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!47991 clean code 

Merge pull request !47991 from xulei/clean_code
This commit is contained in:
i-robot 2023-01-18 08:49:05 +00:00 committed by Gitee
parent 08aa1515d3 8f55d79bc9
commit 622803b69c
No known key found for this signature in database
GPG Key ID: 173E9B9CA92EEF8F
19 changed files with 68 additions and 48 deletions
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3
mindspore/ccsrc/backend/common/somas/somas.cc
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@ -2019,7 +2019,8 @@ size_t Somas::CalcLowerBound() const {
lifetime_lb[time] = 0;
}
size_t lower, upper;
size_t lower;
size_t upper;
for (const auto &tensor : tensors_list_) {
MS_EXCEPTION_IF_NULL(tensor);
if (tensor->lifelong_value_ == kLifeLongGraphAll) {

4
mindspore/ccsrc/backend/common/somas/somas_solver_alg.h
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@ -41,6 +41,8 @@ using std::vector;
namespace mindspore {
namespace somas {
constexpr auto kDefaultAlignmentSize = 512;
class Interval {
public:
Interval() : m_a_(0), m_b_(0) {}
@ -180,7 +182,7 @@ class FootPrint : public std::enable_shared_from_this<FootPrint> {
class FastHeuristic {
public:
FastHeuristic() : m_alignment_(512), m_tensors_allocated_(0) {}
FastHeuristic() : m_alignment_(kDefaultAlignmentSize), m_tensors_allocated_(0) {}
~FastHeuristic() = default;
void setAlignment(const size_t &a) { m_alignment_ = a; }

3
mindspore/ccsrc/backend/common/somas/somas_solver_pre.h
View File

@ -43,6 +43,7 @@ constexpr char const *sortingNames[6] = {"size(>), index(<)",
constexpr char const *branchingNames[4] = {"bestfit", "smallest", "largest", "worstfit"};
constexpr char const *algorithmTypeNames[2] = {"Shared Objects", "Single Object"};
constexpr auto kParallelComputeSizeThreshold = 2000;
constexpr auto kHalfByteSize = 4;
enum Status { FAILED, SUCCESS };
enum AlgorithmType { kManyObjects = 0, kSingleObject, kNumAlgorithmTypes };
enum SortingType {
@ -117,7 +118,7 @@ class DynamicBitSet {
auto *char_value = reinterpret_cast<unsigned char *>(&value);
for (size_t j = 0; j < bit_width_ / CHAR_BIT; j++) {
ret += ones_num_in_hex[static_cast<int>(char_value[j] & 0xF)];
char_value[j] >>= 4;
char_value[j] >>= kHalfByteSize;
ret += ones_num_in_hex[static_cast<int>(char_value[j] & 0xF)];
}
}

4
mindspore/ccsrc/cxx_api/types.cc
View File

@ -312,7 +312,9 @@ MSTensor *MSTensor::CharStringsToTensor(const std::vector<char> &name, const std
}
std::vector<std::vector<char>> MSTensor::TensorToStringChars(const MSTensor &tensor) {
if (tensor == nullptr || tensor.DataType() != DataType::kObjectTypeString || tensor.DataSize() < 4) {
constexpr auto minimum_tensor_size = 4;
if (tensor == nullptr || tensor.DataType() != DataType::kObjectTypeString ||
tensor.DataSize() < minimum_tensor_size) {
MS_LOG(ERROR) << "Invalid tensor.";
return {};
}

3
mindspore/ccsrc/kernel/common_utils.h
View File

@ -210,7 +210,8 @@ struct AsymmetricFunc {
template <typename T>
struct HalfPixelFunc {
T operator()(const T &new_x, const int &old_length, const int &new_length) const {
return new_length > 1 ? (new_x + 0.5) * old_length / new_length - 0.5 : 0;
constexpr auto half_pixel = 0.5;
return new_length > 1 ? (new_x + half_pixel) * old_length / new_length - half_pixel : 0;
}
};

17
mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_device_address.cc
View File

@ -45,8 +45,9 @@
namespace mindspore {
namespace device {
namespace ascend {
const int FLOAT_LEN = sizeof(float);
const int FLOAT16_LEN = 2;
const auto kFloat16Bytes = 2;
const auto kFloatBytes = sizeof(float);
const auto kFloat64Bytes = 8;
bool IsUseTransDataTypeFormat(const std::pair<std::string, std::string> &type_format) {
static const std::set<std::pair<std::string, std::string>> use_trans_data = {
@ -107,8 +108,8 @@ void SyncMemory(void *dst, const void *src, uint64_t size, aclrtMemcpyKind kind)
}
bool FloatToHalfAndSyncHostToDevice(void *dst, size_t dst_size, const void *src, size_t src_size) {
auto elem_num = src_size / FLOAT_LEN;
if (elem_num != (dst_size / FLOAT16_LEN)) {
auto elem_num = src_size / kFloatBytes;
if (elem_num != (dst_size / kFloat16Bytes)) {
MS_EXCEPTION(ArgumentError) << "FloatToHalf failed. size not match src_size[" << src_size << "], dst_size["
<< dst_size << "]";
}
@ -119,7 +120,7 @@ bool FloatToHalfAndSyncHostToDevice(void *dst, size_t dst_size, const void *src,
}
bool Float64ToFloatAndSyncHostToDevice(void *dst, size_t dst_size, const void *src, size_t src_size) {
if (src_size / 2 != dst_size) {
if (src_size / kFloat64Bytes != dst_size / kFloatBytes) {
MS_EXCEPTION(ArgumentError) << "src_size[" << src_size << "], dst_size[" << dst_size << "]";
}
size_t elem_num = dst_size / sizeof(float);
@ -130,8 +131,8 @@ bool Float64ToFloatAndSyncHostToDevice(void *dst, size_t dst_size, const void *s
}
bool SyncDeviceToHostAndHalfToFloat(void *dst, size_t dst_size, const void *src, size_t src_size) {
auto elem_num = src_size / FLOAT16_LEN;
if (elem_num != (dst_size / FLOAT_LEN)) {
auto elem_num = src_size / kFloat16Bytes;
if (elem_num != (dst_size / kFloatBytes)) {
MS_EXCEPTION(ArgumentError) << "HalfToFloat failed. size not match src_size[" << src_size << "], dst_size["
<< dst_size << "]";
}
@ -142,7 +143,7 @@ bool SyncDeviceToHostAndHalfToFloat(void *dst, size_t dst_size, const void *src,
}
bool SyncDeviceToHostAndFloatToFloat64(void *dst, size_t dst_size, const void *src, size_t src_size) {
if (src_size != dst_size / 2) {
if (src_size / kFloatBytes != dst_size / kFloat64Bytes) {
MS_EXCEPTION(ArgumentError) << "src_size[" << src_size << "], dst_size[" << dst_size << "]";
}
size_t elem_num = src_size / sizeof(float);

3
mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_stream_assign.cc
View File

@ -2411,7 +2411,8 @@ void AscendStreamAssign::CheckEventAssign(const NotNull<KernelGraphPtr> &graph_p
<< ", max event id:" << max_event_id << ", event map is:" << event_map;
}
for (const auto &item : std::as_const(event_map)) {
if (item.second.size() != 2) {
constexpr auto pair_size = 2;
if (item.second.size() != pair_size) {
MS_LOG(EXCEPTION) << "Send/recv should be in pair and share one event id, invalid event id is:" << item.first
<< ", event size is:" << item.second.size();
}

3
mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_select_ascend.cc
View File

@ -859,7 +859,8 @@ std::tuple<KernelSelectStatus, std::string, ExceptionType> SelectKernelInfoWithM
KernelType kernel_type) {
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> kernel_info_list;
std::vector<std::shared_ptr<kernel::KernelBuildInfo>> aicpu_kernel_info_list;
std::ostringstream aicore_in_out_info, aicpu_in_out_info;
std::ostringstream aicore_in_out_info;
std::ostringstream aicpu_in_out_info;
std::tuple<KernelSelectStatus, std::string, ExceptionType> result =
std::make_tuple(kStatusAllMatched, "", NoExceptionType);
MS_EXCEPTION_IF_NULL(kernel_node);

12
mindspore/ccsrc/plugin/device/ascend/kernel/hccl/hccl_kernel.h
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@ -93,24 +93,24 @@ class HcclKernelFactory {
std::map<string, HcclKernelCreater> hccl_kernel_map_;
};
class _HcclKernelRegister {
class HcclKernelRegister {
public:
_HcclKernelRegister(const string &name, HcclKernelCreater &&fun) {
HcclKernelRegister(const string &name, HcclKernelCreater &&fun) {
HcclKernelFactory::Get().Register(name, std::move(fun));
}
~_HcclKernelRegister() = default;
~HcclKernelRegister() = default;
};
#define _MS_HCCL_REG_KERNEL_REG(KNAME, clazz) \
#define MS_HCCL_REG_KERNEL_REG(KNAME, clazz) \
static_assert(std::is_base_of<HcclKernel, clazz>::value, " must be base of HcclKernel"); \
static const _HcclKernelRegister g_##KNAME##_##_kernel_reg(#KNAME, []() { \
static const HcclKernelRegister g_##KNAME##_##_kernel_reg(#KNAME, []() { \
std::shared_ptr<clazz> ptr = nullptr; \
ptr = std::make_shared<clazz>(); \
MS_EXCEPTION_IF_NULL(ptr); \
return ptr; \
});
#define MS_HCCL_REG_KERNEL(KNAME, clazz) _MS_HCCL_REG_KERNEL_REG(KNAME, clazz)
#define MS_HCCL_REG_KERNEL(KNAME, clazz) MS_HCCL_REG_KERNEL_REG(KNAME, clazz)
} // namespace kernel
} // namespace mindspore
#endif

8
mindspore/ccsrc/plugin/device/ascend/kernel/host/host_kernel_mod.h
View File

@ -58,17 +58,17 @@ class HostKernelFactory {
std::map<string, HostKernelCreater> hostKernelMap_;
};
class _HostKernelRegister {
class HostKernelRegister {
public:
_HostKernelRegister(const string &name, HostKernelCreater &&fun) {
HostKernelRegister(const string &name, HostKernelCreater &&fun) {
HostKernelFactory::Get().Register(name, std::move(fun));
}
~_HostKernelRegister() = default;
~HostKernelRegister() = default;
};
#define MS_HOST_REG_KERNEL_REG(KNAME, clazz) \
static_assert(std::is_base_of<HostKernelMod, clazz>::value, " must be base of HostKernelMod"); \
static const _HostKernelRegister g_##KNAME##_##_kernel_reg(#KNAME, []() { \
static const HostKernelRegister g_##KNAME##_##_kernel_reg(#KNAME, []() { \
std::shared_ptr<clazz> ptr = nullptr; \
ptr = std::make_shared<clazz>(); \
MS_EXCEPTION_IF_NULL(ptr); \

7
mindspore/ccsrc/plugin/device/ascend/kernel/rts/assign.cc
View File

@ -24,13 +24,16 @@ using MemcpyAsyncTaskInfoPtr = std::shared_ptr<MemcpyAsyncTaskInfo>;
namespace mindspore {
namespace kernel {
namespace {
constexpr auto kAssignInputSize = 2;
}
AssignKernel::AssignKernel() {}
AssignKernel::~AssignKernel() {}
bool AssignKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &, void *stream_ptr) {
if (inputs.size() != 2) {
if (inputs.size() != kAssignInputSize) {
MS_LOG(ERROR) << "inputs size is not two";
return false;
}
@ -52,7 +55,7 @@ bool AssignKernel::Launch(const std::vector<AddressPtr> &inputs, const std::vect
std::vector<TaskInfoPtr> AssignKernel::GenTask(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &, uint32_t stream_id) {
if (inputs.size() != 2) {
if (inputs.size() != kAssignInputSize) {
MS_LOG(EXCEPTION) << "Inputs size should be 2, but got " << inputs.size();
}
stream_id_ = stream_id;

12
mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel.h
View File

@ -61,19 +61,19 @@ class RtKernelFactory {
std::map<string, RtKernelCreater> fmap_;
};
class _RtKernelRegister {
class RtKernelRegister {
public:
_RtKernelRegister(const std::string &name, RtKernelCreater &&fun) {
RtKernelRegister(const std::string &name, RtKernelCreater &&fun) {
RtKernelFactory::Get().Register(name, std::move(fun));
}
~_RtKernelRegister() = default;
~RtKernelRegister() = default;
};
#define _MS_REG_RTKERNEL_REG(KNAME, clazz) \
#define MS_REG_RTKERNEL_REG(KNAME, clazz) \
static_assert(std::is_base_of<RtKernel, clazz>::value, " must be base of RtKernel"); \
static const _RtKernelRegister g_##KNAME##_##_RtKernel_reg(#KNAME, []() { return std::make_shared<clazz>(); });
static const RtKernelRegister g_##KNAME##_##_RtKernel_reg(#KNAME, []() { return std::make_shared<clazz>(); });
#define MS_REG_RTKERNEL(KNAME, clazz) _MS_REG_RTKERNEL_REG(KNAME, clazz)
#define MS_REG_RTKERNEL(KNAME, clazz) MS_REG_RTKERNEL_REG(KNAME, clazz)
} // namespace kernel
} // namespace mindspore

12
mindspore/ccsrc/plugin/device/ascend/kernel/rts/rt_kernel_info.h
View File

@ -54,19 +54,19 @@ class RtKerDescFactory {
std::map<std::string, RtKerDescCreater> fmap_;
};
class _RtKerDescRegister {
class RtKerDescRegister {
public:
_RtKerDescRegister(const std::string &name, RtKerDescCreater &&fun) {
RtKerDescRegister(const std::string &name, RtKerDescCreater &&fun) {
RtKerDescFactory::Get().Register(name, std::move(fun));
}
~_RtKerDescRegister() = default;
~RtKerDescRegister() = default;
};
#define _MS_REG_RTKERNEL_DESC_REG(KNAME, clazz) \
#define MS_REG_RTKERNEL_DESC_REG(KNAME, clazz) \
static_assert(std::is_base_of<RtKerDesc, clazz>::value, " must be base of RtKerDesc"); \
static const _RtKerDescRegister g_##KNAME##_##_rtkernel_desc_reg(#KNAME, []() { return std::make_shared<clazz>(); });
static const RtKerDescRegister g_##KNAME##_##_rtkernel_desc_reg(#KNAME, []() { return std::make_shared<clazz>(); });
#define MS_REG_RTKERNEL_DESC(KNAME, clazz) _MS_REG_RTKERNEL_DESC_REG(KNAME, clazz)
#define MS_REG_RTKERNEL_DESC(KNAME, clazz) MS_REG_RTKERNEL_DESC_REG(KNAME, clazz)
void GetRtKelInfo(const CNodePtr &kernel_node, std::vector<std::shared_ptr<kernel::KernelBuildInfo>> *kernel_info_list);
} // namespace kernel

6
mindspore/ccsrc/plugin/device/ascend/kernel/rts/tensor_copy_slices.cc
View File

@ -38,7 +38,7 @@ TensorCopySlices::~TensorCopySlices() {}
bool TensorCopySlices::Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, void *stream_ptr) {
if (inputs.size() != 2) {
if (inputs.size() != kTensorCopySlicesInputSize) {
MS_LOG(ERROR) << "inputs size is not 2";
return false;
}
@ -88,7 +88,7 @@ bool TensorCopySlices::Init(const mindspore::AnfNodePtr &anf_node) {
void TensorCopySlices::GetInputOutputInfo(const AnfNodePtr &anf_node) {
MS_EXCEPTION_IF_NULL(anf_node);
size_t input_size = common::AnfAlgo::GetInputTensorNum(anf_node);
if (input_size != 2) {
if (input_size != kTensorCopySlicesInputSize) {
MS_LOG(EXCEPTION) << "TensorCopySlices input size is not 2, got " << input_size;
}
input_type_id_ = AnfAlgo::GetPrevNodeOutputDeviceDataType(anf_node, 0);
@ -134,7 +134,7 @@ void TensorCopySlices::GetInputOutputTotalCount(const AnfNodePtr &anf_node) {
std::vector<TaskInfoPtr> TensorCopySlices::GenTask(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &,
const std::vector<AddressPtr> &outputs, uint32_t stream_id) {
if (inputs.size() != 2) {
if (inputs.size() != kTensorCopySlicesInputSize) {
MS_LOG(EXCEPTION) << "inputs size is not 2.";
}
if (outputs.size() != 1) {

3
mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_kernel_compile.cc
View File

@ -781,7 +781,8 @@ void TbeKernelCompileManager::DistributeCompileTask(const std::vector<CNodePtr>
void TbeKernelCompileManager::TbePreBuild(const KernelGraphPtr &kernel_graph) {
MS_EXCEPTION_IF_NULL(kernel_graph);
MS_LOG(INFO) << "Single op pre build start.";
struct timeval start_time, end_time;
struct timeval start_time;
struct timeval end_time;
(void)gettimeofday(&start_time, nullptr);
std::vector<CNodePtr> node_list;
GetAllTbeNodes(kernel_graph, &node_list);

3
mindspore/ccsrc/plugin/device/ascend/optimizer/ir_fission/dynamic_rnn_grad_fission_v2.cc
View File

@ -418,7 +418,8 @@ AnfNodePtr DynamicRnnGradFissionV2::CreateSplitV(const FuncGraphPtr &func_graph,
}
auto split_v = NewCNode(splitv_input, func_graph);
// Set infer data type and shape
ShapeVector shape1, shape2;
ShapeVector shape1;
ShapeVector shape2;
if (specs.batch_size % kCubeSize == 0 && !specs.shape_need_align) {
shape1 = {(origin_input6_shape[kDim0] - 1) * origin_input6_shape[kDim1], origin_input6_shape[kDim2]};
shape2 = {origin_input6_shape[kDim1], origin_input6_shape[kDim2]};

5
mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/avg_pool_grad_unify_mindir.cc
View File

@ -111,7 +111,10 @@ ValueNodePtr CreateMeanMatrixValueNode(const FuncGraphPtr &func_graph, const Anf
<< x_shape << ", kernel_size:" << k_size << ", strides:" << stride
<< trace::DumpSourceLines(node);
}
int64_t pad_top, pad_bottom, pad_left, pad_right;
int64_t pad_top;
int64_t pad_bottom;
int64_t pad_left;
int64_t pad_right;
int64_t h_output =
windowed_output_size(node, x_shape[kDim2], k_size[kDim2], stride[kDim2], pad_mode, &pad_top, &pad_bottom);
int64_t w_output =

5
mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/dropout_unify_mindir.cc
View File

@ -110,8 +110,9 @@ std::vector<int64_t> CalGenMaskOutputShape(const std::vector<int64_t> &shape) {
std::vector<int64_t> CalGenMaskV3OutputShape(const std::vector<int64_t> &shape, TypeId type) {
// [*dim, M, N] -> [*dim, N/16, M/16, 16, 16] if M%16=0 and N%16=0
if (shape.size() >= 2 && shape[shape.size() - 1] % static_cast<int64_t>(kCubeSize) == 0 &&
shape[shape.size() - 2] % static_cast<int64_t>(kCubeSize) == 0) {
constexpr auto cube_h_offset = 2;
if (shape.size() >= cube_h_offset && shape[shape.size() - 1] % static_cast<int64_t>(kCubeSize) == 0 &&
shape[shape.size() - cube_h_offset] % static_cast<int64_t>(kCubeSize) == 0) {
auto fnz_shape = trans::TransShapeToDevice(shape, kOpFormat_FRAC_NZ, type);
return fnz_shape;
}

3
mindspore/ccsrc/plugin/device/ascend/optimizer/mindir/neighbor_exchange_v2_unify_mindir.cc
View File

@ -48,6 +48,7 @@ constexpr int64_t kRankIdFive = 5;
constexpr int64_t kRankIdSix = 6;
constexpr int64_t kRankIdSeven = 7;
constexpr size_t kSizeFour = 4;
constexpr size_t kSizeEight = 8;
constexpr int64_t kInvalidId = -1;
constexpr size_t kMinSplitOutputSize = 2;
@ -215,7 +216,7 @@ AnfNodePtr GetCenter(const FuncGraphPtr &graph, const CNodePtr &neighbor_exchang
std::vector<AnfNodePtr> CreateAllToAllvInputForGrad(const std::vector<int64_t> &send_rank_ids,
const std::vector<std::vector<AnfNodePtr>> &split_outputs,
const std::vector<CNodePtr> &split_nodes) {
if (send_rank_ids.size() != 8) {
if (send_rank_ids.size() != kSizeEight) {
MS_LOG(EXCEPTION) << "Wrong send_rank_ids size: " << send_rank_ids.size() << ", expect size: 8.";
}
if (split_outputs.size() != kSizeFour) {