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!29577 solve code format issue 

Merge pull request !29577 from zhujingxuan/code_format
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i-robot 2022-02-07 08:56:48 +00:00 committed by Gitee
parent 0ec4b29a31 beb12c3d0e
commit 39fee2393d
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GPG Key ID: 173E9B9CA92EEF8F
3 changed files with 8 additions and 9 deletions
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3
mindspore/ccsrc/backend/kernel_compiler/cpu/eigen/solve_triangular_cpu_kernel.cc
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@ -18,7 +18,6 @@
#include <Eigen/Dense> #include <Eigen/Dense>
#include <vector> #include <vector>
#include <string> #include <string>
#include <type_traits>
namespace mindspore { namespace mindspore {
namespace kernel { namespace kernel {
using Eigen::ColMajor; using Eigen::ColMajor;
@ -105,7 +104,7 @@ inline void solve(const MatrixBase<Derived_A> &A, const MatrixBase<Derived_b> &b
template <typename T> template <typename T>
bool SolveTriangularCpuKernelMod<T>::Launch(const std::vector<AddressPtr> &inputs, bool SolveTriangularCpuKernelMod<T>::Launch(const std::vector<AddressPtr> &inputs,
const std::vector<AddressPtr> &workspace, const std::vector<AddressPtr> & /* workspace */,
const std::vector<AddressPtr> &outputs) { const std::vector<AddressPtr> &outputs) {
CHECK_KERNEL_INPUTS_NUM(inputs.size(), kSolveTriangularInputsNum, kernel_name_); CHECK_KERNEL_INPUTS_NUM(inputs.size(), kSolveTriangularInputsNum, kernel_name_);
CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kSolveTriangularOutputsNum, kernel_name_); CHECK_KERNEL_OUTPUTS_NUM(outputs.size(), kSolveTriangularOutputsNum, kernel_name_);

12
mindspore/ccsrc/backend/kernel_compiler/cpu/rank_cpu_kernel.cc
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@ -84,7 +84,7 @@ template <typename T>
void RankCpuKernelMod<T>::SetFunc() { void RankCpuKernelMod<T>::SetFunc() {
switch (method_) { switch (method_) {
case Method::Max: { case Method::Max: {
func_ = [](size_t i, size_t duplicate_count, int culmutive_rank, const AxisIterator &axisIterator, func_ = [](size_t i, size_t duplicate_count, int /* culmutive_rank */, const AxisIterator &axisIterator,
const size_t *const sort_idx, float *const output_addr) { const size_t *const sort_idx, float *const output_addr) {
for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) { for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) {
output_addr[axisIterator.GetPos(sort_idx[j])] = i + 1; output_addr[axisIterator.GetPos(sort_idx[j])] = i + 1;
@ -92,7 +92,7 @@ void RankCpuKernelMod<T>::SetFunc() {
}; };
} break; } break;
case Method::Min: { case Method::Min: {
func_ = [](size_t i, size_t duplicate_count, int culmutive_rank, const AxisIterator &axisIterator, func_ = [](size_t i, size_t duplicate_count, int /* culmutive_rank */, const AxisIterator &axisIterator,
const size_t *const sort_idx, float *const output_addr) { const size_t *const sort_idx, float *const output_addr) {
for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) { for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) {
output_addr[axisIterator.GetPos(sort_idx[j])] = i - duplicate_count + 2; output_addr[axisIterator.GetPos(sort_idx[j])] = i - duplicate_count + 2;
@ -105,7 +105,7 @@ void RankCpuKernelMod<T>::SetFunc() {
// = duplicate_count * (2 * i - duplicate_count + 1) / 2 // = duplicate_count * (2 * i - duplicate_count + 1) / 2
// rank_sum = sum + duplicate_count = duplicate_count * (2 * i - duplicate_count + 3) / 2 // rank_sum = sum + duplicate_count = duplicate_count * (2 * i - duplicate_count + 3) / 2
// avg = rank_sum / duplicate_count = (2 * i - duplicate_count + 3) / 2 // avg = rank_sum / duplicate_count = (2 * i - duplicate_count + 3) / 2
func_ = [](size_t i, size_t duplicate_count, int culmutive_rank, const AxisIterator &axisIterator, func_ = [](size_t i, size_t duplicate_count, int /* culmutive_rank */, const AxisIterator &axisIterator,
const size_t *const sort_idx, float *const output_addr) { const size_t *const sort_idx, float *const output_addr) {
float avg = (2 * i - duplicate_count + 3) / 2.0; float avg = (2 * i - duplicate_count + 3) / 2.0;
for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) { for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) {
@ -114,7 +114,7 @@ void RankCpuKernelMod<T>::SetFunc() {
}; };
} break; } break;
case Method::First: { case Method::First: {
func_ = [](size_t i, size_t duplicate_count, int culmutive_rank, const AxisIterator &axisIterator, func_ = [](size_t i, size_t duplicate_count, int /* culmutive_rank */, const AxisIterator &axisIterator,
const size_t *const sort_idx, float *const output_addr) { const size_t *const sort_idx, float *const output_addr) {
for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) { for (size_t j = i - duplicate_count + 1; j < i + 1; ++j) {
output_addr[axisIterator.GetPos(sort_idx[j])] = j + 1; output_addr[axisIterator.GetPos(sort_idx[j])] = j + 1;
@ -201,7 +201,7 @@ void RankCpuKernelMod<T>::Launch1D(const T *input_addr, size_t *sort_idx, T *val
int culmutive_rank = 1; int culmutive_rank = 1;
size_t duplicate_count = 0; size_t duplicate_count = 0;
int nans_count = 0; size_t nans_count = 0;
for (size_t i = 0; i < n; ++i) { for (size_t i = 0; i < n; ++i) {
duplicate_count++; duplicate_count++;
@ -226,7 +226,7 @@ void RankCpuKernelMod<T>::Launch1D(const T *input_addr, size_t *sort_idx, T *val
template <typename T> template <typename T>
void RankCpuKernelMod<T>::PctConvert(float *output_addr, const AxisIterator &iter, int culmutive_rank, void RankCpuKernelMod<T>::PctConvert(float *output_addr, const AxisIterator &iter, int culmutive_rank,
int nans_count) const { size_t nans_count) const {
const size_t n = iter.AxisSize(); const size_t n = iter.AxisSize();
if (pct_) { if (pct_) {
// pct calculation // pct calculation

2
mindspore/ccsrc/backend/kernel_compiler/cpu/rank_cpu_kernel.h
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@ -79,7 +79,7 @@ class RankCpuKernelMod : public NativeCpuKernelMod {
return std::numeric_limits<T>::min(); return std::numeric_limits<T>::min();
} }
} }
void PctConvert(float *output_addr, const AxisIterator &iter, int culmutive_rank, int nans_count) const; void PctConvert(float *output_addr, const AxisIterator &iter, int culmutive_rank, size_t nans_count) const;
void PctConvert(float *output_addr, const AxisIterator &iter, int culmutive_rank) const; void PctConvert(float *output_addr, const AxisIterator &iter, int culmutive_rank) const;
// shape info // shape info
AxisIterator axisIterator_{}; AxisIterator axisIterator_{};