mailcow/data/Dockerfiles/rspamd/neural.lua

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2020-03-08 19:25:03 +08:00
--[[
Copyright (c) 2016, Vsevolod Stakhov <vsevolod@highsecure.ru>
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.
]]--
if confighelp then
return
end
local rspamd_logger = require "rspamd_logger"
local rspamd_util = require "rspamd_util"
local rspamd_kann = require "rspamd_kann"
local lua_redis = require "lua_redis"
local lua_util = require "lua_util"
local fun = require "fun"
local lua_settings = require "lua_settings"
local meta_functions = require "lua_meta"
local ts = require("tableshape").types
local lua_verdict = require "lua_verdict"
local N = "neural"
-- Module vars
local default_options = {
train = {
max_trains = 1000,
max_epoch = 1000,
max_usages = 10,
max_iterations = 25, -- Torch style
mse = 0.001,
autotrain = true,
train_prob = 1.0,
learn_threads = 1,
learning_rate = 0.01,
},
watch_interval = 60.0,
lock_expire = 600,
learning_spawned = false,
ann_expire = 60 * 60 * 24 * 2, -- 2 days
symbol_spam = 'NEURAL_SPAM',
symbol_ham = 'NEURAL_HAM',
}
local redis_profile_schema = ts.shape{
digest = ts.string,
symbols = ts.array_of(ts.string),
version = ts.number,
redis_key = ts.string,
distance = ts.number:is_optional(),
}
-- Rule structure:
-- * static config fields (see `default_options`)
-- * prefix - name or defined prefix
-- * settings - table of settings indexed by settings id, -1 is used when no settings defined
-- Rule settings element defines elements for specific settings id:
-- * symbols - static symbols profile (defined by config or extracted from symcache)
-- * name - name of settings id
-- * digest - digest of all symbols
-- * ann - dynamic ANN configuration loaded from Redis
-- * train - train data for ANN (e.g. the currently trained ANN)
-- Settings ANN table is loaded from Redis and represents dynamic profile for ANN
-- Some elements are directly stored in Redis, ANN is, in turn loaded dynamically
-- * version - version of ANN loaded from redis
-- * redis_key - name of ANN key in Redis
-- * symbols - symbols in THIS PARTICULAR ANN (might be different from set.symbols)
-- * distance - distance between set.symbols and set.ann.symbols
-- * ann - kann object
local settings = {
rules = {},
prefix = 'rn', -- Neural network default prefix
max_profiles = 3, -- Maximum number of NN profiles stored
}
local module_config = rspamd_config:get_all_opt("neural")
if not module_config then
-- Legacy
module_config = rspamd_config:get_all_opt("fann_redis")
end
-- Lua script that checks if we can store a new training vector
-- Uses the following keys:
-- key1 - ann key
-- key2 - spam or ham
-- key3 - maximum trains
-- key4 - sampling coin (as Redis scripts do not allow math.random calls)
-- returns 1 or 0 + reason: 1 - allow learn, 0 - not allow learn
local redis_lua_script_can_store_train_vec = [[
local prefix = KEYS[1]
local locked = redis.call('HGET', prefix, 'lock')
if locked then return {tostring(-1),'locked by another process till: ' .. locked} end
local nspam = 0
local nham = 0
local lim = tonumber(KEYS[3])
local coin = tonumber(KEYS[4])
local ret = redis.call('LLEN', prefix .. '_spam')
if ret then nspam = tonumber(ret) end
ret = redis.call('LLEN', prefix .. '_ham')
if ret then nham = tonumber(ret) end
if KEYS[2] == 'spam' then
if nspam <= lim then
if nspam > nham then
-- Apply sampling
local skip_rate = 1.0 - nham / (nspam + 1)
if coin < skip_rate then
return {tostring(-(nspam)),'sampled out with probability ' .. tostring(skip_rate)}
end
end
return {tostring(nspam),'can learn'}
else -- Enough learns
return {tostring(-(nspam)),'too many spam samples'}
end
else
if nham <= lim then
if nham > nspam then
-- Apply sampling
local skip_rate = 1.0 - nspam / (nham + 1)
if coin < skip_rate then
return {tostring(-(nham)),'sampled out with probability ' .. tostring(skip_rate)}
end
end
return {tostring(nham),'can learn'}
else
return {tostring(-(nham)),'too many ham samples'}
end
end
return {tostring(-1),'bad input'}
]]
local redis_can_store_train_vec_id = nil
-- Lua script to invalidate ANNs by rank
-- Uses the following keys
-- key1 - prefix for keys
-- key2 - number of elements to leave
local redis_lua_script_maybe_invalidate = [[
local card = redis.call('ZCARD', KEYS[1])
local lim = tonumber(KEYS[2])
if card > lim then
local to_delete = redis.call('ZRANGE', KEYS[1], 0, card - lim - 1)
for _,k in ipairs(to_delete) do
local tb = cjson.decode(k)
redis.call('DEL', tb.redis_key)
-- Also train vectors
redis.call('DEL', tb.redis_key .. '_spam')
redis.call('DEL', tb.redis_key .. '_ham')
end
redis.call('ZREMRANGEBYRANK', KEYS[1], 0, card - lim - 1)
return to_delete
else
return {}
end
]]
local redis_maybe_invalidate_id = nil
-- Lua script to invalidate ANN from redis
-- Uses the following keys
-- key1 - prefix for keys
-- key2 - current time
-- key3 - key expire
-- key4 - hostname
local redis_lua_script_maybe_lock = [[
local locked = redis.call('HGET', KEYS[1], 'lock')
local now = tonumber(KEYS[2])
if locked then
locked = tonumber(locked)
local expire = tonumber(KEYS[3])
if now > locked and (now - locked) < expire then
return {tostring(locked), redis.call('HGET', KEYS[1], 'hostname')}
end
end
redis.call('HSET', KEYS[1], 'lock', tostring(now))
redis.call('HSET', KEYS[1], 'hostname', KEYS[4])
return 1
]]
local redis_maybe_lock_id = nil
-- Lua script to save and unlock ANN in redis
-- Uses the following keys
-- key1 - prefix for ANN
-- key2 - prefix for profile
-- key3 - compressed ANN
-- key4 - profile as JSON
-- key5 - expire in seconds
-- key6 - current time
-- key7 - old key
local redis_lua_script_save_unlock = [[
local now = tonumber(KEYS[6])
redis.call('ZADD', KEYS[2], now, KEYS[4])
redis.call('HSET', KEYS[1], 'ann', KEYS[3])
redis.call('DEL', KEYS[1] .. '_spam')
redis.call('DEL', KEYS[1] .. '_ham')
redis.call('HDEL', KEYS[1], 'lock')
redis.call('HDEL', KEYS[7], 'lock')
redis.call('EXPIRE', KEYS[1], tonumber(KEYS[5]))
return 1
]]
local redis_save_unlock_id = nil
local redis_params
local function load_scripts(params)
redis_can_store_train_vec_id = lua_redis.add_redis_script(redis_lua_script_can_store_train_vec,
params)
redis_maybe_invalidate_id = lua_redis.add_redis_script(redis_lua_script_maybe_invalidate,
params)
redis_maybe_lock_id = lua_redis.add_redis_script(redis_lua_script_maybe_lock,
params)
redis_save_unlock_id = lua_redis.add_redis_script(redis_lua_script_save_unlock,
params)
end
local function result_to_vector(task, profile)
if not profile.zeros then
-- Fill zeros vector
local zeros = {}
for i=1,meta_functions.count_metatokens() do
zeros[i] = 0.0
end
for _,_ in ipairs(profile.symbols) do
zeros[#zeros + 1] = 0.0
end
profile.zeros = zeros
end
local vec = lua_util.shallowcopy(profile.zeros)
local mt = meta_functions.rspamd_gen_metatokens(task)
for i,v in ipairs(mt) do
vec[i] = v
end
task:process_ann_tokens(profile.symbols, vec, #mt, 0.1)
return vec
end
-- Used to generate new ANN key for specific profile
local function new_ann_key(rule, set, version)
local ann_key = string.format('%s_%s_%s_%s_%s', settings.prefix,
rule.prefix, set.name, set.digest:sub(1, 8), tostring(version))
return ann_key
end
-- Extract settings element for a specific settings id
local function get_rule_settings(task, rule)
local sid = task:get_settings_id() or -1
local set = rule.settings[sid]
if not set then return nil end
while type(set) == 'number' do
-- Reference to another settings!
set = rule.settings[set]
end
return set
end
-- Generate redis prefix for specific rule and specific settings
local function redis_ann_prefix(rule, settings_name)
-- We also need to count metatokens:
local n = meta_functions.version
return string.format('%s_%s_%d_%s',
settings.prefix, rule.prefix, n, settings_name)
end
-- Creates and stores ANN profile in Redis
local function new_ann_profile(task, rule, set, version)
local ann_key = new_ann_key(rule, set, version)
local profile = {
symbols = set.symbols,
redis_key = ann_key,
version = version,
digest = set.digest,
distance = 0 -- Since we are using our own profile
}
local ucl = require "ucl"
local profile_serialized = ucl.to_format(profile, 'json-compact', true)
local function add_cb(err, _)
if err then
rspamd_logger.errx(task, 'cannot store ANN profile for %s:%s at %s : %s',
rule.prefix, set.name, profile.redis_key, err)
else
rspamd_logger.infox(task, 'created new ANN profile for %s:%s, data stored at prefix %s',
rule.prefix, set.name, profile.redis_key)
end
end
lua_redis.redis_make_request(task,
rule.redis,
nil,
true, -- is write
add_cb, --callback
'ZADD', -- command
{set.prefix, tostring(rspamd_util.get_time()), profile_serialized}
)
return profile
end
-- ANN filter function, used to insert scores based on the existing symbols
local function ann_scores_filter(task)
for _,rule in pairs(settings.rules) do
local sid = task:get_settings_id() or -1
local ann
local profile
local set = get_rule_settings(task, rule)
if set then
if set.ann then
ann = set.ann.ann
profile = set.ann
else
lua_util.debugm(N, task, 'no ann loaded for %s:%s',
rule.prefix, set.name)
end
else
lua_util.debugm(N, task, 'no ann defined in %s for settings id %s',
rule.prefix, sid)
end
if ann then
local vec = result_to_vector(task, profile)
local score
local out = ann:apply1(vec)
score = out[1]
local symscore = string.format('%.3f', score)
lua_util.debugm(N, task, '%s:%s:%s ann score: %s',
rule.prefix, set.name, set.ann.version, symscore)
if score > 0 then
local result = score
task:insert_result(rule.symbol_spam, result, symscore)
else
local result = -(score)
task:insert_result(rule.symbol_ham, result, symscore)
end
end
end
end
local function create_ann(n, nlayers)
-- We ignore number of layers so far when using kann
local nhidden = math.floor((n + 1) / 2)
local t = rspamd_kann.layer.input(n)
t = rspamd_kann.transform.relu(t)
t = rspamd_kann.transform.tanh(rspamd_kann.layer.dense(t, nhidden));
t = rspamd_kann.layer.cost(t, 1, rspamd_kann.cost.mse)
return rspamd_kann.new.kann(t)
end
local function ann_push_task_result(rule, task, verdict, score, set)
local train_opts = rule.train
local learn_spam, learn_ham
local skip_reason = 'unknown'
if train_opts.autotrain then
if train_opts.spam_score then
learn_spam = score >= train_opts.spam_score
if not learn_spam then
skip_reason = string.format('score < spam_score: %f < %f',
score, train_opts.spam_score)
end
else
learn_spam = verdict == 'spam' or verdict == 'junk'
if not learn_spam then
skip_reason = string.format('verdict: %s',
verdict)
end
end
if train_opts.ham_score then
learn_ham = score <= train_opts.ham_score
if not learn_ham then
skip_reason = string.format('score > ham_score: %f > %f',
score, train_opts.ham_score)
end
else
learn_ham = verdict == 'ham'
if not learn_ham then
skip_reason = string.format('verdict: %s',
verdict)
end
end
else
-- Train by request header
local hdr = task:get_request_header('ANN-Train')
if hdr then
if hdr:lower() == 'spam' then
learn_spam = true
elseif hdr:lower() == 'ham' then
learn_ham = true
else
skip_reason = string.format('no explicit header')
end
end
end
if learn_spam or learn_ham then
local learn_type
if learn_spam then learn_type = 'spam' else learn_type = 'ham' end
local function can_train_cb(err, data)
if not err and type(data) == 'table' then
local nsamples,reason = tonumber(data[1]),data[2]
if nsamples >= 0 then
local coin = math.random()
if coin < 1.0 - train_opts.train_prob then
rspamd_logger.infox(task, 'probabilistically skip sample: %s', coin)
return
end
local vec = result_to_vector(task, set)
local str = rspamd_util.zstd_compress(table.concat(vec, ';'))
local target_key = set.ann.redis_key .. '_' .. learn_type
local function learn_vec_cb(_err)
if _err then
rspamd_logger.errx(task, 'cannot store train vector for %s:%s: %s',
rule.prefix, set.name, _err)
else
lua_util.debugm(N, task,
"add train data for ANN rule " ..
"%s:%s, save %s vector of %s elts in %s key; %s bytes compressed",
rule.prefix, set.name, learn_type, #vec, target_key, #str)
end
end
lua_redis.redis_make_request(task,
rule.redis,
nil,
true, -- is write
learn_vec_cb, --callback
'LPUSH', -- command
{ target_key, str } -- arguments
)
else
-- Negative result returned
rspamd_logger.infox(task, "cannot learn %s ANN %s:%s; redis_key: %s: %s (%s vectors stored)",
learn_type, rule.prefix, set.name, set.ann.redis_key, reason, -tonumber(nsamples))
end
else
if err then
rspamd_logger.errx(task, 'cannot check if we can train %s:%s : %s',
rule.prefix, set.name, err)
else
rspamd_logger.errx(task, 'cannot check if we can train %s:%s : type of Redis key %s is %s, expected table' ..
'please remove this key from Redis manually if you perform upgrade from the previous version',
rule.prefix, set.name, set.ann.redis_key, type(data))
end
end
end
-- Check if we can learn
if set.can_store_vectors then
if not set.ann then
-- Need to create or load a profile corresponding to the current configuration
set.ann = new_ann_profile(task, rule, set, 0)
lua_util.debugm(N, task,
'requested new profile for %s, set.ann is missing',
set.name)
end
lua_redis.exec_redis_script(redis_can_store_train_vec_id,
{task = task, is_write = true},
can_train_cb,
{
set.ann.redis_key,
learn_type,
tostring(train_opts.max_trains),
tostring(math.random()),
})
else
lua_util.debugm(N, task,
'do not push data: train condition not satisfied; reason: not checked existing ANNs')
end
else
lua_util.debugm(N, task,
'do not push data to key %s: train condition not satisfied; reason: %s',
(set.ann or {}).redis_key,
skip_reason)
end
end
--- Offline training logic
-- Closure generator for unlock function
local function gen_unlock_cb(rule, set, ann_key)
return function (err)
if err then
rspamd_logger.errx(rspamd_config, 'cannot unlock ANN %s:%s at %s from redis: %s',
rule.prefix, set.name, ann_key, err)
else
lua_util.debugm(N, rspamd_config, 'unlocked ANN %s:%s at %s',
rule.prefix, set.name, ann_key)
end
end
end
-- This function is intended to extend lock for ANN during training
-- It registers periodic that increases locked key each 30 seconds unless
-- `set.learning_spawned` is set to `true`
local function register_lock_extender(rule, set, ev_base, ann_key)
rspamd_config:add_periodic(ev_base, 30.0,
function()
local function redis_lock_extend_cb(_err, _)
if _err then
rspamd_logger.errx(rspamd_config, 'cannot lock ANN %s from redis: %s',
ann_key, _err)
else
rspamd_logger.infox(rspamd_config, 'extend lock for ANN %s for 30 seconds',
ann_key)
end
end
if set.learning_spawned then
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
redis_lock_extend_cb, --callback
'HINCRBY', -- command
{ann_key, 'lock', '30'}
)
else
lua_util.debugm(N, rspamd_config, "stop lock extension as learning_spawned is false")
return false -- do not plan any more updates
end
return true
end
)
end
-- This function receives training vectors, checks them, spawn learning and saves ANN in Redis
local function spawn_train(worker, ev_base, rule, set, ann_key, ham_vec, spam_vec)
-- Check training data sanity
-- Now we need to join inputs and create the appropriate test vectors
local n = #set.symbols +
meta_functions.rspamd_count_metatokens()
-- Now we can train ann
local train_ann = create_ann(n, 3)
if #ham_vec + #spam_vec < rule.train.max_trains / 2 then
-- Invalidate ANN as it is definitely invalid
-- TODO: add invalidation
assert(false)
else
local inputs, outputs = {}, {}
-- Used to show sparsed vectors in a convenient format (for debugging only)
local function debug_vec(t)
local ret = {}
for i,v in ipairs(t) do
if v ~= 0 then
ret[#ret + 1] = string.format('%d=%.2f', i, v)
end
end
return ret
end
-- Make training set by joining vectors
-- KANN automatically shuffles those samples
-- 1.0 is used for spam and -1.0 is used for ham
-- It implies that output layer can express that (e.g. tanh output)
for _,e in ipairs(spam_vec) do
inputs[#inputs + 1] = e
outputs[#outputs + 1] = {1.0}
--rspamd_logger.debugm(N, rspamd_config, 'spam vector: %s', debug_vec(e))
end
for _,e in ipairs(ham_vec) do
inputs[#inputs + 1] = e
outputs[#outputs + 1] = {-1.0}
--rspamd_logger.debugm(N, rspamd_config, 'ham vector: %s', debug_vec(e))
end
-- Called in child process
local function train()
local log_thresh = rule.train.max_iterations / 10
local seen_nan = false
local function train_cb(iter, train_cost, value_cost)
if (iter * (rule.train.max_iterations / log_thresh)) % (rule.train.max_iterations) == 0 then
if train_cost ~= train_cost and not seen_nan then
-- We have nan :( try to log lot's of stuff to dig into a problem
seen_nan = true
rspamd_logger.errx(rspamd_config, 'ANN %s:%s: train error: observed nan in error cost!; value cost = %s',
rule.prefix, set.name,
value_cost)
for i,e in ipairs(inputs) do
lua_util.debugm(N, rspamd_config, 'train vector %s -> %s',
debug_vec(e), outputs[i][1])
end
end
rspamd_logger.infox(rspamd_config,
"ANN %s:%s: learned from %s redis key in %s iterations, error: %s, value cost: %s",
rule.prefix, set.name,
ann_key,
iter,
train_cost,
value_cost)
end
end
train_ann:train1(inputs, outputs, {
lr = rule.train.learning_rate,
max_epoch = rule.train.max_iterations,
cb = train_cb,
})
if not seen_nan then
local out = train_ann:save()
return out
else
return nil
end
end
set.learning_spawned = true
local function redis_save_cb(err)
if err then
rspamd_logger.errx(rspamd_config, 'cannot save ANN %s:%s to redis key %s: %s',
rule.prefix, set.name, ann_key, err)
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
gen_unlock_cb(rule, set, ann_key), --callback
'HDEL', -- command
{ann_key, 'lock'}
)
else
rspamd_logger.infox(rspamd_config, 'saved ANN %s:%s to redis: %s',
rule.prefix, set.name, set.ann.redis_key)
end
end
local function ann_trained(err, data)
set.learning_spawned = false
if err then
rspamd_logger.errx(rspamd_config, 'cannot train ANN %s:%s : %s',
rule.prefix, set.name, err)
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
gen_unlock_cb(rule, set, ann_key), --callback
'HDEL', -- command
{ann_key, 'lock'}
)
else
local ann_data = rspamd_util.zstd_compress(data)
if not set.ann then
set.ann = {
symbols = set.symbols,
distance = 0,
digest = set.digest,
redis_key = ann_key,
}
end
-- Deserialise ANN from the child process
ann_trained = rspamd_kann.load(data)
local version = (set.ann.version or 0) + 1
set.ann.version = version
set.ann.ann = ann_trained
set.ann.symbols = set.symbols
set.ann.redis_key = new_ann_key(rule, set, version)
local profile = {
symbols = set.symbols,
digest = set.digest,
redis_key = set.ann.redis_key,
version = version
}
local ucl = require "ucl"
local profile_serialized = ucl.to_format(profile, 'json-compact', true)
rspamd_logger.infox(rspamd_config,
'trained ANN %s:%s, %s bytes; redis key: %s (old key %s)',
rule.prefix, set.name, #data, set.ann.redis_key, ann_key)
lua_redis.exec_redis_script(redis_save_unlock_id,
{ev_base = ev_base, is_write = true},
redis_save_cb,
{profile.redis_key,
redis_ann_prefix(rule, set.name),
ann_data,
profile_serialized,
tostring(rule.ann_expire),
tostring(os.time()),
ann_key, -- old key to unlock...
})
end
end
worker:spawn_process{
func = train,
on_complete = ann_trained,
proctitle = string.format("ANN train for %s/%s", rule.prefix, set.name),
}
end
-- Spawn learn and register lock extension
set.learning_spawned = true
register_lock_extender(rule, set, ev_base, ann_key)
end
-- Utility to extract and split saved training vectors to a table of tables
local function process_training_vectors(data)
return fun.totable(fun.map(function(tok)
local _,str = rspamd_util.zstd_decompress(tok)
return fun.totable(fun.map(tonumber, lua_util.str_split(tostring(str), ';')))
end, data))
end
-- This function does the following:
-- * Tries to lock ANN
-- * Loads spam and ham vectors
-- * Spawn learning process
local function do_train_ann(worker, ev_base, rule, set, ann_key)
local spam_elts = {}
local ham_elts = {}
local function redis_ham_cb(err, data)
if err or type(data) ~= 'table' then
rspamd_logger.errx(rspamd_config, 'cannot get ham tokens for ANN %s from redis: %s',
ann_key, err)
-- Unlock on error
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
gen_unlock_cb(rule, set, ann_key), --callback
'HDEL', -- command
{ann_key, 'lock'}
)
else
-- Decompress and convert to numbers each training vector
ham_elts = process_training_vectors(data)
spawn_train(worker, ev_base, rule, set, ann_key, ham_elts, spam_elts)
end
end
-- Spam vectors received
local function redis_spam_cb(err, data)
if err or type(data) ~= 'table' then
rspamd_logger.errx(rspamd_config, 'cannot get spam tokens for ANN %s from redis: %s',
ann_key, err)
-- Unlock ANN on error
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
gen_unlock_cb(rule, set, ann_key), --callback
'HDEL', -- command
{ann_key, 'lock'}
)
else
-- Decompress and convert to numbers each training vector
spam_elts = process_training_vectors(data)
-- Now get ham vectors...
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_ham_cb, --callback
'LRANGE', -- command
{ann_key .. '_ham', '0', '-1'}
)
end
end
local function redis_lock_cb(err, data)
if err then
rspamd_logger.errx(rspamd_config, 'cannot call lock script for ANN %s from redis: %s',
ann_key, err)
elseif type(data) == 'number' and data == 1 then
-- ANN is locked, so we can extract SPAM and HAM vectors and spawn learning
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_spam_cb, --callback
'LRANGE', -- command
{ann_key .. '_spam', '0', '-1'}
)
rspamd_logger.infox(rspamd_config, 'lock ANN %s:%s (key name %s) for learning',
rule.prefix, set.name, ann_key)
else
local lock_tm = tonumber(data[1])
rspamd_logger.infox(rspamd_config, 'do not learn ANN %s:%s (key name %s), ' ..
'locked by another host %s at %s', rule.prefix, set.name, ann_key,
data[2], os.date('%c', lock_tm))
end
end
-- Check if we are already learning this network
if set.learning_spawned then
rspamd_logger.infox(rspamd_config, 'do not learn ANN %s, already learning another ANN',
ann_key)
return
end
-- Call Redis script that tries to acquire a lock
-- This script returns either a boolean or a pair {'lock_time', 'hostname'} when
-- ANN is locked by another host (or a process, meh)
lua_redis.exec_redis_script(redis_maybe_lock_id,
{ev_base = ev_base, is_write = true},
redis_lock_cb,
{
ann_key,
tostring(os.time()),
tostring(rule.watch_interval * 2),
rspamd_util.get_hostname()
})
end
-- This function loads new ann from Redis
-- This is based on `profile` attribute.
-- ANN is loaded from `profile.redis_key`
-- Rank of `profile` key is also increased, unfortunately, it means that we need to
-- serialize profile one more time and set its rank to the current time
-- set.ann fields are set according to Redis data received
local function load_new_ann(rule, ev_base, set, profile, min_diff)
local ann_key = profile.redis_key
local function data_cb(err, data)
if err then
rspamd_logger.errx(rspamd_config, 'cannot get ANN data from key: %s; %s',
ann_key, err)
else
if type(data) == 'string' then
local _err,ann_data = rspamd_util.zstd_decompress(data)
local ann
if _err or not ann_data then
rspamd_logger.errx(rspamd_config, 'cannot decompress ANN for %s from Redis key %s: %s',
rule.prefix .. ':' .. set.name, ann_key, _err)
return
else
ann = rspamd_kann.load(ann_data)
if ann then
set.ann = {
digest = profile.digest,
version = profile.version,
symbols = profile.symbols,
distance = min_diff,
redis_key = profile.redis_key
}
local ucl = require "ucl"
local profile_serialized = ucl.to_format(profile, 'json-compact', true)
set.ann.ann = ann -- To avoid serialization
local function rank_cb(_, _)
-- TODO: maybe add some logging
end
-- Also update rank for the loaded ANN to avoid removal
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
rank_cb, --callback
'ZADD', -- command
{set.prefix, tostring(rspamd_util.get_time()), profile_serialized}
)
rspamd_logger.infox(rspamd_config, 'loaded ANN for %s:%s from %s; %s bytes compressed; version=%s',
rule.prefix, set.name, ann_key, #ann_data, profile.version)
else
rspamd_logger.errx(rspamd_config, 'cannot deserialize ANN for %s:%s from Redis key %s',
rule.prefix, set.name, ann_key)
end
end
else
lua_util.debugm(N, rspamd_config, 'no ANN for %s:%s in Redis key %s',
rule.prefix, set.name, ann_key)
end
end
end
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
data_cb, --callback
'HGET', -- command
{ann_key, 'ann'} -- arguments
)
end
-- Used to check an element in Redis serialized as JSON
-- for some specific rule + some specific setting
-- This function tries to load more fresh or more specific ANNs in lieu of
-- the existing ones.
-- Use this function to load ANNs as `callback` parameter for `check_anns` function
local function process_existing_ann(_, ev_base, rule, set, profiles)
local my_symbols = set.symbols
local min_diff = math.huge
local sel_elt
for _,elt in fun.iter(profiles) do
if elt and elt.symbols then
local dist = lua_util.distance_sorted(elt.symbols, my_symbols)
-- Check distance
if dist < #my_symbols * .3 then
if dist < min_diff then
min_diff = dist
sel_elt = elt
end
end
end
end
if sel_elt then
-- We can load element from ANN
if set.ann then
-- We have an existing ANN, probably the same...
if set.ann.digest == sel_elt.digest then
-- Same ANN, check version
if set.ann.version < sel_elt.version then
-- Load new ann
rspamd_logger.infox(rspamd_config, 'ann %s is changed, ' ..
'our version = %s, remote version = %s',
rule.prefix .. ':' .. set.name,
set.ann.version,
sel_elt.version)
load_new_ann(rule, ev_base, set, sel_elt, min_diff)
else
lua_util.debugm(N, rspamd_config, 'ann %s is not changed, ' ..
'our version = %s, remote version = %s',
rule.prefix .. ':' .. set.name,
set.ann.version,
sel_elt.version)
end
else
-- We have some different ANN, so we need to compare distance
if set.ann.distance > min_diff then
-- Load more specific ANN
rspamd_logger.infox(rspamd_config, 'more specific ann is available for %s, ' ..
'our distance = %s, remote distance = %s',
rule.prefix .. ':' .. set.name,
set.ann.distance,
min_diff)
load_new_ann(rule, ev_base, set, sel_elt, min_diff)
else
lua_util.debugm(N, rspamd_config, 'ann %s is not changed or less specific, ' ..
'our distance = %s, remote distance = %s',
rule.prefix .. ':' .. set.name,
set.ann.distance,
min_diff)
end
end
else
-- We have no ANN, load new one
load_new_ann(rule, ev_base, set, sel_elt, min_diff)
end
end
end
-- This function checks all profiles and selects if we can train our
-- ANN. By our we mean that it has exactly the same symbols in profile.
-- Use this function to train ANN as `callback` parameter for `check_anns` function
local function maybe_train_existing_ann(worker, ev_base, rule, set, profiles)
local my_symbols = set.symbols
local sel_elt
for _,elt in fun.iter(profiles) do
if elt and elt.symbols then
local dist = lua_util.distance_sorted(elt.symbols, my_symbols)
-- Check distance
if dist == 0 then
sel_elt = elt
break
end
end
end
if sel_elt then
-- We have our ANN and that's train vectors, check if we can learn
local ann_key = sel_elt.redis_key
lua_util.debugm(N, rspamd_config, "check if ANN %s needs to be trained",
ann_key)
-- Create continuation closure
local redis_len_cb_gen = function(cont_cb, what, is_final)
return function(err, data)
if err then
rspamd_logger.errx(rspamd_config,
'cannot get ANN %s trains %s from redis: %s', what, ann_key, err)
elseif data and type(data) == 'number' or type(data) == 'string' then
if tonumber(data) and tonumber(data) >= rule.train.max_trains then
if is_final then
rspamd_logger.debugm(N, rspamd_config,
'can start ANN %s learn as it has %s learn vectors; %s required, after checking %s vectors',
ann_key, tonumber(data), rule.train.max_trains, what)
else
rspamd_logger.debugm(N, rspamd_config,
'checked %s vectors in ANN %s: %s vectors; %s required, need to check other class vectors',
what, ann_key, tonumber(data), rule.train.max_trains)
end
cont_cb()
else
rspamd_logger.debugm(N, rspamd_config,
'cannot learn ANN %s now: there are not enough %s learn vectors (has %s vectors; %s required)',
ann_key, what, tonumber(data), rule.train.max_trains)
end
end
end
end
local function initiate_train()
rspamd_logger.infox(rspamd_config,
'need to learn ANN %s after %s required learn vectors',
ann_key, rule.train.max_trains)
do_train_ann(worker, ev_base, rule, set, ann_key)
end
-- Spam vector is OK, check ham vector length
local function check_ham_len()
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_len_cb_gen(initiate_train, 'ham', true), --callback
'LLEN', -- command
{ann_key .. '_ham'}
)
end
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_len_cb_gen(check_ham_len, 'spam', false), --callback
'LLEN', -- command
{ann_key .. '_spam'}
)
end
end
-- Used to deserialise ANN element from a list
local function load_ann_profile(element)
local ucl = require "ucl"
local parser = ucl.parser()
local res,ucl_err = parser:parse_string(element)
if not res then
rspamd_logger.warnx(rspamd_config, 'cannot parse ANN from redis: %s',
ucl_err)
return nil
else
local profile = parser:get_object()
local checked,schema_err = redis_profile_schema:transform(profile)
if not checked then
rspamd_logger.errx(rspamd_config, "cannot parse profile schema: %s", schema_err)
return nil
end
return checked
end
end
-- Function to check or load ANNs from Redis
local function check_anns(worker, cfg, ev_base, rule, process_callback, what)
for _,set in pairs(rule.settings) do
local function members_cb(err, data)
if err then
rspamd_logger.errx(cfg, 'cannot get ANNs list from redis: %s',
err)
set.can_store_vectors = true
elseif type(data) == 'table' then
lua_util.debugm(N, cfg, '%s: process element %s:%s',
what, rule.prefix, set.name)
process_callback(worker, ev_base, rule, set, fun.map(load_ann_profile, data))
set.can_store_vectors = true
end
end
if type(set) == 'table' then
-- Extract all profiles for some specific settings id
-- Get the last `max_profiles` recently used
-- Select the most appropriate to our profile but it should not differ by more
-- than 30% of symbols
lua_redis.redis_make_request_taskless(ev_base,
cfg,
rule.redis,
nil,
false, -- is write
members_cb, --callback
'ZREVRANGE', -- command
{set.prefix, '0', tostring(settings.max_profiles)} -- arguments
)
end
end -- Cycle over all settings
return rule.watch_interval
end
-- Function to clean up old ANNs
local function cleanup_anns(rule, cfg, ev_base)
for _,set in pairs(rule.settings) do
local function invalidate_cb(err, data)
if err then
rspamd_logger.errx(cfg, 'cannot exec invalidate script in redis: %s',
err)
elseif type(data) == 'table' then
for _,expired in ipairs(data) do
local profile = load_ann_profile(expired)
rspamd_logger.infox(cfg, 'invalidated ANN for %s; redis key: %s; version=%s',
rule.prefix .. ':' .. set.name,
profile.redis_key,
profile.version)
end
end
end
if type(set) == 'table' then
lua_redis.exec_redis_script(redis_maybe_invalidate_id,
{ev_base = ev_base, is_write = true},
invalidate_cb,
{set.prefix, tostring(settings.max_profiles)})
end
end
end
local function ann_push_vector(task)
if task:has_flag('skip') then
lua_util.debugm(N, task, 'do not push data for skipped task')
return
end
if not settings.allow_local and lua_util.is_rspamc_or_controller(task) then
lua_util.debugm(N, task, 'do not push data for manual scan')
return
end
local verdict,score = lua_verdict.get_specific_verdict(N, task)
if verdict == 'passthrough' then
lua_util.debugm(N, task, 'ignore task as its verdict is %s(%s)',
verdict, score)
return
end
if score ~= score then
lua_util.debugm(N, task, 'ignore task as its score is nan (%s verdict)',
verdict)
return
end
for _,rule in pairs(settings.rules) do
local set = get_rule_settings(task, rule)
if set then
ann_push_task_result(rule, task, verdict, score, set)
else
lua_util.debugm(N, task, 'settings not found in rule %s', rule.prefix)
end
end
end
-- This function is used to adjust profiles and allowed setting ids for each rule
-- It must be called when all settings are already registered (e.g. at post-init for config)
local function process_rules_settings()
local function process_settings_elt(rule, selt)
local profile = rule.profile[selt.name]
if profile then
-- Use static user defined profile
-- Ensure that we have an array...
lua_util.debugm(N, rspamd_config, "use static profile for %s (%s): %s",
rule.prefix, selt.name, profile)
if not profile[1] then profile = lua_util.keys(profile) end
selt.symbols = profile
else
lua_util.debugm(N, rspamd_config, "use dynamic cfg based profile for %s (%s)",
rule.prefix, selt.name)
end
local function filter_symbols_predicate(sname)
local fl = rspamd_config:get_symbol_flags(sname)
if fl then
fl = lua_util.list_to_hash(fl)
return not (fl.nostat or fl.idempotent or fl.skip)
end
return false
end
-- Generic stuff
table.sort(fun.totable(fun.filter(filter_symbols_predicate, selt.symbols)))
selt.digest = lua_util.table_digest(selt.symbols)
selt.prefix = redis_ann_prefix(rule, selt.name)
lua_redis.register_prefix(selt.prefix, N,
string.format('NN prefix for rule "%s"; settings id "%s"',
rule.prefix, selt.name), {
persistent = true,
type = 'zlist',
})
-- Versions
lua_redis.register_prefix(selt.prefix .. '_\\d+', N,
string.format('NN storage for rule "%s"; settings id "%s"',
rule.prefix, selt.name), {
persistent = true,
type = 'hash',
})
lua_redis.register_prefix(selt.prefix .. '_\\d+_spam', N,
string.format('NN learning set (spam) for rule "%s"; settings id "%s"',
rule.prefix, selt.name), {
persistent = true,
type = 'list',
})
lua_redis.register_prefix(selt.prefix .. '_\\d+_ham', N,
string.format('NN learning set (spam) for rule "%s"; settings id "%s"',
rule.prefix, selt.name), {
persistent = true,
type = 'list',
})
end
for k,rule in pairs(settings.rules) do
if not rule.allowed_settings then
rule.allowed_settings = {}
elseif rule.allowed_settings == 'all' then
-- Extract all settings ids
rule.allowed_settings = lua_util.keys(lua_settings.all_settings())
end
-- Convert to a map <setting_id> -> true
rule.allowed_settings = lua_util.list_to_hash(rule.allowed_settings)
-- Check if we can work without settings
if k == 'default' or type(rule.default) ~= 'boolean' then
rule.default = true
end
rule.settings = {}
if rule.default then
local default_settings = {
symbols = lua_settings.default_symbols(),
name = 'default'
}
process_settings_elt(rule, default_settings)
rule.settings[-1] = default_settings -- Magic constant, but OK as settings are positive int32
end
-- Now, for each allowed settings, we store sorted symbols + digest
-- We set table rule.settings[id] -> { name = name, symbols = symbols, digest = digest }
for s,_ in pairs(rule.allowed_settings) do
-- Here, we have a name, set of symbols and
local settings_id = s
if type(settings_id) ~= 'number' then
settings_id = lua_settings.numeric_settings_id(s)
end
local selt = lua_settings.settings_by_id(settings_id)
local nelt = {
symbols = selt.symbols, -- Already sorted
name = selt.name
}
process_settings_elt(rule, nelt)
for id,ex in pairs(rule.settings) do
if type(ex) == 'table' then
if nelt and lua_util.distance_sorted(ex.symbols, nelt.symbols) == 0 then
-- Equal symbols, add reference
lua_util.debugm(N, rspamd_config,
'added reference from settings id %s to %s; same symbols',
nelt.name, ex.name)
rule.settings[settings_id] = id
nelt = nil
end
end
end
if nelt then
rule.settings[settings_id] = nelt
lua_util.debugm(N, rspamd_config, 'added new settings id %s(%s) to %s',
nelt.name, settings_id, rule.prefix)
end
end
end
end
redis_params = lua_redis.parse_redis_server('neural')
if not redis_params then
redis_params = lua_redis.parse_redis_server('fann_redis')
end
-- Initialization part
if not (module_config and type(module_config) == 'table') or not redis_params then
rspamd_logger.infox(rspamd_config, 'Module is unconfigured')
lua_util.disable_module(N, "redis")
return
end
local rules = module_config['rules']
if not rules then
-- Use legacy configuration
rules = {}
rules['default'] = module_config
end
local id = rspamd_config:register_symbol({
name = 'NEURAL_CHECK',
type = 'postfilter,nostat',
priority = 6,
callback = ann_scores_filter
})
settings = lua_util.override_defaults(settings, module_config)
settings.rules = {} -- Reset unless validated further in the cycle
-- Check all rules
for k,r in pairs(rules) do
local rule_elt = lua_util.override_defaults(default_options, r)
rule_elt['redis'] = redis_params
rule_elt['anns'] = {} -- Store ANNs here
if not rule_elt.prefix then
rule_elt.prefix = k
end
if not rule_elt.name then
rule_elt.name = k
end
if rule_elt.train.max_train then
rule_elt.train.max_trains = rule_elt.train.max_train
end
if not rule_elt.profile then rule_elt.profile = {} end
rspamd_logger.infox(rspamd_config, "register ann rule %s", k)
settings.rules[k] = rule_elt
rspamd_config:set_metric_symbol({
name = rule_elt.symbol_spam,
score = 0.0,
description = 'Neural network SPAM',
group = 'neural'
})
rspamd_config:register_symbol({
name = rule_elt.symbol_spam,
type = 'virtual,nostat',
parent = id
})
rspamd_config:set_metric_symbol({
name = rule_elt.symbol_ham,
score = -0.0,
description = 'Neural network HAM',
group = 'neural'
})
rspamd_config:register_symbol({
name = rule_elt.symbol_ham,
type = 'virtual,nostat',
parent = id
})
end
rspamd_config:register_symbol({
name = 'NEURAL_LEARN',
type = 'idempotent,nostat,explicit_disable',
priority = 5,
callback = ann_push_vector
})
-- Add training scripts
for _,rule in pairs(settings.rules) do
load_scripts(rule.redis)
-- We also need to deal with settings
rspamd_config:add_post_init(process_rules_settings)
-- This function will check ANNs in Redis when a worker is loaded
rspamd_config:add_on_load(function(cfg, ev_base, worker)
if worker:is_scanner() then
rspamd_config:add_periodic(ev_base, 0.0,
function(_, _)
return check_anns(worker, cfg, ev_base, rule, process_existing_ann,
'try_load_ann')
end)
end
if worker:is_primary_controller() then
-- We also want to train neural nets when they have enough data
rspamd_config:add_periodic(ev_base, 0.0,
function(_, _)
-- Clean old ANNs
cleanup_anns(rule, cfg, ev_base)
return check_anns(worker, cfg, ev_base, rule, maybe_train_existing_ann,
'try_train_ann')
end)
end
end)
end