From: MyungJoo Ham Date: Wed, 22 Jul 2020 07:35:32 +0000 (+0900) Subject: CAPI/Single: Optimize no-timeout mode run-time performance X-Git-Tag: accepted/tizen/unified/20200802.223717~15 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=a99045646d3224489b82fbee7166009868b788ca;p=platform%2Fupstream%2Fnnstreamer.git CAPI/Single: Optimize no-timeout mode run-time performance Do not invoke in a secondary thread, but ivoke in the primary thread directly without CV so that there is minimized latencies for single-invoke calls. This has refactored the invoke-output-process routine. This follows-up #2551 Signed-off-by: MyungJoo Ham --- diff --git a/api/capi/src/nnstreamer-capi-single.c b/api/capi/src/nnstreamer-capi-single.c index 0c06121..e519a91 100644 --- a/api/capi/src/nnstreamer-capi-single.c +++ b/api/capi/src/nnstreamer-capi-single.c @@ -111,6 +111,76 @@ typedef struct } ml_single; /** + * @brief Internal function to call subplugin's invoke + */ +static inline int +__invoke (ml_single * single_h, GstTensorMemory * out_tensors) +{ + ml_tensors_data_s *in_data; + unsigned int i; + int status = ML_ERROR_NONE; + GstTensorMemory in_tensors[NNS_TENSOR_SIZE_LIMIT]; + + in_data = (ml_tensors_data_s *) single_h->input; + /** Setup input buffer */ + for (i = 0; i < in_data->num_tensors; i++) { + in_tensors[i].data = in_data->tensors[i].tensor; + in_tensors[i].size = in_data->tensors[i].size; + in_tensors[i].type = (tensor_type) single_h->in_info.info[i].type; + } + + /** Setup output buffer */ + for (i = 0; i < single_h->out_info.num_tensors; i++) { + /** memory will be allocated by tensor_filter_single */ + out_tensors[i].data = NULL; + out_tensors[i].size = ml_tensor_info_get_size (&single_h->out_info.info[i]); + out_tensors[i].type = (tensor_type) single_h->out_info.info[i].type; + } + /** invoke the thread */ + if (!single_h->klass->invoke (single_h->filter, in_tensors, out_tensors)) + status = ML_ERROR_STREAMS_PIPE; + + return status; +} + +/** + * @brief Internal function to post-process given output. + */ +static inline int +__process_output (ml_single * single_h, GstTensorMemory * out_tensors) +{ + unsigned int i; + int status = ML_ERROR_NONE; + ml_tensors_data_s *out_data; + + /** Allocate output buffer */ + if (single_h->ignore_output == FALSE) { + status = ml_tensors_data_create_no_alloc (&single_h->out_info, + single_h->output); + if (status != ML_ERROR_NONE) { + ml_loge ("Failed to allocate the memory block."); + (*single_h->output) = NULL; + return ML_ERROR_OUT_OF_MEMORY; + } + + /** set the result */ + out_data = (ml_tensors_data_s *) (*single_h->output); + for (i = 0; i < single_h->out_info.num_tensors; i++) { + out_data->tensors[i].tensor = out_tensors[i].data; + } + } else { + /** + * Caller of the invoke thread has returned back with timeout + * so, free the memory allocated by the invoke as their is no receiver + */ + for (i = 0; i < single_h->out_info.num_tensors; i++) + g_free (out_tensors[i].data); + } + + return ML_ERROR_NONE; +} + +/** * @brief thread to execute calls to invoke * * @details The thread behavior is detailed as below: @@ -133,10 +203,7 @@ static void * invoke_thread (void *arg) { ml_single *single_h; - GstTensorMemory in_tensors[NNS_TENSOR_SIZE_LIMIT]; GstTensorMemory out_tensors[NNS_TENSOR_SIZE_LIMIT]; - ml_tensors_data_s *in_data, *out_data; - unsigned int i; single_h = (ml_single *) arg; @@ -152,57 +219,17 @@ invoke_thread (void *arg) goto exit; } - in_data = (ml_tensors_data_s *) single_h->input; - - /** Setup input buffer */ - for (i = 0; i < in_data->num_tensors; i++) { - in_tensors[i].data = in_data->tensors[i].tensor; - in_tensors[i].size = in_data->tensors[i].size; - in_tensors[i].type = (tensor_type) single_h->in_info.info[i].type; - } - - /** Setup output buffer */ - for (i = 0; i < single_h->out_info.num_tensors; i++) { - /** memory will be allocated by tensor_filter_single */ - out_tensors[i].data = NULL; - out_tensors[i].size = - ml_tensor_info_get_size (&single_h->out_info.info[i]); - out_tensors[i].type = (tensor_type) single_h->out_info.info[i].type; - } g_mutex_unlock (&single_h->mutex); - - /** invoke the thread */ - if (!single_h->klass->invoke (single_h->filter, in_tensors, out_tensors)) { - status = ML_ERROR_STREAMS_PIPE; - } - + status = __invoke (single_h, out_tensors); g_mutex_lock (&single_h->mutex); + if (status != ML_ERROR_NONE) goto wait_for_next; - /** Allocate output buffer */ - if (single_h->ignore_output == FALSE) { - status = ml_tensors_data_create_no_alloc (&single_h->out_info, - single_h->output); - if (status != ML_ERROR_NONE) { - ml_loge ("Failed to allocate the memory block."); - (*single_h->output) = NULL; - goto wait_for_next; - } + status = __process_output (single_h, out_tensors); - /** set the result */ - out_data = (ml_tensors_data_s *) (*single_h->output); - for (i = 0; i < single_h->out_info.num_tensors; i++) { - out_data->tensors[i].tensor = out_tensors[i].data; - } - } else { - /** - * Caller of the invoke thread has returned back with timeout - * so, free the memory allocated by the invoke as their is no receiver - */ - for (i = 0; i < single_h->out_info.num_tensors; i++) - g_free (out_tensors[i].data); - } + if (status != ML_ERROR_NONE) + goto wait_for_next; /** loop over to wait for the next element */ wait_for_next: @@ -313,7 +340,7 @@ ml_single_set_gst_info (ml_single * single_h, const ml_tensors_info_h info) ml_tensors_info_copy_from_ml (&gst_in_info, info); ret = single_h->klass->set_input_info (single_h->filter, &gst_in_info, - &gst_out_info); + &gst_out_info); if (ret == 0) { ml_tensors_info_copy_from_gst (&single_h->in_info, &gst_in_info); ml_tensors_info_copy_from_gst (&single_h->out_info, &gst_out_info); @@ -629,7 +656,8 @@ ml_single_open_custom (ml_single_h * single, ml_single_preset * info) /* set accelerator, framework, model files and custom option */ fw_name = ml_get_nnfw_subplugin_name (nnfw); hw_name = ml_nnfw_to_str_prop (hw); - g_object_set (filter_obj, "accelerator", hw_name, "framework", fw_name, "model", info->models, NULL); + g_object_set (filter_obj, "accelerator", hw_name, "framework", fw_name, + "model", info->models, NULL); g_free (hw_name); if (info->custom_option) { @@ -840,9 +868,11 @@ ml_single_invoke (ml_single_h single, single_h->state = RUNNING; single_h->ignore_output = FALSE; - g_cond_broadcast (&single_h->cond); if (single_h->timeout > 0) { + /* Wake up "invoke_thread" */ + g_cond_broadcast (&single_h->cond); + /* set timeout */ end_time = g_get_monotonic_time () + single_h->timeout * G_TIME_SPAN_MILLISECOND; @@ -857,14 +887,25 @@ ml_single_invoke (ml_single_h single, /** Free if any output memory was allocated */ if (*single_h->output != NULL) { - ml_tensors_data_destroy ((ml_tensors_data_h) *single_h->output); + ml_tensors_data_destroy ((ml_tensors_data_h) * single_h->output); *single_h->output = NULL; } } } else { - /** @todo calling klass->invoke() may suspend current thread */ - g_cond_wait (&single_h->cond, &single_h->mutex); - status = single_h->status; + /** + * Don't worry. We have locked single_h->mutex, thus there is no + * other thread with ml_single_invoke function on the same handle + * that are in this if-then-else block, which means that there is + * no other thread with active invoke-thread (calling __invoke()) + * with the same handle. Thus we can call __invoke without + * having yet another mutex for __invoke. + */ + GstTensorMemory out_tensors[NNS_TENSOR_SIZE_LIMIT]; + status = __invoke (single_h, out_tensors); + if (status != ML_ERROR_NONE) + goto exit; + status = __process_output (single_h, out_tensors); + single_h->state = IDLE; } exit: