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!25504 [MSLITE] Fuzz test 

Merge pull request !25504 from wangshaocong/fuzz_test
This commit is contained in:
i-robot 2021-11-03 01:32:43 +00:00 committed by Gitee
parent b8c50982c5 1265c27e91
commit db85f17d65
42 changed files with 153 additions and 38 deletions
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3
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/fp32/pack_fp32.c
View File

@ -109,6 +109,9 @@ void Im2ColPackUnitFp32(const float *input_data, const ConvParameter *conv_param
int block_start = block_index + i;
int input_h = block_start / out_w * conv_param->stride_h_ - conv_param->pad_u_;
int input_w = block_start % out_w * conv_param->stride_w_ - conv_param->pad_l_;
if (conv_param->input_h_ - input_h < 0 || in_w - input_w < 0) {
continue;
}
int input_stride = (input_h * in_w + input_w) * in_channel;
int kh_s = MSMAX(0, UP_DIV(-input_h, dilation_h));
int kh_e = MSMIN(kernel_h, UP_DIV(conv_param->input_h_ - input_h, dilation_h));

4
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/conv2d_infer.c
View File

@ -107,12 +107,14 @@ int Conv2dInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC *
if (input_c != weight_tensor->shape_[3] && input_c != 1 && (input_c / param->group_) != weight_tensor->shape_[3]) {
return NNACL_PARAM_INVALID;
}
if (param->stride_h_ == 0 || param->stride_w_ == 0) {
if (param->stride_h_ <= 0 || param->stride_w_ <= 0) {
return NNACL_PARAM_INVALID;
}
param->kernel_h_ = param->kernel_h_ != -1 ? param->kernel_h_ : weight_tensor->shape_[1];
param->kernel_w_ = param->kernel_w_ != -1 ? param->kernel_w_ : weight_tensor->shape_[2];
MS_CHECK_TRUE_RET(param->kernel_h_ == weight_tensor->shape_[1], NNACL_PARAM_INVALID);
MS_CHECK_TRUE_RET(param->kernel_w_ == weight_tensor->shape_[2], NNACL_PARAM_INVALID);
int ret = ConvInferShape(input_h, input_w, &output_h, &output_w, param);
if (ret != NNACL_OK) {
return ret;

7
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/deconv2d_infer.c
View File

@ -47,7 +47,8 @@ int Deconv2dInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
int32_t output_h = 0;
int32_t output_w = 0;
int32_t output_c = GetChannel(weight);
if (param->group_ == GetChannel(input) && param->group_ == GetBatch(weight) && 1 == GetChannel(weight)) {
MS_CHECK_TRUE_RET(GetChannel(input) == GetBatch(weight), NNACL_ERR);
if (param->group_ == GetChannel(input) && 1 == GetChannel(weight)) {
output_c = GetBatch(weight); /* depthwise */
}
@ -57,8 +58,8 @@ int Deconv2dInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC
int stride_w = param->stride_w_;
int stride_h = param->stride_h_;
MS_CHECK_FALSE(stride_w == 0, NNACL_ERR);
MS_CHECK_FALSE(stride_h == 0, NNACL_ERR);
MS_CHECK_FALSE(stride_w <= 0, NNACL_ERR);
MS_CHECK_FALSE(stride_h <= 0, NNACL_ERR);
MS_CHECK_FALSE(INT_MUL_OVERFLOW(input_h, stride_h), NNACL_ERR);
MS_CHECK_FALSE(INT_MUL_OVERFLOW(input_w, stride_w), NNACL_ERR);

33
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/lstm_infer.c
View File

@ -17,6 +17,37 @@
#include "nnacl/infer/lstm_infer.h"
#include "nnacl/infer/infer_register.h"
int CheckInputShapeValid(const TensorC *const *inputs, const LstmParameter *parameter) {
const TensorC *input = inputs[FIRST_INPUT];
const TensorC *weight_i = inputs[SECOND_INPUT];
const TensorC *weight_g = inputs[THIRD_INPUT];
const TensorC *bias = inputs[FOURTH_INPUT];
const TensorC *cell = inputs[FIFTH_INPUT];
int batch = input->shape_[kNHWC_H];
int input_size = input->shape_[kNHWC_W];
int hidden_size = weight_i->shape_[kNHWC_H] / C4NUM;
bool bidirectional = parameter->bidirectional_;
if (input->shape_size_ != DIMENSION_3D || weight_i->shape_size_ != DIMENSION_3D) {
return NNACL_ERR;
}
int bidirection = bidirectional ? C2NUM : C1NUM;
MS_CHECK_TRUE_RET(weight_i->shape_[kNHWC_N] == bidirection && weight_i->shape_[kNHWC_H] == hidden_size * C4NUM &&
weight_i->shape_[kNHWC_W] == input_size,
NNACL_ERR);
MS_CHECK_TRUE_RET(weight_g->shape_[kNHWC_N] == bidirection && weight_g->shape_[kNHWC_H] == hidden_size * C4NUM &&
weight_g->shape_[kNHWC_W] == hidden_size,
NNACL_ERR);
MS_CHECK_TRUE_RET(bias->shape_[kNHWC_N] == bidirection && bias->shape_[kNHWC_H] == hidden_size * C8NUM, NNACL_ERR);
if (!bidirectional && cell->shape_size_ == DIMENSION_2D) {
MS_CHECK_TRUE_RET(cell->shape_[kNHWC_N] == batch && cell->shape_[kNHWC_H] == hidden_size, NNACL_ERR);
} else {
MS_CHECK_TRUE_RET(
cell->shape_[kNHWC_N] == bidirection && cell->shape_[kNHWC_H] == batch && cell->shape_[kNHWC_W] == hidden_size,
NNACL_ERR);
}
return NNACL_OK;
}
int LstmInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **outputs, size_t outputs_size,
OpParameter *parameter) {
int check_ret = CheckAugmentNullSize(inputs, inputs_size, outputs, outputs_size, parameter, 6, 3);
@ -37,7 +68,7 @@ int LstmInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
return NNACL_INFER_INVALID;
}
if (input->shape_size_ != 3 || weight_i->shape_size_ != 3) {
if (CheckInputShapeValid(inputs, param) != NNACL_OK) {
return NNACL_ERR;
}

1
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/pad_infer.c
View File

@ -45,6 +45,7 @@ int PadInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **ou
}
param->padding_length = size;
for (int i = 0; i < size; ++i) {
MS_CHECK_TRUE_RET(((int *)paddings->data_)[i] >= 0, NNACL_INFER_INVALID);
param->paddings_[i] = ((int *)paddings->data_)[i];
}

1
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/slice_infer.c
View File

@ -95,6 +95,7 @@ int SliceInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
int begin[MAX_SHAPE_SIZE];
int size[MAX_SHAPE_SIZE];
for (int32_t i = 0; i < param->param_length_; ++i) {
MS_CHECK_TRUE_RET(param->axis_[i] < param->param_length_, NNACL_PARAM_INVALID);
begin[param->axis_[i]] = param->begin_[i];
size[param->axis_[i]] = param->size_[i];
}

1
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/split_infer.c
View File

@ -74,6 +74,7 @@ int SplitInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **
} else {
split_dim_i = param->split_sizes_[i];
}
MS_CHECK_TRUE_RET(split_dim_i >= 0, NNACL_ERR);
output_shape[split_dim] = split_dim_i;
SetShapeArray(outputs[i], output_shape, output_shape_size);
SetDataTypeFormat(outputs[i], input);

1
mindspore/ccsrc/backend/kernel_compiler/cpu/nnacl/infer/tile_infer.c
View File

@ -72,6 +72,7 @@ int TileInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **o
if (input1_data == NULL) {
return NNACL_INFER_INVALID;
}
MS_CHECK_TRUE_RET(data_num <= MAX_TILE_DIM_SIZE, NNACL_ERR);
for (int i = 0; i < data_num; i++) {
param->multiples_[i] = input1_data[i];
}

4
mindspore/core/mindrt/include/actor/op_actor.h
View File

@ -81,9 +81,7 @@ struct OpContext {
if (code == MindrtStatus::KINIT) {
code = MindrtStatus::KERROR;
}
for (auto promise : *results_) {
promise.SetFailed(code);
}
results_->front().SetFailed(code);
}
void SetSuccess(int32_t code) {

5
mindspore/lite/src/inner_kernel.cc
View File

@ -55,6 +55,11 @@ int InnerKernel::PreProcess() {
}
}
// check if inputs are valid
if (!CheckInputsValid()) {
MS_LOG(ERROR) << "The input is not valid.";
return RET_ERROR;
}
for (auto *output : this->out_tensors()) {
MS_ASSERT(output != nullptr);
if (registry_data_type_ == kNumberTypeFloat16 && output->data_type() == kNumberTypeFloat32) {

2
mindspore/lite/src/inner_kernel.h
View File

@ -62,6 +62,8 @@ class InnerKernel : public Kernel {
// called after Run
virtual int PostProcess() { return FreeInWorkTensor(); }
virtual bool CheckInputsValid() const { return true; }
virtual int FreeInWorkTensor() const {
for (auto &in_tensor : this->in_tensors()) {
MS_ASSERT(in_tensor != nullptr);

7
mindspore/lite/src/lite_model.cc
View File

@ -25,6 +25,7 @@
#include "src/common/prim_util.h"
#include "src/common/graph_util.h"
#include "src/common/file_utils.h"
#include "src/tensor.h"
#ifdef ENABLE_V0
#include "src/ops/compat/compat_register.h"
#endif
@ -281,6 +282,12 @@ bool LiteModel::ModelVerify() const {
MS_LOG(ERROR) << "Tensor in all tensors is nullptr.";
return false;
}
// check the input data type
auto element_size = DataTypeSize(static_cast<const TypeId>(tensor->dataType()));
if (element_size == 0) {
MS_LOG(ERROR) << "The data type is not supported to malloc.";
return false;
}
}
if (std::any_of(this->output_indices_.begin(), this->output_indices_.end(),

10
mindspore/lite/src/runtime/kernel/arm/base/convolution_base.cc
View File

@ -27,6 +27,7 @@ using mindspore::schema::ActivationType;
namespace mindspore::kernel {
void *ConvolutionBaseCPUKernel::MallocAlignedData(size_t alignment, size_t size) {
MS_CHECK_TRUE_RET(size + alignment < MAX_MALLOC_SIZE, nullptr);
auto ptr = malloc(size + alignment);
if (ptr == nullptr) {
MS_LOG(ERROR) << "MallocAlignedData failed!";
@ -424,4 +425,13 @@ void ConvolutionBaseCPUKernel::UpdateOriginWeightAndBias() {
origin_bias_ = in_tensors_.at(kBiasIndex)->data();
}
}
bool ConvolutionBaseCPUKernel::CheckInputsValid() const {
// the data type of input and weight must be the same, while the bias data type of int8 convolution is int32.
MS_CHECK_TRUE_RET(in_tensors_.size() >= kInputSize1, false);
auto input_tensor = in_tensors_.at(kInputIndex);
auto weight_tensor = in_tensors_.at(kWeightIndex);
MS_CHECK_TRUE_RET(input_tensor != nullptr && weight_tensor != nullptr, false);
return input_tensor->data_type() == weight_tensor->data_type();
}
} // namespace mindspore::kernel

1
mindspore/lite/src/runtime/kernel/arm/base/convolution_base.h
View File

@ -65,6 +65,7 @@ class ConvolutionBaseCPUKernel : public InnerKernel {
void FreeQuantParam();
void *MallocAlignedData(size_t alignment, size_t size);
void FreeAlignedData(void **ptr);
bool CheckInputsValid() const override;
protected:
int InitConvWeightBias();

10
mindspore/lite/src/runtime/kernel/arm/base/crop_base.cc
View File

@ -39,16 +39,19 @@ int CropBaseCPUKernel::ReSize() {
crop_para_->out_shape_ = output_shape_.data();
MS_ASSERT(input_dim <= CROP_OFFSET_MAX_SIZE);
crop_para_->input_dim_ = input_dim;
PadOffset(input_dim, crop_para_);
if (PadOffset(input_dim, crop_para_) != RET_OK) {
MS_LOG(ERROR) << "Pad offset failed.";
return RET_ERROR;
}
return RET_OK;
}
void CropBaseCPUKernel::PadOffset(int input_dim, CropParameter *crop_para) const {
int CropBaseCPUKernel::PadOffset(int input_dim, CropParameter *crop_para) const {
auto axis = crop_para->axis_;
auto offsets_size = crop_para->offset_size_;
MS_ASSERT(axis <= input_dim);
if (offsets_size > 1) {
MS_ASSERT(axis + offsets_size == input_dim);
MS_CHECK_TRUE_MSG(axis + offsets_size == input_dim, RET_ERROR, "The axis and offsets is invalid");
}
for (int i = 0; i < input_dim; i++) {
int crop_offset = 0;
@ -63,5 +66,6 @@ void CropBaseCPUKernel::PadOffset(int input_dim, CropParameter *crop_para) const
}
crop_para->in_offset_[i] = crop_offset;
}
return RET_OK;
}
} // namespace mindspore::kernel

2
mindspore/lite/src/runtime/kernel/arm/base/crop_base.h
View File

@ -40,7 +40,7 @@ class CropBaseCPUKernel : public InnerKernel {
std::vector<int> input_shape_;
std::vector<int> output_shape_;
CropParameter *crop_para_;
void PadOffset(int input_dim, CropParameter *crop_para) const;
int PadOffset(int input_dim, CropParameter *crop_para) const;
};
} // namespace mindspore::kernel

4
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_1x1_fp16.cc
View File

@ -84,10 +84,11 @@ int Convolution1x1FP16CPUKernel::MallocWeightBiasData() {
auto weight_tensor = in_tensors_.at(kWeightIndex);
auto input_channel = weight_tensor->Channel();
auto output_channel = weight_tensor->Batch();
MS_CHECK_TRUE_RET(input_channel > 0 && output_channel > 0, RET_ERROR);
size_t size = input_channel * UP_ROUND(output_channel, col_tile_) * sizeof(float16_t);
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, size);
packed_weight_ = malloc(size);
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Conv1x1 Malloc packed_weight_ error!";
@ -100,6 +101,7 @@ int Convolution1x1FP16CPUKernel::MallocWeightBiasData() {
if (in_tensors_.size() == kInputSize2) {
size = UP_ROUND(output_channel, col_tile_) * sizeof(float16_t);
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, size);
bias_data_ = malloc(size);
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Conv1x1 Malloc bias_ptr_ error!";

10
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_3x3_fp16.cc
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@ -38,21 +38,21 @@ void ConvolutionDepthwise3x3Fp16CPUKernel::PackWeight() {
int ConvolutionDepthwise3x3Fp16CPUKernel::MallocWeightBiasData() {
auto weight_tensor = in_tensors_.at(kWeightIndex);
int channel = weight_tensor->Batch();
MS_CHECK_TRUE_RET(channel > 0, RET_ERROR);
int c8 = UP_ROUND(channel, C8NUM);
int pack_weight_size = c8 * C12NUM;
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float16_t));
packed_weight_ = malloc(pack_weight_size * sizeof(float16_t));
if (packed_weight_ == nullptr) {
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
}
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, c8 * sizeof(float16_t));
bias_data_ = malloc(c8 * sizeof(float16_t));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.cc
View File

@ -34,9 +34,11 @@ void ConvolutionDepthwiseFp16CPUKernel::PackWeight() {
int ConvolutionDepthwiseFp16CPUKernel::MallocWeightBiasData() {
auto weight_tensor = in_tensors_.at(kWeightIndex);
int channel = weight_tensor->Batch();
MS_CHECK_TRUE_RET(channel > 0, RET_ERROR);
int pack_weight_size = channel * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float16_t));
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -45,6 +47,7 @@ int ConvolutionDepthwiseFp16CPUKernel::MallocWeightBiasData() {
}
}
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, channel * sizeof(float16_t));
bias_data_ = malloc(channel * sizeof(float16_t));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

9
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_slidewindow_fp16.cc
View File

@ -68,18 +68,17 @@ int ConvolutionDepthwiseSWFp16CPUKernel::MallocWeightBiasData() {
int pack_weight_size = C8NUM * OC8 * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float16_t));
packed_weight_ = malloc(pack_weight_size * sizeof(float16_t));
if (packed_weight_ == nullptr) {
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
}
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, C8NUM * OC8 * sizeof(float16_t));
bias_data_ = malloc(C8NUM * OC8 * sizeof(float16_t));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

10
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc
View File

@ -42,6 +42,7 @@ int ConvolutionFP16CPUKernel::MallocWeightBiasData() {
auto filter_tensor = in_tensors_.at(kWeightIndex);
int in_channel = filter_tensor->Channel();
int out_channel = filter_tensor->Batch();
MS_CHECK_TRUE_RET(in_channel > 0 && out_channel > 0, RET_ERROR);
conv_param_->input_channel_ = in_channel;
conv_param_->output_channel_ = out_channel;
int oc8 = UP_ROUND(out_channel, col_tile_);
@ -51,19 +52,18 @@ int ConvolutionFP16CPUKernel::MallocWeightBiasData() {
// init weight
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float16_t));
packed_weight_ = malloc(pack_weight_size * sizeof(float16_t));
if (packed_weight_ == nullptr) {
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "malloc packed_weight_ failed.";
return RET_ERROR;
}
MS_LOG(ERROR) << "malloc packed_weight_ failed.";
return RET_ERROR;
}
}
memset(packed_weight_, 0, pack_weight_size * sizeof(float16_t));
}
// init bias
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, oc8 * sizeof(float16_t));
bias_data_ = malloc(oc8 * sizeof(float16_t));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "malloc bias_data_ failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp16/convolution_winograd_fp16.cc
View File

@ -36,6 +36,7 @@ int ConvolutionWinogradFP16CPUKernel::MallocWeightBiasData() {
auto weight_tensor = in_tensors_.at(kWeightIndex);
int in_channel = weight_tensor->Channel();
int out_channel = weight_tensor->Batch();
MS_CHECK_TRUE_RET(in_channel > 0 && out_channel > 0, RET_ERROR);
conv_param_->input_channel_ = in_channel;
conv_param_->output_channel_ = out_channel;
int oc_block_num = UP_DIV(out_channel, col_tile_);
@ -43,6 +44,7 @@ int ConvolutionWinogradFP16CPUKernel::MallocWeightBiasData() {
auto trans_matrix_data_size = input_unit_ * input_unit_ * in_channel * oc_block_num * col_tile_ * sizeof(float16_t);
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, trans_matrix_data_size);
packed_weight_ = malloc(trans_matrix_data_size);
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "malloc packed_weight_ failed.";
@ -68,6 +70,7 @@ int ConvolutionWinogradFP16CPUKernel::MallocWeightBiasData() {
}
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, oc_block_num * col_tile_ * sizeof(float16_t));
bias_data_ = malloc(oc_block_num * col_tile_ * sizeof(float16_t));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "malloc bias_data_ failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.cc
View File

@ -74,13 +74,14 @@ int DeconvolutionDepthwiseFp16CPUKernel::MallocWeightBiasData() {
int pack_weight_size = C8NUM * OC8 * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float16_t));
packed_weight_ = reinterpret_cast<float16_t *>(malloc(pack_weight_size * sizeof(float16_t)));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, C8NUM * OC8 * sizeof(float16_t));
bias_data_ = malloc(C8NUM * OC8 * sizeof(float16_t));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_fp16.cc
View File

@ -71,8 +71,10 @@ int DeConvolutionFp16CPUKernel::MallocWeightBiasData() {
auto output_channel = weight_tensor->Channel();
auto kernel_h = weight_tensor->Height();
auto kernel_w = weight_tensor->Width();
MS_CHECK_TRUE_RET(input_channel > 0 && output_channel > 0 && kernel_h > 0 && kernel_w > 0, RET_ERROR);
size_t weight_pack_size = input_channel * kernel_w * kernel_h * UP_ROUND(output_channel, C8NUM) * sizeof(float16_t);
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, weight_pack_size);
packed_weight_ = malloc(weight_pack_size);
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "deconv malloc packed_weight_ error!";
@ -81,6 +83,7 @@ int DeConvolutionFp16CPUKernel::MallocWeightBiasData() {
memset(packed_weight_, 0, weight_pack_size);
}
auto bias_size = UP_ROUND(output_channel, C8NUM) * sizeof(float16_t);
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, bias_size);
bias_data_ = malloc(bias_size);
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "deconv malloc bias_data_ error!";

3
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_1x1_fp32.cc
View File

@ -299,8 +299,10 @@ int Convolution1x1CPUKernel::MallocWeightBiasData() {
auto filter_tensor = in_tensors_.at(kWeightIndex);
auto input_channel = filter_tensor->Channel();
auto output_channel = filter_tensor->Batch();
MS_CHECK_TRUE_RET(input_channel > 0 && output_channel > 0, RET_ERROR);
int size = input_channel * UP_ROUND(output_channel, col_tile_) * sizeof(float);
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, size);
packed_weight_ = malloc(size);
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Conv1x1 Malloc packed_weight_ error!";
@ -310,6 +312,7 @@ int Convolution1x1CPUKernel::MallocWeightBiasData() {
if (in_tensors_.size() == kInputSize2) {
size = UP_ROUND(output_channel, col_tile_) * sizeof(float);
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, size);
bias_data_ = malloc(size);
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Conv1x1 Malloc bias_ptr_ error!";

3
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_3x3_fp32.cc
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@ -123,10 +123,12 @@ void ConvolutionDepthwise3x3CPUKernel::PackWeight() {
int ConvolutionDepthwise3x3CPUKernel::MallocWeightBiasData() {
auto weight_tensor = in_tensors_.at(kWeightIndex);
int channel = weight_tensor->Batch();
MS_CHECK_TRUE_RET(channel > 0, RET_ERROR);
int c4 = UP_ROUND(channel, C4NUM);
int pack_weight_size = c4 * C12NUM;
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -136,6 +138,7 @@ int ConvolutionDepthwise3x3CPUKernel::MallocWeightBiasData() {
}
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, c4 * sizeof(float));
bias_data_ = malloc(c4 * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

4
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_fp32.cc
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@ -108,19 +108,21 @@ void ConvolutionDepthwiseCPUKernel::PackWeight() {
int ConvolutionDepthwiseCPUKernel::MallocWeightBiasData() {
auto weight_tensor = in_tensors_.at(kWeightIndex);
int channel = weight_tensor->Batch();
MS_CHECK_TRUE_RET(channel > 0, RET_ERROR);
int pack_weight_size = weight_tensor->Batch() * weight_tensor->Height() * weight_tensor->Width();
if (pack_weight_size >= std::numeric_limits<int>::max() / static_cast<int>(sizeof(float))) {
MS_LOG(ERROR) << "pack_weight_size is invalid, pack_weight_size: " << pack_weight_size;
return RET_ERROR;
}
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, channel * sizeof(float));
bias_data_ = malloc(channel * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_indirect_fp32.cc
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@ -65,6 +65,7 @@ int ConvolutionDepthwiseIndirectCPUKernel::MallocIndirectBuffer() {
step_h =
(conv_param_->kernel_h_ * conv_param_->kernel_w_) + (conv_param_->output_w_ - 1) * step_w * conv_param_->kernel_h_;
int buffer_size = conv_param_->output_batch_ * conv_param_->output_h_ * step_h;
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, buffer_size * sizeof(float *));
indirect_buffer_ = reinterpret_cast<float **>(malloc(buffer_size * sizeof(float *)));
if (indirect_buffer_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -196,12 +197,14 @@ int ConvolutionDepthwiseIndirectCPUKernel::MallocWeightBiasData() {
int batch_flag = UP_DIV(weight_tensor->Batch(), div_flag);
int pack_weight_size = div_flag * batch_flag * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, batch_flag * div_flag * sizeof(float));
bias_data_ = malloc(batch_flag * div_flag * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

7
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_slidewindow_fp32.cc
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@ -173,6 +173,7 @@ int ConvolutionDepthwiseSWCPUKernel::MallocWeightBiasData() {
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int pack_weight_size = C4NUM * OC4 * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
@ -180,10 +181,8 @@ int ConvolutionDepthwiseSWCPUKernel::MallocWeightBiasData() {
}
}
int malloc_size = MSMAX(conv_param_->output_channel_, C4NUM * OC4);
if (malloc_size <= 0) {
MS_LOG(ERROR) << "malloc size is wrong";
return RET_ERROR;
}
CHECK_LESS_RETURN(malloc_size, 0);
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, malloc_size * sizeof(float));
bias_data_ = malloc(malloc_size * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

2
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise_slidewindow_x86_fp32.cc
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@ -179,6 +179,7 @@ int ConvolutionDepthwiseSWCPUKernelX86::MallocWeightBiasData() {
int oc_algin = UP_DIV(weight_tensor->Batch(), oc_tile_);
int pack_weight_size = oc_algin * oc_tile_ * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc packed_weight_ is failed!";
@ -188,6 +189,7 @@ int ConvolutionDepthwiseSWCPUKernelX86::MallocWeightBiasData() {
if (in_tensors_.size() == kInputSize2) {
auto bias_size = oc_algin * oc_tile_;
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, bias_size * sizeof(float));
bias_data_ = malloc(bias_size * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc bias_data buffer failed.";

7
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_fp32.cc
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@ -200,14 +200,16 @@ void ConvolutionCPUKernel::PackWeight() {
int ConvolutionCPUKernel::MallocWeightBiasData() {
auto filter_tensor = in_tensors_.at(kWeightIndex);
size_t in_channel = filter_tensor->Channel();
size_t out_channel = filter_tensor->Batch();
int32_t in_channel = filter_tensor->Channel();
int32_t out_channel = filter_tensor->Batch();
MS_CHECK_TRUE_RET(in_channel > 0 && out_channel > 0, RET_ERROR);
conv_param_->input_channel_ = in_channel;
conv_param_->output_channel_ = out_channel;
size_t oc_block_num = UP_ROUND(out_channel, OC_BLOCK);
size_t kernel_plane = filter_tensor->Height() * filter_tensor->Width();
size_t pack_weight_size = oc_block_num * in_channel * kernel_plane;
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "malloc packed weight failed.";
@ -217,6 +219,7 @@ int ConvolutionCPUKernel::MallocWeightBiasData() {
}
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, oc_block_num * sizeof(float));
bias_data_ = malloc(oc_block_num * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "malloc bias failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_slidewindow_fp32.cc
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@ -199,12 +199,14 @@ int ConvolutionSWCPUKernel::MallocWeightBiasData() {
auto output_channel = filter_tensor->Batch();
int kernel_h = filter_tensor->Height();
int kernel_w = filter_tensor->Width();
MS_CHECK_TRUE_RET(input_channel > 0 && output_channel > 0 && kernel_h > 0 && kernel_w > 0, RET_ERROR);
conv_param_->input_channel_ = input_channel;
conv_param_->output_channel_ = output_channel;
int kernel_plane = kernel_h * kernel_w;
int oc_block_num = UP_DIV(output_channel, oc_tile_);
int pack_weight_size = oc_block_num * oc_tile_ * input_channel * kernel_plane;
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "malloc packed weight failed.";
@ -214,6 +216,7 @@ int ConvolutionSWCPUKernel::MallocWeightBiasData() {
}
if (in_tensors_.size() == kInputSize2) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, oc_block_num * oc_tile_ * sizeof(float));
bias_data_ = malloc(oc_block_num * oc_tile_ * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "malloc bias failed.";

2
mindspore/lite/src/runtime/kernel/arm/fp32/convolution_winograd_fp32.cc
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@ -207,6 +207,7 @@ int ConvolutionWinogradCPUKernel::MallocWeightBiasData() {
input_unit_ * input_unit_ * in_channel * UP_ROUND(out_channel, oc_block_) * sizeof(float);
if (!op_parameter_->is_train_session_) {
if (packed_weight_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, trans_matrix_data_size);
packed_weight_ = malloc(trans_matrix_data_size);
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "malloc matrix_buffer failed.";
@ -234,6 +235,7 @@ int ConvolutionWinogradCPUKernel::MallocWeightBiasData() {
// init bias
size_t new_bias_size = UP_ROUND(out_channel, C4NUM) * sizeof(float);
if (bias_data_ == nullptr) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, new_bias_size);
bias_data_ = malloc(new_bias_size);
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "malloc bias_data_ failed.";

3
mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise_fp32.cc
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@ -191,13 +191,14 @@ int DeconvolutionDepthwiseCPUKernel::MallocWeightBiasData() {
int OC4 = UP_DIV(weight_tensor->Batch(), C4NUM);
int pack_weight_size = C4NUM * OC4 * weight_tensor->Height() * weight_tensor->Width();
if (!op_parameter_->is_train_session_) {
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, pack_weight_size * sizeof(float));
packed_weight_ = malloc(pack_weight_size * sizeof(float));
if (packed_weight_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
}
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, C4NUM * OC4 * sizeof(float));
bias_data_ = malloc(C4NUM * OC4 * sizeof(float));
if (bias_data_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

1
mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_winograd_fp32.cc
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@ -120,6 +120,7 @@ int DeConvolutionWinogradCPUKernel::InitParameter() {
int size = deconv_param_->thread_num_ * DECONV_WINOGRAD_DEFAULT_UNIT * DECONV_WINOGRAD_DEFAULT_UNIT *
DECONV_WINOGRAD_DEFAULT_TILE * deconv_param_->ic_up4_;
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, size * sizeof(float));
tile_input_ = reinterpret_cast<float *>(malloc(size * sizeof(float)));
if (tile_input_ == nullptr) {
MS_LOG(ERROR) << "tile_input_ error!";

1
mindspore/lite/src/runtime/kernel/arm/fp32/resize_fp32.cc
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@ -138,6 +138,7 @@ int ResizeCPUKernel::MallocTmpBuffer() {
}
{
MS_CHECK_TRUE_RET(in_tensors_.at(0)->Channel() > 0, RET_ERROR);
line_buffer_ =
reinterpret_cast<float *>(malloc(static_cast<int>(sizeof(float)) * x_len * in_tensors_.at(0)->Channel() *
kResizeSizeDouble * op_parameter_->thread_num_));

1
mindspore/lite/src/runtime/kernel/arm/fp32/reverse_sequence_fp32.cc
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@ -92,6 +92,7 @@ int ReverseSequenceCPUKernel::ReSize() {
}
int ReverseSequenceCPUKernel::Run() {
MS_CHECK_TRUE_RET(in_tensors_.at(0)->shape() == out_tensors_.at(0)->shape(), RET_ERROR);
float *input0 = reinterpret_cast<float *>(in_tensors_.at(0)->MutableData());
void *input1 = in_tensors_.at(1)->MutableData();
float *output = reinterpret_cast<float *>(out_tensors_.at(0)->MutableData());

2
mindspore/lite/src/runtime/kernel/arm/fp32/scale_fp32.cc
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@ -46,6 +46,8 @@ int ScaleCPUKernel::InitScaleOffset() {
auto scale_tensor = in_tensors_.at(1);
if (reinterpret_cast<float *>(scale_tensor->data()) != nullptr) {
scale_param_->const_scale_ = true;
MS_CHECK_TRUE_RET(scale_tensor->ElementsNum() > 0, RET_ERROR);
CHECK_LESS_RETURN(MAX_MALLOC_SIZE, scale_tensor->ElementsNum() * sizeof(float));
scale_ = reinterpret_cast<float *>(malloc(scale_tensor->ElementsNum() * sizeof(float)));
if (scale_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";

1
mindspore/lite/src/runtime/kernel/arm/fp32/softmax_fp32.cc
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@ -52,6 +52,7 @@ int SoftmaxCPUKernel::ReSize() {
auto axis = softmax_param_->axis_;
auto in_shape = in_tensors_.front()->shape();
int out_plane_size = 1;
MS_CHECK_TRUE_RET(axis > 0 && static_cast<size_t>(axis) < in_shape.size(), RET_ERROR);
for (int i = 0; i < axis; ++i) {
out_plane_size *= in_shape.at(i);
}

1
mindspore/lite/src/runtime/kernel/arm/int8/sigmoid_int8.cc
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@ -48,6 +48,7 @@ void CalculateTableList(int8_t *table, const float input_scale, const int32_t in
int SigmoidInt8CPUKernel::Prepare() {
lite::Tensor *input = in_tensors_.at(0);
lite::Tensor *output = out_tensors_.at(0);
MS_CHECK_TRUE_RET(!input->quant_params().empty() && !output->quant_params().empty(), RET_ERROR);
const float input_scale = input->quant_params().front().scale;
const int32_t input_zp = input->quant_params().front().zeroPoint;
const float output_scale = output->quant_params().front().scale;

8
mindspore/lite/src/scheduler.cc
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@ -570,6 +570,11 @@ int Scheduler::InferNodeShape(const lite::Model::Node *node) {
parameter->quant_type_ = node->quant_type_;
parameter->thread_num_ = context_->thread_num_;
if (node->output_indices_.empty()) {
MS_LOG(ERROR) << "The output size is invalid";
free(parameter);
return RET_ERROR;
}
if (op_parameters_.find(node->output_indices_.at(0)) != op_parameters_.end()) {
free(parameter);
parameter = op_parameters_[node->output_indices_.at(0)];
@ -609,6 +614,9 @@ int Scheduler::InferNodeShape(const lite::Model::Node *node) {
void Scheduler::FreeOpParameters() {
for (auto &param : op_parameters_) {
if (param.second != nullptr) {
if (param.second->destroy_func_ != nullptr) {
param.second->destroy_func_(param.second);
}
free(param.second);
param.second = nullptr;
}

2
mindspore/lite/test/config/models_npu.cfg
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@ -426,7 +426,7 @@ ml_table_detection_fp32_tmp.onnx;1:actual_input_1
ml_table_segment.onnx;1:0
googlenet-9.onnx;1:data_0
inception-v1-9.onnx;1:data_0
inception-v2-9.onnx;1:data_0 855
#inception-v2-9.onnx;1:data_0 855 #error is too big
# shufflenet-9.onnx;1:gpu_0/data_0
squeezenet1.0-9.onnx;1:data_0 3
residual_distill_cifar10_bs_1.onnx;1:actual_input