提交 d1e75433 编写于 作者: L liaogang

Merge branch 'clang-format' of https://github.com/gangliao/Paddle; branch...

Merge branch 'clang-format' of https://github.com/gangliao/Paddle; branch 'develop' of https://github.com/PaddlePaddle/Paddle into clang-format
......@@ -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)
......
......@@ -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} ${base_filename}.cpplint)
endif()
endforeach()
endif()
......
......@@ -187,7 +187,13 @@ function(cc_library TARGET_NAME)
endif()
# cpplint code style
add_style_check_target(${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} ${nv_library_DEPS})
......
......@@ -118,7 +118,6 @@ endfunction()
macro(add_unittest_without_exec TARGET_NAME)
add_executable(${TARGET_NAME} ${ARGN})
link_paddle_test(${TARGET_NAME})
add_style_check_target(${TARGET_NAME} ${ARGN})
endmacro()
# add_unittest
......
......@@ -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)
......@@ -26,7 +28,7 @@ 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)
......
/* 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
/* 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));
......
......@@ -59,19 +59,17 @@ 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
// `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();
}
......@@ -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
......@@ -78,14 +78,14 @@ class FcOp : public ops::NetOp {
{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);
}
}
......@@ -163,13 +163,12 @@ TEST(Backward, simple_op_grad) {
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(4UL, gop->inputs_.size());
ASSERT_EQ(f::OperatorBase::EMPTY_VAR_NAME(), gop->inputs_[0]);
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) {
......@@ -177,7 +176,7 @@ 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"});
......@@ -210,8 +209,8 @@ 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()},
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, {});
......@@ -242,24 +241,21 @@ TEST(Backward, net_input_of_network_not_need_grad) {
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<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) {
......@@ -311,17 +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) {
......@@ -331,12 +325,10 @@ 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");
......@@ -368,23 +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");
*/
}
......@@ -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>()) {}
......
......@@ -56,8 +56,7 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
for (const auto& arg : src_arg_list) {
std::string src_name = arg.name();
std::string dst_name =
is_grad ? src_name + OperatorBase::GRAD_VAR_SUFFIX() : src_name;
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 =
......@@ -65,10 +64,9 @@ static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
int src_end = src_format == nullptr ? src_arg_idx + 1
: src_format->at(src_arg_idx + 1);
for (int i = src_begin; i < src_end; ++i) {
std::string s = is_grad ? src_inout[i] + OperatorBase::GRAD_VAR_SUFFIX()
: arg.ignore_gradient()
? OperatorBase::EMPTY_VAR_NAME()
: src_inout[i];
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) {
......
/* 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/operator.h"
......
......@@ -83,24 +83,21 @@ TEST(GradOpBuilder, MutiInOut) {
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input("Out1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Inputs("Out2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
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::OperatorBase::GRAD_VAR_SUFFIX(),
"out2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
{"out2_1" + f::kGradVarSuffix, "out2_2" + f::kGradVarSuffix}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_3" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Output("In3" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"in3" + f::OperatorBase::GRAD_VAR_SUFFIX());
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) {
......@@ -116,30 +113,25 @@ TEST(GradOpBuilder, IOIgnoredInGradient) {
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({f::OperatorBase::EMPTY_VAR_NAME(),
f::OperatorBase::EMPTY_VAR_NAME()}));
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::OperatorBase::EMPTY_VAR_NAME());
EXPECT_EQ(
grad_test_op->Inputs("Out1_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
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::OperatorBase::GRAD_VAR_SUFFIX(),
"out1_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::OperatorBase::GRAD_VAR_SUFFIX()),
"out2" + f::OperatorBase::GRAD_VAR_SUFFIX());
{"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::OperatorBase::GRAD_VAR_SUFFIX()),
"in1" + f::OperatorBase::GRAD_VAR_SUFFIX());
EXPECT_EQ(
grad_test_op->Outputs("In2_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in2_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in2_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
EXPECT_EQ(
grad_test_op->Outputs("In3_mult" + f::OperatorBase::GRAD_VAR_SUFFIX()),
std::vector<std::string>({"in3_1" + f::OperatorBase::GRAD_VAR_SUFFIX(),
"in3_2" + f::OperatorBase::GRAD_VAR_SUFFIX()}));
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}));
}
......@@ -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.
......
......@@ -22,7 +22,7 @@ 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.
......
......@@ -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
......@@ -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);
......@@ -314,7 +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_;
......@@ -336,12 +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));
......@@ -353,7 +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);
}
};
......
......@@ -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
......@@ -43,25 +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"; }
static std::string GRAD_VAR_NAME(const std::string& name) {
return name + GRAD_VAR_SUFFIX();
}
/// Variables with this suffix are supposed to be filled up with zeros.
static std::string ZERO_VAR_SUFFIX() { return "@ZERO"; }
virtual ~OperatorBase() {}
template <typename T>
......@@ -284,7 +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();
}
......
......@@ -105,7 +105,16 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDATensorSetFromArray<float>)
.def("set", PyCUDATensorSetFromArray<int>)
#endif
.def("shape", [](Tensor &self) { return 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_<Variable>(m, "Variable", R"DOC(Variable Class.
......@@ -154,8 +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", OperatorBase::EMPTY_VAR_NAME)
.def("temp", 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",
......
......@@ -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_) {
......
......@@ -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
......@@ -412,6 +419,7 @@ protected:
struct Generator {
GeneratorConfig config;
std::vector<int> ids; // store generated sequences
std::vector<real> idsProb; // log probability of each generated word
Argument outArg; // final output argument
};
bool generating_;
......
# 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)
......
......@@ -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:
......
......@@ -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;
......
......@@ -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);
......
......@@ -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);
}
......
......@@ -39,7 +39,7 @@ class BuddyAllocator {
public:
void* Alloc(size_t unaligned_size);
void Free(void*);
void Free(void* ptr);
size_t Used();
public:
......
......@@ -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;
......
......@@ -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:
......
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
class AddOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of AddOp must be two");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
......@@ -33,7 +33,7 @@ protected:
};
class AddOpMaker : public OpProtoAndCheckerMaker {
public:
public:
AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of add op");
......@@ -48,7 +48,7 @@ The equation is: Out = X + Y
};
class AddOpGrad : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {}
};
......
/* 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"
......
......@@ -20,7 +20,7 @@ namespace operators {
template <typename Place, typename T>
class AddKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
auto input0 = context.Input<Tensor>(0);
auto input1 = context.Input<Tensor>(1);
......
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
class OnehotCrossEntropyOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2,
"Input size of OnehotCrossEntropyOp must be two");
......@@ -37,7 +37,7 @@ protected:
};
class OnehotCrossEntropyOpMaker : public OpProtoAndCheckerMaker {
public:
public:
OnehotCrossEntropyOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of OnehotCrossEntropyOp");
......@@ -54,8 +54,7 @@ OnehotCrossEntropy Operator.
} // namespace operators
} // namespace paddle
REGISTER_OP(onehot_cross_entropy,
ops::OnehotCrossEntropyOp,
REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp,
ops::OnehotCrossEntropyOpMaker);
REGISTER_OP_CPU_KERNEL(onehot_cross_entropy,
ops::OnehotCrossEntropyOpKernel<ops::CPUPlace, float>);
/* 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"
......
......@@ -20,7 +20,7 @@ namespace operators {
template <typename Place, typename T>
class OnehotCrossEntropyOpKernel : public OpKernel {
public:
public:
constexpr T LOG_THRESHOLD() const { return static_cast<T>(1e-20); }
void Compute(const ExecutionContext& ctx) const override {
......
......@@ -18,31 +18,29 @@ namespace paddle {
namespace operators {
class FullyConnectedOp : public NetOp {
public:
public:
void Init() override {
AddOp(OpRegistry::CreateOp("mul",
{
Input("X"), Input("W"),
},
{Output("before_act")},
{}));
{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")},
{}));
{Output("before_act")}, {}));
}
auto activation = GetAttr<std::string>("activation");
AddOp(OpRegistry::CreateOp(
activation, {Output("before_act")}, {Output("Y")}, {}));
AddOp(OpRegistry::CreateOp(activation, {Output("before_act")},
{Output("Y")}, {}));
CompleteAddOp(false);
}
};
class FullyConnectedOpMaker : public OpProtoAndCheckerMaker {
public:
public:
FullyConnectedOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input of fc operator");
......
......@@ -20,7 +20,7 @@ namespace paddle {
namespace operators {
class FillZerosLikeOp : public framework::OperatorWithKernel {
protected:
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1UL,
"Input size of FillZerosLikeOp must be one.");
......@@ -36,7 +36,7 @@ protected:
};
class FillZerosLikeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
public:
FillZerosLikeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: framework::OpProtoAndCheckerMaker(proto, op_checker) {
......@@ -52,8 +52,7 @@ The output will have the same size with input.
} // namespace operators
} // namespace paddle
REGISTER_OP(fill_zeros_like,
paddle::operators::FillZerosLikeOp,
REGISTER_OP(fill_zeros_like, paddle::operators::FillZerosLikeOp,
paddle::operators::FillZerosLikeOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_zeros_like,
......
/* 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"
......
......@@ -22,7 +22,7 @@ namespace operators {
template <typename Place, typename T>
class FillZerosLikeKernel : public framework::OpKernel {
public:
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* output = context.Output<framework::Tensor>(0);
output->mutable_data<T>(context.GetPlace());
......
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
class MeanOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Input size of AddOp must be one");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
......@@ -29,7 +29,7 @@ protected:
};
class MeanOpMaker : public OpProtoAndCheckerMaker {
public:
public:
MeanOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
......@@ -39,9 +39,9 @@ public:
};
class MeanGradOp : public OperatorWithKernel {
protected:
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());
}
};
......
/* 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"
......
......@@ -20,7 +20,7 @@ namespace operators {
template <typename Place, typename T>
class MeanKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
......@@ -37,12 +37,12 @@ public:
template <typename Place, typename T>
class MeanGradKernel : public OpKernel {
public:
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());
......
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
class MulOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
auto dim0 = ctx.Input<Tensor>(0)->dims();
......@@ -34,7 +34,7 @@ protected:
};
class MulOpMaker : public OpProtoAndCheckerMaker {
public:
public:
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of mul op");
......@@ -49,7 +49,7 @@ The equation is: Out = X * Y
};
class MulOpGrad : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "MulGrad";
......
......@@ -21,7 +21,7 @@ namespace operators {
template <typename Place, typename T>
class MulKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> dim_pair = {
{Eigen::IndexPair<Eigen::DenseIndex>(1, 0)}};
......
......@@ -40,7 +40,7 @@ namespace operators {
* it defines.
*/
class NetOp : public framework::OperatorBase {
public:
public:
/**
* Infer all the operators' input and output variables' shapes, will be called
* before every mini-batch
......@@ -90,7 +90,7 @@ public:
std::vector<std::shared_ptr<OperatorBase>> ops_;
private:
private:
bool add_op_done_{false};
template <typename T, typename KeyType>
......
......@@ -12,7 +12,7 @@ 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;
}
......@@ -23,7 +23,7 @@ public:
};
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 {}
......
......@@ -28,20 +28,18 @@ namespace operators {
namespace rnn {
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode) {
PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
for (size_t i = 0; i < inlinks.size(); ++i) {
auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
PADDLE_ENFORCE(input_var != nullptr,
"input link [%s] is not in scope.",
PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
DDim dims = input->dims();
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>();
......@@ -54,23 +52,21 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
}
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len,
const std::vector<Link>& outlinks, const size_t seq_len,
bool infer_shape_mode) {
for (size_t i = 0; i < outlinks.size(); i++) {
auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
PADDLE_ENFORCE(output_var != nullptr,
"output link [%s] is not in scope.",
PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
outlinks[i].external);
Tensor* output = output_var->GetMutable<Tensor>();
if (infer_shape_mode) {
DDim step_dims = step_scopes[0]
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++) {
......@@ -87,22 +83,16 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
void LinkMemories(const std::vector<Scope*>& scopes,
const std::vector<rnn::MemoryAttr>& memories,
const size_t step_id,
const int offset,
const size_t step_id, const int offset,
bool infer_shape_mode) {
PADDLE_ENFORCE(step_id < scopes.size(),
"step [%d] is out of range of step scopes' size [%d]",
step_id,
"step [%d] is out of range of step scopes' size [%d]", step_id,
scopes.size());
PADDLE_ENFORCE(static_cast<int>(step_id) + offset >= 0,
"offset [%d] must be large than -[%d]",
offset,
step_id);
"offset [%d] must be large than -[%d]", offset, step_id);
PADDLE_ENFORCE(step_id + offset < scopes.size(),
"offset [%d] is out of range, it must be less than (%d - %d)",
offset,
scopes.size(),
step_id);
offset, scopes.size(), step_id);
auto scope = scopes[step_id];
auto linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
......@@ -116,8 +106,7 @@ void LinkMemories(const std::vector<Scope*>& scopes,
}
}
void InitArgument(const ArgumentName& name,
Argument* arg,
void InitArgument(const ArgumentName& name, Argument* arg,
const OperatorBase& op) {
arg->step_net = op.Input(name.step_net);
arg->step_scopes = op.Output(name.step_scopes);
......@@ -126,8 +115,7 @@ void InitArgument(const ArgumentName& name,
auto inlink_alias = op.GetAttr<std::vector<std::string>>(name.inlink_alias);
PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
"the size of inlinks and inlink_alias don't match:%d,%d",
inlinks.size(),
inlink_alias.size());
inlinks.size(), inlink_alias.size());
for (size_t i = 0; i < inlinks.size(); ++i) {
rnn::Link link;
link.external = inlinks[i];
......@@ -139,8 +127,7 @@ void InitArgument(const ArgumentName& name,
auto outlink_alias = op.GetAttr<std::vector<std::string>>(name.outlink_alias);
PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
"the size of outlinks and outlink_alias don't match:%d,%d",
outlinks.size(),
outlink_alias.size());
outlinks.size(), outlink_alias.size());
for (size_t i = 0; i < outlinks.size(); ++i) {
rnn::Link link;
link.external = outlinks[i];
......@@ -156,12 +143,10 @@ void InitArgument(const ArgumentName& name,
PADDLE_ENFORCE(memories.size() == boot_memories.size(),
"the size of memories, boot_memories don't match:%d,%d",
memories.size(),
boot_memories.size());
memories.size(), boot_memories.size());
PADDLE_ENFORCE(pre_memories.size() == boot_memories.size(),
"the size of pre_memories, boot_memories don't match:%d,%d",
pre_memories.size(),
boot_memories.size());
pre_memories.size(), boot_memories.size());
PADDLE_ENFORCE(memories.size() > 0, "more than 1 memories should be set");
for (size_t i = 0; i < memories.size(); ++i) {
......@@ -181,39 +166,39 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
->dims()[0];
CreateScopes(scope);
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
true /*infer_shape_mode*/);
InitMemories(step_scopes[0], true /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (size_t i = 0; i < seq_len_; i++) {
if (i > 0) {
rnn::LinkMemories(
step_scopes, arg_->memories, i, -1, true /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, arg_->memories, i, -1,
true /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->InferShape(*step_scopes[i]);
}
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
true /*infer_shape_mode*/);
}
void RecurrentAlgorithm::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
InitMemories(step_scopes[0], false /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
for (size_t step_id = 0; step_id < seq_len_; step_id++) {
if (step_id > 0) {
rnn::LinkMemories(
step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
false /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
}
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
false /*infer_shape_mode*/);
}
void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
......@@ -245,8 +230,7 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
for (auto& attr : arg_->memories) {
Tensor* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
"memory [%s]'s boot variable [%s] not exists",
attr.var,
"memory [%s]'s boot variable [%s] not exists", attr.var,
attr.boot_var);
Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
if (infer_shape_mode) {
......@@ -257,25 +241,15 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
}
}
const rnn::ArgumentName RecurrentOp::kArgName{"step_net",
"step_scopes",
"inlinks",
"outlinks",
"inlink_alias",
"outlink_alias",
"memories",
"pre_memories",
"boot_memories"};
const rnn::ArgumentName RecurrentGradientOp::kArgName{"step_net",
"step_scopes",
"outlink@grad",
"inlink@grad",
"inlink_alias",
"outlink_alias",
"memories",
"pre_memories",
"boot_memories@grad"};
const rnn::ArgumentName RecurrentOp::kArgName{
"step_net", "step_scopes", "inlinks",
"outlinks", "inlink_alias", "outlink_alias",
"memories", "pre_memories", "boot_memories"};
const rnn::ArgumentName RecurrentGradientOp::kArgName{
"step_net", "step_scopes", "outlink@grad",
"inlink@grad", "inlink_alias", "outlink_alias",
"memories", "pre_memories", "boot_memories@grad"};
void RecurrentOp::Init() {
OperatorBase::Init();
......@@ -285,7 +259,7 @@ void RecurrentOp::Init() {
}
class RecurrentAlgorithmProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
public:
RecurrentAlgorithmProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
......@@ -316,31 +290,29 @@ public:
void RecurrentGradientAlgorithm::Run(
const Scope& scope, const platform::DeviceContext& dev_ctx) const {
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, false /*infer_shape_mode*/);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
false /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(
step_scopes, arg_->memories, step_id, 1, false /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
false /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->Run(*step_scopes[step_id], dev_ctx);
}
LinkBootMemoryGradients(step_scopes[0], false);
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
false /*infer_shape_mode*/);
}
void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
Scope* step_scope, bool infer_shape_mode) const {
for (auto& attr : arg_->memories) {
PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr,
"memory variable [%s] does not exists",
attr.var);
"memory variable [%s] does not exists", attr.var);
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
"boot variable [%s] does not exists",
attr.boot_var);
"boot variable [%s] does not exists", attr.boot_var);
Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
Tensor* boot_mem_grad =
step_scope->NewVar(attr.boot_var)->GetMutable<Tensor>();
......@@ -357,19 +329,19 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
->GetMutable<Tensor>()
->dims()[0];
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(
step_scopes, arg_->inlinks, seq_len_, true /*infer_shape_mode*/);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
true /*infer_shape_mode*/);
Variable* net = scope.FindVar(arg_->step_net);
PADDLE_ENFORCE(net != nullptr, "failed to get step net");
for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
if (static_cast<size_t>(step_id) != seq_len_ - 1) {
rnn::LinkMemories(
step_scopes, arg_->memories, step_id, 1, true /*infer_shape_mode*/);
rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
true /*infer_shape_mode*/);
}
net->GetMutable<NetOp>()->InferShape(*step_scopes[step_id]);
}
rnn::ConcatOutputs(
step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/);
rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
true /*infer_shape_mode*/);
LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
}
......@@ -383,6 +355,5 @@ void RecurrentGradientOp::Init() {
} // namespace operators
} // namespace paddle
REGISTER_OP(recurrent_op,
paddle::operators::RecurrentOp,
REGISTER_OP(recurrent_op, paddle::operators::RecurrentOp,
paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker);
......@@ -19,8 +19,6 @@
namespace paddle {
namespace operators {
using namespace paddle::framework;
namespace rnn {
/**
......@@ -70,31 +68,27 @@ struct ArgumentName {
/**
* Prepare inputs for each step net.
*/
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks,
const size_t seq_len,
void SegmentInputs(const std::vector<framework::Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
bool infer_shape_mode);
/**
* Process outputs of step nets and merge to variables.
*/
void ConcatOutputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& outlinks,
const size_t seq_len,
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,
const std::vector<MemoryAttr>& memories,
const size_t step_id,
const int offset,
bool infer_shape_mode);
void LinkMemories(const std::vector<framework::Scope*>& step_scopes,
const std::vector<MemoryAttr>& memories, const size_t step_id,
const int offset, bool infer_shape_mode);
void InitArgument(const ArgumentName& name, Argument* arg);
}; // namespace rnn
// The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
// TODO:
// TODO(Yan Chunwei):
// 1. No-padding computing for sequences with indifinite length in one batch.
// 2. Hierarchical RNN for sequence with sub-sequence.
// 3. Internal Memory.
......@@ -102,32 +96,35 @@ void InitArgument(const ArgumentName& name, Argument* arg);
// Refer to: https://arxiv.org/pdf/1502.02367.pdf
class RecurrentAlgorithm {
public:
void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const;
public:
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:
protected:
/*
* The step scopes will be stored in the father scope as a variable.
*
* 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:
private:
std::unique_ptr<rnn::Argument> arg_;
mutable size_t seq_len_;
};
......@@ -143,69 +140,73 @@ class RecurrentGradientAlgorithm {
* lot, and the latter is a wrapper acts like an dapter for it to make RNN an
* operator.
*/
public:
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*>>();
protected:
inline const std::vector<framework::Scope*>& GetStepScopes(
const framework::Scope& scope) const {
return *scope.FindVar(arg_->step_scopes)
->GetMutable<std::vector<framework::Scope*>>();
}
private:
private:
std::unique_ptr<rnn::Argument> arg_;
mutable size_t seq_len_;
};
class RecurrentOp final : public OperatorBase {
public:
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,
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
static const rnn::ArgumentName kArgName;
private:
private:
RecurrentAlgorithm alg_;
};
class RecurrentGradientOp final : public OperatorBase {
public:
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,
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
alg_.Run(scope, dev_ctx);
}
static const rnn::ArgumentName kArgName;
private:
private:
RecurrentGradientAlgorithm alg_;
};
......
......@@ -16,6 +16,7 @@
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/framework/ddim.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
......@@ -24,8 +25,11 @@
namespace paddle {
namespace operators {
using framework::make_ddim;
using framework::DDim;
class RecurrentOpTest : public ::testing::Test {
protected:
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepNet();
......@@ -72,7 +76,7 @@ protected:
}
void CreateRNNOp() {
OpDesc op_desc;
framework::OpDesc op_desc;
op_desc.set_type("recurrent_op");
// inlinks 0
......@@ -170,7 +174,7 @@ TEST_F(RecurrentOpTest, Run) {
}
class RecurrentGradientAlgorithmTest : public ::testing::Test {
protected:
protected:
virtual void SetUp() override {
CreateGlobalVariables();
CreateStepScopes();
......@@ -273,13 +277,11 @@ protected:
LOG(INFO) << "create variable step_net";
Variable* var = scope_.NewVar("step_net");
auto net = var->GetMutable<NetOp>();
net->AddOp(OpRegistry::CreateOp("mul",
{"rnn/h_pre", "rnn/w", "rnn/s_grad"},
{"rnn/h_pre_grad", "rnn/w_grad"},
{}));
net->AddOp(OpRegistry::CreateOp("mul", {"rnn/h_pre", "rnn/w", "rnn/s_grad"},
{"rnn/h_pre_grad", "rnn/w_grad"}, {}));
net->AddOp(OpRegistry::CreateOp(
"add_two", {"rnn/h_grad"}, {"rnn/x_grad", "rnn/s_grad"}, {}));
net->AddOp(OpRegistry::CreateOp("add_two", {"rnn/h_grad"},
{"rnn/x_grad", "rnn/s_grad"}, {}));
net->CompleteAddOp();
}
......@@ -293,9 +295,7 @@ protected:
inlink.internal = "rnn/x";
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
rnn::SegmentInputs(*step_scopes,
std::vector<rnn::Link>{inlink},
10,
rnn::SegmentInputs(*step_scopes, std::vector<rnn::Link>{inlink}, 10,
true /*infer_shape_mode*/);
}
......@@ -310,8 +310,8 @@ protected:
auto step_scopes =
scope_.FindVar("step_scopes")->GetMutable<std::vector<Scope*>>();
for (int i = 1; i < 10; ++i) {
rnn::LinkMemories(
*step_scopes, memories, i, -1, true /*infer_shape_mode*/);
rnn::LinkMemories(*step_scopes, memories, i, -1,
true /*infer_shape_mode*/);
}
}
......
......@@ -17,7 +17,7 @@ namespace paddle {
namespace operators {
class RowWiseAddOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2UL,
"Two inputs is needed by rowwise add");
......@@ -33,7 +33,7 @@ protected:
};
class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
public:
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The left input of row-wise add op, must be matrix");
......
/* 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"
......
......@@ -20,7 +20,7 @@ namespace operators {
template <typename Place, typename T>
class RowWiseAddKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
auto out = context.Output<Tensor>(0);
out->mutable_data<T>(context.GetPlace());
......
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
class SGDOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of SGDOp must be two");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of SGDOp must be one");
......@@ -32,7 +32,7 @@ protected:
};
class SGDOpMaker : public OpProtoAndCheckerMaker {
public:
public:
SGDOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("param", "input parameter");
......
/* 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"
......
......@@ -20,7 +20,7 @@ namespace operators {
template <typename Place, typename T>
class SGDOpKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& ctx) const override {
auto param = ctx.Input<Tensor>("param");
auto grad = ctx.Input<Tensor>("grad");
......
......@@ -17,7 +17,7 @@ namespace paddle {
namespace operators {
class SigmoidOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1, "Sigmoid Op only have one input");
PADDLE_ENFORCE(ctx.OutputSize() == 1, "Sigmoid Op only have one output");
......@@ -26,7 +26,7 @@ protected:
};
class SigmoidOpMaker : public OpProtoAndCheckerMaker {
public:
public:
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "sigmoid input");
......@@ -36,7 +36,7 @@ public:
};
class SigmoidOpGrad : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {}
std::string DebugString() const override {
LOG(INFO) << "SigmoidGrad";
......
/* 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"
......
......@@ -21,7 +21,7 @@ namespace operators {
template <typename Place, typename T>
class SigmoidKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>(0);
auto output = context.Output<Tensor>(0);
......
......@@ -18,7 +18,7 @@ namespace paddle {
namespace operators {
class SoftmaxOp : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 1UL,
"Only one input is need for softmax");
......@@ -31,7 +31,7 @@ protected:
};
class SoftmaxOpMaker : public OpProtoAndCheckerMaker {
public:
public:
SoftmaxOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "input of softmax");
......@@ -41,19 +41,19 @@ public:
};
class SoftmaxOpGrad : public OperatorWithKernel {
protected:
protected:
void InferShape(const InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.InputSize() == 3UL,
"Input of SoftmaxOpGrad should be 3, X, Y, YG");
PADDLE_ENFORCE(ctx.OutputSize() == 1UL,
"Output of SoftmaxOpGrad should be 1");
PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null");
PADDLE_ENFORCE(ctx.InputVar(GRAD_VAR_NAME("Y")) != nullptr,
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>(GRAD_VAR_NAME("Y"))->dims(),
ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
"the shape of Input(0) and Input(1) should be the same");
ctx.Output<Tensor>(GRAD_VAR_NAME("X"))
ctx.Output<Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("Y")->dims());
}
};
......
/* 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"
......
......@@ -24,7 +24,7 @@ namespace operators {
template <typename Place, typename T>
class SoftmaxKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
auto input = context.Input<Tensor>("X");
auto output = context.Output<Tensor>("Y");
......@@ -63,13 +63,13 @@ public:
template <typename Place, typename T>
class SoftmaxGradKernel : public OpKernel {
public:
public:
void Compute(const ExecutionContext& context) const override {
std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
auto Y = context.Input<Tensor>("Y");
auto dY = context.Input<Tensor>(OperatorBase::GRAD_VAR_NAME("Y"));
auto dX = context.Output<Tensor>(OperatorBase::GRAD_VAR_NAME("X"));
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];
......
......@@ -26,21 +26,16 @@ using OperatorBase = framework::OperatorBase;
using InferShapeContext = framework::InferShapeContext;
using ExecutionContext = framework::ExecutionContext;
using Variable = framework::Variable;
template <typename T,
int MajorType = Eigen::RowMajor,
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename T,
int MajorType = Eigen::RowMajor,
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T,
int MajorType = Eigen::RowMajor,
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T,
size_t D,
int MajorType = Eigen::RowMajor,
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Tensor = framework::Tensor;
......
......@@ -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();
......
......@@ -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();
......
/* 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 {
......
/* 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 {
......
/* 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
......@@ -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
......@@ -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);
}
......@@ -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; }
......
......@@ -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_; }
......
#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"],
)
......@@ -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);
......
trainer/tests/compare_sparse_data
......@@ -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")
......@@ -32,19 +32,24 @@ __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:
def must_mkdirs(path):
try:
os.makedirs(DATA_HOME)
except OSError as exc:
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
pass
must_mkdirs(DATA_HOME)
def md5file(fname):
hash_md5 = hashlib.md5()
f = open(fname, "rb")
......@@ -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:
......
......@@ -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")
......@@ -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")
......@@ -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")
......@@ -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")
......@@ -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__':
......
......@@ -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")
......@@ -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")
......@@ -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")
import paddle.v2.framework.core as core
import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
import paddle.v2.framework.proto.attr_type_pb2 as attr_type_pb2
import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2
import cStringIO
......@@ -57,7 +57,7 @@ class OpDescCreationMethod(object):
op_desc.attrs.extend([out_format])
if len(tmp_index) != 0:
tmp_index_attr = op_desc.attrs.add()
tmp_index_attr.type = attr_type_pb2.INTS
tmp_index_attr.type = attribute_pb2.INTS
tmp_index_attr.name = "temporary_index"
tmp_index_attr.ints.extend(tmp_index)
......@@ -73,17 +73,17 @@ class OpDescCreationMethod(object):
new_attr = op_desc.attrs.add()
new_attr.name = attr.name
new_attr.type = attr.type
if attr.type == attr_type_pb2.INT:
if attr.type == attribute_pb2.INT:
new_attr.i = user_defined_attr
elif attr.type == attr_type_pb2.FLOAT:
elif attr.type == attribute_pb2.FLOAT:
new_attr.f = user_defined_attr
elif attr.type == attr_type_pb2.STRING:
elif attr.type == attribute_pb2.STRING:
new_attr.s = user_defined_attr
elif attr.type == attr_type_pb2.INTS:
elif attr.type == attribute_pb2.INTS:
new_attr.ints.extend(user_defined_attr)
elif attr.type == attr_type_pb2.FLOATS:
elif attr.type == attribute_pb2.FLOATS:
new_attr.floats.extend(user_defined_attr)
elif attr.type == attr_type_pb2.STRINGS:
elif attr.type == attribute_pb2.STRINGS:
new_attr.strings.extend(user_defined_attr)
else:
raise NotImplementedError("Not support attribute type " +
......@@ -109,7 +109,7 @@ class OpDescCreationMethod(object):
retv = []
if multiple:
var_format = op_desc_pb2.AttrDesc()
var_format.type = attr_type_pb2.INTS
var_format.type = attribute_pb2.INTS
var_format.name = "%s_format" % in_out
var_format.ints.append(0)
......@@ -185,17 +185,17 @@ def get_docstring_from_op_proto(op_proto):
for attr in op_proto.attrs:
attr_type = None
if attr.type == attr_type_pb2.INT:
if attr.type == attribute_pb2.INT:
attr_type = "int"
elif attr.type == attr_type_pb2.FLOAT:
elif attr.type == attribute_pb2.FLOAT:
attr_type = "float"
elif attr.type == attr_type_pb2.STRING:
elif attr.type == attribute_pb2.STRING:
attr_type = "basestr"
elif attr.type == attr_type_pb2.INTS:
elif attr.type == attribute_pb2.INTS:
attr_type = "list of int"
elif attr.type == attr_type_pb2.FLOATS:
elif attr.type == attribute_pb2.FLOATS:
attr_type = "list of float"
elif attr.type == attr_type_pb2.STRINGS:
elif attr.type == attribute_pb2.STRINGS:
attr_type = "list of basestr"
if attr_type is None:
......
......@@ -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)
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()
......@@ -3,7 +3,7 @@ import paddle.v2.framework.create_op_creation_methods as creation
import paddle.v2.framework.core as core
import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
import paddle.v2.framework.proto.attr_type_pb2 as attr_type_pb2
import paddle.v2.framework.proto.attribute_pb2 as attribute_pb2
class TestGetAllProtos(unittest.TestCase):
......@@ -76,7 +76,7 @@ class TestOpDescCreationMethod(unittest.TestCase):
expected1.type = 'fc'
attr = expected1.attrs.add()
attr.name = 'input_format'
attr.type = attr_type_pb2.INTS
attr.type = attribute_pb2.INTS
attr.ints.extend([0, 1, 2, 3])
self.assertEqual(expected1, generated1)
......@@ -88,7 +88,7 @@ class TestOpDescCreationMethod(unittest.TestCase):
expected2.type = 'fc'
attr = expected2.attrs.add()
attr.name = 'input_format'
attr.type = attr_type_pb2.INTS
attr.type = attribute_pb2.INTS
attr.ints.extend([0, 3, 6, 7])
self.assertEqual(expected2, generated2)
......@@ -105,12 +105,12 @@ class TestOpDescCreationMethod(unittest.TestCase):
attr.comment = ""
attr.type = type
__add_attr__("int_attr", attr_type_pb2.INT)
__add_attr__("float_attr", attr_type_pb2.FLOAT)
__add_attr__("string_attr", attr_type_pb2.STRING)
__add_attr__("ints_attr", attr_type_pb2.INTS)
__add_attr__("floats_attr", attr_type_pb2.FLOATS)
__add_attr__("strings_attr", attr_type_pb2.STRINGS)
__add_attr__("int_attr", attribute_pb2.INT)
__add_attr__("float_attr", attribute_pb2.FLOAT)
__add_attr__("string_attr", attribute_pb2.STRING)
__add_attr__("ints_attr", attribute_pb2.INTS)
__add_attr__("floats_attr", attribute_pb2.FLOATS)
__add_attr__("strings_attr", attribute_pb2.STRINGS)
op.comment = ""
self.assertTrue(op.IsInitialized())
......@@ -131,32 +131,32 @@ class TestOpDescCreationMethod(unittest.TestCase):
expected.inputs.extend(['a'])
attr = expected.attrs.add()
attr.name = "int_attr"
attr.type = attr_type_pb2.INT
attr.type = attribute_pb2.INT
attr.i = 10
attr = expected.attrs.add()
attr.name = "float_attr"
attr.type = attr_type_pb2.FLOAT
attr.type = attribute_pb2.FLOAT
attr.f = 3.2
attr = expected.attrs.add()
attr.name = "string_attr"
attr.type = attr_type_pb2.STRING
attr.type = attribute_pb2.STRING
attr.s = "test_str"
attr = expected.attrs.add()
attr.name = "ints_attr"
attr.type = attr_type_pb2.INTS
attr.type = attribute_pb2.INTS
attr.ints.extend([0, 1, 2, 3, 4])
attr = expected.attrs.add()
attr.name = 'floats_attr'
attr.type = attr_type_pb2.FLOATS
attr.type = attribute_pb2.FLOATS
attr.floats.extend([0.2, 3.2, 4.5])
attr = expected.attrs.add()
attr.name = 'strings_attr'
attr.type = attr_type_pb2.STRINGS
attr.type = attribute_pb2.STRINGS
attr.strings.extend(['a', 'b', 'c'])
self.assertEqual(expected, generated)
......@@ -185,7 +185,7 @@ class TestOpDescCreationMethod(unittest.TestCase):
desc.type = "test"
attr = desc.attrs.add()
attr.name = "temporary_index"
attr.type = attr_type_pb2.INTS
attr.type = attribute_pb2.INTS
attr.ints.append(2)
self.assertEqual(generated, desc)
......@@ -219,7 +219,7 @@ This op is used for unit test, not a real op.
test_str = op.attrs.add()
test_str.name = "str_attr"
test_str.type = attr_type_pb2.STRING
test_str.type = attribute_pb2.STRING
test_str.comment = "A string attribute for test op"
actual = creation.get_docstring_from_op_proto(op)
......
import paddle.v2.framework.proto.op_proto_pb2
import paddle.v2.framework.proto.attr_type_pb2
import paddle.v2.framework.proto.op_proto_pb2 as op_proto_lib
import paddle.v2.framework.proto.attribute_pb2 as attr_type_lib
import unittest
class TestFrameworkProto(unittest.TestCase):
def test_all(self):
op_proto_lib = paddle.v2.framework.proto.op_proto_pb2
attr_type_lib = paddle.v2.framework.proto.attr_type_pb2
op_proto = op_proto_lib.OpProto()
ipt0 = op_proto.inputs.add()
ipt0.name = "a"
......
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