gpt4all/gpt4all-backend/llmodel_shared.cpp
Jared Van Bortel bd307abfe6
backend: fix a crash on inputs greater than n_ctx (#2498)
This fixes a regression in commit 4fc4d94b ("fix chat-style prompt
templates (#1970)"), which moved some return statements into a new
function (LLModel::decodePrompt) without making them return from the
parent as well.

Signed-off-by: Jared Van Bortel <jared@nomic.ai>
2024-07-01 11:33:46 -04:00

313 lines
12 KiB
C++

#include "llmodel.h"
#include <algorithm>
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <functional>
#include <iostream>
#include <optional>
#include <regex>
#include <stdexcept>
#include <string>
#include <unordered_set>
#include <vector>
// TODO(cebtenzzre): replace this with llama_kv_cache_seq_shift for llamamodel (GPT-J needs this as-is)
void LLModel::recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate)
{
int n_keep = shouldAddBOS();
const int32_t n_discard = (promptCtx.n_ctx - n_keep) * promptCtx.contextErase;
// Erase the first percentage of context from the tokens
std::cerr << implementation().modelType() << ": reached the end of the context window so resizing\n";
promptCtx.tokens.erase(promptCtx.tokens.begin() + n_keep, promptCtx.tokens.begin() + n_keep + n_discard);
size_t i = n_keep;
promptCtx.n_past = n_keep;
while (i < promptCtx.tokens.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, promptCtx.tokens.size());
std::vector<int32_t> batch(promptCtx.tokens.begin() + i, promptCtx.tokens.begin() + batch_end);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
if (!evalTokens(promptCtx, batch)) {
std::cerr << "LLModel ERROR: Failed to process prompt\n";
goto stop_generating;
}
promptCtx.n_past += batch.size();
if (!recalculate(true))
goto stop_generating;
i = batch_end;
}
assert(promptCtx.n_past == int32_t(promptCtx.tokens.size()));
stop_generating:
recalculate(false);
}
static bool parsePromptTemplate(const std::string &tmpl, std::vector<std::smatch> &placeholders, std::string &err)
{
static const std::regex placeholderRegex(R"(%[1-2](?![0-9]))");
auto it = std::sregex_iterator(tmpl.begin(), tmpl.end(), placeholderRegex);
placeholders.clear();
placeholders.insert(placeholders.end(), it, std::sregex_iterator());
if (placeholders.size() > 2) {
err = "ERROR: expected at most two placeholders, got " + std::to_string(placeholders.size());
return false;
}
if (placeholders.size() >= 1 && placeholders[0].str() != "%1") {
err = "ERROR: first placeholder must be %1, got " + placeholders[0].str();
return false;
}
if (placeholders.size() >= 2 && placeholders[1].str() != "%2") {
err = "ERROR: second placeholder must be %2, got " + placeholders[1].str();
return false;
}
return true;
}
void LLModel::prompt(const std::string &prompt,
const std::string &promptTemplate,
std::function<bool(int32_t)> promptCallback,
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &promptCtx,
bool special,
std::string *fakeReply)
{
if (!isModelLoaded()) {
std::cerr << implementation().modelType() << " ERROR: prompt won't work with an unloaded model!\n";
return;
}
if (!supportsCompletion()) {
std::string errorMessage = "ERROR: this model does not support text completion or chat!";
responseCallback(-1, errorMessage);
std::cerr << implementation().modelType() << " " << errorMessage << "\n";
return;
}
// parse the prompt template
std::vector<std::smatch> placeholders;
{
std::string err;
if (!parsePromptTemplate(promptTemplate, placeholders, err)) {
responseCallback(-1, err);
std::cerr << err << "\n";
return;
}
}
auto old_n_past = promptCtx.n_past; // prepare to fake n_past for tokenize
// tokenize the user prompt
std::vector<Token> embd_inp;
if (placeholders.empty()) {
// this is unusual, but well-defined
std::cerr << __func__ << ": prompt template has no placeholder\n";
embd_inp = tokenize(promptCtx, promptTemplate, true);
} else {
// template: beginning of user prompt
const auto &phUser = placeholders[0];
std::string userPrefix(phUser.prefix());
if (!userPrefix.empty()) {
embd_inp = tokenize(promptCtx, userPrefix, true);
promptCtx.n_past += embd_inp.size();
}
// user input (shouldn't have special token processing)
auto tokens = tokenize(promptCtx, prompt, special);
embd_inp.insert(embd_inp.end(), tokens.begin(), tokens.end());
promptCtx.n_past += tokens.size();
// template: end of user prompt + start of assistant prompt
size_t start = phUser.position() + phUser.length();
size_t end = placeholders.size() >= 2 ? placeholders[1].position() : promptTemplate.length();
auto userToAsst = promptTemplate.substr(start, end - start);
if (!userToAsst.empty()) {
tokens = tokenize(promptCtx, userToAsst, true);
embd_inp.insert(embd_inp.end(), tokens.begin(), tokens.end());
promptCtx.n_past += tokens.size();
}
}
promptCtx.n_past = old_n_past; // restore n_past so decodePrompt can increment it
// decode the user prompt
if (!decodePrompt(promptCallback, responseCallback, recalculateCallback, promptCtx, embd_inp))
return; // error
// decode the assistant's reply, either generated or spoofed
if (fakeReply == nullptr) {
generateResponse(responseCallback, recalculateCallback, promptCtx);
} else {
embd_inp = tokenize(promptCtx, *fakeReply, false);
if (!decodePrompt(promptCallback, responseCallback, recalculateCallback, promptCtx, embd_inp))
return; // error
}
// decode the rest of the prompt template
// template: end of assistant prompt
std::string asstSuffix;
if (placeholders.size() >= 2) {
size_t start = placeholders[1].position() + placeholders[1].length();
asstSuffix = promptTemplate.substr(start);
} else {
asstSuffix = "\n\n"; // default to a blank link, good for e.g. Alpaca
}
if (!asstSuffix.empty()) {
embd_inp = tokenize(promptCtx, asstSuffix, true);
decodePrompt(promptCallback, responseCallback, recalculateCallback, promptCtx, embd_inp);
}
}
// returns false on error
bool LLModel::decodePrompt(std::function<bool(int32_t)> promptCallback,
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &promptCtx,
std::vector<Token> embd_inp) {
// save the context size
promptCtx.n_ctx = contextLength();
if ((int) embd_inp.size() > promptCtx.n_ctx - 4) {
responseCallback(-1, "ERROR: The prompt size exceeds the context window size and cannot be processed.");
std::cerr << implementation().modelType() << " ERROR: The prompt is " << embd_inp.size() <<
" tokens and the context window is " << promptCtx.n_ctx << "!\n";
return false;
}
promptCtx.n_predict = std::min(promptCtx.n_predict, promptCtx.n_ctx - (int) embd_inp.size());
promptCtx.n_past = std::min(promptCtx.n_past, promptCtx.n_ctx);
promptCtx.n_batch = std::min(promptCtx.n_batch, LLMODEL_MAX_PROMPT_BATCH);
// process the prompt in batches
size_t i = 0;
while (i < embd_inp.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, embd_inp.size());
std::vector<Token> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);
// Check if the context has run out...
if (promptCtx.n_past + int32_t(batch.size()) > promptCtx.n_ctx) {
recalculateContext(promptCtx, recalculateCallback);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
}
if (!evalTokens(promptCtx, batch)) {
std::cerr << implementation().modelType() << " ERROR: Failed to process prompt\n";
return false;
}
size_t tokens = batch_end - i;
for (size_t t = 0; t < tokens; ++t) {
if (int32_t(promptCtx.tokens.size()) == promptCtx.n_ctx)
promptCtx.tokens.erase(promptCtx.tokens.begin());
promptCtx.tokens.push_back(batch.at(t));
promptCtx.n_past += 1;
if (!promptCallback(batch.at(t)))
return false;
}
i = batch_end;
}
return true;
}
void LLModel::generateResponse(std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &promptCtx) {
std::string cachedResponse;
std::vector<Token> cachedTokens;
std::unordered_set<std::string> reversePrompts
= { "### Instruction", "### Prompt", "### Response", "### Human", "### Assistant", "### Context" };
// predict next tokens
for (int i = 0; i < promptCtx.n_predict; i++) {
// sample next token
auto id = sampleToken(promptCtx);
// Check if the context has run out...
if (promptCtx.n_past + 1 > promptCtx.n_ctx) {
recalculateContext(promptCtx, recalculateCallback);
assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
}
if (!evalTokens(promptCtx, { id })) {
std::cerr << implementation().modelType() << " ERROR: Failed to predict next token\n";
return;
}
// display text
for (const auto token : endTokens()) {
if (id == token) return;
}
const std::string str = tokenToString(id);
// Check if the provided str is part of our reverse prompts
bool foundPartialReversePrompt = false;
const std::string completed = cachedResponse + std::string(str);
if (reversePrompts.find(completed) != reversePrompts.end())
return;
// Check if it partially matches our reverse prompts and if so, cache
for (const auto& s : reversePrompts) {
if (s.compare(0, completed.size(), completed) == 0) {
foundPartialReversePrompt = true;
cachedResponse = completed;
break;
}
}
// Regardless the token gets added to our cache
cachedTokens.push_back(id);
// Continue if we have found a partial match
if (foundPartialReversePrompt)
continue;
// Empty the cache
for (auto t : cachedTokens) {
if (int32_t(promptCtx.tokens.size()) == promptCtx.n_ctx)
promptCtx.tokens.erase(promptCtx.tokens.begin());
promptCtx.tokens.push_back(t);
promptCtx.n_past += 1;
//TODO: Conversion to std::string can be avoided here...
if (!responseCallback(t, std::string(tokenToString(t))))
return;
}
cachedTokens.clear();
}
}
void LLModel::embed(
const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
size_t *tokenCount, bool doMean, bool atlas, EmbedCancelCallback *cancelCb
) {
(void)texts;
(void)embeddings;
(void)prefix;
(void)dimensionality;
(void)tokenCount;
(void)doMean;
(void)atlas;
(void)cancelCb;
throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
}
void LLModel::embed(
const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
bool doMean, bool atlas
) {
(void)texts;
(void)embeddings;
(void)isRetrieval;
(void)dimensionality;
(void)tokenCount;
(void)doMean;
(void)atlas;
throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
}