dnn_backend_torch.cpp

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/*
 * Copyright (c) 2024
 *
 * This file is part of FFmpeg.
 *
 * FFmpeg is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Lesser General Public
 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * FFmpeg is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with FFmpeg; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
 */
 
/**
 * @file
 * DNN Torch backend implementation.
 */
 
#include <torch/torch.h>
#include <torch/script.h>
 
extern "C" {
#include "dnn_io_proc.h"
#include "dnn_backend_common.h"
#include "libavutil/opt.h"
#include "libavutil/mem.h"
#include "queue.h"
#include "safe_queue.h"
}
 
typedef struct THModel {
    DNNModel model;
    DnnContext *ctx;
    torch::jit::Module *jit_model;
    SafeQueue *request_queue;
    Queue *task_queue;
    Queue *lltask_queue;
} THModel;
 
typedef struct THInferRequest {
    torch::Tensor *output;
    torch::Tensor *input_tensor;
} THInferRequest;
 
typedef struct THRequestItem {
    THInferRequest *infer_request;
    LastLevelTaskItem *lltask;
    DNNAsyncExecModule exec_module;
} THRequestItem;
 
 
#define OFFSET(x) offsetof(THOptions, x)
#define FLAGS AV_OPT_FLAG_FILTERING_PARAM
static const AVOption dnn_th_options[] = {
    { "optimize", "turn on graph executor optimization", OFFSET(optimize), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, 1, FLAGS},
    { NULL }
};
 
static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
{
    THModel *th_model = (THModel *)task->model;
    DnnContext *ctx = th_model->ctx;
    LastLevelTaskItem *lltask = (LastLevelTaskItem *)av_malloc(sizeof(*lltask));
    if (!lltask) {
        av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for LastLevelTaskItem\n");
        return AVERROR(ENOMEM);
    }
    task->inference_todo = 1;
    task->inference_done = 0;
    lltask->task = task;
    if (ff_queue_push_back(lltask_queue, lltask) < 0) {
        av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
        av_freep(&lltask);
        return AVERROR(ENOMEM);
    }
    return 0;
}
 
static void th_free_request(THInferRequest *request)
{
    if (!request)
        return;
    if (request->output) {
        delete(request->output);
        request->output = NULL;
    }
    if (request->input_tensor) {
        delete(request->input_tensor);
        request->input_tensor = NULL;
    }
    return;
}
 
static inline void destroy_request_item(THRequestItem **arg)
{
    THRequestItem *item;
    if (!arg || !*arg) {
        return;
    }
    item = *arg;
    th_free_request(item->infer_request);
    av_freep(&item->infer_request);
    av_freep(&item->lltask);
    ff_dnn_async_module_cleanup(&item->exec_module);
    av_freep(arg);
}
 
static void dnn_free_model_th(DNNModel **model)
{
    THModel *th_model;
    if (!model || !*model)
        return;
 
    th_model = (THModel *)(*model);
 
    if (th_model->request_queue) {
        while (ff_safe_queue_size(th_model->request_queue) != 0) {
            THRequestItem *item = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
            destroy_request_item(&item);
        }
        ff_safe_queue_destroy(th_model->request_queue);
    }
 
    if (th_model->lltask_queue)
        ff_queue_destroy(th_model->lltask_queue);
    if (th_model->task_queue)
        ff_queue_destroy(th_model->task_queue);
 
    if (th_model->jit_model)
        delete th_model->jit_model;
 
    av_freep(&th_model);
    *model = NULL;
}
 
static int get_input_th(DNNModel *model, DNNData *input, const char *input_name)
{
    input->dt = DNN_FLOAT;
    input->order = DCO_RGB;
    input->layout = DL_NCHW;
    input->dims[0] = 1;
    input->dims[1] = 3;
    input->dims[2] = -1;
    input->dims[3] = -1;
    return 0;
}
 
static void deleter(void *arg)
{
    av_freep(&arg);
}
 
static int fill_model_input_th(THModel *th_model, THRequestItem *request)
{
    LastLevelTaskItem *lltask = NULL;
    TaskItem *task = NULL;
    THInferRequest *infer_request = NULL;
    DNNData input = { 0 };
    DnnContext *ctx = th_model->ctx;
    int ret, width_idx, height_idx, channel_idx;
 
    lltask = (LastLevelTaskItem *)ff_queue_pop_front(th_model->lltask_queue);
    if (!lltask) {
        ret = AVERROR(EINVAL);
        goto err;
    }
    request->lltask = lltask;
    task = lltask->task;
    infer_request = request->infer_request;
 
    ret = get_input_th(&th_model->model, &input, NULL);
    if ( ret != 0) {
        goto err;
    }
    width_idx = dnn_get_width_idx_by_layout(input.layout);
    height_idx = dnn_get_height_idx_by_layout(input.layout);
    channel_idx = dnn_get_channel_idx_by_layout(input.layout);
    input.dims[height_idx] = task->in_frame->height;
    input.dims[width_idx] = task->in_frame->width;
    input.data = av_malloc(input.dims[height_idx] * input.dims[width_idx] *
                           input.dims[channel_idx] * sizeof(float));
    if (!input.data)
        return AVERROR(ENOMEM);
    infer_request->input_tensor = new torch::Tensor();
    infer_request->output = new torch::Tensor();
 
    switch (th_model->model.func_type) {
    case DFT_PROCESS_FRAME:
        input.scale = 255;
        if (task->do_ioproc) {
            if (th_model->model.frame_pre_proc != NULL) {
                th_model->model.frame_pre_proc(task->in_frame, &input, th_model->model.filter_ctx);
            } else {
                ff_proc_from_frame_to_dnn(task->in_frame, &input, ctx);
            }
        }
        break;
    default:
        avpriv_report_missing_feature(NULL, "model function type %d", th_model->model.func_type);
        break;
    }
    *infer_request->input_tensor = torch::from_blob(input.data,
        {1, input.dims[channel_idx], input.dims[height_idx], input.dims[width_idx]},
        deleter, torch::kFloat32);
    return 0;
 
err:
    th_free_request(infer_request);
    return ret;
}
 
static int th_start_inference(void *args)
{
    THRequestItem *request = (THRequestItem *)args;
    THInferRequest *infer_request = NULL;
    LastLevelTaskItem *lltask = NULL;
    TaskItem *task = NULL;
    THModel *th_model = NULL;
    DnnContext *ctx = NULL;
    std::vector<torch::jit::IValue> inputs;
    torch::NoGradGuard no_grad;
 
    if (!request) {
        av_log(NULL, AV_LOG_ERROR, "THRequestItem is NULL\n");
        return AVERROR(EINVAL);
    }
    infer_request = request->infer_request;
    lltask = request->lltask;
    task = lltask->task;
    th_model = (THModel *)task->model;
    ctx = th_model->ctx;
 
    if (ctx->torch_option.optimize)
        torch::jit::setGraphExecutorOptimize(true);
    else
        torch::jit::setGraphExecutorOptimize(false);
 
    if (!infer_request->input_tensor || !infer_request->output) {
        av_log(ctx, AV_LOG_ERROR, "input or output tensor is NULL\n");
        return DNN_GENERIC_ERROR;
    }
    // Transfer tensor to the same device as model
    c10::Device device = (*th_model->jit_model->parameters().begin()).device();
    if (infer_request->input_tensor->device() != device)
        *infer_request->input_tensor = infer_request->input_tensor->to(device);
    inputs.push_back(*infer_request->input_tensor);
 
    *infer_request->output = th_model->jit_model->forward(inputs).toTensor();
 
    return 0;
}
 
static void infer_completion_callback(void *args) {
    THRequestItem *request = (THRequestItem*)args;
    LastLevelTaskItem *lltask = request->lltask;
    TaskItem *task = lltask->task;
    DNNData outputs = { 0 };
    THInferRequest *infer_request = request->infer_request;
    THModel *th_model = (THModel *)task->model;
    torch::Tensor *output = infer_request->output;
 
    c10::IntArrayRef sizes = output->sizes();
    outputs.order = DCO_RGB;
    outputs.layout = DL_NCHW;
    outputs.dt = DNN_FLOAT;
    if (sizes.size() == 4) {
        // 4 dimensions: [batch_size, channel, height, width]
        // this format of data is normally used for video frame SR
        outputs.dims[0] = sizes.at(0); // N
        outputs.dims[1] = sizes.at(1); // C
        outputs.dims[2] = sizes.at(2); // H
        outputs.dims[3] = sizes.at(3); // W
    } else {
        avpriv_report_missing_feature(th_model->ctx, "Support of this kind of model");
        goto err;
    }
 
    switch (th_model->model.func_type) {
    case DFT_PROCESS_FRAME:
        if (task->do_ioproc) {
            // Post process can only deal with CPU memory.
            if (output->device() != torch::kCPU)
                *output = output->to(torch::kCPU);
            outputs.scale = 255;
            outputs.data = output->data_ptr();
            if (th_model->model.frame_post_proc != NULL) {
                th_model->model.frame_post_proc(task->out_frame, &outputs, th_model->model.filter_ctx);
            } else {
                ff_proc_from_dnn_to_frame(task->out_frame, &outputs, th_model->ctx);
            }
        } else {
            task->out_frame->width = outputs.dims[dnn_get_width_idx_by_layout(outputs.layout)];
            task->out_frame->height = outputs.dims[dnn_get_height_idx_by_layout(outputs.layout)];
        }
        break;
    default:
        avpriv_report_missing_feature(th_model->ctx, "model function type %d", th_model->model.func_type);
        goto err;
    }
    task->inference_done++;
    av_freep(&request->lltask);
err:
    th_free_request(infer_request);
 
    if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
        destroy_request_item(&request);
        av_log(th_model->ctx, AV_LOG_ERROR, "Unable to push back request_queue when failed to start inference.\n");
    }
}
 
static int execute_model_th(THRequestItem *request, Queue *lltask_queue)
{
    THModel *th_model = NULL;
    LastLevelTaskItem *lltask;
    TaskItem *task = NULL;
    int ret = 0;
 
    if (ff_queue_size(lltask_queue) == 0) {
        destroy_request_item(&request);
        return 0;
    }
 
    lltask = (LastLevelTaskItem *)ff_queue_peek_front(lltask_queue);
    if (lltask == NULL) {
        av_log(NULL, AV_LOG_ERROR, "Failed to get LastLevelTaskItem\n");
        ret = AVERROR(EINVAL);
        goto err;
    }
    task = lltask->task;
    th_model = (THModel *)task->model;
 
    ret = fill_model_input_th(th_model, request);
    if (ret != 0) {
        goto err;
    }
 
    if (task->async) {
        ret = ff_dnn_start_inference_async(th_model->ctx, &request->exec_module);
        if (ret != 0) {
            goto err;
        }
        return 0;
    } else {
        // Synchronous execution path
        ret = th_start_inference((void *)(request));
        if (ret != 0) {
            goto err;
        }
        infer_completion_callback(request);
        return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
    }
 
err:
    th_free_request(request->infer_request);
    if (ff_safe_queue_push_back(th_model->request_queue, request) < 0) {
        destroy_request_item(&request);
    }
    return ret;
}
 
static int get_output_th(DNNModel *model, const char *input_name, int input_width, int input_height,
                                   const char *output_name, int *output_width, int *output_height)
{
    int ret = 0;
    THModel *th_model = (THModel*) model;
    DnnContext *ctx = th_model->ctx;
    TaskItem task = { 0 };
    THRequestItem *request = NULL;
    DNNExecBaseParams exec_params = {
        .input_name     = input_name,
        .output_names   = &output_name,
        .nb_output      = 1,
        .in_frame       = NULL,
        .out_frame      = NULL,
    };
    ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, th_model, input_height, input_width, ctx);
    if ( ret != 0) {
        goto err;
    }
 
    ret = extract_lltask_from_task(&task, th_model->lltask_queue);
    if ( ret != 0) {
        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
        goto err;
    }
 
    request = (THRequestItem*) ff_safe_queue_pop_front(th_model->request_queue);
    if (!request) {
        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
        ret = AVERROR(EINVAL);
        goto err;
    }
 
    ret = execute_model_th(request, th_model->lltask_queue);
    *output_width = task.out_frame->width;
    *output_height = task.out_frame->height;
 
err:
    av_frame_free(&task.out_frame);
    av_frame_free(&task.in_frame);
    return ret;
}
 
static THInferRequest *th_create_inference_request(void)
{
    THInferRequest *request = (THInferRequest *)av_malloc(sizeof(THInferRequest));
    if (!request) {
        return NULL;
    }
    request->input_tensor = NULL;
    request->output = NULL;
    return request;
}
 
static DNNModel *dnn_load_model_th(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx)
{
    DNNModel *model = NULL;
    THModel *th_model = NULL;
    THRequestItem *item = NULL;
    const char *device_name = ctx->device ? ctx->device : "cpu";
 
    th_model = (THModel *)av_mallocz(sizeof(THModel));
    if (!th_model)
        return NULL;
    model = &th_model->model;
    th_model->ctx = ctx;
 
    c10::Device device = c10::Device(device_name);
    if (device.is_xpu()) {
        if (!at::hasXPU()) {
            av_log(ctx, AV_LOG_ERROR, "No XPU device found\n");
            goto fail;
        }
#if TORCH_VERSION_MAJOR > 2 || (TORCH_VERSION_MAJOR == 2 && TORCH_VERSION_MINOR >= 6)
        at::detail::getXPUHooks().init();
#else
        at::detail::getXPUHooks().initXPU();
#endif
    } else if (device.is_cuda()) {
        // CUDA device - works for both NVIDIA CUDA and AMD ROCm (which uses CUDA-compatible API)
        if (!torch::cuda::is_available()) {
            av_log(ctx, AV_LOG_ERROR, "CUDA/ROCm is not available\n");
            goto fail;
        }
        av_log(ctx, AV_LOG_INFO, "Using CUDA/ROCm device: %s\n", device_name);
    } else if (!device.is_cpu()) {
        av_log(ctx, AV_LOG_ERROR, "Not supported device:\"%s\"\n", device_name);
        goto fail;
    }
 
    try {
        th_model->jit_model = new torch::jit::Module;
        (*th_model->jit_model) = torch::jit::load(ctx->model_filename);
        th_model->jit_model->to(device);
    } catch (const c10::Error& e) {
        av_log(ctx, AV_LOG_ERROR, "Failed to load torch model\n");
        goto fail;
    }
 
    th_model->request_queue = ff_safe_queue_create();
    if (!th_model->request_queue) {
        goto fail;
    }
 
    item = (THRequestItem *)av_mallocz(sizeof(THRequestItem));
    if (!item) {
        goto fail;
    }
    item->infer_request = th_create_inference_request();
    if (!item->infer_request) {
        goto fail;
    }
 
    item->exec_module.start_inference = &th_start_inference;
    item->exec_module.callback = &infer_completion_callback;
    item->exec_module.args = item;
 
    if (ff_safe_queue_push_back(th_model->request_queue, item) < 0) {
        goto fail;
    }
    item = NULL;
 
    th_model->task_queue = ff_queue_create();
    th_model->lltask_queue = ff_queue_create();
 
    model->get_input = &get_input_th;
    model->get_output = &get_output_th;
    model->filter_ctx = filter_ctx;
    model->func_type = func_type;
    return model;
 
fail:
    if (item) {
        destroy_request_item(&item);
    }
    dnn_free_model_th(&model);
    return NULL;
}
 
static int dnn_execute_model_th(const DNNModel *model, DNNExecBaseParams *exec_params)
{
    THModel *th_model = (THModel *)model;
    DnnContext *ctx = th_model->ctx;
    TaskItem *task;
    THRequestItem *request;
    int ret = 0;
 
    ret = ff_check_exec_params(ctx, DNN_TH, model->func_type, exec_params);
    if (ret != 0) {
        av_log(ctx, AV_LOG_ERROR, "exec parameter checking fail.\n");
        return ret;
    }
 
    task = (TaskItem *)av_malloc(sizeof(TaskItem));
    if (!task) {
        av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
        return AVERROR(ENOMEM);
    }
 
    ret = ff_dnn_fill_task(task, exec_params, th_model, ctx->async, 1);
    if (ret != 0) {
        av_freep(&task);
        av_log(ctx, AV_LOG_ERROR, "unable to fill task.\n");
        return ret;
    }
 
    ret = ff_queue_push_back(th_model->task_queue, task);
    if (ret < 0) {
        av_freep(&task);
        av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
        return ret;
    }
 
    ret = extract_lltask_from_task(task, th_model->lltask_queue);
    if (ret != 0) {
        av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
        return ret;
    }
 
    request = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
    if (!request) {
        av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
        return AVERROR(EINVAL);
    }
 
    return execute_model_th(request, th_model->lltask_queue);
}
 
static DNNAsyncStatusType dnn_get_result_th(const DNNModel *model, AVFrame **in, AVFrame **out)
{
    THModel *th_model = (THModel *)model;
    return ff_dnn_get_result_common(th_model->task_queue, in, out);
}
 
static int dnn_flush_th(const DNNModel *model)
{
    THModel *th_model = (THModel *)model;
    THRequestItem *request;
 
    if (ff_queue_size(th_model->lltask_queue) == 0)
        // no pending task need to flush
        return 0;
 
    request = (THRequestItem *)ff_safe_queue_pop_front(th_model->request_queue);
    if (!request) {
        av_log(th_model->ctx, AV_LOG_ERROR, "unable to get infer request.\n");
        return AVERROR(EINVAL);
    }
 
    return execute_model_th(request, th_model->lltask_queue);
}
 
extern const DNNModule ff_dnn_backend_torch = {
    .clazz          = DNN_DEFINE_CLASS(dnn_th),
    .type           = DNN_TH,
    .load_model     = dnn_load_model_th,
    .execute_model  = dnn_execute_model_th,
    .get_result     = dnn_get_result_th,
    .flush          = dnn_flush_th,
    .free_model     = dnn_free_model_th,
};