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|
/*-
* Copyright 2019 Vsevolod Stakhov
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "lua_common.h"
#include "contrib/kann/kann.h"
/***
* @module rspamd_kann
* `rspamd_kann` is a Lua interface to kann library
*/
#define KANN_NODE_CLASS "rspamd{kann_node}"
#define KANN_NETWORK_CLASS "rspamd{kann}"
/* Simple macros to define behaviour */
#define KANN_LAYER_DEF(name) static int lua_kann_layer_ ## name (lua_State *L)
#define KANN_LAYER_INTERFACE(name) {#name, lua_kann_layer_ ## name}
#define KANN_TRANSFORM_DEF(name) static int lua_kann_transform_ ## name (lua_State *L)
#define KANN_TRANSFORM_INTERFACE(name) {#name, lua_kann_transform_ ## name}
#define KANN_LOSS_DEF(name) static int lua_kann_loss_ ## name (lua_State *L)
#define KANN_LOSS_INTERFACE(name) {#name, lua_kann_loss_ ## name}
#define KANN_NEW_DEF(name) static int lua_kann_new_ ## name (lua_State *L)
#define KANN_NEW_INTERFACE(name) {#name, lua_kann_new_ ## name}
/*
* Forwarded declarations
*/
static kad_node_t *lua_check_kann_node (lua_State *L, int pos);
/* Layers */
KANN_LAYER_DEF(input);
KANN_LAYER_DEF(dense);
KANN_LAYER_DEF(layernorm);
KANN_LAYER_DEF(rnn);
KANN_LAYER_DEF(lstm);
KANN_LAYER_DEF(gru);
KANN_LAYER_DEF(conv2d);
KANN_LAYER_DEF(conv1d);
KANN_LAYER_DEF(cost);
static luaL_reg rspamd_kann_layers_f[] = {
KANN_LAYER_INTERFACE(input),
KANN_LAYER_INTERFACE(dense),
KANN_LAYER_INTERFACE(layernorm),
KANN_LAYER_INTERFACE(rnn),
KANN_LAYER_INTERFACE(lstm),
KANN_LAYER_INTERFACE(gru),
KANN_LAYER_INTERFACE(conv2d),
KANN_LAYER_INTERFACE(conv1d),
KANN_LAYER_INTERFACE(cost),
{NULL, NULL},
};
/* Transition and composition functions */
/* General transform */
KANN_TRANSFORM_DEF (add);
KANN_TRANSFORM_DEF (sub);
KANN_TRANSFORM_DEF (mul);
KANN_TRANSFORM_DEF (cmul);
KANN_TRANSFORM_DEF (matmul);
KANN_TRANSFORM_DEF (square);
KANN_TRANSFORM_DEF (sigm);
KANN_TRANSFORM_DEF (tanh);
KANN_TRANSFORM_DEF (relu);
KANN_TRANSFORM_DEF (softmax);
KANN_TRANSFORM_DEF (1minus);
KANN_TRANSFORM_DEF (exp);
KANN_TRANSFORM_DEF (log);
KANN_TRANSFORM_DEF (sin);
static luaL_reg rspamd_kann_transform_f[] = {
KANN_TRANSFORM_INTERFACE (add),
KANN_TRANSFORM_INTERFACE (sub),
KANN_TRANSFORM_INTERFACE (mul),
KANN_TRANSFORM_INTERFACE (cmul),
KANN_TRANSFORM_INTERFACE (matmul),
KANN_TRANSFORM_INTERFACE (square),
KANN_TRANSFORM_INTERFACE (sigm),
KANN_TRANSFORM_INTERFACE (tanh),
KANN_TRANSFORM_INTERFACE (relu),
KANN_TRANSFORM_INTERFACE (softmax),
KANN_TRANSFORM_INTERFACE (1minus),
KANN_TRANSFORM_INTERFACE (exp),
KANN_TRANSFORM_INTERFACE (log),
KANN_TRANSFORM_INTERFACE (sin),
{NULL, NULL},
};
/* Loss functions */
KANN_LOSS_DEF (mse);
KANN_LOSS_DEF (ce_multi);
KANN_LOSS_DEF (ce_bin);
KANN_LOSS_DEF (ce_bin_neg);
KANN_LOSS_DEF (ce_multi_weighted);
static luaL_reg rspamd_kann_loss_f[] = {
KANN_LOSS_INTERFACE (mse),
KANN_LOSS_INTERFACE (ce_multi),
KANN_LOSS_INTERFACE (ce_bin),
KANN_LOSS_INTERFACE (ce_bin_neg),
KANN_LOSS_INTERFACE (ce_multi_weighted),
{NULL, NULL},
};
/* Creation functions */
KANN_NEW_DEF (leaf);
KANN_NEW_DEF (scalar);
KANN_NEW_DEF (weight);
KANN_NEW_DEF (bias);
KANN_NEW_DEF (weight_conv2d);
KANN_NEW_DEF (weight_conv1d);
KANN_NEW_DEF (kann);
static luaL_reg rspamd_kann_new_f[] = {
KANN_NEW_INTERFACE (leaf),
KANN_NEW_INTERFACE (scalar),
KANN_NEW_INTERFACE (weight),
KANN_NEW_INTERFACE (bias),
KANN_NEW_INTERFACE (weight_conv2d),
KANN_NEW_INTERFACE (weight_conv1d),
KANN_NEW_INTERFACE (kann),
{NULL, NULL},
};
LUA_FUNCTION_DEF (kann, load);
LUA_FUNCTION_DEF (kann, destroy);
LUA_FUNCTION_DEF (kann, save);
LUA_FUNCTION_DEF (kann, train1);
LUA_FUNCTION_DEF (kann, apply1);
static luaL_reg rspamd_kann_m[] = {
LUA_INTERFACE_DEF (kann, save),
LUA_INTERFACE_DEF (kann, train1),
LUA_INTERFACE_DEF (kann, apply1),
{"__gc", lua_kann_destroy},
{NULL, NULL},
};
static int
rspamd_kann_table_to_flags (lua_State *L, int table_pos)
{
int result = 0;
lua_pushvalue (L, table_pos);
for (lua_pushnil (L); lua_next (L, -2); lua_pop (L, 1)) {
int fl = lua_tointeger (L, -1);
result |= fl;
}
lua_pop (L, 1);
return result;
}
static gint
lua_load_kann (lua_State * L)
{
lua_newtable (L);
/* Flags */
lua_pushstring (L, "flag");
lua_newtable (L);
lua_pushinteger (L, KANN_F_IN);
lua_setfield (L, -2, "in");
lua_pushinteger (L, KANN_F_COST);
lua_setfield (L, -2, "cost");
lua_pushinteger (L, KANN_F_OUT);
lua_setfield (L, -2, "out");
lua_pushinteger (L, KANN_F_TRUTH);
lua_setfield (L, -2, "truth");
lua_settable (L, -3);
/* Cost type */
lua_pushstring (L, "cost");
lua_newtable (L);
/* binary cross-entropy cost, used with sigmoid */
lua_pushinteger (L, KANN_C_CEB);
lua_setfield (L, -2, "ceb");
/* multi-class cross-entropy cost, used with softmax */
lua_pushinteger (L, KANN_C_CEM);
lua_setfield (L, -2, "cem");
/* binary cross-entropy-like cost, used with tanh */
lua_pushinteger (L, KANN_C_CEB_NEG);
lua_setfield (L, -2, "ceb_neg");
lua_pushinteger (L, KANN_C_MSE);
lua_setfield (L, -2, "mse");
lua_settable (L, -3);
/* RNN flag */
lua_pushstring (L, "rnn");
lua_newtable (L);
/* apply layer normalization */
lua_pushinteger (L, KANN_RNN_NORM);
lua_setfield (L, -2, "norm");
/* take the initial hidden values as variables */
lua_pushinteger (L, KANN_RNN_VAR_H0);
lua_setfield (L, -2, "var_h0");
lua_settable (L, -3);
/* Layers */
lua_pushstring (L, "layer");
lua_newtable (L);
luaL_register (L, NULL, rspamd_kann_layers_f);
lua_settable (L, -3);
/* Transforms */
lua_pushstring (L, "transform");
lua_newtable (L);
luaL_register (L, NULL, rspamd_kann_transform_f);
lua_settable (L, -3);
/* Cost */
lua_pushstring (L, "loss");
lua_newtable (L);
luaL_register (L, NULL, rspamd_kann_loss_f);
lua_settable (L, -3);
/* Create functions */
lua_pushstring (L, "new");
lua_newtable (L);
luaL_register (L, NULL, rspamd_kann_new_f);
lua_settable (L, -3);
/* Load ann from memory or file */
lua_pushstring (L, "load");
lua_pushcfunction (L, lua_kann_load);
lua_settable (L, -3);
return 1;
}
static kad_node_t *
lua_check_kann_node (lua_State *L, int pos)
{
void *ud = rspamd_lua_check_udata (L, pos, KANN_NODE_CLASS);
luaL_argcheck (L, ud != NULL, pos, "'kann_node' expected");
return ud ? *((kad_node_t **)ud) : NULL;
}
static kann_t *
lua_check_kann (lua_State *L, int pos)
{
void *ud = rspamd_lua_check_udata (L, pos, KANN_NETWORK_CLASS);
luaL_argcheck (L, ud != NULL, pos, "'kann' expected");
return ud ? *((kann_t **)ud) : NULL;
}
void luaopen_kann (lua_State *L)
{
/* Metatables */
rspamd_lua_new_class (L, KANN_NODE_CLASS, NULL); /* TODO: add methods */
lua_pop (L, 1); /* No need in metatable... */
rspamd_lua_new_class (L, KANN_NETWORK_CLASS, rspamd_kann_m);
lua_pop (L, 1); /* No need in metatable... */
rspamd_lua_add_preload (L, "rspamd_kann", lua_load_kann);
lua_settop (L, 0);
}
/* Layers implementation */
#define PUSH_KAD_NODE(n) do { \
kad_node_t **pt; \
pt = lua_newuserdata (L, sizeof (kad_node_t *)); \
*pt = (n); \
rspamd_lua_setclass (L, KANN_NODE_CLASS, -1); \
} while(0)
#define PUSH_KAN_NETWORK(n) do { \
kann_t **pn; \
pn = lua_newuserdata (L, sizeof (kann_t *)); \
*pn = (n); \
rspamd_lua_setclass (L, KANN_NETWORK_CLASS, -1); \
} while(0)
#define PROCESS_KAD_FLAGS(n, pos) do { \
int fl = 0; \
if (lua_type(L, (pos)) == LUA_TTABLE) { fl = rspamd_kann_table_to_flags (L, (pos)); } \
else if (lua_type(L, (pos)) == LUA_TNUMBER) { fl = lua_tointeger (L, (pos)); } \
(n)->ext_flag |= fl; \
}while(0)
/***
* @function kann.layer.input(ninputs[, flags])
* Creates an input layer for ANN
* @param {int} ninputs number of inputs
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_input (lua_State *L)
{
gint nnodes = luaL_checkinteger (L, 1);
if (nnodes > 0) {
kad_node_t *t;
t = kann_layer_input (nnodes);
PROCESS_KAD_FLAGS (t, 2);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, nnodes required");
}
return 1;
}
/***
* @function kann.layer.dense(in, ninputs[, flags])
* Creates a dense layer (e.g. for hidden layer)
* @param {kann_node} in kann node
* @param {int} ninputs number of dense nodes
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_dense (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
gint nnodes = luaL_checkinteger (L, 2);
if (in != NULL && nnodes > 0) {
kad_node_t *t;
t = kann_layer_dense (in, nnodes);
PROCESS_KAD_FLAGS (t, 3);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input + nnodes required");
}
return 1;
}
/***
* @function kann.layer.dropout(in, ratio[, flags])
* Creates a dropout layer
* @param {kann_node} in kann node
* @param {float} ratio drop ratio
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_layerdropout (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
double r = luaL_checknumber (L, 2);
if (in != NULL) {
kad_node_t *t;
t = kann_layer_dropout (in, r);
PROCESS_KAD_FLAGS (t, 3);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input + rate required");
}
return 1;
}
/***
* @function kann.layer.dropout(in [, flags])
* Creates a normalisation layer
* @param {kann_node} in kann node
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_layernorm (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
if (in != NULL) {
kad_node_t *t;
t = kann_layer_layernorm (in);
PROCESS_KAD_FLAGS (t, 2);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input required");
}
return 1;
}
/***
* @function kann.layer.rnn(in, nnodes[, rnn_flags, [, flags]])
* Creates a recursive NN layer
* @param {kann_node} in kann node
* @param {int} nnodes number of cells
* @param {int} rnnflags rnn flags
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_rnn (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
gint nnodes = luaL_checkinteger (L, 2);
gint rnnflags = 0;
if (in != NULL && nnodes > 0) {
kad_node_t *t;
if (lua_type (L, 3) == LUA_TNUMBER) {
rnnflags = lua_tointeger (L, 3);
}
t = kann_layer_rnn (in, nnodes, rnnflags);
PROCESS_KAD_FLAGS (t, 4);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input + nnodes required");
}
return 1;
}
/***
* @function kann.layer.lstm(in, nnodes[, rnn_flags, [, flags]])
* Creates a recursive NN layer using LSTM cells
* @param {kann_node} in kann node
* @param {int} nnodes number of cells
* @param {int} rnnflags rnn flags
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_lstm (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
gint nnodes = luaL_checkinteger (L, 2);
gint rnnflags = 0;
if (in != NULL && nnodes > 0) {
kad_node_t *t;
if (lua_type (L, 3) == LUA_TNUMBER) {
rnnflags = lua_tointeger (L, 3);
}
t = kann_layer_lstm (in, nnodes, rnnflags);
PROCESS_KAD_FLAGS (t, 4);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input + nnodes required");
}
return 1;
}
/***
* @function kann.layer.rnn(in, nnodes[, rnn_flags, [, flags]])
* Creates a recursive NN layer using GRU cells
* @param {kann_node} in kann node
* @param {int} nnodes number of cells
* @param {int} rnnflags rnn flags
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_gru (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
gint nnodes = luaL_checkinteger (L, 2);
gint rnnflags = 0;
if (in != NULL && nnodes > 0) {
kad_node_t *t;
if (lua_type (L, 3) == LUA_TNUMBER) {
rnnflags = lua_tointeger (L, 3);
}
t = kann_layer_gru (in, nnodes, rnnflags);
PROCESS_KAD_FLAGS (t, 4);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input + nnodes required");
}
return 1;
}
/***
* @function kann.layer.conv2d(in, n_flt, k_rows, k_cols, stride_rows, stride_cols, pad_rows, pad_columns[, flags])
* Creates a 2D convolution layer
* @param {kann_node} in kann node
* @param {int} n_flt number of filters
* @param {int} k_rows kernel rows
* @param {int} k_cols kernel columns
* @param {int} stride_rows stride rows
* @param {int} stride_cols stride columns
* @param {int} pad_rows padding rows
* @param {int} pad_columns padding columns
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_conv2d (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
int n_flt = luaL_checkinteger (L, 2);
int k_rows = luaL_checkinteger (L, 3);
int k_cols = luaL_checkinteger (L, 4);
int stride_r = luaL_checkinteger (L, 5);
int stride_c = luaL_checkinteger (L, 6);
int pad_r = luaL_checkinteger (L, 7);
int pad_c = luaL_checkinteger (L, 8);
if (in != NULL) {
kad_node_t *t;
t = kann_layer_conv2d (in, n_flt, k_rows, k_cols, stride_r, stride_c,
pad_r, pad_c);
PROCESS_KAD_FLAGS (t, 9);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input, nflt, kx, ky, stridex, stridey, padx, pady are required");
}
return 1;
}
/***
* @function kann.layer.conv1d(in, n_flt, kern_size, stride_size, pad_size[, flags])
* Creates 1D convolution layer
* @param {kann_node} in kann node
* @param {int} n_flt number of filters
* @param {int} kern_size kernel rows
* @param {int} stride_size stride rows
* @param {int} pad_size padding rows
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_conv1d (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
int n_flt = luaL_checkinteger (L, 2);
int k_size = luaL_checkinteger (L, 3);
int stride = luaL_checkinteger (L, 4);
int pad = luaL_checkinteger (L, 5);
if (in != NULL) {
kad_node_t *t;
t = kann_layer_conv1d (in, n_flt, k_size, stride, pad);
PROCESS_KAD_FLAGS (t, 6);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input, nflt, k, stride, pad required");
}
return 1;
}
/***
* @function kann.layer.cost(in, nout, cost_type[, flags])
* Creates 1D convolution layer
* @param {kann_node} in kann node
* @param {int} nout number of outputs
* @param {int} cost_type see kann.cost table
* @param {table|int} flags optional flags
* @return {kann_node} kann node object (should be used to combine ANN)
*/
static int
lua_kann_layer_cost (lua_State *L)
{
kad_node_t *in = lua_check_kann_node (L, 1);
int nout = luaL_checkinteger (L, 2);
int cost_type = luaL_checkinteger (L, 3);
if (in != NULL && nout > 0) {
kad_node_t *t;
t = kann_layer_cost (in, nout, cost_type);
PROCESS_KAD_FLAGS (t, 4);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments, input, nout and cost_type are required");
}
return 1;
}
/* Generic helpers */
static int
lua_kann_call_unary_function (lua_State *L, const char *name,
kad_node_t *(*func)(kad_node_t *))
{
kad_node_t *in = lua_check_kann_node (L, 1);
if (in != NULL) {
kad_node_t *t;
t = func (in);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments for %s, input required", name);
}
return 1;
}
static int
lua_kann_call_binary_function (lua_State *L, const char *name,
kad_node_t *(*func)(kad_node_t *, kad_node_t *))
{
kad_node_t *x = lua_check_kann_node (L, 1);
kad_node_t *y = lua_check_kann_node (L, 2);
if (x != NULL && y != NULL) {
kad_node_t *t;
t = func (x, y);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments for %s, 2 inputs required", name);
}
return 1;
}
#define LUA_UNARY_TRANSFORM_FUNC_IMPL(name) \
static int lua_kann_transform_ ##name (lua_State *L) \
{ \
return lua_kann_call_unary_function(L, #name, kad_##name); \
}
#define LUA_BINARY_TRANSFORM_FUNC_IMPL(name) \
static int lua_kann_transform_ ##name (lua_State *L) \
{ \
return lua_kann_call_binary_function(L, #name, kad_##name); \
}
#define LUA_LOSS_FUNC_IMPL(name) \
static int lua_kann_loss_ ##name (lua_State *L) \
{ \
return lua_kann_call_binary_function(L, #name, kad_##name); \
}
/* Transform functions registered via macro helpers */
LUA_BINARY_TRANSFORM_FUNC_IMPL (add)
LUA_BINARY_TRANSFORM_FUNC_IMPL (sub)
LUA_BINARY_TRANSFORM_FUNC_IMPL (mul)
LUA_BINARY_TRANSFORM_FUNC_IMPL (cmul)
LUA_BINARY_TRANSFORM_FUNC_IMPL (matmul)
LUA_UNARY_TRANSFORM_FUNC_IMPL (square)
LUA_UNARY_TRANSFORM_FUNC_IMPL (sigm)
LUA_UNARY_TRANSFORM_FUNC_IMPL (tanh)
LUA_UNARY_TRANSFORM_FUNC_IMPL (relu)
LUA_UNARY_TRANSFORM_FUNC_IMPL (softmax)
LUA_UNARY_TRANSFORM_FUNC_IMPL (1minus)
LUA_UNARY_TRANSFORM_FUNC_IMPL (exp)
LUA_UNARY_TRANSFORM_FUNC_IMPL (log)
LUA_UNARY_TRANSFORM_FUNC_IMPL (sin)
/* Generic cost functions */
LUA_LOSS_FUNC_IMPL (mse)
LUA_LOSS_FUNC_IMPL (ce_multi)
LUA_LOSS_FUNC_IMPL (ce_bin)
LUA_LOSS_FUNC_IMPL (ce_bin_neg)
/* The only case of ternary weight function */
static int
lua_kann_loss_ce_multi_weighted (lua_State *L)
{
kad_node_t *pred = lua_check_kann_node (L, 1);
kad_node_t *truth = lua_check_kann_node (L, 2);
kad_node_t *weight = lua_check_kann_node (L, 3);
if (pred != NULL && truth != NULL && weight != NULL) {
kad_node_t *t;
t = kad_ce_multi_weighted (pred, truth, weight);
PUSH_KAD_NODE (t);
}
else {
return luaL_error (L, "invalid arguments for ce_multi_weighted, 3 inputs required");
}
return 1;
}
/* Creation functions */
static int
lua_kann_new_scalar (lua_State *L)
{
gint flag = luaL_checkinteger (L, 1);
double x = luaL_checknumber (L, 2);
kad_node_t *t;
t = kann_new_scalar (flag, x);
PROCESS_KAD_FLAGS (t, 3);
PUSH_KAD_NODE (t);
return 1;
}
static int
lua_kann_new_weight (lua_State *L)
{
gint nrow = luaL_checkinteger (L, 1);
gint ncol = luaL_checkinteger (L, 2);
kad_node_t *t;
t = kann_new_weight (nrow, ncol);
PROCESS_KAD_FLAGS (t, 3);
PUSH_KAD_NODE (t);
return 1;
}
static int
lua_kann_new_bias (lua_State *L)
{
gint n = luaL_checkinteger (L, 1);
kad_node_t *t;
t = kann_new_bias (n);
PROCESS_KAD_FLAGS (t, 2);
PUSH_KAD_NODE (t);
return 1;
}
static int
lua_kann_new_weight_conv2d (lua_State *L)
{
gint nout = luaL_checkinteger (L, 1);
gint nin = luaL_checkinteger (L, 2);
gint krow = luaL_checkinteger (L, 3);
gint kcol = luaL_checkinteger (L, 4);
kad_node_t *t;
t = kann_new_weight_conv2d (nout, nin, krow, kcol);
PROCESS_KAD_FLAGS (t, 5);
PUSH_KAD_NODE (t);
return 1;
}
static int
lua_kann_new_weight_conv1d (lua_State *L)
{
gint nout = luaL_checkinteger (L, 1);
gint nin = luaL_checkinteger (L, 2);
gint klen = luaL_checkinteger (L, 3);
kad_node_t *t;
t = kann_new_weight_conv1d (nout, nin, klen);
PROCESS_KAD_FLAGS (t, 4);
PUSH_KAD_NODE (t);
return 1;
}
static int
lua_kann_new_leaf (lua_State *L)
{
gint dim = luaL_checkinteger (L, 1), i, *ar;
kad_node_t *t;
if (dim >= 1 && dim < KAD_MAX_DIM && lua_istable (L, 2)) {
ar = g_malloc0 (sizeof (ar) * dim);
for (i = 0; i < dim; i ++) {
lua_rawgeti (L, 2, i + 1);
ar[i] = lua_tointeger (L, -1);
lua_pop (L, 1);
}
t = kann_new_leaf_array (NULL, NULL, 0, 0.0, dim, ar);
PROCESS_KAD_FLAGS (t, 3);
PUSH_KAD_NODE (t);
g_free (ar);
}
else {
return luaL_error (L, "invalid arguments for new.leaf, "
"dim and vector of elements are required");
}
return 1;
}
static int
lua_kann_new_kann (lua_State *L)
{
kad_node_t *cost = lua_check_kann_node (L, 1);
kann_t *k;
if (cost) {
k = kann_new (cost, 0);
PUSH_KAN_NETWORK (k);
}
else {
return luaL_error (L, "invalid arguments for new.kann, "
"cost node is required");
}
return 1;
}
static int
lua_kann_destroy (lua_State *L)
{
kann_t *k = lua_check_kann (L, 1);
kann_delete (k);
return 0;
}
static int
lua_kann_save (lua_State *L)
{
kann_t *k = lua_check_kann (L, 1);
if (k) {
if (lua_istable (L, 2)) {
lua_getfield (L, 2, "filename");
if (lua_isstring (L, -1)) {
const gchar *fname = lua_tostring (L, -1);
FILE *f;
f = fopen (fname, "w");
if (!f) {
lua_pop (L, 1);
return luaL_error (L, "cannot open %s for writing: %s",
fname, strerror (errno));
}
kann_save_fp (f, k);
fclose (f);
lua_pushboolean (L, true);
}
else {
lua_pop (L, 1);
return luaL_error (L, "invalid arguments: missing filename");
}
lua_pop (L, 1);
}
else {
/* Save to Rspamd text */
#ifndef HAVE_OPENMEMSTREAM
return luaL_error (L, "no support of saving to memory on your system");
#endif
FILE *f;
char *buf = NULL;
size_t buflen;
struct rspamd_lua_text *t;
f = open_memstream (&buf, &buflen);
g_assert (f != NULL);
kann_save_fp (f, k);
fclose (f);
t = lua_newuserdata (L, sizeof (*t));
rspamd_lua_setclass (L, "rspamd{text}", -1);
t->flags = RSPAMD_TEXT_FLAG_OWN;
t->start = (const gchar *)buf;
t->len = buflen;
}
}
else {
return luaL_error (L, "invalid arguments");
}
return 1;
}
static int
lua_kann_load (lua_State *L)
{
kann_t *k;
FILE *f = NULL;
if (lua_istable (L, 1)) {
lua_getfield (L, 2, "filename");
if (lua_isstring (L, -1)) {
const gchar *fname = lua_tostring (L, -1);
f = fopen (fname, "rb");
}
else {
lua_pop (L, 1);
return luaL_error (L, "invalid arguments: missing filename");
}
lua_pop (L, 1);
}
else if (lua_isstring (L, 1)) {
gsize dlen;
const gchar *data;
data = lua_tolstring (L, 1, &dlen);
#ifndef HAVE_FMEMOPEN
return luaL_error (L, "no support of loading from memory on your system");
#endif
f = fmemopen ((void *)data, dlen, "rb");
}
else if (lua_isuserdata (L, 1)) {
struct rspamd_lua_text *t;
t = lua_check_text (L, 1);
#ifndef HAVE_FMEMOPEN
return luaL_error (L, "no support of loading from memory on your system");
#endif
f = fmemopen ((void *)t->start, t->len, "rb");
}
if (f == NULL) {
return luaL_error (L, "invalid arguments or cannot open file");
}
k = kann_load_fp (f);
fclose (f);
if (k == NULL) {
lua_pushnil (L);
}
else {
PUSH_KAN_NETWORK (k);
}
return 1;
}
struct rspamd_kann_train_cbdata {
lua_State *L;
kann_t *k;
gint cbref;
};
static void
lua_kann_train_cb (int iter, float train_cost, float val_cost, void *ud)
{
struct rspamd_kann_train_cbdata *cbd = (struct rspamd_kann_train_cbdata *)ud;
if (cbd->cbref != -1) {
gint err_idx;
lua_State *L = cbd->L;
lua_pushcfunction (L, &rspamd_lua_traceback);
err_idx = lua_gettop (L);
lua_rawgeti (L, LUA_REGISTRYINDEX, cbd->cbref);
lua_pushinteger (L, iter);
lua_pushnumber (L, train_cost);
lua_pushnumber (L, val_cost);
if (lua_pcall (L, 3, 0, err_idx) != 0) {
msg_err ("cannot run lua train callback: %s",
lua_tostring (L, -1));
}
lua_settop (L, err_idx - 1);
}
}
#define FREE_VEC(a, n) do { for(int i = 0; i < (n); i ++) g_free((a)[i]); g_free(a); } while(0)
static int
lua_kann_train1 (lua_State *L)
{
kann_t *k = lua_check_kann (L, 1);
/* Default train params */
double lr = 0.001;
gint64 mini_size = 64;
gint64 max_epoch = 25;
gint64 max_drop_streak = 10;
double frac_val = 0.1;
gint cbref = -1;
if (k && lua_istable (L, 2) && lua_istable (L, 3)) {
int n = rspamd_lua_table_size (L, 2);
int n_in = kann_dim_in (k);
int n_out = kann_dim_out (k);
if (n_in <= 0) {
return luaL_error (L, "invalid inputs count: %d", n_in);
}
if (n_out <= 0) {
return luaL_error (L, "invalid outputs count: %d", n_in);
}
if (n != rspamd_lua_table_size (L, 3) || n == 0) {
return luaL_error (L, "invalid dimensions: outputs size must be "
"equal to inputs and non zero");
}
if (lua_istable (L, 4)) {
GError *err = NULL;
if (!rspamd_lua_parse_table_arguments (L, 4, &err,
RSPAMD_LUA_PARSE_ARGUMENTS_IGNORE_MISSING,
"lr=N;mini_size=I;max_epoch=I;max_drop_streak=I;frac_val=N;cb=F",
&lr, &mini_size, &max_epoch, &max_drop_streak, &frac_val, &cbref)) {
n = luaL_error (L, "invalid params: %s",
err ? err->message : "unknown error");
g_error_free (err);
return n;
}
}
float **x, **y;
/* Fill vectors */
x = (float **)g_malloc0 (sizeof (float *) * n);
y = (float **)g_malloc0 (sizeof (float *) * n);
for (int s = 0; s < n; s ++) {
/* Inputs */
lua_rawgeti (L, 2, s + 1);
x[s] = (float *)g_malloc (sizeof (float) * n_in);
if (rspamd_lua_table_size (L, -1) != n_in) {
FREE_VEC (x, n);
FREE_VEC (y, n);
n = luaL_error (L, "invalid params at pos %d: "
"bad input dimension %d; %d expected",
s + 1,
(int)rspamd_lua_table_size (L, -1),
n_in);
return n;
}
for (int i = 0; i < n_in; i ++) {
lua_rawgeti (L, -1, i + 1);
x[s][i] = lua_tonumber (L, -1);
lua_pop (L, 1);
}
lua_pop (L, 1);
/* Outputs */
y[s] = (float *)g_malloc (sizeof (float) * n_out);
lua_rawgeti (L, 3, s + 1);
if (rspamd_lua_table_size (L, -1) != n_out) {
FREE_VEC (x, n);
FREE_VEC (y, n);
n = luaL_error (L, "invalid params at pos %d: "
"bad output dimension %d; "
"%d expected",
s + 1,
(int)rspamd_lua_table_size (L, -1),
n_out);
return n;
}
for (int i = 0; i < n_out; i ++) {
lua_rawgeti (L, -1, i + 1);
y[s][i] = lua_tonumber (L, -1);
lua_pop (L, 1);
}
lua_pop (L, 1);
}
struct rspamd_kann_train_cbdata cbd;
cbd.cbref = cbref;
cbd.k = k;
cbd.L = L;
int niters = kann_train_fnn1 (k, lr,
mini_size, max_epoch, max_drop_streak,
frac_val, n, x, y, lua_kann_train_cb, &cbd);
lua_pushinteger (L, niters);
FREE_VEC (x, n);
FREE_VEC (y, n);
}
else {
return luaL_error (L, "invalid arguments: kann, inputs, outputs and"
" optional params are expected");
}
return 1;
}
static int
lua_kann_apply1 (lua_State *L)
{
kann_t *k = lua_check_kann (L, 1);
if (k && lua_istable (L, 2)) {
gsize vec_len = rspamd_lua_table_size (L, 2);
float *vec = (float *)g_malloc (sizeof (float) * vec_len);
int i_out;
int n_in = kann_dim_in (k);
if (n_in <= 0) {
return luaL_error (L, "invalid inputs count: %d", n_in);
}
if (n_in != vec_len) {
return luaL_error (L, "invalid params: bad input dimension %d; %d expected",
(int)vec_len, n_in);
}
for (gsize i = 0; i < vec_len; i ++) {
lua_rawgeti (L, 2, i + 1);
vec[i] = lua_tonumber (L, -1);
lua_pop (L, 1);
}
i_out = kann_find (k, KANN_F_OUT, 0);
if (i_out <= 0) {
g_free (vec);
return luaL_error (L, "invalid ANN: output layer is missing or is "
"at the input pos");
}
kann_set_batch_size (k, 1);
kann_feed_bind (k, KANN_F_IN, 0, &vec);
kad_eval_at (k->n, k->v, i_out);
gsize outlen = kad_len (k->v[i_out]);
lua_createtable (L, outlen, 0);
for (gsize i = 0; i < outlen; i ++) {
lua_pushnumber (L, k->v[i_out]->x[i]);
lua_rawseti (L, -2, i + 1);
}
g_free (vec);
}
else {
return luaL_error (L, "invalid arguments: rspamd{kann} expected");
}
return 1;
}
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