#pragma once
#include <stddef.h>
#include <stdint.h>
#include <stdbool.h>
#include <pthreadpool.h>
#ifdef __cplusplus
extern "C" {
#endif
/**
* @brief Status code for any NNPACK function call.
*/
enum nnp_status {
/** The call succeeded, and all output arguments now contain valid data. */
nnp_status_success = 0,
/** NNPACK function was called with batch_size == 0. */
nnp_status_invalid_batch_size = 2,
/** NNPACK function was called with channels == 0. */
nnp_status_invalid_channels = 3,
/** NNPACK function was called with input_channels == 0. */
nnp_status_invalid_input_channels = 4,
/** NNPACK function was called with output_channels == 0. */
nnp_status_invalid_output_channels = 5,
/** NNPACK function was called with input_size.height == 0 or input_size.width == 0 */
nnp_status_invalid_input_size = 10,
/** NNPACK function was called with input_stride.height == 0 or input_stride.width == 0 */
nnp_status_invalid_input_stride = 11,
/** NNPACK function was called with input_padding not less than respective kernel (or pooling) size, i.e.:
*
* - input_padding.left >= kernel_size.width (>= pooling_size.width)
* - input_padding.right >= kernel_size.width (>= pooling_size.width)
* - input_padding.top >= kernel_size.height (>= pooling_size.height)
* - input_padding.bottom >= kernel_size.height (>= pooling_size.height)
*/
nnp_status_invalid_input_padding = 12,
/** NNPACK function was called with kernel_size.height == 0 or kernel_size.width == 0 */
nnp_status_invalid_kernel_size = 13,
/** NNPACK function was called with pooling_size.height == 0 or pooling_size.width == 0 */
nnp_status_invalid_pooling_size = 14,
/** NNPACK function was called with pooling_stride.height == 0 or pooling_stride.width == 0 */
nnp_status_invalid_pooling_stride = 15,
/** NNPACK function was called with convolution algorithm not in nnp_convolution_algorithm enumeration */
nnp_status_invalid_algorithm = 16,
/** NNPACK function was called with convolution transform strategy not in nnp_convolution_transform_strategy enum */
nnp_status_invalid_transform_strategy = 17,
/** NNPACK function was called with output_subsampling.height == 0 or output_subsampling.width == 0 */
nnp_status_invalid_output_subsampling = 13,
/** NNPACK function was called with activation not in nnp_activation enum */
nnp_status_invalid_activation = 14,
/** NNPACK function was called with invalid activation parameters */
nnp_status_invalid_activation_parameters = 15,
/** NNPACK does not support the particular input size for the function */
nnp_status_unsupported_input_size = 20,
/** NNPACK does not support the particular input stride for the function */
nnp_status_unsupported_input_stride = 21,
/** NNPACK does not support the particular input padding for the function */
nnp_status_unsupported_input_padding = 22,
/** NNPACK does not support the particular kernel size for the function */
nnp_status_unsupported_kernel_size = 23,
/** NNPACK does not support the particular pooling size for the function */
nnp_status_unsupported_pooling_size = 24,
/** NNPACK does not support the particular pooling stride for the function */
nnp_status_unsupported_pooling_stride = 25,
/** NNPACK does not support the particular convolution algorithm for the function */
nnp_status_unsupported_algorithm = 26,
/** NNPACK does not support the particular convolution transform strategy for the algorithm */
nnp_status_unsupported_transform_strategy = 27,
/** NNPACK does not support the particular activation function for the function */
nnp_status_unsupported_activation = 28,
/** NNPACK does not support the particular activation function parameters for the function */
nnp_status_unsupported_activation_parameters = 29,
/** NNPACK function was called before the library was initialized */
nnp_status_uninitialized = 50,
/** NNPACK does not implement this function for the host CPU */
nnp_status_unsupported_hardware = 51,
/** NNPACK failed to allocate memory for temporary buffers */
nnp_status_out_of_memory = 52,
/** Scratch space buffer is too small */
nnp_status_insufficient_buffer = 53,
/** Scratch space buffer is not properly aligned */
nnp_status_misaligned_buffer = 54
};
/**
* @brief Activation applied applied after a convolutional or fully-connected layer.
*/
enum nnp_activation {
/** Identity activation f(x) := x, i.e. no transformation */
nnp_activation_identity = 0,
/** ReLU activation f(x) := max(0, x) */
nnp_activation_relu = 1,
};
/**
* @brief Algorithm for computing convolutional layers.
*/
enum nnp_convolution_algorithm {
/** Let NNPACK choose the algorithm depending on layer parameters */
nnp_convolution_algorithm_auto = 0,
/** Tiled convolution based on 2D Fourier transform with 8x8 blocks. Supports kernels up to 8x8. */
nnp_convolution_algorithm_ft8x8 = 1,
/** Tiled convolution based on 2D Fourier transform with 16x16 blocks. Supports kernels up to 16x16. */
nnp_convolution_algorithm_ft16x16 = 2,
/** Tiled convolution based on 2D Winograd transform F(3x3, 6x6) with 8x8 blocks. Supports only 3x3 kernels. */
nnp_convolution_algorithm_wt8x8 = 3,
/** Direct convolution via implicit GEMM. */
nnp_convolution_algorithm_implicit_gemm = 4,
/** Direct convolution implementation. */
nnp_convolution_algorithm_direct = 5,
/**
* Tiled convolution based on 2D Winograd transform F(3x3, 6x6) with 8x8 blocks in FP16.
* Supports only 3x3 kernels. Implemented only for new ARM processors (with NEON-HP),
* on non-supported processors falls back to nnp_convolution_algorithm_wt8x8.
*/
nnp_convolution_algorithm_wt8x8_fp16 = 6,
};
enum nnp_convolution_transform_strategy {
nnp_convolution_transform_strategy_compute = 1,
nnp_convolution_transform_strategy_precompute = 2,
nnp_convolution_transform_strategy_reuse = 3
};
/* For backward compatibility */
#define nnp_convolution_transform_strategy_block_based nnp_convolution_transform_strategy_compute
#define nnp_convolution_transform_strategy_tuple_based nnp_convolution_transform_strategy_compute
/**
* @brief Size of images, kernels, and pooling filters in NNPACK.
*/
struct nnp_size {
/** Width (horizontal size) of an image, kernel, or pooling filter. */
size_t width;
/** Height (vertical size) of an image, kernel, or pooling filter. */
size_t height;
};
/**
* @brief Padding of images in NNPACK.
*/
struct nnp_padding {
/** Padding above the image data */
size_t top;
/** Padding on the right of image data */
size_t right;
/** Padding below the image data */
size_t bottom;
/** Padding on the left of image data */
size_t left;
};
/**
* @brief Profiling information about time spent in different phases of a function call.
*/
struct nnp_profile {
/** Time spent inside the function call, in seconds. */
double total;
/** Time spend on transformation of the input or input gradient tensor, in seconds. */
double input_transform;
/** Time spend on transformation of the kernel or kernel gradient tensor, in seconds. */
double kernel_transform;
/** Time spend on transformation of the output or output gradient tensor, in seconds. */
double output_transform;
/** Time spend on multiplication-accumulation of transformed coefficients, in seconds. */
double block_multiplication;
};
enum nnp_status nnp_initialize(void);
enum nnp_status nnp_deinitialize(void);
/**
* @brief Computes output of a 2D convolutional layer from input and kernel tensors.
* @details This function targets training of convolutional neural networks and performs forward propagation.
* It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
* For minibatch size 1, use nnp_convolution_inference for optimal performance.
* @param algorithm The type of algorithm to use for convolution. Possible values are:
*
* - nnp_convolution_algorithm_auto -- let the function choose the algorithm.
* - nnp_convolution_algorithm_ft8x8 -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
* Supports kernels up to 8x8.
* - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
* Supports kernels up to 16x16.
* - nnp_convolution_algorithm_wt8x8 -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
* Supports only 3x3 kernels.
*
* @param batch_size The number of images on the input and output of the convolutional layer.
* @param input_channels The number of channels (AKA features, dimensions) in the input images.
* @param output_channels The number of channels (AKA features, dimensions) in the output images.
* @param input_size Size of input images, excluding implicit zero-padding.
* @param input_padding Implicit zero-padding of input images.
* @param kernel_size Kernel size.
* @param[in] input A 4D tensor input[batch_size][input_channels][input_size.height][input_size.width].
* @param[in] kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
* @param[in] bias A 1D array bias[output_channels].
* @param[out] output A 4D tensor output[batch_size][output_channels][output_size.height][output_size.width] where
* output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
* (kernel_size.height - 1)
* output_size.width = (input_padding.left + input_size.width + input_padding.right) -
* (kernel_size.width - 1)
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
* @param[out] profile An optional pointer to profiling structure.
* If provided, the structure would record time spent in different phases of the computation.
*/
enum nnp_status nnp_convolution_output(
enum nnp_convolution_algorithm algorithm,
size_t batch_size,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
const float* input,
const float* kernel,
const float* bias,
float* output,
void* workspace_buffer,
size_t* workspace_size,
enum nnp_activation activation,
const void* activation_parameters,
pthreadpool_t threadpool,
struct nnp_profile* profile);
/**
* @brief Computes gradient of input of a 2D convolutional layer from gradient of output and kernel tensors.
* @details This function targets training of convolutional neural networks and performs backward propagation.
* It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
* @param algorithm The type of algorithm to use for convolution. Possible values are:
*
* - nnp_convolution_algorithm_auto -- let the function choose the algorithm.
* - nnp_convolution_algorithm_ft8x8 -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
* Supports kernels up to 8x8.
* - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
* Supports kernels up to 16x16.
* - nnp_convolution_algorithm_wt8x8 -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
* Supports only 3x3 kernels.
*
* @param batch_size The number of images (and their gradients) on the input and output of the convolutional layer.
* @param input_channels The number of channels (AKA features, dimensions) in the input images (and gradients).
* @param output_channels The number of channels (AKA features, dimensions) in the output images (and gradients).
* @param input_size Size of input images and their gradients, excluding implicit zero-padding.
* @param input_padding Implicit zero-padding of input images.
* @param kernel_size Kernel size.
* @param[in] grad_output A 4D tensor grad_output[batch_size][output_channels][output_size.height][output_size.width]
* where
* output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
* (kernel_size.height - 1)
* output_size.width = (input_padding.left + input_size.width + input_padding.right) -
* (kernel_size.width - 1)
* @param[in] kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
* @param[out] grad_input A 4D tensor grad_input[batch_size][input_channels][input_size.height][input_size.width].
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
* @param[out] profile An optional pointer to profiling structure.
* If provided, the structure would record time spent in different phases of the computation.
*/
enum nnp_status nnp_convolution_input_gradient(
enum nnp_convolution_algorithm algorithm,
size_t batch_size,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
const float* grad_output,
const float* kernel,
float* grad_input,
void* workspace_buffer,
size_t* workspace_size,
enum nnp_activation activation,
const void* activation_parameters,
pthreadpool_t threadpool,
struct nnp_profile* profile);
/**
* @brief Computes gradient of kernel of a 2D convolutional layer from gradient of output and input tensors.
* @details This function targets training of convolutional neural networks and performs backward propagation.
* It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
* @param algorithm The type of algorithm to use for convolution. Possible values are:
*
* - nnp_convolution_algorithm_auto -- let the function choose the algorithm.
* - nnp_convolution_algorithm_ft8x8 -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
* Supports kernels up to 8x8.
* - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
* Supports kernels up to 16x16.
*
* @param batch_size The number of images (and their gradients) on the input and output of the convolutional layer.
* @param input_channels The number of channels (AKA features, dimensions) in the input images.
* @param output_channels The number of channels (AKA features, dimensions) in the output images (and gradients).
* @param input_size Size of input images and their gradients, excluding implicit zero-padding.
* @param input_padding Implicit zero-padding of input images.
* @param kernel_size Kernel size.
* @param[in] input A 4D tensor input[batch_size][input_channels][input_size.height][input_size.width].
* @param[in] grad_output A 4D tensor grad_output[batch_size][output_channels][output_size.height][output_size.width]
* where
* output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
* (kernel_size.height - 1)
* output_size.width = (input_padding.left + input_size.width + input_padding.right) -
* (kernel_size.width - 1)
* @param[out] grad_kernel A 4D tensor
* grad_kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
* @param[out] profile An optional pointer to profiling structure.
* If provided, the structure would record time spent in different phases of the computation.
*/
enum nnp_status nnp_convolution_kernel_gradient(
enum nnp_convolution_algorithm algorithm,
size_t batch_size,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
const float* input,
const float* grad_output,
float* grad_kernel,
void* workspace_buffer,
size_t* workspace_size,
enum nnp_activation activation,
const void* activation_parameters,
pthreadpool_t threadpool,
struct nnp_profile* profile);
/**
* @brief Computes output of a 2D convolutional layer for a single input image and a kernel tensor.
* @details This function targets prediction with convolutional neural networks and performs forward propagation.
* @param algorithm The type of algorithm to use for convolution. Possible values are:
*
* - nnp_convolution_algorithm_auto -- let the function choose the algorithm.
* - nnp_convolution_algorithm_ft8x8 -- tiled convolution based on 2D Fourier transform with 8x8 blocks.
* Supports kernels up to 8x8.
* - nnp_convolution_algorithm_ft16x16 -- tiled convolution based on 2D Fourier transform with 16x16 blocks.
* Supports kernels up to 16x16.
* - nnp_convolution_algorithm_wt8x8 -- tiled convolution based on 2D Winograd transform F(3x3, 6x6).
* Supports only 3x3 kernels.
*
* @param transform_strategy A strategy that guides computation of kernel transforms coefficients.
* Possible values are:
*
* - nnp_convolution_transform_strategy_block_based -- do multiplication-accumulations on blocks of transformed
* coefficients.
* - nnp_convolution_transform_strategy_tuple_based -- do multiplication-accumulations on tuples of transformed
* coefficients.
*
* @param input_channels The number of channels (AKA features, dimensions) in the input image.
* @param output_channels The number of channels (AKA features, dimensions) in the output image.
* @param input_size Size of input image, excluding implicit zero-padding.
* @param input_padding Implicit zero-padding of input image.
* @param kernel_size Kernel size.
* @param output_subsampling Subsample region for output, also known as convolution stride.
* @param[in] input A 3D tensor input[input_channels][input_size.height][input_size.width].
* @param[in] kernel A 4D tensor kernel[output_channels][input_channels][kernel_size.height][kernel_size.width].
* @param[in] bias A 1D array bias[output_channels].
* @param[out] output A 3D tensor output[output_channels][output_size.height][output_size.width] where
* output_size.height = (input_padding.top + input_size.height + input_padding.bottom) -
* (kernel_size.height - 1)
* output_size.width = (input_padding.left + input_size.width + input_padding.right) -
* (kernel_size.width - 1)
* @param[in] workspace_buffer Buffer for scratch memory used during computation. Buffer must be aligned on 64 bytes.
* If workspace_buffer is NULL and workspace_size is non-NULL, NNPACK would store the size
* of required workspace memory at the workspace_size location, and exit without
* computations.
* If workspace_buffer is NULL and workspace_size is NULL, NNPACK would allocate memory
* before and deallocate after this computation, potentially at significant runtime cost.
* @param[in,out] workspace_size Pointer to the size of workspace buffer.
* If workspace_buffer is NULL, NNPACK will write the size of required scratch memory to
* the location specified by this pointer.
* If workspace_buffer is non-NULL, NNPACK expects workspace_size to specify the size of
* the buffer, in bytes.
* If workspace_size is NULL, workspace_buffer must be NULL as well. In this case NNPACK
* would allocate memory before and deallocate after this computation, potentially at
* significant runtime cost.
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
* @param[out] profile An optional pointer to profiling structure.
* If provided, the structure would record time spent in different phases of the computation.
*/
enum nnp_status nnp_convolution_inference(
enum nnp_convolution_algorithm algorithm,
enum nnp_convolution_transform_strategy transform_strategy,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
struct nnp_size output_subsampling,
const float* input,
const float* kernel,
const float* bias,
float* output,
void* workspace_buffer,
size_t* workspace_size,
enum nnp_activation activation,
const void* activation_parameters,
pthreadpool_t threadpool,
struct nnp_profile* profile);
/**
* @brief Computes output of a fully connected layer from input and kernel matrices.
* @details This function targets training of convolutional neural networks and performs forward propagation.
* It is optimized for moderate minibatch sizes (64-128) and can be inefficient on a small minibatch.
* For minibatch size 1, use nnp_fully_connected_inference for optimal performance.
* @param batch_size The number of vectors on the input and output of the fully connected layer.
* @param input_channels The number of channels (AKA features, dimensions) in the input matrix.
* @param output_channels The number of channels (AKA features, dimensions) in the output matrix.
* @param[in] input A 2D matrix input[batch_size][input_channels].
* @param[in] kernel A 2D matrix kernel[output_channels][input_channels].
* @param[out] output A 2D matrix output[batch_size][output_channels].
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_fully_connected_output(
size_t batch_size,
size_t input_channels,
size_t output_channels,
const float input[],
const float kernel[],
float output[],
pthreadpool_t threadpool,
struct nnp_profile* profile);
/**
* @brief Computes output of a fully connected layer for a single input vector and a kernel matrix.
* @details This function targets prediction with convolutional neural networks and performs forward propagation.
* @param input_channels The number of channels (AKA features, dimensions) in the input vector.
* @param output_channels The number of channels (AKA features, dimensions) in the output vector.
* @param[in] input A 1D array input[input_channels] of FP32 elements.
* @param[in] kernel A 2D matrix kernel[output_channels][input_channels] of FP32 elements.
* @param[out] output A 1D array output[output_channels] of FP32 elements.
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_fully_connected_inference(
size_t input_channels,
size_t output_channels,
const float* input,
const float* kernel,
float* output,
pthreadpool_t threadpool);
/**
* @brief Computes output of a fully connected layer for a single input vector and a kernel matrix.
* @details This function targets prediction with convolutional neural networks and performs forward propagation.
* @param input_channels The number of channels (AKA features, dimensions) in the input vector.
* @param output_channels The number of channels (AKA features, dimensions) in the output vector.
* @param[in] input A 1D array input[input_channels] of FP32 elements.
* @param[in] kernel A 2D matrix kernel[output_channels][input_channels] of FP16 (ARM alternative format) elements.
* @param[out] output A 1D array output[output_channels] of FP32 elements.
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_fully_connected_inference_f16f32(
size_t input_channels,
size_t output_channels,
const float* input,
const void* kernel,
float* output,
pthreadpool_t threadpool);
/**
* @brief Computes output of a max-pooling layer for an input tensor.
* @details This function targets both prediction and training of convolutional neural networks and performs forward
* propagation. Is is optimized for both large and small minibatch sizes.
* @param batch_size The number of images on the input and output of the max-pooling layer.
* @param channels The number of channels (AKA features, dimensions) in both input and output images.
* @param input_size Size of input images, excluding implicit zero-padding.
* @param input_padding Implicit padding of input images. The padding pixels are ignored by the pooling filter, but
* affect the output size.
* @param pooling_size Size of the pooling filter. Only 2x2 filter are currently supported.
* @param pooling_stride Stride of the pooling filter. Only 2x2 strides are currently supported.
* @param[in] input A 4D tensor input[batch_size][channels][input_size.height][input_size.width].
* @param[out] output A 4D tensor output[batch_size][channels][output_size.height][output_size.width] where
* output_size.height = ceil(
* (input_padding.top + input_size.height + input_padding.bottom - pooling_size.height) /
* pooling_stride.height) + 1
* output_size.width = ceil(
* (input_padding.left + input_size.width + input_padding.right - pooling_size.width) /
* pooling_stride.width) + 1
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_max_pooling_output(
size_t batch_size,
size_t channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size pooling_size,
struct nnp_size pooling_stride,
const float input[],
float output[],
pthreadpool_t threadpool);
/**
* @brief Computes output of a softmax layer for an input matrix.
* @details This function targets both prediction and training of convolutional neural networks and performs forward
* propagation. Is is optimized for both large and small minibatch sizes.
* @param batch_size The number of vectors on the input and output of the softmax layer.
* @param channels The number of channels (AKA features, dimensions) in both input and output vectors.
* @param[in] input A 2D matrix input[batch_size][channels].
* @param[out] output A 2D matrix output[batch_size][channels].
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_softmax_output(
size_t batch_size,
size_t channels,
const float input[],
float output[],
pthreadpool_t threadpool);
/**
* @brief Computes output of a rectified linear unit (ReLU) layer for an input matrix.
* @details This function targets both prediction and training of convolutional neural networks and performs forward
* propagation. Is is optimized for both large and small minibatch sizes.
* @param batch_size The number of vectors on the input and output of the ReLU layer.
* @param channels The number of channels (AKA features, dimensions) in both input and output matrices.
* @param[in] input A 2D matrix input[batch_size][channels].
* @param[out] output A 2D matrix output[batch_size][channels].
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_relu_output(
size_t batch_size,
size_t channels,
const float input[],
float output[],
float negative_slope,
pthreadpool_t threadpool);
/**
* @brief Computes gradient of input of a rectified linear unit (ReLU) layer from gradient of output and input matrices.
* @details This function targets training of convolutional neural networks and performs backward propagation.
* Is is optimized for both large and small minibatch sizes.
* @param batch_size The number of vectors on the input and output of the ReLU layer.
* @param channels The number of channels (AKA features, dimensions) in both input and output matrices.
* @param[in] input A 2D matrix input[batch_size][channels].
* @param[out] output A 2D matrix output[batch_size][channels].
* @param threadpool A thread pool for parallelization of the computation.
* If threadpool is NULL, the computation would run on the caller thread without parallelization.
*/
enum nnp_status nnp_relu_input_gradient(
size_t batch_size,
size_t channels,
const float grad_output[],
const float input[],
float grad_input[],
float negative_slope,
pthreadpool_t threadpool);
#ifdef __cplusplus
} /* extern "C" */
#endif
#ifdef __cplusplus
// Backward compatible implementations for nnp_convolution_*, if we are in C++
// mode.
inline enum nnp_status nnp_convolution_output(
enum nnp_convolution_algorithm algorithm,
size_t batch_size,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
const float input[],
const float kernel[],
const float bias[],
float output[],
pthreadpool_t threadpool,
struct nnp_profile* profile)
{
return nnp_convolution_output(
algorithm,
batch_size, input_channels, output_channels,
input_size, input_padding, kernel_size,
input, kernel, bias, output,
NULL, NULL,
nnp_activation_identity, NULL, threadpool, profile);
}
inline enum nnp_status nnp_convolution_input_gradient(
enum nnp_convolution_algorithm algorithm,
size_t batch_size,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
const float grad_output[],
const float kernel[],
float grad_input[],
pthreadpool_t threadpool,
struct nnp_profile* profile)
{
return nnp_convolution_input_gradient(
algorithm,
batch_size, input_channels, output_channels,
input_size, input_padding, kernel_size,
grad_output, kernel, grad_input,
NULL, NULL,
nnp_activation_identity, NULL, threadpool, profile);
}
inline enum nnp_status nnp_convolution_kernel_gradient(
enum nnp_convolution_algorithm algorithm,
size_t batch_size,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
const float input[],
const float grad_output[],
float grad_kernel[],
pthreadpool_t threadpool,
struct nnp_profile* profile)
{
return nnp_convolution_kernel_gradient(
algorithm,
batch_size, input_channels, output_channels,
input_size, input_padding, kernel_size,
input, grad_output, grad_kernel,
NULL, NULL,
nnp_activation_identity, NULL, threadpool, profile);
}
inline enum nnp_status nnp_convolution_inference(
enum nnp_convolution_algorithm algorithm,
enum nnp_convolution_transform_strategy transform_strategy,
size_t input_channels,
size_t output_channels,
struct nnp_size input_size,
struct nnp_padding input_padding,
struct nnp_size kernel_size,
struct nnp_size output_subsampling,
const float input[],
const float kernel[],
const float bias[],
float output[],
pthreadpool_t threadpool,
struct nnp_profile* profile) {
return nnp_convolution_inference(
algorithm, transform_strategy,
input_channels, output_channels,
input_size, input_padding, kernel_size, output_subsampling,
input, kernel, bias, output, NULL, NULL,
nnp_activation_identity, NULL,
threadpool, profile);
}
#endif // __cplusplus