diff --git a/bangc-ops/mlu_op.h b/bangc-ops/mlu_op.h index d1c19436ed..c5ca3c9d77 100644 --- a/bangc-ops/mlu_op.h +++ b/bangc-ops/mlu_op.h @@ -13992,7 +13992,7 @@ mluOpRoiPoolingBackward(mluOpHandle_t handle, * @param[in] handle * Handle to a Cambricon MLUOP context that is used to manage MLU devices and queues in the * sync_batchnorm_stats operation. For detailed information, see ::mluOpHandle_t. - * @param[in] input_desc + * @param[in] x_desc * The descriptor of the input tensor. For detailed information, * see ::mluOpTensorDescriptor_t. * @param[out] workspace_size @@ -14016,7 +14016,7 @@ mluOpRoiPoolingBackward(mluOpHandle_t handle, * * @par Note * - This API is only used along with ::mluOpSyncBatchNormStats_v2. - * - The ::mluOpSyncBatchNormStats does not require this API. + * - ::mluOpSyncBatchNormStats does not require this API. * * @par Example * - None. @@ -14034,8 +14034,8 @@ mluOpGetSyncBatchNormStatsWorkspaceSize(mluOpHandle_t handle, * @brief Computes the local mean and the local inverse standard deviation for each channel * across a batch of data in the training scenario. * - * mluOpSyncBatchNormStats_v2 is used in convolution network, including but not limited to - * ResNet (Deep Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). + * ::mluOpSyncBatchNormStats_v2 is used in convolution network, including but not limited to + * ResNet (Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). * * Compared with ::mluOpSyncBatchNormStats, this function allows you to allocate some extra * workspace as an input parameter. If you just set \b workspace to NULL and \b workspace_size @@ -14050,8 +14050,7 @@ mluOpGetSyncBatchNormStatsWorkspaceSize(mluOpHandle_t handle, * @param[in] x * Pointer to the MLU memory that stores the input tensor \b x. * @param[in] workspace - * Pointer to the MLU memory that is used as an extra workspace for the - * ::mluOpSyncBatchNormStats_v2. + * Pointer to the MLU memory that is used as an extra workspace for ::mluOpSyncBatchNormStats_v2. * @param[in] workspace_size * The size of the extra workspace in bytes that needs to be used in * the ::mluOpSyncBatchNormStats_v2. You can get the size of the workspace with @@ -14082,7 +14081,7 @@ mluOpGetSyncBatchNormStatsWorkspaceSize(mluOpHandle_t handle, * @par Data Layout * - The supported data layout of the input tensor is shown as follows: * - x tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and \p MLUOP_LAYOUT_NLC. - * - The layout of the output tensors are shown as follows: + * - The layout of the output tensors is shown as follows: * - mean tensor: \p MLUOP_LAYOUT_ARRAY. * - invstd tensor: \p MLUOP_LAYOUT_ARRAY. * @@ -14131,7 +14130,7 @@ mluOpSyncBatchNormStats_v2(mluOpHandle_t handle, * across a batch of data in the training scenario. * * SyncBatchnormStats is used in CNN, including but not limited to - * ResNet (Deep Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). + * ResNet (Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). * * @param[in] handle * Handle to a Cambricon MLUOP context that is used to manage MLU devices and queues in the @@ -14167,9 +14166,9 @@ mluOpSyncBatchNormStats_v2(mluOpHandle_t handle, * - half - float - float - float. * * @par Data Layout - * - The supported data layout of the input tensor is shown as following: + * - The supported data layout of the input tensor is shown as follows: * - x tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and \p MLUOP_LAYOUT_NLC. - * - The layout of the output tensors are shown as following: + * - The layout of the output tensors is shown as follows: * - mean tensor: \p MLUOP_LAYOUT_ARRAY. * - invstd tensor: \p MLUOP_LAYOUT_ARRAY. * @@ -14211,7 +14210,7 @@ mluOpSyncBatchNormStats(mluOpHandle_t handle, // Group:SyncBatchNormGatherStatsWithCounts /*! - * @brief Computes the global mean and the global inverse standard deviation across aggragation + * @brief Computes the global mean and the global inverse standard deviation across aggregation * of the local mean and local inverse standard deviation of multiple MLU devices. * * @param[in] handle @@ -14222,13 +14221,13 @@ mluOpSyncBatchNormStats(mluOpHandle_t handle, * The descriptor of the input tensor \b mean_all. For detailed information, see * ::mluOpTensorDescriptor_t. * @param[in] mean_all - * Pointer to the MLU memory that stores the input tensor tensor \b mean_all, which is + * Pointer to the MLU memory that stores the input tensor \b mean_all, which is * the local mean of multiple MLU devices. * @param[in] invstd_all_desc * The descriptor of the input tensor \b invstd_all. For detailed information, see * ::mluOpTensorDescriptor_t. * @param[in] invstd_all - * Pointer to the MLU memory that stores the input tensor tensor \n invstd_all, which + * Pointer to the MLU memory that stores the input tensor \n invstd_all, which * is the local inverse standard deviation of multiple MLU devices. * @param[in] moving_mean_desc * The descriptor of the input tensor \b moving_mean. For detailed information, see @@ -14277,7 +14276,7 @@ mluOpSyncBatchNormStats(mluOpHandle_t handle, * - float - float - half - half - float - float - half - float - float. * * @par Data Layout - * - The supported data layout of the input tensors are shown as the following: + * - The supported data layout of the input tensors is shown as follows: * - mean_all tensor: \p MLUOP_LAYOUT_NC. * - invstd_all tensor: \p MLUOP_LAYOUT_NC. * - moving_mean tensor: \p MLUOP_LAYOUT_ARRAY. @@ -14285,7 +14284,7 @@ mluOpSyncBatchNormStats(mluOpHandle_t handle, * - momentum: Scalar. * - eps: Scalar. * - count_all tensor: \p MLUOP_LAYOUT_ARRAY. - * - The layout of the output tensors are shown as the following: + * - The layout of the output tensors is shown as follows: * - mean tensor: \p MLUOP_LAYOUT_ARRAY. * - invstd tensor: \p MLUOP_LAYOUT_ARRAY. * @@ -14344,7 +14343,7 @@ mluOpSyncBatchNormGatherStatsWithCounts(mluOpHandle_t handle, * inverse variance and scaling factors. * * Batch Normalization is used in artificial intelligence, including but not limited to - * ResNet (Deep Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). + * ResNet (Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). * * @param[in] handle * Handle to a Cambricon MLUOP context that is used to manage MLU devices and queues in the sync batchnorm @@ -14392,7 +14391,7 @@ mluOpSyncBatchNormGatherStatsWithCounts(mluOpHandle_t handle, * - half - float - float - float - float - half. * * @par Data Layout - * - The supported data layout of \b x, \b mean, \b invstd, \b filter, \b bias and \b y are as follows: + * - The supported data layout of \b x, \b mean, \b invstd, \b filter, \b bias and \b y is as follows: * - x tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and \p MLUOP_LAYOUT_NLC. * - mean tensor: \p MLUOP_LAYOUT_ARRAY. * - invstd tensor: \p MLUOP_LAYOUT_ARRAY. @@ -14409,7 +14408,7 @@ mluOpSyncBatchNormGatherStatsWithCounts(mluOpHandle_t handle, * * @par note * - The \b mean, \b invstd, \b filter and \b \b bias must be 1D tensors and the length of their dimensions - * should be the same as the the length of the lowest dimension of \b x. + * should be the same as the length of the lowest dimension of \b x. * - The length of each dimension of \b x and \b y must be the same. * * @par Example @@ -14463,7 +14462,7 @@ mluOpSyncBatchNormElemt(mluOpHandle_t handle, * @param[in] handle * Handle to a Cambricon MLUOP context that is used to manage MLU devices and queues in the mse_loss * operation. For detailed information, see ::mluOpHandle_t. - * @param[in] desc_x + * @param[in] x_desc * The descriptor of the input tensor. For detailed information, see * ::mluOpTensorDescriptor_t. * @param[out] workspace_size @@ -14487,7 +14486,7 @@ mluOpSyncBatchNormElemt(mluOpHandle_t handle, * * @par note * - This API is only used along with ::mluOpSyncBatchnormBackwardReduce_v2. - * - The ::mluOpSyncBatchnormBackwardReduce does not require this API. + * - ::mluOpSyncBatchnormBackwardReduce does not require this API. * * @par Example * - None. @@ -14497,16 +14496,16 @@ mluOpSyncBatchNormElemt(mluOpHandle_t handle, */ mluOpStatus_t MLUOP_WIN_API mluOpGetSyncBatchnormBackwardReduceWorkspaceSize(mluOpHandle_t handle, - const mluOpTensorDescriptor_t desc_x, + const mluOpTensorDescriptor_t x_desc, size_t *workspace_size); // Group:SyncBatchnormBackwardReduce /*! - * @brief Applies Syncronized Batch Normalization Reduce operator to backwardly compute grad + * @brief Applies Synchronized Batch Normalization Reduce operator to backwardly compute grad * filters, grad bias, sum_dy and sum_dy_xmu on each MLU device. * * Batch Normalization is used in convolution network, including but not limited to - * ResNet (Deep Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). + * ResNet (Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). * * Compared with ::mluOpSyncBatchnormBackwardReduce, this function allows you to allocate some extra * workspace as an input parameter. If you just set \b workspace to NULL and \b workspace_size to 0, @@ -14537,7 +14536,7 @@ mluOpGetSyncBatchnormBackwardReduceWorkspaceSize(mluOpHandle_t handle, * Pointer to the MLU memory that stores the tensor \b invstd, which denotes the inversed * standard deviation of input \b x. * @param[in] workspace - * Pointer to the MLU memory that is used as an extra workspace for the + * Pointer to the MLU memory that is used as an extra workspace for * ::mluOpSyncBatchnormBackwardReduce_v2. * @param[in] workspace_size * The size of the extra workspace in bytes that needs to be used in @@ -14596,7 +14595,7 @@ mluOpGetSyncBatchnormBackwardReduceWorkspaceSize(mluOpHandle_t handle, * * @par Data Layout * - The supported data layout of \b dz, \b x, \b mean, \b invstd, \b dfilter, \b dbias, \b sum_dy - * and \b sum_dy_xmu are as follows: + * and \b sum_dy_xmu is as follows: * - dz tensor: \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NLC, \p MLUOP_LAYOUT_NC. * - x tensor: \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NLC, \p MLUOP_LAYOUT_NC. * - mean tensor: \p MLUOP_LAYOUT_ARRAY. @@ -14615,7 +14614,7 @@ mluOpGetSyncBatchnormBackwardReduceWorkspaceSize(mluOpHandle_t handle, * * @par note * - The \b mean, \b invstd, \b dfilter, \b bias, \b sum_dy and \b sum_dy_xmu must be 1D tensors - * and the length of the dimensions of these tensors should be the same as the the length of + * and the length of the dimensions of these tensors should be the same as the length of * the lowest dimension of \b x. * - The length of each dimension of \b x and \b dz must be the same. * @@ -14674,11 +14673,11 @@ mluOpSyncBatchnormBackwardReduce_v2(mluOpHandle_t handle, // Group:SyncBatchnormBackwardReduce /*! - * @brief Applies Syncronized Batch Normalization Reduce operator to backwardly compute grad filters, + * @brief Applies Synchronized Batch Normalization Reduce operator to backwardly compute grad filters, * grad bias, sum_dy and sum_dy_xmu on each MLU device. * * Batch Normalization is used in CNN, including but not limited to - * ResNet (Deep Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). + * ResNet (Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). * * @param[in] handle * Handle to a Cambricon MLUOP context that is used to manage MLU devices and queues in the @@ -14755,7 +14754,7 @@ mluOpSyncBatchnormBackwardReduce_v2(mluOpHandle_t handle, * * @par Data Layout * - The supported data layout of \b dz, \b x, \b mean, \b invstd, \b dfilter, \b dbias, \b sum_dy and - * \b sum_dy_xmu are as follows: + * \b sum_dy_xmu is as follows: * - dz tensor: \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NLC, \p MLUOP_LAYOUT_NC. * - x tensor: \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NLC, \p MLUOP_LAYOUT_NC. * - mean tensor: \p MLUOP_LAYOUT_ARRAY. @@ -14773,7 +14772,7 @@ mluOpSyncBatchnormBackwardReduce_v2(mluOpHandle_t handle, * * @par note * - The \b mean, \b invstd, \b dfilter, \b bias, \b sum_dy and \b sum_dy_xmu must be 1D tensors and the - * length of the dimensions of these tensors should be the same as the the length of the lowest dimension of \b x. + * length of the dimensions of these tensors should be the same as the length of the lowest dimension of \b x. * - The length of each dimension of \b x and \b dz must be the same. * * @par Example @@ -14832,7 +14831,7 @@ mluOpSyncBatchnormBackwardReduce(mluOpHandle_t handle, * @brief Computes the gradients of input in the training scenario. * * This function is used in artificial intelligence, including but not limited - * to ResNet (Deep Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). + * to ResNet (Residual Network), Yolo (You Only Look Once) and R-CNN (Regions with CNN features). * * @param[in] handle * Handle to a Cambricon MLUOP context that is used to manage MLU devices and queues in the @@ -14891,7 +14890,7 @@ mluOpSyncBatchnormBackwardReduce(mluOpHandle_t handle, * * @par Data Layout * - The supported data layout of \b diff_y, \b x, \b mean, \b invstd, \b filter, \b mean_dy, - * \b mean_dy_xmu and \b diff_x are as follows: + * \b mean_dy_xmu and \b diff_x is as follows: * - diff_y tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and * \p MLUOP_LAYOUT_NLC. * - x tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and \p MLUOP_LAYOUT_NLC. @@ -14912,7 +14911,7 @@ mluOpSyncBatchnormBackwardReduce(mluOpHandle_t handle, * * @par note * - The \b mean, \b invstd, \b filter, \b mean_dy and \b mean_dy_xmu must be 1D tensors and the - * length of the dimension of these tensors should be the same as the the length of the lowest + * length of the dimension of these tensors should be the same as the length of the lowest * dimension of \b x. * - The length of each dimension of \b diff_y, \b x and \b diff_x must be the same. * @@ -14959,7 +14958,7 @@ mluOpSyncBatchNormBackwardElemt(mluOpHandle_t handle, /*! * @brief Computes the gradients of input in the training scenario. * - * This function is used in ResNet (Deep Residual Network), Yolo (You Only Look Once) and + * This function is used in ResNet (Residual Network), Yolo (You Only Look Once) and * R-CNN (Regions with CNN features). * * Compared with ::mluOpSyncBatchNormBackwardElemt, this function first computes the intermediate @@ -15028,7 +15027,7 @@ mluOpSyncBatchNormBackwardElemt(mluOpHandle_t handle, * * @par Data Layout * - The supported data layouts of \b diff_y, \b x, \b mean, \b invstd, \b filter, \b sum_dy, - * \b sum_dy_xmu and \b diff_x are as follows: + * \b sum_dy_xmu and \b diff_x is as follows: * - diff_y tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and * \p MLUOP_LAYOUT_NLC. * - x tensor: \p MLUOP_LAYOUT_NHWC, \p MLUOP_LAYOUT_NDHWC, \p MLUOP_LAYOUT_NC and \p MLUOP_LAYOUT_NLC. @@ -15049,7 +15048,7 @@ mluOpSyncBatchNormBackwardElemt(mluOpHandle_t handle, * * @par note * - The \b mean, \b invstd, \b filter, \b sum_dy and \b sum_dy_xmu must be 1D tensors and the - * length of the dimension of these tensors should be the same as the the length of the lowest + * length of the dimension of these tensors should be the same as the length of the lowest * dimension of \b x. * - The length of each dimension of \b diff_y, \b x and \b diff_x must be the same. *