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!45023 [Lite] sync code check 

Merge pull request !45023 from yangruoqi713/master
This commit is contained in:
i-robot 2022-11-04 01:41:36 +00:00 committed by Gitee
parent 689bc9a819 cf2e9e9784
commit de4a5a18fa
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GPG Key ID: 173E9B9CA92EEF8F
45 changed files with 121 additions and 136 deletions
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2
mindspore/ccsrc/plugin/device/cpu/kernel/maxpool_grad_grad_cpu_kernel.cc
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@ -146,7 +146,7 @@ int MaxPoolGradGradCpuKernelMod::Resize(const BaseOperatorPtr &base_operator,
param_->output_channel_ = LongToInt(out_shapes_[kDim1]);
param_->output_h_ = LongToInt(out_shapes_[height_index_]);
param_->output_w_ = LongToInt(out_shapes_[width_index_]);
output_elements_ = std::accumulate(out_shapes_.begin(), out_shapes_.end(), 1, std::multiplies<size_t>());
output_elements_ = LongToSize(std::accumulate(out_shapes_.begin(), out_shapes_.end(), 1, std::multiplies<int64_t>()));
if (dim_ == kMaxPool3DGradGradDim) {
reinterpret_cast<Pooling3DParameter *>(param_)->input_d_ = LongToInt(in_shapes_[depth_index_]);

4
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/base/gather_d_base.c
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@ -82,14 +82,14 @@ int InitCalVec(size_t *in_strides, size_t *out_strides, size_t *pos, const size_
#define COPY_TASK_IMPL(type0, type1) \
int CopyTask_Input_##type0##_Index_##type1( \
type0 *output, const type0 *input, const type1 *index, size_t cur_dim, size_t *pos, const int dim, \
type0 *output, const type0 *input, const type1 *index, size_t cur_dim, size_t *pos, const size_t dim, \
const size_t *output_shape, const size_t output_shape_size, const size_t *in_strides, const size_t *out_strides) { \
if (pos == NULL || out_strides == NULL || in_strides == NULL) { \
return NNACL_NULL_PTR; \
} \
for (size_t i = 0; i < output_shape[cur_dim]; ++i) { \
pos[cur_dim] = i; \
if (cur_dim == (int)output_shape_size - 1) { \
if (cur_dim == output_shape_size - 1) { \
size_t input_offset = 0; \
size_t out_offset = 0; \
for (size_t j = 0; j < output_shape_size; ++j) { \

8
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/base/gather_d_base.h
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@ -28,10 +28,10 @@ extern "C" {
GatherD_Input_##type0##_Index_##type1(output, input, index, input_shape, input_shape_size, output_shape, \
output_shape_size, dim)
#define GATHER_D_IMPL_DECLARATION(type0, type1) \
int GatherD_Input_##type0##_Index_##type1(type0 *output, const type0 *input, type1 *index, \
const size_t *input_shape, const size_t input_shape_size, \
const size_t *output_shape, const size_t output_shape_size, const int dim)
#define GATHER_D_IMPL_DECLARATION(type0, type1) \
int GatherD_Input_##type0##_Index_##type1( \
type0 *output, const type0 *input, type1 *index, const size_t *input_shape, const size_t input_shape_size, \
const size_t *output_shape, const size_t output_shape_size, const size_t dim)
GATHER_D_IMPL_DECLARATION(bool, int32_t);
GATHER_D_IMPL_DECLARATION(bool, int64_t);

2
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/base/zeroslike_base.h
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@ -22,7 +22,7 @@
extern "C" {
#endif
static inline void ApproximateZerosLike(void *output, int data_size) {
static inline void ApproximateZerosLike(void *output, size_t data_size) {
(void)memset(output, 0, data_size);
return;
}

6
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp32/invert_permutation_fp32.c
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@ -18,11 +18,11 @@
#include "nnacl/errorcode.h"
#include "nnacl/op_base.h"
int InvertPermutation(const int *input, int *output, int num) {
int InvertPermutation(const int *input, int *output, size_t num) {
NNACL_CHECK_NULL_RETURN_ERR(input);
NNACL_CHECK_NULL_RETURN_ERR(output);
for (int i = 0; i < num; i++) {
int index = input[i];
for (size_t i = 0; i < num; i++) {
size_t index = (size_t)input[i];
if (index >= num) {
return NNACL_ERR;
}

4
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp32/invert_permutation_fp32.h
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@ -16,10 +16,12 @@
#ifndef MINDSPORE_NNACL_INVERT_PERMUTATION_FP32_H_
#define MINDSPORE_NNACL_INVERT_PERMUTATION_FP32_H_
#include <stddef.h>
#ifdef __cplusplus
extern "C" {
#endif
int InvertPermutation(const int *input, int *output, int num);
int InvertPermutation(const int *input, int *output, size_t num);
#ifdef __cplusplus
}
#endif

2
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp32/winograd_transform.c
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@ -155,7 +155,7 @@ void WinogradInputTransformOptStep(const float *input_data, float *trans_input,
// input transform
const int block_tile = C12NUM;
int dst_ic8_offset = dst_plane_offset + ic * block_tile * input_unit * input_unit * channel_tile;
size_t dst_step = input_unit * block_tile * channel_tile;
size_t dst_step = (size_t)(input_unit * block_tile * channel_tile);
float *trans_input_ptr = trans_input + dst_ic8_offset;
func(tmp_data, trans_input_ptr, channel_tile, dst_step, block_tile * channel_tile);
}

1
mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/kernel/exp.c
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@ -69,7 +69,6 @@ int exp_do_compute(void *param, int task_id, float lhs_scale, float rhs_scale) {
void *output_data = exp_stru->base.out[0].data_;
NNACL_CHECK_NULL_RETURN_ERR(output_data);
int ret = exp_stru->base.funcs->ExpFusion(input_data, output_data, exp_param, task_id);
return ret;
}

2
mindspore/lite/src/litert/cxx_api/tensor/tensor_impl.h
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@ -163,7 +163,7 @@ class LiteTensorImpl : public MutableTensorImpl {
return lite_tensor_->format();
}
void SetFormat(mindspore::Format format) override {
void SetFormat(const mindspore::Format format) override {
if (lite_tensor_ == nullptr) {
MS_LOG(ERROR) << "Invalid tensor.";
return;

2
mindspore/lite/src/litert/kernel/cpu/base/group_convolution_creator.cc
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@ -87,7 +87,7 @@ lite::Tensor *CreateConstTensor(const lite::Tensor *tensor, const std::vector<in
delete new_tensor;
return nullptr;
}
memcpy(new_tensor->data(), reinterpret_cast<void *>(new_tensor_data), new_tensor->Size());
(void)memcpy(new_tensor->data(), reinterpret_cast<void *>(new_tensor_data), new_tensor->Size());
return new_tensor;
}

2
mindspore/lite/src/litert/kernel/cpu/base/reduce_base.cc
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@ -107,7 +107,7 @@ int ReduceBaseCPUKernel::Prepare() {
MS_CHECK_TRUE_MSG(axes_tensor != nullptr, RET_ERROR, "axes-tensor is a nullptr.");
MS_CHECK_FALSE_MSG((axes_tensor->data_type() != kNumberTypeInt && axes_tensor->data_type() != kNumberTypeInt32),
RET_ERROR, "The data type of axes tensor should be int32");
num_axes_ = axes_tensor->ElementsNum();
num_axes_ = static_cast<int>(axes_tensor->ElementsNum());
if (axes_tensor->data() != nullptr && (num_axes_ <= 0 || num_axes_ > MAX_SHAPE_SIZE)) {
MS_LOG(ERROR) << "input axes invalid.";
return RET_ERROR;

2
mindspore/lite/src/litert/kernel/cpu/fp32/affine_fp32.cc
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@ -348,7 +348,7 @@ OpParameter *AffineFp32CPUKernel::MatmulParameterCreate() {
matmul_param->a_transpose_ = origin_matmul->a_transpose_;
matmul_param->has_bias_ = origin_matmul->has_bias_;
matmul_param->act_type_ = origin_matmul->act_type_;
matmul_param->op_parameter_.thread_num_ = this->context()->thread_num_;
matmul_param->op_parameter_.thread_num_ = op_parameter_->thread_num_;
return reinterpret_cast<OpParameter *>(matmul_param);
}

5
mindspore/lite/src/litert/kernel/cpu/fp32/batchnorm_fp32.h
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@ -38,10 +38,7 @@ class BatchnormCPUKernel : public LiteKernel {
int SetupVirtualBatch(int virtual_batch_multiplier, int param) override;
virtual int InitConstTensor();
virtual int DoExecute(int task_id);
virtual int Batchnorm2Scale(const void *scale_data, const void *bias_data, const void *mean_data,
const void *var_data, float eps, int kernel_num) {
return RET_OK;
}
virtual int Batchnorm2Scale(const void *, const void *, const void *, const void *, float, int) { return RET_OK; }
virtual int set_momentum(float momentum);
virtual float get_momentum();
virtual int RestoreDefaultMomentum();

3
mindspore/lite/src/litert/kernel/cpu/fp32/convolution_delegate_fp32.cc
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@ -312,9 +312,8 @@ kernel::LiteKernel *ConvolutionDelegateCPUKernel::CpuConvFp32KernelSelect() {
op_parameter_ = nullptr;
return nullptr;
}
kernel->set_name("act_" + name_);
}
kernel->set_name("act_" + name_);
return kernel;
}

2
mindspore/lite/src/litert/kernel/cpu/fp32/fill_fp32.cc
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@ -43,7 +43,7 @@ int FillCPUKernel::ReSize() {
}
auto output = out_tensors_.front();
CHECK_NULL_RETURN(output);
data_size_ = output->ElementsNum();
data_size_ = static_cast<int>(output->ElementsNum());
thread_sz_count_ = MSMIN(thread_num_, data_size_);
if (thread_sz_count_ != 0) {
thread_sz_stride_ = UP_DIV(data_size_, thread_sz_count_);

8
mindspore/lite/src/litert/kernel/cpu/fp32/group_convolution_fp32.cc
View File

@ -21,7 +21,7 @@ using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_OK;
namespace mindspore::kernel {
int GroupConvolutionFp32CPUKernel::Separate(int task_id) {
int GroupConvolutionFp32CPUKernel::Separate(const int &task_id) const {
auto plane_step = UP_DIV(in_plane_, in_thread_num_);
MS_CHECK_INT_MUL_NOT_OVERFLOW(plane_step, task_id, RET_ERROR);
auto begin_plane = plane_step * task_id;
@ -31,7 +31,7 @@ int GroupConvolutionFp32CPUKernel::Separate(int task_id) {
auto src_ptr = sub_in_src_ + begin_plane * ori_in_channel_;
auto dst_ptr = sub_in_dst_ + begin_plane * sub_in_channel_;
for (int i = begin_plane; i < end_plane; ++i) {
memcpy(dst_ptr, src_ptr, sub_in_channel_ * sizeof(float));
(void)memcpy(dst_ptr, src_ptr, sub_in_channel_ * sizeof(float));
src_ptr += ori_in_channel_;
dst_ptr += sub_in_channel_;
}
@ -63,7 +63,7 @@ int GroupConvolutionFp32CPUKernel::SeparateInput(int group_id) {
return RET_OK;
}
int GroupConvolutionFp32CPUKernel::Concat(int task_id) {
int GroupConvolutionFp32CPUKernel::Concat(const int &task_id) const {
auto plane_step = UP_DIV(out_plane_, out_thread_num_);
MS_CHECK_INT_MUL_NOT_OVERFLOW(plane_step, task_id, RET_ERROR);
auto begin_plane = plane_step * task_id;
@ -73,7 +73,7 @@ int GroupConvolutionFp32CPUKernel::Concat(int task_id) {
auto src_ptr = sub_out_src_ + begin_plane * sub_out_channel_;
auto dst_ptr = sub_out_dst_ + begin_plane * ori_out_channel_;
for (int i = begin_plane; i < end_plane; ++i) {
memcpy(dst_ptr, src_ptr, sub_out_channel_ * sizeof(float));
(void)memcpy(dst_ptr, src_ptr, sub_out_channel_ * sizeof(float));
src_ptr += sub_out_channel_;
dst_ptr += ori_out_channel_;
}

4
mindspore/lite/src/litert/kernel/cpu/fp32/group_convolution_fp32.h
View File

@ -37,8 +37,8 @@ class GroupConvolutionFp32CPUKernel : public GroupConvolutionBaseCPUKernel {
int SeparateInput(int group_id) override;
int PostConcat(int group_id) override;
int Separate(int task_id);
int Concat(int task_id);
int Separate(const int &task_id) const;
int Concat(const int &task_id) const;
private:
float *sub_in_src_ = nullptr;

4
mindspore/lite/src/litert/kernel/cpu/fp32/groupnorm_fp32.cc
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@ -36,9 +36,9 @@ GroupnormCPUKernel::GroupnormCPUKernel(OpParameter *parameter, const std::vector
}
for (size_t i = 0; i < in_tensors_.size(); i++) {
Tensor2TensorC(in_tensors_.at(i), &(in_[i]));
(void)Tensor2TensorC(in_tensors_.at(i), &(in_[i]));
}
Tensor2TensorC(out_tensors_.at(0), &(out_[0]));
(void)Tensor2TensorC(out_tensors_.at(0), &(out_[0]));
}
GroupnormCPUKernel::~GroupnormCPUKernel() {

1
mindspore/lite/src/litert/kernel/cpu/fp32/instance_norm_fp32.h
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@ -44,7 +44,6 @@ class InstanceNormCPUKernel : public LiteKernel {
}
}
private:
InstanceNormParameter *param_ = nullptr;
float *src_data_ = nullptr;
float *tmp_src_data_ = nullptr;

2
mindspore/lite/src/litert/kernel/cpu/fp32/invert_permutation_fp32.cc
View File

@ -59,7 +59,7 @@ int InvertPermutationCPUKernel::Run() {
auto output_ptr = reinterpret_cast<int32_t *>(out_tensor->data());
CHECK_NULL_RETURN(input_ptr);
CHECK_NULL_RETURN(output_ptr);
auto ret = InvertPermutation(input_ptr, output_ptr, in_tensors_[0]->ElementsNum());
auto ret = InvertPermutation(input_ptr, output_ptr, static_cast<int>(in_tensors_[0]->ElementsNum()));
if (ret != NNACL_OK) {
MS_LOG(ERROR) << "null pointer dereferencing.";
return RET_ERROR;

9
mindspore/lite/src/litert/kernel/cpu/fp32/layer_norm_fp32.cc
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@ -40,10 +40,11 @@ int LayerNormCPUKernel::ReSize() {
auto input = in_tensors_.front();
CHECK_NULL_RETURN(input);
auto shape = input->shape();
param_->begin_norm_axis_ =
param_->begin_norm_axis_ >= 0 ? param_->begin_norm_axis_ : param_->begin_norm_axis_ + shape.size();
param_->begin_params_axis_ =
param_->begin_params_axis_ >= 0 ? param_->begin_params_axis_ : param_->begin_params_axis_ + shape.size();
param_->begin_norm_axis_ = param_->begin_norm_axis_ >= 0 ? param_->begin_norm_axis_
: param_->begin_norm_axis_ + static_cast<int>(shape.size());
param_->begin_params_axis_ = param_->begin_params_axis_ >= 0
? param_->begin_params_axis_
: param_->begin_params_axis_ + static_cast<int>(shape.size());
MS_CHECK_LT(param_->begin_norm_axis_, static_cast<int>(shape.size()), RET_ERROR);
MS_CHECK_LT(param_->begin_params_axis_, static_cast<int>(shape.size()), RET_ERROR);
param_->norm_outer_size_ = 1;

10
mindspore/lite/src/litert/kernel/cpu/int8/group_convolution_int8.cc
View File

@ -21,10 +21,13 @@ using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_OK;
namespace mindspore::kernel {
int GroupConvolutionInt8CPUKernel::Separate(int task_id) {
int GroupConvolutionInt8CPUKernel::Separate(const int &task_id) const {
auto plane_step = UP_DIV(in_plane_, in_thread_num_);
MS_CHECK_INT_MUL_NOT_OVERFLOW(plane_step, task_id, RET_ERROR);
auto begin_plane = plane_step * task_id;
auto end_plane = MSMIN(in_plane_, plane_step * (task_id + 1));
MS_CHECK_INT_MUL_NOT_OVERFLOW(begin_plane, ori_in_channel_, RET_ERROR);
MS_CHECK_INT_MUL_NOT_OVERFLOW(begin_plane, sub_in_channel_, RET_ERROR);
auto src_ptr = sub_in_src_ + begin_plane * ori_in_channel_;
auto dst_ptr = sub_in_dst_ + begin_plane * sub_in_channel_;
for (int i = begin_plane; i < end_plane; ++i) {
@ -59,10 +62,13 @@ int GroupConvolutionInt8CPUKernel::SeparateInput(int group_id) {
return RET_OK;
}
int GroupConvolutionInt8CPUKernel::Concat(int task_id) {
int GroupConvolutionInt8CPUKernel::Concat(const int &task_id) const {
auto plane_step = UP_DIV(out_plane_, out_thread_num_);
MS_CHECK_INT_MUL_NOT_OVERFLOW(plane_step, task_id, RET_ERROR);
auto begin_plane = plane_step * task_id;
auto end_plane = MSMIN(out_plane_, plane_step * (task_id + 1));
MS_CHECK_INT_MUL_NOT_OVERFLOW(begin_plane, sub_out_channel_, RET_ERROR);
MS_CHECK_INT_MUL_NOT_OVERFLOW(begin_plane, ori_out_channel_, RET_ERROR);
auto src_ptr = sub_out_src_ + begin_plane * sub_out_channel_;
auto dst_ptr = sub_out_dst_ + begin_plane * ori_out_channel_;
for (int i = begin_plane; i < end_plane; ++i) {

4
mindspore/lite/src/litert/kernel/cpu/int8/group_convolution_int8.h
View File

@ -37,8 +37,8 @@ class GroupConvolutionInt8CPUKernel : public GroupConvolutionBaseCPUKernel {
int SeparateInput(int group_id) override;
int PostConcat(int group_id) override;
int Separate(int task_id);
int Concat(int task_id);
int Separate(const int &task_id) const;
int Concat(const int &task_id) const;
private:
int8_t *sub_in_src_ = nullptr;

2
mindspore/lite/src/litert/kernel_exec.cc
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@ -91,7 +91,7 @@ int KernelExec::DoExecute() {
return ret;
}
void KernelExec::RepalceKernel(std::shared_ptr<Kernel> kernel) {
void KernelExec::RepalceKernel(const std::shared_ptr<Kernel> kernel) {
if (desc_.provider == kBuiltin) {
std::static_pointer_cast<LiteKernel>(kernel_)->set_parameter(nullptr); // set nullptr, don't release op_parameter
kernel_.reset();

16
mindspore/lite/src/litert/kernel_exec.h
View File

@ -234,7 +234,7 @@ class KernelExec {
MS_ASSERT(index < kernel_->inputs().size());
auto impl = std::make_shared<mindspore::LiteTensorImpl>(in_tensor);
auto tensor_in = mindspore::MSTensor(impl);
kernel_->set_input(tensor_in, index);
kernel_->set_input(tensor_in, static_cast<int>(index));
}
}
@ -261,7 +261,7 @@ class KernelExec {
MS_ASSERT(index < kernel_->outputs().size());
auto impl = std::make_shared<mindspore::LiteTensorImpl>(out_tensor);
auto tensor_out = mindspore::MSTensor(impl);
kernel_->set_output(tensor_out, index);
kernel_->set_output(tensor_out, static_cast<int>(index));
}
}
@ -317,8 +317,8 @@ class KernelExec {
}
}
size_t FindInTensorIndex(lite::Tensor *tensor) {
int index = 0;
size_t FindInTensorIndex(const lite::Tensor *tensor) {
size_t index = 0;
for (size_t i = 0; i < in_tensors().size(); i++) {
if (tensor == in_tensors().at(i)) {
index = i;
@ -328,8 +328,8 @@ class KernelExec {
return index;
}
size_t FindOutTensorIndex(lite::Tensor *tensor) {
int index = 0;
size_t FindOutTensorIndex(const lite::Tensor *tensor) {
size_t index = 0;
for (size_t i = 0; i < out_tensors().size(); i++) {
if (tensor == out_tensors().at(i)) {
index = i;
@ -339,9 +339,9 @@ class KernelExec {
return index;
}
void RemoveInKernel(KernelExec *kernel) { lite::VectorErase(&(this->in_kernels_), kernel); }
void RemoveInKernel(KernelExec *kernel) { (void)lite::VectorErase(&(this->in_kernels_), kernel); }
void RemoveOutKernel(KernelExec *kernel) { lite::VectorErase(&(this->out_kernels_), kernel); }
void RemoveOutKernel(KernelExec *kernel) { (void)lite::VectorErase(&(this->out_kernels_), kernel); }
void set_in_kernels(const std::vector<KernelExec *> &kernel) { this->in_kernels_ = kernel; }

6
mindspore/lite/src/litert/kernel_exec_util.cc
View File

@ -266,7 +266,7 @@ void KernelExecUtil::FindAllInoutKernelsInSubgraphKernel(const std::vector<Kerne
KernelExecUtil::FindAllInoutKernels(all_kernels);
}
KernelExec *KernelExecUtil::FindInKernelForInTensor(KernelExec *kernel, lite::Tensor *tensor) {
KernelExec *KernelExecUtil::FindInKernelForInTensor(const KernelExec *kernel, lite::Tensor *tensor) {
for (auto in_kernel : kernel->in_kernels()) {
if (lite::IsContain(in_kernel->out_tensors(), tensor)) {
return in_kernel;
@ -275,7 +275,7 @@ KernelExec *KernelExecUtil::FindInKernelForInTensor(KernelExec *kernel, lite::Te
return nullptr;
}
std::vector<KernelExec *> KernelExecUtil::FindOutKernelsForOutTensor(KernelExec *kernel, lite::Tensor *tensor) {
std::vector<KernelExec *> KernelExecUtil::FindOutKernelsForOutTensor(const KernelExec *kernel, lite::Tensor *tensor) {
std::vector<KernelExec *> out_kernels;
for (auto out_kernel : kernel->out_kernels()) {
if (lite::IsContain(out_kernel->in_tensors(), tensor)) {
@ -285,7 +285,7 @@ std::vector<KernelExec *> KernelExecUtil::FindOutKernelsForOutTensor(KernelExec
return out_kernels;
}
int KernelExecUtil::SetKernelTensorDataType(kernel::KernelExec *kernel) {
int KernelExecUtil::SetKernelTensorDataType(const kernel::KernelExec *kernel) {
CHECK_NULL_RETURN(kernel);
if (kernel->desc().arch != kernel::KERNEL_ARCH::kCPU) {
return RET_OK;

6
mindspore/lite/src/litert/kernel_exec_util.h
View File

@ -45,9 +45,9 @@ class KernelExecUtil {
// find in_kernels_ and out_kernels of kernel, sub_graph and nodes_ in sub_graph
static void FindAllInoutKernels(const std::vector<KernelExec *> &kernels);
static void FindAllInoutKernelsInSubgraphKernel(const std::vector<KernelExec *> &kernels);
static KernelExec *FindInKernelForInTensor(KernelExec *kernel, lite::Tensor *tensor);
static std::vector<KernelExec *> FindOutKernelsForOutTensor(KernelExec *kernel, lite::Tensor *tensor);
static int SetKernelTensorDataType(kernel::KernelExec *kernel);
static KernelExec *FindInKernelForInTensor(const KernelExec *kernel, lite::Tensor *tensor);
static std::vector<KernelExec *> FindOutKernelsForOutTensor(const KernelExec *kernel, lite::Tensor *tensor);
static int SetKernelTensorDataType(const kernel::KernelExec *kernel);
static SubGraphKernel *CreateSubGraphKernel(const std::vector<KernelExec *> &kernels,
const std::vector<lite::Tensor *> *in_tensors,
const std::vector<lite::Tensor *> *out_tensors, SubGraphType type,

5
mindspore/lite/src/litert/lite_kernel.h
View File

@ -164,11 +164,11 @@ class LiteKernel : public Abstractkernel {
return mindspore::lite::RET_OK;
}
virtual int SetupVirtualBatch(int virtual_batch_multiplier, int param) { return mindspore::lite::RET_OK; }
virtual int SetupVirtualBatch(int, int) { return mindspore::lite::RET_OK; }
bool IsEval() const override { return !this->train_mode_; }
void SetTrainable(bool trainable = true) override { this->trainable_ = trainable; }
void SetTrainable(bool trainable) override { this->trainable_ = trainable; }
bool IsTrainable() const override { return this->trainable_; }
@ -186,7 +186,6 @@ class LiteKernel : public Abstractkernel {
workspace_ = ws;
}
}
const lite::InnerContext *context() const { return this->ms_context_; }
bool ws_allocated_ = false;
protected:

39
mindspore/lite/src/litert/pass/decrease_transpose_algo.cc
View File

@ -25,16 +25,12 @@ int TransFullyFusion(kernel::SubGraphKernel *subgraph, kernel::KernelExec *trans
CHECK_NULL_RETURN(trans_kernel0);
CHECK_NULL_RETURN(trans_kernel1);
auto in_tensor = trans_kernel0->in_tensors().at(0);
auto out_tensor = trans_kernel1->out_tensors().at(0);
auto in_kernel = kernel::KernelExecUtil::FindInKernelForInTensor(trans_kernel0, in_tensor);
auto out_kernels = kernel::KernelExecUtil::FindOutKernelsForOutTensor(trans_kernel1, out_tensor);
auto ret = subgraph->UpdateInOutKernels(in_kernel, out_kernels, trans_kernel0, trans_kernel1);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update kernel link failed when fusing kernel " << trans_kernel0->name() << " and "
<< trans_kernel1->name();
return RET_ERROR;
}
ret = subgraph->UpdateInOutTensors(in_kernel, out_kernels, in_tensor, out_tensor, true);
subgraph->UpdateInOutKernels(in_kernel, out_kernels, trans_kernel0, trans_kernel1);
auto ret = subgraph->UpdateInOutTensors(in_kernel, out_kernels, in_tensor, out_tensor, true);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update tensor failed when fusing kernel " << trans_kernel0->name() << " and "
<< trans_kernel1->name();
@ -59,12 +55,7 @@ int TransHeadTailFusion(kernel::SubGraphKernel *subgraph, kernel::KernelExec *tr
auto out_tensor = trans_kernel1->out_tensors().at(0);
auto in_kernel = kernel::KernelExecUtil::FindInKernelForInTensor(trans_kernel0, in_tensor);
auto out_kernels = kernel::KernelExecUtil::FindOutKernelsForOutTensor(trans_kernel1, out_tensor);
auto ret = subgraph->UpdateInOutKernels(in_kernel, out_kernels, trans_kernel0, trans_kernel1);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update kernel link failed when fusing kernel " << trans_kernel0->name() << " and "
<< trans_kernel1->name();
return RET_ERROR;
}
subgraph->UpdateInOutKernels(in_kernel, out_kernels, trans_kernel0, trans_kernel1);
// new trans kernel: src_format -> dst_format
auto trans_name = trans_kernel0->name() + "_and_" + trans_kernel1->name() + "_fusion";
auto kernel = CreateFormatTranspose(in_tensor, out_tensor, trans_info, trans_name, ctx, desc);
@ -98,7 +89,7 @@ int DecreaseTransposeAlgo::TransTransFusion(kernel::SubGraphKernel *subgraph) {
while (kernel_iter != kernels->end()) {
auto &kernel = *kernel_iter;
CHECK_NULL_RETURN(kernel);
kernel_iter++;
(void)kernel_iter++;
if (kernel->in_kernels().size() == 0 || !IsContain(subgraph->nodes(), kernel->in_kernels().at(0))) {
continue;
@ -227,11 +218,7 @@ int DoPreFusion(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel, st
}
} else {
auto pre_in_kernel = kernel::KernelExecUtil::FindInKernelForInTensor(in_kernel, in_kernel->in_tensors().at(0));
ret = subgraph->UpdateInOutKernels(pre_in_kernel, {kernel}, in_kernel, in_kernel);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update kernel link failed when removing kernel " << in_kernel->name();
return RET_ERROR;
}
subgraph->UpdateInOutKernels(pre_in_kernel, {kernel}, in_kernel, in_kernel);
ret = subgraph->UpdateInOutTensors(pre_in_kernel, {kernel}, in_kernel->in_tensors().at(0), in_tensor, true);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update tensor failed when removing kernel " << in_kernel->name();
@ -248,7 +235,7 @@ int DoPreFusion(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel, st
return RET_OK;
}
int DoPostFusion(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel, std::vector<Tensor *> *all_tensors,
int DoPostFusion(kernel::SubGraphKernel *subgraph, const kernel::KernelExec *kernel, std::vector<Tensor *> *all_tensors,
const TransInfoPair &post_trans) {
for (size_t i = 0; i < kernel->out_tensors().size(); i++) {
auto tensor = kernel->out_tensors().at(i);
@ -259,13 +246,13 @@ int DoPostFusion(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel, s
TransInfoPair out_kernel_trans;
auto ret = GetTransposeInfo(out_kernel, &out_kernel_trans);
if (ret == RET_OK && IsSameTranspose(post_trans, out_kernel_trans)) {
to_deletes.emplace_back(out_kernel);
(void)to_deletes.emplace_back(out_kernel);
continue;
}
auto in_tensor_of_out_kernel_idx = out_kernel->FindInTensorIndex(tensor);
ret = InsertPreTranspose(subgraph, out_kernel, all_tensors,
TransInfoPair(post_trans.dst_format_, post_trans.src_format_),
static_cast<int>(in_tensor_of_out_kernel_idx));
ret =
InsertPreTranspose(subgraph, out_kernel, all_tensors,
TransInfoPair(post_trans.dst_format_, post_trans.src_format_), in_tensor_of_out_kernel_idx);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Insert pre transpose kernel for op: " << out_kernel->name() << " input tensor "
<< in_tensor_of_out_kernel_idx << " failed.";
@ -293,7 +280,7 @@ int DecreaseTransposeAlgo::DecreaseTransposeForSingleKernel(kernel::SubGraphKern
TransInfoPair pre_trans;
TransInfoPair post_trans;
if (!transpose_strategy_.CheckFusion(kernel, &pre_trans, &post_trans)) {
kernel_iter++;
(void)kernel_iter++;
continue;
}
auto ret = transpose_strategy_.ChangeKernelAxis(kernel, post_trans);
@ -314,7 +301,7 @@ int DecreaseTransposeAlgo::DecreaseTransposeForSingleKernel(kernel::SubGraphKern
return RET_ERROR;
}
kernel_iter = find(kernels->begin(), kernels->end(), kernel);
kernel_iter++;
(void)kernel_iter++;
}
return RET_OK;
}

6
mindspore/lite/src/litert/pass/delete_isolated_kernel.cc
View File

@ -20,7 +20,7 @@
#include "src/litert/kernel_exec_util.h"
namespace mindspore::lite::pass {
int DeleteIsolatedKernel::Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *tensors) {
int DeleteIsolatedKernel::Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *) {
subgraph->SetInNodes(kernel::KernelExecUtil::SubgraphInputNodes(subgraph->nodes()));
std::set<kernel::KernelExec *> visited; // record the kernel that will be executed
@ -28,7 +28,7 @@ int DeleteIsolatedKernel::Run(kernel::SubGraphKernel *subgraph, std::vector<Tens
for (auto in_kernel : subgraph->in_nodes()) {
kernel_queue.push(in_kernel);
visited.insert(in_kernel);
(void)visited.insert(in_kernel);
}
while (!kernel_queue.empty()) {
auto kernel = kernel_queue.front();
@ -39,7 +39,7 @@ int DeleteIsolatedKernel::Run(kernel::SubGraphKernel *subgraph, std::vector<Tens
continue;
}
kernel_queue.push(out_kernel);
visited.insert(out_kernel);
(void)visited.insert(out_kernel);
}
}

2
mindspore/lite/src/litert/pass/delete_isolated_kernel.h
View File

@ -25,7 +25,7 @@ class DeleteIsolatedKernel : public RuntimePass {
public:
DeleteIsolatedKernel() {}
~DeleteIsolatedKernel() override = default;
int Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *tensors = nullptr) override;
int Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *) override;
};
} // namespace mindspore::lite::pass
#endif // MINDSPORE_LITE_SRC_RUNTIME_PASS_DELETE_ISOLATED_KERNEL_H_

2
mindspore/lite/src/litert/pass/infershape_pass.cc
View File

@ -19,7 +19,7 @@
#include "src/litert/kernel_exec_util.h"
namespace mindspore::lite::pass {
int Infershape::Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *tensors) {
int Infershape::Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *) {
auto kernels = &(subgraph->nodes());
for (const auto &kernel : *kernels) {
CHECK_NULL_RETURN(kernel);

2
mindspore/lite/src/litert/pass/infershape_pass.h
View File

@ -25,7 +25,7 @@ class Infershape : public RuntimePass {
public:
Infershape() {}
~Infershape() override = default;
int Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *tensors = nullptr) override;
int Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *) override;
};
} // namespace mindspore::lite::pass
#endif // MINDSPORE_LITE_SRC_RUNTIME_PASS_INFERSHAPE_PASS_H_

10
mindspore/lite/src/litert/pass/pass_utils.cc
View File

@ -61,8 +61,8 @@ kernel::KernelExec *CreateFormatTranspose(Tensor *input, Tensor *output, const T
MS_LOG(ERROR) << "Malloc FormatTransposeParameter failed.";
return nullptr;
}
memset(param, 0, sizeof(FormatTransposeParameter));
param->op_parameter_.type_ = schema::PrimitiveType_FormatTranspose;
(void)memset(param, 0, sizeof(FormatTransposeParameter));
param->op_parameter_.type_ = static_cast<int>(schema::PrimitiveType_FormatTranspose);
param->src_format_ = trans_info.src_format_;
param->dst_format_ = trans_info.dst_format_;
kernel::KernelKey format_transpose_key = desc;
@ -80,7 +80,7 @@ kernel::KernelExec *CreateFormatTranspose(Tensor *input, Tensor *output, const T
return kernel;
}
void SetShape(Tensor *src_tensor, Tensor *dst_tensor) {
void SetShape(const Tensor *src_tensor, Tensor *dst_tensor) {
auto shape = src_tensor->shape();
auto invalid_shape = {-1};
if (shape.size() != DIMENSION_4D) {
@ -105,7 +105,7 @@ void SetShape(Tensor *src_tensor, Tensor *dst_tensor) {
}
int InsertPreTranspose(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel, std::vector<Tensor *> *all_tensors,
const TransInfoPair &trans_info, const int &index) {
const TransInfoPair &trans_info, const size_t &index) {
auto trans_name = kernel->name() + "_pre_" + std::to_string(index);
auto in_tensor = kernel->in_tensors().at(index);
auto out_tensor = new (std::nothrow) Tensor(in_tensor->data_type(), {}, (Format)trans_info.dst_format_);
@ -126,7 +126,7 @@ int InsertPreTranspose(kernel::SubGraphKernel *subgraph, kernel::KernelExec *ker
}
int InsertPostTranspose(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel,
std::vector<Tensor *> *all_tensors, const TransInfoPair &trans_info, const int &index) {
std::vector<Tensor *> *all_tensors, const TransInfoPair &trans_info, const size_t &index) {
auto trans_name = kernel->name() + "_post_" + std::to_string(index);
auto out_tensor = kernel->out_tensors().at(index);

6
mindspore/lite/src/litert/pass/pass_utils.h
View File

@ -41,13 +41,13 @@ kernel::KernelExec *CreateFormatTranspose(Tensor *input, Tensor *output, const T
const std::string &name, const lite::InnerContext *ctx,
const kernel::KernelKey &desc);
void SetShape(Tensor *src_tensor, Tensor *dst_tensor);
void SetShape(const Tensor *src_tensor, Tensor *dst_tensor);
int InsertPreTranspose(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel, std::vector<Tensor *> *all_tensors,
const TransInfoPair &trans_info, const int &index);
const TransInfoPair &trans_info, const size_t &index);
int InsertPostTranspose(kernel::SubGraphKernel *subgraph, kernel::KernelExec *kernel,
std::vector<Tensor *> *all_tensors, const TransInfoPair &trans_info, const int &index);
std::vector<Tensor *> *all_tensors, const TransInfoPair &trans_info, const size_t &index);
int GetTransposeInfo(const kernel::KernelExec *kernel, TransInfoPair *trans_info);
} // namespace mindspore::lite::pass

2
mindspore/lite/src/litert/pass/runtime_optimizer.cc
View File

@ -21,7 +21,7 @@ RuntimeOptimizer::~RuntimeOptimizer() { passes_.clear(); }
int RuntimeOptimizer::AddPass(RuntimePassPtr pass) {
CHECK_NULL_RETURN(pass);
this->passes_.emplace_back(pass);
(void)this->passes_.emplace_back(pass);
return RET_OK;
}

2
mindspore/lite/src/litert/pass/to_nchw_format.h
View File

@ -36,7 +36,7 @@ namespace mindspore::lite::pass {
class ToNCHWFormat : public RuntimePass {
public:
ToNCHWFormat(Format src_format, Format dst_format, std::set<schema::PrimitiveType> to_trans_kernels)
ToNCHWFormat(const Format &src_format, const Format &dst_format, std::set<schema::PrimitiveType> to_trans_kernels)
: src_format_(src_format), dst_format_(dst_format), to_trans_kernels_(to_trans_kernels) {}
~ToNCHWFormat() override = default;
int Run(kernel::SubGraphKernel *subgraph, std::vector<Tensor *> *tensors) override;

13
mindspore/lite/src/litert/pass/transpose_strategy.cc
View File

@ -56,7 +56,7 @@ size_t TransposeStrategy::GetTransCount(const std::vector<kernel::KernelExec *>
return count;
}
bool CheckInTensorsShape(kernel::KernelExec *kernel, const Format &runtime_format) {
bool CheckInTensorsShape(const kernel::KernelExec *kernel, const Format &runtime_format) {
// If teh fusion is valid, kernel will be executed in runtime_format.
// Only check arithmetic (two input) kernel input tensors.
// If broadcast for various formats is supported, this function can be deleted.
@ -87,7 +87,8 @@ bool CheckInTensorsShape(kernel::KernelExec *kernel, const Format &runtime_forma
return true;
}
bool TransposeStrategy::CheckFusion(kernel::KernelExec *kernel, TransInfoPair *pre_trans, TransInfoPair *post_trans) {
bool TransposeStrategy::CheckFusion(const kernel::KernelExec *kernel, TransInfoPair *pre_trans,
TransInfoPair *post_trans) {
if (dynamic_format_kernel_lists.find(kernel->type()) == dynamic_format_kernel_lists.end()) {
return false;
}
@ -155,24 +156,24 @@ int TransFormAxis(int axis, const TransInfoPair &trans) {
return axis;
}
int HandleArgMinMaxKernel(kernel::KernelExec *kernel, const TransInfoPair &trans) {
int HandleArgMinMaxKernel(const kernel::KernelExec *kernel, const TransInfoPair &trans) {
auto arg_min_max_param = reinterpret_cast<ArgMinMaxParameter *>(kernel->op_parameter());
CHECK_NULL_RETURN(arg_min_max_param);
arg_min_max_param->axis_ = TransFormAxis(arg_min_max_param->axis_, trans);
return RET_OK;
}
int HandleConcatKernel(kernel::KernelExec *kernel, const TransInfoPair &trans) {
int HandleConcatKernel(const kernel::KernelExec *kernel, const TransInfoPair &trans) {
auto concat_param = reinterpret_cast<ConcatParameter *>(kernel->op_parameter());
CHECK_NULL_RETURN(concat_param);
concat_param->axis_ = TransFormAxis(concat_param->axis_, trans);
return RET_OK;
}
int HandleCropKernel(kernel::KernelExec *kernel, const TransInfoPair &trans) {
int HandleCropKernel(const kernel::KernelExec *kernel, const TransInfoPair &trans) {
auto crop_param = reinterpret_cast<CropParameter *>(kernel->op_parameter());
CHECK_NULL_RETURN(crop_param);
crop_param->axis_ = TransFormAxis(crop_param->axis_, trans);
crop_param->axis_ = TransFormAxis(static_cast<int>(crop_param->axis_), trans);
return RET_OK;
}

2
mindspore/lite/src/litert/pass/transpose_strategy.h
View File

@ -91,7 +91,7 @@ class TransposeStrategy {
~TransposeStrategy() = default;
size_t GetTransCount(const std::vector<kernel::KernelExec *> &kernels, TransInfoPair *trans_info);
bool CheckFusion(kernel::KernelExec *kernel, TransInfoPair *pre_trans, TransInfoPair *post_trans);
bool CheckFusion(const kernel::KernelExec *kernel, TransInfoPair *pre_trans, TransInfoPair *post_trans);
int ChangeKernelAxis(kernel::KernelExec *kernel, const TransInfoPair &post_trans);
};
} // namespace mindspore::lite::pass

23
mindspore/lite/src/litert/sub_graph_kernel.cc
View File

@ -190,7 +190,7 @@ int SubGraphKernel::TopologicalSortNodes() {
while (!kernel_queue.empty()) {
auto cur_kernel = kernel_queue.front();
nodes_.emplace_back(cur_kernel);
(void)nodes_.emplace_back(cur_kernel);
kernel_queue.pop();
CHECK_NULL_RETURN(cur_kernel);
auto next_kernels = cur_kernel->out_kernels();
@ -218,7 +218,7 @@ int SubGraphKernel::TopologicalSortNodes() {
return RET_OK;
}
void SubGraphKernel::InsertInEdge(KernelExec *kernel, KernelExec *replace_kernel, const int &tensor_index) {
void SubGraphKernel::InsertInEdge(KernelExec *kernel, KernelExec *replace_kernel, const size_t &tensor_index) {
// replace_kernel is a kernel with ont input tensor and output tensor
auto in_kernel = KernelExecUtil::FindInKernelForInTensor(kernel, kernel->in_tensors().at(tensor_index));
if (in_kernel != nullptr) {
@ -234,7 +234,7 @@ void SubGraphKernel::InsertInEdge(KernelExec *kernel, KernelExec *replace_kernel
nodes_.push_back(replace_kernel);
}
void SubGraphKernel::InsertOutEdge(KernelExec *kernel, KernelExec *replace_kernel, const int &tensor_index) {
void SubGraphKernel::InsertOutEdge(KernelExec *kernel, KernelExec *replace_kernel, const size_t &tensor_index) {
// replace_kernel is a kernel with ont input tensor and output tensor
auto out_kernels = KernelExecUtil::FindOutKernelsForOutTensor(kernel, kernel->out_tensors().at(tensor_index));
for (const auto &post_kernel : out_kernels) {
@ -253,8 +253,8 @@ void SubGraphKernel::InsertOutEdge(KernelExec *kernel, KernelExec *replace_kerne
// in_kernel -> in_post_kernel -> out_pre_kernel -> out_kernels.
// remove in_post_kernel and out_pre_kernel, link in_kernel and out_kernels.
// in_post_kernel and out_pre_kernel can be the same kernel sometimes.
int SubGraphKernel::UpdateInOutKernels(KernelExec *in_kernel, std::vector<KernelExec *> out_kernels,
KernelExec *in_post_kernel, KernelExec *out_pre_kernel) {
void SubGraphKernel::UpdateInOutKernels(KernelExec *in_kernel, std::vector<KernelExec *> out_kernels,
KernelExec *in_post_kernel, KernelExec *out_pre_kernel) {
for (const auto &out_kernel : out_kernels) {
out_kernel->RemoveInKernel(out_pre_kernel);
out_pre_kernel->RemoveOutKernel(out_kernel);
@ -280,7 +280,7 @@ int SubGraphKernel::UpdateInOutKernels(KernelExec *in_kernel, std::vector<Kernel
in_nodes_.push_back(out_kernel);
}
if (in_post_kernel->out_kernels().empty() && !lite::IsContain(out_nodes_, in_post_kernel)) {
lite::VectorErase(&in_nodes_, in_post_kernel);
(void)lite::VectorErase(&in_nodes_, in_post_kernel);
}
}
@ -288,10 +288,9 @@ int SubGraphKernel::UpdateInOutKernels(KernelExec *in_kernel, std::vector<Kernel
if (lite::IsContain(out_nodes_, out_pre_kernel) && in_kernel != nullptr) {
out_nodes_.push_back(in_kernel);
if (out_pre_kernel->in_kernels().empty() && !lite::IsContain(in_nodes_, out_pre_kernel)) {
lite::VectorErase(&out_nodes_, out_pre_kernel);
(void)lite::VectorErase(&out_nodes_, out_pre_kernel);
}
}
return RET_OK;
}
// Update tensor according to the subgraph.
@ -340,14 +339,10 @@ int SubGraphKernel::DeleteSingleWayNode(KernelExec *kernel, bool keep_input) {
}
// update kernel link
auto ret = UpdateInOutKernels(in_kernel, out_kernels, kernel, kernel);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update kernel link failed when removing kernel " << kernel->name();
return RET_ERROR;
}
UpdateInOutKernels(in_kernel, out_kernels, kernel, kernel);
// update tensor link
ret = UpdateInOutTensors(in_kernel, out_kernels, in_tensor, out_tensor, keep_input);
auto ret = UpdateInOutTensors(in_kernel, out_kernels, in_tensor, out_tensor, keep_input);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Update tensor failed when removing kernel " << kernel->name();
return RET_ERROR;

12
mindspore/lite/src/litert/sub_graph_kernel.h
View File

@ -112,20 +112,20 @@ class SubGraphKernel : public KernelExec {
std::vector<KernelExec *> out_nodes() { return this->out_nodes_; }
void SetInNodes(std::vector<KernelExec *> in_nodes) { in_nodes_ = in_nodes; }
void SetInNodes(const std::vector<KernelExec *> &in_nodes) { in_nodes_ = in_nodes; }
void SetOutNodes(std::vector<KernelExec *> out_nodes) { out_nodes_ = out_nodes; }
void SetOutNodes(const std::vector<KernelExec *> &out_nodes) { out_nodes_ = out_nodes; }
void SetSchemaVersion(int schema_version) { schema_version_ = schema_version; }
int TopologicalSortNodes();
void InsertInEdge(KernelExec *kernel, KernelExec *replace_kernel, const int &tensor_index);
void InsertInEdge(KernelExec *kernel, KernelExec *replace_kernel, const size_t &tensor_index);
void InsertOutEdge(KernelExec *kernel, KernelExec *replace_kernel, const int &tensor_index);
void InsertOutEdge(KernelExec *kernel, KernelExec *replace_kernel, const size_t &tensor_index);
int UpdateInOutKernels(KernelExec *in_kernel, std::vector<KernelExec *> out_kernels, KernelExec *in_post_kernel,
KernelExec *out_pre_kernel);
void UpdateInOutKernels(KernelExec *in_kernel, std::vector<KernelExec *> out_kernels, KernelExec *in_post_kernel,
KernelExec *out_pre_kernel);
int UpdateInOutTensors(KernelExec *in_kernel, std::vector<KernelExec *> out_kernels, lite::Tensor *in_tensor,
lite::Tensor *out_tensor, bool keep_input);

2
mindspore/lite/tools/common/meta_graph_serializer.cc
View File

@ -38,7 +38,7 @@ constexpr size_t kFlatbuffersBuilderInitSize = 1024;
void ChangeMod(const std::string &file_path) {
#ifndef _MSC_VER
if (access(file_path.c_str(), F_OK) == 0) {
chmod(file_path.c_str(), S_IWUSR | S_IRUSR);
(void)chmod(file_path.c_str(), S_IWUSR | S_IRUSR);
}
#endif
}

2
mindspore/lite/tools/lite_exporter/fetch_content.cc
View File

@ -246,7 +246,7 @@ int FetchFromSequenceValue(const ValueNodePtr &value_node, DataInfo *data_info)
}
} // namespace
int FetchFromDefaultParam(const ParameterPtr &param_node, const converter::FmkType &fmk_type, DataInfo *data_info,
int FetchFromDefaultParam(const ParameterPtr &param_node, const converter::FmkType &, DataInfo *data_info,
bool copy_data) {
MS_ASSERT(param_node != nullptr && data_info != nullptr);
ShapeVector shape_vector;

6
mindspore/lite/tools/optimizer/const_fold/fold_utils.cc
View File

@ -72,8 +72,9 @@ ParameterPtr CreateNewParamter(const FuncGraphPtr &func_graph, Tensor *tensor) {
}
return parameter;
}
kernel::KernelExec *GetKernelExec(std::vector<Tensor *> inputs, std::vector<Tensor *> *outputs, const CNodePtr &cnode,
lite::InnerContext *context, mindspore::Context *ms_context) {
const lite::InnerContext *context, const mindspore::Context *ms_context) {
MS_ASSERT(outputs != nullptr && cnode != nullptr && context != nullptr && ms_context != nullptr);
OpParameter *parameter = nullptr;
auto ret = lite::FetchOpParameterFromNode(cnode->input(0), &parameter);
@ -92,8 +93,7 @@ kernel::KernelExec *GetKernelExec(std::vector<Tensor *> inputs, std::vector<Tens
return nullptr;
}
auto data_type = inputs.front()->data_type();
kernel::KernelKey desc{kernel::KERNEL_ARCH::kCPU, data_type, NHWC,
static_cast<schema::PrimitiveType>(parameter->type_)};
kernel::KernelKey desc{kernel::KERNEL_ARCH::kCPU, data_type, NHWC, parameter->type_};
kernel::KernelExec *kernel_exec = nullptr;
ret = lite::KernelRegistry::GetInstance()->GetKernelExec(inputs, *outputs, context, ms_context, desc, parameter,
&kernel_exec);

2
mindspore/lite/tools/optimizer/graph/decrease_transpose_algo.cc
View File

@ -588,7 +588,7 @@ int DecreaseTransposeAlgo::SetSubGraphInput(const CNodePtr &cnode, const FuncGra
auto last_underline = node_name.find_last_of("_");
node_name = node_name.substr(0, last_underline);
last_underline = node_name.find_last_of("_");
auto index = 0;
size_t index = 0;
try {
index = std::stoi(node_name.substr(last_underline + 1)) + static_cast<int>(kInputSizeThree);
} catch (const std::exception &e) {