@@ -21,6 +21,7 @@ | |||
#include "framework/common/string_util.h" | |||
#include "graph/ge_context.h" | |||
#include "runtime/base.h" | |||
#include "graph/load/new_model_manager/davinci_model.h" | |||
namespace { | |||
const char *const kJobID = "jobID"; | |||
@@ -39,10 +40,12 @@ const std::string kConfigNumsdev = "devNums"; | |||
const std::string kConfigDevIdList = "devIdList"; | |||
const std::string kProfStart = "prof_start"; | |||
const std::string kProfStop = "prof_stop"; | |||
const std::string kProfModelSubscribe = "prof_model_subscribe"; | |||
const std::string kProfModelUnsubscribe = "prof_model_cancel_subscribe"; | |||
} // namespace | |||
namespace ge { | |||
ProfilingManager::ProfilingManager() {} | |||
ProfilingManager::ProfilingManager() : subscribe_count_(0) {} | |||
ProfilingManager::~ProfilingManager() {} | |||
@@ -54,6 +57,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ProfilingManager &ProfilingMana | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ge::Status ProfilingManager::Init(const Options &options) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
vector<int32_t>().swap(device_id_); | |||
subscribe_count_ = 0; | |||
job_id_ = options.job_id; | |||
GELOGI("ProfilingManager::Init job_id:%s", job_id_.c_str()); | |||
@@ -382,7 +386,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::StopProf | |||
} | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::ProfilingTaskDescInfo( | |||
const std::vector<TaskDescInfo> &task_desc_info, const int32_t &device_id) { | |||
uint32_t model_id, const std::vector<TaskDescInfo> &task_desc_info, const int32_t &device_id) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
Msprof::Engine::Reporter *reporter = PluginImpl::GetPluginReporter(); | |||
if (reporter == nullptr) { | |||
@@ -401,7 +405,8 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::Profilin | |||
.append(op_name).append(" ") | |||
.append(std::to_string(block_dim).append(" ") | |||
.append(std::to_string(task_id)).append(" ") | |||
.append(std::to_string(stream_id)).append("\n")); | |||
.append(std::to_string(stream_id)).append(" ") | |||
.append(std::to_string(model_id)).append("\n")); | |||
Msprof::Engine::ReporterData reporter_data{}; | |||
reporter_data.deviceId = device_id; | |||
@@ -425,7 +430,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::Profilin | |||
} | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::ProfilingGraphDescInfo( | |||
const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info, const int32_t &device_id) { | |||
uint32_t model_id, const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info, const int32_t &device_id) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
Msprof::Engine::Reporter *reporter = PluginImpl::GetPluginReporter(); | |||
GE_IF_BOOL_EXEC(reporter == nullptr, GELOGI("Profiling report is nullptr!"); return;); | |||
@@ -483,6 +488,8 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::Profilin | |||
data.append("\""); | |||
} | |||
data.append(" model_id:").append(std::to_string(model_id)); | |||
data.append("\n"); | |||
Msprof::Engine::ReporterData reporter_data{}; | |||
@@ -537,7 +544,9 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::PluginUn | |||
} | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::ReportProfilingData( | |||
const std::vector<TaskDescInfo> &task_desc_info, const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info) { | |||
uint32_t model_id, const std::vector<TaskDescInfo> &task_desc_info, | |||
const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info, | |||
bool check_device) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
int32_t logic_device_id = 0; | |||
rtError_t rt_ret = rtGetDevice(&logic_device_id); | |||
@@ -546,7 +555,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::ReportPr | |||
return; | |||
} | |||
GELOGI("current logic_device_id:%d", logic_device_id); | |||
if (!is_acl_api_mode_) { | |||
if (check_device) { | |||
auto ret = std::find(device_id_.begin(), device_id_.end(), logic_device_id); | |||
if (ret == device_id_.end()) { | |||
GELOGE(FAILED, "get valid phy_device_id failed, profiling report failed."); | |||
@@ -554,9 +563,9 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY void ProfilingManager::ReportPr | |||
} | |||
} | |||
GELOGI("start ProfilingTaskDescInfo."); | |||
ProfilingTaskDescInfo(task_desc_info, logic_device_id); | |||
ProfilingTaskDescInfo(model_id, task_desc_info, logic_device_id); | |||
GELOGI("start ProfilingGraphDescInfo."); | |||
ProfilingGraphDescInfo(compute_graph_desc_info, logic_device_id); | |||
ProfilingGraphDescInfo(model_id, compute_graph_desc_info, logic_device_id); | |||
GELOGI("Report profiling data for GE end."); | |||
#endif | |||
} | |||
@@ -581,6 +590,105 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY uint64_t ProfilingManager::GetP | |||
return module; | |||
} | |||
void ProfilingManager::UpdateSubscribeDeviceModuleMap(std::string prof_type, | |||
uint32_t device_id, | |||
uint64_t module) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
if (prof_type == kProfModelSubscribe) { | |||
if (subs_dev_module_.find(device_id) != subs_dev_module_.end()) { | |||
subs_dev_module_[device_id].subscribe_count++; | |||
} else { | |||
DeviceSubsInfo dev_info; | |||
dev_info.module = module; | |||
dev_info.subscribe_count = 1; | |||
subs_dev_module_[device_id] = dev_info; | |||
} | |||
} else if (prof_type == kProfModelUnsubscribe) { | |||
if (subs_dev_module_.find(device_id) != subs_dev_module_.end()) { | |||
if (subs_dev_module_[device_id].subscribe_count > 0) { | |||
subs_dev_module_[device_id].subscribe_count--; | |||
} | |||
} | |||
} else { | |||
GELOGI("No need to update device_id module map."); | |||
} | |||
#endif | |||
} | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status ProfilingManager::ProfModelSubscribe( | |||
uint64_t module, void *model) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
std::lock_guard<std::mutex> lock(mutex_); | |||
uint64_t model_load_mask = module & PROF_MODEL_LOAD_MASK; | |||
if ((subscribe_count_ == 0) && (model_load_mask == PROF_MODEL_LOAD_MASK)) { | |||
// register framework to profiling | |||
int32_t result = Msprof::Engine::Init(GE_PROFILING_MODULE, &engine_); | |||
if (result != SUCCESS) { | |||
GELOGE(FAILED, "Register profiling engine failed."); | |||
return FAILED; | |||
} | |||
GELOGI("Prof subscribe: model load profiling on."); | |||
} | |||
subscribe_count_++; | |||
auto davinci_model = static_cast<DavinciModel *>(model); | |||
int32_t device_num = 1; | |||
uint32_t device[1]; | |||
device[0] = davinci_model->GetDeviceId(); | |||
rtError_t rt_ret = rtProfilerStart(module, device_num, device); | |||
if (rt_ret != RT_ERROR_NONE) { | |||
GELOGE(FAILED, "Runtime profiler start failed."); | |||
return FAILED; | |||
} | |||
UpdateSubscribeDeviceModuleMap(kProfModelSubscribe, device[0], module); | |||
// Report profiling data | |||
Status p_ret = davinci_model->ReportProfilingData(false); | |||
if (p_ret != SUCCESS) { | |||
GELOGE(p_ret, "Report profiling data failed."); | |||
return p_ret; | |||
} | |||
#endif | |||
return SUCCESS; | |||
} | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status ProfilingManager::ProfModelUnsubscribe( | |||
void *model) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
std::lock_guard<std::mutex> lock(mutex_); | |||
if (subscribe_count_ == 0) { | |||
GELOGW("The profiler has not been subscribed, you do not need to cannel the subscription."); | |||
return SUCCESS; | |||
} | |||
auto davinci_model = static_cast<DavinciModel *>(model); | |||
int32_t dev_num = 1; | |||
uint32_t device[1]; | |||
device[0] = davinci_model->GetDeviceId(); | |||
auto iter = subs_dev_module_.find(device[0]); | |||
if (iter != subs_dev_module_.end()) { | |||
if (subs_dev_module_[device[0]].subscribe_count == 1) { | |||
rtError_t rt_ret = rtProfilerStop(subs_dev_module_[device[0]].module, dev_num, device); | |||
if (rt_ret != RT_ERROR_NONE) { | |||
GELOGE(FAILED, "Runtime profiler stop failed."); | |||
return FAILED; | |||
} | |||
} | |||
UpdateSubscribeDeviceModuleMap(kProfModelUnsubscribe, device[0], subs_dev_module_[device[0]].module); | |||
} | |||
subscribe_count_--; | |||
if (subscribe_count_ == 0) { | |||
int32_t ret = Msprof::Engine::UnInit(GE_PROFILING_MODULE); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "Profiling plugin uninit failed, ret:%d", ret); | |||
return ret; | |||
} | |||
} | |||
#endif | |||
return SUCCESS; | |||
} | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status ProfilingManager::ProfInit(uint64_t module) { | |||
#ifdef DAVINCI_SUPPORT_PROFILING | |||
std::lock_guard<std::mutex> lock(mutex_); | |||
@@ -748,6 +856,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY Status ProfilingManager::ProfSt | |||
device_id_ptr[i] = static_cast<uint32_t>(device_list[i]); | |||
} | |||
GELOGI("Runtime config param: 0x%llx, device num: %d.", module, device_num); | |||
rtError_t rt_ret = rtProfilerStart(module, device_num, device_id_ptr.get()); | |||
if (rt_ret != RT_ERROR_NONE) { | |||
GELOGE(FAILED, "Runtime profiler config proc failed."); | |||
@@ -39,6 +39,10 @@ namespace { | |||
const std::string GE_PROFILING_MODULE = "Framework"; | |||
} // namespace | |||
namespace ge { | |||
struct DeviceSubsInfo { | |||
uint64_t module; | |||
uint32_t subscribe_count; | |||
}; | |||
// register Plugin | |||
class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY PluginImpl : public Msprof::Engine::PluginIntf { | |||
public: | |||
@@ -73,6 +77,9 @@ class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ProfilingManager { | |||
ge::Status InitFromOptions(const Options &options); | |||
ge::Status InitFromAclCfg(const std::string &config); | |||
ge::Status StartProfiling(int32_t iter, int32_t device_id); | |||
void UpdateSubscribeDeviceModuleMap(std::string prof_type, uint32_t device_id, uint64_t module); | |||
ge::Status ProfModelSubscribe(uint64_t module, void *model); | |||
ge::Status ProfModelUnsubscribe(void *model); | |||
ge::Status ProfInit(uint64_t module); | |||
ge::Status ProfFinalize(); | |||
ge::Status ProfStartProfiling(uint64_t module, const std::map<std::string, std::string> &config_para); | |||
@@ -84,13 +91,16 @@ class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ProfilingManager { | |||
bool ProfilingModelLoadOn() const { return is_load_profiling_; } | |||
bool ProfilingModelExecuteOn() const; | |||
bool ProfilingOn() const { return is_load_profiling_ && is_execute_profiling_; } // only used by command pattern | |||
bool IsAclApiMode() const { return is_acl_api_mode_; } | |||
int32_t GetOpTraceIterNum() const { return op_trace_iter_num_; } | |||
void ReportProfilingData(const std::vector<TaskDescInfo> &task_desc_info, | |||
const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info); | |||
void ReportProfilingData(uint32_t model_id, const std::vector<TaskDescInfo> &task_desc_info, | |||
const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info, | |||
bool check_device); | |||
void Report(const int32_t &device_id, const string &data, Msprof::Engine::Reporter &reporter, | |||
Msprof::Engine::ReporterData &reporter_data); | |||
void ProfilingTaskDescInfo(const std::vector<TaskDescInfo> &task_desc_info, const int32_t &device_id); | |||
void ProfilingGraphDescInfo(const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info, | |||
void ProfilingTaskDescInfo(uint32_t model_id, const std::vector<TaskDescInfo> &task_desc_info, | |||
const int32_t &device_id); | |||
void ProfilingGraphDescInfo(uint32_t model_id, const std::vector<ComputeGraphDescInfo> &compute_graph_desc_info, | |||
const int32_t &device_id); | |||
void SetProfilingConfig(const string &profiling_cfg); | |||
vector<int32_t> GetProfilingDeviceId() const { return device_id_; } | |||
@@ -122,6 +132,8 @@ class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ProfilingManager { | |||
string task_trace_conf_; | |||
const ProfilingEngineImpl engine_; | |||
map<int32_t, uint64_t> device_id_module_map_; // key: device_id, value: profiling on module | |||
map<uint32_t, DeviceSubsInfo> subs_dev_module_; // key: device_id, value: profiling on module | |||
uint32_t subscribe_count_; | |||
std::mutex mutex_; | |||
}; | |||
} // namespace ge | |||
@@ -54,6 +54,7 @@ const std::map<std::string, std::string> PROFILE_COMPONENT_MAP{ | |||
{"runtime", RTS_PROFILE}, | |||
}; | |||
const std::string PROFILE_CONFIG = "config"; | |||
const std::string PROFILE_MODEL_ID = "modelId"; | |||
REGISTER_OPTYPE_DEFINE(DATA, "Data"); | |||
REGISTER_OPTYPE_DEFINE(AIPPDATA, "AippData"); | |||
@@ -1062,6 +1062,19 @@ Status GeExecutor::ReleaseSingleOpResource(void *stream) { | |||
return SingleOpManager::GetInstance().ReleaseResource(stream); | |||
} | |||
Status GeExecutor::GetDeviceIdByModelId(uint32_t model_id, uint32_t &device_id) { | |||
auto model_manager = ModelManager::GetInstance(); | |||
GE_CHECK_NOTNULL(model_manager); | |||
auto davinci_model = model_manager->GetModel(model_id); | |||
if (davinci_model == nullptr) { | |||
GELOGE(FAILED, "Model id: %d is invaild or model is not loaded.", model_id); | |||
return FAILED; | |||
} | |||
device_id = davinci_model->GetDeviceId(); | |||
return SUCCESS; | |||
} | |||
Status GeExecutor::GetBatchInfoSize(uint32_t model_id, size_t &shape_count) { | |||
std::vector<std::vector<int64_t>> batch_info; | |||
int32_t dynamic_type = static_cast<int32_t>(FIXED); | |||
@@ -32,11 +32,6 @@ Status LabelAllocator::AssignFunctionalLabels() { | |||
return INTERNAL_ERROR; | |||
} | |||
if (compute_graph_->GetGraphUnknownFlag()) { | |||
GELOGD("Graph[%s] is unknown graph, skip label allocator.", compute_graph_->GetName().c_str()); | |||
return SUCCESS; | |||
} | |||
// Add label task for sub graph. | |||
GELOGI("AssignFunctionalLabels start: %s.", compute_graph_->GetName().c_str()); | |||
std::set<NodePtr> functional_nodes; | |||
@@ -62,7 +57,7 @@ Status LabelAllocator::AssignFunctionalLabels() { | |||
} | |||
(void)AttrUtils::SetInt(*compute_graph_, ATTR_MODEL_LABEL_NUM, label_index); | |||
GELOGI("AssignFunctionalLabels success."); | |||
GELOGI("AssignFunctionalLabels success, Num: %u.", label_index); | |||
return SUCCESS; | |||
} | |||
@@ -72,13 +67,29 @@ bool LabelAllocator::CollectFunctionalNode(ComputeGraphPtr &graph, std::set<Node | |||
return false; | |||
} | |||
NodePtr parent = graph->GetParentNode(); | |||
if (parent == nullptr) { | |||
GELOGE(INTERNAL_ERROR, "ComputeGraph owner not set: %s.", graph->GetName().c_str()); | |||
if (graph->GetGraphUnknownFlag()) { | |||
GELOGD("Graph[%s] is unknown graph, skip label allocator.", graph->GetName().c_str()); | |||
return true; | |||
} | |||
NodePtr func_node = graph->GetParentNode(); | |||
if (func_node == nullptr) { | |||
GELOGE(INTERNAL_ERROR, "Parent functional node not set: %s.", graph->GetName().c_str()); | |||
return false; | |||
} | |||
(void)functional_nodes.insert(parent); // unique functional node. | |||
ComputeGraphPtr owner_graph = func_node->GetOwnerComputeGraph(); | |||
if (owner_graph == nullptr) { | |||
GELOGE(INTERNAL_ERROR, "ComputeGraph owner not set: %s.", func_node->GetName().c_str()); | |||
return false; | |||
} | |||
if (owner_graph->GetGraphUnknownFlag()) { | |||
GELOGD("Graph[%s] is unknown graph, skip label allocator.", owner_graph->GetName().c_str()); | |||
return true; | |||
} | |||
(void)functional_nodes.insert(func_node); // unique functional node. | |||
return true; | |||
} | |||
} // namespace ge |
@@ -880,6 +880,15 @@ MemoryBlock *BlockMemAssigner::ApplyMemory(size_t block_size, size_t real_size, | |||
GELOGI("Unreusable block."); | |||
continue; | |||
} | |||
std::string batch_label; | |||
if (reusable_block->IsSameLabel(batch_label)) { | |||
std::string op_label; | |||
(void)ge::AttrUtils::GetStr(node_op_desc, ATTR_NAME_BATCH_LABEL, op_label); | |||
if (batch_label != op_label) { | |||
GELOGI("label diff, op name %s", node_op_desc->GetName().c_str()); | |||
continue; | |||
} | |||
} | |||
// A node can reuse blocks of the same stream and preorder streams | |||
if (CanReuseBySize(reusable_block_counts_, *reusable_block, block_size, real_size, continuous)) { | |||
@@ -416,6 +416,14 @@ Status ModelBuilder::BuildModelDef(ge::Model &model) { | |||
return FAILED); | |||
GELOGI("For model, max_mem_offset_: %zu, p2p_mem_size: %zu, zero_copy_mem_size_: %zu", max_mem_offset_, | |||
p2p_mem_offset_, zero_copy_mem_size_); | |||
string fp_ceiling_mode; | |||
if (ge::GetContext().GetOption("ge.fpCeilingMode", fp_ceiling_mode) == SUCCESS) { | |||
if (!ge::AttrUtils::SetStr(&model, ATTR_FP_CEILING_MODE, fp_ceiling_mode)) { | |||
GELOGE(FAILED, "Failed to set attr ATTR_FP_CEILING_MODE"); | |||
return FAILED; | |||
} | |||
GELOGI("Set attr ATTR_FP_CEILING_MODE to model, value is %s.", fp_ceiling_mode.c_str()); | |||
} | |||
string ge_core_type; | |||
Status ret = ge::GetContext().GetOption(kCoreType, ge_core_type); | |||
@@ -690,8 +698,8 @@ Status ModelBuilder::BuildModelForGetTask(ge::Model &model) { | |||
GE_TIMESTAMP_END(AssignLogicalStreams, "GraphBuilder::AssignLogicalStreams"); | |||
// Assign functional op labels. | |||
label_num_ = 0; | |||
(void)AttrUtils::GetInt(*compute_graph_, ATTR_MODEL_LABEL_NUM, label_num_); | |||
auto root_graph = GraphUtils::FindRootGraph(compute_graph_); | |||
(void)AttrUtils::GetInt(*root_graph, ATTR_MODEL_LABEL_NUM, label_num_); | |||
GE_TIMESTAMP_START(AssignMemory); | |||
MemoryAssigner mem_assigner(compute_graph_); | |||
@@ -82,4 +82,13 @@ bool TransOpUtil::CheckPrecisionLoss(const ge::NodePtr &src_node) { | |||
} | |||
return true; | |||
} | |||
std::string TransOpUtil::TransopMapToString() { | |||
std::string buffer; | |||
for (auto &key : Instance().transop_index_map_) { | |||
buffer += key.first + " "; | |||
} | |||
return buffer; | |||
} | |||
} // namespace ge |
@@ -35,6 +35,8 @@ class GE_FUNC_HOST_VISIBILITY GE_FUNC_DEV_VISIBILITY TransOpUtil { | |||
static bool CheckPrecisionLoss(const NodePtr &src_node); | |||
static std::string TransopMapToString(); | |||
private: | |||
TransOpUtil(); | |||
@@ -86,6 +86,7 @@ class DataDumper { | |||
void SetDumpProperties(const DumpProperties &dump_properties) { dump_properties_ = dump_properties; } | |||
const DumpProperties &GetDumpProperties() const { return dump_properties_; } | |||
bool GetOpDescInfo(uint32_t stream_id, uint32_t task_id, OpDescInfo &op_desc_info) const; | |||
const std::vector<OpDescInfo> &GetAllOpDescInfo() const { return op_desc_info_; } | |||
// Dump exception info | |||
Status DumpExceptionInput(const OpDescInfo &op_desc_info, const string &dump_file); | |||
@@ -88,6 +88,7 @@ const uint32_t kDataMemAlignSizeCompare = 64; | |||
const uint32_t kDumpL1FusionOpMByteSize = 2 * 1024 * 1024; | |||
const uint32_t kDumpFlagOfL1Fusion = 0; | |||
const char *const kDefaultBatchLable = "Batch_default"; | |||
const int32_t kInvalidStream = -1; | |||
inline bool IsDataOp(const std::string &node_type) { | |||
return node_type == DATA_TYPE || node_type == AIPP_DATA_TYPE || node_type == ANN_DATA_TYPE; | |||
@@ -258,7 +259,6 @@ Status DavinciModel::Assign(const GeModelPtr &ge_model) { | |||
/// | |||
void DavinciModel::Shrink() { | |||
ge_model_.reset(); // delete object. | |||
op_list_.clear(); | |||
} | |||
Status DavinciModel::InitModelMem(void *dev_ptr, size_t mem_size, void *weight_ptr, size_t weight_size) { | |||
@@ -611,7 +611,9 @@ Status DavinciModel::Init(void *dev_ptr, size_t mem_size, void *weight_ptr, size | |||
GE_DISMISS_GUARD(stream); | |||
stream_list_.push_back(stream); | |||
GELOGD("Stream index:%u, stream:%p.", i, stream); | |||
int32_t rt_stream_id = kInvalidStream; | |||
(void)rtGetStreamId(stream, &rt_stream_id); | |||
GELOGI("Logical stream index:%u, stream:%p, rtstream: %d.", i, stream, rt_stream_id); | |||
} | |||
for (uint32_t i = 0; i < EventNum(); i++) { | |||
@@ -653,18 +655,6 @@ Status DavinciModel::Init(void *dev_ptr, size_t mem_size, void *weight_ptr, size | |||
GE_IF_BOOL_EXEC(IsBroadCastOpData(node), | |||
(void)ge::AttrUtils::SetStr(op_desc, VAR_ATTR_VAR_IS_BROADCAST, "var_is_restore");); | |||
} | |||
// for profiling | |||
op_name_map_ = compute_graph->GetGraphOpName(); | |||
vector<string> op_name; | |||
GE_IF_BOOL_EXEC(ge::AttrUtils::GetListStr(ge_model_, ATTR_MODEL_TASK_INDEX_OP_NAME, op_name), | |||
GELOGI("get str of task_index_op_name")); | |||
if (op_name_map_.empty()) { | |||
for (size_t idx = 0; idx < op_name.size(); idx++) { | |||
op_name_map_[idx] = op_name[idx]; | |||
} | |||
GELOGI("Infer profiling: op_name_size(%zu)", op_name.size()); | |||
} | |||
GE_CHK_STATUS_RET(InitNodes(compute_graph), "Init nodes failed"); | |||
@@ -676,7 +666,9 @@ Status DavinciModel::Init(void *dev_ptr, size_t mem_size, void *weight_ptr, size | |||
auto all_dump_model = GetDumpProperties().GetAllDumpModel(); | |||
bool findByOmName = all_dump_model.find(om_name_) != all_dump_model.end(); | |||
bool findByModelName = all_dump_model.find(name_) != all_dump_model.end(); | |||
if (all_dump_model.find(ge::DUMP_ALL_MODEL) != all_dump_model.end() || findByOmName || findByModelName) { | |||
bool dump_l1fusion_op = (all_dump_model.find(ge::DUMP_ALL_MODEL) != all_dump_model.end()) || | |||
findByOmName || findByModelName; | |||
if (dump_l1fusion_op) { | |||
// malloc 2M for dump l1fusion op | |||
GE_CHK_RT_RET(rtMalloc(&l1_fusion_addr_, kDumpL1FusionOpMByteSize, RT_MEMORY_DDR)); | |||
@@ -690,16 +682,21 @@ Status DavinciModel::Init(void *dev_ptr, size_t mem_size, void *weight_ptr, size | |||
need_destroy_aicpu_kernel_ = IsAicpuKernelConnectSpecifiedLayer(); | |||
(void)ge::AttrUtils::GetListStr(ge_model_, ATTR_MODEL_OUT_NODES_NAME, out_node_name_); | |||
string fp_ceiling_mode; | |||
if (ge::AttrUtils::GetStr(ge_model_, ATTR_FP_CEILING_MODE, fp_ceiling_mode)) { | |||
GELOGI("Get attr ATTR_FP_CEILING_MODE from model, value is %s.", fp_ceiling_mode.c_str()); | |||
// mode 0: Do not perform saturation processing. By default, IEEE754 is used. | |||
GE_CHK_RT_RET(rtSetCtxINFMode((fp_ceiling_mode != "0"))); | |||
} | |||
// collect profiling for ge | |||
if (ProfilingManager::Instance().ProfilingModelLoadOn()) { | |||
std::vector<ComputeGraphDescInfo> compute_graph_desc_info; | |||
Status ret1 = GetComputeGraphInfo(compute_graph, compute_graph_desc_info); | |||
if (ret1 != SUCCESS) { | |||
GELOGE(ret1, "GetComputeGraphInfo failed."); | |||
return ret1; | |||
auto &profiling_manager = ProfilingManager::Instance(); | |||
if (profiling_manager.ProfilingModelLoadOn()) { | |||
Status p_ret = ReportProfilingData(!profiling_manager.IsAclApiMode()); | |||
if (p_ret != SUCCESS) { | |||
GELOGE(p_ret, "Report profiling data failed."); | |||
return p_ret; | |||
} | |||
ProfilingManager::Instance().ReportProfilingData(GetTaskDescInfo(), compute_graph_desc_info); | |||
GE_CHK_STATUS(SinkModelProfile(), "Sink model profile failed."); | |||
} | |||
Shrink(); | |||
@@ -707,6 +704,20 @@ Status DavinciModel::Init(void *dev_ptr, size_t mem_size, void *weight_ptr, size | |||
return ret; | |||
} | |||
Status DavinciModel::ReportProfilingData(bool check_device) { | |||
std::vector<ComputeGraphDescInfo> compute_graph_desc_info; | |||
Status ret = GetComputeGraphInfo(compute_graph_desc_info); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "GetComputeGraphInfo failed."); | |||
return ret; | |||
} | |||
ProfilingManager::Instance().ReportProfilingData(model_id_, GetTaskDescInfo(), compute_graph_desc_info, check_device); | |||
GE_CHK_STATUS(SinkModelProfile(), "Sink model profiler failed."); | |||
op_list_.clear(); | |||
return SUCCESS; | |||
} | |||
/// | |||
/// @ingroup ge | |||
/// @brief Travel all nodes and determine if destruction is required. | |||
@@ -2900,34 +2911,25 @@ Status DavinciModel::DistributeTask() { | |||
SaveDumpTask(task->GetTaskID(), task->GetStreamId(), op, task->GetDumpArgs()); | |||
} | |||
} | |||
// get op_name by task_index | |||
if (task->GetCtx() != nullptr) { | |||
auto iter = op_name_map_.find(task_index); | |||
if (iter == op_name_map_.end()) { | |||
continue; | |||
} | |||
// else task index is found in op_name_map_ | |||
TaskDescInfo task_desc_info; | |||
string op_name = op_name_map_[task_index]; | |||
if (!om_name_.empty()) { | |||
task_desc_info.model_name = om_name_; | |||
} else { | |||
task_desc_info.model_name = name_; | |||
} | |||
task_desc_info.op_name = op_name; | |||
task_desc_info.block_dim = model_task_def->task(task_index).kernel().block_dim(); | |||
task_desc_info.task_id = task->GetTaskID(); | |||
task_desc_info.stream_id = task->GetStreamId(); | |||
task_desc_info_.emplace_back(task_desc_info); | |||
if (flag) { | |||
if (task->GetSktTaskID() != 0xFFFFFFFF) { | |||
TaskDescInfo task_desc_info; | |||
string op_name = "super_kernel_" + to_string(task_index); | |||
task_desc_info.op_name = op_name; | |||
task_desc_info.task_id = task->GetSktTaskID(); | |||
task_desc_info_.emplace_back(task_desc_info); | |||
} | |||
// Load task info for profiling | |||
TaskDescInfo task_desc_info; | |||
if (!om_name_.empty()) { | |||
task_desc_info.model_name = om_name_; | |||
} else { | |||
task_desc_info.model_name = name_; | |||
} | |||
task_desc_info.op_name = op->GetName(); | |||
task_desc_info.block_dim = model_task_def->task(task_index).kernel().block_dim(); | |||
task_desc_info.task_id = task->GetTaskID(); | |||
task_desc_info.stream_id = task->GetStreamId(); | |||
task_desc_info_.emplace_back(task_desc_info); | |||
if (flag) { | |||
if (task->GetSktTaskID() != 0xFFFFFFFF) { | |||
TaskDescInfo task_desc_info; | |||
string op_name = "super_kernel_" + to_string(task_index); | |||
task_desc_info.op_name = op_name; | |||
task_desc_info.task_id = task->GetSktTaskID(); | |||
task_desc_info_.emplace_back(task_desc_info); | |||
} | |||
} | |||
} | |||
@@ -3817,50 +3819,31 @@ void DavinciModel::SaveHcclFollowStream(int64_t main_stream_id, rtStream_t strea | |||
main_follow_stream_mapping_[main_stream_id].emplace_back(stream); | |||
} | |||
Status DavinciModel::GetComputeGraphInfo(const ComputeGraphPtr &graph, vector<ComputeGraphDescInfo> &graph_desc_info) { | |||
Status DavinciModel::GetComputeGraphInfo(vector<ComputeGraphDescInfo> &graph_desc_info) { | |||
GELOGI("GetComputeGraphInfo start."); | |||
for (auto &node : graph->GetAllNodes()) { | |||
auto &all_op_desc = data_dumper_.GetAllOpDescInfo(); | |||
for (auto &op_desc : all_op_desc) { | |||
ComputeGraphDescInfo compute_graph_info; | |||
auto op_desc = node->GetOpDesc(); | |||
if (op_desc == nullptr) { | |||
GELOGE(PARAM_INVALID, "op_desc is nullptr."); | |||
return PARAM_INVALID; | |||
if (!om_name_.empty()) { | |||
compute_graph_info.model_name = om_name_; | |||
} else { | |||
compute_graph_info.model_name = name_; | |||
} | |||
compute_graph_info.op_name = op_desc.op_name; | |||
compute_graph_info.op_type = op_desc.op_type; | |||
compute_graph_info.input_format = op_desc.input_format; | |||
compute_graph_info.input_shape = op_desc.input_shape; | |||
compute_graph_info.input_data_type = op_desc.input_data_type; | |||
compute_graph_info.output_format = op_desc.output_format; | |||
compute_graph_info.output_shape = op_desc.output_shape; | |||
compute_graph_info.output_data_type = op_desc.output_data_type; | |||
auto op_mode = static_cast<uint32_t>(domi::ImplyType::INVALID); | |||
if (AttrUtils::GetInt(op_desc, ATTR_NAME_IMPLY_TYPE, op_mode) && | |||
op_mode == static_cast<uint32_t>(domi::ImplyType::TVM)) { | |||
if (!om_name_.empty()) { | |||
compute_graph_info.model_name = om_name_; | |||
} else { | |||
compute_graph_info.model_name = name_; | |||
} | |||
compute_graph_info.op_name = op_desc->GetName(); | |||
compute_graph_info.op_type = op_desc->GetType(); | |||
for (size_t i = 0; i < op_desc->GetAllInputsSize(); ++i) { | |||
GeTensorDescPtr input_desc = op_desc->MutableInputDesc(i); | |||
if (input_desc == nullptr) { | |||
continue; | |||
} | |||
compute_graph_info.input_format.emplace_back(input_desc->GetFormat()); | |||
compute_graph_info.input_shape.emplace_back(input_desc->GetShape().GetDims()); | |||
compute_graph_info.input_data_type.emplace_back(input_desc->GetDataType()); | |||
} | |||
for (size_t j = 0; j < op_desc->GetOutputsSize(); ++j) { | |||
GeTensorDesc output_desc = op_desc->GetOutputDesc(j); | |||
compute_graph_info.output_format.emplace_back(output_desc.GetFormat()); | |||
compute_graph_info.output_shape.emplace_back(output_desc.GetShape().GetDims()); | |||
compute_graph_info.output_data_type.emplace_back(output_desc.GetDataType()); | |||
} | |||
graph_desc_info.emplace_back(compute_graph_info); | |||
} | |||
graph_desc_info.emplace_back(compute_graph_info); | |||
} | |||
GELOGI("GetComputeGraphInfo end."); | |||
return SUCCESS; | |||
} | |||
void DavinciModel::SetTotalFixedAddrsSize(string tensor_name, int64_t fix_addr_size) { | |||
if (tensor_name_to_fixed_addr_size_.find(tensor_name) == tensor_name_to_fixed_addr_size_.end()) { | |||
tensor_name_to_fixed_addr_size_[tensor_name] = total_fixed_addr_size_; | |||
@@ -439,6 +439,8 @@ class DavinciModel { | |||
Status SinkTimeProfile(const InputData ¤t_data); | |||
Status ReportProfilingData(bool check_device = true); | |||
void SaveDumpOpInfo(const RuntimeParam &model_param, const OpDescPtr &op, uint32_t task_id, uint32_t stream_id) { | |||
data_dumper_.SaveDumpOpInfo(model_param, op, task_id, stream_id); | |||
} | |||
@@ -830,7 +832,7 @@ class DavinciModel { | |||
Status TransAllVarData(ComputeGraphPtr &graph, uint32_t graph_id); | |||
// get desc info of graph for profiling | |||
Status GetComputeGraphInfo(const ComputeGraphPtr &graph, vector<ComputeGraphDescInfo> &graph_desc_info); | |||
Status GetComputeGraphInfo(vector<ComputeGraphDescInfo> &graph_desc_info); | |||
void SetDataDumperArgs(const ComputeGraphPtr &compute_graph); | |||
@@ -949,7 +951,6 @@ class DavinciModel { | |||
std::map<std::string, uint32_t> used_tbe_handle_map_; | |||
// for profiling task and graph info | |||
std::map<uint32_t, std::string> op_name_map_; | |||
std::vector<TaskDescInfo> task_desc_info_; | |||
int64_t maxDumpOpNum_; | |||
@@ -43,6 +43,8 @@ const std::string kCmdTypeProfInit = "prof_init"; | |||
const std::string kCmdTypeProfFinalize = "prof_finalize"; | |||
const std::string kCmdTypeProfStart = "prof_start"; | |||
const std::string kCmdTypeProfStop = "prof_stop"; | |||
const std::string kCmdTypeProfModelSubscribe = "prof_model_subscribe"; | |||
const std::string kCmdTypeProfModelUnsubscribe = "prof_model_cancel_subscribe"; | |||
const char *const kBatchLoadBuf = "batchLoadsoFrombuf"; | |||
const char *const kDeleteCustOp = "deleteCustOp"; | |||
struct CustAicpuSoBuf { | |||
@@ -334,11 +336,9 @@ Status ModelManager::LoadModelOnline(uint32_t &model_id, const shared_ptr<ge::Ge | |||
GELOGI("Parse model %u success.", model_id); | |||
if (ProfilingManager::Instance().ProfilingModelLoadOn()) { | |||
davinci_model->SetProfileTime(MODEL_LOAD_START, (timespec.tv_sec * 1000 * 1000 * 1000 + | |||
timespec.tv_nsec)); // 1000 ^ 3 converts second to nanosecond | |||
davinci_model->SetProfileTime(MODEL_LOAD_END); | |||
} | |||
davinci_model->SetProfileTime(MODEL_LOAD_START, (timespec.tv_sec * 1000 * 1000 * 1000 + | |||
timespec.tv_nsec)); // 1000 ^ 3 converts second to nanosecond | |||
davinci_model->SetProfileTime(MODEL_LOAD_END); | |||
} while (0); | |||
GE_CHK_RT(rtDeviceReset(static_cast<int32_t>(GetContext().DeviceId()))); | |||
@@ -565,7 +565,9 @@ Status ModelManager::HandleCommand(const Command &command) { | |||
{kCmdTypeProfile, HandleProfileCommand}, {kCmdTypeDump, HandleDumpCommand}, | |||
{kCmdTypeProfiling, HandleAclProfilingCommand}, {kCmdTypeProfInit, HandleProfInitCommand}, | |||
{kCmdTypeProfFinalize, HandleProfFinalizeCommand}, {kCmdTypeProfStart, HandleProfStartCommand}, | |||
{kCmdTypeProfStop, HandleProfStopCommand}}; | |||
{kCmdTypeProfStop, HandleProfStopCommand}, | |||
{kCmdTypeProfModelSubscribe, HandleProfModelSubscribeCommand}, | |||
{kCmdTypeProfModelUnsubscribe, HandleProfModelUnsubscribeCommand}}; | |||
auto iter = cmds.find(command.cmd_type); | |||
if (iter == cmds.end()) { | |||
@@ -591,6 +593,77 @@ Status ModelManager::HandleAclProfilingCommand(const Command &command) { | |||
return SUCCESS; | |||
} | |||
Status ModelManager::GetModelByCmd(const Command &command, | |||
std::shared_ptr<DavinciModel> &davinci_model) { | |||
if (command.cmd_params.size() < kCmdParSize) { | |||
GELOGE(PARAM_INVALID, "When the cmd_type is '%s', the size of cmd_params must larger than 2.", | |||
command.cmd_type.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
std::string map_key = command.cmd_params[0]; | |||
std::string value = command.cmd_params[1]; | |||
if (map_key == PROFILE_MODEL_ID) { | |||
int32_t model_id = 0; | |||
try { | |||
model_id = std::stoi(value); | |||
} catch (std::invalid_argument &) { | |||
GELOGE(PARAM_INVALID, "Model id: %s is invalid.", value.c_str()); | |||
return PARAM_INVALID; | |||
} catch (std::out_of_range &) { | |||
GELOGE(PARAM_INVALID, "Model id: %s is out of range.", value.c_str()); | |||
return PARAM_INVALID; | |||
} catch (...) { | |||
GELOGE(FAILED, "Model id: %s cannot change to int.", value.c_str()); | |||
return FAILED; | |||
} | |||
auto model_manager = ModelManager::GetInstance(); | |||
GE_CHECK_NOTNULL(model_manager); | |||
davinci_model = model_manager->GetModel(static_cast<uint32_t>(model_id)); | |||
if (davinci_model == nullptr) { | |||
GELOGE(FAILED, "Model id: %d is invaild or model is not loaded.", model_id); | |||
return FAILED; | |||
} | |||
} else { | |||
GELOGE(FAILED, "The model_id parameter is not found in the command."); | |||
return FAILED; | |||
} | |||
return SUCCESS; | |||
} | |||
Status ModelManager::HandleProfModelSubscribeCommand(const Command &command) { | |||
std::shared_ptr<DavinciModel> davinci_model = nullptr; | |||
Status ret = GetModelByCmd(command, davinci_model); | |||
if (ret != SUCCESS) { | |||
return ret; | |||
} | |||
if (ProfilingManager::Instance().ProfModelSubscribe(command.module_index, | |||
static_cast<void *>(davinci_model.get())) != SUCCESS) { | |||
GELOGE(FAILED, "Handle prof model subscribe failed."); | |||
return FAILED; | |||
} | |||
return SUCCESS; | |||
} | |||
Status ModelManager::HandleProfModelUnsubscribeCommand(const Command &command) { | |||
std::shared_ptr<DavinciModel> davinci_model = nullptr; | |||
Status ret = GetModelByCmd(command, davinci_model); | |||
if (ret != SUCCESS) { | |||
return ret; | |||
} | |||
if (ProfilingManager::Instance().ProfModelUnsubscribe(static_cast<void *>(davinci_model.get())) != SUCCESS) { | |||
GELOGE(FAILED, "Handle prof model unsubscribe failed."); | |||
return FAILED; | |||
} | |||
return SUCCESS; | |||
} | |||
Status ModelManager::HandleProfInitCommand(const Command &command) { | |||
uint64_t module_index = command.module_index; | |||
if (ProfilingManager::Instance().ProfInit(module_index) != SUCCESS) { | |||
@@ -973,11 +1046,9 @@ Status ModelManager::LoadModelOffline(uint32_t &model_id, const ModelData &model | |||
GELOGI("Parse model %u success.", model_id); | |||
if (ProfilingManager::Instance().ProfilingModelLoadOn()) { | |||
davinci_model->SetProfileTime(MODEL_LOAD_START, (timespec.tv_sec * 1000 * 1000 * 1000 + | |||
timespec.tv_nsec)); // 1000 ^ 3 converts second to nanosecond | |||
davinci_model->SetProfileTime(MODEL_LOAD_END); | |||
} | |||
davinci_model->SetProfileTime(MODEL_LOAD_START, (timespec.tv_sec * 1000 * 1000 * 1000 + | |||
timespec.tv_nsec)); // 1000 ^ 3 converts second to nanosecond | |||
davinci_model->SetProfileTime(MODEL_LOAD_END); | |||
GE_IF_BOOL_EXEC(ret == SUCCESS, device_count++); | |||
return SUCCESS; | |||
@@ -158,10 +158,15 @@ class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ModelManager { | |||
static ge::Status HandleAclProfilingCommand(const Command &command); | |||
static ge::Status HandleProfileCommand(const Command &command); | |||
static ge::Status HandleDumpCommand(const Command &command); | |||
static ge::Status HandleProfModelSubscribeCommand(const Command &command); | |||
static ge::Status HandleProfModelUnsubscribeCommand(const Command &command); | |||
static ge::Status HandleProfInitCommand(const Command &command); | |||
static ge::Status HandleProfFinalizeCommand(const Command &command); | |||
static ge::Status HandleProfStartCommand(const Command &command); | |||
static ge::Status HandleProfStopCommand(const Command &command); | |||
static ge::Status GetModelByCmd(const Command &command, | |||
std::shared_ptr<DavinciModel> &davinci_model); | |||
/// | |||
/// @ingroup domi_ome | |||
/// @brief get model memory usage | |||
@@ -45,7 +45,7 @@ Status ZeroCopyTask::SetTaskArgsOffset(uintptr_t addr, size_t offset) { | |||
if (it == task_addr_offset_.end()) { | |||
task_addr_offset_[addr] = {offset}; | |||
} else { | |||
it->second.push_back(offset); | |||
it->second.insert(offset); | |||
} | |||
GELOGI("[ZCPY] %s set task, virtual_addr: 0x%lx, args_addr: %p, size: %zu, offset: %zu", name_.c_str(), addr, | |||
@@ -103,7 +103,7 @@ class ZeroCopyTask { | |||
bool is_updated_; | |||
string batch_label_; | |||
// <address from Op, {offset in args}> | |||
map<uintptr_t, vector<size_t>> task_addr_offset_; | |||
map<uintptr_t, set<size_t>> task_addr_offset_; | |||
}; | |||
} // namespace ge | |||
#endif // GE_GRAPH_LOAD_NEW_MODEL_MANAGER_ZERO_COPY_TASK_H_ |
@@ -133,6 +133,22 @@ bool IsTailingOptimization() { | |||
GELOGW("OPTION_EXEC_ENABLE_TAILING_OPTIMIZATION not set, use BFSTopologicalSorting by default."); | |||
return false; | |||
} | |||
ge::Status CheckFpCeilingMode() { | |||
static const std::unordered_set<std::string> kValidFpCeilingMode = {"0", "1", "2"}; | |||
string mode; | |||
auto ret = ge::GetContext().GetOption("ge.fpCeilingMode", mode); | |||
if (ret == ge::GRAPH_SUCCESS) { | |||
if (kValidFpCeilingMode.count(mode) == 0) { | |||
GELOGE(ge::GE_GRAPH_OPTIONS_INVALID, "The fp_ceiling_mode %s is invalid, options are 0, 1, and 2.", mode.c_str()); | |||
return ge::GE_GRAPH_OPTIONS_INVALID; | |||
} | |||
GELOGI("The parameter fp_ceiling_mode is set to %s.", mode.c_str()); | |||
return ge::SUCCESS; | |||
} | |||
GELOGW("The parameter fp_ceiling_mode is not set."); | |||
return ge::SUCCESS; | |||
} | |||
} // namespace | |||
namespace ge { | |||
@@ -168,6 +184,12 @@ Status GraphManager::Initialize(const std::map<string, string> &options) { | |||
return ret; | |||
} | |||
ret = CheckFpCeilingMode(); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "[Initialize] Check fp-ceiling-mode options failed."); | |||
return ret; | |||
} | |||
ret = graph_context_->Initialize(options); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "[Initialize] GraphContext initialize failed."); | |||
@@ -25,6 +25,10 @@ | |||
namespace ge { | |||
Status MemcpyAddrAsyncPass::Run(ComputeGraphPtr graph) { | |||
GE_CHECK_NOTNULL(graph); | |||
if (graph->GetGraphUnknownFlag()) { | |||
GELOGD("Graph[%s] is unknown graph, skip.", graph->GetName().c_str()); | |||
return SUCCESS; | |||
} | |||
int64_t value = 0; | |||
rtError_t rt_ret = rtGetRtCapability(FEATURE_TYPE_MEMCPY, MEMCPY_INFO_SUPPORT_ZEROCOPY, &value); | |||
@@ -201,9 +205,10 @@ NodePtr MemcpyAddrAsyncPass::CreateMemcpyAddrAsyncNode(const ComputeGraphPtr &gr | |||
const OutDataAnchorPtr &out_data_anchor, | |||
const NodePtr &out_of_user_data) { | |||
GELOGD("Start CreateMemcpyAddrAsyncNode."); | |||
static uint32_t new_node_index = 0; | |||
OpDescPtr pre_op_desc = out_data_anchor->GetOwnerNode()->GetOpDesc(); | |||
GE_CHK_BOOL_EXEC(pre_op_desc != nullptr, return nullptr, "Op_desc of pre node is invalid."); | |||
std::string node_name = pre_op_desc->GetName() + "_" + MEMCPYADDRASYNC; | |||
std::string node_name = pre_op_desc->GetName() + "_" + MEMCPYADDRASYNC + "_" + std::to_string(new_node_index++); | |||
OpDescPtr op_desc = MakeShared<OpDesc>(node_name, MEMCPYADDRASYNC); | |||
GE_CHECK_NOTNULL_EXEC(op_desc, return nullptr); | |||
@@ -103,6 +103,12 @@ Status NetOutputPass::GetOutputNode(const ge::ComputeGraphPtr &graph, std::vecto | |||
GELOGI("user set out node [%s] is found in user def targets, out node is prio!", ele.first->GetName().c_str()); | |||
targets_.erase(iter); | |||
} | |||
auto op_desc = ele.first->GetOpDesc(); | |||
GE_CHECK_NOTNULL(op_desc); | |||
if (op_desc->HasAttr(ATTR_ATC_USER_DEFINE_OUTPUT_NODES)) { | |||
is_user_define_ouput_nodes = true; | |||
} | |||
output_nodes_info.push_back({ele.first, ele.second, -1}); | |||
} | |||
GELOGI("Output node set by user or leaf node, size:%zu.", output_nodes_info.size()); | |||
@@ -414,7 +420,7 @@ Status NetOutputPass::ProcessWithNetoutput(const ge::ComputeGraphPtr &graph, con | |||
Status NetOutputPass::AddCtrlEdgesBetweenLeafAndNetOutput(const ge::ComputeGraphPtr &graph, | |||
const ge::NodePtr &net_out_node) { | |||
GE_CHECK_NOTNULL(net_out_node); | |||
if (!GetLocalOmgContext().user_out_nodes.empty()) { | |||
if (!GetLocalOmgContext().user_out_nodes.empty() || is_user_define_ouput_nodes) { | |||
GELOGI("No need to add ctrl edge to netoutput because user out nodes have been set."); | |||
return SUCCESS; | |||
} | |||
@@ -220,6 +220,7 @@ class NetOutputPass : public GraphPass { | |||
bool is_include_special_node_ = false; | |||
std::set<NodePtr> targets_; | |||
friend class ReUpdateNetOutputPass; | |||
bool is_user_define_ouput_nodes = false; | |||
}; | |||
} // namespace ge | |||
#endif // GE_GRAPH_PASSES_NET_OUTPUT_PASS_H_ |
@@ -117,7 +117,6 @@ | |||
#include "graph/passes/variable_op_pass.h" | |||
#include "graph/passes/variable_prepare_op_pass.h" | |||
#include "graph/passes/variable_ref_delete_op_pass.h" | |||
#include "graph/passes/mark_agnostic_pass.h" | |||
namespace ge { | |||
@@ -219,6 +218,9 @@ NodePtr CreateTransNode(const std::string &name, const std::string &node_type, c | |||
auto index = TransOpUtil::GetTransOpDataIndex(node_type); | |||
if (index < 0) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E19025", {"situation", "reason"}, | |||
{"The trans node type[" + node_type + "]", "it must be " + TransOpUtil::TransopMapToString()}); | |||
GELOGE(INTERNAL_ERROR, "The trans node type %s does not exists", node_type.c_str()); | |||
return nullptr; | |||
} | |||
@@ -387,6 +389,8 @@ Status RecoverTransRoadForVar(const NodePtr &var, const VarTransRoad &road) { | |||
auto trans_name = var->GetName() + "_trans_" + std::to_string(index++); | |||
auto ret = RecoverOneTransNodeForVar(trans_name, *iter, last_node, last_node); | |||
if (ret != SUCCESS) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E15001", {"variable", "index", "type"}, {var->GetName(), std::to_string(index), iter->node_type}); | |||
GELOGE(INTERNAL_ERROR, "Failed to recover trans node for variable %s, index %d, type %s", var->GetName().c_str(), | |||
index, iter->node_type.c_str()); | |||
return INTERNAL_ERROR; | |||
@@ -419,6 +423,8 @@ Status RecoverTransRoadForVarRef(const std::set<NodePtr> &nodes, const VarTransR | |||
auto trans_name = var->GetName() + "_trans_" + std::to_string(index++); | |||
auto ret = RecoverOneTransNodeForVarRef(trans_name, *iter, last_node, last_node); | |||
if (ret != SUCCESS) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E15001", {"variable", "index", "type"}, {var->GetName(), std::to_string(index), iter->node_type}); | |||
GELOGE(INTERNAL_ERROR, "Failed to recover trans node for variable %s, index %d, type %s", | |||
var->GetName().c_str(), index, iter->node_type.c_str()); | |||
return INTERNAL_ERROR; | |||
@@ -571,6 +577,8 @@ Status CheckIfDynamicBatchScene(NodePtr &data_node, bool &is_dynamic_batch, Node | |||
std::string related_node_name; | |||
if (AttrUtils::GetStr(data_node->GetOpDesc(), kMbatchSwitchnName, related_node_name)) { | |||
if (related_node_name.empty()) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E15002", {"opname", "value", "reason"}, {data_node->GetName(), "flag", "but the value is empty"}); | |||
GELOGE(INTERNAL_ERROR, "The data node %s has switchn node flag, but the value is empty", | |||
data_node->GetName().c_str()); | |||
return INTERNAL_ERROR; | |||
@@ -582,6 +590,9 @@ Status CheckIfDynamicBatchScene(NodePtr &data_node, bool &is_dynamic_batch, Node | |||
} | |||
} | |||
if (switchn_node == nullptr) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E15002", {"opname", "value", "reason"}, | |||
{data_node->GetName(), related_node_name, "but can not find it on the graph"}); | |||
GELOGE(INTERNAL_ERROR, "The data node %s has switchn node %s, but can not find it on the graph", | |||
data_node->GetName().c_str(), related_node_name.c_str()); | |||
return INTERNAL_ERROR; | |||
@@ -682,6 +693,10 @@ Status ProcessInputNC1HWC0DynShape(NodePtr &node_ptr, bool &is_dynamic_batch, No | |||
ge::GeShape old_shape = input->GetShape(); | |||
bool support = ((old_format == FORMAT_NC1HWC0) || (old_format == FORMAT_NCHW) || (old_format == FORMAT_NHWC)); | |||
if (!support) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E19014", {"opname", "value", "reason"}, | |||
{op_desc->GetName(), "format[" + TypeUtils::FormatToSerialString(old_format) + "]", | |||
"only support FORMAT_NC1HWC0,FORMAT_NCHW,FORMAT_NHWC"}); | |||
GELOGE(INTERNAL_ERROR, "The format [%s] is unsupported", TypeUtils::FormatToSerialString(old_format).c_str()); | |||
return FAILED; | |||
} | |||
@@ -762,6 +777,9 @@ Status GetStorageFormatAndShape(OpDescPtr &op_desc, const GeTensorDescPtr &tenso | |||
op_desc->GetName().c_str(), TypeUtils::FormatToSerialString(storage_format).c_str(), | |||
formats::JoinToString(storage_shape).c_str()); | |||
} else { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"15003", {"opname", "format"}, | |||
{op_desc->GetName(), TypeUtils::FormatToSerialString(storage_format)}); | |||
GELOGE(PARAM_INVALID, "Update node by storage format failed, storage_shape not set. " | |||
"node: [%s], storage_format [%s]", | |||
op_desc->GetName().c_str(), TypeUtils::FormatToSerialString(storage_format).c_str()); | |||
@@ -900,9 +918,14 @@ Status ProcessNetoutputNodeDynShape(NodePtr &node) { | |||
// check if is_output_adjust_hw_layout is set | |||
if (NeedUpdateFormatByOutputTypeParm(op_desc, index)) { | |||
if ((old_format != FORMAT_NCHW) && (old_format != FORMAT_NHWC) && (old_format != FORMAT_NC1HWC0)) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E19014", {"opname", "value", "reason"}, | |||
{op_desc->GetName(), "format[" + TypeUtils::FormatToSerialString(old_format) + "]", | |||
"only support FORMAT_NC1HWC0,FORMAT_NCHW,FORMAT_NHWC"}); | |||
GELOGE(INTERNAL_ERROR, "Format is not one of NCHW, NHWC, NC1HWC0."); | |||
return FAILED; | |||
} | |||
GeTensorDesc old_desc(old_shape, old_format, old_dtype); | |||
if (ProcessNetoutputNodeFp16Nc1hwc0DynShape(old_desc, net_output_input_desc, src_node) != SUCCESS) { | |||
GELOGE(INTERNAL_ERROR, "Process netoutput fp16 nc1hwc0."); | |||
@@ -1035,6 +1058,9 @@ Status GraphPrepare::CheckRefInputNode(const NodePtr &node, const std::string &i | |||
} | |||
bool is_acceptable = (acceptable_types.find(input_type) != acceptable_types.end()); | |||
if (!is_acceptable) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E15005", {"opname", "optype", "opname1", "optype1"}, | |||
{op_desc->GetName(), node->GetType(), input_op_desc->GetName(), input_op_desc->GetType()}); | |||
GELOGE(PARAM_INVALID, "The ref input of ref node %s[%s] must be ref node or variable, but %s[%s]isn't.", | |||
node->GetName().c_str(), node->GetType().c_str(), input_op_desc->GetName().c_str(), | |||
input_op_desc->GetType().c_str()); | |||
@@ -1127,6 +1153,9 @@ Status GraphPrepare::UpdateInput(const std::vector<GeTensor> &user_input) { | |||
} | |||
if ((index < 0) || (static_cast<size_t>(index) >= user_input.size())) { | |||
std::string situation = "data op index[" + std::to_string(index) + "]"; | |||
std::string reason = "it must less than user_input size[" + std::to_string(user_input.size()) + "]"; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, {situation, reason}); | |||
GELOGE(PARAM_INVALID, "user_input size = %zu, graph data op index = %ld.", user_input.size(), index); | |||
return FAILED; | |||
} | |||
@@ -1139,6 +1168,9 @@ Status GraphPrepare::UpdateInput(const std::vector<GeTensor> &user_input) { | |||
if (need_check_internal_format) { | |||
bool is_internal = TypeUtils::IsInternalFormat(format) || TypeUtils::IsInternalFormat(origin_format); | |||
if (is_internal) { | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, | |||
{"Input format[" + TypeUtils::FormatToSerialString(format) + "] or origin_format[" + | |||
TypeUtils::FormatToSerialString(origin_format) + "]", "it is not support"}); | |||
GELOGE(PARAM_INVALID, "Input format %s or origin_format %s is not support.", | |||
TypeUtils::FormatToSerialString(format).c_str(), | |||
TypeUtils::FormatToSerialString(origin_format).c_str()); | |||
@@ -1150,6 +1182,8 @@ Status GraphPrepare::UpdateInput(const std::vector<GeTensor> &user_input) { | |||
uint32_t length = 1; | |||
bool type_ret = TypeUtils::GetDataTypeLength(data_type, length); | |||
if (!type_ret) { | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, | |||
{"Input datatype[" + TypeUtils::DataTypeToSerialString(data_type) + "]", "it is not support"}); | |||
GELOGE(PARAM_INVALID, "Input datatype %s is not support.", | |||
TypeUtils::DataTypeToSerialString(data_type).c_str()); | |||
return FAILED; | |||
@@ -1164,6 +1198,10 @@ Status GraphPrepare::UpdateInput(const std::vector<GeTensor> &user_input) { | |||
return FAILED); | |||
bool size_check = (size != 0 && shape_size != size); | |||
if (size_check) { | |||
std::string situation = "input data size[" + std::to_string(size) + | |||
"] and shape_size[" + std::to_string(size) + "]"; | |||
std::string reason = "because size != 0 and shape_size != size"; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, {situation, reason}); | |||
GELOGE(PARAM_INVALID, "input data size =%ld, shape_size =%ld.", size, shape_size); | |||
return FAILED; | |||
} | |||
@@ -1503,6 +1541,8 @@ Status GraphPrepare::VerifyConstOp(const NodePtr &node) { | |||
uint32_t length = 1; | |||
bool type_ret = TypeUtils::GetDataTypeLength(data_type, length); | |||
if (!type_ret) { | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, | |||
{"Input datatype[" + TypeUtils::DataTypeToSerialString(data_type) + "]", "it is not support"}); | |||
GELOGE(PARAM_INVALID, "Input datatype %s is not support.", TypeUtils::DataTypeToSerialString(data_type).c_str()); | |||
return FAILED; | |||
} | |||
@@ -1512,14 +1552,20 @@ Status GraphPrepare::VerifyConstOp(const NodePtr &node) { | |||
if (shape_size == 0) { | |||
if (ge_tensor_desc.GetShape().GetDims().size() == 0) { | |||
// shape = [], means it's a sclar tensor. | |||
GE_CHK_BOOL_EXEC(data_size / length == 1, return PARAM_INVALID, "Const is invalid scalar tensor."); | |||
GE_CHK_BOOL_EXEC(data_size / length == 1, | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {"Const is invalid scalar tensor."}); | |||
return PARAM_INVALID, "Const is invalid scalar tensor."); | |||
} else { | |||
// shape = [x, y, 0,...], means it's a vector tensor that value is []. | |||
GE_CHK_BOOL_EXEC(data_size == 0, return PARAM_INVALID, "Const is invalid vector scalar."); | |||
GE_CHK_BOOL_EXEC(data_size == 0, | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {"Const is invalid vector scalar."}); | |||
return PARAM_INVALID, "Const is invalid vector scalar."); | |||
} | |||
} else { | |||
GE_CHK_BOOL_EXEC(data_size == static_cast<size_t>(shape_size * length) && data_size != 0, return PARAM_INVALID, | |||
"Const input data size is not equal with tensor desc shape"); | |||
GE_CHK_BOOL_EXEC(data_size == static_cast<size_t>(shape_size * length) && data_size != 0, | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E10043", {"reason"}, {"Const input data size is not equal with tensor desc shape"}); | |||
return PARAM_INVALID, "Const input data size is not equal with tensor desc shape"); | |||
} | |||
return SUCCESS; | |||
} | |||
@@ -1543,6 +1589,9 @@ Status GraphPrepare::CheckUserInput(const std::vector<GeTensor> &user_input) { | |||
return GE_GRAPH_INIT_FAILED; | |||
} | |||
if ((index < 0) || (static_cast<size_t>(index) >= user_input.size())) { | |||
std::string situation = "data op index[" + std::to_string(index) + "]"; | |||
std::string reason = "it must less than user_input size[" + std::to_string(user_input.size()) + "]"; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, {situation, reason}); | |||
GELOGE(GE_GRAPH_INIT_FAILED, "user_input size:%zu, data op index:%ld.", user_input.size(), index); | |||
return GE_GRAPH_INIT_FAILED; | |||
} | |||
@@ -1550,6 +1599,9 @@ Status GraphPrepare::CheckUserInput(const std::vector<GeTensor> &user_input) { | |||
for (size_t i = 0; i < desc.GetShape().GetDimNum(); ++i) { | |||
if (desc.GetShape().GetDim(i) < 0) { | |||
std::string situation = "data dim[" + std::to_string(i) + "][" + std::to_string(desc.GetShape().GetDim(i)) + "]" ; | |||
std::string reason = "it need >= 0"; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E19025", {"situation", "reason"}, {situation, reason}); | |||
GELOGE(GE_GRAPH_INIT_FAILED, "data dim %zu is not supported, need >= 0, real:%ld.", i, | |||
desc.GetShape().GetDim(i)); | |||
return GE_GRAPH_INIT_FAILED; | |||
@@ -1627,7 +1679,6 @@ Status GraphPrepare::PrepareOptimize() { | |||
try { | |||
(void)original_graph_passes.AddPass("PrepareOptimize::ShapeOperateOpRemovePass", new ShapeOperateOpRemovePass); | |||
(void)original_graph_passes.AddPass("PrepareOptimize::ReplaceTransShapePass", new ReplaceTransShapePass); | |||
(void)original_graph_passes.AddPass("PrepareOptimize::MarkAgnosticPass", new MarkAgnosticPass); | |||
} catch (std::bad_alloc &e) { | |||
GELOGE(INTERNAL_ERROR, "Add pass failed, bad memory allocation occurs."); | |||
return INTERNAL_ERROR; | |||
@@ -53,16 +53,6 @@ | |||
} \ | |||
} while (0) | |||
#define AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(expr, _status, errormsg) \ | |||
do { \ | |||
bool b = (expr); \ | |||
if (!b) { \ | |||
GELOGE(_status, errormsg); \ | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {errormsg}); \ | |||
return _status; \ | |||
} \ | |||
} while (0) | |||
namespace { | |||
const int32_t DEFAULT_MATRIX_R0C0_YUV2RGB = 298; | |||
const int32_t DEFAULT_MATRIX_R0C1_YUV2RGB = 0; | |||
@@ -316,9 +306,8 @@ NodePtr AippOp::FindDataByIndex(const ComputeGraphPtr &graph, int rank) { | |||
} | |||
return node; | |||
} | |||
GELOGE(PARAM_INVALID, "Can not find the data node by index %d", rank); | |||
string errormsg = "Can not find the data node by aipp parameter related_input_rank " + to_string(rank); | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {errormsg}); | |||
string error_msg = "Can not find the data node by aipp parameter related_input_rank " + to_string(rank); | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return nullptr; | |||
} | |||
Status AippOp::GetAndCheckTarget(const ComputeGraphPtr &graph, int rank, NodePtr &target, | |||
@@ -363,10 +352,10 @@ Status AippOp::GetAndCheckTarget(const ComputeGraphPtr &graph, int rank, NodePtr | |||
} | |||
if (!edge_indexes.empty() && (*edge_indexes.rbegin() >= data_node->GetOutDataNodes().size())) { | |||
GELOGE(PARAM_INVALID, "input_edge_idx %u should smaller than out edge size of target input %zu", | |||
*edge_indexes.rbegin(), data_node->GetOutDataNodes().size()); | |||
string errormsg = "The aipp parameter input_edge_idx should be smaller than the target input's outnodes."; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {errormsg}); | |||
string error_msg = "The aipp parameter input_edge_idx[" + std::to_string(*edge_indexes.rbegin()) + | |||
"] should be smaller than the target input[" + | |||
std::to_string(data_node->GetOutDataNodes().size()) +"]'s outnodes."; | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
target = data_node; | |||
@@ -439,8 +428,7 @@ Status AippOp::ConvertRelatedInputNameToRank() { | |||
if (!convert_flag) { | |||
string error_msg = "Top name " + related_input_name + "convert rank failed, Please" | |||
" ensure top name in aipp config is the top name of data node."; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {error_msg}); | |||
GELOGE(PARAM_INVALID, "Top name[%s] converts rank failed.", related_input_name.c_str()); | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
@@ -537,87 +525,87 @@ Status AippOp::SetDefaultParams() { | |||
Status AippOp::ValidateParams() { | |||
GE_CHECK_NOTNULL(aipp_params_); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->aipp_mode() != domi::AippOpParams::undefined, PARAM_INVALID, | |||
"When insert AIPP op, aipp_mode must be configured as static or dynamic "); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->var_reci_chn_0_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_0 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->var_reci_chn_1_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_1 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->var_reci_chn_2_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_2 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->var_reci_chn_3_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_3 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r0c0_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r0c0 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r0c1_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r0c1 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r0c2_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r0c2 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r1c0_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r1c0 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r1c1_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r1c1 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r1c2_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r1c2 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r2c0_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r2c0 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r2c1_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r2c1 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->matrix_r2c2_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r2c2 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->output_bias_0_size() <= 1, PARAM_INVALID, | |||
"The parameter output_bias_0 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->output_bias_1_size() <= 1, PARAM_INVALID, | |||
"The parameter output_bias_1 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->output_bias_2_size() <= 1, PARAM_INVALID, | |||
"The parameter output_bias_2 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->input_bias_0_size() <= 1, PARAM_INVALID, | |||
"The parameter input_bias_0 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->input_bias_1_size() <= 1, PARAM_INVALID, | |||
"The parameter input_bias_1 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->input_bias_2_size() <= 1, PARAM_INVALID, | |||
"The parameter input_bias_2 can not be configed repeatedly"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->input_edge_idx_size() <= 1, PARAM_INVALID, | |||
"The parameter input_edge_idx can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->aipp_mode() != domi::AippOpParams::undefined, PARAM_INVALID, | |||
"When insert AIPP op, aipp_mode must be configured as static or dynamic "); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->var_reci_chn_0_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_0 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->var_reci_chn_1_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_1 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->var_reci_chn_2_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_2 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->var_reci_chn_3_size() <= 1, PARAM_INVALID, | |||
"The parameter var_reci_chn_3 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r0c0_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r0c0 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r0c1_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r0c1 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r0c2_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r0c2 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r1c0_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r1c0 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r1c1_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r1c1 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r1c2_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r1c2 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r2c0_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r2c0 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r2c1_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r2c1 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->matrix_r2c2_size() <= 1, PARAM_INVALID, | |||
"The parameter matrix_r2c2 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->output_bias_0_size() <= 1, PARAM_INVALID, | |||
"The parameter output_bias_0 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->output_bias_1_size() <= 1, PARAM_INVALID, | |||
"The parameter output_bias_1 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->output_bias_2_size() <= 1, PARAM_INVALID, | |||
"The parameter output_bias_2 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->input_bias_0_size() <= 1, PARAM_INVALID, | |||
"The parameter input_bias_0 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->input_bias_1_size() <= 1, PARAM_INVALID, | |||
"The parameter input_bias_1 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->input_bias_2_size() <= 1, PARAM_INVALID, | |||
"The parameter input_bias_2 can not be configed repeatedly"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->input_edge_idx_size() <= 1, PARAM_INVALID, | |||
"The parameter input_edge_idx can not be configed repeatedly"); | |||
const domi::AippOpParams::AippMode aipp_mode = aipp_params_->aipp_mode(); | |||
if (aipp_mode == domi::AippOpParams::dynamic) { | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG( | |||
GE_CHK_LOG_AND_ERRORMSG( | |||
aipp_params_->max_src_image_size() > 0, PARAM_INVALID, | |||
"For dynamic AIPP params, max_src_image_size must be set which number should be greater than 0"); | |||
} else { | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->input_format() != domi::AippOpParams::UNDEFINED, PARAM_INVALID, | |||
"Input format of AIPP conf is undefined"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->src_image_size_w() >= 0, PARAM_INVALID, | |||
"Src_image_size_w must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->src_image_size_h() >= 0, PARAM_INVALID, | |||
"Src_image_size_h must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->load_start_pos_w() >= 0, PARAM_INVALID, | |||
"Load_start_pos_w must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->load_start_pos_h() >= 0, PARAM_INVALID, | |||
"Load_start_pos_h must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->crop_size_w() >= 0, PARAM_INVALID, | |||
"Crop_size_w must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->resize_output_w() >= 0, PARAM_INVALID, | |||
"Resize_output_w must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->resize_output_h() >= 0, PARAM_INVALID, | |||
"Resize_output_h must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->left_padding_size() >= 0, PARAM_INVALID, | |||
"Left_padding_size must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->right_padding_size() >= 0, PARAM_INVALID, | |||
"Right_padding_size must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->top_padding_size() >= 0, PARAM_INVALID, | |||
"Top_padding_size must not be configed smaller than 0"); | |||
AIPP_RETURN_STATUS_AND_REPROT_ERRORMSG(aipp_params_->bottom_padding_size() >= 0, PARAM_INVALID, | |||
"Bottom_padding_size must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->input_format() != domi::AippOpParams::UNDEFINED, PARAM_INVALID, | |||
"Input format of AIPP conf is undefined"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->src_image_size_w() >= 0, PARAM_INVALID, | |||
"Src_image_size_w must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->src_image_size_h() >= 0, PARAM_INVALID, | |||
"Src_image_size_h must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->load_start_pos_w() >= 0, PARAM_INVALID, | |||
"Load_start_pos_w must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->load_start_pos_h() >= 0, PARAM_INVALID, | |||
"Load_start_pos_h must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->crop_size_w() >= 0, PARAM_INVALID, | |||
"Crop_size_w must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->resize_output_w() >= 0, PARAM_INVALID, | |||
"Resize_output_w must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->resize_output_h() >= 0, PARAM_INVALID, | |||
"Resize_output_h must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->left_padding_size() >= 0, PARAM_INVALID, | |||
"Left_padding_size must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->right_padding_size() >= 0, PARAM_INVALID, | |||
"Right_padding_size must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->top_padding_size() >= 0, PARAM_INVALID, | |||
"Top_padding_size must not be configed smaller than 0"); | |||
GE_CHK_LOG_AND_ERRORMSG(aipp_params_->bottom_padding_size() >= 0, PARAM_INVALID, | |||
"Bottom_padding_size must not be configed smaller than 0"); | |||
} | |||
return SUCCESS; | |||
@@ -790,17 +778,20 @@ Status AippOp::CreateAippData(const NodePtr &aipp_node) { | |||
int64_t batch_count = -1; | |||
if (GetDataDimN(data_node, ori_data_format, batch_count) != ge::SUCCESS) { | |||
GELOGE(PARAM_INVALID, "Get data_node dims and transfer to nchw_dims failed!"); | |||
string error_msg = "Get data_node dims and transfer to nchw_dims failed!"; | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
if (batch_count <= 0) { | |||
GELOGE(PARAM_INVALID, "Batch count %ld is invalid", batch_count); | |||
string error_msg = "Batch count[" + std::to_string(batch_count) + "] is invalid, it must positive."; | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
int64_t max_dynamic_aipp_size = CalcMaxSize(batch_count); | |||
if (max_dynamic_aipp_size < 0) { | |||
GELOGE(PARAM_INVALID, "The dynamic aipp size is not positive."); | |||
string error_msg = "The dynamic aipp size is not positive"; | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
@@ -124,19 +124,13 @@ Status InsertNewOpUtil::CheckInputNamePositionNotRepeat() { | |||
if (another_item->related_input_name().empty()) { | |||
string error_msg = "Can not both set related_input_name and related_input_rank!" | |||
" Please ensure param is the same with the first aipp config(related_input_name)."; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {error_msg}); | |||
GELOGE(PARAM_INVALID, | |||
"Can not both set related_input_rank and related_input_name!" | |||
" Please ensure param is the same with the first aipp config(related_input_name)."); | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
if (item->related_input_name() == another_item->related_input_name()) { | |||
string error_msg = "Can not insert aipp to the same postion! Please ensure related_input_name" | |||
" param is different in different aipp config."; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {error_msg}); | |||
GELOGE(PARAM_INVALID, | |||
"Can not insert aipp op to the same postion! Please ensure related_input_rank param " | |||
"is different in different aipp config."); | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
} | |||
@@ -156,19 +150,13 @@ Status InsertNewOpUtil::CheckInputRankPositionNoRepeat() { | |||
if (!another_item->related_input_name().empty()) { | |||
string error_msg = "Can not both set related_input_rank and related_input_name!" | |||
" Please ensure param is the same with the first aipp config(related_input_rank)."; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {error_msg}); | |||
GELOGE(PARAM_INVALID, | |||
"Can not both set related_input_rank and related_input_name!" | |||
" Please ensure param is the same with the first aipp config(related_input_rank)."); | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
if (item->related_input_rank() == another_item->related_input_rank()) { | |||
string error_msg = "Can not insert aipp to the same postion! Please ensure related_input_rank" | |||
" param is different in different aipp config."; | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {error_msg}); | |||
GELOGE(PARAM_INVALID, | |||
"Can not insert aipp op to the same postion! Please ensure related_input_rank param " | |||
"is different in different aipp config."); | |||
GE_ERRORLOG_AND_ERRORMSG(PARAM_INVALID, error_msg.c_str()); | |||
return PARAM_INVALID; | |||
} | |||
} | |||
@@ -224,9 +212,10 @@ Status InsertNewOpUtil::CheckGraph(const ComputeGraphPtr &graph) { | |||
} | |||
} | |||
} | |||
GE_CHK_BOOL_RET_STATUS((aippNodes.size() == 0) || (aippNodes.size() == next_nodes_cnt), PARAM_INVALID, | |||
"Can not config part of outputs of Data node to support AIPP, config all " | |||
"of the outputs of Data to support AIPP, or config none of them"); | |||
GE_CHK_LOG_AND_ERRORMSG((aippNodes.size() == 0) || (aippNodes.size() == next_nodes_cnt), | |||
PARAM_INVALID, | |||
"Can not config part of outputs of Data node to support AIPP, config all " | |||
"of the outputs of Data to support AIPP, or config none of them"); | |||
std::unique_ptr<domi::AippOpParams> aippParams(new (std::nothrow) domi::AippOpParams()); | |||
GE_CHECK_NOTNULL(aippParams); | |||
@@ -238,16 +227,19 @@ Status InsertNewOpUtil::CheckGraph(const ComputeGraphPtr &graph) { | |||
GE_CHK_STATUS(GetAippParams(currAippParam, aippNodes[i])); | |||
if (aippMode == domi::AippOpParams::static_) { | |||
GE_CHK_BOOL_RET_STATUS(aippParams->input_format() == currAippParam->input_format(), PARAM_INVALID, | |||
"The input_format of all aipp_ops after one Data should be the same"); | |||
GE_CHK_BOOL_RET_STATUS(aippParams->src_image_size_w() == currAippParam->src_image_size_w(), PARAM_INVALID, | |||
"The src_image_size_w of all aipp_ops after one Data should be the same"); | |||
GE_CHK_BOOL_RET_STATUS(aippParams->src_image_size_h() == currAippParam->src_image_size_h(), PARAM_INVALID, | |||
"The src_image_size_h of all aipp_ops after one Data should be the same"); | |||
GE_CHK_LOG_AND_ERRORMSG( | |||
aippParams->input_format() == currAippParam->input_format(), | |||
PARAM_INVALID, "The input_format of all aipp_ops after one Data should be the same"); | |||
GE_CHK_LOG_AND_ERRORMSG( | |||
aippParams->src_image_size_w() == currAippParam->src_image_size_w(), | |||
PARAM_INVALID, "The src_image_size_w of all aipp_ops after one Data should be the same"); | |||
GE_CHK_LOG_AND_ERRORMSG( | |||
aippParams->src_image_size_h() == currAippParam->src_image_size_h(), | |||
PARAM_INVALID, "The src_image_size_h of all aipp_ops after one Data should be the same"); | |||
} else { | |||
GE_CHK_BOOL_RET_STATUS(aippParams->max_src_image_size() == currAippParam->max_src_image_size(), | |||
PARAM_INVALID, | |||
"The max_src_image_size of all aipp_ops after one Data should be the same"); | |||
GE_CHK_LOG_AND_ERRORMSG( | |||
aippParams->max_src_image_size() == currAippParam->max_src_image_size(), | |||
PARAM_INVALID, "The max_src_image_size of all aipp_ops after one Data should be the same"); | |||
} | |||
}); | |||
} | |||
@@ -290,7 +282,8 @@ Status InsertNewOpUtil::UpdateDataNodeByAipp(const ComputeGraphPtr &graph) { | |||
for (auto &switchn : updated_switchn) { | |||
auto data_iter = switchn_names_to_data.find(switchn->GetName()); | |||
if (data_iter == switchn_names_to_data.end()) { | |||
GELOGE(INTERNAL_ERROR, "Failed to find relative data node by switchn %s", switchn->GetName().c_str()); | |||
string error_msg = "Failed to find relative data node by switchn[" + switchn->GetName() + "]"; | |||
GE_ERRORLOG_AND_ERRORMSG(INTERNAL_ERROR, error_msg.c_str()); | |||
return INTERNAL_ERROR; | |||
} | |||
GE_RETURN_IF_ERROR(UpdateDataBySwitchN(switchn, data_iter->second)); | |||
@@ -477,7 +470,8 @@ Status InsertNewOpUtil::UpdateDataBySwitchN(const NodePtr &switchn, const NodePt | |||
} | |||
} | |||
if (max_index >= switchn->GetOpDesc()->GetOutputsSize()) { | |||
GELOGE(INTERNAL_ERROR, "No max size found from switchn node %s", switchn->GetName().c_str()); | |||
string error_msg = "No max size found from switchn node[" + switchn->GetName()+ "]"; | |||
GE_ERRORLOG_AND_ERRORMSG(INTERNAL_ERROR, error_msg.c_str()); | |||
return INTERNAL_ERROR; | |||
} | |||
auto output_desc = switchn->GetOpDesc()->MutableOutputDesc(max_index); | |||
@@ -595,6 +595,8 @@ Status MultiBatchGraphCopyer::CheckCopyResult(const std::vector<NodePtr> &start_ | |||
} | |||
auto dims = NodeUtils::GetOutputDesc(*node, kDataOutIndex).GetShape().GetDims(); | |||
if (!IsAllDimsPositive(dims)) { | |||
ErrorManager::GetInstance().ATCReportErrMessage("E15004", {"opname", "shape"}, | |||
{node->GetName(), formats::ShapeToString(dims)}); | |||
GELOGE(INTERNAL_ERROR, "Failed to copy multi batch graph, the node %s still has unknown shape %s", | |||
node->GetName().c_str(), formats::ShapeToString(dims).c_str()); | |||
return INTERNAL_ERROR; | |||
@@ -1025,6 +1027,13 @@ Status MultiBatchGraphCopyer::InsertIdentityAfterSwitchN() { | |||
} | |||
Status ProcessMultiBatch(ComputeGraphPtr &graph) { | |||
const char *multi_batch_with_case = std::getenv("MULTI_BATCH_WITH_CASE"); | |||
if (multi_batch_with_case != nullptr) { | |||
PassManager pass_manager; | |||
GE_CHK_STATUS_RET(pass_manager.AddPass("MultiBatchClonePass", new (std::nothrow) MultiBatchClonePass)); | |||
return pass_manager.Run(graph); | |||
} | |||
std::vector<std::vector<int64_t>> shapes; | |||
if (!InitDynamicParams(shapes)) { | |||
GELOGD("There is no multi-batch options, no need to process multi-batch copy"); | |||
@@ -124,6 +124,8 @@ Status ParserDataToDynmaicInfo(const vector<vector<int64_t>> &shapes, | |||
auto tmp_index = cur_data_index; | |||
for (size_t i = 0; i < static_cast<size_t>(dynamic_dims_num); ++i) { | |||
if (tmp_index >= dynamic_gear_info.size()) { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E10045", {"name", "shape"}, {data_name, formats::JoinToString(data_shape)}); | |||
GELOGE(PARAM_INVALID, "Data: %s shape: %s make dynamic dims overflow", data_name.c_str(), | |||
formats::JoinToString(data_shape).c_str()); | |||
return FAILED; | |||
@@ -131,6 +133,8 @@ Status ParserDataToDynmaicInfo(const vector<vector<int64_t>> &shapes, | |||
one_gear.push_back(dynamic_gear_info[tmp_index++]); | |||
} | |||
} else { | |||
ErrorManager::GetInstance().ATCReportErrMessage( | |||
"E10046", {"name", "shape"}, {data_name, formats::JoinToString(data_shape)}); | |||
GELOGE(PARAM_INVALID, "Dynamic dims num of data: %s shape: %s can not be more than one gear dynamic info size", | |||
data_name.c_str(), formats::JoinToString(data_shape).c_str()); | |||
return FAILED; | |||
@@ -100,7 +100,9 @@ Status SliceKernel::Compute(const OpDescPtr attr, const std::vector<ConstGeTenso | |||
} | |||
// construct tensorDesc | |||
ge::GeShape output_shape(output_dims); | |||
GeTensorDesc output_tensor_desc(output_shape, FORMAT_NCHW, data_type); | |||
auto attr_output_tensor_desc = attr->GetOutputDesc(0); | |||
GeTensorDesc output_tensor_desc(attr_output_tensor_desc); | |||
output_tensor_desc.SetShape(output_shape); | |||
GeTensorPtr output_ptr = MakeShared<GeTensor>(output_tensor_desc); | |||
if (output_ptr == nullptr) { | |||
GELOGW("make_shared ge::GeTensor failed, node name %s.", attr->GetName().c_str()); | |||
@@ -259,7 +259,9 @@ Status NodeDoneCallback::ProfilingReport() { | |||
return profiling_ret; | |||
} | |||
ProfilingManager::Instance().ReportProfilingData(task_desc_info, compute_graph_info); | |||
auto &profiling_manager = ProfilingManager::Instance(); | |||
profiling_manager.ReportProfilingData(model->GetModelId(), task_desc_info, compute_graph_info, | |||
!profiling_manager.IsAclApiMode()); | |||
return SUCCESS; | |||
} | |||
@@ -17,8 +17,6 @@ | |||
#include "aicore_node_executor.h" | |||
#include "cce/taskdown_common.hpp" | |||
#include "hybrid/executor/hybrid_execution_context.h" | |||
#include "init/gelib.h" | |||
#include "hybrid/executor/hybrid_execution_context.h" | |||
namespace ge { | |||
namespace hybrid { | |||
@@ -28,19 +26,10 @@ AiCoreNodeTask::AiCoreNodeTask(std::vector<std::unique_ptr<AiCoreOpTask>> &&task | |||
} | |||
Status AiCoreNodeExecutor::Initialize() { | |||
auto ge_lib = GELib::GetInstance(); | |||
GE_CHECK_NOTNULL(ge_lib); | |||
if (!ge_lib->InitFlag()) { | |||
GELOGE(GE_CLI_GE_NOT_INITIALIZED, "Ge_lib is uninitialized, failed."); | |||
return GE_CLI_GE_NOT_INITIALIZED; | |||
compiler_ = TaskCompilerFactory::GetInstance().GetTaskCompiler(); | |||
if (compiler_ != nullptr) { | |||
GE_CHK_STATUS_RET(compiler_->Initialize(), "Failed to init aicore task compiler."); | |||
} | |||
auto &kernel_manager = ge_lib->OpsKernelManagerObj(); | |||
auto aic_ops_store = kernel_manager.GetOpsKernelInfoStore("AIcoreEngine"); | |||
GE_CHECK_NOTNULL(aic_ops_store); | |||
compiler_.reset(new(std::nothrow)AiCoreTaskCompiler(aic_ops_store)); | |||
GE_CHECK_NOTNULL(compiler_); | |||
return SUCCESS; | |||
} | |||
@@ -120,6 +109,12 @@ Status AiCoreNodeExecutor::CompileTask(const HybridModel &model, | |||
GE_CHECK_NOTNULL(op_desc); | |||
GELOGI("AiCoreNodeExecutor(%s) CompileTask Start.", node->GetName().c_str()); | |||
auto ori_node_name = node->GetName(); | |||
if (compiler_ == nullptr) { | |||
GELOGE(FAILED, "[%s] Can not find any valid aicore task compiler.", ori_node_name.c_str()); | |||
return FAILED; | |||
} | |||
AiCoreNodeTaskRegistry ®istry = AiCoreNodeTaskRegistry::GetInstance(); | |||
std::string shape_key; | |||
GE_CHK_STATUS_RET(GenNodeKey(node, shape_key), "GenNodeKey failed, op name = %s.", node->GetName().c_str()); | |||
@@ -133,7 +128,6 @@ Status AiCoreNodeExecutor::CompileTask(const HybridModel &model, | |||
} | |||
std::vector<domi::TaskDef> task_defs; | |||
auto ori_node_name = node->GetName(); | |||
op_desc->SetName(ori_node_name + "_" + shape_key); | |||
GE_CHK_STATUS_RET(compiler_->CompileOp(node, task_defs), "Compile op(%s) failed.", ori_node_name.c_str()); | |||
op_desc->SetName(ori_node_name); | |||
@@ -239,5 +233,23 @@ bool AiCoreNodeTask::IsNoOp(TaskContext &task_context) { | |||
return true; | |||
} | |||
TaskCompilerFactory &TaskCompilerFactory::GetInstance() { | |||
static TaskCompilerFactory instance; | |||
return instance; | |||
} | |||
void TaskCompilerFactory::Register(CreateFn fn) { | |||
compiler_func_ = fn; | |||
} | |||
std::unique_ptr<TaskCompiler> TaskCompilerFactory::GetTaskCompiler() { | |||
auto compiler_instance = std::unique_ptr<TaskCompiler>(compiler_func_()); | |||
return compiler_instance; | |||
} | |||
CompilerFunctionRegistrar::CompilerFunctionRegistrar(CreateFn fn) { | |||
TaskCompilerFactory::GetInstance().Register(fn); | |||
} | |||
} // namespace hybrid | |||
} // namespace ge |
@@ -18,13 +18,21 @@ | |||
#define GE_HYBRID_KERNEL_AICORE_NODE_EXECUTOR_H_ | |||
#include "hybrid/node_executor/aicore/aicore_task_builder.h" | |||
#include "hybrid/node_executor/aicore/aicore_task_compiler.h" | |||
#include "hybrid/node_executor/node_executor.h" | |||
#include <map> | |||
#include <mutex> | |||
namespace ge { | |||
namespace hybrid { | |||
class TaskCompiler { | |||
public: | |||
TaskCompiler() = default; | |||
virtual ~TaskCompiler() = default; | |||
virtual Status CompileOp(const NodePtr &node, std::vector<domi::TaskDef> &tasks) = 0; | |||
virtual Status Initialize() = 0; | |||
}; | |||
class AiCoreNodeTaskRegistry { | |||
public: | |||
~AiCoreNodeTaskRegistry() = default; | |||
@@ -65,8 +73,33 @@ class AiCoreNodeExecutor : public NodeExecutor { | |||
private: | |||
static Status GenNodeKey(const NodePtr &node, std::string &node_key); | |||
std::unique_ptr<AiCoreTaskCompiler> compiler_; | |||
std::unique_ptr<TaskCompiler> compiler_; | |||
}; | |||
using CreateFn = TaskCompiler *(*)(); | |||
class TaskCompilerFactory { | |||
public: | |||
static TaskCompilerFactory &GetInstance(); | |||
void Register(CreateFn fn); | |||
std::unique_ptr<TaskCompiler> GetTaskCompiler(); | |||
private: | |||
CreateFn compiler_func_; | |||
}; | |||
class CompilerFunctionRegistrar { | |||
public: | |||
CompilerFunctionRegistrar(CreateFn fn); | |||
~CompilerFunctionRegistrar() = default; | |||
}; | |||
} // namespace hybrid | |||
} // namespace ge | |||
#endif //GE_HYBRID_KERNEL_AICORE_NODE_EXECUTOR_H_ | |||
#define REGISTER_TASK_COMPILER(compiler) \ | |||
static ::ge::hybrid::CompilerFunctionRegistrar register_compiler_function \ | |||
__attribute__((unused)) = \ | |||
::ge::hybrid::CompilerFunctionRegistrar([]()->::ge::hybrid::TaskCompiler* { \ | |||
return new (std::nothrow) compiler(); \ | |||
}) \ | |||
#endif //GE_HYBRID_KERNEL_AICORE_NODE_EXECUTOR_H_ |
@@ -18,6 +18,7 @@ | |||
#include "framework/common/debug/log.h" | |||
#include "graph/debug/ge_attr_define.h" | |||
#include "opskernel_manager/ops_kernel_builder_manager.h" | |||
#include "init/gelib.h" | |||
namespace ge { | |||
namespace hybrid { | |||
@@ -25,11 +26,22 @@ namespace { | |||
uintptr_t kWeightBase = 0x10000000; | |||
uintptr_t kMemBase = 0x20000000; | |||
uint64_t kFakeSize = 0x10000000UL; | |||
REGISTER_TASK_COMPILER(AiCoreTaskCompiler); | |||
} | |||
std::mutex AiCoreTaskCompiler::mu_; | |||
AiCoreTaskCompiler::AiCoreTaskCompiler(OpsKernelInfoStorePtr aic_kernel_store) | |||
: aic_kernel_store_(std::move(aic_kernel_store)) {} | |||
Status AiCoreTaskCompiler::Initialize() { | |||
auto ge_lib = GELib::GetInstance(); | |||
GE_CHECK_NOTNULL(ge_lib); | |||
if (!ge_lib->InitFlag()) { | |||
GELOGE(GE_CLI_GE_NOT_INITIALIZED, "Ge_lib is uninitialized, failed."); | |||
return GE_CLI_GE_NOT_INITIALIZED; | |||
} | |||
auto &kernel_manager = ge_lib->OpsKernelManagerObj(); | |||
aic_kernel_store_ = kernel_manager.GetOpsKernelInfoStore("AIcoreEngine"); | |||
GE_CHECK_NOTNULL(aic_kernel_store_); | |||
return SUCCESS; | |||
} | |||
Status AiCoreTaskCompiler::DoCompileOp(const NodePtr &node) const { | |||
GE_CHECK_NOTNULL(node); | |||
@@ -19,15 +19,17 @@ | |||
#include <mutex> | |||
#include "opskernel_manager/ops_kernel_manager.h" | |||
#include "aicore_node_executor.h" | |||
namespace ge { | |||
namespace hybrid { | |||
class AiCoreTaskCompiler { | |||
class AiCoreTaskCompiler : public TaskCompiler { | |||
public: | |||
explicit AiCoreTaskCompiler(OpsKernelInfoStorePtr aic_kernel_store); | |||
AiCoreTaskCompiler() = default; | |||
~AiCoreTaskCompiler() = default; | |||
Status CompileOp(const NodePtr &node, std::vector<domi::TaskDef> &tasks); | |||
Status CompileOp(const NodePtr &node, std::vector<domi::TaskDef> &tasks) override; | |||
Status Initialize() override; | |||
private: | |||
Status DoCompileOp(const NodePtr &node) const; | |||
Status DoGenerateTask(const Node &node, std::vector<domi::TaskDef> &tasks); | |||
@@ -56,6 +56,7 @@ const int kDefaultDeviceIdForInfer = -1; | |||
const uint32_t kAicoreOverflow = (0x1 << 0); | |||
const uint32_t kAtomicOverflow = (0x1 << 1); | |||
const uint32_t kAllOverflow = (kAicoreOverflow | kAtomicOverflow); | |||
const char *const kGlobalOptionFpCeilingModeDefault = "2"; | |||
} // namespace | |||
static std::shared_ptr<GELib> instancePtr_ = nullptr; | |||
@@ -79,6 +80,11 @@ Status GELib::Initialize(const map<string, string> &options) { | |||
return ret; | |||
} | |||
instancePtr_->SetDefaultPrecisionMode(new_options); | |||
if (new_options.find("ge.fpCeilingMode") == new_options.end()) { | |||
new_options["ge.fpCeilingMode"] = kGlobalOptionFpCeilingModeDefault; | |||
} | |||
GetMutableGlobalOptions().insert(new_options.begin(), new_options.end()); | |||
GetThreadLocalContext().SetGlobalOption(GetMutableGlobalOptions()); | |||
GE_TIMESTAMP_START(Init); | |||
@@ -32,7 +32,6 @@ | |||
#include "graph/anchor.h" | |||
#include "graph/debug/ge_attr_define.h" | |||
#include "graph/graph.h" | |||
#include "graph/manager/graph_var_manager.h" | |||
#include "graph/op_desc.h" | |||
#include "graph/utils/graph_utils.h" | |||
#include "graph/utils/type_utils.h" | |||
@@ -64,8 +63,6 @@ using std::vector; | |||
static bool is_dynamic_input = false; | |||
// 310 limited 8G size | |||
const char *const kGraphMemoryManagerMallocMaxSize = "8*1024*1024*1024"; | |||
const char *const kModeSupport = "only support 0(model to framework model), " | |||
"1(framework model to json), 3(only pre-check), 5(pbtxt to json)"; | |||
const char *const kModelToJsonSupport = "only support 0(Caffe) 3(TensorFlow) 5(Onnx)"; | |||
@@ -908,13 +905,6 @@ domi::Status GenerateModel(std::map<string, string> &options, std::string output | |||
return domi::FAILED; | |||
} | |||
geRet = ge::VarManager::Instance(0)->SetMemoryMallocSize(options); | |||
if (geRet != ge::SUCCESS) { | |||
GELOGE(ge::FAILED, "SetMemoryMallocSize failed."); | |||
(void)ge::GELib::GetInstance()->Finalize(); | |||
return domi::FAILED; | |||
} | |||
ge::Graph graph; | |||
std::vector<ge::GeTensor> inputs; | |||
if (FLAGS_framework == domi::MINDSPORE) { | |||
@@ -1016,7 +1006,6 @@ static void SetEnvForSingleOp(std::map<string, string> &options) { | |||
options.emplace(ge::OP_SELECT_IMPL_MODE, FLAGS_op_select_implmode); | |||
options.emplace(ge::OPTYPELIST_FOR_IMPLMODE, FLAGS_optypelist_for_implmode); | |||
options.emplace(ge::AUTO_TUNE_MODE, FLAGS_auto_tune_mode); | |||
options.emplace(ge::GRAPH_MEMORY_MAX_SIZE, kGraphMemoryManagerMallocMaxSize); | |||
options.emplace(ge::OP_DEBUG_LEVEL, to_string(FLAGS_op_debug_level)); | |||
options.emplace(ge::DEBUG_DIR, FLAGS_debug_dir); | |||
options.emplace(ge::OP_COMPILER_CACHE_DIR, FLAGS_op_compiler_cache_dir); | |||
@@ -1053,13 +1042,6 @@ domi::Status GenerateSingleOp(const std::string& json_file_path) { | |||
return domi::FAILED; | |||
} | |||
ret = ge::VarManager::Instance(0)->SetMemoryMallocSize(options); | |||
if (ret != ge::SUCCESS) { | |||
GELOGE(ge::FAILED, "SetMemoryMallocSize failed."); | |||
(void)ge::GELib::GetInstance()->Finalize(); | |||
return domi::FAILED; | |||
} | |||
vector<ge::SingleOpBuildParam> build_params; | |||
if (ge::SingleOpParser::ParseSingleOpList(json_file_path, build_params) != ge::SUCCESS) { | |||
DOMI_LOGE("parse single op json file failed"); | |||
@@ -1158,8 +1140,6 @@ domi::Status GenerateOmModel() { | |||
(FLAGS_enable_compress_weight == "true") ? | |||
ge::kEnableCompressWeightTrue : ge::kEnableCompressWeightFalse)); | |||
options.insert(std::pair<string, string>(string(ge::GRAPH_MEMORY_MAX_SIZE), kGraphMemoryManagerMallocMaxSize)); | |||
options.insert(std::pair<string, string>(string(ge::ENABLE_SINGLE_STREAM), FLAGS_enable_single_stream)); | |||
options.insert(std::pair<string, string>(string(ge::DEBUG_DIR), FLAGS_debug_dir)); | |||
@@ -485,6 +485,10 @@ Status SetOutputNodeInfo(ge::Graph &graph, const std::string &output_type, const | |||
GELOGE(domi::FAILED, "Check out node (%s) fail.", user_out_nodes[i].first.c_str()); | |||
return domi::FAILED; | |||
} | |||
// add user_define_output_nodes attr. | |||
(void)ge::AttrUtils::SetStr(op_desc, ATTR_ATC_USER_DEFINE_OUTPUT_NODES, "true"); | |||
if (i < output_formats.size()) { | |||
if (output_formats[i] == domi::DOMI_TENSOR_NC1HWC0) { | |||
GELOGI("The output node [%s] should be set NC1HWC0", user_out_nodes[i].first.c_str()); | |||
@@ -339,6 +339,7 @@ const std::set<std::string> ir_builder_suppported_options = {INPUT_FORMAT, | |||
OUT_NODES, | |||
INPUT_FP16_NODES, | |||
LOG_LEVEL, | |||
OP_DEBUG_LEVEL, | |||
DEBUG_DIR, | |||
OP_COMPILER_CACHE_DIR, | |||
OP_COMPILER_CACHE_MODE}; | |||
@@ -28,7 +28,7 @@ | |||
#if !defined(__ANDROID__) && !defined(ANDROID) | |||
#define DOMI_LOGE(...) GE_LOG_ERROR(GE_MODULE_NAME, ge::FAILED, __VA_ARGS__) | |||
#else | |||
#include<android/log.h> | |||
#include <android/log.h> | |||
#if defined(BUILD_VERSION_PERF) | |||
#define DOMI_LOGE(fmt, ...) | |||
#else | |||
@@ -83,12 +83,12 @@ | |||
} while (0); | |||
// If expr is not GRAPH_SUCCESS, print the log and return FAILED | |||
#define GE_CHK_GRAPH_STATUS_RET(expr, ...) \ | |||
do { \ | |||
if ((expr) != ge::GRAPH_SUCCESS) { \ | |||
DOMI_LOGE(__VA_ARGS__); \ | |||
return FAILED; \ | |||
} \ | |||
#define GE_CHK_GRAPH_STATUS_RET(expr, ...) \ | |||
do { \ | |||
if ((expr) != ge::GRAPH_SUCCESS) { \ | |||
DOMI_LOGE(__VA_ARGS__); \ | |||
return FAILED; \ | |||
} \ | |||
} while (0); | |||
// If expr is not SUCCESS, print the log and execute a custom statement | |||
@@ -99,13 +99,13 @@ | |||
} while (0); | |||
// If expr is not true, print the log and return the specified status | |||
#define GE_CHK_BOOL_RET_STATUS(expr, _status, ...) \ | |||
do { \ | |||
bool b = (expr); \ | |||
if (!b) { \ | |||
GELOGE(_status, __VA_ARGS__); \ | |||
return _status; \ | |||
} \ | |||
#define GE_CHK_BOOL_RET_STATUS(expr, _status, ...) \ | |||
do { \ | |||
bool b = (expr); \ | |||
if (!b) { \ | |||
GELOGE(_status, __VA_ARGS__); \ | |||
return _status; \ | |||
} \ | |||
} while (0); | |||
// If expr is not true, print the log and return the specified status | |||
@@ -253,4 +253,20 @@ | |||
exec_expr1; \ | |||
} | |||
#define GE_ERRORLOG_AND_ERRORMSG(_status, errormsg) \ | |||
{ \ | |||
GELOGE(_status, "%s", errormsg); \ | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {errormsg}); \ | |||
} | |||
#define GE_CHK_LOG_AND_ERRORMSG(expr, _status, errormsg) \ | |||
do { \ | |||
bool b = (expr); \ | |||
if (!b) { \ | |||
GELOGE(_status, "%s", errormsg); \ | |||
ErrorManager::GetInstance().ATCReportErrMessage("E10043", {"reason"}, {errormsg}); \ | |||
return _status; \ | |||
} \ | |||
} while (0) | |||
#endif // INC_FRAMEWORK_COMMON_DEBUG_LOG_H_ |
@@ -70,6 +70,7 @@ FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string PROFIL | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string PROFILE_STOP_VALUE; | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::map<std::string, std::string> PROFILE_COMPONENT_MAP; | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string PROFILE_CONFIG; | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string PROFILE_MODEL_ID; | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASKS; | |||
FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY extern const std::string MODEL_ATTR_TASK_GEN_BASE_ADDR; | |||
@@ -567,10 +568,10 @@ enum ModelCheckType { | |||
/// @brief dynamic input type | |||
/// | |||
enum DynamicInputType { | |||
FIXED = 0, // default mode | |||
DYNAMIC_BATCH = 1, | |||
DYNAMIC_IMAGE = 2, | |||
DYNAMIC_DIMS = 3 | |||
FIXED = 0, // default mode | |||
DYNAMIC_BATCH = 1, | |||
DYNAMIC_IMAGE = 2, | |||
DYNAMIC_DIMS = 3 | |||
}; | |||
/// | |||
@@ -38,14 +38,14 @@ class DynamicSingleOp; | |||
struct RunModelData { | |||
uint32_t index; // Data index | |||
uint32_t modelId; | |||
std::vector<DataBuffer> blobs; // All input/output data buffer | |||
uint32_t timestamp; // Data creation time | |||
uint32_t timeout; // Processing timeout | |||
uint64_t request_id = 0; // Request ID | |||
uint64_t dynamic_batch_size = 0; // Dynamic batch size scene, set dynamic size, not supported by default:0 | |||
uint64_t dynamic_image_height = 0; // Dynamic image size scene, set image height, not supported by default:0 | |||
uint64_t dynamic_image_width = 0; // Dynamic image size scene, set image width, not supported by default:0 | |||
std::vector<uint64_t> dynamic_dims; // Dynamic dims scene, set dynamic dims, not supported by default:empty | |||
std::vector<DataBuffer> blobs; // All input/output data buffer | |||
uint32_t timestamp; // Data creation time | |||
uint32_t timeout; // Processing timeout | |||
uint64_t request_id = 0; // Request ID | |||
uint64_t dynamic_batch_size = 0; // Dynamic batch size scene, set dynamic size, not supported by default:0 | |||
uint64_t dynamic_image_height = 0; // Dynamic image size scene, set image height, not supported by default:0 | |||
uint64_t dynamic_image_width = 0; // Dynamic image size scene, set image width, not supported by default:0 | |||
std::vector<uint64_t> dynamic_dims; // Dynamic dims scene, set dynamic dims, not supported by default:empty | |||
}; | |||
class GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY GeExecutor { | |||
@@ -264,14 +264,14 @@ class GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY GeExecutor { | |||
static ge::Status LoadDynamicSingleOp(const std::string &model_name, const ge::ModelData &modelData, void *stream, | |||
DynamicSingleOp **single_op); | |||
static ge::Status ExecuteAsync(DynamicSingleOp *executor, | |||
const std::vector<GeTensorDesc> &input_desc, | |||
const std::vector<DataBuffer> &inputs, | |||
std::vector<GeTensorDesc> &output_desc, | |||
static ge::Status ExecuteAsync(DynamicSingleOp *executor, const std::vector<GeTensorDesc> &input_desc, | |||
const std::vector<DataBuffer> &inputs, std::vector<GeTensorDesc> &output_desc, | |||
std::vector<DataBuffer> &outputs); | |||
static ge::Status ReleaseSingleOpResource(void *stream); | |||
static ge::Status GetDeviceIdByModelId(uint32_t model_id, uint32_t &device_id); | |||
ge::Status GetBatchInfoSize(uint32_t model_id, size_t &shape_count); | |||
ge::Status GetOrigInputInfo(uint32_t model_id, uint32_t index, OriginInputInfo &orig_input_info); | |||
ge::Status GetAllAippInputOutputDims(uint32_t model_id, uint32_t index, std::vector<InputOutputDims> &input_dims, | |||
@@ -1 +1 @@ | |||
Subproject commit 0d0d2fb016d44f9a575ad8f8c2cb8858bba3acec | |||
Subproject commit 37465b85d30b67a0edcc6ea4acd2f11a9697c7af |
@@ -1 +1 @@ | |||
Subproject commit 84ea76e94054fcfac5b80ded6e0ec4db1f37d3e0 | |||
Subproject commit 5fa1f3ed9b1785b9fd1623d624de91838dff615e |