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mindspore2022/mindspore/ccsrc/backend/kernel_compiler/common_utils.cc

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/**
* Copyright 2019 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/common_utils.h"
#include <unordered_map>
#include <map>
#include <iostream>
#include <utility>
#include <fstream>
#include <algorithm>
#include <thread>
#include "nlohmann/json.hpp"
#include "backend/session/anf_runtime_algorithm.h"
#include "utils/ms_utils.h"
#include "ir/manager.h"
#include "ir/meta_tensor.h"
#include "base/core_ops.h"
#include "ir/graph_utils.h"
#include "utils/ms_context.h"
#include "mindspore/ccsrc/debug/common.h"
namespace mindspore {
namespace kernel {
constexpr char kAxis[] = "axis";
constexpr char kTypeInt32[] = "Int32";
const std::unordered_map<std::string, TypeId> type_id_maps = {
{"float", TypeId::kNumberTypeFloat32}, {"float16", TypeId::kNumberTypeFloat16},
{"float32", TypeId::kNumberTypeFloat32}, {"float64", TypeId::kNumberTypeFloat64},
{"int", TypeId::kNumberTypeInt}, {"int8", TypeId::kNumberTypeInt8},
{"int16", TypeId::kNumberTypeInt16}, {"int32", TypeId::kNumberTypeInt32},
{"int64", TypeId::kNumberTypeInt64}, {"uint", TypeId::kNumberTypeUInt},
{"uint8", TypeId::kNumberTypeUInt8}, {"uint16", TypeId::kNumberTypeUInt16},
{"uint32", TypeId::kNumberTypeUInt32}, {"uint64", TypeId::kNumberTypeUInt64},
{"bool", TypeId::kNumberTypeBool}, {"complex64", TypeId::kNumberTypeComplex64}};
const std::map<TypeId, std::string> type_id_str_map = {
{TypeId::kNumberTypeFloat32, "float32"}, {TypeId::kNumberTypeFloat16, "float16"},
{TypeId::kNumberTypeFloat, "float"}, {TypeId::kNumberTypeFloat64, "float64"},
{TypeId::kNumberTypeInt, "int"}, {TypeId::kNumberTypeInt8, "int8"},
{TypeId::kNumberTypeInt16, "int16"}, {TypeId::kNumberTypeInt32, "int32"},
{TypeId::kNumberTypeInt64, "int64"}, {TypeId::kNumberTypeUInt, "uint"},
{TypeId::kNumberTypeUInt8, "uint8"}, {TypeId::kNumberTypeUInt16, "uint16"},
{TypeId::kNumberTypeUInt32, "uint32"}, {TypeId::kNumberTypeUInt64, "uint64"},
{TypeId::kNumberTypeBool, "bool"}, {TypeId::kNumberTypeComplex64, "complex64"}};
const std::unordered_map<std::string, std::string> dtype_shortdtype_map_ = {
{"float16", "f16"}, {"float32", "f32"}, {"float64", "f64"}, {"int8", "i8"}, {"int16", "i16"}, {"int32", "i32"},
{"int64", "i64"}, {"uint8", "u8"}, {"uint16", "u16"}, {"uint32", "u32"}, {"uint64", "u64"}, {"bool", "bool"},
};
const std::unordered_map<std::string, size_t> dtype_nbyte_map = {
{"float16", sizeof(float) / 2}, {"float32", sizeof(float)}, {"float64", sizeof(float) * 2},
{"int8", sizeof(int) / 4}, {"int16", sizeof(int) / 2}, {"int32", sizeof(int)},
{"int64", sizeof(int) * 2}, {"uint8", sizeof(int) / 4}, {"uint16", sizeof(int) / 2},
{"uint32", sizeof(int)}, {"uint64", sizeof(int) * 2}, {"bool", sizeof(char)},
{"complex64", sizeof(float) * 2}};
// Define all patterns here for different schedule
const std::unordered_map<FusionType, std::string> fusion_type_name_maps = {
{FusionType::BN_UPDATE_GRAD, "bn_update_grad"},
{FusionType::BN_GRAD_REDUCE, "bn_grad_reduce"},
{FusionType::LAYER_NORM_GRAD, "layer_norm_grad"},
{FusionType::L2LOSS_MUL_ADDN, "l2loss_mul_addn"},
{FusionType::ELEMWISE, "ElemWise"},
{FusionType::PURE_BROADCAST, "PureBroadcast"},
{FusionType::COMMREDUCE, "CommReduce"},
{FusionType::SEGMENT, "Segment"},
{FusionType::INPLACE, "Inplace"},
{FusionType::MATMUL, "Matmul"},
{FusionType::MATMUL_V2, "Matmul_v2"},
{FusionType::GEMM, "GEMM"},
{FusionType::CONV, "Convolution"},
{FusionType::CONV2D_BACKPROP_INPUT, "Conv2d_backprop_input"},
{FusionType::CONV2D_BACKPROP_FILTER, "Conv2d_backprop_filter"},
{FusionType::CONV3D_BACKPROP_INPUT, "Conv3d_backprop_input"},
{FusionType::CONV3D_BACKPROP_FILTER, "Conv3d_backprop_filter"},
{FusionType::CUBE_LAYER_NORM, "cube_layer_norm"},
{FusionType::OPAQUE, "Opaque"},
{FusionType::BN_REDUCE, "bn_reduce"},
{FusionType::BN_UPDATE, "bn_update"},
{FusionType::SOFTMAX_CROSS_ENTROPY_WITH_LOGITS, "softmax_cross_entropy_with_logits"},
{FusionType::L2_NORMALIZE, "l2_normalize"},
{FusionType::SOFTMAX, "softmax_pattern"},
{FusionType::L2_LOSS, "l2_loss"},
{FusionType::ASCEND_QUANT, "quant"},
{FusionType::ASCEND_DEQUANT, "dequant"},
{FusionType::ASCEND_ANTI_QUANT, "anti_quant"},
{FusionType::STRIDED_READ, "strided_read"},
{FusionType::STRIDED_WRITE, "strided_write"},
{FusionType::ASCEND_DEQUANT_S16, "dequant_s16"},
{FusionType::ASCEND_REQUANT, "requant"},
{FusionType::ASCEND_REQUANT_S16, "requant_s16"},
{FusionType::MAX_POOL, "MaxPool"},
{FusionType::DEPTHWISECONV, "DepthwiseConvolution"},
{FusionType::CONV3D, "Conv3d"},
{FusionType::POOL2D, "Pool2d"},
{FusionType::POOL3D, "Pool3d"},
{FusionType::READ_SELECT, "read_select"},
{FusionType::WRITE_SELECT, "write_select"},
{FusionType::COSINE_EMBEDDING_LOSS, "cosine_embedding_loss"},
{FusionType::DILATION_PATTERN, "dilation"},
{FusionType::BROAD_CAST, "Broadcast"},
{FusionType::BATCH_MATMUL, "BatchMatmul"},
{FusionType::CONFUSION_TRANSPOSE, "confusiontranspose"},
{FusionType::UNKNOWN_FUSION_TYPE, ""}};
std::string GetFusionNameByType(const kernel::FusionType &type) {
auto iter = fusion_type_name_maps.find(type);
if (iter == fusion_type_name_maps.end()) {
MS_LOG(EXCEPTION) << "Illegal fusion type: " << type;
}
return iter->second;
}
FusionType GetFusionTypeByName(const std::string &name) {
std::string fusion_name_upper = name;
transform(fusion_name_upper.begin(), fusion_name_upper.end(), fusion_name_upper.begin(), ::toupper);
auto iter =
std::find_if(fusion_type_name_maps.begin(), fusion_type_name_maps.end(), [&fusion_name_upper](const auto &it) {
std::string name_upper = it.second;
transform(name_upper.begin(), name_upper.end(), name_upper.begin(), ::toupper);
return fusion_name_upper == name_upper;
});
if (iter == fusion_type_name_maps.end()) {
MS_LOG(EXCEPTION) << "Illegal fusion name: " << name;
}
return iter->first;
}
void KernelMeta::Initialize() {
kernel_meta_path_ = std::string(kGpuKernelMeta) + "/";
#if defined(_WIN32) || defined(_WIN64)
auto ret = mkdir(kernel_meta_path_.c_str());
#else
auto ret = mkdir(kernel_meta_path_.c_str(), S_IRWXG | S_IRWXU);
#endif
if (ret != 0) {
MS_LOG(INFO) << "kernel dir [" << kernel_meta_path_ << "], will be created later";
}
initialized_ = true;
}
std::string KernelMeta::Search(const std::string &kernel_name) const {
if (!initialized_) {
return "";
}
auto iter = kernel_meta_map_.find(kernel_name);
if (iter == kernel_meta_map_.end()) {
return "";
} else {
return iter->second;
}
}
bool KernelMeta::Insert(const std::string &kernel_name, const std::string &kernel_json) {
if (!initialized_) {
return false;
}
kernel_meta_map_[kernel_name] = kernel_json;
return true;
}
bool CheckCache(const std::string &kernel_name) {
// check cache.
KernelMeta *bin_map = KernelMeta::GetInstance();
if (bin_map == nullptr) {
MS_LOG(DEBUG) << "Kernel cache is invalid, kernel_name: " << kernel_name;
return false;
}
std::string kernel_json = bin_map->Search(kernel_name);
bool ret = (!kernel_json.empty());
if (ret) {
MS_LOG(INFO) << "Kernel name:" << kernel_name << " has registered.";
} else {
MS_LOG(INFO) << "Kernel name:" << kernel_name << " will been registered.";
}
return ret;
}
KernelPackPtr SearchCache(const std::string &kernel_name, const std::string &processor) {
// search cache.
KernelMeta *bin_map = KernelMeta::GetInstance();
if (bin_map == nullptr) {
MS_LOG(DEBUG) << "kernel cache is invalid, kernel_name: " << kernel_name;
return nullptr;
}
std::string kernel_json = bin_map->Search(kernel_name);
if (!kernel_json.empty()) {
KernelPackPtr kernel_pack = std::make_shared<KernelPack>();
// just a tmp solution.
if (!kernel_pack->ReadFromJsonFile(kernel_json, processor)) {
MS_LOG(ERROR) << "Read cache json and bin file failed[" << kernel_json << "].";
return nullptr;
} else {
return kernel_pack;
}
} else {
MS_LOG(INFO) << "The cache kernel not found[" << kernel_name << "].";
return nullptr;
}
}
KernelPackPtr InsertCache(const std::string &kernel_name, const std::string &processor) {
MS_LOG(INFO) << "Insert cache for kernel:" << kernel_name << ", processr:" << processor;
KernelMeta *bin_map = KernelMeta::GetInstance();
std::string kernel_json;
if (processor == kProcessorAiCore || processor == kProcessorAiCpu) {
kernel_json = kCceKernelMeta;
} else {
kernel_json = bin_map->kernel_meta_path();
}
(void)kernel_json.append(kernel_name).append(kJsonSuffix);
KernelPackPtr kernel_pack = std::make_shared<KernelPack>();
if (!kernel_pack->ReadFromJsonFile(kernel_json, processor)) {
MS_LOG(ERROR) << "Read json and bin file failed[" << kernel_json << "].";
return nullptr;
}
if (bin_map == nullptr) {
MS_LOG(DEBUG) << "Kernel cache is invalid, kernel name :" << kernel_name;
return nullptr;
}
if (bin_map->Insert(kernel_name, kernel_json)) {
MS_LOG(INFO) << "Kernel insert cache success[" << kernel_json << "], kernel name[" << kernel_name << "].";
}
return kernel_pack;
}
TypeId DtypeToTypeId(const std::string &dtypes) {
auto iter = type_id_maps.find(dtypes);
if (iter != type_id_maps.end()) {
return iter->second;
} else {
MS_EXCEPTION(ArgumentError) << "Illegal input device dtype:" << dtypes;
}
}
std::string TypeId2String(TypeId type_id, bool unknown_as_default) {
auto iter = type_id_str_map.find(type_id);
if (iter == type_id_str_map.end()) {
if (!unknown_as_default) {
MS_EXCEPTION(ArgumentError) << "Illegal input dtype." << TypeIdLabel(type_id);
}
MS_LOG(INFO) << "Using default dtype: float32";
return "float32";
}
return iter->second;
}
std::string Dtype2ShortType(const std::string &dtype) {
auto iter = dtype_shortdtype_map_.find(dtype);
if (iter != dtype_shortdtype_map_.end()) {
return iter->second;
} else {
MS_EXCEPTION(ArgumentError) << "Illegal input dtype:" << dtype;
}
}
size_t GetDtypeNbyte(const std::string &dtype) {
auto iter = dtype_nbyte_map.find(dtype);
if (iter != dtype_nbyte_map.end()) {
return iter->second;
} else {
MS_EXCEPTION(ArgumentError) << "Illegal input dtype:" << dtype;
}
}
bool SetInputKernelBuilderInfo(const std::vector<std::shared_ptr<OpIOInfo>> &inputs, size_t real_input_num,
size_t builder_idex, const std::vector<int64_t> &dyn_input_sizes,
const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder) {
MS_EXCEPTION_IF_NULL(builder);
std::vector<TypeId> inputs_device_type;
std::vector<std::string> inputs_format;
size_t dyn_input_idx = 0;
size_t kernel_info_index = 0;
MS_EXCEPTION_IF_NULL(inputs[0]);
size_t kernel_info_cnt = inputs[0]->dtypes().size();
for (const auto &input : inputs) {
MS_EXCEPTION_IF_NULL(input);
std::string param_type = input->param_type();
std::vector<std::string> dtypes = input->dtypes();
std::vector<std::string> formats = input->formats();
if (dtypes.size() != kernel_info_cnt || formats.size() != kernel_info_cnt) {
MS_LOG(DEBUG) << "Set input kernel builder info failed, dtyps size != formats size. dtypes size: "
<< dtypes.size() << ", formats size : " << formats.size();
return false;
}
if (param_type == "dynamic") {
if (dyn_input_sizes.empty()) {
MS_LOG(DEBUG) << "Set input kernel builder info failed, dyn_input_sizes's size is 0 when param_type is dynamic";
return false;
}
for (int64_t t = 0; t < dyn_input_sizes[dyn_input_idx]; t++) {
kernel_info_index++;
auto type_id = DtypeToTypeId(dtypes[builder_idex]);
inputs_device_type.push_back(type_id);
inputs_format.push_back(formats[builder_idex]);
}
dyn_input_idx++;
} else if (param_type == "required") {
kernel_info_index++;
auto type_id = DtypeToTypeId(dtypes[builder_idex]);
inputs_device_type.push_back(type_id);
inputs_format.push_back(formats[builder_idex]);
} else {
if (kernel_info_index < real_input_num) {
MS_LOG(INFO) << "Set input kernel builder info, input type is optional, input index is :" << kernel_info_index;
kernel_info_index++;
auto type_id = DtypeToTypeId(dtypes[builder_idex]);
inputs_device_type.push_back(type_id);
inputs_format.push_back(formats[builder_idex]);
}
}
}
builder->SetInputsDeviceType(inputs_device_type);
builder->SetInputsFormat(inputs_format);
return true;
}
bool SetOutputKernelBuilderInfo(const std::vector<std::shared_ptr<OpIOInfo>> &outputs, size_t builder_idex,
const size_t &real_output_num,
const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder) {
// not now but in the next we need to support dynamic output case
MS_EXCEPTION_IF_NULL(builder);
size_t output_idx = 0;
std::vector<TypeId> outputs_device_type;
std::vector<std::string> outputs_format;
MS_EXCEPTION_IF_NULL(outputs[0]);
size_t kernel_info_cnt = outputs[0]->dtypes().size();
for (const auto &output : outputs) {
MS_EXCEPTION_IF_NULL(output);
if (output_idx >= real_output_num) {
MS_LOG(DEBUG) << "real_output_num:" << real_output_num << ", output_idx:" << output_idx << " is out of limit!";
continue;
}
size_t output_num = 0;
if (output->param_type() == "dynamic") {
if (outputs.size() > 1) {
MS_EXCEPTION(ArgumentError) << "Dynamic output is unsupported multi output!";
}
output_num = real_output_num;
} else if (output->param_type() == "required") {
output_num = 1;
} else {
if (output_idx < real_output_num) {
MS_LOG(DEBUG) << "Set output kernel builder info, output type is optional, output index is :" << output_idx;
output_num = 1;
}
}
for (size_t i = 0; i < output_num; i++) {
std::vector<std::string> dtypes = output->dtypes();
std::vector<std::string> formats = output->formats();
if (dtypes.size() != kernel_info_cnt || formats.size() != kernel_info_cnt) {
MS_LOG(DEBUG) << "Set output kernel builder info, dtyps size != formats size.";
return false;
}
auto type_id = DtypeToTypeId(dtypes[builder_idex]);
outputs_device_type.push_back(type_id);
outputs_format.push_back(formats[builder_idex]);
output_idx++;
}
}
builder->SetOutputsFormat(outputs_format);
builder->SetOutputsDeviceType(outputs_device_type);
return true;
}
void SetKernelBuildInfo(const std::shared_ptr<KernelBuildInfo::KernelBuildInfoBuilder> &builder, Processor processor,
const std::shared_ptr<const OpInfo> &op_info_ptr) {
MS_EXCEPTION_IF_NULL(builder);
MS_EXCEPTION_IF_NULL(op_info_ptr);
auto imply_type = op_info_ptr->imply_type();
builder->SetProcessor(processor);
std::string fusion_name = op_info_ptr->fusion_type();
auto fusion_type = GetFusionTypeByName(fusion_name);
builder->SetFusionType(fusion_type);
if (imply_type == kAKG) {
builder->SetKernelType(AKG_KERNEL);
} else if (imply_type == kAICPU) {
builder->SetKernelType(AICPU_KERNEL);
} else {
builder->SetKernelType(TBE_KERNEL);
}
}
bool ParseMetadata(const CNodePtr &kernel_node, const std::shared_ptr<const OpInfo> &op_info_ptr, Processor processor,
std::vector<std::shared_ptr<KernelBuildInfo>> *const kernel_info_list) {
MS_EXCEPTION_IF_NULL(kernel_node);
MS_EXCEPTION_IF_NULL(kernel_info_list);
size_t real_input_num = AnfAlgo::GetInputTensorNum(kernel_node);
size_t real_output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
std::vector<std::shared_ptr<OpIOInfo>> inputs = op_info_ptr->inputs_ptr();
std::vector<std::shared_ptr<OpIOInfo>> outputs = op_info_ptr->outputs_ptr();
std::vector<int64_t> dyn_input_sizes;
auto primitive = AnfAlgo::GetCNodePrimitive(kernel_node);
MS_EXCEPTION_IF_NULL(primitive);
auto op_name = AnfAlgo::GetCNodeName(kernel_node);
if (primitive->GetAttr("dyn_input_sizes") != nullptr) {
dyn_input_sizes = GetValue<std::vector<int64_t>>(primitive->GetAttr("dyn_input_sizes"));
}
if (inputs.size() > 0) {
if (inputs[0] == nullptr) {
MS_LOG(EXCEPTION) << "Inputs[0] is nullptr. Op name: " << op_name;
}
size_t kernel_info_cnt = inputs[0]->dtypes().size();
for (size_t j = 0; j < kernel_info_cnt; j++) {
auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
MS_EXCEPTION_IF_NULL(builder);
SetKernelBuildInfo(builder, processor, op_info_ptr);
if (!SetInputKernelBuilderInfo(inputs, real_input_num, j, dyn_input_sizes, builder)) {
MS_LOG(DEBUG) << "Parse kernel metadata, set inputs kernel builder info failed. Op name: " << op_name;
return false;
}
if (outputs.size() > 0) {
if (!SetOutputKernelBuilderInfo(outputs, j, real_output_num, builder)) {
MS_LOG(DEBUG) << "Parse kernel metadata, set outputs kernel builder info failed. Op name: " << op_name;
return false;
}
}
kernel_info_list->push_back(builder->Build());
}
} else if (outputs.size() > 0) {
if (outputs[0] == nullptr) {
MS_LOG(EXCEPTION) << "Outputs[0] is nullptr. Op name: " << op_name;
}
size_t kernel_info_cnt = outputs[0]->dtypes().size();
for (size_t j = 0; j < kernel_info_cnt; j++) {
auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
MS_EXCEPTION_IF_NULL(builder);
SetKernelBuildInfo(builder, processor, op_info_ptr);
if (!SetOutputKernelBuilderInfo(outputs, j, real_output_num, builder)) {
MS_LOG(DEBUG) << "Parse kernel metadata, set outputs kernel builder info failed. Op name: " << op_name;
return false;
}
kernel_info_list->push_back(builder->Build());
}
} else {
if (processor == AICPU) {
auto builder = std::make_shared<KernelBuildInfo::KernelBuildInfoBuilder>();
MS_EXCEPTION_IF_NULL(builder);
SetKernelBuildInfo(builder, processor, op_info_ptr);
kernel_info_list->push_back(builder->Build());
}
}
return true;
}
void SaveJsonInfo(const std::string &json_name, const std::string &info, const std::string &base_path) {
std::string path = base_path + json_name + kInfoSuffix;
auto realpath = Common::GetRealPath(path);
if (!realpath.has_value()) {
MS_LOG(ERROR) << "Get real path failed, path=" << path;
return;
}
ChangeFileMode(realpath.value(), S_IWUSR);
std::ofstream filewrite(realpath.value());
if (!filewrite.is_open()) {
MS_LOG(ERROR) << "Open file '" << realpath.value() << "' failed!";
return;
}
filewrite << info << std::endl;
filewrite.close();
ChangeFileMode(realpath.value(), S_IRUSR);
}
Processor GetProcessor(const string &processor) {
if (processor == kProcessorAiCore) return Processor::AICORE;
if (processor == kProcessorAiCpu) return Processor::AICPU;
if (processor == kProcessorCuda) return Processor::CUDA;
MS_LOG(DEBUG) << "Unknown processor type.";
return Processor::UNKNOWN;
}
std::string GetProcessor(const AnfNodePtr &anf_node) {
MS_EXCEPTION_IF_NULL(anf_node);
std::string device;
switch (AnfAlgo::GetProcessor(anf_node)) {
case Processor::AICORE:
device = kProcessorAiCore;
break;
case Processor::AICPU:
device = kProcessorAiCpu;
break;
case Processor::CUDA:
device = kProcessorCuda;
break;
default:
MS_LOG(DEBUG) << "Unknown processor type.";
break;
}
return device;
}
bool IsSameShape(const std::vector<size_t> &shape_a, const std::vector<size_t> &shape_b) {
if (shape_a.size() != shape_b.size()) {
return false;
}
for (size_t i = 0; i < shape_a.size(); ++i) {
if (shape_a[i] != shape_b[i]) {
return false;
}
}
return true;
}
int Sign(float x) {
if (x > 0) {
return 1;
}
if (x < 0) {
return -1;
}
return 0;
}
std::pair<AnfNodePtr, size_t> GetKernelInput(const AnfNodePtr &anf_node, size_t index) {
MS_EXCEPTION_IF_NULL(anf_node);
if (index >= AnfAlgo::GetInputTensorNum(anf_node)) {
MS_EXCEPTION(ArgumentError) << "Index is out of the size of anf_node inputs. Node info : ["
<< anf_node->DebugString() << "]";
}
auto cnode = anf_node->cast<CNodePtr>();
if (cnode == nullptr) {
return AnfAlgo::VisitKernel(anf_node, 0);
} else {
return AnfAlgo::VisitKernel(anf_node->cast<CNodePtr>()->input(index + 1), 0);
}
}
std::vector<std::pair<AnfNodePtr, std::pair<size_t, size_t>>> GetInputIndex(const std::vector<AnfNodePtr> &node_list,
const std::vector<AnfNodePtr> &input_list) {
std::vector<std::pair<AnfNodePtr, std::pair<size_t, size_t>>> input_index;
for (size_t i = 0; i < input_list.size(); ++i) {
auto const &input = input_list[i];
MS_EXCEPTION_IF_NULL(input);
bool found = false;
auto mng = input->func_graph()->manager();
MS_EXCEPTION_IF_NULL(mng);
const NodeUsersMap &users = mng->node_users();
auto input_users = users.find(input);
if (input_users == users.end() || input_users->second.empty()) {
MS_EXCEPTION(ArgumentError) << "Input [" << i << "][" << input->DebugString(2) << "] of ["
<< input->func_graph()->ToString() << "] has no users.";
}
for (auto const &input_user : input_users->second) {
for (auto const &anf_node : node_list) {
if (anf_node != input_user.first) {
continue;
}
std::vector<int64_t> dyn_input_sizes;
auto prim = AnfAlgo::GetCNodePrimitive(anf_node);
MS_EXCEPTION_IF_NULL(prim);
if (prim->GetAttr(kAttrDynInputSizes) != nullptr) {
dyn_input_sizes = GetValue<const std::vector<int64_t>>(prim->GetAttr(kAttrDynInputSizes));
}
if (dyn_input_sizes.empty()) {
input_index.push_back(std::make_pair(anf_node, std::make_pair(IntToSize(input_user.second - 1), 0)));
found = true;
break;
} else {
int used_as_idx = input_user.second - 1;
int accum_idx = 0;
size_t dyn_i = 0;
for (; dyn_i < dyn_input_sizes.size(); ++dyn_i) {
accum_idx += LongToInt(dyn_input_sizes[dyn_i]);
if (used_as_idx < accum_idx) {
input_index.push_back(std::make_pair(
anf_node, std::make_pair(dyn_i, IntToSize(used_as_idx - (accum_idx - dyn_input_sizes[dyn_i])))));
break;
}
}
if (dyn_i != dyn_input_sizes.size()) {
found = true;
break;
}
}
}
if (found) {
break;
}
}
if (!found) {
MS_EXCEPTION(ArgumentError) << "Input [" << i << "][" << input->DebugString(2) << "] of ["
<< input->func_graph()->ToString() << "] found no related kernel info.";
}
}
return input_index;
}
std::vector<std::pair<AnfNodePtr, size_t>> GetOutputIndex(const std::vector<AnfNodePtr> &node_list,
const std::vector<AnfNodePtr> &input_list,
const std::vector<AnfNodePtr> &output_list) {
std::vector<std::pair<AnfNodePtr, size_t>> output_index;
for (size_t i = 0; i < output_list.size(); ++i) {
auto const &output = output_list[i];
MS_EXCEPTION_IF_NULL(output);
bool found = false;
auto pree_node = AnfAlgo::VisitKernel(output, 0);
auto pos = std::find(std::begin(node_list), std::end(node_list), pree_node.first);
if (pos != std::end(node_list)) {
output_index.push_back(pree_node);
continue;
}
auto ret = std::find(std::begin(input_list), std::end(input_list), pree_node.first);
if (ret != std::end(input_list)) {
output_index.push_back(std::make_pair(pree_node.first, 0));
found = true;
}
if (!found) {
MS_EXCEPTION(ArgumentError) << "Output [" << i << "][" << output->DebugString(2) << "] of ["
<< output->func_graph()->ToString() << "] found no related kernel info.";
}
}
return output_index;
}
void GetValidKernelNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr> *node_list) {
MS_EXCEPTION_IF_NULL(node_list);
MS_EXCEPTION_IF_NULL(func_graph);
std::vector<AnfNodePtr> node_lists = TopoSort(func_graph->get_return());
for (auto const &node : node_lists) {
if (!AnfAlgo::IsRealKernel(node) || !node->isa<CNode>()) {
continue;
}
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (IsValueNode<Primitive>(cnode->input(kAnfPrimitiveIndex))) {
node_list->push_back(node);
}
}
}
void GetValidKernelNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr> *node_list,
std::vector<AnfNodePtr> *input_list, std::vector<AnfNodePtr> *output_list) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(node_list);
MS_EXCEPTION_IF_NULL(input_list);
GetValidKernelNodes(func_graph, node_list);
auto parameters = func_graph->parameters();
input_list->insert(input_list->begin(), parameters.begin(), parameters.end());
GetFuncGraphOutputNodes(func_graph, output_list);
}
void GetFuncGraphOutputNodes(const FuncGraphPtr &func_graph, std::vector<AnfNodePtr> *output_list) {
MS_EXCEPTION_IF_NULL(func_graph);
MS_EXCEPTION_IF_NULL(output_list);
auto func_output = func_graph->output();
MS_EXCEPTION_IF_NULL(func_output);
if (func_output->isa<CNode>()) {
// multi output.
auto cnode = func_output->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
auto input0 = cnode->input(kAnfPrimitiveIndex);
MS_EXCEPTION_IF_NULL(input0);
if (IsPrimitive(input0, prim::kPrimMakeTuple)) {
for (size_t input_idx = 1; input_idx < cnode->inputs().size(); ++input_idx) {
auto input_node = cnode->input(input_idx);
MS_EXCEPTION_IF_NULL(input_node);
if (input_node->isa<CNode>() && AnfAlgo::GetInputTensorNum(input_node) == 0) {
continue;
}
output_list->push_back(AnfAlgo::VisitKernel(input_node, 0).first);
}
} else {
// single output.
output_list->push_back(AnfAlgo::VisitKernel(func_output, 0).first);
}
} else {
// single output.
output_list->push_back(AnfAlgo::VisitKernel(func_output, 0).first);
}
}
bool GetInputTensorValue(const AnfNodePtr &anf_node, size_t input_idx, nlohmann::json *const node_json) {
MS_EXCEPTION_IF_NULL(anf_node);
MS_EXCEPTION_IF_NULL(node_json);
auto cnode = anf_node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (input_idx + 1 >= cnode->size()) {
MS_EXCEPTION(ArgumentError) << "input_idx [" << input_idx << "] is out of index of inputs of ["
<< cnode->inputs().size() << "][" << cnode->DebugString() << "]";
}
auto input_node = cnode->input(input_idx + 1);
if (!IsValueNode<tensor::Tensor>(input_node)) {
return false;
}
auto tensor = GetValueNode<tensor::TensorPtr>(input_node);
if (tensor == nullptr) {
MS_LOG(DEBUG) << "Value of input node is nullptr, op: [" << input_node->DebugString() << "]";
return false;
}
auto type_id = tensor->data_type();
auto *data = tensor->data_c();
MS_EXCEPTION_IF_NULL(data);
if (tensor->DataSize() > 1) {
// not const tensor.
MS_LOG(WARNING) << "Not take value of tensor whose datasize greater than 1, [" << input_node->DebugString(2) << "]";
return false;
}
if (type_id == kFloat64->type_id()) {
(*node_json)["value"] = static_cast<double *>(data)[0];
} else if (type_id == kFloat32->type_id()) {
(*node_json)["value"] = static_cast<float *>(data)[0];
} else if (type_id == kFloat16->type_id()) {
float16 *val = static_cast<float16 *>(data);
(*node_json)["value"] = static_cast<float>(val[0]);
} else if (type_id == kUInt64->type_id()) {
(*node_json)["value"] = static_cast<uint64_t *>(data)[0];
} else if (type_id == kUInt32->type_id()) {
(*node_json)["value"] = static_cast<uint32_t *>(data)[0];
} else if (type_id == kUInt16->type_id()) {
(*node_json)["value"] = static_cast<uint16_t *>(data)[0];
} else if (type_id == kUInt8->type_id()) {
(*node_json)["value"] = static_cast<uint8_t *>(data)[0];
} else if (type_id == kInt64->type_id()) {
(*node_json)["value"] = static_cast<int64_t *>(data)[0];
} else if (type_id == kInt32->type_id()) {
(*node_json)["value"] = static_cast<int32_t *>(data)[0];
} else if (type_id == kInt16->type_id()) {
(*node_json)["value"] = static_cast<int16_t *>(data)[0];
} else if (type_id == kInt8->type_id()) {
(*node_json)["value"] = static_cast<int8_t *>(data)[0];
} else if (type_id == kBool->type_id()) {
(*node_json)["value"] = static_cast<bool *>(data)[0];
} else {
MS_LOG(EXCEPTION) << "Unknown value type of tensor[" << cnode->DebugString() << "]";
}
return true;
}
bool IsWeightBoundary(const AnfNodePtr &node) {
if (node->isa<ValueNode>()) {
return true;
}
if (node->isa<Parameter>() && AnfAlgo::IsParameterWeight(node->cast<ParameterPtr>())) {
return true;
}
return false;
}
std::vector<int64_t> GetReduceAttrAxis(const CNodePtr &cnode) {
if (AnfAlgo::GetInputTensorNum(cnode) != AnfAlgo::GetOutputTensorNum(cnode) &&
AnfAlgo::GetInputTensorNum(cnode) != 1) {
MS_LOG(EXCEPTION) << "The reduce node [" << cnode->DebugString() << "] is not single input or single output ";
}
std::vector<int64_t> axis;
auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode, 0);
auto primitive = AnfAlgo::GetCNodePrimitive(cnode);
MS_EXCEPTION_IF_NULL(primitive);
auto axis_attr = primitive->GetAttr(kAxis);
if (axis_attr == nullptr) {
MS_LOG(ERROR) << "This node doesn't have axis attr. Node info [" << cnode->DebugString() << "]";
return std::vector<int64_t>();
}
std::vector<int64_t> axis_list;
if (axis_attr->isa<Int64Imm>()) {
(void)axis_list.emplace_back(GetValue<int64_t>(axis_attr));
} else {
axis_list = GetValue<std::vector<int64_t>>(axis_attr);
}
for (const auto &elem : axis_list) {
if (elem < 0) {
(void)axis.emplace_back(input_shape.size() + elem);
} else {
(void)axis.emplace_back(elem);
}
}
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(axis), cnode);
return axis;
}
std::string GetProcessorStr(const AnfNodePtr &anf_node) {
MS_EXCEPTION_IF_NULL(anf_node);
std::string processor = kProcessorUnknown;
auto kernel_info = dynamic_cast<device::KernelInfo *>(anf_node->kernel_info());
MS_EXCEPTION_IF_NULL(kernel_info);
auto build_info = kernel_info->select_kernel_build_info();
// we may call this before kernel select.
if (build_info == nullptr) {
return processor;
}
switch (build_info->processor()) {
case Processor::AICORE:
processor = kProcessorAiCore;
break;
case Processor::AICPU:
processor = kProcessorAiCpu;
break;
case Processor::CUDA:
processor = kProcessorCuda;
break;
default:
MS_LOG(ERROR) << "Unknown processor type.";
break;
}
return processor;
}
Processor GetProcessorFromContext() {
kernel::Processor processor = kernel::Processor::UNKNOWN;
auto context_ptr = MsContext::GetInstance();
MS_EXCEPTION_IF_NULL(context_ptr);
auto device_info = context_ptr->get_param<std::string>(MS_CTX_DEVICE_TARGET);
if (device_info == kGPUDevice) {
processor = kernel::Processor::CUDA;
} else if (device_info == kAscendDevice) {
processor = kernel::Processor::AICORE;
}
return processor;
}
std::string GetStrProcessorFromContext() {
auto processor = GetProcessorFromContext();
string str_processor = kernel::kProcessorUnknown;
if (processor == kernel::Processor::CUDA) {
str_processor = kernel::kProcessorCuda;
} else if (processor == kernel::Processor::AICORE) {
str_processor = kernel::kProcessorAiCore;
}
return str_processor;
}
float Scaling(size_t in_size, size_t out_size, bool align_corners) {
return (align_corners && out_size > 1) ? (in_size - 1) / static_cast<float>(out_size - 1)
: in_size / static_cast<float>(out_size);
}
float ScaleGrid(const int x, const float scale) { return static_cast<float>(x) * scale; }
void ComputeInterpolationWeights(const size_t out_size, const size_t in_size, const float scale,
CachedInterpolation *interpolation) {
interpolation[out_size].lower = 0;
interpolation[out_size].upper = 0;
for (size_t i = 0; i <= out_size - 1; ++i) {
const float in = ScaleGrid(i, scale);
const float in_f = std::floor(in);
interpolation[i].lower = std::max(static_cast<size_t>(in_f), static_cast<size_t>(0));
interpolation[i].upper = std::min(static_cast<size_t>(std::ceil(in)), in_size - 1);
interpolation[i].lerp = in - in_f;
}
}
bool GetShapeSize(const std::vector<size_t> &shape, const TypePtr &type_ptr, int64_t *size_i) {
MS_EXCEPTION_IF_NULL(type_ptr);
size_t type_byte = GetTypeByte(type_ptr);
if (type_byte == 0) {
return false;
}
for (size_t j = 0; j < shape.size(); j++) {
size_i[0] = LongMulWithOverflowCheck(size_i[0], static_cast<int>(shape[j]));
}
size_i[0] = LongMulWithOverflowCheck(size_i[0], SizeToInt(type_byte));
return true;
}
void CastShapeSizeToLong(const std::vector<size_t> &shape, std::vector<int64_t> *long_shape) {
MS_EXCEPTION_IF_NULL(long_shape);
std::transform(shape.begin(), shape.end(), std::back_inserter(*long_shape), SizeToLong);
}
void CheckSliceValid(const std::vector<int64_t> &start, const std::vector<int64_t> &stop,
const std::vector<int64_t> &step, const std::vector<int64_t> &input_shape) {
if (start.size() != stop.size() || start.size() != step.size() || start.size() > input_shape.size()) {
MS_LOG(EXCEPTION)
<< "TensorCopySlices requires the length of begin, stride and end must be equal and less than input dimension.";
}
size_t size = start.size();
for (size_t i = 0; i < size; ++i) {
if (stop[i] <= start[i]) {
MS_LOG(EXCEPTION) << "Invalid slice: (" << start[i] << ", " << stop[i] << " ," << step[i] << ")";
}
// Operator need to be generalized in the future. Only support to copy continuous memory now.
if (step[i] != 1) {
MS_LOG(EXCEPTION) << "The element in step only support 1, but got:" << step;
}
}
size_t slice_pos = size;
for (size_t i = 0; i < size; ++i) {
if (stop[i] - start[i] > 1) {
slice_pos = i;
break;
}
}
for (size_t i = slice_pos + 1; i < size; ++i) {
if (stop[i] - start[i] != input_shape[i]) {
MS_LOG(EXCEPTION) << "Only support copy continuous memory now. For example tensor[0, 0:100] is fine, "
"but tensor[0:100, 0] is not supported.";
}
}
}
size_t GetCopySize(const std::vector<int64_t> &dim_offset, const std::vector<int64_t> &start,
const std::vector<int64_t> &stop) {
for (size_t i = 0; i < start.size(); ++i) {
if (stop[i] - start[i] != 1) {
return SizetMulWithOverflowCheck(LongToSize(stop[i] - start[i]), LongToSize(dim_offset[i]));
}
}
return LongToSize(dim_offset[start.size() - 1]);
}
std::vector<int64_t> CalDimOffset(const std::vector<int64_t> &input_shape) {
std::vector<int64_t> dim_offset;
int64_t offset = 1;
for (auto iter = input_shape.rbegin(); iter != input_shape.rend(); ++iter) {
dim_offset.push_back(offset);
offset = offset * (*iter);
}
std::reverse(dim_offset.begin(), dim_offset.end());
return dim_offset;
}
size_t CalOffset(const std::vector<int64_t> &start, const std::vector<int64_t> &stop,
const std::vector<int64_t> &dim_offset) {
size_t size = start.size();
size_t offset = 0;
for (size_t i = 0; i < size; ++i) {
offset += SizetMulWithOverflowCheck(LongToSize(dim_offset[i]), start[i]);
if (stop[i] - start[i] != 1) {
break;
}
}
return offset;
}
size_t UnitSizeInBytes(const mindspore::TypeId &t) {
size_t bytes = 0;
switch (t) {
case kNumberTypeBool:
case kNumberTypeInt8:
case kNumberTypeUInt8:
bytes = sizeof(int8_t);
break;
case kNumberTypeInt16:
case kNumberTypeUInt16:
case kNumberTypeFloat16:
bytes = sizeof(int16_t);
break;
case kNumberTypeInt:
case kNumberTypeUInt:
case kNumberTypeInt32:
case kNumberTypeUInt32:
case kNumberTypeFloat:
case kNumberTypeFloat32:
bytes = sizeof(int32_t);
break;
case kNumberTypeUInt64:
case kNumberTypeInt64:
case kNumberTypeFloat64:
bytes = sizeof(int64_t);
break;
default:
MS_LOG(EXCEPTION) << "Invalid types " << t;
break;
}
return bytes;
}
} // namespace kernel
} // namespace mindspore
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