提交 27d55e61 编写于 作者: J jiaopu

move sth from device_info to backends/mlu

上级 e0a5364f
...@@ -116,12 +116,8 @@ int TargetWrapperMlu::MLUCoreNumber() { return mlu_core_number_; } ...@@ -116,12 +116,8 @@ int TargetWrapperMlu::MLUCoreNumber() { return mlu_core_number_; }
bool TargetWrapperMlu::UseFirstConv() { return use_first_conv_; } bool TargetWrapperMlu::UseFirstConv() { return use_first_conv_; }
// const std::vector<float>& TargetWrapperMlu::MeanVec() const { return
// mean_vec_; }
const std::vector<float>& TargetWrapperMlu::MeanVec() { return mean_vec_; } const std::vector<float>& TargetWrapperMlu::MeanVec() { return mean_vec_; }
// const std::vector<float>& TargetWrapperMlu::StdVec() const { return std_vec_;
// }
const std::vector<float>& TargetWrapperMlu::StdVec() { return std_vec_; } const std::vector<float>& TargetWrapperMlu::StdVec() { return std_vec_; }
DataLayoutType TargetWrapperMlu::InputLayout() { return input_layout_; } DataLayoutType TargetWrapperMlu::InputLayout() { return input_layout_; }
......
...@@ -53,8 +53,6 @@ class TargetWrapper<TARGET(kMLU)> { ...@@ -53,8 +53,6 @@ class TargetWrapper<TARGET(kMLU)> {
static cnmlCoreVersion_t MLUCoreVersion(); static cnmlCoreVersion_t MLUCoreVersion();
static int MLUCoreNumber(); static int MLUCoreNumber();
static bool UseFirstConv(); static bool UseFirstConv();
// static const std::vector<float>& MeanVec() const;
// static const std::vector<float>& StdVec() const;
static const std::vector<float>& MeanVec(); static const std::vector<float>& MeanVec();
static const std::vector<float>& StdVec(); static const std::vector<float>& StdVec();
static DataLayoutType InputLayout(); static DataLayoutType InputLayout();
......
...@@ -66,15 +66,6 @@ thread_local std::vector<int> DeviceInfo::active_ids_; ...@@ -66,15 +66,6 @@ thread_local std::vector<int> DeviceInfo::active_ids_;
thread_local TensorLite DeviceInfo::workspace_; thread_local TensorLite DeviceInfo::workspace_;
thread_local int64_t DeviceInfo::count_ = 0; thread_local int64_t DeviceInfo::count_ = 0;
// #ifdef LITE_WITH_MLU
// thread_local cnmlCoreVersion_t DeviceInfo::mlu_core_version_{CNML_MLU270};
// thread_local int DeviceInfo::mlu_core_number_{1};
// thread_local bool DeviceInfo::use_first_conv_{false};
// thread_local std::vector<float> DeviceInfo::mean_vec_;
// thread_local std::vector<float> DeviceInfo::std_vec_;
// thread_local DataLayoutType DeviceInfo::input_layout_{DATALAYOUT(kNCHW)};
// #endif
#ifdef TARGET_IOS #ifdef TARGET_IOS
const int DEFAULT_L1_CACHE_SIZE = 64 * 1024; const int DEFAULT_L1_CACHE_SIZE = 64 * 1024;
const int DEFAULT_L2_CACHE_SIZE = 2048 * 1024; const int DEFAULT_L2_CACHE_SIZE = 2048 * 1024;
...@@ -1089,45 +1080,6 @@ int DeviceInfo::Setup() { ...@@ -1089,45 +1080,6 @@ int DeviceInfo::Setup() {
return 0; return 0;
} }
// #ifdef LITE_WITH_MLU
// void DeviceInfo::SetMLURunMode(lite_api::MLUCoreVersion core_version,
// int core_number,
// bool use_first_conv,
// const std::vector<float>& mean_vec,
// const std::vector<float>& std_vec,
// DataLayoutType input_layout) {
// switch (core_version) {
// case (lite_api::MLUCoreVersion::MLU_220):
// mlu_core_version_ = CNML_MLU220;
// break;
// case (lite_api::MLUCoreVersion::MLU_270):
// mlu_core_version_ = CNML_MLU270;
// break;
// default:
// mlu_core_version_ = CNML_MLU270;
// break;
// }
// mlu_core_number_ = core_number;
// use_first_conv_ = use_first_conv;
// mean_vec_ = mean_vec;
// std_vec_ = std_vec;
// input_layout_ = input_layout;
// }
//
// cnmlCoreVersion_t DeviceInfo::MLUCoreVersion() { return mlu_core_version_; }
//
// int DeviceInfo::MLUCoreNumber() { return mlu_core_number_; }
//
// bool DeviceInfo::UseFirstConv() { return use_first_conv_; }
//
// const std::vector<float>& DeviceInfo::MeanVec() const { return mean_vec_; }
//
// const std::vector<float>& DeviceInfo::StdVec() const { return std_vec_; }
//
// DataLayoutType DeviceInfo::InputLayout() const { return input_layout_; }
//
// #endif // LITE_WITH_MLU
void DeviceInfo::SetRunMode(lite_api::PowerMode mode, int thread_num) { void DeviceInfo::SetRunMode(lite_api::PowerMode mode, int thread_num) {
#ifdef ARM_WITH_OMP #ifdef ARM_WITH_OMP
thread_num = std::min(thread_num, core_num_); thread_num = std::min(thread_num, core_num_);
......
...@@ -55,20 +55,6 @@ class DeviceInfo { ...@@ -55,20 +55,6 @@ class DeviceInfo {
int Setup(); int Setup();
void SetRunMode(lite_api::PowerMode mode, int thread_num); void SetRunMode(lite_api::PowerMode mode, int thread_num);
// #ifdef LITE_WITH_MLU
// void SetMLURunMode(lite_api::MLUCoreVersion core_version,
// int core_number,
// bool use_first_conv,
// const std::vector<float>& mean_vec,
// const std::vector<float>& std_vec,
// DataLayoutType input_layout);
// cnmlCoreVersion_t MLUCoreVersion();
// int MLUCoreNumber();
// bool UseFirstConv();
// const std::vector<float>& MeanVec() const;
// const std::vector<float>& StdVec() const;
// DataLayoutType InputLayout() const;
// #endif
void SetCache(int l1size, int l2size, int l3size); void SetCache(int l1size, int l2size, int l3size);
void SetArch(ARMArch arch) { arch_ = arch; } void SetArch(ARMArch arch) { arch_ = arch; }
...@@ -120,15 +106,6 @@ class DeviceInfo { ...@@ -120,15 +106,6 @@ class DeviceInfo {
static thread_local TensorLite workspace_; static thread_local TensorLite workspace_;
static thread_local int64_t count_; static thread_local int64_t count_;
// #ifdef LITE_WITH_MLU
// static thread_local cnmlCoreVersion_t mlu_core_version_;
// static thread_local int mlu_core_number_;
// static thread_local bool use_first_conv_;
// static thread_local std::vector<float> mean_vec_;
// static thread_local std::vector<float> std_vec_;
// static thread_local DataLayoutType input_layout_;
// #endif
void SetDotInfo(int argc, ...); void SetDotInfo(int argc, ...);
void SetFP16Info(int argc, ...); void SetFP16Info(int argc, ...);
void SetFP32Info(int argc, ...); void SetFP32Info(int argc, ...);
......
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