gpt4all/gpt4all-backend/llmodel.h
Aaron Miller 88616fde7f
llmodel: change tokenToString to not use string_view (#968)
fixes a definite use-after-free and likely avoids some other
potential ones - std::string will convert to a std::string_view
automatically but as soon as the std::string in question goes out of
scope it is already freed and the string_view is pointing at freed
memory - this is *mostly* fine if its returning a reference to the
tokenizer's internal vocab table but it's, imo, too easy to return a
reference to a dynamically constructed string with this as replit is
doing (and unfortunately needs to do to convert the internal whitespace
replacement symbol back to a space)
2023-06-13 07:14:02 -04:00

102 lines
3.7 KiB
C++

#ifndef LLMODEL_H
#define LLMODEL_H
#include <string>
#include <functional>
#include <vector>
#include <string_view>
#include <fstream>
#include <cstdint>
#include <limits>
class Dlhandle;
class LLModel {
public:
using Token = int32_t;
class Implementation {
LLModel *(*construct_)();
public:
Implementation(Dlhandle&&);
Implementation(const Implementation&) = delete;
Implementation(Implementation&&);
~Implementation();
static bool isImplementation(const Dlhandle&);
std::string_view modelType, buildVariant;
bool (*magicMatch)(std::ifstream& f);
Dlhandle *dlhandle;
// The only way an implementation should be constructed
LLModel *construct() const {
auto fres = construct_();
fres->m_implementation = this;
return fres;
}
};
struct PromptContext {
std::vector<float> logits; // logits of current context
std::vector<int32_t> tokens; // current tokens in the context window
int32_t n_past = 0; // number of tokens in past conversation
int32_t n_ctx = 0; // number of tokens possible in context window
int32_t n_predict = 200;
int32_t top_k = 40;
float top_p = 0.9f;
float temp = 0.9f;
int32_t n_batch = 9;
float repeat_penalty = 1.10f;
int32_t repeat_last_n = 64; // last n tokens to penalize
float contextErase = 0.75f; // percent of context to erase if we exceed the context
// window
};
explicit LLModel() {}
virtual ~LLModel() {}
virtual bool loadModel(const std::string &modelPath) = 0;
virtual bool isModelLoaded() const = 0;
virtual size_t stateSize() const { return 0; }
virtual size_t saveState(uint8_t */*dest*/) const { return 0; }
virtual size_t restoreState(const uint8_t */*src*/) { return 0; }
virtual void prompt(const std::string &prompt,
std::function<bool(int32_t)> promptCallback,
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &ctx);
virtual void setThreadCount(int32_t /*n_threads*/) {}
virtual int32_t threadCount() const { return 1; }
const Implementation& implementation() const {
return *m_implementation;
}
static const std::vector<Implementation>& implementationList();
static const Implementation *implementation(std::ifstream& f, const std::string& buildVariant);
static LLModel *construct(const std::string &modelPath, std::string buildVariant = "auto");
static void setImplementationsSearchPath(const std::string& path);
static const std::string& implementationsSearchPath();
protected:
// These are pure virtual because subclasses need to implement as the default implementation of
// 'prompt' above calls these functions
virtual std::vector<Token> tokenize(PromptContext &, const std::string&) const = 0;
virtual std::string tokenToString(Token) const = 0;
virtual Token sampleToken(PromptContext &ctx) const = 0;
virtual bool evalTokens(PromptContext &/*ctx*/, const std::vector<int32_t>& /*tokens*/) const = 0;
virtual int32_t contextLength() const = 0;
virtual const std::vector<Token>& endTokens() const = 0;
// This is a helper function called from the default implementation of 'prompt' but it can be
// shared by all base classes so it isn't virtual
void recalculateContext(PromptContext &promptCtx, std::function<bool(bool)> recalculate);
const Implementation *m_implementation = nullptr;
};
#endif // LLMODEL_H