[FFmpeg-devel] [PATCH v2 2/2] dnn_backend_native_layer_conv2d.c:Add mutithread function
xujunzz at sjtu.edu.cn
xujunzz at sjtu.edu.cn
Fri Sep 4 15:46:04 EEST 2020
From: Xu Jun <xujunzz at sjtu.edu.cn>
v2: add check for HAVE_PTHREAD_CANCEL and modify FATE test
dnn-layer-conv2d-test.c
Use pthread to multithread dnn_execute_layer_conv2d.
Can be tested with command "./ffmpeg_g -i input.png -vf \
format=yuvj420p,dnn_processing=dnn_backend=native:model= \
espcn.model:input=x:output=y:options=conv2d_threads=23 \
-y sr_native.jpg -benchmark"
before patch: utime=11.238s stime=0.005s rtime=11.248s
after patch: utime=20.817s stime=0.047s rtime=1.051s
on my 3900X 12c24t @4.2GHz
About the increase of utime, it's because that CPU HyperThreading
technology makes logical cores twice of physical cores while cpu's
counting performance improves less than double. And utime sums
all cpu's logical cores' runtime. As a result, using threads num
near cpu's logical core's number will double utime, while reduce
rtime less than half for HyperThreading CPUs.
Signed-off-by: Xu Jun <xujunzz at sjtu.edu.cn>
---
.../dnn/dnn_backend_native_layer_conv2d.c | 107 ++++++++++++++++--
tests/dnn/dnn-layer-conv2d-test.c | 12 +-
2 files changed, 106 insertions(+), 13 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
index d079795bf8..4068a13ab4 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
@@ -19,10 +19,27 @@
*/
#include "libavutil/avassert.h"
+#include "libavutil/thread.h"
+#include "libavutil/cpu.h"
#include "dnn_backend_native_layer_conv2d.h"
#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
+//struct to pass parameters
+typedef struct thread_common_param{
+ DnnOperand *operands;
+ const int32_t *input_operand_indexes;
+ int32_t output_operand_index;
+ const void *parameters;
+ NativeContext *ctx;
+ int thread_num;
+} thread_common_param;
+
+typedef struct thread_param{
+ thread_common_param *thread_common_param;
+ int thread_index;
+} thread_param;
+
int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
{
ConvolutionalParams *conv_params;
@@ -88,17 +105,20 @@ int dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int fil
return dnn_size;
}
-int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
- int32_t output_operand_index, const void *parameters, NativeContext *ctx)
+static void * dnn_execute_layer_conv2d_thread(void *threadarg)
{
+ //pass parameters
+ thread_param *thread_param = (struct thread_param *)threadarg;
+ thread_common_param *thread_common_param = thread_param->thread_common_param;
+ DnnOperand *operands = thread_common_param->operands;
float *output;
- int32_t input_operand_index = input_operand_indexes[0];
+ int32_t input_operand_index = thread_common_param->input_operand_indexes[0];
int number = operands[input_operand_index].dims[0];
int height = operands[input_operand_index].dims[1];
int width = operands[input_operand_index].dims[2];
int channel = operands[input_operand_index].dims[3];
const float *input = operands[input_operand_index].data;
- const ConvolutionalParams *conv_params = (const ConvolutionalParams *)parameters;
+ const ConvolutionalParams *conv_params = (const ConvolutionalParams *)(thread_common_param->parameters);
int radius = conv_params->kernel_size >> 1;
int src_linesize = width * conv_params->input_num;
@@ -106,7 +126,11 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
int filter_size = conv_params->kernel_size * filter_linesize;
int pad_size = (conv_params->padding_method == VALID) ? (conv_params->kernel_size - 1) / 2 * conv_params->dilation : 0;
- DnnOperand *output_operand = &operands[output_operand_index];
+ int thread_stride = (height - pad_size * 2) / thread_common_param->thread_num;
+ int thread_start = thread_stride * thread_param->thread_index + pad_size;
+ int thread_end = (thread_param->thread_index == thread_common_param->thread_num - 1) ? (height - pad_size) : (thread_start + thread_stride);
+
+ DnnOperand *output_operand = &operands[thread_common_param->output_operand_index];
output_operand->dims[0] = number;
output_operand->dims[1] = height - pad_size * 2;
output_operand->dims[2] = width - pad_size * 2;
@@ -114,19 +138,21 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
output_operand->data_type = operands[input_operand_index].data_type;
output_operand->length = calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
- av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ av_log(thread_common_param->ctx, AV_LOG_ERROR, "The output data length overflow\n");
+ return (void *)DNN_ERROR;
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
- av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ av_log(thread_common_param->ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
+ return (void *)DNN_ERROR;
}
+
output = output_operand->data;
+ output += (conv_params->output_num) * (width - 2 * pad_size) * (thread_start - pad_size);
av_assert0(channel == conv_params->input_num);
- for (int y = pad_size; y < height - pad_size; ++y) {
+ for (int y = thread_start; y < thread_end; ++y) {
for (int x = pad_size; x < width - pad_size; ++x) {
for (int n_filter = 0; n_filter < conv_params->output_num; ++n_filter) {
if (conv_params->has_bias)
@@ -174,5 +200,64 @@ int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_
output += conv_params->output_num;
}
}
- return 0;
+ return (void *)0;
+}
+
+
+int dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters, NativeContext *ctx)
+{
+ int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
+ ? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
+#if HAVE_PTHREAD_CANCEL
+ pthread_t *thread_id = av_malloc(thread_num * sizeof(pthread_t));
+#endif
+ thread_param **thread_param = av_malloc(thread_num * sizeof(*thread_param));
+ void *res;
+ int error_flag = 0;
+
+ //struct used to pass parameters
+ thread_common_param thread_common_param;
+ thread_common_param.operands = operands;
+ thread_common_param.input_operand_indexes = input_operand_indexes;
+ thread_common_param.output_operand_index = output_operand_index;
+ thread_common_param.parameters = parameters;
+ thread_common_param.ctx = ctx;
+#if HAVE_PTHREAD_CANCEL
+ thread_common_param.thread_num = thread_num;
+
+ //create threads
+ for (int i = 0; i < thread_num; i++){
+ thread_param[i] = av_malloc(sizeof(thread_param));
+ thread_param[i]->thread_common_param = &thread_common_param;
+ thread_param[i]->thread_index = i;
+ pthread_create(&thread_id[i], NULL, dnn_execute_layer_conv2d_thread, (void *)thread_param[i]);
+ }
+
+ //join threads, res gets function return
+ for (int i = 0; i < thread_num; i++){
+ pthread_join(thread_id[i], &res);
+ if ((int)res != 0)
+ error_flag = (int)res;
+ }
+
+ //release memory
+ av_free(thread_id);
+
+ for (int i = 0; i < thread_num; i++){
+ av_free(thread_param[i]);
+ }
+#else
+ thread_common_param.thread_num = 1;
+ thread_param[0] = av_malloc(sizeof(thread_param));
+ thread_param[0]->thread_common_param = &thread_common_param;
+ thread_param[0]->thread_index = 0;
+ res = dnn_execute_layer_conv2d_thread((void *)thread_param[0]);
+ if ((int)res != 0)
+ error_flag = (int)res;
+ av_free(thread_param[0]);
+#endif
+
+ av_free(thread_param);
+ return error_flag;
}
diff --git a/tests/dnn/dnn-layer-conv2d-test.c b/tests/dnn/dnn-layer-conv2d-test.c
index 836839cc64..f695fcee0d 100644
--- a/tests/dnn/dnn-layer-conv2d-test.c
+++ b/tests/dnn/dnn-layer-conv2d-test.c
@@ -96,6 +96,10 @@ static int test_with_same_dilate(void)
};
float bias[2] = { -1.6574852, -0.72915393 };
+ NativeContext ctx;
+ ctx.class = &dnn_native_class;
+ ctx.options.conv2d_threads = 1;
+
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
@@ -114,7 +118,7 @@ static int test_with_same_dilate(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, NULL);
+ dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, &ctx);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
@@ -196,6 +200,10 @@ static int test_with_valid(void)
};
float bias[2] = { -0.4773722, -0.19620377 };
+ NativeContext ctx;
+ ctx.class = &dnn_native_class;
+ ctx.options.conv2d_threads = 1;
+
params.activation = TANH;
params.has_bias = 1;
params.biases = bias;
@@ -214,7 +222,7 @@ static int test_with_valid(void)
operands[1].data = NULL;
input_indexes[0] = 0;
- dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, NULL);
+ dnn_execute_layer_conv2d(operands, input_indexes, 1, ¶ms, &ctx);
output = operands[1].data;
for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
--
2.28.0
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