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提交 c004b35a 编写于 作者: Y Yan Chunwei 提交者： GitHub
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init code gen for light framework(#17905)

上级 4e7431ef
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Showing with 1145 addition and 58 deletion
paddle/fluid/lite/CMakeLists.txt
浏览文件 @ c004b35a
......@@ -34,7 +34,7 @@ endfunction()
function (lite_deps TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS ARGS)
set(multiValueArgs DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS LIGHT_DEPS HVY_DEPS ARGS)
cmake_parse_arguments(lite_deps "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(deps ${lite_deps_DEPS})
......@@ -63,14 +63,35 @@ function (lite_deps TARGET)
endforeach(var)
endif()
if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
foreach(var ${lite_deps_LIGHT_DEPS})
set(deps ${deps} ${var})
endforeach(var)
endif()
if (NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
foreach(var ${lite_deps_HVY_DEPS})
set(deps ${deps} ${var})
endforeach(var)
endif()
set(${TARGET} ${deps} PARENT_SCOPE)
endfunction()
# cc_library with branch support.
# The branches:
# X86_DEPS: works only when LITE_WITH_X86 is ON.
# CUDA_DEPS: LITE_WITH_CUDA
# ARM_DEPS: LITE_WITH_ARM
# PROFILE_DEPS: LITE_WITH_PROFILE
# LIGHT_DEPS: LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
# HVY_DEPS: NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
function(lite_cc_library TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS ARGS)
set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS LIGHT_DEPS
HVY_DEPS ARGS)
cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(deps "")
......@@ -79,7 +100,10 @@ function(lite_cc_library TARGET)
X86_DEPS ${args_X86_DEPS}
CUDA_DEPS ${args_CUDA_DEPS}
ARM_DEPS ${args_ARM_DEPS}
PROFILE_DEPS ${args_PROFILE_DEPS})
PROFILE_DEPS ${args_PROFILE_DEPS}
LIGHT_DEPS ${args_LIGHT_DEPS}
HVY_DEPS ${args_HVY_DEPS}
)
cc_library(${TARGET} SRCS ${args_SRCS} DEPS ${deps} ${args_DEPS})
endfunction()
......@@ -87,7 +111,8 @@ endfunction()
function(lite_cc_binary TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS ARGS)
set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS
LIGHT_DEPS HVY_DEPS ARGS)
cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(deps "")
......@@ -97,6 +122,8 @@ function(lite_cc_binary TARGET)
CUDA_DEPS ${args_CUDA_DEPS}
ARM_DEPS ${args_ARM_DEPS}
PROFILE_DEPS ${args_PROFILE_DEPS}
LIGHT_DEPS ${args_LIGHT_DEPS}
HVY_DEPS ${args_HVY_DEPS}
)
cc_binary(${TARGET} SRCS ${args_SRCS} DEPS ${deps} ${args_DEPS})
endfunction()
......@@ -112,7 +139,9 @@ endfunction()
function(lite_cc_test TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS ARGS)
set(multiValueArgs SRCS DEPS X86_DEPS CUDA_DEPS ARM_DEPS PROFILE_DEPS
LIGHT_DEPS HVY_DEPS
ARGS)
cmake_parse_arguments(args "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(deps "")
......@@ -122,6 +151,8 @@ function(lite_cc_test TARGET)
CUDA_DEPS ${args_CUDA_DEPS}
ARM_DEPS ${args_ARM_DEPS}
PROFILE_DEPS ${args_PROFILE_DEPS}
LIGHT_DEPS ${args_LIGHT_DEPS}
HVY_DEPS ${args_HVY_DEPS}
)
_lite_cc_test(${TARGET} SRCS ${args_SRCS} DEPS ${deps} ARGS ${args_ARGS})
register_test_offline("${TARGET}")
......@@ -137,3 +168,4 @@ add_subdirectory(kernels)
add_subdirectory(model_parser)
add_subdirectory(utils)
add_subdirectory(api)
add_subdirectory(gen_code)
paddle/fluid/lite/api/cxx_api_test.cc
浏览文件 @ c004b35a
......@@ -76,6 +76,7 @@ TEST(CXXApi, save_model) {
predictor.Build(FLAGS_model_dir, Place{TARGET(kCUDA), PRECISION(kFloat)},
valid_places);
LOG(INFO) << "Save optimized model to " << FLAGS_optimized_model;
predictor.SaveModel(FLAGS_optimized_model);
}
#endif // LITE_WITH_LIGHT_WEIGHT_FRAMEWORK
......
paddle/fluid/lite/core/CMakeLists.txt
浏览文件 @ c004b35a
......@@ -8,7 +8,7 @@ lite_cc_library(target_wrapper_lite SRCS target_wrapper.cc
lite_cc_library(memory_lite SRCS memory.cc DEPS target_wrapper_lite)
lite_cc_library(lite_tensor SRCS lite_tensor.cc DEPS memory_lite target_wrapper_lite)
if (NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
cc_library(hvy_tensor SRCS hvy_tensor.cc DEPS lod_tensor)
lite_cc_library(hvy_tensor SRCS hvy_tensor.cc DEPS lod_tensor HVY_DEPS framework_proto)
endif()
if(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
......@@ -26,8 +26,7 @@ cc_library(scope_lite SRCS scope.cc DEPS ${tensor_lite})
cc_library(cpu_info_lite SRCS cpu_info.cc)
cc_library(context_lite SRCS context.cc DEPS ${tensor_lite} any_lite cpu_info_lite)
cc_library(op_lite SRCS op_lite.cc DEPS scope_lite op_registry_lite target_wrapper_lite
cpp_op_desc_lite
${tensor_lite})
cpp_op_desc_lite ${tensor_lite})
cc_library(types_lite SRCS types.cc)
cc_library(type_system SRCS type_system.cc DEPS ${tensor_lite} target_wrapper_lite)
......
paddle/fluid/lite/core/context.h
浏览文件 @ c004b35a
......@@ -173,6 +173,11 @@ class Context<TargetType::kX86> {
new ::paddle::framework::ExecutionContext(*x86_device_context_));
}
Context(Context&& ctx) {
x86_device_context_ = std::move(ctx.x86_device_context_);
x86_execution_context_ = std::move(ctx.x86_execution_context_);
}
// NOTE: InitOnce should only be used by ContextScheduler
void InitOnce() {}
......
paddle/fluid/lite/core/hvy_tensor.h
浏览文件 @ c004b35a
......@@ -21,6 +21,7 @@
#pragma once
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/lite/core/target_wrapper.h"
#include "paddle/fluid/lite/core/tensor.h"
namespace paddle {
......@@ -65,6 +66,14 @@ class TensorHvy : public TensorBase<TensorHvy> {
using DDimT = DDimHvy;
using LoDT = framework::LoD;
template <typename DType, typename DimT, TargetType Target>
void Assign(DType* data, const DimT& dim) {
Resize(dim);
auto* dst = mutable_data<DType>(Target);
CopySync<Target>(dst, data, dim.production() * sizeof(DType),
IoDirection::HtoD);
}
TargetType target() const {
if (platform::is_gpu_place(data_.place())) {
return TARGET(kCUDA);
......@@ -95,7 +104,6 @@ class TensorHvy : public TensorBase<TensorHvy> {
const void* raw_data() const { return data_.raw_data(); }
void Resize(const DDimHvy& dims) {
LOG(INFO) << "dims.size " << dims.size();
data_.Resize(framework::make_ddim(dims.Vectorize()));
}
......
paddle/fluid/lite/core/kernel.h
浏览文件 @ c004b35a
......@@ -150,7 +150,7 @@ class KernelBase {
void Torch() {}
protected:
std::unique_ptr<KernelContext> ctx_;
std::unique_ptr<KernelContext> ctx_{nullptr};
mutable operators::param_t param_;
// The corresponding op type.
std::string op_type_{};
......
paddle/fluid/lite/core/lite_tensor.h
浏览文件 @ c004b35a
......@@ -61,6 +61,14 @@ class TensorLite : public TensorBase<TensorLite> {
TensorLite() : buffer_(std::make_shared<Buffer>()) {}
template <typename DType, typename DimT, TargetType Target>
void Assign(DType *data, const DimT &dim) {
Resize(dim);
auto *dst = mutable_data<DType>(Target);
CopySync<Target>(dst, data, dim.product() * sizeof(DType),
IoDirection::HtoD);
}
template <typename T>
const T *data() const {
return static_cast<const T *>(buffer_->data());
......
paddle/fluid/lite/core/op_lite.cc
浏览文件 @ c004b35a
......@@ -28,8 +28,7 @@ std::vector<std::unique_ptr<KernelBase>> OpLite::CreateKernels(
CHECK(!op_type_.empty()) << "op_type_ should be set first";
auto pick_kernel = [&](const Place &place) {
auto ks = KernelRegistry::Global().Create(
(kernel_type.empty() ? op_type_ : kernel_type), place.target,
auto ks = KernelRegistry::Global().Create(op_type_, place.target,
place.precision, place.layout);
for (auto &&it : ks) {
AttachKernel(it.get());
......@@ -37,6 +36,15 @@ std::vector<std::unique_ptr<KernelBase>> OpLite::CreateKernels(
}
};
if (!kernel_type.empty()) {
Place place;
std::string op_type, alias;
KernelBase::ParseKernelType(kernel_type, &op_type, &alias, &place);
pick_kernel(place);
CHECK(!kernels.empty()) << "no kernel for kernel type " << kernel_type;
return kernels;
}
std::set<Place> place_set;
for (auto place : places) {
place_set.insert(place);
......
paddle/fluid/lite/core/op_registry.h
浏览文件 @ c004b35a
......@@ -91,9 +91,9 @@ class KernelRegistry final {
void Register(const std::string &name,
typename KernelRegistryForTarget<Target, Precision,
Layout>::creator_t &&creator) {
VLOG(3) << "register for " << TargetToStr(Target) << ":"
<< PrecisionToStr(Precision) << "//"
<< GetKernelOffset<Target, Precision, Layout>();
// VLOG(3) << "register for " << TargetToStr(Target) << ":"
//<< PrecisionToStr(Precision) << "//"
//<< GetKernelOffset<Target, Precision, Layout>();
using kernel_registor_t =
KernelRegistryForTarget<Target, Precision, Layout>;
auto &varient = registries_[GetKernelOffset<Target, Precision, Layout>()];
......@@ -153,9 +153,6 @@ class KernelRegistor : public lite::Registor<KernelType> {
public:
KernelRegistor(const std::string &op_type, const std::string &alias)
: Registor<KernelType>([=] {
VLOG(3) << "Register kernel " << op_type << " for "
<< TargetToStr(target) << " " << PrecisionToStr(precision)
<< " " << DataLayoutToStr(layout) << " alias " << alias;
KernelRegistry::Global().Register<target, precision, layout>(
op_type, [=]() -> std::unique_ptr<KernelType> {
std::unique_ptr<KernelType> x(new KernelType);
......
paddle/fluid/lite/core/scope.cc
浏览文件 @ c004b35a
......@@ -17,7 +17,13 @@
namespace paddle {
namespace lite {
Scope::~Scope() {}
Scope::~Scope() {
for (auto *x : kids_) {
if (x) {
delete x;
}
}
}
Scope &Scope::NewScope() const {
kids_.push_back(new Scope);
......
paddle/fluid/lite/core/target_wrapper.h
浏览文件 @ c004b35a
......@@ -63,7 +63,8 @@ static const std::string& TargetToStr(TargetType target) {
}
static const std::string& PrecisionToStr(PrecisionType precision) {
static const std::string precision2string[] = {"unk", "float", "int8", "any"};
static const std::string precision2string[] = {"unk", "float", "int8_t",
"any"};
auto x = static_cast<int>(precision);
CHECK_LT(x, static_cast<int>(PRECISION(NUM)));
return precision2string[x];
......@@ -76,6 +77,29 @@ static const std::string& DataLayoutToStr(DataLayoutType layout) {
return datalayout2string[x];
}
static const std::string& TargetRepr(TargetType target) {
static const std::string target2string[] = {"kUnk", "kHost", "kX86", "kCUDA",
"kAny"};
auto x = static_cast<int>(target);
CHECK_LT(x, static_cast<int>(TARGET(NUM)));
return target2string[x];
}
static const std::string& PrecisionRepr(PrecisionType precision) {
static const std::string precision2string[] = {"kUnk", "kFloat", "kInt8",
"kAny"};
auto x = static_cast<int>(precision);
CHECK_LT(x, static_cast<int>(PRECISION(NUM)));
return precision2string[x];
}
static const std::string& DataLayoutRepr(DataLayoutType layout) {
static const std::string datalayout2string[] = {"kUnk", "kNCHW", "kAny"};
auto x = static_cast<int>(layout);
CHECK_LT(x, static_cast<int>(DATALAYOUT(NUM)));
return datalayout2string[x];
}
/*
* Place specifies the execution context of a Kernel or input/output for a
* kernel. It is used to make the analysis of the MIR more clear and accurate.
......@@ -228,5 +252,20 @@ class TargetWrapper<TARGET(kCUDA), cudaStream_t, cudaEvent_t> {
};
#endif // LITE_WITH_CUDA
template <TargetType Target>
void CopySync(void* dst, void* src, size_t size, IoDirection dir) {
switch (Target) {
case TARGET(kX86):
case TARGET(kHost):
case TARGET(kARM):
TargetWrapperX86::MemcpySync(dst, src, size, IoDirection::HtoH);
break;
#ifdef LITE_WITH_CUDA
case TARGET(kCUDA):
TargetWrapperCuda::MemcpySync(dst, src, size, dir);
#endif
}
}
} // namespace lite
} // namespace paddle
paddle/fluid/lite/core/tensor.h
浏览文件 @ c004b35a
......@@ -47,7 +47,8 @@ class DDimBase {
DDimBase() = default;
explicit DDimBase(const std::vector<int64_t> &x) { self()->ConstructFrom(x); }
value_type operator[](int offset) const { return (*self())[offset]; }
value_type operator[](int offset) const { return (*const_self())[offset]; }
value_type &operator[](int offset) { return (*self())[offset]; }
std::vector<int64_t> Vectorize() const { return self()->Vectorize(); }
size_t size() const { return const_self()->size(); }
bool empty() const { return const_self()->empty(); }
......@@ -73,18 +74,19 @@ class DDimBase {
{Slice(0, col).production(), Slice(col, size()).production()}));
}
friend std::ostream &operator<<(std::ostream &os, const DDimT &dims) {
if (dims.empty()) {
os << "[]";
return os;
std::string repr() const {
std::stringstream ss;
ss << "{";
for (size_t i = 0; i < this->size() - 1; i++) {
ss << (*this)[i] << ",";
}
os << "[";
for (size_t i = 0; i < dims.size() - 1; i++) {
os << dims[i] << " ";
if (!this->empty()) ss << (*this)[size() - 1];
ss << "}";
return ss.str();
}
if (!dims.empty()) os << dims[dims.size() - 1];
os << "]";
friend std::ostream &operator<<(std::ostream &os, const DDimT &dims) {
os << dims.repr();
return os;
}
......@@ -102,6 +104,12 @@ template <typename TensorT>
class TensorBase {
public:
TensorBase() = default;
template <typename T, typename DimT>
void Assign(T *data, const DimT &dim) {
self()->Assign(data, dim);
}
TargetType target() const { return self()->target(); }
template <typename T>
......
paddle/fluid/lite/core/variable.h
浏览文件 @ c004b35a
......@@ -24,7 +24,7 @@ namespace lite {
class Variable {
public:
template <typename T>
const T& Get() {
const T& Get() const {
return blob_.get<T>();
}
......
paddle/fluid/lite/gen_code/CMakeLists.txt 0 → 100644
浏览文件 @ c004b35a
lite_cc_library(gen_code_lite SRCS gen_code.cc
DEPS program_lite op_lite scope
cpp_op_desc_lite
HVY_DEPS operator)
lite_cc_library(paddle_infer_gencode SRCS paddle_infer.cc DEPS program_lite utils_lite)
lite_cc_test(test_gen_code_lite SRCS gen_code_test.cc DEPS gen_code_lite ${tensor_lite}
mul_op_lite
compatible_pb_lite
model_parser_lite
X86_DEPS mul_compute_x86
ARGS --optimized_model=${LITE_MODEL_DIR}/lite_naive_model_opt SERIAL)
lite_cc_library(__generated_code__
SRCS ${CMAKE_BINARY_DIR}/paddle/fluid/lite/gen_code/__generated_code__.cc
DEPS scope_lite op_lite kernel_lite paddle_infer_gencode
)
lite_cc_test(test_generated_code SRCS generated_code_test.cc DEPS __generated_code__
${ops_lite} ${host_kernels}
X86_DEPS ${x86_kernels}
)
add_dependencies(__generated_code__ test_gen_code_lite)
paddle/fluid/lite/gen_code/gen_code.cc 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#include "paddle/fluid/lite/gen_code/gen_code.h"
#include <algorithm>
#include <string>
#include <vector>
namespace paddle {
namespace lite {
namespace gencode {
void Module::AddWeight(const std::string &name, const TensorRepr &tensor) {
auto w_name = WeightUniqueName();
Line(string_format("// Create weight: %s", name.c_str()));
// auto* w0 = scope.Var("w0")->GetMutable<lite::Tensor>();
Line(string_format("auto* %s = scope->Var(%s)->GetMutable<lite::Tensor>();",
w_name.c_str(), Repr(name).c_str()));
// lite::DDim w_ddim({1, 2})
Line(string_format("lite::DDim %s_ddim(std::vector<int64_t>(%s));",
w_name.c_str(), tensor.ddim.repr().c_str()));
// std::vector<float> w_data({});
auto w_data_repr = DataRepr(
std::string(static_cast<const char *>(tensor.raw_data), tensor.num_bytes),
tensor.dtype);
Line(string_format("std::vector<%s> %s_data({%s});",
PrecisionToStr(tensor.dtype).c_str(), w_name.c_str(),
w_data_repr.c_str()));
// w0->Assign<float, lite::DDim, TARGET(kX86)>(w0_data.data(), w0_ddim);
Line(string_format(
"%s->Assign<%s, lite::DDim, TARGET(kX86)>(%s_data.data(), %s_ddim);",
w_name.c_str(), PrecisionToStr(tensor.dtype).c_str(), w_name.c_str(),
w_name.c_str()));
Line("");
}
void Module::AddHeaderIncludeGenCode() {
Line("");
Line("#include <string>");
Line("#include <vector>");
Line("#include \"paddle/fluid/lite/core/compatible_tensor.h\"");
Line("#include \"paddle/fluid/lite/core/context.h\"");
Line("#include \"paddle/fluid/lite/gen_code/paddle_infer.h\"");
Line("#include \"paddle/fluid/lite/core/op_registry.h\"");
Line("#include \"paddle/fluid/lite/core/scope.h\"");
Line("#include \"paddle/fluid/lite/model_parser/cpp/op_desc.h\"");
Line("");
Line("");
}
std::string Module::DataRepr(const std::string &raw_data, PrecisionType dtype) {
std::stringstream ss;
switch (dtype) {
case PRECISION(kFloat): {
const float *raw = reinterpret_cast<const float *>(raw_data.c_str());
int num_elems = raw_data.size() / sizeof(float);
if (num_elems) {
for (int i = 0; i < num_elems - 1; i++) {
ss << raw[i] << ",";
}
ss << raw[num_elems - 1];
}
} break;
default:
LOG(FATAL) << "Unsupported type " << PrecisionToStr(dtype);
}
return ss.str();
}
void Module::AddOpDescHelper(const std::string &op_id,
const cpp::OpDesc &desc) {
std::string desc_var = op_id + "_desc";
Line(string_format("lite::cpp::OpDesc %s;", desc_var.c_str()));
auto vec_str_repr = [](const std::vector<std::string> &vec) {
return Repr(vec);
};
for (auto &item : desc.inputs()) {
Line(string_format("%s.SetInput(%s, %s);", desc_var.c_str(),
Repr(item.first).c_str(),
vec_str_repr(item.second).c_str()));
}
for (auto &item : desc.outputs()) {
Line(string_format("%s.SetOutput(%s, %s);", desc_var.c_str(),
Repr(item.first).c_str(),
vec_str_repr(item.second).c_str()));
}
auto attr_repr = [&](const std::string &name) -> std::string {
using AttrType = OpDescAPI::AttrType;
auto type = desc.GetAttrType(name);
switch (type) {
case AttrType::INT:
return std::to_string(desc.GetAttr<int>(name));
case AttrType::FLOAT:
return std::to_string(desc.GetAttr<float>(name));
case AttrType::BOOLEAN:
return std::to_string(desc.GetAttr<bool>(name));
case AttrType::STRING:
return "\"" + desc.GetAttr<std::string>(name) + "\"";
case AttrType::STRINGS: {
std::vector<std::string> tmp;
auto vals = desc.GetAttr<std::vector<std::string>>(name);
std::transform(vals.begin(), vals.end(), std::back_inserter(tmp),
[](const std::string &x) { return Repr(x); });
return "{" + Join(tmp, ",") + "}";
}
default:
LOG(FATAL) << "Unsupported attribute type: " << static_cast<int>(type);
}
return "";
};
auto attr_type_repr = [&](const std::string &name) -> std::string {
using AttrType = OpDescAPI::AttrType;
auto type = desc.GetAttrType(name);
switch (type) {
case AttrType::INT:
return "int";
case AttrType::FLOAT:
return "float";
case AttrType::BOOLEAN:
return "bool";
case AttrType::STRING:
return "std::string";
case AttrType::STRINGS:
return "std::vector<std::string>";
default:
LOG(FATAL) << "Unsupported attribute type: " << static_cast<int>(type);
}
return "unk_t";
};
for (auto &item : desc.AttrNames()) {
// Drop the python information.
if (item == "op_callstack") continue;
auto attr_type = attr_type_repr(item);
auto attr_val = attr_repr(item);
Line(string_format("%s.SetAttr<%s>(%s, %s);", //
desc_var.c_str(), attr_type.c_str(), Repr(item).c_str(),
attr_val.c_str()));
}
}
void Module::AddOp(const cpp::OpDesc &op) {
auto op_name = OpUniqueName();
AddOpDescHelper(op_name, op);
Line(string_format("// Create Op: %s", op.Type().c_str()));
Line(string_format("auto %s = lite::LiteOpRegistry::Global().Create(\"%s\");",
op_name.c_str(), op.Type().c_str()));
CHECK(op.HasAttr(kKernelTypeAttr))
<< "the kernel type should be specified before generate code.";
auto kernel_type = op.GetAttr<std::string>(kKernelTypeAttr);
Line(string_format("%s->Attach(%s, exec_scope);", op_name.c_str(),
(op_name + "_desc").c_str()));
// Create kernel
auto kernel_name = KernelUniqueName();
Line(string_format(
"auto %s = std::move(%s->CreateKernels(valid_places, \"%s\").front());",
kernel_name.c_str(), op_name.c_str(), kernel_type.c_str()));
// Set Context for kernel
// clang-format off
Line(string_format("%s->SetContext(lite::ContextScheduler::Global().NewContext(%s->target()));", kernel_name.c_str(), kernel_name.c_str())); // NOLINT
// clang-format on
Line(string_format("ops.push_back(%s);", op_name.c_str()));
Line(string_format("kernels.push_back(std::move(%s));", kernel_name.c_str()));
op_kinds_.insert(op.Type());
kernel_kinds_.insert(kernel_type);
}
} // namespace gencode
} // namespace lite
} // namespace paddle
paddle/fluid/lite/gen_code/gen_code.h 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#pragma once
#include <set>
#include <string>
#include <vector>
#include "paddle/fluid/lite/core/compatible_tensor.h"
#include "paddle/fluid/lite/core/framework.pb.h"
#include "paddle/fluid/lite/core/program.h"
#include "paddle/fluid/lite/core/target_wrapper.h"
#include "paddle/fluid/lite/model_parser/cpp/op_desc.h"
#include "paddle/fluid/lite/model_parser/desc_apis.h"
#include "paddle/fluid/lite/utils/string.h"
namespace paddle {
namespace lite {
namespace gencode {
struct TensorRepr {
TensorRepr() = default;
TensorRepr(PrecisionType dtype, const std::vector<int64_t> &ddim,
void *raw_data, size_t num_bytes)
: dtype(dtype), ddim(ddim), raw_data(raw_data), num_bytes(num_bytes) {}
PrecisionType dtype;
lite::DDim ddim;
const void *raw_data;
size_t num_bytes{};
};
class Module {
std::vector<cpp::OpDesc> ops;
std::vector<TensorRepr> weights;
std::vector<std::string> tmp_vars_;
std::stringstream stream_;
std::set<std::string> kernel_kinds_;
std::set<std::string> op_kinds_;
int line_indent_{};
const int indent_unit_{2};
public:
void NewOp(const cpp::OpDesc &desc) { ops.push_back(desc); }
void NewWeight(const TensorRepr &x) { weights.push_back(x); }
void NewTmpVar(const std::string &x) { tmp_vars_.push_back(x); }
std::stringstream &stream() { return stream_; }
void AddHeaderIncludeGenCode();
void AddNamespaceBegin() {
Line("namespace paddle {");
Line("namespace gencode{");
Line("");
}
void AddNamespaceEnd() {
Line("");
Line("} // namespace gencode");
Line("} // namespace paddle");
}
void AddInitFuncBegin() {
Line("void PaddlePredictor::Init() {");
Line("");
IncIndent();
}
void AddInitFuncEnd() {
DecIndent();
Line("");
Line("}");
}
void AddScopeDecl() {
Line("lite::Scope* scope = static_cast<lite::Scope*>(raw_scope_);");
// clang-format off
Line("lite::Scope* exec_scope = static_cast<lite::Scope*>(raw_exe_scope_);"); // NOLINT
// clang-format on
// Create feed and fetch in exec_scope.
Line(string_format("exec_scope->Var(%s);", Repr("feed").c_str()));
Line(string_format("exec_scope->Var(%s);", Repr("fetch").c_str()));
}
void AddValidPlaceDecl() {
// clang-format off
Line("std::vector<lite::Place> valid_places({lite::Place({TARGET(kX86), PRECISION(kFloat), DATALAYOUT(kNCHW)}), lite::Place({TARGET(kHost), PRECISION(kAny), DATALAYOUT(kAny)})});"); // NOLINT
// clang-format on
}
void AddMemberCast() {
Line("// Cast the raw members");
// clang-format off
Line(string_format("auto& ops = *static_cast<std::vector<std::shared_ptr<lite::OpLite>>*>(raw_ops_);")); // NOLINT
Line(string_format("auto& kernels = *static_cast<std::vector<std::unique_ptr<lite::KernelBase>>*>(raw_kernels_);")); // NOLINT
// clang-format on
Line("");
}
void AddWeight(const std::string &name, const TensorRepr &tensor);
void AddTmpVar(const std::string &x) {
Line(string_format("// Create temporary variable: %s", x.c_str()));
Line(string_format("exec_scope->Var(%s);", Repr(x).c_str()));
Line("");
}
void AddOp(const cpp::OpDesc &op);
void AddOpDescHelper(const std::string &op_id, const cpp::OpDesc &desc);
void AddOpCompileDeps() {
Line("");
Line("// Add Operator compile deps");
for (auto &op_type : op_kinds_) {
Line(string_format("USE_LITE_OP(%s)", op_type.c_str()));
}
Line("");
}
void AddKernelCompileDeps() {
Line("// Add Kernel compile deps");
std::string op_type, alias;
Place place;
for (auto &kernel_type : kernel_kinds_) {
KernelBase::ParseKernelType(kernel_type, &op_type, &alias, &place);
Line(string_format("USE_LITE_KERNEL(%s, %s, %s, %s, %s)", //
op_type.c_str(), //
TargetRepr(place.target).c_str(),
PrecisionRepr(place.precision).c_str(),
DataLayoutRepr(place.layout).c_str(), alias.c_str()));
}
}
private:
std::string WeightUniqueName() const {
return "w_" + std::to_string(weight_counter_++);
}
std::string TmpVarUniqueName() const {
return "tmp_" + std::to_string(tmp_var_counter_++);
}
std::string OpUniqueName() const {
return "op_" + std::to_string(op_counter_++);
}
std::string KernelUniqueName() const {
return "kernel_" + std::to_string(kernel_counter_++);
}
std::string DataRepr(const std::string &raw_data, PrecisionType dtype);
void IncIndent() { line_indent_++; }
void DecIndent() { line_indent_--; }
void Line(const std::string &x) {
std::string indent_str(line_indent_ * indent_unit_, ' ');
stream() << indent_str << x << "\n";
}
private:
mutable int weight_counter_{};
mutable int tmp_var_counter_{};
mutable int op_counter_{};
mutable int kernel_counter_{};
};
class ProgramCodeGenerator {
public:
ProgramCodeGenerator(const framework::proto::ProgramDesc &program,
const lite::Scope &exec_scope)
: program_(program), exec_scope_(exec_scope) {
LOG(INFO) << program.DebugString();
}
std::string GenCode() {
Module m;
m.AddHeaderIncludeGenCode();
m.AddNamespaceBegin();
m.AddInitFuncBegin();
m.AddMemberCast();
m.AddScopeDecl();
m.AddValidPlaceDecl();
AddWeights(&m);
AddTmpVars(&m);
AddOps(&m);
m.AddInitFuncEnd();
m.AddNamespaceEnd();
m.AddOpCompileDeps();
m.AddKernelCompileDeps();
return m.stream().str();
}
void AddWeights(Module *m) {
for (auto &var : program_.blocks(0).vars()) {
if (var.persistable()) {
auto name = var.name();
if (name == "feed" || name == "fetch") continue;
const auto &tensor = exec_scope_.FindVar(name)->Get<lite::Tensor>();
TensorRepr repr;
TensorToRepr(tensor, &repr);
m->AddWeight(name, repr);
}
}
}
void AddTmpVars(Module *m) {
for (auto &var : program_.blocks(0).vars()) {
if (!var.persistable()) {
m->AddTmpVar(var.name());
}
}
}
void AddOps(Module *m) {
for (auto &op : program_.blocks(0).ops()) {
pb::OpDesc pb_desc(op);
cpp::OpDesc cpp_desc;
TransformOpDescPbToCpp(pb_desc, &cpp_desc);
m->AddOp(cpp_desc);
}
}
private:
void TensorToRepr(const lite::Tensor &tensor, TensorRepr *repr) {
repr->ddim = tensor.dims();
// TODO(Superjomn) support other types.
repr->dtype = PRECISION(kFloat);
repr->raw_data = tensor.data<float>();
repr->num_bytes = repr->ddim.production() * sizeof(float);
}
private:
const framework::proto::ProgramDesc &program_;
const lite::Scope &exec_scope_;
};
} // namespace gencode
} // namespace lite
} // namespace paddle
paddle/fluid/lite/gen_code/gen_code_test.cc 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#include "paddle/fluid/lite/gen_code/gen_code.h"
#include <gflags/gflags.h>
#include <gtest/gtest.h>
#include <fstream>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/lite/core/compatible_tensor.h"
#include "paddle/fluid/lite/core/context.h"
#include "paddle/fluid/lite/core/op_registry.h"
#include "paddle/fluid/lite/core/scope.h"
#include "paddle/fluid/lite/model_parser/cpp/op_desc.h"
#include "paddle/fluid/lite/model_parser/model_parser.h"
DEFINE_string(optimized_model, "", "");
DEFINE_string(generated_code_file, "__generated_code__.cc", "");
namespace paddle {
namespace lite {
namespace gencode {
// Manually construct a program.
TEST(gen_code, manual) {
// For holding the weights.
lite::Scope scope;
// For holding the temporary variables.
auto &tmp_scope = scope.NewScope();
// Create weight variables.
auto *w0 = scope.Var("w0")->GetMutable<lite::Tensor>();
// Create temporary variables.
auto *a = tmp_scope.Var("x")->GetMutable<lite::Tensor>();
tmp_scope.Var("out")->GetMutable<lite::Tensor>();
// Set weights.
std::vector<float> w0_data({0, 1, 2, 3});
w0->Assign<float, lite::DDim, TARGET(kX86)>(
w0_data.data(), lite::DDim{std::vector<int64_t>({2, 2})});
std::vector<float> a_data({0, 1, 2, 3});
a->Assign<float, lite::DDim, TARGET(kX86)>(
a_data.data(), lite::DDim{std::vector<int64_t>({2, 2})});
std::vector<Place> valid_places({
Place{TARGET(kX86), PRECISION(kFloat)},
Place{TARGET(kHost), PRECISION(kFloat)},
Place{TARGET(kHost), PRECISION(kAny)},
});
auto mul_op = LiteOpRegistry::Global().Create("mul");
cpp::OpDesc mul_op_desc;
mul_op_desc.SetType("mul");
mul_op_desc.SetInput("X", {"x"});
mul_op_desc.SetInput("Y", {"w0"});
mul_op_desc.SetAttr("x_num_col_dims", 1);
mul_op_desc.SetAttr("y_num_col_dims", 1);
mul_op_desc.SetOutput("Out", {"out"});
mul_op->Attach(mul_op_desc, &tmp_scope);
auto mul_kernel = std::move(mul_op->CreateKernels(valid_places).front());
auto fc_ctx = ContextScheduler::Global().NewContext(TARGET(kX86));
mul_op->CheckShape();
mul_op->InferShape();
mul_kernel->SetContext(std::move(fc_ctx));
mul_kernel->Launch();
}
TEST(gen_code, auto_gen) {
std::vector<float> w0_data({0, 1, 2, 3});
TensorRepr w0(PRECISION(kFloat), std::vector<int64_t>({2, 2}), w0_data.data(),
w0_data.size() * sizeof(float));
std::vector<float> w1_data({0.01, 1.2, 2.3, 3.4, 1.1, 2.2});
TensorRepr w1(PRECISION(kFloat), std::vector<int64_t>({3, 2}), w1_data.data(),
w1_data.size() * sizeof(float));
cpp::OpDesc op0;
op0.SetType("mul");
op0.SetInput("X", {"a", "b"});
op0.SetOutput("Out", {"out0"});
op0.SetAttr<std::string>("desc", "this is a desc");
op0.SetAttr<int>("x_col", 1);
op0.SetAttr<int>("y_col", 2);
op0.SetAttr<std::string>(kKernelTypeAttr, "x86");
gencode::Module module;
module.AddHeaderIncludeGenCode();
module.AddNamespaceBegin();
module.AddInitFuncBegin();
module.AddMemberCast();
module.AddWeight("w0", w0);
module.AddWeight("w1", w1);
module.AddTmpVar("a");
module.AddTmpVar("b");
module.AddOp(op0);
module.AddInitFuncEnd();
module.AddNamespaceEnd();
LOG(INFO) << module.stream().str();
}
TEST(gen_code, optimized_program) {
lite::Scope scope;
framework::proto::ProgramDesc desc;
LoadModel(FLAGS_optimized_model, &scope, &desc);
ProgramCodeGenerator codegen(desc, scope);
std::ofstream file(FLAGS_generated_code_file);
file << codegen.GenCode();
file.close();
}
} // namespace gencode
} // namespace lite
} // namespace paddle
USE_LITE_OP(mul);
USE_LITE_KERNEL(mul, kX86, kFloat, kNCHW, def);
paddle/fluid/lite/gen_code/generated_code_test.cc 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/lite/gen_code/paddle_infer.h"
namespace paddle {
namespace lite {
TEST(PaddlePredictor, Init) {
gencode::PaddlePredictor predictor;
predictor.Init();
}
TEST(PaddlePredictor, Run) {
gencode::PaddlePredictor predictor;
predictor.Init();
LOG(INFO) << "run the generated code";
auto input_tensor = predictor.GetInput(0);
input_tensor->Resize(std::vector<int64_t>({100, 100}));
auto* data = input_tensor->mutable_data<float>();
for (int i = 0; i < 100 * 100; i++) {
data[i] = i;
}
predictor.Run();
auto output_tensor = predictor.GetOutput(0);
LOG(INFO) << "output: " << output_tensor->data<float>()[0];
}
} // namespace lite
} // namespace paddle
paddle/fluid/lite/gen_code/paddle_infer.cc 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#include "paddle/fluid/lite/gen_code/paddle_infer.h"
#include "paddle/fluid/lite/core/compatible_tensor.h"
#include "paddle/fluid/lite/core/op_lite.h"
namespace paddle {
namespace gencode {
void Tensor::Resize(const Tensor::ddim_t &shape) {
CHECK(raw_mutable_tensor_);
auto *tensor = static_cast<lite::Tensor *>(raw_mutable_tensor_);
tensor->Resize(shape);
}
#define FOR_EACH_TYPE(HANDLE) \
HANDLE(int); \
HANDLE(float); \
HANDLE(int8_t); \
HANDLE(int64_t);
#define IMPL_DATA(T) \
template <> \
const T *Tensor::data<T>() const { \
CHECK(raw_tensor_); \
const auto *tensor = static_cast<const lite::Tensor *>(raw_tensor_); \
return tensor->data<T>(); \
}
FOR_EACH_TYPE(IMPL_DATA);
#undef IMPL_DATA
#define IMPL_MUTABLE_DATA(T) \
template <> \
T *Tensor::mutable_data<T>() { \
CHECK(raw_mutable_tensor_); \
auto *tensor = static_cast<lite::Tensor *>(raw_mutable_tensor_); \
return tensor->mutable_data<T>(); \
}
FOR_EACH_TYPE(IMPL_MUTABLE_DATA);
#undef IMPL_MUTABLE_DATA
PaddlePredictor::PaddlePredictor() {
raw_ops_ = new std::vector<std::shared_ptr<lite::OpLite>>;
raw_kernels_ = new std::vector<std::unique_ptr<lite::KernelBase>>;
raw_scope_ = new lite::Scope;
raw_exe_scope_ = &(static_cast<lite::Scope *>(raw_scope_)->NewScope());
}
std::unique_ptr<Tensor> PaddlePredictor::GetTensor(
const std::string &id) const {
auto *exe_scope = static_cast<lite::Scope *>(raw_exe_scope_);
const auto *var = exe_scope->FindVar(id);
const auto &tensor = var->Get<lite::Tensor>();
return std::unique_ptr<Tensor>(new Tensor(&tensor, nullptr));
}
std::unique_ptr<Tensor> PaddlePredictor::GetMutableTensor(
const std::string &id) {
auto *exe_scope = static_cast<lite::Scope *>(raw_exe_scope_);
auto *var = exe_scope->FindVar(id);
auto *tensor = var->GetMutable<lite::Tensor>();
return std::unique_ptr<Tensor>(new Tensor(nullptr, tensor));
}
#define CAST_OPS \
auto *ops = \
static_cast<std::vector<std::shared_ptr<lite::OpLite>> *>(raw_ops_);
#define CAST_KERNELS \
auto *kernels = \
static_cast<std::vector<std::unique_ptr<lite::KernelBase>> *>( \
raw_kernels_);
#define CAST_SCOPE auto *scope = static_cast<lite::Scope *>(raw_scope_);
PaddlePredictor::~PaddlePredictor() {
CAST_OPS
CAST_KERNELS
CAST_SCOPE
if (ops) {
delete ops;
}
if (kernels) {
delete kernels;
}
if (scope) {
delete scope;
}
}
void PaddlePredictor::Run() {
CAST_OPS
CAST_KERNELS
CHECK(ops);
CHECK(kernels);
CHECK_EQ(ops->size(), kernels->size());
for (size_t i = 0; i < ops->size(); i++) {
LOG(INFO) << "Running the " << i << "-th operator";
ops->at(i)->InferShape();
kernels->at(i)->Launch();
}
}
std::unique_ptr<Tensor> PaddlePredictor::GetInput(size_t offset) {
auto *exec_scope = static_cast<lite::Scope *>(raw_exe_scope_);
auto *_feed_list = exec_scope->FindVar("feed");
CHECK(_feed_list) << "no feed variable in exec_scope";
auto *feed_list = _feed_list->GetMutable<std::vector<lite::Tensor>>();
if (offset >= feed_list->size()) {
feed_list->resize(offset + 1);
}
return std::unique_ptr<Tensor>(new Tensor(nullptr, &feed_list->at(offset)));
}
std::unique_ptr<Tensor> PaddlePredictor::GetOutput(size_t offset) {
auto *exec_scope = static_cast<lite::Scope *>(raw_exe_scope_);
auto *_fetch_list = exec_scope->FindVar("fetch");
CHECK(_fetch_list) << "no fatch variable in exec_scope";
auto &fetch_list = *_fetch_list->GetMutable<std::vector<lite::Tensor>>();
CHECK_LT(offset, fetch_list.size()) << "offset " << offset << " overflow";
return std::unique_ptr<Tensor>(new Tensor(&fetch_list.at(offset), nullptr));
}
} // namespace gencode
} // namespace paddle
paddle/fluid/lite/gen_code/paddle_infer.h 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#pragma once
#include <memory>
#include <string>
#include <vector>
namespace paddle {
namespace gencode {
/// Zero Copy Tensor.
class Tensor {
public:
using ddim_t = std::vector<int64_t>;
Tensor(const void *raw_tensor, void *raw_mutable_tensor)
: raw_tensor_(raw_tensor), raw_mutable_tensor_(raw_mutable_tensor) {}
void Resize(const ddim_t &shape);
template <typename T>
const T *data() const;
template <typename T>
T *mutable_data();
private:
const void *raw_tensor_;
void *raw_mutable_tensor_{};
};
/*
* Predictor for the generated code.
*/
class PaddlePredictor {
public:
void Init();
std::unique_ptr<Tensor> GetTensor(const std::string &id) const;
std::unique_ptr<Tensor> GetMutableTensor(const std::string &id);
// Get offset-th col of feed.
std::unique_ptr<Tensor> GetInput(size_t offset);
std::unique_ptr<Tensor> GetOutput(size_t offset);
void Run();
PaddlePredictor();
~PaddlePredictor();
private:
void *raw_ops_;
void *raw_kernels_;
void *raw_scope_{};
void *raw_exe_scope_{}; // raw_exe_scope is not owned.
};
} // namespace gencode
} // namespace paddle
paddle/fluid/lite/model_parser/cpp/op_desc.cc
浏览文件 @ c004b35a
......@@ -14,6 +14,7 @@
#include "paddle/fluid/lite/model_parser/cpp/op_desc.h"
#include <set>
#include <utility>
namespace paddle {
namespace lite {
......@@ -48,7 +49,8 @@ FindAttr(const cpp::OpDesc& desc, const std::string& name) {
template <> \
T OpDesc::GetAttr<T>(const std::string& name) const { \
auto pair = FindAttr(*this, name); \
CHECK(pair.second->second == AttrType::repr__); \
CHECK(pair.second->second == AttrType::repr__) \
<< "required type is " << #repr__ << " not match the true type"; \
return pair.first->second.get<T>(); \
}
......
paddle/fluid/lite/model_parser/pb/op_desc.cc
浏览文件 @ c004b35a
......@@ -44,7 +44,7 @@ FindAttr(framework::proto::OpDesc *desc, const std::string &name) {
}
SET_IMPL_ONE(int, INT, i);
SET_IMPL_ONE(float, FLOAT, f);
SET_IMPL_ONE(bool, FLOAT, f);
SET_IMPL_ONE(bool, BOOLEAN, b);
template <>
void OpDesc::SetAttr<std::vector<int>>(const std::string &name,
......
paddle/fluid/lite/operators/feed_op.cc
浏览文件 @ c004b35a
......@@ -38,8 +38,8 @@ class FeedOp : public OpLite {
auto feed_var_name = opdesc.Input("X").front();
auto* feed_var = scope->FindVar(feed_var_name);
CHECK(feed_var);
auto& feed_tensor_list = feed_var->Get<std::vector<lite::Tensor>>();
param_.feed_list = &feed_tensor_list;
auto* feed_tensor_list = feed_var->GetMutable<std::vector<lite::Tensor>>();
param_.feed_list = feed_tensor_list;
auto out_name = opdesc.Output("Out").front();
auto* out_var = scope->FindVar(out_name);
......
paddle/fluid/lite/operators/mul_op.h
浏览文件 @ c004b35a
......@@ -45,10 +45,11 @@ class MulOpLite : public OpLite {
CHECK(var);
param_.x = var->GetMutable<Tensor>();
var = scope->FindVar(W);
CHECK(var);
CHECK(var) << "no var called " << W;
param_.y = var->GetMutable<Tensor>();
CHECK(scope->FindVar(out));
param_.output = scope->FindVar(out)->GetMutable<Tensor>();
var = scope->FindVar(out);
CHECK(var) << "no var called " << out;
param_.output = var->GetMutable<Tensor>();
param_.x_num_col_dims = op_desc.GetAttr<int>("x_num_col_dims");
param_.y_num_col_dims = op_desc.GetAttr<int>("y_num_col_dims");
......
paddle/fluid/lite/tools/build.sh
浏览文件 @ c004b35a
......@@ -4,15 +4,26 @@ set -ex
TESTS_FILE="./lite_tests.txt"
readonly common_flags="-DWITH_LITE=ON -DLITE_WITH_LIGHT_WEIGHT_FRAMEWORK=OFF -DWITH_PYTHON=OFF -DWITH_TESTING=ON -DLITE_WITH_ARM=OFF"
# for code gen, a source file is generated after a test, but is dependended by some targets in cmake.
# here we fake an empty file to make cmake works.
function prepare_for_codegen {
# in build directory
mkdir -p ./paddle/fluid/lite/gen_code
touch ./paddle/fluid/lite/gen_code/__generated_code__.cc
}
function cmake_x86 {
prepare_for_codegen
cmake .. -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DLITE_WITH_X86=ON ${common_flags}
}
function cmake_x86_for_CI {
prepare_for_codegen
cmake .. -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DLITE_WITH_X86=ON ${common_flags} -DLITE_WITH_PROFILE=ON
}
function cmake_gpu {
prepare_for_codegen
cmake .. " -DWITH_GPU=ON {common_flags} -DLITE_WITH_GPU=ON"
}
......@@ -31,18 +42,19 @@ function cmake_arm {
-DARM_TARGET_OS=$1 -DARM_TARGET_ARCH_ABI=$2
}
function build {
file=$1
for _test in $(cat $file); do
make $_test -j$(expr $(nproc) - 2)
done
}
# function build {
# file=$1
# for _test in $(cat $file); do
# make $_test -j$(expr $(nproc) - 2)
# done
# }
# It will eagerly test all lite related unittests.
function test_lite {
local file=$1
echo "file: ${file}"
for _test in $(cat $file); do
make $_test -j$(expr $(nproc) - 2)
ctest -R $_test -V
done
}
......@@ -86,7 +98,7 @@ function build_test_server {
cd ./build
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/paddle/build/third_party/install/mklml/lib"
cmake_x86_for_CI
build $TESTS_FILE
#build $TESTS_FILE
test_lite $TESTS_FILE
}
......@@ -165,10 +177,10 @@ function main {
TESTS_FILE="${i#*=}"
shift
;;
build)
build $TESTS_FILE
shift
;;
# build)
# build $TESTS_FILE
# shift
# ;;
cmake_x86)
cmake_x86
shift
......
paddle/fluid/lite/utils/CMakeLists.txt
浏览文件 @ c004b35a
......@@ -8,4 +8,4 @@ set(utils_DEPS glog)
lite_cc_test(test_varient SRCS varient_test.cc DEPS utils_lite)
cc_library(any_lite SRCS any.cc)
cc_library(utils_lite SRCS cp_logging.cc DEPS ${utils_DEPS} any_lite)
cc_library(utils_lite SRCS cp_logging.cc string.cc DEPS ${utils_DEPS} any_lite)
paddle/fluid/lite/utils/string.cc 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#include "paddle/fluid/lite/utils/string.h"
namespace paddle {
namespace lite {} // namespace lite
} // namespace paddle
paddle/fluid/lite/utils/string.h 0 → 100644
浏览文件 @ c004b35a
// Copyright (c) 2019 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.
#pragma once
#include <stdarg.h> // For va_start, etc.
#include <algorithm>
#include <cstring>
#include <memory> // For std::unique_ptr
#include <sstream>
#include <string>
#include <vector>
namespace paddle {
namespace lite {
static std::string string_format(const std::string fmt_str, ...) {
/* Reserve two times as much as the length of the fmt_str */
int final_n, n = (static_cast<int>(fmt_str.size())) * 2;
std::unique_ptr<char[]> formatted;
va_list ap;
while (1) {
formatted.reset(
new char[n]); /* Wrap the plain char array into the unique_ptr */
std::strcpy(&formatted[0], fmt_str.c_str()); // NOLINT
va_start(ap, fmt_str);
final_n = vsnprintf(&formatted[0], n, fmt_str.c_str(), ap);
va_end(ap);
if (final_n < 0 || final_n >= n)
n += abs(final_n - n + 1);
else
break;
}
return std::string(formatted.get());
}
template <typename T>
static std::string to_string_with_precision(const T& v, const int n = 6) {
std::stringstream ss;
ss.precision(n);
ss << std::fixed << v;
return ss.str();
}
static std::string Join(const std::vector<std::string>& vec,
const std::string& delim) {
if (vec.empty()) return "";
std::stringstream ss;
for (size_t i = 0; i < vec.size() - 1; i++) ss << vec[i] << delim;
if (!vec.empty()) {
ss << vec.back();
}
return ss.str();
}
static std::string Repr(const std::string& x) { return "\"" + x + "\""; }
static std::string Repr(const std::vector<std::string>& v) {
std::vector<std::string> tmp;
std::transform(v.begin(), v.end(), std::back_inserter(tmp),
[](const std::string& x) { return Repr(x); });
return "{" + Join(tmp, ",") + "}";
}
} // namespace lite
} // namespace paddle
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