@@ -681,8 +681,35 @@ Status Session::BuildGraph(uint32_t graph_id, const std::vector<InputTensorInfo> | |||
return SUCCESS; | |||
} | |||
// Build Graph | |||
Status Session::BuildGraph(uint32_t graph_id, const std::vector<ge::Tensor> &inputs) { | |||
ErrorManager::GetInstance().SetStage(error_message::kModelCompile, error_message::kOther); | |||
ErrorManager::GetInstance().GenWorkStreamIdBySessionGraph(sessionId_, graph_id); | |||
std::shared_ptr<GELib> instance_ptr = ge::GELib::GetInstance(); | |||
if (instance_ptr == nullptr || !instance_ptr->InitFlag()) { | |||
GELOGE(GE_CLI_GE_NOT_INITIALIZED, | |||
"[Build][Graph]Failed, the GELib instance is nullptr or is not InitFlag, " | |||
"session_id %lu, graph_id %u", sessionId_, graph_id); | |||
REPORT_INNER_ERROR("E19999", | |||
"Build graph failed, the GELib instance is nullptr or is not InitFlag, " | |||
"session_id %lu, graph_id %u", sessionId_, graph_id); | |||
return FAILED; | |||
} | |||
GELOGT(TRACE_RUNNING, "Building Graph"); | |||
Status ret = instance_ptr->SessionManagerObj().BuildGraph(sessionId_, graph_id, inputs); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, | |||
"[Build][Graph]Failed, error code:%u, session_id:%lu, graph_id:%u.", | |||
ret, sessionId_, graph_id); | |||
REPORT_CALL_ERROR("E19999", "Build graph failed , error code:%u, " | |||
"session_id:%lu, graph_id:%u", ret, sessionId_, graph_id); | |||
return FAILED; | |||
} | |||
return SUCCESS; | |||
} | |||
// Run Graph Asynchronously | |||
Status Session::RunGraphAsync(uint32_t graph_id, const std::vector<InputTensorInfo> &inputs, | |||
Status Session::RunGraphAsync(uint32_t graph_id, const std::vector<ge::Tensor> &inputs, | |||
RunAsyncCallback callback) { | |||
ErrorManager::GetInstance().SetStage(error_message::kModelExecute, error_message::kModelExecute); | |||
ErrorManager::GetInstance().GenWorkStreamIdBySessionGraph(sessionId_, graph_id); | |||
@@ -382,7 +382,7 @@ Status GraphExecutor::ExecuteGraph(GraphId graph_id, const GeRootModelPtr &ge_ro | |||
} | |||
Status GraphExecutor::ExecuteGraphAsync(GraphId graph_id, const GeRootModelPtr &ge_root_model, | |||
const std::vector<InputTensorInfo> &input_tensor, | |||
const std::vector<ge::Tensor> &input_tensor, | |||
const RunAsyncCallback& callback) { | |||
GELOGI("[GraphExecutor] Start to async execute graph, graph_id=%u", graph_id); | |||
if (graph_id != last_graph_id_) { | |||
@@ -529,7 +529,7 @@ Status GraphExecutor::SetCallback(uint32_t model_id, const GeRootModelPtr &ge_ro | |||
return SUCCESS; | |||
} | |||
Status GraphExecutor::AsyncExecuteModel(const GeRootModelPtr &ge_root_model, const std::vector<InputTensorInfo> &inputs, | |||
Status GraphExecutor::AsyncExecuteModel(const GeRootModelPtr &ge_root_model, const std::vector<ge::Tensor> &inputs, | |||
const RunAsyncCallback &callback) { | |||
uint32_t model_id = GetExecuteModelId(ge_root_model); | |||
if (model_id == kInvalidModelId) { | |||
@@ -50,7 +50,7 @@ class GraphExecutor { | |||
std::vector<GeTensor> &output_tensor); | |||
ge::Status ExecuteGraphAsync(GraphId graph_id, const GeRootModelPtr &ge_root_model, | |||
const std::vector<InputTensorInfo> &input_tensor, const RunAsyncCallback &callback); | |||
const std::vector<ge::Tensor> &input_tensor, const RunAsyncCallback &callback); | |||
Status ExecuteGraphWithStream(GraphId graph_id, | |||
rtStream_t stream, | |||
@@ -137,7 +137,7 @@ class GraphExecutor { | |||
Status SyncExecuteModel(uint32_t model_id, const std::vector<GeTensor> &input_tensor, | |||
std::vector<GeTensor> &output_tensor); | |||
Status AsyncExecuteModel(const GeRootModelPtr &ge_root_model, const std::vector<InputTensorInfo> &input_tensor, | |||
Status AsyncExecuteModel(const GeRootModelPtr &ge_root_model, const std::vector<ge::Tensor> &input_tensor, | |||
const RunAsyncCallback &callback); | |||
void InitModelIdInfo(std::vector<uint32_t> &out_model_id_info, std::vector<SubGraphInfoPtr> &sub_graph_vec, | |||
@@ -122,6 +122,8 @@ const char* const kInferEndTime = "infer_end_time"; | |||
const char* const kOutputBeginTime = "output_start_time"; | |||
const char* const kOutputEndTime = "output_end_time"; | |||
const uint32_t kStringHeadElems = 2; | |||
const uint32_t kPlacementHostData = 0; | |||
const size_t kAlignment = 64; | |||
inline bool IsDataOp(const std::string &node_type) { | |||
return (node_type == DATA_TYPE) || (node_type == AIPP_DATA_TYPE) || (node_type == ANN_DATA_TYPE); | |||
@@ -2261,8 +2263,7 @@ Status DavinciModel::GetOutputDescInfo(vector<InputOutputDescInfo> &output_descs | |||
return SUCCESS; | |||
} | |||
Status DavinciModel::CopyInputData(const InputData &input_data, bool device_data) { | |||
rtMemcpyKind_t kind = device_data ? RT_MEMCPY_DEVICE_TO_DEVICE : RT_MEMCPY_HOST_TO_DEVICE; | |||
Status DavinciModel::CopyInputData(const InputData &input_data) { | |||
const std::vector<DataBuffer> &blobs = input_data.blobs; | |||
for (const auto &data : input_data_info_) { | |||
if (data.first >= blobs.size()) { | |||
@@ -2275,6 +2276,8 @@ Status DavinciModel::CopyInputData(const InputData &input_data, bool device_data | |||
} | |||
const DataBuffer &data_buf = blobs[data.first]; | |||
rtMemcpyKind_t kind = | |||
data_buf.placement == kPlacementHostData ? RT_MEMCPY_HOST_TO_DEVICE : RT_MEMCPY_DEVICE_TO_DEVICE; | |||
if (data_buf.length == 0) { | |||
GELOGW("No data need to memcpy!"); | |||
return SUCCESS; | |||
@@ -2615,7 +2618,7 @@ Status DavinciModel::InitOutputTensorInfo(const OpDescPtr &op_desc) { | |||
return SUCCESS; | |||
} | |||
Status DavinciModel::GenOutputTensorInfo(OutputData *output_data, vector<OutputTensorInfo> &outputs) { | |||
Status DavinciModel::GenOutputTensorInfo(OutputData *output_data, vector<ge::Tensor> &outputs) { | |||
GE_CHECK_NOTNULL(output_data); | |||
if (!output_data->blobs.empty()) { | |||
GELOGI("No need to generate output tensor info, model id:%u", model_id_); | |||
@@ -2644,26 +2647,25 @@ Status DavinciModel::GenOutputTensorInfo(OutputData *output_data, vector<OutputT | |||
GELOGI("Output blobs size:%zu, model id:%u", output_buffer_size_.size(), model_id_); | |||
for (size_t i = 0; i < output_buffer_size.size(); ++i) { | |||
std::unique_ptr<uint8_t[]> data_buf(new (std::nothrow) uint8_t[output_buffer_size[i]]); | |||
if (data_buf == nullptr) { | |||
REPORT_CALL_ERROR("E19999", "New buffer failed, size:%ld, model_id:%u", | |||
output_buffer_size[i], model_id_); | |||
GELOGE(GE_GRAPH_MALLOC_FAILED, "Malloc buffer failed."); | |||
return GE_GRAPH_MALLOC_FAILED; | |||
} | |||
output_data->blobs.push_back({data_buf.get(), static_cast<uint64_t>(output_buffer_size[i]), false}); | |||
OutputTensorInfo output; | |||
output.dims = output_shape_info[i]; | |||
output.data = std::move(data_buf); | |||
output.length = output_buffer_size[i]; | |||
outputs.emplace_back(std::move(output)); | |||
auto aligned_ptr = MakeShared<AlignedPtr>(output_buffer_size[i], kAlignment); | |||
GE_CHECK_NOTNULL(aligned_ptr); | |||
GeShape ge_shape(output_shape_info[i]); | |||
GeTensorDesc tensor_desc; | |||
tensor_desc.SetShape(ge_shape); | |||
GeTensor ge_tensor(tensor_desc); | |||
ge_tensor.SetData(aligned_ptr, output_buffer_size[i]); | |||
ge::Tensor output_tensor = TensorAdapter::AsTensor(ge_tensor); | |||
auto data_ptr = aligned_ptr->MutableGet(); | |||
output_data->blobs.push_back( | |||
{reinterpret_cast<void *>(data_ptr), static_cast<uint64_t>(output_buffer_size[i]), false}); | |||
outputs.emplace_back(std::move(output_tensor)); | |||
GELOGD("Output index:%zu, output dims is %s, data length:%lu.", i, | |||
formats::JoinToString(output.dims).c_str(), output.length); | |||
formats::JoinToString(output_shape_info[i]).c_str(), output_buffer_size[i]); | |||
} | |||
return SUCCESS; | |||
} | |||
/// | |||
/// @ingroup ge | |||
/// @brief send Output Op result to upper layer | |||
@@ -2678,7 +2680,7 @@ Status DavinciModel::GenOutputTensorInfo(OutputData *output_data, vector<OutputT | |||
Status DavinciModel::ReturnResult(uint32_t data_id, const bool rslt_flg, const bool seq_end_flag, | |||
OutputData *output_data) { | |||
GE_CHK_BOOL_EXEC(listener_ != nullptr, return PARAM_INVALID, "listener_ is null."); | |||
std::vector<ge::OutputTensorInfo> outputs; | |||
std::vector<ge::Tensor> outputs; | |||
// return result is not required | |||
if (!rslt_flg && !seq_end_flag) { | |||
@@ -2742,7 +2744,7 @@ Status DavinciModel::ReturnNoOutput(uint32_t data_id) { | |||
GELOGI("ReturnNoOutput model id:%u.", model_id_); | |||
GE_CHK_BOOL_EXEC(listener_ != nullptr, return PARAM_INVALID, "listener_ is null!"); | |||
std::vector<ge::OutputTensorInfo> outputs; | |||
std::vector<ge::Tensor> outputs; | |||
GE_CHK_STATUS(listener_->OnComputeDone(model_id_, data_id, SUCCESS, outputs), "OnComputeDone failed."); | |||
return SUCCESS; | |||
} | |||
@@ -2798,7 +2800,7 @@ void *DavinciModel::Run(DavinciModel *model) { | |||
GELOGI("Copy input data, model id:%u", model_id); | |||
GE_IF_BOOL_EXEC(ProfilingManager::Instance().ProfilingModelExecuteOn(), | |||
model->SetProfileTime(MODEL_PRE_PROC_START)); | |||
ret = model->CopyInputData(current_data, false); | |||
ret = model->CopyInputData(current_data); | |||
GE_CHK_BOOL_TRUE_EXEC_WITH_LOG( | |||
ret != SUCCESS, (void)model->ReturnResult(current_data.index, false, false, data_wrapper->GetOutput()); | |||
continue, "Copy input data to model failed."); // [No need to check value] | |||
@@ -639,7 +639,7 @@ class DavinciModel { | |||
Status UpdateIoTaskArgs(const map<uint32_t, ZeroCopyOffset> &data_info, bool is_input, | |||
const vector<DataBuffer> &blobs, bool is_dynamic, const string &batch_label); | |||
Status CopyInputData(const InputData &input_data, bool device_data = false); | |||
Status CopyInputData(const InputData &input_data); | |||
Status CopyOutputData(uint32_t data_id, OutputData &output_data, rtMemcpyKind_t kind); | |||
@@ -884,7 +884,7 @@ class DavinciModel { | |||
Status SinkTimeProfile(const InputData ¤t_data); | |||
Status InitOutputTensorInfo(const OpDescPtr &op_desc); | |||
Status GenOutputTensorInfo(OutputData *output_data, vector<OutputTensorInfo> &outputs); | |||
Status GenOutputTensorInfo(OutputData *output_data, vector<ge::Tensor> &outputs); | |||
Status InitInputDescInfo(const OpDescPtr &op_desc); | |||
Status InitOutputDescInfo(const OpDescPtr &op_desc, const vector<string> &out_node_name); | |||
@@ -542,7 +542,7 @@ Status ModelManager::GetCurDynamicDims(const vector<vector<int64_t>> &user_real_ | |||
/// @brief load Input and output TensorInfo for Model | |||
/// @return Status run result | |||
/// | |||
Status ModelManager::DataInputTensor(uint32_t model_id, const std::vector<InputTensorInfo> &inputs) { | |||
Status ModelManager::DataInputTensor(uint32_t model_id, const std::vector<ge::Tensor> &inputs) { | |||
std::shared_ptr<DavinciModel> model = GetModel(model_id); | |||
auto hybrid_model = GetHybridModel(model_id); | |||
if (hybrid_model == nullptr) { | |||
@@ -556,9 +556,11 @@ Status ModelManager::DataInputTensor(uint32_t model_id, const std::vector<InputT | |||
input_data.index = 0; | |||
for (size_t i = 0; i < inputs.size(); ++i) { | |||
DataBuffer data; | |||
data.data = inputs[i].data; | |||
data.length = inputs[i].length; | |||
input_data.shapes.emplace_back(inputs[i].dims); | |||
const TensorDesc &tensor_desc = inputs[i].GetTensorDesc(); | |||
data.data = reinterpret_cast<void *>(const_cast<uint8_t *>(inputs[i].GetData())); | |||
data.length = inputs[i].GetSize(); | |||
data.placement = static_cast<uint32_t>(tensor_desc.GetPlacement()); | |||
input_data.shapes.emplace_back(tensor_desc.GetShape().GetDims()); | |||
input_data.blobs.push_back(data); | |||
} | |||
if (!GetLocalOmgContext().user_input_dims.empty() && GetLocalOmgContext().need_multi_batch) { | |||
@@ -608,7 +610,6 @@ Status ModelManager::DataInputTensor(uint32_t model_id, const std::vector<InputT | |||
return SUCCESS; | |||
} | |||
/// | |||
/// @ingroup domi_ome | |||
/// @brief create model thread, start to execute model | |||
@@ -122,7 +122,7 @@ class FMK_FUNC_HOST_VISIBILITY FMK_FUNC_DEV_VISIBILITY ModelManager { | |||
/// | |||
ge::Status DataInput(const InputData &input_data, OutputData &output_data); | |||
ge::Status DataInputTensor(uint32_t model_id, const std::vector<InputTensorInfo> &inputs); | |||
ge::Status DataInputTensor(uint32_t model_id, const std::vector<ge::Tensor> &inputs); | |||
/// | |||
/// @ingroup domi_ome | |||
@@ -106,6 +106,7 @@ | |||
#include "graph/common/omg_util.h" | |||
#include "common/formats/utils/formats_trans_utils.h" | |||
#include "register/custom_pass_helper.h" | |||
#include "external/graph/types.h" | |||
namespace { | |||
const char *const kSummary = "Summary"; | |||
@@ -126,6 +127,7 @@ const uint32_t kNotAdded = 0; | |||
const uint32_t kStartAdd = 1; | |||
const uint32_t kDoneAdded = 2; | |||
const uint32_t kNeverLoaded = 0; | |||
const size_t kAlignment = 64; | |||
bool IsTailingOptimization() { | |||
string is_tailing_optimization_option; | |||
@@ -368,9 +370,9 @@ void GraphManager::RemoveAddGraphCondition(GraphId graph_id) { | |||
auto it = graph_id_to_add_graph_cond_.find(graph_id); | |||
if (it != graph_id_to_add_graph_cond_.end()) { | |||
graph_id_to_add_graph_cond_.erase(it); | |||
GELOGD("Successfully removed add_graph_cond of graph [id:%u].", graph_id); | |||
GELOGD("Successfully remove add_graph_cond of graph [id:%u].", graph_id); | |||
} else { | |||
GELOGD("Graph [id:%u] has not been added. no need to remove.", graph_id); | |||
GELOGD("Graph [id:%u] has not been added, no need to be removed.", graph_id); | |||
} | |||
} | |||
@@ -537,7 +539,7 @@ Status GraphManager::CheckGraphAdded(const GraphId &graph_id, const Graph &graph | |||
bool graph_has_been_added = false; | |||
if (AttrUtils::GetBool(*compute_graph, ATTR_NAME_GRAPH_HAS_BEEN_ADDED, graph_has_been_added) | |||
&& graph_has_been_added) { | |||
REPORT_INNER_ERROR("E19999", "Get Attr:%s from graph:%u fail", | |||
REPORT_INNER_ERROR("E19999", "Get Attr:%s from graph:%u fail.", | |||
ATTR_NAME_GRAPH_HAS_BEEN_ADDED.c_str(), graph_id); | |||
GELOGE(GE_GRAPH_GRAPH_ALREADY_EXIST, | |||
"[GraphManager] same graph object can not be added again, graph_id = %u.", graph_id); | |||
@@ -896,7 +898,7 @@ Status GraphManager::PreRunAfterOptimizeSubGraph(const GraphNodePtr &graph_node, | |||
} | |||
Status GraphManager::SetRtContext(rtContext_t rt_context, rtCtxMode_t mode, uint64_t session_id, uint32_t graph_id) { | |||
GELOGD("set rt_context: session id: %lu, graph id: %u, mode %d, device id:%u.", | |||
GELOGD("Set rt_context: session id: %lu, graph id: %u, mode %d, device id:%u.", | |||
session_id, graph_id, static_cast<int>(mode), ge::GetContext().DeviceId()); | |||
rtError_t rt_ret = rtCtxCreate(&rt_context, mode, ge::GetContext().DeviceId()); | |||
@@ -942,7 +944,7 @@ Status GraphManager::PreRun(const GraphNodePtr &graph_node, const std::vector<Ge | |||
GE_CHK_STATUS_RET(analyzer_instance->BuildJsonObject(session_id, compute_graph->GetGraphID()), | |||
"BuildJsonObject Failed") | |||
GEEVENT("PreRun start: graph node size %zu, session id %lu, graph id %u, graph name %s", | |||
GEEVENT("PreRun start: graph node size %zu, session id %lu, graph id %u, graph name %s.", | |||
compute_graph->GetDirectNodesSize(), session_id, compute_graph->GetGraphID(), | |||
compute_graph->GetName().c_str()); | |||
GE_DUMP(compute_graph, "PreRunBegin"); | |||
@@ -963,7 +965,7 @@ Status GraphManager::PreRun(const GraphNodePtr &graph_node, const std::vector<Ge | |||
if (run_optimize_original_graph) { | |||
Status ret = PreRunOptimizeOriginalGraph(graph_node, inputs, compute_graph, session_id); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "Run PreRunOptimizeOriginalGraph failed for graph:%s", compute_graph->GetName().c_str()); | |||
GELOGE(ret, "Run PreRunOptimizeOriginalGraph failed for graph:%s.", compute_graph->GetName().c_str()); | |||
return ret; | |||
} | |||
} | |||
@@ -1058,7 +1060,7 @@ Status GraphManager::StartForRunGraph(const GraphNodePtr &graph_node, const std: | |||
// release rts generate context | |||
RtContextUtil::GetInstance().DestroyRtContexts(session_id, graph_node->GetGraphId()); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "PreRun Failed. graph_id:%u.", graph_node->GetGraphId()); | |||
GELOGE(ret, "PreRun Failed, graph_id:%u.", graph_node->GetGraphId()); | |||
return ret; | |||
} | |||
} | |||
@@ -2943,7 +2945,7 @@ Status GraphManager::ProcessSubGraphWithMultiThreads(GraphManager *graph_manager | |||
} | |||
// run graph async on session | |||
Status GraphManager::RunGraphAsync(const GraphId &graph_id, const std::vector<ge::InputTensorInfo> &inputs, | |||
Status GraphManager::RunGraphAsync(const GraphId &graph_id, const std::vector<ge::Tensor> &inputs, | |||
uint64_t session_id, RunAsyncCallback callback) { | |||
ErrorManager::GetInstance().SetStage(error_message::kModelExecute, error_message::kModelExecute); | |||
GELOGI("[GraphManager] Start to run graph async, graph_id=%u, inputsSize=%zu.", graph_id, inputs.size()); | |||
@@ -3015,14 +3017,6 @@ Status GraphManager::IncreBuild(const GraphNodePtr &graph_node, GeModelPtr &ge_m | |||
return FAILED; | |||
} | |||
void GraphManager::ConstructGeInput(const vector<InputTensorInfo> &inputs, vector<GeTensor> &ge_inputs) { | |||
for (auto const &input : inputs) { | |||
GeTensorDesc input_tensor_desc(GeShape(input.dims)); | |||
input_tensor_desc.SetDataType(static_cast<ge::DataType>(input.data_type)); | |||
ge_inputs.emplace_back(input_tensor_desc); | |||
} | |||
} | |||
Status GraphManager::CheckIncreBuildAndPreRun(GraphManager *graph_manager, const PreRunArgs &args, | |||
GraphNodePtr &graph_node, GeRootModelPtr &ge_root_model) { | |||
if (!graph_manager->IsGraphNeedBuild(graph_node)) { | |||
@@ -3041,7 +3035,9 @@ Status GraphManager::CheckIncreBuildAndPreRun(GraphManager *graph_manager, const | |||
GeModelPtr ge_model = nullptr; | |||
if (graph_manager->IncreBuild(graph_node, ge_model) != SUCCESS) { | |||
std::vector<GeTensor> ge_inputs; | |||
ConstructGeInput(args.input_tensor, ge_inputs); | |||
for (const auto &item: args.input_tensor) { | |||
ge_inputs.emplace_back(TensorAdapter::AsGeTensor(item)); | |||
} | |||
Status ret = graph_manager->PreRun(graph_node, ge_inputs, ge_root_model, args.session_id); | |||
// release rts generate context | |||
RtContextUtil::GetInstance().DestroyRtContexts(args.session_id, graph_node->GetGraphId()); | |||
@@ -3153,20 +3149,19 @@ void GraphManager::PreRunThread(GraphManager *graph_manager) { | |||
} | |||
} | |||
void GraphManager::ParseInputsDimsForData(const std::vector<InputTensorInfo> &input_tensor) { | |||
void GraphManager::ParseInputsDimsForData(const std::vector<ge::Tensor> &input_tensor) { | |||
GELOGD("Start parse input dims from data."); | |||
for (size_t i = 0; i < input_tensor.size(); ++i) { | |||
std::vector<int64_t> dynamic_dim; | |||
for (size_t j = 0; j < input_tensor[i].dims.size(); ++j) { | |||
dynamic_dim.emplace_back(input_tensor[i].dims[j]); | |||
} | |||
GELOGD("Input tensor dims is %s.", formats::JoinToString(dynamic_dim).c_str()); | |||
GetLocalOmgContext().user_real_input_dims.emplace_back(input_tensor[i].dims); | |||
const TensorDesc &tensor_desc = input_tensor[i].GetTensorDesc(); | |||
const Shape &shape = tensor_desc.GetShape(); | |||
const auto &shape_dims = shape.GetDims(); | |||
GELOGD("Input tensor dims is %s.", formats::JoinToString(shape_dims).c_str()); | |||
GetLocalOmgContext().user_real_input_dims.emplace_back(shape_dims); | |||
} | |||
} | |||
Status GraphManager::ParseInputsDimsForGetNexNosinkAndData(const vector<NodePtr> &dynamic_nodes, | |||
const std::vector<InputTensorInfo> &input_tensor) { | |||
const std::vector<ge::Tensor> &input_tensor) { | |||
GELOGD("Start parse inputs dims when coexist data and getnext sink."); | |||
for (size_t i = 0; i < dynamic_nodes.size(); ++i) { | |||
auto op_desc = dynamic_nodes.at(i)->GetOpDesc(); | |||
@@ -3189,13 +3184,16 @@ Status GraphManager::ParseInputsDimsForGetNexNosinkAndData(const vector<NodePtr> | |||
return PARAM_INVALID; | |||
} | |||
GetLocalOmgContext().user_real_input_dims.emplace_back(input_tensor.at(index).dims); | |||
GELOGI("Shape dims of %zu data is %s.", index, formats::JoinToString(input_tensor.at(index).dims).c_str()); | |||
const TensorDesc &tensor_desc = input_tensor[i].GetTensorDesc(); | |||
const Shape &shape = tensor_desc.GetShape(); | |||
const auto &shape_dims = shape.GetDims(); | |||
GELOGI("Shape dims of %zu data is %s.", index, formats::JoinToString(shape_dims).c_str()); | |||
GetLocalOmgContext().user_real_input_dims.emplace_back(std::move(shape_dims)); | |||
} | |||
return SUCCESS; | |||
} | |||
Status GraphManager::ParseInputsDims(const std::vector<InputTensorInfo> &input_tensor) { | |||
Status GraphManager::ParseInputsDims(const std::vector<ge::Tensor> &input_tensor) { | |||
GELOGI("Start parse input dims of %zu input tensor.", input_tensor.size()); | |||
GetLocalOmgContext().user_real_input_dims.clear(); | |||
if (!GetLocalOmgContext().dynamic_node_type.empty()) { | |||
@@ -3326,13 +3324,13 @@ void GraphManager::ReturnError(GraphManager *graph_manager, RunAsyncCallback cal | |||
} | |||
StopQueue(graph_manager); | |||
GELOGE(ret, "%s.", log.c_str()); | |||
std::vector<ge::OutputTensorInfo> outputs; | |||
std::vector<ge::Tensor> outputs; | |||
callback(ret, outputs); | |||
} | |||
void GraphManager::ReturnError(GraphManager *graph_manager, GraphNodePtr &graph_node, | |||
RunAsyncCallback callback, Status ret, const string &log) { | |||
std::vector<ge::OutputTensorInfo> outputs; | |||
void GraphManager::ReturnError(GraphManager *graph_manager, GraphNodePtr &graph_node, RunAsyncCallback callback, | |||
Status ret, const string &log) { | |||
std::vector<ge::Tensor> outputs; | |||
auto compute_graph = GraphUtils::GetComputeGraph(*graph_node->GetGraph()); | |||
if (graph_manager == nullptr || compute_graph == nullptr) { | |||
REPORT_INNER_ERROR("E19999", "Param graph_manager or compute_graph in graph_node is nullptr, " | |||
@@ -3348,9 +3346,10 @@ void GraphManager::ReturnError(GraphManager *graph_manager, GraphNodePtr &graph_ | |||
} | |||
for (size_t i = 0; i < node->GetAllInDataAnchorsSize(); i++) { | |||
auto input_desc = node->GetOpDesc()->MutableInputDesc(i); | |||
ge::OutputTensorInfo tensor; | |||
tensor.dims = input_desc->GetShape().GetDims(); | |||
tensor.data_type = static_cast<uint32_t>(input_desc->GetDataType()); | |||
GeShape ge_shape(input_desc->GetShape().GetDims()); | |||
GeTensorDesc ge_tensor_desc; | |||
ge_tensor_desc.SetShape(ge_shape); | |||
GeTensor ge_tensor(ge_tensor_desc); | |||
int64_t len = 1; | |||
if (input_desc->GetShape().GetDims() != std::vector<int64_t>({})) { | |||
len = input_desc->GetShape().GetShapeSize(); | |||
@@ -3366,30 +3365,19 @@ void GraphManager::ReturnError(GraphManager *graph_manager, GraphNodePtr &graph_ | |||
GELOGI("getted shape size is 0.Do process as empty tensor!"); | |||
len = 1; | |||
} | |||
auto size = GetSizeByDataType(input_desc->GetDataType()); | |||
if (size <= 0) { | |||
REPORT_INNER_ERROR("E19999", "data_type:%s of op:%s(%s) is not support, input_index:%zu check invalid", | |||
ge::TypeUtils::DataTypeToSerialString(input_desc->GetDataType()).c_str(), | |||
node->GetName().c_str(), node->GetType().c_str(), i); | |||
GELOGE(PARAM_INVALID, "Failed to get cube size, the data type %s is invalid", | |||
ge::TypeUtils::DataTypeToSerialString(input_desc->GetDataType()).c_str()); | |||
callback(GRAPH_FAILED, outputs); | |||
auto length = GetSizeInBytes(len, input_desc->GetDataType()); | |||
auto aligned_ptr = MakeShared<AlignedPtr>(length, kAlignment); | |||
if (aligned_ptr == nullptr) { | |||
REPORT_INNER_ERROR("E19999", "Aligned_ptr is nullptr"); | |||
GELOGE(GRAPH_FAILED, "[Analyze Mode] Aligned_ptr is nullptr"); | |||
return; | |||
} | |||
if (CheckInt64MulOverflow(len, static_cast<int64_t>(size)) != true) { | |||
REPORT_INNER_ERROR("E19999", "shape_size:%ld of op:%s(%s) will overflow after multiply by " | |||
"size:%u of data_type:%s, input_index:%zu, check invalid", len, | |||
node->GetName().c_str(), node->GetType().c_str(), size, | |||
ge::TypeUtils::DataTypeToSerialString(input_desc->GetDataType()).c_str(), i); | |||
GELOGE(MEMALLOC_FAILED, "int64 multiply happens overflow! a:%ld b:%d", len, size); | |||
callback(GRAPH_FAILED, outputs); | |||
return; | |||
} | |||
tensor.length = len * size; | |||
tensor.data.reset(new(std::nothrow) uint8_t[tensor.length]); | |||
ge_tensor.SetData(aligned_ptr, length); | |||
ge::Tensor tensor = TensorAdapter::AsTensor(ge_tensor); | |||
// To avoid global step too small and can not stop, totally set a bigger value | |||
for (int64_t i = 0; i < tensor.length; i++) { | |||
tensor.data[i] = 0x7F; // here stands for a positive max value | |||
auto ptr = aligned_ptr->MutableGet(); | |||
for (int64_t i = 0; i < length; i++) { | |||
ptr[i] = 0x7F; // here stands for a positive max value | |||
} | |||
outputs.emplace_back(std::move(tensor)); | |||
} | |||
@@ -3737,7 +3725,7 @@ void GraphManager::UpdateLocalOmgContext(GraphId graph_id) { | |||
if (iter != omg_contexts_.end()) { | |||
SetLocalOmgContext(iter->second); | |||
} else { | |||
GELOGW("OmgContext of graph %u not found.", graph_id); | |||
GELOGW("OmgContext of graph %u is not found.", graph_id); | |||
} | |||
} | |||
@@ -3767,9 +3755,9 @@ void GraphManager::RemoveGraphCount(GraphId graph_id) { | |||
std::lock_guard<std::mutex> lock(graph_count_mutex_); | |||
auto it = graph_count_.find(graph_id); | |||
if (it == graph_count_.end()) { | |||
GELOGW("Graph of id: %u has not been added, count cannot be decreased.", graph_id); | |||
GELOGW("Graph of id: %u has not been added, count cannot be decreased", graph_id); | |||
} else { | |||
GELOGD("RemoveGraphCount success, graph count of id[%u] is %u.", graph_id, graph_count_[graph_id]); | |||
GELOGD("RemoveGraphCount success, graph count of id[%u] is %u", graph_id, graph_count_[graph_id]); | |||
graph_count_.erase(it); | |||
} | |||
} | |||
@@ -162,9 +162,8 @@ class GraphManager { | |||
/// @param [out] callback: callback while run graph async finish | |||
/// @return Status result of function | |||
/// | |||
Status RunGraphAsync(const GraphId &graph_id, const std::vector<ge::InputTensorInfo> &inputs, | |||
Status RunGraphAsync(const GraphId &graph_id, const std::vector<ge::Tensor> &inputs, | |||
uint64_t session_id, RunAsyncCallback callback); | |||
/// | |||
/// @ingroup ge_graph | |||
/// @brief me register the callback function to get the result of summary or checkpoin | |||
@@ -221,7 +220,7 @@ class GraphManager { | |||
struct PreRunArgs { | |||
GraphId graph_id; | |||
std::vector<ge::InputTensorInfo> input_tensor; | |||
std::vector<ge::Tensor> input_tensor; | |||
uint64_t session_id; | |||
struct error_message::Context error_context; | |||
GEThreadLocalContext context; | |||
@@ -233,7 +232,7 @@ class GraphManager { | |||
GraphId graph_id; | |||
uint64_t session_id; | |||
struct error_message::Context error_context; | |||
std::vector<ge::InputTensorInfo> input_tensor; | |||
std::vector<ge::Tensor> input_tensor; | |||
GeRootModelPtr ge_root_model; | |||
GEThreadLocalContext context; | |||
RunAsyncCallback callback; | |||
@@ -252,10 +251,10 @@ class GraphManager { | |||
uint64_t session_id, | |||
const struct error_message::Context &error_context, | |||
const GEThreadLocalContext &ge_context); | |||
Status ParseInputsDims(const std::vector<InputTensorInfo> &input_tensor); | |||
void ParseInputsDimsForData(const std::vector<InputTensorInfo> &input_tensor); | |||
Status ParseInputsDims(const std::vector<ge::Tensor> &input_tensor); | |||
void ParseInputsDimsForData(const std::vector<ge::Tensor> &input_tensor); | |||
Status ParseInputsDimsForGetNexNosinkAndData(const vector<NodePtr> &dynamic_nodes, | |||
const std::vector<InputTensorInfo> &input_tensor); | |||
const std::vector<ge::Tensor> &input_tensor); | |||
Status RunCustomPass(const GraphNodePtr &graph_node); | |||
Status PreRun(const GraphNodePtr &graph_node, const std::vector<GeTensor> &inputs, GeRootModelPtr &ge_root_model, | |||
uint64_t session_id = INVALID_SESSION_ID); | |||
@@ -369,7 +368,6 @@ class GraphManager { | |||
void RemoveModelCacheHelper(const GraphId &graph_id); | |||
ModelCacheHelperPtr FindModelCacheHelper(GraphId graph_id); | |||
static void ConstructGeInput(const std::vector<InputTensorInfo> &inputs, std::vector<GeTensor> &ge_inputs); | |||
static void PreRunThread(GraphManager *graph_manager); | |||
static void RunThread(GraphManager *graph_manager); | |||
static void StopQueue(GraphManager *graph_manager); | |||
@@ -114,7 +114,7 @@ GraphModelListener::GraphModelListener(std::mutex &mutex, std::condition_variabl | |||
: result_code_(0), is_finished_(false), mutex_(mutex), condition_(cond) {} | |||
Status GraphModelListener::OnComputeDone(uint32_t model_id, uint32_t task_id, uint32_t result, | |||
std::vector<ge::OutputTensorInfo> &outputs) { | |||
std::vector<ge::Tensor> &outputs) { | |||
GELOGI( | |||
"[GraphManager] graph compute call back, model_id:%u, task_id:%u, " | |||
"resultCode:%u.", | |||
@@ -151,7 +151,7 @@ void RunAsyncListener::SetCallback(const RunAsyncCallback &callback) { | |||
} | |||
Status RunAsyncListener::OnComputeDone(uint32_t model_id, uint32_t task_id, uint32_t result, | |||
std::vector<ge::OutputTensorInfo> &outputs) { | |||
std::vector<ge::Tensor> &outputs) { | |||
GELOGI("[GraphManager] run graph async call back, modelId:%u, taskId:%u, resultCode:%u.", | |||
model_id, task_id, result); | |||
GE_CHECK_NOTNULL(callback_); | |||
@@ -130,7 +130,7 @@ class RunAsyncListener : public ge::ModelListener { | |||
// callback | |||
Status OnComputeDone(uint32_t model_id, uint32_t task_id, uint32_t result, | |||
std::vector<ge::OutputTensorInfo> &outputs) override; | |||
std::vector<ge::Tensor> &outputs) override; | |||
private: | |||
RunAsyncCallback callback_; | |||
@@ -224,7 +224,7 @@ class GraphModelListener : public ge::ModelListener { | |||
// callback | |||
Status OnComputeDone(uint32_t model_id, uint32_t task_id, uint32_t result, | |||
std::vector<ge::OutputTensorInfo> &outputs) override; | |||
std::vector<ge::Tensor> &outputs) override; | |||
Status ResetResult(); | |||
@@ -26,6 +26,7 @@ namespace { | |||
const int kDataOutputIndex = 0; | |||
const size_t kMinimumPiplineStages = 2; | |||
const int kDefaultLoopCount = 10; | |||
const size_t kAlignment = 64; | |||
} | |||
HybridModelAsyncExecutor::HybridModelAsyncExecutor(HybridModel *model) | |||
: model_(model), run_flag_(false), data_dumper_(nullptr) { | |||
@@ -70,6 +71,8 @@ Status HybridModelAsyncExecutor::Start(const std::shared_ptr<ModelListener> &lis | |||
GetThreadLocalContext() = *executor_->GetContext()->ge_context; | |||
GetContext().SetSessionId(executor_->GetContext()->session_id); | |||
GetContext().SetContextId(executor_->GetContext()->context_id); | |||
GE_CHECK_NOTNULL(executor_->GetContext()->ge_context); | |||
GetThreadLocalContext() = *executor_->GetContext()->ge_context; | |||
return RunInternal(); | |||
}); | |||
@@ -197,7 +200,7 @@ Status HybridModelAsyncExecutor::HandleResult(Status exec_ret, | |||
HybridModelExecutor::ExecuteArgs &args, | |||
OutputData *output_data) { | |||
GELOGD("Start to handle result. model id = %u, data index = %u, execution ret = %u", model_id_, data_id, exec_ret); | |||
std::vector<ge::OutputTensorInfo> output_tensor_info_list; | |||
std::vector<ge::Tensor> output_tensor_info_list; | |||
if (args.is_eos) { | |||
GELOGI("End of sequence, model id = %u", model_id_); | |||
GE_CHK_STATUS_RET_NOLOG(OnComputeDone(data_id, END_OF_SEQUENCE, output_tensor_info_list)); | |||
@@ -368,7 +371,7 @@ Status HybridModelAsyncExecutor::InitInputDesc() { | |||
} | |||
Status HybridModelAsyncExecutor::OnComputeDone(uint32_t data_index, uint32_t result_code, | |||
std::vector<ge::OutputTensorInfo> &outputs) { | |||
std::vector<ge::Tensor> &outputs) { | |||
GELOGD("OnComputeDone. model id = %u, data index = %u, execution ret = %u", model_id_, data_index, result_code); | |||
if (listener_ != nullptr) { | |||
GE_CHK_STATUS(listener_->OnComputeDone(model_id_, data_index, result_code, outputs), | |||
@@ -378,9 +381,8 @@ Status HybridModelAsyncExecutor::OnComputeDone(uint32_t data_index, uint32_t res | |||
return result_code; | |||
} | |||
Status HybridModelAsyncExecutor::CopyOutputs(HybridModelExecutor::ExecuteArgs &args, | |||
OutputData *output_data, | |||
std::vector<ge::OutputTensorInfo> &outputs) { | |||
Status HybridModelAsyncExecutor::CopyOutputs(HybridModelExecutor::ExecuteArgs &args, OutputData *output_data, | |||
std::vector<ge::Tensor> &outputs) { | |||
// copy output data from op to designated position | |||
std::vector<ConstGeTensorDescPtr> &output_tensor_desc_list = args.output_desc; | |||
std::vector<TensorValue> &output_tensors = args.outputs; | |||
@@ -395,6 +397,12 @@ Status HybridModelAsyncExecutor::CopyOutputs(HybridModelExecutor::ExecuteArgs &a | |||
} | |||
GELOGD("Number of outputs = %zu", output_tensor_desc_list.size()); | |||
string execute_mode; | |||
auto result = ge::GetContext().GetOption(OPTION_EXEC_DYNAMIC_EXECUTE_MODE, execute_mode); | |||
if (result != SUCCESS) { | |||
GELOGW("Can not get dynamic execute mode attr"); | |||
} | |||
GELOGD("The dynamic execute is %s", execute_mode.c_str()); | |||
for (size_t i = 0; i < output_tensors.size(); ++i) { | |||
GELOGD("Start to process output[%zu]", i); | |||
auto &output_tensor = output_tensors[i]; | |||
@@ -429,32 +437,28 @@ Status HybridModelAsyncExecutor::CopyOutputs(HybridModelExecutor::ExecuteArgs &a | |||
return INTERNAL_ERROR; | |||
} | |||
ge::OutputTensorInfo output; | |||
output.data_type = static_cast<uint32_t>(tensor_desc->GetDataType()); | |||
output.dims = tensor_desc->GetShape().GetDims(); | |||
output.length = output_size; | |||
GeShape ge_shape(tensor_desc->GetShape().GetDims()); | |||
GeTensorDesc ge_tensor_desc; | |||
ge_tensor_desc.SetShape(ge_shape); | |||
GeTensor ge_tensor(ge_tensor_desc); | |||
if (output_size > 0) { | |||
std::unique_ptr<uint8_t[]> data_buf(new(std::nothrow) uint8_t[output_size]); | |||
auto aligned_ptr = MakeShared<AlignedPtr>(output_size, kAlignment); | |||
GE_CHECK_NOTNULL(aligned_ptr); | |||
auto data_buf = aligned_ptr->MutableGet(); | |||
GE_CHECK_NOTNULL(data_buf); | |||
GE_CHK_RT_RET(rtMemcpy(data_buf.get(), | |||
output_size, | |||
output_tensor.GetData(), | |||
output_size, | |||
RT_MEMCPY_DEVICE_TO_HOST)); | |||
output.data = std::move(data_buf); | |||
output_data->blobs.emplace_back(data_buf.get(), static_cast<uint32_t>(output_size), false); | |||
GE_CHK_RT_RET(rtMemcpy(data_buf, output_size, output_tensor.GetData(), output_size, RT_MEMCPY_DEVICE_TO_HOST)); | |||
ge_tensor.SetData(aligned_ptr, output_size); | |||
output_data->blobs.emplace_back(data_buf, static_cast<uint32_t>(output_size), false); | |||
} else { | |||
GELOGW("Output[%zu] is empty. shape = [%s]", i, tensor_desc->GetShape().ToString().c_str()); | |||
output.data = nullptr; | |||
ge_tensor.SetData(nullptr, 0U); | |||
output_data->blobs.emplace_back(nullptr, 0U, false); | |||
} | |||
outputs.emplace_back(std::move(output)); | |||
GELOGD("Output[%zu] added, type = %s, shape = [%s], size = %ld", | |||
i, | |||
auto tensor = TensorAdapter::AsTensor(ge_tensor); | |||
outputs.emplace_back(std::move(tensor)); | |||
GELOGD("Output[%zu] added, type = %s, shape = [%s], size = %ld", i, | |||
TypeUtils::DataTypeToSerialString(tensor_desc->GetDataType()).c_str(), | |||
tensor_desc->GetShape().ToString().c_str(), | |||
output_size); | |||
tensor_desc->GetShape().ToString().c_str(), output_size); | |||
} | |||
return SUCCESS; | |||
@@ -507,7 +511,7 @@ Status HybridModelAsyncExecutor::Execute(const vector<GeTensor> &inputs, vector< | |||
GELOGD("Done copying input data successfully."); | |||
GE_CHK_STATUS_RET(executor_->Execute(args), "[Invoke][Execute] Failed, model_id = %u.", model_id_); | |||
std::vector<ge::OutputTensorInfo> output_tensor_info_list; | |||
std::vector<ge::Tensor> output_tensor_info_list; | |||
OutputData output_data; | |||
GE_CHK_STATUS_RET(CopyOutputs(args, &output_data, output_tensor_info_list), | |||
"[Invoke][CopyOutputs]Failed to copy outputs, model_id = %u.", model_id_); | |||
@@ -517,15 +521,15 @@ Status HybridModelAsyncExecutor::Execute(const vector<GeTensor> &inputs, vector< | |||
outputs.resize(output_tensor_info_list.size()); | |||
for (auto &out_tensor_info : output_tensor_info_list) { | |||
auto &ge_tensor = outputs[out_index]; | |||
if (out_tensor_info.length > 0) { | |||
GE_CHK_GRAPH_STATUS_RET(ge_tensor.SetData(out_tensor_info.data.get(), out_tensor_info.length), | |||
if (out_tensor_info.GetSize() > 0) { | |||
GE_CHK_GRAPH_STATUS_RET(ge_tensor.SetData(out_tensor_info.GetData(), out_tensor_info.GetSize()), | |||
"Failed to set output[%d].", out_index); | |||
} | |||
ge_tensor.MutableTensorDesc() = *args.output_desc[out_index]; | |||
GELOGD("Set output[%d], tensor size = %ld, shape = [%s]", | |||
out_index, | |||
out_tensor_info.length, | |||
out_tensor_info.GetSize(), | |||
ge_tensor.MutableTensorDesc().MutableShape().ToString().c_str()); | |||
++out_index; | |||
} | |||
@@ -77,9 +77,9 @@ class HybridModelAsyncExecutor { | |||
Status CopyOutputs(HybridModelExecutor::ExecuteArgs &args, | |||
OutputData *output_data, | |||
std::vector<ge::OutputTensorInfo> &outputs); | |||
std::vector<ge::Tensor> &outputs); | |||
Status OnComputeDone(uint32_t data_index, uint32_t result_code, std::vector<ge::OutputTensorInfo> &outputs); | |||
Status OnComputeDone(uint32_t data_index, uint32_t result_code, std::vector<ge::Tensor> &outputs); | |||
Status PreRun(InputData ¤t_data, HybridModelExecutor::ExecuteArgs &args); | |||
@@ -408,7 +408,26 @@ Status InnerSession::BuildGraph(uint32_t graph_id, const std::vector<InputTensor | |||
return ret; | |||
} | |||
Status InnerSession::RunGraphAsync(uint32_t graph_id, const std::vector<InputTensorInfo> &inputs, | |||
Status InnerSession::BuildGraph(uint32_t graph_id, const std::vector<ge::Tensor> &inputs) { | |||
UpdateThreadContext(graph_id); | |||
GELOGI("[InnerSession:%lu] build graph on session, graph_id=%u.", session_id_, graph_id); | |||
std::vector<ge::GeTensor> ge_inputs; | |||
for (const auto &input : inputs) { | |||
ge_inputs.emplace_back(TensorAdapter::AsGeTensor(input)); | |||
} | |||
GeRootModelPtr ge_root_model = nullptr; | |||
Status ret = graph_manager_.BuildGraph(graph_id, ge_inputs, ge_root_model, session_id_, true); | |||
if (ret != SUCCESS) { | |||
GELOGE(ret, "[Build][Graph] failed, InnerSession:%lu graph_id=%u.", session_id_, graph_id); | |||
REPORT_CALL_ERROR("E19999", | |||
"GraphManager BuildGraph failed, InnerSession:%lu graph_id=%u.", session_id_, graph_id); | |||
return ret; | |||
} | |||
GELOGI("[InnerSession:%lu] build graph success, graph_id=%u.", session_id_, graph_id); | |||
return ret; | |||
} | |||
Status InnerSession::RunGraphAsync(uint32_t graph_id, const std::vector<ge::Tensor> &inputs, | |||
RunAsyncCallback callback) { | |||
UpdateThreadContext(graph_id); | |||
GELOGI("[InnerSession:%lu] run graph on session, graph_id=%u.", session_id_, graph_id); | |||
@@ -422,7 +441,6 @@ Status InnerSession::RunGraphAsync(uint32_t graph_id, const std::vector<InputTen | |||
GELOGI("[InnerSession:%lu] run graph success, graph_id=%u.", session_id_, graph_id); | |||
return ret; | |||
} | |||
const GraphManager &InnerSession::getGraphManagerObj() const { return graph_manager_; } | |||
void InnerSession::UpdateThreadContext(const std::map<std::string, std::string> &options) { | |||
@@ -48,7 +48,9 @@ class InnerSession { | |||
Status BuildGraph(uint32_t graph_id, const std::vector<InputTensorInfo> &inputs); | |||
Status RunGraphAsync(uint32_t graph_id, const std::vector<InputTensorInfo> &inputs, RunAsyncCallback callback); | |||
Status BuildGraph(uint32_t graph_id, const std::vector<ge::Tensor> &inputs); | |||
Status RunGraphAsync(uint32_t graph_id, const std::vector<ge::Tensor> &inputs, RunAsyncCallback callback); | |||
Status Finalize(); | |||
@@ -384,8 +384,29 @@ Status SessionManager::BuildGraph(SessionId session_id, uint32_t graph_id, const | |||
return innerSession->BuildGraph(graph_id, inputs); | |||
} | |||
Status SessionManager::BuildGraph(SessionId session_id, uint32_t graph_id, const std::vector<ge::Tensor> &inputs) { | |||
if (!init_flag_) { | |||
GELOGE(GE_SESSION_MANAGER_NOT_INIT, "[Build][Graph]fail for Session manager is not initialized," | |||
"session_id:%lu, graph_id:%u.", session_id, graph_id); | |||
REPORT_INNER_ERROR("E19999", "BuildGraph fail for Session manager is not initialized," | |||
"session_id:%lu, graph_id:%u.", session_id, graph_id); | |||
return GE_SESSION_MANAGER_NOT_INIT; | |||
} | |||
SessionPtr innerSession = nullptr; | |||
{ | |||
std::lock_guard<std::mutex> lock(mutex_); | |||
std::map<SessionId, SessionPtr>::iterator it = session_manager_map_.find(session_id); | |||
if (it == session_manager_map_.end()) { | |||
return GE_SESSION_NOT_EXIST; | |||
} else { | |||
innerSession = it->second; | |||
} | |||
} | |||
return innerSession->BuildGraph(graph_id, inputs); | |||
} | |||
Status SessionManager::RunGraphAsync(SessionId session_id, uint32_t graph_id, | |||
const std::vector<InputTensorInfo> &inputs, RunAsyncCallback callback) { | |||
const std::vector<ge::Tensor> &inputs, RunAsyncCallback callback) { | |||
if (!init_flag_) { | |||
GELOGE(GE_SESSION_MANAGER_NOT_INIT, | |||
"[AsyncRun][Graph]fail for Session manager is not initialized, session_id:%lu, graph_id:%u.", | |||
@@ -139,6 +139,8 @@ class SessionManager { | |||
/// | |||
Status BuildGraph(SessionId session_id, uint32_t graph_id, const std::vector<InputTensorInfo> &inputs); | |||
Status BuildGraph(SessionId session_id, uint32_t graph_id, const std::vector<ge::Tensor> &inputs); | |||
/// | |||
/// @ingroup ge_session | |||
/// @brief run a graph of the session with specific session id for train asynchronously | |||
@@ -147,7 +149,7 @@ class SessionManager { | |||
/// @param [in] inputs input data | |||
/// @return Status result of function | |||
/// | |||
Status RunGraphAsync(SessionId session_id, uint32_t graph_id, const std::vector<InputTensorInfo> &inputs, | |||
Status RunGraphAsync(SessionId session_id, uint32_t graph_id, const std::vector<ge::Tensor> &inputs, | |||
RunAsyncCallback callback); | |||
/// | |||
@@ -142,6 +142,8 @@ class GE_FUNC_VISIBILITY Session { | |||
/// | |||
Status BuildGraph(uint32_t graphId, const std::vector<InputTensorInfo> &inputs); | |||
Status BuildGraph(uint32_t graphId, const std::vector<ge::Tensor> &inputs); | |||
/// | |||
/// @ingroup ge_graph | |||
/// @brief run graph in the session with specific session id asynchronously | |||
@@ -152,7 +154,7 @@ class GE_FUNC_VISIBILITY Session { | |||
/// Please ensure that the implementation of the function is trusted. | |||
/// @return Status result of function | |||
/// | |||
Status RunGraphAsync(uint32_t graphId, const std::vector<ge::InputTensorInfo> &inputs, RunAsyncCallback callback); | |||
Status RunGraphAsync(uint32_t graphId, const std::vector<ge::Tensor> &inputs, RunAsyncCallback callback); | |||
/// | |||
/// @ingroup ge_graph | |||
@@ -23,6 +23,7 @@ | |||
#include <set> | |||
#include <functional> | |||
#include <memory> | |||
#include "graph/tensor.h" | |||
namespace ge { | |||
// Option key: graph run mode | |||
@@ -356,7 +357,8 @@ struct OutputTensorInfo { | |||
}; | |||
using Status = uint32_t; | |||
using RunAsyncCallback = std::function<void(Status, std::vector<ge::OutputTensorInfo> &)>; | |||
using RunAsyncCallback = std::function<void(Status, std::vector<ge::Tensor> &)>; | |||
// for ir build | |||
namespace ir_option { | |||
static const char *const INPUT_FORMAT = "input_format"; | |||
@@ -226,7 +226,7 @@ class GE_FUNC_VISIBILITY ModelListener { | |||
/// @param [in] resultCode Execution results | |||
/// | |||
virtual Status OnComputeDone(uint32_t model_id, uint32_t data_index, uint32_t result_code, | |||
std::vector<ge::OutputTensorInfo> &outputs) = 0; | |||
std::vector<ge::Tensor> &outputs) = 0; | |||
}; | |||
// OMM configuration item | |||
@@ -97,6 +97,7 @@ set(GRAPH_SRC_FILES | |||
"${GE_CODE_DIR}/metadef/graph/ge_tensor.cc" | |||
"${GE_CODE_DIR}/metadef/graph/ref_relation.cc" | |||
"${GE_CODE_DIR}/metadef/graph/tensor.cc" | |||
"${GE_CODE_DIR}/metadef/graph/types.cc" | |||
"${GE_CODE_DIR}/metadef/graph/detail/attributes_holder.cc" | |||
"${GE_CODE_DIR}/metadef/graph/utils/anchor_utils.cc" | |||
"${GE_CODE_DIR}/metadef/graph/utils/graph_utils.cc" | |||
@@ -793,6 +794,8 @@ set(MULTI_PARTS_TEST_FILES | |||
"graph/manager/graph_manager_unittest.cc" | |||
"session/omg_omg_unittest.cc" | |||
"session/ge_api_unittest.cc" | |||
"session/inner_session_unittest.cc" | |||
"session/session_manager_unittest.cc" | |||
) | |||
set(GENERATOR_TEST_FILES | |||
@@ -115,7 +115,7 @@ TEST_F(UtestGraphExecuteTest, test_set_callback) { | |||
ComputeGraphPtr graph = MakeShared<ComputeGraph>("test"); | |||
// is_unknown_shape_graph_ = false | |||
GeRootModelPtr ge_root_model = MakeShared<GeRootModel>(graph); | |||
RunAsyncCallback callback = [](Status, std::vector<ge::OutputTensorInfo> &) {}; | |||
RunAsyncCallback callback = [](Status, std::vector<ge::Tensor> &) {}; | |||
auto model_manager = ModelManager::GetInstance(); | |||
auto listener = MakeShared<RunAsyncListener>(); | |||
@@ -75,7 +75,7 @@ class DModelListener : public ge::ModelListener { | |||
DModelListener() { | |||
}; | |||
Status OnComputeDone(uint32_t model_id, uint32_t data_index, uint32_t resultCode, | |||
std::vector<ge::OutputTensorInfo> &outputs) { | |||
std::vector<ge::Tensor> &outputs) { | |||
GELOGI("In Call back. OnComputeDone"); | |||
return SUCCESS; | |||
} | |||
@@ -276,7 +276,7 @@ TEST_F(UtestGeExecutor, execute_graph_with_stream) { | |||
EXPECT_EQ(model.task_list_.size(), 2); | |||
OutputData output_data; | |||
vector<OutputTensorInfo> outputs; | |||
vector<Tensor> outputs; | |||
EXPECT_EQ(model.GenOutputTensorInfo(&output_data, outputs), SUCCESS); | |||
@@ -32,7 +32,7 @@ extern OpDescPtr CreateOpDesc(string name, string type); | |||
class DModelListener : public ModelListener { | |||
public: | |||
DModelListener(){}; | |||
uint32_t OnComputeDone(uint32_t model_id, uint32_t data_index, uint32_t result, vector<OutputTensorInfo> &outputs) { | |||
uint32_t OnComputeDone(uint32_t model_id, uint32_t data_index, uint32_t result, vector<ge::Tensor> &outputs) { | |||
return 0; | |||
} | |||
}; | |||
@@ -138,7 +138,7 @@ TEST_F(UtestDavinciModel, init_success) { | |||
EXPECT_EQ(model.task_list_.size(), 2); | |||
OutputData output_data; | |||
vector<OutputTensorInfo> outputs; | |||
vector<ge::Tensor> outputs; | |||
EXPECT_EQ(model.GenOutputTensorInfo(&output_data, outputs), SUCCESS); | |||
EXPECT_EQ(output_data.blobs.size(), 1); | |||
EXPECT_EQ(outputs.size(), 1); | |||
@@ -1024,7 +1024,7 @@ TEST_F(UtestDavinciModel, NnExecute) { | |||
rtStream_t stream = nullptr; | |||
InputData input_data; | |||
OutputData output_data; | |||
vector<OutputTensorInfo> outputs; | |||
vector<ge::Tensor> outputs; | |||
EXPECT_EQ(model.GenOutputTensorInfo(&output_data, outputs), SUCCESS); | |||
EXPECT_EQ(output_data.blobs.size(), 1); | |||
EXPECT_EQ(outputs.size(), 1); | |||
@@ -414,8 +414,8 @@ TEST_F(UtestModelManagerModelManager, test_data_input_tensor) { | |||
mm.model_map_[1] = model; | |||
mm.hybrid_model_map_[1] = std::make_shared<hybrid::HybridDavinciModel>(); | |||
auto input_tensor = InputTensorInfo(); | |||
vector<InputTensorInfo> inputs; | |||
ge::Tensor input_tensor; | |||
vector<ge::Tensor> inputs; | |||
inputs.emplace_back(input_tensor); | |||
auto ret = mm.DataInputTensor(model_id,inputs); | |||
EXPECT_EQ(PARAM_INVALID, ret); // HybridDavinciModel::impl_ is null. | |||
@@ -280,7 +280,7 @@ TEST_F(UtestGraphManagerTest, test_pre_run_thread) { | |||
graph_manager.thread_run_flag_ = true; | |||
GraphId graph_id = 1; | |||
std::vector<ge::InputTensorInfo> input_tensor; | |||
std::vector<ge::Tensor> input_tensor; | |||
uint64_t session_id = 0; | |||
error_message::Context error_context; | |||
GEThreadLocalContext context; | |||
@@ -306,7 +306,7 @@ TEST_F(UtestGraphManagerTest, test_pre_run_thread_2) { | |||
graph_manager.IncreaseGraphCount(graph_id); | |||
graph_manager.IncreaseGraphCount(graph_id); | |||
graph_node_1->SetBuildFlag(true); | |||
std::vector<ge::InputTensorInfo> input_tensor; | |||
std::vector<ge::Tensor> input_tensor; | |||
uint64_t session_id = 0; | |||
error_message::Context error_context; | |||
GEThreadLocalContext context; | |||
@@ -381,7 +381,7 @@ TEST_F(UtestGraphManagerTest, test_check_incre_build_and_pre_run_2) { | |||
ComputeGraphPtr compute_graph = MakeShared<ComputeGraph>("test_graph"); | |||
GeRootModelPtr ge_root_model = MakeShared<GeRootModel>(compute_graph); | |||
GraphManager::PreRunArgs arg; | |||
arg.callback = [](Status, std::vector<ge::OutputTensorInfo> &) {}; | |||
arg.callback = [](Status, std::vector<ge::Tensor> &) {}; | |||
GraphNodePtr graph_node = MakeShared<ge::GraphNode>(graph_id); | |||
graph_node->SetBuildFlag(true); | |||
graph_node->Lock(); | |||
@@ -397,7 +397,7 @@ TEST_F(UtestGraphManagerTest, test_check_incre_build_and_pre_run_3) { | |||
ComputeGraphPtr compute_graph = MakeShared<ComputeGraph>("test_graph"); | |||
GeRootModelPtr ge_root_model = MakeShared<GeRootModel>(compute_graph); | |||
GraphManager::PreRunArgs arg; | |||
arg.callback = [](Status, std::vector<ge::OutputTensorInfo> &) {}; | |||
arg.callback = [](Status, std::vector<ge::Tensor> &) {}; | |||
GraphNodePtr graph_node = MakeShared<ge::GraphNode>(graph_id); | |||
graph_node->SetBuildFlag(false); | |||
graph_node->Lock(); | |||
@@ -434,3 +434,34 @@ TEST_F(UtestGraphManagerTest, test_add_graph_with_copy_fail) { | |||
status = graph_manager.AddGraphWithCopy(graph_id, graph, options, context); | |||
EXPECT_NE(status, ge::SUCCESS); | |||
} | |||
TEST_F(UtestGraphManagerTest, ParseInputsDimsForData_success) { | |||
GraphManager graph_manager; | |||
std::vector<ge::Tensor> input_tensors; | |||
ge::Tensor tensor; | |||
input_tensors.emplace_back(tensor); | |||
graph_manager.ParseInputsDimsForData(input_tensors); | |||
} | |||
// TEST_F(UtestGraphManagerTest, ParseInputsDimsForGetNexNosinkAndData_success) { | |||
// GraphManager graph_manager; | |||
// ge::ComputeGraphPtr graph = std::make_shared<ge::ComputeGraph>("default"); | |||
// // save1 | |||
// ge::OpDescPtr save_op = std::make_shared<ge::OpDesc>(); | |||
// save_op->SetType("Save"); | |||
// save_op->SetName("Save1"); | |||
// save_op->AddInputDesc(ge::GeTensorDesc()); | |||
// save_op->AddOutputDesc(ge::GeTensorDesc()); | |||
// AttrUtils::SetInt(save_op, ATTR_NAME_INDEX, 1); | |||
// ge::NodePtr save_node = graph->AddNode(save_op); | |||
// std::vector<NodePtr> nodes; | |||
// nodes.emplace_back(save_node); | |||
// ge::Tensor tensor; | |||
// std::vector<Tensor> input_tensors; | |||
// input_tensors.emplace_back(tensor); | |||
// auto ret = graph_manager.ParseInputsDimsForGetNexNosinkAndData(nodes, input_tensors); | |||
// EXPECT_EQ(ret, ge::SUCCESS); | |||
// } |
@@ -55,4 +55,12 @@ TEST_F(UtestGeApi, run_graph_with_stream) { | |||
ret = inner_session.RunGraphWithStreamAsync(10, nullptr, inputs, outputs); | |||
ASSERT_NE(ret, SUCCESS); | |||
} | |||
TEST_F(UtestGeApi, build_graph_success) { | |||
vector<Tensor> inputs; | |||
std::map<std::string, std::string> options; | |||
Session session(options); | |||
auto ret = session.BuildGraph(1, inputs); | |||
ASSERT_NE(ret, SUCCESS); | |||
} | |||
} // namespace ge |
@@ -0,0 +1,47 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#include <gtest/gtest.h> | |||
#define private public | |||
#define protected public | |||
#include "session/inner_session.h" | |||
#undef private | |||
#undef protected | |||
using namespace std; | |||
namespace ge { | |||
class Utest_Inner_session : public testing::Test { | |||
protected: | |||
void SetUp() override {} | |||
void TearDown() override {} | |||
}; | |||
TEST_F(Utest_Inner_session, build_graph_success) { | |||
std::map <string, string> options; | |||
uint64_t session_id = 1; | |||
InnerSession inner_seesion(session_id, options); | |||
std::vector<ge::Tensor> inputs; | |||
ge::Tensor tensor; | |||
inputs.emplace_back(tensor); | |||
Status ret = inner_seesion.BuildGraph(1, inputs); | |||
EXPECT_NE(ret, ge::SUCCESS); | |||
} | |||
} // namespace ge |
@@ -0,0 +1,78 @@ | |||
/** | |||
* Copyright 2019-2020 Huawei Technologies Co., Ltd | |||
* | |||
* Licensed under the Apache License, Version 2.0 (the "License"); | |||
* you may not use this file except in compliance with the License. | |||
* You may obtain a copy of the License at | |||
* | |||
* http://www.apache.org/licenses/LICENSE-2.0 | |||
* | |||
* Unless required by applicable law or agreed to in writing, software | |||
* distributed under the License is distributed on an "AS IS" BASIS, | |||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |||
* See the License for the specific language governing permissions and | |||
* limitations under the License. | |||
*/ | |||
#include <gtest/gtest.h> | |||
#define private public | |||
#define protected public | |||
#include "session/session_manager.h" | |||
#undef private | |||
#undef protected | |||
using namespace std; | |||
namespace ge { | |||
class Utest_SessionManager : public testing::Test { | |||
protected: | |||
void SetUp() override {} | |||
void TearDown() override {} | |||
}; | |||
TEST_F(Utest_SessionManager, build_graph_failed) { | |||
map<string, string> session_manager_option; | |||
map<string, string> session_option; | |||
SessionManager *session_manager = new SessionManager(); | |||
uint64_t session_id = 0; | |||
uint32_t graph_id = 0; | |||
std::vector<ge::Tensor> inputs; | |||
Status ret = session_manager->BuildGraph(session_id, graph_id, inputs); | |||
EXPECT_EQ(ret, ge::GE_SESSION_MANAGER_NOT_INIT); | |||
session_manager->Initialize(session_manager_option); | |||
ret = session_manager->BuildGraph(session_id, graph_id, inputs); | |||
EXPECT_NE(ret, ge::SUCCESS); | |||
delete session_manager; | |||
} | |||
TEST_F(Utest_SessionManager, RungraphAsync_before_init) { | |||
SessionManager *session_manager = new SessionManager(); | |||
SessionId session_id; | |||
uint32_t graph_id = 0; | |||
std::vector<ge::Tensor> inputs; | |||
RunAsyncCallback callback; | |||
Status ret = session_manager->RunGraphAsync(session_id, graph_id, inputs, callback); | |||
EXPECT_EQ(ret, ge::GE_SESSION_MANAGER_NOT_INIT); | |||
delete session_manager; | |||
} | |||
TEST_F(Utest_SessionManager, RungraphAsync_failed) { | |||
map<string, string> session_manager_option; | |||
SessionManager *session_manager = new SessionManager(); | |||
session_manager->Initialize(session_manager_option); | |||
SessionId session_id; | |||
uint32_t graph_id = 0; | |||
std::vector<ge::Tensor> inputs; | |||
RunAsyncCallback callback; | |||
Status ret = session_manager->RunGraphAsync(session_id, graph_id, inputs, callback); | |||
EXPECT_EQ(ret, ge::GE_SESSION_NOT_EXIST); | |||
delete session_manager; | |||
} | |||
} // namespace ge |