update include file 0325

third_party/fwkacllib/inc/ops/nn_pooling_ops.h

@@ -1444,8 +1444,7 @@ REG_OP(MaxPoolV3Grad)
 
 *@par Inputs:
 *x: A tensor of shape is 4d, format is support NHWC.
-*filter: A tensor of shape is 3d, the type is same with x,
-and the c dimension is same with x. \n
+*filter: A tensor of shape is 3d, the type is same with x, and the c dimension is same with x. \n
 
 *@par Attributes:
 *@li strides: A required list of 4 ints, specifying the stride of the sliding window. The strides of the N and C dimensions are 1.
@@ -1473,6 +1472,82 @@ REG_OP(Dilation2D)
     .ATTR(data_format, String, "NHWC")
     .OP_END_FACTORY_REG(Dilation2D)
 
+/*
+* @brief Performs Dilation2DBackpropFilter on the input. \n
+
+*@par Inputs:
+*x: A tensor of shape is 4d, format is support NHWC
+*filter: A tensor of shape is 3d the type is same with x,
+*out_backprop: Has the same type and format as input "x" and the c dimension is same with x. \n
+
+*@par Attributes
+*@li stride: A required list of 4 ints, specifying the stride of the sliding window. The strides of the N and C
+dimension are 1
+*@li rates: A required list of 4 ints, the rates of the N and C dimensions are 1
+*@li padding_mode: A optional string. Defaults to "SAME", it support SAME and VALID
+*@li pads: A optional list of 4 ints. \n
+
+*@par Outputs:
+*y: The output tensor. Has the same type and format as input "filter" . \n
+
+*@par Third-party framework compatibility
+* Compatible with the TensorFlow operator Dilation2D
+*/
+
+REG_OP(Dilation2DBackpropFilter)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .INPUT(filter,
+           TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .INPUT(out_backprop,
+           TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .OUTPUT(y,
+            TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .REQUIRED_ATTR(strides, ListInt)
+    .REQUIRED_ATTR(rates, ListInt)
+    .ATTR(padding_mode, String, "SAME")
+    .ATTR(pads, ListInt, {0, 0, 0, 0})
+    .ATTR(ceil_mode, Bool, false)
+    .ATTR(data_format, String, "NHWC")
+    .OP_END_FACTORY_REG(Dilation2DBackpropFilter)
+
+/*
+* @brief Performs Dilation2DBackpropInput on the input. \n
+
+*@par Inputs:
+*x: A tensor of shape is 4d, format is support NHWC
+*filter: A tensor of shape is 3d the type is same with x,
+*out_backprop: Has the same type and format as input "x" and the c dimension is same with x. \n
+
+*@par Attributes
+*@li stride: A required list of 4 ints, specifying the stride of the sliding window. The strides of the N and C
+dimension are 1
+*@li rates: A required list of 4 ints, the rates of the N and C dimensions are 1
+*@li padding_mode: A optional string. Defaults to "SAME", it support SAME and VALID
+*@li pads: A optional list of 4 ints. \n
+
+*@par Outputs:
+*y: The output tensor. Has the same type and format as input "filter" . \n
+
+*@par Third-party framework compatibility
+* Compatible with the TensorFlow operator Dilation2D
+*/
+
+REG_OP(Dilation2DBackpropInput)
+    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .INPUT(filter,
+           TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .INPUT(out_backprop,
+           TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .OUTPUT(y,
+            TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_UINT8, DT_INT16, DT_INT8, DT_UINT16}))
+    .REQUIRED_ATTR(strides, ListInt)
+    .REQUIRED_ATTR(rates, ListInt)
+    .ATTR(padding_mode, String, "SAME")
+    .ATTR(pads, ListInt, {0, 0, 0, 0})
+    .ATTR(ceil_mode, Bool, false)
+    .ATTR(data_format, String, "NHWC")
+    .OP_END_FACTORY_REG(Dilation2DBackpropInput)
+
 /**
 * @brief Applies a 2D adaptive average pooling over  
 *       an input signal composed of several input planes.  \n

third_party/fwkacllib/inc/toolchain/prof_callback.h

@@ -74,6 +74,7 @@ enum MsprofReporterCallbackType {
     MSPROF_REPORTER_REPORT = 0,           // report data
     MSPROF_REPORTER_INIT,                 // init reporter
     MSPROF_REPORTER_UNINIT,               // uninit reporter
+    MSPROF_REPORTER_DATA_MAX_LEN,         // data max length for calling report callback
 };
 
 /**

third_party/fwkacllib/inc/toolchain/prof_reporter.h

@@ -41,42 +41,44 @@ namespace Engine {
  *  the Reporter class .used to send data to profiling
  */
 class MSVP_PROF_API Reporter {
- public:
-  virtual ~Reporter() {}
+public:
+    virtual ~Reporter() {}
 
- public:
-  /**
-   * @ingroup reporter
-   * @name  : Report
-   * @brief : API of libmsprof, report data to libmsprof, it's a non-blocking function \n
-              The data will be firstly appended to cache, if the cache is full, data will be ignored
-   * @param data [IN] const ReporterData * the data send to libmsporf
-   * @retval PROFILING_SUCCESS 0 (success)
-   * @retval PROFILING_FAILED -1 (failed)
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_reporter.h
-   * @since c60
-   * @see Flush
-   */
-  virtual int Report(const ReporterData *data) = 0;
+public:
+    /**
+     * @ingroup reporter
+     * @name  : Report
+     * @brief : API of libmsprof, report data to libmsprof, it's a non-blocking function \n
+                The data will be firstly appended to cache, if the cache is full, data will be ignored
+    * @param data [IN] const ReporterData * the data send to libmsporf
+    * @retval PROFILING_SUCCESS 0 (success)
+    * @retval PROFILING_FAILED -1 (failed)
+    *
+    * @par depend:
+    * @li libmsprof
+    * @li prof_reporter.h
+    * @since c60
+    * @see Flush
+    */
+    virtual int Report(const ReporterData *data) = 0;
 
-  /**
-   * @ingroup reporter
-   * @name  : Flush
-   * @brief : API of libmsprof, notify libmsprof send data over, it's a blocking function \n
-              The all datas of cache will be write to file or send to host
-   * @retval PROFILING_SUCCESS 0 (success)
-   * @retval PROFILING_FAILED -1 (failed)
-   *
-   * @par depend:
-   * @li libmsprof
-   * @li prof_reporter.h
-   * @since c60
-   * @see ProfMgrStop
-   */
-  virtual int Flush() = 0;
+    /**
+     * @ingroup reporter
+     * @name  : Flush
+     * @brief : API of libmsprof, notify libmsprof send data over, it's a blocking function \n
+                The all datas of cache will be write to file or send to host
+    * @retval PROFILING_SUCCESS 0 (success)
+    * @retval PROFILING_FAILED -1 (failed)
+    *
+    * @par depend:
+    * @li libmsprof
+    * @li prof_reporter.h
+    * @since c60
+    * @see ProfMgrStop
+    */
+    virtual int Flush() = 0;
+
+    virtual uint32_t GetReportDataMaxLen() = 0;
 };
 
 }  // namespace Engine