337 lines
14 KiB
C
337 lines
14 KiB
C
|
#ifndef DL_LIB_H
|
||
|
|
#define DL_LIB_H
|
||
|
|
#ifdef __cplusplus
|
||
|
|
extern "C" {
|
||
|
|
#endif
|
||
|
|
|
||
|
|
#include "dl_lib_matrix.h"
|
||
|
|
#include "dl_lib_matrixq.h"
|
||
|
|
#include "dl_lib_matrix3d.h"
|
||
|
|
#include "dl_lib_matrix3dq.h"
|
||
|
|
|
||
|
|
typedef int padding_state;
|
||
|
|
/**
|
||
|
|
* @brief Does a fast version of the exp() operation on a floating point number.
|
||
|
|
*
|
||
|
|
* As described in https://codingforspeed.com/using-faster-exponential-approximation/
|
||
|
|
* Should be good til an input of 5 or so with a steps factor of 8.
|
||
|
|
*
|
||
|
|
* @param in Floating point input
|
||
|
|
* @param steps Approximation steps. More is more precise. 8 or 10 should be good enough for most purposes.
|
||
|
|
* @return Exp()'ed output
|
||
|
|
*/
|
||
|
|
fptp_t fast_exp(double x, int steps);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a softmax operation on a matrix.
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so,
|
||
|
|
output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_softmax(const dl_matrix2d_t *in,
|
||
|
|
dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a softmax operation on a quantized matrix.
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_softmax_q(const dl_matrix2dq_t *in, dl_matrix2dq_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a sigmoid operation on a floating point number
|
||
|
|
*
|
||
|
|
* @param in Floating point input
|
||
|
|
* @return Sigmoid output
|
||
|
|
*/
|
||
|
|
fptp_t dl_sigmoid_op(fptp_t in);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a sigmoid operation on a matrix.
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_sigmoid(const dl_matrix2d_t *in, dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a tanh operation on a floating point number
|
||
|
|
*
|
||
|
|
* @param in Floating point input number
|
||
|
|
* @return Tanh value
|
||
|
|
*/
|
||
|
|
fptp_t dl_tanh_op(fptp_t v);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a tanh operation on a matrix.
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_tanh(const dl_matrix2d_t *in, dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a relu (Rectifier Linear Unit) operation on a floating point number
|
||
|
|
*
|
||
|
|
* @param in Floating point input
|
||
|
|
* @param clip If value is higher than this, it will be clipped to this value
|
||
|
|
* @return Relu output
|
||
|
|
*/
|
||
|
|
fptp_t dl_relu_op(fptp_t in, fptp_t clip);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a ReLu operation on a matrix.
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param clip If values are higher than this, they will be clipped to this value
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_relu(const dl_matrix2d_t *in, fptp_t clip, dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Fully connected layer operation
|
||
|
|
*
|
||
|
|
* @param in Input vector
|
||
|
|
* @param weight Weights of the neurons
|
||
|
|
* @param bias Biases for the neurons. Can be NULL if a bias of 0 is required.
|
||
|
|
* @param out Output array. Outputs are placed here. Needs to be an initialized, weight->w by in->h in size, matrix.
|
||
|
|
*/
|
||
|
|
void dl_fully_connect_layer(const dl_matrix2d_t *in,
|
||
|
|
const dl_matrix2d_t *weight,
|
||
|
|
const dl_matrix2d_t *bias,
|
||
|
|
dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Pre-calculate the sqrtvari variable for the batch_normalize function.
|
||
|
|
* The sqrtvari matrix depends on the variance and epsilon values, which normally are constant. Hence,
|
||
|
|
* this matrix only needs to be calculated once. This function does that.
|
||
|
|
*
|
||
|
|
* @param
|
||
|
|
* @return
|
||
|
|
*/
|
||
|
|
void dl_batch_normalize_get_sqrtvar(const dl_matrix2d_t *variance,
|
||
|
|
fptp_t epsilon,
|
||
|
|
dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Batch-normalize a matrix
|
||
|
|
*
|
||
|
|
* @param m The matrix to normalize
|
||
|
|
* @param offset Offset matrix
|
||
|
|
* @param scale Scale matrix
|
||
|
|
* @param mean Mean matrix
|
||
|
|
* @param sqrtvari Matrix precalculated using dl_batch_normalize_get_sqrtvar
|
||
|
|
* @return
|
||
|
|
*/
|
||
|
|
void dl_batch_normalize(dl_matrix2d_t *m,
|
||
|
|
const dl_matrix2d_t *offset,
|
||
|
|
const dl_matrix2d_t *scale,
|
||
|
|
const dl_matrix2d_t *mean,
|
||
|
|
const dl_matrix2d_t *sqrtvari);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Do a basic LSTM layer pass.
|
||
|
|
*
|
||
|
|
* @warning Returns state_h pointer, so do not free result.
|
||
|
|
|
||
|
|
* @param in Input vector
|
||
|
|
* @param state_c Internal state of the LSTM network
|
||
|
|
* @param state_h Internal state (previous output values) of the LSTM network
|
||
|
|
* @param weights Weights for the neurons
|
||
|
|
* @param bias Bias for the neurons. Can be NULL if no bias is required
|
||
|
|
* @return Output values of the neurons
|
||
|
|
*/
|
||
|
|
dl_matrix2d_t *dl_basic_lstm_layer(const dl_matrix2d_t *in,
|
||
|
|
dl_matrix2d_t *state_c,
|
||
|
|
dl_matrix2d_t *state_h,
|
||
|
|
const dl_matrix2d_t *weight,
|
||
|
|
const dl_matrix2d_t *bias);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Do a basic LSTM layer pass, partial quantized version.
|
||
|
|
* This LSTM function accepts 16-bit fixed-point weights and 32-bit float-point bias.
|
||
|
|
*
|
||
|
|
* @warning Returns state_h pointer, so do not free result.
|
||
|
|
|
||
|
|
* @param in Input vector
|
||
|
|
* @param state_c Internal state of the LSTM network
|
||
|
|
* @param state_h Internal state (previous output values) of the LSTM network
|
||
|
|
* @param weights Weights for the neurons, need to be quantised
|
||
|
|
* @param bias Bias for the neurons. Can be NULL if no bias is required
|
||
|
|
* @return Output values of the neurons
|
||
|
|
*/
|
||
|
|
dl_matrix2d_t *dl_basic_lstm_layer_quantised_weights(const dl_matrix2d_t *in,
|
||
|
|
dl_matrix2d_t *state_c,
|
||
|
|
dl_matrix2d_t *state_h,
|
||
|
|
const dl_matrix2dq_t *weight,
|
||
|
|
const dl_matrix2d_t *bias);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Do a fully-connected layer pass, fully-quantized version.
|
||
|
|
*
|
||
|
|
* @param in Input vector
|
||
|
|
* @param weight Weights of the neurons
|
||
|
|
* @param bias Bias values of the neurons. Can be NULL if no bias is needed.
|
||
|
|
* @param shift Number of bits to shift the result back by. See dl_lib_matrixq.h for more info
|
||
|
|
* @return Output values of the neurons
|
||
|
|
*/
|
||
|
|
void dl_fully_connect_layer_q(const dl_matrix2dq_t *in,
|
||
|
|
const dl_matrix2dq_t *weight,
|
||
|
|
const dl_matrix2dq_t *bias,
|
||
|
|
dl_matrix2dq_t *out,
|
||
|
|
int shift);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Do a basic LSTM layer pass, fully-quantized version
|
||
|
|
*
|
||
|
|
* @warning Returns state_h pointer, so do not free result.
|
||
|
|
|
||
|
|
* @param in Input vector
|
||
|
|
* @param state_c Internal state of the LSTM network
|
||
|
|
* @param state_h Internal state (previous output values) of the LSTM network
|
||
|
|
* @param weights Weights for the neurons
|
||
|
|
* @param bias Bias for the neurons. Can be NULL if no bias is required
|
||
|
|
* @param shift Number of bits to shift the result back by. See dl_lib_matrixq.h for more info
|
||
|
|
* @return Output values of the neurons
|
||
|
|
*/
|
||
|
|
dl_matrix2dq_t *dl_basic_lstm_layer_q(const dl_matrix2dq_t *in,
|
||
|
|
dl_matrix2dq_t *state_c,
|
||
|
|
dl_matrix2dq_t *state_h,
|
||
|
|
const dl_matrix2dq_t *weight,
|
||
|
|
const dl_matrix2dq_t *bias,
|
||
|
|
int shift);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Batch-normalize a matrix, fully-quantized version
|
||
|
|
*
|
||
|
|
* @param m The matrix to normalize
|
||
|
|
* @param offset Offset matrix
|
||
|
|
* @param scale Scale matrix
|
||
|
|
* @param mean Mean matrix
|
||
|
|
* @param sqrtvari Matrix precalculated using dl_batch_normalize_get_sqrtvar
|
||
|
|
* @param shift Number of bits to shift the result back by. See dl_lib_matrixq.h for more info
|
||
|
|
* @return
|
||
|
|
*/
|
||
|
|
void dl_batch_normalize_q(dl_matrix2dq_t *m,
|
||
|
|
const dl_matrix2dq_t *offset,
|
||
|
|
const dl_matrix2dq_t *scale,
|
||
|
|
const dl_matrix2dq_t *mean,
|
||
|
|
const dl_matrix2dq_t *sqrtvari,
|
||
|
|
int shift);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a relu (Rectifier Linear Unit) operation on a fixed-point number
|
||
|
|
* This accepts and returns fixed-point 32-bit number with the last 15 bits being the bits after the decimal
|
||
|
|
* point. (Equivalent to a mantissa in a quantized matrix with exponent -15.)
|
||
|
|
*
|
||
|
|
* @param in Fixed-point input
|
||
|
|
* @param clip If value is higher than this, it will be clipped to this value
|
||
|
|
* @return Relu output
|
||
|
|
*/
|
||
|
|
qtp_t dl_relu_q_op(qtp_t in,
|
||
|
|
qtp_t clip);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a ReLu operation on a matrix, quantized version
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param clip If values are higher than this, they will be clipped to this value
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_relu_q(const dl_matrix2dq_t *in,
|
||
|
|
fptp_t clip,
|
||
|
|
dl_matrix2dq_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a sigmoid operation on a fixed-point number.
|
||
|
|
* This accepts and returns a fixed-point 32-bit number with the last 15 bits being the bits after the decimal
|
||
|
|
* point. (Equivalent to a mantissa in a quantized matrix with exponent -15.)
|
||
|
|
*
|
||
|
|
* @param in Fixed-point input
|
||
|
|
* @return Sigmoid output
|
||
|
|
*/
|
||
|
|
int dl_sigmoid_op_q(const int in);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a sigmoid operation on a matrix, quantized version
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_sigmoid_q(const dl_matrix2dq_t *in,
|
||
|
|
dl_matrix2dq_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Does a tanh operation on a matrix, quantized version
|
||
|
|
*
|
||
|
|
* @param in Input matrix
|
||
|
|
* @param out Output matrix. Can be the same as the input matrix; if so, output results overwrite the input.
|
||
|
|
*/
|
||
|
|
void dl_tanh_q(const dl_matrix2dq_t *in,
|
||
|
|
dl_matrix2dq_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Do a basic CNN layer pass.
|
||
|
|
*
|
||
|
|
* @Warning This just supports the single channel input image, and the output is single row matrix.
|
||
|
|
That is to say, the height of output is 1, and the weight of output is out_channels*out_image_width*out_image_height
|
||
|
|
*
|
||
|
|
* @param in Input single channel image
|
||
|
|
* @param weight Weights of the neurons, weight->w = out_channels, weight->h = filter_width*filter_height
|
||
|
|
* @param bias Bias for the CNN layer.
|
||
|
|
* @param filter_height The height of convolution kernel
|
||
|
|
* @param filter_width The width of convolution kernel
|
||
|
|
* @param out_channels The number of output channels of convolution kernel
|
||
|
|
* @param stride_x The step length of the convolution window in x(width) direction
|
||
|
|
* @param stride_y The step length of the convolution window in y(height) direction
|
||
|
|
* @param pad One of `"VALID"` or `"SAME"`, 0 is "VALID" and the other is "SAME"
|
||
|
|
* @param out The result of CNN layer, out->h=1.
|
||
|
|
* @return The result of CNN layer.
|
||
|
|
*/
|
||
|
|
dl_matrix2d_t *dl_basic_conv_layer(const dl_matrix2d_t *in,
|
||
|
|
const dl_matrix2d_t *weight,
|
||
|
|
const dl_matrix2d_t *bias,
|
||
|
|
int filter_width,
|
||
|
|
int filter_height,
|
||
|
|
const int out_channels,
|
||
|
|
const int stride_x,
|
||
|
|
const int stride_y,
|
||
|
|
padding_state pad,
|
||
|
|
const dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
/**
|
||
|
|
* @brief Do a basic CNN layer pass, quantised wersion.
|
||
|
|
*
|
||
|
|
* @Warning This just supports the single channel input image, and the output is single row matrix.
|
||
|
|
That is to say, the height of output is 1, and the weight of output is out_channels*out_image_width*out_image_height
|
||
|
|
*
|
||
|
|
* @param in Input single channel image
|
||
|
|
* @param weight Weights of the neurons, weight->w = out_channels, weight->h = filter_width*filter_height,
|
||
|
|
* @param bias Bias of the neurons.
|
||
|
|
* @param filter_height The height of convolution kernel
|
||
|
|
* @param filter_width The width of convolution kernel
|
||
|
|
* @param out_channels The number of output channels of convolution kernel
|
||
|
|
* @param stride_x The step length of the convolution window in x(width) direction
|
||
|
|
* @param stride_y The step length of the convolution window in y(height) direction
|
||
|
|
* @param pad One of `"VALID"` or `"SAME"`, 0 is "VALID" and the other is "SAME"
|
||
|
|
* @param out The result of CNN layer, out->h=1
|
||
|
|
* @return The result of CNN layer
|
||
|
|
*/
|
||
|
|
dl_matrix2d_t *dl_basic_conv_layer_quantised_weight(const dl_matrix2d_t *in,
|
||
|
|
const dl_matrix2dq_t *weight,
|
||
|
|
const dl_matrix2d_t *bias,
|
||
|
|
int filter_width,
|
||
|
|
int filter_height,
|
||
|
|
const int out_channels,
|
||
|
|
const int stride_x,
|
||
|
|
const int stride_y,
|
||
|
|
padding_state pad,
|
||
|
|
const dl_matrix2d_t *out);
|
||
|
|
|
||
|
|
#ifdef __cplusplus
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
#endif
|