Method exec

dnn/src/cuda/local/backward_data.cpp:22–47

namespace cuda {

void LocalBackwardDataImpl::exec(
        _megdnn_tensor_in filter, _megdnn_tensor_in diff, _megdnn_tensor_out grad,
        _megdnn_workspace workspace) {
    check_exec(filter.layout, diff.layout, grad.layout, workspace.size);
    megdnn_assert(param().mode == Mode::CROSS_CORRELATION);
    auto N = grad.layout.shape[0], IC = grad.layout.shape[1], IH = grad.layout.shape[2],
         IW = grad.layout.shape[3];
    auto OC = diff.layout.shape[1], OH = diff.layout.shape[2],
         OW = diff.layout.shape[3];
    auto FH = filter.layout.shape[3], FW = filter.layout.shape[4];
    auto handle = concrete_handle(this->handle());
    auto stream = cuda_stream(this->handle());
    auto cublas = cublas_handle(this->handle());
    auto one = handle->one_device();
    auto zero = handle->zero_device();
    if (use_cuda_convnet(filter.layout, diff.layout, grad.layout)) {
        local::backward_data_proxy_convnet(
                filter.ptr<dt_float32>(), diff.ptr<dt_float32>(),
                grad.ptr<dt_float32>(), reinterpret_cast<float*>(workspace.raw_ptr), N,
                IC, IH, IW, OC, OH, OW, FH, FW, IC * IH * IW, OC * OH * OW,
                param().pad_h, param().pad_w, param().stride_h, param().stride_w,
                cublas, stream, one, zero);
    } else {
        local::boom_backward_data();
    }
}

size_t LocalBackwardDataImpl::get_workspace_in_bytes(
        const TensorLayout& filter, const TensorLayout& diff,