template <typename T>
......@@ -280,7 +285,7 @@ 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 @ 329370e8
......@@ -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", []() { return kEmptyVarName; })
.def("temp", []() { return kTempVarName; });
// 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 @ 329370e8
......@@ -26,19 +26,17 @@ limitations under the License. */
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace pybind {
namespace details { // forward declare
template <bool less, size_t i, typename... args>
struct CastToPyBufferImpl;
} // namespace details
} // namespace pybind
namespace framework {
namespace details {
template <bool less, size_t i, typename... args>
struct CastToPyBufferImpl;
}
class Tensor {
public:
template <bool less, size_t i, typename... args>
friend struct paddle::pybind::details::CastToPyBufferImpl;
friend struct details::CastToPyBufferImpl;
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
......
paddle/pybind/tensor_bind.h → paddle/framework/tensor_py.h
浏览文件 @ 329370e8
......@@ -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/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @ 329370e8
......@@ -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 @ 329370e8
......@@ -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/tests/CMakeLists.txt
浏览文件 @ 329370e8
# gserver pacakge unittests
file(GLOB_RECURSE GSERVER_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.h")
file(GLOB_RECURSE GSERVER_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cpp")
add_style_check_target(paddle_gserver ${GSERVER_SOURCES})
add_style_check_target(paddle_gserver ${GSERVER_HEADER})
################### test_ProtoDataProvider ############
add_unittest_without_exec(test_ProtoDataProvider
test_ProtoDataProvider.cpp)
......@@ -50,7 +55,7 @@ add_unittest_without_exec(test_DetectionOutput
test_DetectionOutput.cpp
LayerGradUtil.cpp)
add_test(NAME test_DetectionOutput
add_test(NAME test_DetectionOutput
COMMAND test_DetectionOutput)
################# test_ConvUnify #######################
add_unittest_without_exec(test_ConvUnify
......
paddle/gserver/tests/LayerGradUtil.cpp
浏览文件 @ 329370e8
......@@ -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 @ 329370e8
......@@ -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/math/MathUtils.cpp
浏览文件 @ 329370e8
......@@ -25,7 +25,7 @@ namespace paddle {
*/
void sparseRand(
int* major, int* minor, int nnz, int majorLen, int minorMax, bool useGpu) {
CHECK(size_t(nnz) > size_t(1));
CHECK(size_t(nnz) >= size_t(1));
int* cpuMajor;
int* cpuMinor;
CpuIVector cpuMinorVec(nnz);
......
paddle/math/tests/test_matrixCompare.cpp
浏览文件 @ 329370e8
......@@ -79,8 +79,8 @@ void testMatrixMaxSequence(int batchSize, int inputDim) {
}
TEST(Matrix, maxSequence) {
for (auto batchSize : {1, 10, 128, 1000, 6000}) {
for (auto inputDim : {1, 32, 100, 512}) {
for (auto batchSize : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
for (auto inputDim : {1, 7, 131}) { // prime numbers close to 1, 8, 128
VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
testMatrixMaxSequence(batchSize, inputDim);
}
......@@ -240,14 +240,10 @@ TEST(Matrix, unary) {
// inverse matrix
testMatrixInverse(height);
#else
LOG(WARNING) << "Cannot run Matrix Inverse Unit Test.\n"
<< "Failed to find lapack library in current system.\n"
<< "To address this issue, Please adopt one of the following "
"approaches: \n"
<< "1. Simply issue `sudo apt-get install liblapacke-dev` to "
"avoid re-build source code. \n"
<< "2. Install MKL/Openblas/ATLAS and re-build PaddlePaddle "
"source code.";
LOG(WARNING) << "This version of PaddlePaddle was not built with LAPACK"
<< "support so we cannot test matrix inverse. To test "
<< "matrix inverse, please install LAPACKE "
<< "and MKL/Openblas/ATLAS, and re-build PaddlePaddle.";
#endif
}
}
......@@ -341,8 +337,8 @@ void testMatrixSoftmaxBp(int height, int width) {
}
TEST(Matrix, softmax) {
for (auto height : {1, 11, 73, 128, 200}) {
for (auto width : {1, 32, 100, 512, 1000}) {
for (auto height : {1, 3, 131}) { // prime numbers close to 1, 4, 127
for (auto width : {1, 17, 251}) { // prime numbers close to 1, 16, 256
VLOG(3) << " height=" << height << " width=" << width;
testMatrixSoftmax(height, width);
......@@ -527,7 +523,7 @@ void testVectorRowFunc(int size) {
}
TEST(Vector, rowFunc) {
for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
for (auto size : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
VLOG(3) << " size=" << size;
testVectorRowFunc(size);
}
......@@ -604,7 +600,7 @@ void testVectorIsEqual(int size) {
}
TEST(Vector, Equal) {
for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
for (auto size : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
VLOG(3) << " size=" << size;
testVectorReset<int>(size);
testVectorReset<real>(size);
......@@ -635,9 +631,8 @@ void testMatrixTopK(int samples, int dim, int beamSize) {
}
TEST(Matrix, topK) {
for (auto samples : {1, 5, 31, 90, 150, 500}) {
for (auto dim :
{1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
for (auto samples : {1, 17, 131}) { // prime numbers close to 1, 16, 127
for (auto dim : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
for (auto beamSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
if (beamSize > dim) continue;
VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
......@@ -650,6 +645,7 @@ TEST(Matrix, topK) {
void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
int nnz = samples * dim * ratio;
if (nnz < 1) nnz = 1; // Because sparseRand in MathUtil.cpp requires this.
MatrixPtr cpuSrc = std::make_shared<CpuSparseMatrix>(samples, dim, nnz);
MatrixPtr gpuSrc = std::make_shared<GpuSparseMatrix>(samples, dim, nnz);
MatrixPtr cpuVal = std::make_shared<CpuMatrix>(samples, beamSize);
......@@ -683,9 +679,9 @@ void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
}
TEST(SMatrix, topK) {
for (auto samples : {1, 5, 100}) {
for (auto dim : {10000, 10000, 50000}) {
for (auto beamSize : {1, 5, 40, 100, 500}) {
for (auto samples : {1, 3, 61}) {
for (auto dim : {1, 3, 61}) {
for (auto beamSize : {1, 3, 61}) {
for (auto ratio : {0.01, 0.001}) {
if (beamSize > dim) continue;
VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
......@@ -806,10 +802,9 @@ void testClassificationError(int numSamples, int dim, int topkSize) {
}
TEST(Matrix, classificationError) {
for (auto numSamples : {1, 5, 31, 90, 150, 300}) {
for (auto dim :
{1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
for (auto topkSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
for (auto numSamples : {1, 3, 31}) {
for (auto dim : {1, 3, 31}) {
for (auto topkSize : {1, 3, (int)rand() % dim + 1}) {
if (topkSize > dim) continue;
VLOG(3) << " sample= " << numSamples << " topkSize= " << topkSize
<< " dim= " << dim;
......@@ -1016,13 +1011,15 @@ void testAvgPoolFwdBwd(int numSamples,
TensorCheckErr(*inputGrad, *inputGpuGrad);
}
// TODO(yi): I noticed many such blindly combinatorial tests in this
// file. They are no help to locate defects at all.
TEST(Matrix, PoolFwdBwd) {
for (auto numSamples : {5, 32}) {
for (auto channels : {1, 9, 32}) {
for (auto imgSizeH : {14, 28}) {
for (auto imgSizeW : {16, 30}) {
for (auto sizeX : {2, 5}) {
for (auto sizeY : {2, 5}) {
for (auto numSamples : {1, 3}) {
for (auto channels : {1, 3}) {
for (auto imgSizeH : {13, 17}) {
for (auto imgSizeW : {17, 19}) {
for (auto sizeX : {2, 3}) {
for (auto sizeY : {2, 3}) {
for (auto sH : {1, 2}) {
for (auto sW : {1, 2}) {
for (auto pH : {0, (sizeY - 1) / 2}) {
......@@ -1128,8 +1125,8 @@ TEST(Matrix, MaxOutFwdBwd) {
}
TEST(CpuMatrix, copyFrom) {
const size_t height = 1000;
const size_t width = 1000;
const size_t height = 31;
const size_t width = 53;
CpuMatrix cpu(height, width);
GpuMatrix gpu(height, width);
CpuMatrix copy(height, width);
......@@ -1149,6 +1146,10 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
IVectorPtr cpuSequence;
generateSequenceStartPositions(batchSize, cpuSequence);
for (int i = 0; i < cpuSequence->getSize(); ++i) {
(cpuSequence->getData())[i] += 1; // so no way that maxSeqLen is 0;
}
IVectorPtr gpuSequence = IVector::create(cpuSequence->getSize(), true);
gpuSequence->copyFrom(*cpuSequence);
......@@ -1156,45 +1157,46 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
size_t maxSeqLen = *std::max_element(cpuSequence->getData(),
cpuSequence->getData() + numSeq);
printf("numSeq = %ld, maxSeqLen = %ld\n", numSeq, maxSeqLen);
MatrixPtr cBatch = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
MatrixPtr gBatch = std::make_shared<GpuMatrix>(numSeq * maxSeqLen, inputDim);
MatrixPtr cCheck = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
hl_sequence2batch_copy_padding(gBatch->getData(),
gpuInput->getData(),
cpuSequence->getData(),
inputDim,
maxSeqLen,
numSeq,
false,
true);
cCheck->copyFrom(*gBatch);
int* seqStart = cpuSequence->getData();
float* batchData = cBatch->getData();
float* seqData = cpuInput->getData();
for (size_t i = 0; i < maxSeqLen; i++) {
for (size_t j = 0; j < numSeq; j++) {
size_t sequenceStart = seqStart[j];
size_t sequenceLength = seqStart[j + 1] - seqStart[j];
if (i < sequenceLength) {
memcpy(batchData + (i * numSeq + j) * inputDim,
seqData + (sequenceStart + i) * inputDim,
inputDim * sizeof(real));
} else {
memset(batchData + (i * numSeq + j) * inputDim,
0,
inputDim * sizeof(real));
}
}
}
TensorCheckErr(*cBatch, *cCheck);
// hl_sequence2batch_copy_padding(gBatch->getData(),
// gpuInput->getData(),
// cpuSequence->getData(),
// inputDim,
// maxSeqLen,
// numSeq,
// false,
// true);
// cCheck->copyFrom(*gBatch);
// int* seqStart = cpuSequence->getData();
// float* batchData = cBatch->getData();
// float* seqData = cpuInput->getData();
// for (size_t i = 0; i < maxSeqLen; i++) {
// for (size_t j = 0; j < numSeq; j++) {
// size_t sequenceStart = seqStart[j];
// size_t sequenceLength = seqStart[j + 1] - seqStart[j];
// if (i < sequenceLength) {
// memcpy(batchData + (i * numSeq + j) * inputDim,
// seqData + (sequenceStart + i) * inputDim,
// inputDim * sizeof(real));
// } else {
// memset(batchData + (i * numSeq + j) * inputDim,
// 0,
// inputDim * sizeof(real));
// }
// }
// }
// TensorCheckErr(*cBatch, *cCheck);
}
TEST(Matrix, warpCTC) {
for (auto batchSize : {51, 526, 2884}) {
for (auto inputDim : {32, 512, 2026}) {
for (auto batchSize : {1, 3, 17}) {
for (auto inputDim : {1, 3, 31}) {
VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
testBatch2seqPadding(batchSize, inputDim);
}
......
paddle/memory/detail/buddy_allocator.h
浏览文件 @ 329370e8
......@@ -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 @ 329370e8
......@@ -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 @ 329370e8
......@@ -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 @ 329370e8
......@@ -41,6 +41,9 @@ function(op_library TARGET)
endif()
endfunction()
cc_library(net_op SRCS net_op.cc DEPS op_registry)
cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
op_library(add_op SRCS add_op.cc add_op.cu)
cc_test(add_op_test SRCS add_op_test.cc DEPS add_op)
......@@ -59,6 +62,6 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library(fc_op
SRCS fc_op.cc
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net)
op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net)
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net_op)
cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)
paddle/operators/add_op.cu
浏览文件 @ 329370e8
/* 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/cross_entropy_op.cu
浏览文件 @ 329370e8
/* 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/fc_op.cc
浏览文件 @ 329370e8
......@@ -27,7 +27,7 @@ public:
{Output("before_act")},
{}));
auto b = Input("b");
if (b != EMPTY_VAR_NAME()) {
if (b != framework::kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add",
{Output("before_act"), Input("b")},
{Output("before_act")},
......
paddle/operators/fill_zeros_like_op.cu
浏览文件 @ 329370e8
/* 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 @ 329370e8
......@@ -41,7 +41,7 @@ public:
class MeanGradOp : public OperatorWithKernel {
protected:
void InferShape(const InferShapeContext &ctx) const override {
ctx.Output<Tensor>("X" + GRAD_VAR_SUFFIX())
ctx.Output<Tensor>("X" + framework::kGradVarSuffix)
->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......
paddle/operators/mean_op.cu
浏览文件 @ 329370e8
/* 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>);
\ No newline at end of file
REGISTER_OP_GPU_KERNEL(mean_grad, ops::MeanGradKernel<ops::GPUPlace, float>);
paddle/operators/mean_op.h
浏览文件 @ 329370e8
......@@ -39,10 +39,10 @@ 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());
auto OG = context.Input<Tensor>("Out" + framework::kGradVarSuffix);
PADDLE_ENFORCE(framework::product(OG->dims()) == 1,
"Mean Gradient should be scalar");
auto IG = context.Output<Tensor>("X" + OperatorBase::GRAD_VAR_SUFFIX());
auto IG = context.Output<Tensor>("X" + framework::kGradVarSuffix);
IG->mutable_data<T>(context.GetPlace());
T ig_size = (T)framework::product(IG->dims());
......
paddle/operators/mul_op.cu
浏览文件 @ 329370e8
......@@ -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/framework/net.cc → paddle/operators/net_op.cc
浏览文件 @ 329370e8
......@@ -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 @ 329370e8
......@@ -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 @ 329370e8
#include "paddle/operators/net_op.h"
#include <gtest/gtest.h>
#include <paddle/framework/net.h>
#include <paddle/framework/op_registry.h>
#include <paddle/framework/operator.h>
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace framework {
namespace operators {
static int infer_shape_cnt = 0;
static int run_cnt = 0;
class TestOp : public OperatorBase {
public:
public:
void InferShape(const framework::Scope& scope) const override {
++infer_shape_cnt;
}
......@@ -21,7 +23,7 @@ class TestOp : public OperatorBase {
};
class EmptyOp : public OperatorBase {
public:
public:
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
......@@ -73,7 +75,7 @@ TEST(OpKernel, all) {
ASSERT_THROW(net->AddOp(op2), paddle::platform::EnforceNotMet);
}
TEST(Net, insert_op) {
TEST(NetOp, insert_op) {
NetOp net;
auto op1 = std::make_shared<EmptyOp>();
op1->inputs_ = {"x", "w1", "b1"};
......@@ -85,5 +87,5 @@ TEST(Net, insert_op) {
ASSERT_EQ(3UL, net.ops_.size());
}
} // namespace framework
} // namespace operators
} // namespace paddle
paddle/operators/recurrent_op.cc
浏览文件 @ 329370e8
......@@ -18,8 +18,8 @@
#include <cstring>
#include <sstream>
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/platform/enforce.h"
namespace paddle {
......@@ -38,10 +38,10 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
"input link [%s] is not in scope.",
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
DDim dims = input->dims();
framework::DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
DDim step_dims = slice_ddim(dims, 1, dims.size());
framework::DDim step_dims = slice_ddim(dims, 1, dims.size());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
......@@ -64,13 +64,13 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
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();
framework::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));
output->Resize(framework::make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
......
paddle/operators/recurrent_op.h
浏览文件 @ 329370e8
......@@ -19,8 +19,6 @@
namespace paddle {
namespace operators {
using namespace paddle::framework;
namespace rnn {
/**
......@@ -70,7 +68,7 @@ struct ArgumentName {
/**
* Prepare inputs for each step net.
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
void SegmentInputs(const std::vector<framework::Scope*>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len,
bool infer_shape_mode);
......@@ -78,12 +76,12 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
void ConcatOutputs(const std::vector<framework::Scope*>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len,
bool infer_shape_mode);
void LinkMemories(const std::vector<Scope*>& step_scopes,
void LinkMemories(const std::vector<framework::Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories,
const size_t step_id,
const int offset,
......@@ -94,7 +92,7 @@ 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.
......@@ -103,14 +101,15 @@ void InitArgument(const ArgumentName& name, Argument* arg);
class RecurrentAlgorithm {
public:
void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const;
void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
/**
* InferShape must be called before Run.
*/
void InferShape(const Scope& scope) const;
void InferShape(const framework::Scope& scope) const;
protected:
/*
......@@ -119,13 +118,15 @@ protected:
* NOTE the scopes are reused in both the forward and backward, so just
* create once and expand its size if more steps need.
*/
void CreateScopes(const Scope& scope) const;
void CreateScopes(const framework::Scope& scope) const;
const std::vector<Scope*>& GetStepScopes(const Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
const std::vector<framework::Scope*>& GetStepScopes(
const framework::Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)
->GetMutable<std::vector<framework::Scope*>>();
}
void InitMemories(Scope* step_scopes, bool infer_shape_mode) const;
void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
private:
std::unique_ptr<rnn::Argument> arg_;
......@@ -146,18 +147,22 @@ class RecurrentGradientAlgorithm {
public:
void Init(std::unique_ptr<rnn::Argument> arg) { arg_ = std::move(arg); }
void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const;
void LinkBootMemoryGradients(Scope* step_scopes, bool infer_shape_mode) const;
void LinkBootMemoryGradients(framework::Scope* step_scopes,
bool infer_shape_mode) const;
/**
* InferShape must be called before Run.
*/
void InferShape(const Scope& scope) const;
void InferShape(const framework::Scope& scope) const;
protected:
inline const std::vector<Scope*>& GetStepScopes(const Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
inline const std::vector<framework::Scope*>& GetStepScopes(
const framework::Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)
->GetMutable<std::vector<framework::Scope*>>();
}
private:
......@@ -165,19 +170,19 @@ private:
mutable size_t seq_len_;
};
class RecurrentOp final : public OperatorBase {
class RecurrentOp final : public framework::OperatorBase {
public:
void Init() override;
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const Scope& scope) const override {
void InferShape(const framework::Scope& scope) const override {
alg_.InferShape(scope);
}
virtual void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
......@@ -187,19 +192,19 @@ private:
RecurrentAlgorithm alg_;
};
class RecurrentGradientOp final : public OperatorBase {
class RecurrentGradientOp final : public framework::OperatorBase {
public:
void Init() override;
/**
* InferShape must be called before Run.
*/
virtual void InferShape(const Scope& scope) const override {
void InferShape(const framework::Scope& scope) const override {
alg_.InferShape(scope);
}
virtual void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
......
paddle/operators/recurrent_op_test.cc
浏览文件 @ 329370e8
......@@ -11,18 +11,23 @@
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/ddim.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/recurrent_op.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
using framework::make_ddim;
using framework::DDim;
class RecurrentOpTest : public ::testing::Test {
protected:
virtual void SetUp() override {
......@@ -71,7 +76,7 @@ protected:
}
void CreateRNNOp() {
OpDesc op_desc;
framework::OpDesc op_desc;
op_desc.set_type("recurrent_op");
// inlinks 0
......
paddle/operators/rowwise_add_op.cu
浏览文件 @ 329370e8
/* 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 @ 329370e8
/* 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/sigmoid_op.cu
浏览文件 @ 329370e8
/* 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/softmax_op.cc
浏览文件 @ 329370e8
/* 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(framework::GradVarName("Y")) != nullptr,
"Input(Y@GRAD) should not be null");
PADDLE_ENFORCE(ctx.Input<Tensor>("Y")->dims() ==
ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
"the shape of Input(0) and Input(1) should be the same");
ctx.Output<Tensor>(framework::GradVarName("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 @ 329370e8
/* 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 @ 329370e8
/* 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>(framework::GradVarName("Y"));
auto dX = context.Output<Tensor>(framework::GradVarName("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 @ 329370e8
......@@ -15,13 +15,14 @@
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
using OpKernel = framework::OpKernel;
using OperatorBase = framework::OperatorBase;
using InferShapeContext = framework::InferShapeContext;
using ExecutionContext = framework::ExecutionContext;
using Variable = framework::Variable;
......@@ -43,15 +44,16 @@ template <typename T,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Tensor = framework::Tensor;
using Scope = framework::Scope;
using OperatorWithKernel = framework::OperatorWithKernel;
using OperatorBase = framework::OperatorBase;
using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker;
using OpProto = framework::OpProto;
using OpAttrChecker = framework::OpAttrChecker;
using CPUPlace = platform::CPUPlace;
using GPUPlace = platform::GPUPlace;
using NetOp = framework::NetOp;
using OpRegistry = framework::OpRegistry;
using OperatorBase = framework::OperatorBase;
} // namespace operators
} // namespace paddle
......
paddle/platform/device_context.h
浏览文件 @ 329370e8
......@@ -40,7 +40,7 @@ class DeviceContext {
class CPUDeviceContext : public DeviceContext {
public:
CPUDeviceContext();
CPUDeviceContext(CPUPlace);
explicit CPUDeviceContext(CPUPlace);
virtual ~CPUDeviceContext() {}
Eigen::DefaultDevice* eigen_device() const;
......@@ -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/device_context_test.cc
浏览文件 @ 329370e8
......@@ -15,24 +15,28 @@ limitations under the License. */
#include "paddle/platform/device_context.h"
#include "gtest/gtest.h"
using DEVICE_GPU = Eigen::GpuDevice;
TEST(Device, Init) {
using paddle::platform::DeviceContext;
using paddle::platform::CUDADeviceContext;
using paddle::platform::GPUPlace;
int count = paddle::platform::GetDeviceCount();
for (int i = 0; i < count; i++) {
paddle::platform::DeviceContext* device_context =
new paddle::platform::CUDADeviceContext(i);
DeviceContext* device_context = new CUDADeviceContext(GPUPlace(i));
Eigen::GpuDevice* gpu_device =
device_context->template get_eigen_device<DEVICE_GPU>();
device_context->template get_eigen_device<Eigen::GpuDevice>();
ASSERT_NE(nullptr, gpu_device);
delete device_context;
}
}
TEST(Device, CUDADeviceContext) {
using paddle::platform::CUDADeviceContext;
using paddle::platform::GPUPlace;
int count = paddle::platform::GetDeviceCount();
for (int i = 0; i < count; i++) {
paddle::platform::CUDADeviceContext* device_context =
new paddle::platform::CUDADeviceContext(i);
CUDADeviceContext* device_context = new CUDADeviceContext(GPUPlace(i));
Eigen::GpuDevice* gpu_device = device_context->eigen_device();
ASSERT_NE(nullptr, gpu_device);
cudnnHandle_t cudnn_handle = device_context->cudnn_handle();
......
paddle/platform/dynload/cublas.cc
浏览文件 @ 329370e8
/* 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 @ 329370e8
/* 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 @ 329370e8
/* 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/enforce.h
浏览文件 @ 329370e8
......@@ -162,5 +162,50 @@ inline void throw_on_error(T e) {
} \
} while (0)
/*
* Some enforce helpers here, usage:
* int a = 1;
* int b = 2;
* PADDLE_ENFORCE_EQ(a, b);
*
* will raise an expression described as follows:
* "enforce a == b failed, 1 != 2" with detailed stack infomation.
*
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
*/
#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, ==, !=, __VA_ARGS__)
#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, !=, ==, __VA_ARGS__)
#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >, <=, __VA_ARGS__)
#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >=, <, __VA_ARGS__)
#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <, >=, __VA_ARGS__)
#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
// if two values have different data types, choose a compatible type for them.
template <typename T1, typename T2>
struct CompatibleType {
static const bool t1_to_t2 = std::is_convertible<T1, T2>::value;
typedef typename std::conditional<t1_to_t2, T2, T1>::type type;
};
#define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...) \
PADDLE_ENFORCE(__COMPATIBLE_TYPE(__VAL0, __VAL1, __VAL0) \
__CMP __COMPATIBLE_TYPE(__VAL0, __VAL1, __VAL1), \
"enforce %s " #__CMP " %s failed, %s " #__INV_CMP " %s\n%s", \
#__VAL0, #__VAL1, std::to_string(__VAL0), \
std::to_string(__VAL1), \
paddle::string::Sprintf("" __VA_ARGS__));
#define __COMPATIBLE_TYPE(__VAL0, __VAL1, __VAL) \
typename paddle::platform::CompatibleType<decltype(__VAL0), \
decltype(__VAL1)>::type(__VAL)
} // namespace platform
} // namespace paddle
paddle/platform/enforce_test.cc
浏览文件 @ 329370e8
......@@ -34,3 +34,165 @@ TEST(ENFORCE, FAILED) {
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE, NO_ARG_OK) {
int a = 2;
int b = 2;
PADDLE_ENFORCE_EQ(a, b);
// test enforce with extra message.
PADDLE_ENFORCE_EQ(a, b, "some thing wrong %s", "info");
}
TEST(ENFORCE_EQ, NO_EXTRA_MSG_FAIL) {
int a = 2;
bool in_catch = false;
try {
PADDLE_ENFORCE_EQ(a, 1 + 3);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce a == 1 + 3 failed, 2 != 4";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_EQ, EXTRA_MSG_FAIL) {
int a = 2;
bool in_catch = false;
try {
PADDLE_ENFORCE_EQ(a, 1 + 3, "%s size not match", "their");
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg =
"enforce a == 1 + 3 failed, 2 != 4\ntheir size not match";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_NE, OK) {
PADDLE_ENFORCE_NE(1, 2);
PADDLE_ENFORCE_NE(1.0, 2UL);
}
TEST(ENFORCE_NE, FAIL) {
bool in_catch = false;
try {
// 2UL here to check data type compatible
PADDLE_ENFORCE_NE(1.0, 1UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1.0 != 1UL failed, 1.000000 == 1";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_GT, OK) { PADDLE_ENFORCE_GT(2, 1); }
TEST(ENFORCE_GT, FAIL) {
bool in_catch = false;
try {
// 2UL here to check data type compatible
PADDLE_ENFORCE_GT(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1 > 2UL failed, 1 <= 2";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_GE, OK) {
PADDLE_ENFORCE_GE(2, 2UL);
PADDLE_ENFORCE_GE(3, 2UL);
PADDLE_ENFORCE_GE(3, 2);
PADDLE_ENFORCE_GE(3.21, 2UL);
}
TEST(ENFORCE_GE, FAIL) {
bool in_catch = false;
try {
PADDLE_ENFORCE_GE(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1 >= 2UL failed, 1 < 2";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_LE, OK) {
PADDLE_ENFORCE_LE(1, 1);
PADDLE_ENFORCE_LE(1, 1UL);
PADDLE_ENFORCE_LE(2, 3UL);
PADDLE_ENFORCE_LE(2UL, 3);
PADDLE_ENFORCE_LE(2UL, 3.2);
}
TEST(ENFORCE_LE, FAIL) {
bool in_catch = false;
try {
PADDLE_ENFORCE_GT(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1 > 2UL failed, 1 <= 2";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
TEST(ENFORCE_LT, OK) {
PADDLE_ENFORCE_LT(3, 10);
PADDLE_ENFORCE_LT(2, 3UL);
PADDLE_ENFORCE_LT(2UL, 3);
}
TEST(ENFORCE_LT, FAIL) {
bool in_catch = false;
try {
PADDLE_ENFORCE_LT(1UL, 0.12);
} catch (paddle::platform::EnforceNotMet error) {
in_catch = true;
const std::string msg = "enforce 1UL < 0.12 failed, 1 >= 0.12";
const char* what = error.what();
for (size_t i = 0; i < msg.length(); ++i) {
ASSERT_EQ(what[i], msg[i]);
}
}
ASSERT_TRUE(in_catch);
}
paddle/platform/place.h
浏览文件 @ 329370e8
......@@ -32,7 +32,7 @@ struct CPUPlace {
struct GPUPlace {
GPUPlace() : GPUPlace(0) {}
GPUPlace(int d) : device(d) {}
explicit GPUPlace(int d) : device(d) {}
// needed for variant equality comparison
inline bool operator==(const GPUPlace &o) const { return device == o.device; }
......
paddle/string/piece.h
浏览文件 @ 329370e8
......@@ -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: accept C string into Piece.
Piece(const std::string& s); // NOLINT: accept C++ string into Piece.
const char* data() const { return data_; }
size_t len() const { return size_; }
......
paddle/trainer/tests/sample_trainer_config_compare_sparse.conf 0 → 100644
浏览文件 @ 329370e8
#edit-mode: -*- python -*-
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
# Note: when making change to this file, please make sure
# sample_trainer_config_rnn.conf is changed accordingly so that the uniitest
# for comparing these two nets can pass (test_CompareTwoNets)
default_initial_std(0.1)
default_device(0)
word_dim = 999
l1 = 0
l2 = 0
model_type("nn")
sparse_update = get_config_arg("sparse_update", bool, False)
TrainData(ProtoData(
type = "proto_sequence",
files = ('trainer/tests/train_sparse.list'),
))
Settings(
algorithm='sgd',
batch_size=100,
learning_rate=0.0001,
learning_rate_decay_a=4e-08,
learning_rate_decay_b=0.0,
learning_rate_schedule='poly',
)
wordvec_dim = 32
layer2_dim = 16
layer3_dim = 16
hidden_dim = 32
slot_names = ["qb", "qw", "tb", "tw"]
def ltr_network(network_name,
word_dim=word_dim,
wordvec_dim=wordvec_dim,
layer2_dim=layer2_dim,
layer3_dim=layer3_dim,
hidden_dim=hidden_dim,
slot_names=slot_names,
l1=l1,
l2=l2):
slotnum = len(slot_names)
for i in xrange(slotnum):
Inputs(slot_names[i] + network_name)
for i in xrange(slotnum):
Layer(
name = slot_names[i] + network_name,
type = "data",
size = word_dim,
device = -1,
)
Layer(
name = slot_names[i] + "_embedding_" + network_name,
type = "mixed",
size = wordvec_dim,
bias = False,
device = -1,
inputs = TableProjection(slot_names[i] + network_name,
parameter_name = "embedding.w0",
decay_rate_l1=l1,
sparse_remote_update = True,
sparse_update = sparse_update,
),
)
Layer(
name = slot_names[i] + "_rnn1_" + network_name,
type = "recurrent",
active_type = "tanh",
bias = Bias(initial_std = 0,
parameter_name = "rnn1.bias"),
inputs = Input(slot_names[i] + "_embedding_" + network_name,
parameter_name = "rnn1.w0")
)
Layer(
name = slot_names[i] + "_rnnlast_" + network_name,
type = "seqlastins",
inputs = [
slot_names[i] + "_rnn1_" + network_name,
],
)
Layer(
name = "layer2_" + network_name,
type = "fc",
active_type = "tanh",
size = layer2_dim,
bias = Bias(parameter_name = "layer2.bias"),
inputs = [Input(slot_name + "_rnnlast_" + network_name,
parameter_name = "_layer2_" + slot_name + ".w",
decay_rate = l2,
initial_smart = True) for slot_name in slot_names]
)
Layer(
name = "layer3_" + network_name,
type = "fc",
active_type = "tanh",
size = layer3_dim,
bias = Bias(parameter_name = "layer3.bias"),
inputs = [
Input("layer2_" + network_name,
parameter_name = "_layer3.w",
decay_rate = l2,
initial_smart = True),
]
)
Layer(
name = "output_" + network_name,
type = "fc",
size = 1,
bias = False,
inputs = [
Input("layer3_" + network_name,
parameter_name = "_layerO.w"),
],
)
ltr_network("left")
ltr_network("right")
Inputs("label")
Layer(
name = "label",
type = "data",
size = 1,
)
Outputs("cost", "qb_rnnlast_left")
Layer(
name = "cost",
type = "rank-cost",
inputs = ["output_left", "output_right", "label"],
)
paddle/trainer/tests/test_CompareSparse.cpp
浏览文件 @ 329370e8
......@@ -23,7 +23,7 @@ using namespace paddle; // NOLINT
using namespace std; // NOLINT
static const string& configFile1 =
"trainer/tests/sample_trainer_config_qb_rnn.conf";
"trainer/tests/sample_trainer_config_compare_sparse.conf";
DECLARE_bool(use_gpu);
DECLARE_string(config);
......
paddle/trainer/tests/train_sparse.list 0 → 100644
浏览文件 @ 329370e8
trainer/tests/compare_sparse_data
python/paddle/v2/dataset/cifar.py
浏览文件 @ 329370e8
......@@ -133,7 +133,7 @@ def convert(path):
"""
Converts dataset to recordio format
"""
paddle.v2.dataset.common.convert(path, train100(), 10, "cifar_train100")
paddle.v2.dataset.common.convert(path, test100(), 10, "cifar_test100")
paddle.v2.dataset.common.convert(path, train10(), 10, "cifar_train10")
paddle.v2.dataset.common.convert(path, test10(), 10, "cifar_test10")
paddle.v2.dataset.common.convert(path, train100(), 1000, "cifar_train100")
paddle.v2.dataset.common.convert(path, test100(), 1000, "cifar_test100")
paddle.v2.dataset.common.convert(path, train10(), 1000, "cifar_train10")
paddle.v2.dataset.common.convert(path, test10(), 1000, "cifar_test10")
python/paddle/v2/dataset/common.py
浏览文件 @ 329370e8
......@@ -32,17 +32,22 @@ __all__ = [
DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset')
# When running unit tests, there could be multiple processes that
# trying to create DATA_HOME directory simultaneously, so we cannot
# use a if condition to check for the existence of the directory;
# instead, we use the filesystem as the synchronization mechanism by
# catching returned errors.
try:
os.makedirs(DATA_HOME)
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
pass
def must_mkdirs(path):
try:
os.makedirs(DATA_HOME)
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
pass
must_mkdirs(DATA_HOME)
def md5file(fname):
......@@ -93,6 +98,19 @@ def fetch_all():
"fetch")()
def fetch_all_recordio(path):
for module_name in filter(lambda x: not x.startswith("__"),
dir(paddle.v2.dataset)):
if "convert" in dir(
importlib.import_module("paddle.v2.dataset.%s" % module_name)) and \
not module_name == "common":
ds_path = os.path.join(path, module_name)
must_mkdirs(ds_path)
getattr(
importlib.import_module("paddle.v2.dataset.%s" % module_name),
"convert")(ds_path)
def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
"""
you can call the function as:
......
python/paddle/v2/dataset/conll05.py
浏览文件 @ 329370e8
......@@ -233,5 +233,5 @@ def convert(path):
"""
Converts dataset to recordio format
"""
paddle.v2.dataset.common.convert(path, test(), 10, "conl105_train")
paddle.v2.dataset.common.convert(path, test(), 10, "conl105_test")
paddle.v2.dataset.common.convert(path, test(), 1000, "conl105_train")
paddle.v2.dataset.common.convert(path, test(), 1000, "conl105_test")
python/paddle/v2/dataset/imdb.py
浏览文件 @ 329370e8
......@@ -173,5 +173,5 @@ def convert(path):
Converts dataset to recordio format
"""
w = word_dict()
paddle.v2.dataset.common.convert(path, lambda: train(w), 10, "imdb_train")
paddle.v2.dataset.common.convert(path, lambda: test(w), 10, "imdb_test")
paddle.v2.dataset.common.convert(path, lambda: train(w), 1000, "imdb_train")
paddle.v2.dataset.common.convert(path, lambda: test(w), 1000, "imdb_test")
python/paddle/v2/dataset/imikolov.py
浏览文件 @ 329370e8
......@@ -155,6 +155,7 @@ def convert(path):
N = 5
word_dict = build_dict()
paddle.v2.dataset.common.convert(path,
train(word_dict, N), 10, "imikolov_train")
train(word_dict, N), 1000,
"imikolov_train")
paddle.v2.dataset.common.convert(path,
test(word_dict, N), 10, "imikolov_test")
test(word_dict, N), 1000, "imikolov_test")
python/paddle/v2/dataset/mnist.py
浏览文件 @ 329370e8
......@@ -119,5 +119,5 @@ def convert(path):
"""
Converts dataset to recordio format
"""
paddle.v2.dataset.common.convert(path, train(), 10, "minist_train")
paddle.v2.dataset.common.convert(path, test(), 10, "minist_test")
paddle.v2.dataset.common.convert(path, train(), 1000, "minist_train")
paddle.v2.dataset.common.convert(path, test(), 1000, "minist_test")
python/paddle/v2/dataset/movielens.py
浏览文件 @ 329370e8
......@@ -254,8 +254,8 @@ def convert(path):
"""
Converts dataset to recordio format
"""
paddle.v2.dataset.common.convert(path, train(), 10, "movielens_train")
paddle.v2.dataset.common.convert(path, test(), 10, "movielens_test")
paddle.v2.dataset.common.convert(path, train(), 1000, "movielens_train")
paddle.v2.dataset.common.convert(path, test(), 1000, "movielens_test")
if __name__ == '__main__':
......
python/paddle/v2/dataset/sentiment.py
浏览文件 @ 329370e8
......@@ -137,5 +137,5 @@ def convert(path):
"""
Converts dataset to recordio format
"""
paddle.v2.dataset.common.convert(path, train, 10, "sentiment_train")
paddle.v2.dataset.common.convert(path, test, 10, "sentiment_test")
paddle.v2.dataset.common.convert(path, train, 1000, "sentiment_train")
paddle.v2.dataset.common.convert(path, test, 1000, "sentiment_test")
python/paddle/v2/dataset/uci_housing.py
浏览文件 @ 329370e8
......@@ -119,5 +119,5 @@ def convert(path):
"""
Converts dataset to recordio format
"""
paddle.v2.dataset.common.convert(path, train(), 10, "uci_housing_train")
paddle.v2.dataset.common.convert(path, test(), 10, "uci_houseing_test")
paddle.v2.dataset.common.convert(path, train(), 1000, "uci_housing_train")
paddle.v2.dataset.common.convert(path, test(), 1000, "uci_houseing_test")
python/paddle/v2/dataset/wmt14.py
浏览文件 @ 329370e8
......@@ -169,5 +169,6 @@ def convert(path):
Converts dataset to recordio format
"""
dict_size = 30000
paddle.v2.dataset.common.convert(path, train(dict_size), 10, "wmt14_train")
paddle.v2.dataset.common.convert(path, test(dict_size), 10, "wmt14_test")
paddle.v2.dataset.common.convert(path,
train(dict_size), 1000, "wmt14_train")
paddle.v2.dataset.common.convert(path, test(dict_size), 1000, "wmt14_test")
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ 329370e8
......@@ -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 @ 329370e8
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 @ 329370e8
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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