FT_DSP.gitlink.net/master/html/_sources/functionlib/dsplib/reduce.rst.txt

Reduce
=================

对指定维度进行归约。

输入：
        - **src_data** - 输入数据的地址
        - **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。

**ReduceParameter定义：**

.. code-block:: c
    :linenos:

    typedef struct ReduceParameter {
        void** data_buffers_; // 用于存储中间计算结果
        int* outer_sizes_; // 处理某个规约轴时，该轴之前所有轴的元素数
        int* inner_sizes_; // 某个规约轴之后的所有元素数
        int* axis_sizes_; // 规约轴的元素数
        int total_num_; // 输入张量的总元素数
        int num_axes_; // 待规约轴的数目
        int mode_; // 规约模式
        int output_num_; // 输出张量的总元素数
        /**该算子会根据ReduceParameter中的mode_参数选择实际规约所使用的方法。共有如下几种方法：
           Reduce_Mean=0,
           Reduce_Max=1,
           Reduce_Min=2,
           Reduce_Prod=3,
           Reduce_Sum=4,
           Reduce_SumSquare=5,
           Reduce_ASum=6,
           Reduce_L2Norm=7
        **/
    } ReduceParameter;

输出：
        - **dst_data** - 输出地址。

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

.. note::
    - FT78NE 支持int8, int16, int32, fp32, fp64
    - MT7004 支持fp16, fp32, int16, int32

**共享存储版本:**

.. c:function:: void i8_reduce_s(int8_t* src_data, int8_t* dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void i16_reduce_s(int16_t* src_data, half* dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void i32_reduce_s(int* src_data, float* dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void hp_reduce_s(half* src_data, half* dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void fp_reduce_s(float* src_data, float* dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void dp_reduce_s(double* src_data, double* dst_data, ReduceParameter* param, int core_mask)

**C调用示例：**

.. code-block:: c
    :linenos:
    :emphasize-lines: 50

    void PackParam(ReduceParameter* param, int ndim, int* input_shape, int num_axes, int* axes) {
        int tmp_input_shape[8];
        int total_num = 1;
        int i, j, k;
        for (i = 0; i < ndim; i++) {
            tmp_input_shape[i] = input_shape[i];
            total_num *= input_shape[i];
        }
        param->total_num_ = total_num;
        int offset_size = 0;
        for (i = 0; i < num_axes; ++i) {
            int axis = axes[i];
            int outer_size = 1;
            for (j = 0; j < axis; j++) {
                outer_size *= tmp_input_shape[j];
            }
            param->outer_sizes_[offset_size] = outer_size;
            int inner_size = 1;
            for (k = axis + 1; k < ndim; k++) {
                inner_size *= tmp_input_shape[k];
            }
            param->inner_sizes_[offset_size] = inner_size;
            param->axis_sizes_[offset_size] = tmp_input_shape[axis];
            offset_size++;
            tmp_input_shape[axis] = 1;
        }
    }

    void TestReduceSMCFp32(int* input_shape, int ndim, int* axes, int num_axes, int mode, int keep_dims, 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 = (float*)0x88000000;
        float* output = (float*)0x98000000;
        ReduceParameter* param = (ReduceParameter*)0xA8480000;
        if (logic_core_id == 0) {
            param->num_axes_ = num_axes;
            param->mode_ = mode;
            param->data_buffers_ = (void**)0xA8483000;
            param->inner_sizes_ = (int*)0xA8484000;
            param->outer_sizes_ = (int*)0xA8485000;
            param->axis_sizes_ = (int*)0xA8486000;
            int i;
            for (i = 0; i < num_axes - 1; i++) {
                param->data_buffers_[i] = (void*)(0xA8490000 + 0x1000000);
            }
            PackParam(param, ndim, input_shape, num_axes, axes);
        }
        sys_bar(0, core_num); // 初始化参数完成后进行同步
        fp_reduce_s(input, check, param, core_mask);
    }

    void main(){
        int input_shape[3] = {4, 5, 5};
        int ndim = 3;
        int axes[1] = {1};
        int num_axes = 1;
        int mode = 7;
        int keep_dims = 1;
        int core_mask = 0b1111;
        TestReduceSMCFp32(input_shape, ndim, axes, num_axes, mode, keep_dims, core_mask);
    }

**私有存储版本:**

.. c:function:: void i8_reduce_p(int8_t* src_data, int8_t* dst_data, void* tmp_src_data, void* tmp_dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void i16_reduce_p(int16_t* src_data, half* dst_data, void* tmp_src_data, void* tmp_dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void i32_reduce_p(int* src_data, float* dst_data, void* tmp_src_data, void* tmp_dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void hp_reduce_p(half* src_data, half* dst_data, void* tmp_src_data, void* tmp_dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void fp_reduce_p(float* src_data, float* dst_data, void* tmp_src_data, void* tmp_dst_data, ReduceParameter* param, int core_mask)
.. c:function:: void dp_reduce_p(double* src_data, double* dst_data, void* tmp_src_data, void* tmp_dst_data, ReduceParameter* param, int core_mask)

**C调用示例：**

.. code-block:: c
    :linenos:
    :emphasize-lines: 59

    void PackParam(ReduceParameter* param, int ndim, int* input_shape, int num_axes, int* axes) {
        int tmp_input_shape[8];
        int total_num = 1;
        int i, j, k;
        for (i = 0; i < ndim; i++) {
            tmp_input_shape[i] = input_shape[i];
            total_num *= input_shape[i];
        }
        param->total_num_ = total_num;
        int offset_size = 0;
        for (i = 0; i < num_axes; ++i) {
            int axis = axes[i];
            int outer_size = 1;
            for (j = 0; j < axis; j++) {
                outer_size *= tmp_input_shape[j];
            }
            param->outer_sizes_[offset_size] = outer_size;
            int inner_size = 1;
            for (k = axis + 1; k < ndim; k++) {
                inner_size *= tmp_input_shape[k];
            }
            param->inner_sizes_[offset_size] = inner_size;
            param->axis_sizes_[offset_size] = tmp_input_shape[axis];
            offset_size++;
            tmp_input_shape[axis] = 1;
        }
    }

    void TestReduceL2Fp32(int* input_shape, int ndim, int* axes, int num_axes, int mode, int keep_dims, int core_mask) {
        float* input = (float*)0x10000000; // 原始输入输出数据需分配在AM中
        float* output = (float*)0x10010000;
        float* tmp_input = (float*)0x88000000; // 临时输入输出空间需分配在DDR或SMC中
        float* tmp_output = (float*)0x98000000; 
        ReduceParameter* param = (ReduceParameter*)0x10020000;
        param->num_axes_ = num_axes;
        param->mode_ = mode;
        param->data_buffers_ = (void**)0x10021000;
        param->inner_sizes_ = (int*)0x10022000;
        param->outer_sizes_ = (int*)0x10023000;
        param->axis_sizes_ = (int*)0x10024000;
        int i, j;
        for (i = 0; i < ndim; i++) {
            int reduce_axis = 0;
            for (j = 0; j < num_axes; j++) {
                if (axes[j] == i) {
                    reduce_axis = 1;
                    break;
                }
            }
            if (!reduce_axis) {
                length *= input_shape[i];
            }
        }
        for (i = 0; i < num_axes - 1; i++) {
            param->data_buffers_[i] = (void*)(0xA8490000 + 0x1000000); // 每一个中间计算结果空间都需分配在DDR或SMC中
        }
        param->output_num_ = length;
        PackParam(param, ndim, input_shape, num_axes, axes);
        fp_reduce_p(input, check, param, core_mask);
    }

    void main() {
        int input_shape[3] = {4, 5, 5};
        int ndim = 3;
        int axes[1] = {1};
        int num_axes = 1;
        int mode = 7;
        int keep_dims = 1;
        int core_mask = 0b0001; // 私有存储版本只能设置为一个核心启动
        TestReduceL2Fp32(input_shape, ndim, axes, num_axes, mode, keep_dims, core_mask);
    }