Message ID | 20200904124604.179561-2-xujunzz@sjtu.edu.cn |
---|---|
State | Superseded |
Headers | show |
Series | [FFmpeg-devel,v2,1/2] dnn_backend_native.c: parse options in native backend | expand |
Context | Check | Description |
---|---|---|
andriy/default | pending | |
andriy/make | success | Make finished |
andriy/make_fate | success | Make fate finished |
> -----Original Message----- > From: ffmpeg-devel <ffmpeg-devel-bounces@ffmpeg.org> On Behalf Of > xujunzz@sjtu.edu.cn > Sent: 2020年9月4日 20:46 > To: ffmpeg-devel@ffmpeg.org > Cc: xujunzz@sjtu.edu.cn > Subject: [FFmpeg-devel] [PATCH v2 2/2] > dnn_backend_native_layer_conv2d.c:Add mutithread function > > From: Xu Jun <xujunzz@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@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++) { LGTM, thanks.
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++) {