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Diffstat (limited to 'contrib/kann/kann.h')
| -rw-r--r-- | contrib/kann/kann.h | 240 |
1 files changed, 240 insertions, 0 deletions
diff --git a/contrib/kann/kann.h b/contrib/kann/kann.h new file mode 100644 index 0000000..af0de5f --- /dev/null +++ b/contrib/kann/kann.h @@ -0,0 +1,240 @@ +/* + The MIT License + + Copyright (c) 2018-2019 Dana-Farber Cancer Institute + 2016-2018 Broad Institute + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal in the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS + BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN + ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN + CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. +*/ + +#ifndef KANN_H +#define KANN_H + +#define KANN_VERSION "r536" + +#define KANN_F_IN 0x1 /* input */ +#define KANN_F_OUT 0x2 /* output */ +#define KANN_F_TRUTH 0x4 /* truth output */ +#define KANN_F_COST 0x8 /* final cost */ + +#define KANN_C_CEB 1 /* binary cross-entropy cost, used with sigmoid */ +#define KANN_C_CEM 2 /* multi-class cross-entropy cost, used with softmax */ +#define KANN_C_CEB_NEG 3 /* binary cross-enytopy-like cost, used with tanh */ +#define KANN_C_MSE 4 /* mean square error */ + +#define KANN_RNN_VAR_H0 0x1 /* take the initial hidden values as variables */ +#define KANN_RNN_NORM 0x2 /* apply layer normalization */ + +#include "kautodiff.h" + +typedef struct { + int n; /* number of nodes in the computational graph */ + kad_node_t **v; /* list of nodes */ + float *x, *g, *c; /* collated variable values, gradients and constant values */ + void *mt; /* auxiliary data for multi-threading; NULL if multi-threading disabled */ +} kann_t; + +extern int kann_verbose; + +#define kann_size_var(a) kad_size_var((a)->n, (a)->v) +#define kann_size_const(a) kad_size_const((a)->n, (a)->v) +#define kann_dim_in(a) kann_feed_dim((a), KANN_F_IN, 0) +#define kann_dim_out(a) kann_feed_dim((a), KANN_F_TRUTH, 0) +#define kann_srand(seed) kad_srand(0, (seed)) +#define kann_drand() kad_drand(0) +#define kann_set_batch_size(ann, B) kad_sync_dim((ann)->n, (ann)->v, (B)) + +#ifdef __cplusplus +extern "C" { +#endif + +/** + * Generate a network from a computational graph + * + * A network must have at least one scalar cost node (i.e. whose n_d==0). It + * may optionally contain other cost nodes or output nodes not leading to the + * primary cost node. + * + * @param cost cost node (must be a scalar, i.e. cost->n_d==0) + * @param n_rest number of other nodes without predecessors + * @param ... other nodes (of type kad_node_t*) without predecessors + * + * @return network on success, or NULL otherwise + */ +kann_t *kann_new(kad_node_t *cost, int n_rest, ...); + +/** + * Unroll an RNN + * + * @param a network + * @param len number of unrolls + * + * @return an unrolled network, or NULL if the network is not an RNN + */ +kann_t *kann_unroll(kann_t *a, ...); + +kann_t *kann_unroll_array(kann_t *a, int *len); +kann_t *kann_clone(kann_t *a, int batch_size); +void kann_delete(kann_t *a); /* delete a network generated by kann_new() or kann_layer_final() */ +void kann_delete_unrolled(kann_t *a); /* delete a network generated by kann_unroll() */ + +/** + * Enable/disable multi-threading (requiring pthread) + * + * KANN splits a mini-batch to $n_threads mini-mini-batches and puts each of + * them on one thread. So far, only kann_cost() takes the advantage of + * multi-threading. + * + * @param ann network + * @param n_threads number of threads; <=1 to completely disable multi-threading + * @param max_batch_size max mini-batch size; shall no smaller than n_threads + */ +void kann_mt(kann_t *ann, int n_threads, int max_batch_size); + +/** + * Bind float arrays to feed nodes + * + * @param a network + * @param ext_flag required external flags + * @param ext_label required external label + * @param x pointers (size equal to the number of matching feed nodes) + * + * @return number of matching feed nodes + */ +int kann_feed_bind(kann_t *a, uint32_t ext_flag, int32_t ext_label, float **x); + +/** + * Compute the cost and optionally gradients + * + * @param a network + * @param cost_label required external label + * @param cal_grad whether to compute gradients + * + * @return cost + */ +float kann_cost(kann_t *a, int cost_label, int cal_grad); + +int kann_eval(kann_t *a, uint32_t ext_flag, int ext_label); +int kann_eval_out(kann_t *a); +int kann_class_error(const kann_t *ann, int *base); + +/** + * Find a node + * + * @param a network + * @param ext_flag required external flags; set to 0 to match all flags + * @param ext_label required external label + * + * @return >=0 if found; -1 if not found; -2 if found multiple + */ +int kann_find(const kann_t *a, uint32_t ext_flag, int32_t ext_label); + +/** + * Get the size of a feed node, assuming mini-batch size 1 + * + * @param a network + * @param ext_flag required external flags + * @param ext_label required external label + * + * @return size>=0; -1 if not found; -2 if found multiple + */ +int kann_feed_dim(const kann_t *a, uint32_t ext_flag, int32_t ext_label); + +/** + * Get an RNN ready for continuous feeding + * + * @param a network + */ +void kann_rnn_start(kann_t *a); + +void kann_rnn_end(kann_t *a); + +/** + * Switch between training and prediction networks (effective only when there are switch nodes) + * + * @param a network + * @param is_train 0 for prediction network and non-zero for training net + */ +void kann_switch(kann_t *a, int is_train); + +/** + * RMSprop update + * + * @param n number of variables + * @param h0 learning rate + * @param h per-variable learning rate; NULL if not applicable + * @param decay RMSprop decay; use 0.9 if unsure + * @param g gradient, of size n + * @param t variables to change + * @param r memory, of size n + */ +void kann_RMSprop(int n, float h0, const float *h, float decay, const float *g, float *t, float *r); + +void kann_shuffle(int n, int *s); +float kann_grad_clip(float thres, int n, float *g); + +/* common layers */ +kad_node_t *kann_layer_input(int n1); +kad_node_t *kann_layer_dense(kad_node_t *in, int n1); +kad_node_t *kann_layer_dropout(kad_node_t *t, float r); +kad_node_t *kann_layer_layernorm(kad_node_t *in); +kad_node_t *kann_layer_rnn(kad_node_t *in, int n1, int rnn_flag); +kad_node_t *kann_layer_lstm(kad_node_t *in, int n1, int rnn_flag); +kad_node_t *kann_layer_gru(kad_node_t *in, int n1, int rnn_flag); +kad_node_t *kann_layer_conv2d(kad_node_t *in, int n_flt, int k_rows, int k_cols, int stride_r, int stride_c, int pad_r, int pad_c); +kad_node_t *kann_layer_conv1d(kad_node_t *in, int n_flt, int k_size, int stride, int pad); +kad_node_t *kann_layer_cost(kad_node_t *t, int n_out, int cost_type); + +kad_node_t *kann_new_leaf(uint8_t flag, float x0_01, int n_d, ...); /* flag can be KAD_CONST or KAD_VAR */ +kad_node_t *kann_new_scalar(uint8_t flag, float x); +kad_node_t *kann_new_weight(int n_row, int n_col); +kad_node_t *kann_new_bias(int n); +kad_node_t *kann_new_weight_conv2d(int n_out, int n_in, int k_row, int k_col); +kad_node_t *kann_new_weight_conv1d(int n_out, int n_in, int kernel_len); + +kad_node_t *kann_new_leaf_array(int *offset, kad_node_p *par, uint8_t flag, float x0_01, int n_d, int32_t d[KAD_MAX_DIM]); + +kad_node_t *kann_new_leaf2(int *offset, kad_node_p *par, uint8_t flag, float x0_01, int n_d, ...); +kad_node_t *kann_layer_dense2(int *offset, kad_node_p *par, kad_node_t *in, int n1); +kad_node_t *kann_layer_dropout2(int *offset, kad_node_p *par, kad_node_t *t, float r); +kad_node_t *kann_layer_layernorm2(int *offset, kad_node_t **par, kad_node_t *in); +kad_node_t *kann_layer_rnn2(int *offset, kad_node_t **par, kad_node_t *in, kad_node_t *h0, int rnn_flag); +kad_node_t *kann_layer_gru2(int *offset, kad_node_t **par, kad_node_t *in, kad_node_t *h0, int rnn_flag); + +/* operations on network with a single input node and a single output node */ +typedef void (*kann_train_cb)(int iter, float train_cost, float val_cost, void *ud); +int kann_train_fnn1(kann_t *ann, float lr, int mini_size, int max_epoch, + int max_drop_streak, float frac_val, int n, + float **_x, float **_y, kann_train_cb cb, void *ud); +float kann_cost_fnn1(kann_t *a, int n, float **x, float **y); +const float *kann_apply1(kann_t *a, float *x); + +/* model I/O */ +void kann_save_fp(FILE *fp, kann_t *ann); +void kann_save(const char *fn, kann_t *ann); +kann_t *kann_load_fp(FILE *fp); +kann_t *kann_load(const char *fn); + +#ifdef __cplusplus +} +#endif + +#endif |