Adder
=================

Adder是一种卷积替代算子，它使用L1距离度量（绝对差的和）代替传统卷积中的点积操作。与标准卷积不同，Adder通过计算特征与卷积核之间的绝对差的负和来进行特征提取。假定输入X，filter表示为F，它按以下公式计算：

.. math::

   Y(m,n,t) = - \sum_{i=0}^{d} \sum_{j=0}^{d} \sum_{k=0}^{C_{in}} |X(m+i, n+j, k) - F(i,j,k,t)|

输入：
        - **input_x** - 输入数据的地址
        - **input_w** - 输入卷积核权重的地址
        - **bias** - 输入偏置的地址
        - **param** - 算子计算所需参数的结构体。其各成员见下述。
        - **core_mask** - 核掩码。

**AdderParameter定义：**

.. code-block:: c
    :linenos:

    typedef struct AdderParameter {
        void* workspace_; // 用于存放中间计算结果
        int output_batch_; // 输出数据总批次
        int input_batch_; // 输入数据总批次
        int input_h_; // 输入数据h维度大小
        int input_w_; // 输入数据w维度大小
        int output_h_; // 输出数据h维度大小
        int output_w_; // 输出数据w维度大小
        int input_channel_; // 输入数据通道数
        int output_channel_; // 输出数据通道数
        int kernel_h_; // 卷积核h维度大小
        int kernel_w_; // 卷积核w维度大小
        int group_; // 组数
        int pad_l_; // 左填充大小
        int pad_u_; // 上填充大小
        int dilation_h_; // 卷积核h维度膨胀尺寸大小
        int dilation_w_; // 卷积核w维度膨胀尺寸大小
        int stride_h_; // 卷积核h维度步长
        int stride_w_; // 卷积核w维度步长
        int buffer_size_; // 为分块计算所分配的缓存大小
    } AdderParameter;

输出：
        - **out_y** - 输出地址。

支持平台：
        ``FT78NE``
        ``MT7004``

.. note::
    - FT78NE 支持int8, fp32
    - MT7004 支持fp16, fp32

**共享存储版本:**

.. c:function:: void i8_adder_s(int8_t* input_x, int8_t* input_w, int8_t* out_y, int* bias, AdderParameter *param, int core_mask)
.. c:function:: void hp_adder_s(half* input_x, half* input_w, half* out_y, half* bias, AdderParameter *param, int core_mask)
.. c:function:: void fp_adder_s(float* input_x, float* input_w, float* out_y, float* bias, AdderParameter *param, int core_mask)

**C调用示例：**

.. code-block:: c
    :linenos:
    :emphasize-lines: 32

    void TestAdderSMCFp32(int* input_shape, int* weight_shape, int* output_shape, int* stride, int* padding, int* dilation, int groups, float* bias, int core_mask) {
        int core_id = get_core_id();
        int logic_core_id = GetLogicCoreId(core_mask, core_id);
        int core_num = GetCoreNum(core_mask);
        float* input_data = (float*)0x88000000;
        float* weight = (float*)0x89000000;
        float* output_data = (float*)0x90000000;
        float* bias_data = (float*)0x91000000;
        AdderParameter* param = (AdderParameter*)0x92000000;
        if (logic_core_id == 0) {
            memcpy(bias_data, bias, sizeof(float) * output_shape[3]);
            param->dilation_h_ = dilation[0];
            param->dilation_w_ = dilation[1];
            param->group_ = groups;
            param->input_batch_ = input_shape[0];
            param->input_h_ = input_shape[1];
            param->input_w_ = input_shape[2];
            param->input_channel_ = input_shape[3];
            param->kernel_h_ = weight_shape[1];
            param->kernel_w_ = weight_shape[2];
            param->output_batch_ = output_shape[0];
            param->output_h_ = output_shape[1];
            param->output_w_ = output_shape[2];
            param->output_channel_ = output_shape[3];
            param->stride_h_ = stride[0];
            param->stride_w_ = stride[0];
            param->pad_u_ = padding[0];
            param->pad_l_ = padding[2];
            param->workspace_ = (float*)0x10000000; // workspace空间需分配在AM内，计算过程中会将数据搬运到workspace空间内进行计算
        }
        sys_bar(0, core_num); // 初始化参数完成后进行同步
        fp_adder_s(input_data, weight, output_data, bias_data, param, core_mask);
    }

    void main(){
        int in_channel = 4;
        int out_channel = 4;
        int groups = 4;
        int input_shape[4] = {1, 30, 30, in_channel}; // NHWC
        int weight_shape[4] = {out_channel, 3, 3, in_channel / groups};
        int output_shape[4] = {1, 10, 10, out_channel}; // NHWC
        int stride[2] = {2, 2};
        int padding[4] = {1, 1, 1, 1};
        int dilation[2]= {2, 2};
        float bias[4] = {0, 0, 0, 0};
        int core_mask = 0b1111;
        TestAdderSMCFp32(input_shape, weight_shape, output_shape, stride, padding, dilation, groups, bias, core_mask);
    }

**私有存储版本:**

.. c:function:: void i8_adder_p(int8_t* input_x, int8_t* input_w, int8_t* out_y, int* bias, ConvParameter *conv_param, ConvQuantParameter quant_param, int core_mask)
.. c:function:: void hp_adder_p(half* input_x, half* input_w, half* out_y, half* bias, ConvParameter *conv_param, int core_mask)
.. c:function:: void fp_adder_p(float* input_x, float* input_w, float* out_y, float* bias, ConvParameter *conv_param, int core_mask)

**C调用示例：**

.. code-block:: c
    :linenos:
    :emphasize-lines: 27

    void TestAdderL2Fp32(int* input_shape, int* weight_shape, int* output_shape, int* stride, int* padding, int* dilation, int groups, float* bias, int core_mask) {
        float* input_data = (float*)0x10010000; // 私有存储版本地址设置在AM内
        float* weight = (float*)0x10020000;
        float* output_data = (float*)0x10030000;
        float* bias_data = (float*)0x10040000;
        AdderParameter* param = (AdderParameter*)0x10060000;
        memcpy(bias_data, bias, sizeof(float) * output_shape[3]);
        param->dilation_h_ = dilation[0];
        param->dilation_w_ = dilation[1];
        param->group_ = groups;
        param->input_batch_ = input_shape[0];
        param->input_h_ = input_shape[1];
        param->input_w_ = input_shape[2];
        param->input_channel_ = input_shape[3];
        param->kernel_h_ = weight_shape[1];
        param->kernel_w_ = weight_shape[2];
        param->output_batch_ = output_shape[0];
        param->output_h_ = output_shape[1];
        param->output_w_ = output_shape[2];
        param->output_channel_ = output_shape[3];
        param->stride_h_ = stride[0];
        param->stride_w_ = stride[0];
        param->pad_u_ = padding[0];
        param->pad_l_ = padding[2];
        param->workspace_ = (float*)0x10070000;
        param->buffer_size_ = 2048; // 私有存储版本中，必须设置该参数，用于确定分块计算的大小
        fp_adder_p(input_data, weight, output_data, bias_data, param, core_mask);
    }

    void main(){
        int in_channel = 4;
        int out_channel = 4;
        int groups = 4;
        int input_shape[4] = {1, 30, 30, in_channel}; // NHWC
        int weight_shape[4] = {out_channel, 3, 3, in_channel / groups};
        int output_shape[4] = {1, 10, 10, out_channel}; // NHWC
        int stride[2] = {2, 2};
        int padding[4] = {1, 1, 1, 1};
        int dilation[2]= {2, 2};
        float bias[4] = {0, 0, 0, 0};
        int core_mask = 0b0001; // 私有存储版本只能设置为一个核心启动
        TestAdderL2Fp32(input_shape, weight_shape, output_shape, stride, padding, dilation, groups, bias, core_mask);
    }