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mindspore2022/mindspore/ccsrc/debug/debug_services.cc

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/**
* Copyright 2019-2020 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 "debug/debug_services.h"
#include <dirent.h>
#include <fstream>
#include <algorithm>
#include <map>
#include <unordered_set>
#ifdef ONLINE_DBG_MODE
#include "backend/session/anf_runtime_algorithm.h"
#endif
#include "debug/debugger/tensor_summary.h"
#ifdef ONLINE_DBG_MODE
namespace mindspore {
#endif
DebugServices::DebugServices() {
tensor_loader_ = new TensorLoader();
uint32_t iter_num = -1;
tensor_loader_->set_iter_num(iter_num);
}
DebugServices::DebugServices(const DebugServices &other) {
tensor_loader_ = other.tensor_loader_;
watchpoint_table = other.watchpoint_table;
}
DebugServices &DebugServices::operator=(const DebugServices &other) {
if (this != &other) {
tensor_loader_ = other.tensor_loader_;
watchpoint_table = other.watchpoint_table;
}
return *this;
}
DebugServices::~DebugServices() { delete tensor_loader_; }
void DebugServices::AddWatchpoint(
unsigned int id, unsigned int watch_condition, float parameter,
const std::vector<std::tuple<std::string, bool>> &check_node_list, const std::vector<parameter_t> &parameter_list,
const std::vector<std::tuple<std::string, std::vector<uint32_t>>> *check_node_device_list,
const std::vector<std::tuple<std::string, std::vector<uint32_t>>> *check_node_graph_list) {
std::lock_guard<std::mutex> lg(lock_);
watchpoint_t watchpoint_item;
watchpoint_item.id = id;
watchpoint_item.condition.type = static_cast<CONDITION_TYPE>(watch_condition);
watchpoint_item.condition.parameter = parameter;
watchpoint_item.check_node_list = check_node_list;
if (check_node_device_list != nullptr) {
watchpoint_item.check_node_device_list = *check_node_device_list;
}
if (check_node_graph_list != nullptr) {
watchpoint_item.check_node_graph_list = *check_node_graph_list;
}
watchpoint_item.parameter_list = parameter_list;
watchpoint_table[id] = watchpoint_item;
}
void DebugServices::RemoveWatchpoint(unsigned int id) {
std::lock_guard<std::mutex> lg(lock_);
watchpoint_table.erase(id);
}
std::unique_ptr<ITensorSummary> GetSummaryPtr(const std::shared_ptr<TensorData> &tensor, void *previous_tensor_ptr,
uint32_t num_elements, int tensor_dtype) {
switch (tensor_dtype) {
case DbgDataType::DT_UINT8: {
return std::make_unique<TensorSummary<uint8_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_INT8: {
return std::make_unique<TensorSummary<int8_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_UINT16: {
return std::make_unique<TensorSummary<uint16_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_INT16: {
return std::make_unique<TensorSummary<int16_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_UINT32: {
return std::make_unique<TensorSummary<uint32_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_INT32:
case DbgDataType::DT_BASE_INT: {
return std::make_unique<TensorSummary<int32_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_UINT64: {
return std::make_unique<TensorSummary<uint64_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_INT64: {
return std::make_unique<TensorSummary<int64_t>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_FLOAT16: {
return std::make_unique<TensorSummary<float16>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_FLOAT32:
case DbgDataType::DT_BASE_FLOAT: {
return std::make_unique<TensorSummary<float>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_FLOAT64: {
return std::make_unique<TensorSummary<double>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
case DbgDataType::DT_BOOL: {
return std::make_unique<TensorSummary<bool>>(tensor->GetDataPtr(), previous_tensor_ptr, num_elements);
}
default:
MS_LOG(INFO) << "Unsupported tensor type";
// return a null pointer
return std::unique_ptr<TensorSummary<int32_t>>{};
}
}
#ifdef OFFLINE_DBG_MODE
void *DebugServices::GetPrevTensor(const std::shared_ptr<TensorData> &tensor, bool previous_iter_tensor_needed) {
void *previous_tensor_ptr = nullptr;
std::shared_ptr<TensorData> tensor_prev;
if (previous_iter_tensor_needed && tensor->GetIteration() > 1) {
// read data in offline mode
std::vector<std::shared_ptr<TensorData>> result_list_prev;
ReadDumpedTensor(std::vector<std::string>{tensor->GetName()}, std::vector<size_t>{tensor->GetSlot()},
std::vector<unsigned int>{tensor->GetDeviceId()},
std::vector<unsigned int>{tensor->GetIteration() - 1},
std::vector<unsigned int>{tensor->GetRootGraphId()}, &result_list_prev);
tensor_prev = result_list_prev[0];
if (!tensor_prev->GetByteSize()) {
tensor_prev.reset();
} else {
previous_tensor_ptr = tensor_prev->GetDataPtr();
}
}
return previous_tensor_ptr;
}
#endif
void DebugServices::AddWatchPointsToCheck(bool init_dbg_suspend, bool step_end, bool recheck,
const std::string &tensor_name, const std::string &tensor_name_no_slot,
bool *previous_iter_tensor_needed, std::string *qualified_tensor_name,
std::vector<watchpoint_t> *watchpoints_to_check) {
for (auto w_table_item : watchpoint_table) {
auto wp = std::get<1>(w_table_item);
// check ONLY init conditions on initial suspended state.
// skip other conditions on initial suspended state
if (init_dbg_suspend && (wp.condition.type != INIT)) continue;
// skip init condition if not init suspend
if ((wp.condition.type == INIT) && !init_dbg_suspend) continue;
// check change conditions only on step end.
if (wp.change_condition() && !step_end) continue;
// if recheck, ignore the cache results and reanalyze everything.
// if not a recheck, check only unanalyzed tensors
if (!recheck && wp_id_cache[tensor_name].count(wp.id)) continue;
std::string found = wp.FindQualifiedTensorName(tensor_name_no_slot);
if (!found.empty()) {
*qualified_tensor_name = found;
watchpoints_to_check->push_back(w_table_item.second);
#ifdef OFFLINE_DBG_MODE
if (wp.change_condition()) {
*previous_iter_tensor_needed = true;
}
#endif
}
}
}
void DebugServices::AddAnalyzedTensorToCache(const bool recheck, const unsigned int id,
const std::string &tensor_name) {
// add analyzed tensor to cache
if (!recheck) {
wp_id_cache[tensor_name].insert(id);
}
}
void DebugServices::CheckWatchpoints(std::vector<std::string> *name, std::vector<std::string> *slot,
std::vector<int> *condition, std::vector<unsigned int> *watchpoint_id,
std::vector<std::vector<parameter_t>> *parameters,
std::vector<int32_t> *error_codes, const std::vector<std::string> &op_overflows,
std::vector<std::shared_ptr<TensorData>> *tensor_list, const bool init_dbg_suspend,
const bool step_end, const bool recheck, std::vector<unsigned int> *device_id,
std::vector<unsigned int> *root_graph_id) {
std::lock_guard<std::mutex> lg(lock_);
if (watchpoint_table.empty()) return;
for (auto &tensor : *tensor_list) {
#ifdef OFFLINE_DBG_MODE
// read data in offline mode
std::vector<std::shared_ptr<TensorData>> result_list;
ReadDumpedTensor(std::vector<std::string>{tensor->GetName()}, std::vector<size_t>{tensor->GetSlot()},
std::vector<unsigned int>{tensor->GetDeviceId()},
std::vector<unsigned int>{tensor->GetIteration()},
std::vector<unsigned int>{tensor->GetRootGraphId()}, &result_list);
tensor = result_list[0];
if (!tensor->GetByteSize()) {
tensor.reset();
continue;
}
#endif
const auto tensor_name = tensor->GetName();
const auto tensor_name_no_slot = tensor_name.substr(0, tensor_name.find_first_of(':'));
const auto tensor_slot = std::to_string(tensor->GetSlot());
// no elements to analyze
if (tensor->GetByteSize() == 0) continue;
int tensor_dtype = tensor->GetType();
std::vector<watchpoint_t> watchpoints_to_check;
std::string qualified_tensor_name;
bool previous_iter_tensor_needed = false;
// Add do nothing line in case offline debug is off, prevent unused var warning
(void)previous_iter_tensor_needed;
AddWatchPointsToCheck(init_dbg_suspend, step_end, recheck, tensor_name, tensor_name_no_slot,
&previous_iter_tensor_needed, &qualified_tensor_name, &watchpoints_to_check);
// no wp set on current tensor
if (watchpoints_to_check.empty()) continue;
uint32_t num_elements = tensor->GetNumElements();
#ifdef OFFLINE_DBG_MODE
void *previous_tensor_ptr = GetPrevTensor(tensor, previous_iter_tensor_needed);
#else
void *previous_tensor_ptr =
tensor_loader_->GetPrevTensor(tensor_name) ? tensor_loader_->GetPrevTensor(tensor_name)->GetDataPtr() : nullptr;
#endif
std::unique_ptr<ITensorSummary> base_summary_ptr;
if (!(watchpoints_to_check.size() == 1 && watchpoints_to_check[0].condition.type == IS_OVERFLOW)) {
base_summary_ptr = GetSummaryPtr(tensor, previous_tensor_ptr, num_elements, tensor_dtype);
if (base_summary_ptr != nullptr) {
base_summary_ptr->SummarizeTensor(watchpoints_to_check);
}
}
for (auto &wp : watchpoints_to_check) {
bool is_hit = false;
int error_code = 0;
std::vector<parameter_t> parameter_list = {};
if (wp.condition.type == IS_OVERFLOW) {
is_hit = (std::find(op_overflows.begin(), op_overflows.end(), tensor_name_no_slot) != op_overflows.end());
} else if (base_summary_ptr != nullptr) {
auto item = base_summary_ptr->IsWatchpointHit(wp);
is_hit = std::get<0>(item);
error_code = std::get<1>(item);
parameter_list = std::get<2>(item);
}
AddAnalyzedTensorToCache(recheck, wp.id, tensor_name);
if (is_hit || error_code) {
name->push_back(qualified_tensor_name);
slot->push_back(tensor_slot);
condition->push_back(wp.condition.type);
watchpoint_id->push_back(wp.id);
if (device_id != nullptr) {
device_id->push_back(tensor->GetDeviceId());
}
if (root_graph_id != nullptr) {
root_graph_id->push_back(tensor->GetRootGraphId());
}
parameters->push_back(parameter_list);
error_codes->push_back(error_code);
}
}
#ifdef OFFLINE_DBG_MODE
// in offline mode remove the need for the data
tensor.reset();
#endif
}
}
#ifdef OFFLINE_DBG_MODE
void DebugServices::GetSlotInfo(const std::string &file_name, const std::string &dump_name,
const std::string &specific_dump_dir, std::vector<size_t> *slot_list) {
if (is_sync_mode) {
// get the slot from the name
std::string delimiter = "_";
unsigned int start_pos = dump_name.length();
unsigned int end_pos = file_name.find(delimiter, start_pos);
std::string item = file_name.substr(start_pos, end_pos - start_pos);
slot_list->push_back(std::stoul(item));
} else {
std::string out_dir = "/tmp/" + file_name;
std::string input_file = specific_dump_dir + "/" + file_name;
std::string log_enabled = DbgLogger::verbose ? "" : "> /dev/null";
std::string convert_command =
"python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " + input_file + " -out " +
out_dir + " -t bin " + log_enabled;
(void)(system(convert_command.c_str()) + 1);
convert_command = "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " +
input_file + " -out " + out_dir + " -f NCHW -t bin " + log_enabled;
(void)(system(convert_command.c_str()) + 1);
std::string prefix_converted_dump_file_name = file_name + ".output.";
DIR *convert_dir_ptr = opendir(out_dir.c_str());
if (convert_dir_ptr != nullptr) {
struct dirent *convert_dir_contents = nullptr;
while ((convert_dir_contents = readdir(convert_dir_ptr)) != NULL) {
if (convert_dir_contents->d_type == DT_REG) {
std::string converted_file_name = convert_dir_contents->d_name;
std::size_t nd_file = converted_file_name.rfind(".ND.bin");
std::size_t fractal_z_file = converted_file_name.rfind(".FRACTAL_Z.bin");
std::size_t nchw_file = converted_file_name.rfind(".NCHW.bin");
if (nd_file == std::string::npos && nchw_file == std::string::npos && fractal_z_file == std::string::npos) {
continue;
}
std::size_t found_c = converted_file_name.find(prefix_converted_dump_file_name);
if (found_c != 0) {
continue;
}
std::size_t slot_start_pos = prefix_converted_dump_file_name.length();
std::size_t slot_end_pos = converted_file_name.find(".", slot_start_pos) - 1;
std::string slot_item = converted_file_name.substr(slot_start_pos, slot_end_pos - slot_start_pos + 1);
slot_list->push_back(std::stoul(slot_item));
}
}
} else {
MS_LOG(INFO) << out_dir << " directory does not exist!";
}
closedir(convert_dir_ptr);
// std::string delete_cmd = "rm -rf " + out_dir;
// system(delete_cmd.c_str());
}
}
std::size_t DebugServices::GetShapeTypeInfo(const std::string &specific_dump_dir, std::size_t slot,
const std::string &prefix_dump_file_name, std::string *file_name,
std::string *type_name, std::string *out_dir, std::vector<int64_t> *shape) {
std::size_t found = 0;
if (is_sync_mode) {
found = file_name->rfind(prefix_dump_file_name, 0);
} else {
std::string file_name_w_o_prefix = file_name->substr(file_name->find('.') + 1);
found = file_name_w_o_prefix.rfind(prefix_dump_file_name, 0);
}
if (found != 0) {
return found;
}
if (is_sync_mode) {
// found a file, now get the shape and type
// find "_shape_" in the filename
std::string shape_delimiter = "_shape_";
unsigned int str_pos = file_name->find(shape_delimiter) + shape_delimiter.length();
// read numbers with '_' delimter until you read a non-number, that will be the type name
bool number_found = true;
std::string delimiter = "_";
while (number_found) {
unsigned int end_pos = file_name->find(delimiter, str_pos);
std::string item = file_name->substr(str_pos, end_pos - str_pos);
bool is_number = !item.empty() && std::find_if(item.begin(), item.end(),
[](unsigned char c) { return !std::isdigit(c); }) == item.end();
if (is_number) {
shape->push_back(std::stoul(item));
str_pos = end_pos + 1;
} else {
*type_name = item;
number_found = false;
}
}
} else {
*out_dir = "/tmp/" + *file_name;
std::string input_file = specific_dump_dir + "/" + *file_name;
std::string log_enabled = DbgLogger::verbose ? "" : "> /dev/null";
std::string convert_command =
"python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " + input_file + " -out " +
*out_dir + " -t bin " + log_enabled;
(void)(system(convert_command.c_str()) + 1);
convert_command = "python /usr/local/Ascend/toolkit/tools/operator_cmp/compare/msaccucmp.pyc convert -d " +
input_file + " -out " + *out_dir + " -f NCHW -t bin " + log_enabled;
(void)(system(convert_command.c_str()) + 1);
std::string prefix_converted_dump_file_name = *file_name + ".output." + std::to_string(slot);
*file_name = "";
DIR *convert_dir_ptr = opendir(out_dir->c_str());
if (convert_dir_ptr != nullptr) {
struct dirent *convert_dir_contents = nullptr;
while ((convert_dir_contents = readdir(convert_dir_ptr)) != NULL) {
if (convert_dir_contents->d_type == DT_REG) {
std::string converted_file_name = convert_dir_contents->d_name;
std::size_t nd_file = converted_file_name.rfind(".ND.bin");
std::size_t fractal_z_file = converted_file_name.rfind(".FRACTAL_Z.bin");
std::size_t nchw_file = converted_file_name.rfind(".NCHW.bin");
if (nd_file == std::string::npos && nchw_file == std::string::npos && fractal_z_file == std::string::npos) {
continue;
}
std::size_t found_c = converted_file_name.rfind(prefix_converted_dump_file_name, 0);
if (found_c != 0) {
continue;
}
*file_name = converted_file_name;
}
}
} else {
MS_LOG(INFO) << *out_dir << " directory does not exist!";
}
closedir(convert_dir_ptr);
if (*file_name == "") {
MS_LOG(WARNING) << out_dir << ": no valid files found post msaccucmp exec";
return 1;
}
// std::string delete_cmd = "rm -rf " + out_dir;
// system(delete_cmd.c_str());
// found a file, now get the shape and type
std::stringstream check_filename(*file_name);
std::vector<std::string> tokens;
std::string intermediate;
while (getline(check_filename, intermediate, '.')) {
tokens.push_back(intermediate);
}
*type_name = tokens[8];
std::string shape_str = tokens[7];
std::stringstream check_shape(shape_str);
while (getline(check_shape, intermediate, '_')) {
shape->push_back(std::stoul(intermediate));
}
}
return 0;
}
void DebugServices::ReadDumpedTensor(std::vector<std::string> backend_name, std::vector<size_t> slot,
std::vector<unsigned int> device_id, std::vector<unsigned int> iteration,
std::vector<unsigned int> root_graph_id,
std::vector<std::shared_ptr<TensorData>> *result_list) {
for (unsigned int i = 0; i < backend_name.size(); i++) {
// form prefix of the tensor file to read from graph pb node name
std::string dump_style_kernel_name = backend_name[i];
const std::string strsrc = "/";
std::string strdst;
if (is_sync_mode) {
strdst = "--";
} else {
strdst = "_";
}
std::string::size_type pos = 0;
std::string::size_type srclen = strsrc.size();
std::string::size_type dstlen = strdst.size();
// remove slot from name
std::size_t found_colon = dump_style_kernel_name.find_last_of(":");
dump_style_kernel_name = dump_style_kernel_name.substr(0, found_colon);
while ((pos = dump_style_kernel_name.find(strsrc, pos)) != std::string::npos) {
dump_style_kernel_name.replace(pos, srclen, strdst);
pos += dstlen;
}
std::string prefix_dump_file_name = dump_style_kernel_name;
if (is_sync_mode) {
prefix_dump_file_name += "_output_" + std::to_string(slot[i]) + "_";
}
std::string specific_dump_dir;
if (is_sync_mode) {
specific_dump_dir =
dump_dir + "/device_" + std::to_string(device_id[i]) + "/iteration_" + std::to_string(iteration[i]);
} else {
specific_dump_dir = dump_dir + "/device_" + std::to_string(device_id[i]) + "/" + net_name + "_graph_" +
std::to_string(root_graph_id[i]) + "/" + std::to_string(root_graph_id[i]) + "/" +
std::to_string(iteration[i]);
}
// search files in dir for the one that meets the filename prefix and read the file into memory
DIR *d;
d = opendir(specific_dump_dir.c_str());
std::vector<char> *buffer = NULL;
std::string type_name = "";
std::vector<int64_t> shape;
uint64_t data_size = 0;
if (d != nullptr) {
struct dirent *dir = nullptr;
while ((dir = readdir(d)) != NULL) {
if (dir->d_type == DT_REG) {
std::string file_name = dir->d_name;
std::string out_dir;
std::size_t found = GetShapeTypeInfo(specific_dump_dir, slot[i], prefix_dump_file_name, &file_name,
&type_name, &out_dir, &shape);
if (found != 0) {
continue;
}
// read the tensor data from the file
std::string file_path;
if (is_sync_mode) {
file_path = specific_dump_dir + "/" + file_name;
} else {
file_path = out_dir + "/" + file_name;
}
std::ifstream infile;
infile.open(file_path.c_str(), std::ios::binary | std::ios::ate);
if (!infile.is_open()) {
MS_LOG(ERROR) << "Failed to open bin file " << file_name;
break;
}
uint64_t file_size = infile.tellg();
infile.seekg(0, std::ios::beg);
buffer = new std::vector<char>(file_size);
if (!infile.read(buffer->data(), file_size)) {
MS_LOG(ERROR) << "Failed to read in bin file " << file_name;
break;
}
data_size = file_size;
infile.close();
}
}
} else {
MS_LOG(INFO) << "directory does not exist!";
}
closedir(d);
// call LoadNewTensor to store tensor in internal cache
auto tensor_data = std::make_shared<TensorData>();
tensor_data->SetName(backend_name[i]);
tensor_data->SetExecutionOrder(0);
tensor_data->SetSlot(slot[i]);
tensor_data->SetIteration(iteration[i]);
tensor_data->SetDeviceId(device_id[i]);
tensor_data->SetRootGraphId(root_graph_id[i]);
if (data_size) {
tensor_data->SetDataPtr(buffer->data());
} else {
tensor_data->SetDataPtr(NULL);
}
tensor_data->SetByteSize(data_size);
tensor_data->SetType(type_name);
tensor_data->SetShape(shape);
if (data_size) {
tensor_loader_->LoadNewTensor(tensor_data, false);
}
// add to result_list
result_list->push_back(tensor_data);
}
}
void ReplaceSrcFileName(const bool is_sync_mode, std::string *dump_style_name) {
const std::string strsrc = "/";
std::string strdst;
if (is_sync_mode) {
strdst = "--";
} else {
strdst = "_";
}
std::string::size_type pos = 0;
std::string::size_type srclen = strsrc.size();
std::string::size_type dstlen = strdst.size();
while ((pos = dump_style_name->find(strsrc, pos)) != std::string::npos) {
dump_style_name->replace(pos, srclen, strdst);
pos += dstlen;
}
}
std::vector<std::shared_ptr<TensorData>> DebugServices::ReadNeededDumpedTensors(unsigned int iteration) {
// get a list of nodes and the devices they are on to monitor
std::vector<std::shared_ptr<TensorData>> tensor_list;
std::map<std::tuple<uint32_t, uint32_t>, std::unordered_set<std::string>> device_and_graph_to_nodes;
for (auto w_table_item : watchpoint_table) {
auto wp = std::get<1>(w_table_item);
for (auto check_node : wp.check_node_list) {
unsigned int index = 0;
std::string w_name = std::get<0>(check_node);
bool w_is_param = std::get<1>(check_node);
std::string node_name = w_name;
if (w_is_param) {
std::size_t found = node_name.find_last_of("/");
node_name = node_name.substr(found + 1);
}
std::vector<uint32_t> devices = std::get<1>(wp.check_node_device_list[index]);
std::vector<uint32_t> graphs = std::get<1>(wp.check_node_graph_list[index]);
for (auto device : devices) {
for (auto graph : graphs) {
std::tuple<uint32_t, uint32_t> key(device, graph);
device_and_graph_to_nodes[key].insert(node_name);
}
}
index++;
}
}
// scan each device/iteration dir for the watched nodes for each device, and add to tensor_list
// as they are found
for (auto const &device_and_graph_item : device_and_graph_to_nodes) {
std::tuple<uint32_t, uint32_t> device_and_graph = device_and_graph_item.first;
uint32_t device_id = std::get<0>(device_and_graph);
uint32_t root_graph_id = std::get<1>(device_and_graph);
std::unordered_set<std::string> wp_nodes = device_and_graph_item.second;
std::vector<std::tuple<std::string, std::string>> proto_to_dump;
std::string specific_dump_dir;
if (is_sync_mode) {
specific_dump_dir = dump_dir + "/device_" + std::to_string(device_id) + "/iteration_" + std::to_string(iteration);
} else {
specific_dump_dir = dump_dir + "/device_" + std::to_string(device_id) + "/" + net_name + "_graph_" +
std::to_string(root_graph_id) + "/" + std::to_string(root_graph_id) + "/" +
std::to_string(iteration);
}
// convert node names to dump style
for (auto node : wp_nodes) {
std::string orig_name = node;
std::string dump_style_name = node;
ReplaceSrcFileName(is_sync_mode, &dump_style_name);
if (is_sync_mode) {
dump_style_name.append("_output_");
}
proto_to_dump.push_back(std::tuple<std::string, std::string>(orig_name, dump_style_name));
}
// search files in dir for the one that meets the filename prefix and read the file into memory
DIR *d;
d = opendir(specific_dump_dir.c_str());
if (d != nullptr) {
struct dirent *dir = nullptr;
while ((dir = readdir(d)) != NULL) {
if (dir->d_type == DT_REG) {
std::string file_name = dir->d_name;
for (auto &node : proto_to_dump) {
std::string dump_name = std::get<1>(node);
std::size_t found = 0;
if (is_sync_mode) {
found = file_name.rfind(dump_name, 0);
} else {
std::string file_name_w_o_prefix = file_name.substr(file_name.find('.') + 1);
found = file_name_w_o_prefix.rfind(dump_name, 0);
}
if (found == 0) {
std::vector<size_t> slot_list;
GetSlotInfo(file_name, dump_name, specific_dump_dir, &slot_list);
for (auto slot : slot_list) {
// add a TensorData entry (data will be read when needed)
std::vector<int64_t> shape;
std::string orig_name = std::get<0>(node);
auto tensor_data = std::make_shared<TensorData>();
tensor_data->SetName(orig_name);
tensor_data->SetExecutionOrder(0);
tensor_data->SetSlot(slot);
tensor_data->SetIteration(iteration);
tensor_data->SetDeviceId(device_id);
tensor_data->SetRootGraphId(root_graph_id);
tensor_data->SetDataPtr(NULL);
tensor_data->SetByteSize(0);
tensor_data->SetType("");
tensor_data->SetShape(shape);
tensor_list.push_back(tensor_data);
}
break;
}
}
}
}
}
}
return tensor_list;
}
#endif
void DebugServices::ReadNodesTensors(std::vector<std::string> name, std::vector<std::string> *ret_name,
std::vector<char *> *data_ptr, std::vector<ssize_t> *data_size,
std::vector<unsigned int> *dtype, std::vector<std::vector<int64_t>> *shape) {
std::vector<std::tuple<std::string, std::shared_ptr<TensorData>>> result_list;
tensor_loader_->SearchTensors(name, &result_list);
for (auto result : result_list) {
if (!std::get<1>(result)) {
continue;
}
ret_name->push_back(std::get<0>(result));
data_ptr->push_back(reinterpret_cast<char *>(std::get<1>(result)->GetDataPtr()));
data_size->push_back(std::get<1>(result)->GetByteSize());
dtype->push_back(std::get<1>(result)->GetType());
shape->push_back(std::get<1>(result)->GetShape());
}
}
#ifdef ONLINE_DBG_MODE
bool DebugServices::IsWatchPoint(const std::string &kernel_name, const CNodePtr &kernel) const {
bool ret = false;
for (auto w_table_item : watchpoint_table) {
auto check_node_list = std::get<1>(w_table_item).check_node_list;
for (auto check_node : check_node_list) {
std::string w_name = std::get<0>(check_node);
bool w_type = std::get<1>(check_node);
if ((w_type == true &&
((kernel_name.find(w_name) != string::npos && kernel_name.rfind(w_name, 0) == 0) || w_name == "*")) ||
(w_type == false && (kernel_name == w_name || IsWatchPointNodeInput(w_name, kernel)))) {
ret = true;
return ret;
}
}
}
return ret;
}
bool DebugServices::IsWatchPointNodeInput(const std::string &w_name, const CNodePtr &kernel) const {
if (kernel) {
auto input_size = AnfAlgo::GetInputTensorNum(kernel);
for (size_t j = 0; j < input_size; ++j) {
auto input_kernel = kernel->input(j + 1);
std::string input_kernel_name = input_kernel->fullname_with_scope();
auto found = w_name.find_last_of('/');
if (found != std::string::npos && w_name.substr(found + 1) == input_kernel_name) return true;
}
return false;
} else {
return false;
}
}
#endif
void DebugServices::EmptyTensor() { tensor_loader_->EmptyTensor(); }
std::vector<std::shared_ptr<TensorData>> DebugServices::GetTensor() const { return tensor_loader_->GetTensor(); }
std::vector<std::shared_ptr<TensorData>> DebugServices::GetNodeTensorMap(const std::string &node_name) const {
return tensor_loader_->GetNodeTensorMap(node_name);
}
uint32_t DebugServices::GetTensorLoaderIterNum() const { return tensor_loader_->GetIterNum(); }
void DebugServices::SetTensorLoaderIterNum(uint32_t iter_num) { tensor_loader_->set_iter_num(iter_num); }
void DebugServices::EmptyPrevTensor() { tensor_loader_->EmptyPrevTensor(); }
void DebugServices::EmptyCurrentTensor() { tensor_loader_->EmptyCurrentTensor(); }
#ifdef ONLINE_DBG_MODE
bool DebugServices::DumpTensorToFile(const std::string &tensor_name, bool trans_flag, const std::string &filepath,
const std::string &host_fmt, const std::vector<int64_t> &host_shape,
TypeId host_type, TypeId addr_type_id, const std::string &addr_format,
size_t slot) const {
return tensor_loader_->DumpTensorToFile(tensor_name, trans_flag, filepath, host_fmt, host_shape, host_type,
addr_type_id, addr_format, slot);
}
#endif
bool DebugServices::LoadNewTensor(const std::shared_ptr<TensorData> &tensor, bool keep_prev) {
return tensor_loader_->LoadNewTensor(tensor, keep_prev);
}
std::unordered_map<unsigned int, DebugServices::watchpoint_t> DebugServices::GetWatchpointTable() {
return watchpoint_table;
}
void DebugServices::ResetLoadedTensors() {
wp_id_cache.clear();
MS_LOG(INFO) << "Resetting loaded tensors";
tensor_loader_->MoveParametersCurrentToPrev();
tensor_loader_->EmptyCurrentTensor();
// will move parameters from previous to current map
tensor_loader_->SwapCurrentPrev();
}
#ifdef ONLINE_DBG_MODE
std::vector<std::shared_ptr<TensorData>> DebugServices::GetNodeTensor(const CNodePtr &kernel) {
MS_EXCEPTION_IF_NULL(kernel);
std::vector<std::shared_ptr<TensorData>> result;
auto output_size = AnfAlgo::GetOutputTensorNum(kernel);
auto kernel_name = kernel->fullname_with_scope();
for (size_t j = 0; j < output_size; ++j) {
auto tensor_name_with_slot = kernel_name + ":" + std::to_string(j);
auto tensor = tensor_loader_->GetTensor(tensor_name_with_slot);
if (tensor) result.push_back(tensor);
}
return result;
}
#endif
bool DebugServices::TensorExistsInCurrent(std::string tensor_name) {
return tensor_loader_->TensorExistsInCurrent(tensor_name);
}
void DebugServices::MoveTensorCurrentToPrev(std::string tensor_name) {
tensor_loader_->MoveTensorCurrentToPrev(tensor_name);
}
void DebugServices::SetNetName(std::string net_name) { this->net_name = net_name; }
std::string DebugServices::GetNetName() { return net_name; }
void DebugServices::SetDumpDir(std::string dump_dir) { this->dump_dir = dump_dir; }
std::string DebugServices::GetDumpDir() { return dump_dir; }
void DebugServices::SetSyncMode(bool is_sync_mode) { this->is_sync_mode = is_sync_mode; }
bool DebugServices::GetSyncMode() { return is_sync_mode; }
#ifdef ONLINE_DBG_MODE
} // namespace mindspore
#endif
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