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expose n_gpu_layers parameter of llama.cpp (#1890)

Browse Source 
Also dynamically limit the GPU layers and context length fields to the maximum supported by the model.

Signed-off-by: Jared Van Bortel <jared@nomic.ai>
pull/1788/head^2
Jared Van Bortel 8 months ago committed by GitHub
parent f549d5a70a
commit 061d1969f8
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
31 changed files with 381 additions and 157 deletions
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6
gpt4all-backend/bert.cpp
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@ -709,9 +709,10 @@ Bert::~Bert() {
bert_free(d_ptr->ctx);
}
bool Bert::loadModel(const std::string &modelPath, int n_ctx)
bool Bert::loadModel(const std::string &modelPath, int n_ctx, int ngl)
{
(void)n_ctx;
(void)ngl;
d_ptr->ctx = bert_load_from_file(modelPath.c_str());
d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
d_ptr->modelLoaded = d_ptr->ctx != nullptr;
@ -724,10 +725,11 @@ bool Bert::isModelLoaded() const
return d_ptr->modelLoaded;
}
size_t Bert::requiredMem(const std::string &modelPath, int n_ctx)
size_t Bert::requiredMem(const std::string &modelPath, int n_ctx, int ngl)
{
(void)modelPath;
(void)n_ctx;
(void)ngl;
return 0;
}

4
gpt4all-backend/bert_impl.h
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@ -18,9 +18,9 @@ public:
bool supportsEmbedding() const override { return true; }
bool supportsCompletion() const override { return true; }
bool loadModel(const std::string &modelPath, int n_ctx) override;
bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
bool isModelLoaded() const override;
size_t requiredMem(const std::string &modelPath, int n_ctx) override;
size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
size_t stateSize() const override;
size_t saveState(uint8_t *dest) const override;
size_t restoreState(const uint8_t *src) override;

6
gpt4all-backend/gptj.cpp
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@ -672,8 +672,9 @@ GPTJ::GPTJ()
d_ptr->modelLoaded = false;
}
size_t GPTJ::requiredMem(const std::string &modelPath, int n_ctx) {
size_t GPTJ::requiredMem(const std::string &modelPath, int n_ctx, int ngl) {
(void)n_ctx;
(void)ngl;
gptj_model dummy_model;
gpt_vocab dummy_vocab;
size_t mem_req;
@ -681,8 +682,9 @@ size_t GPTJ::requiredMem(const std::string &modelPath, int n_ctx) {
return mem_req;
}
bool GPTJ::loadModel(const std::string &modelPath, int n_ctx) {
bool GPTJ::loadModel(const std::string &modelPath, int n_ctx, int ngl) {
(void)n_ctx;
(void)ngl;
std::mt19937 rng(time(NULL));
d_ptr->rng = rng;

4
gpt4all-backend/gptj_impl.h
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@ -17,9 +17,9 @@ public:
bool supportsEmbedding() const override { return false; }
bool supportsCompletion() const override { return true; }
bool loadModel(const std::string &modelPath, int n_ctx) override;
bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
bool isModelLoaded() const override;
size_t requiredMem(const std::string &modelPath, int n_ctx) override;
size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
size_t stateSize() const override;
size_t saveState(uint8_t *dest) const override;
size_t restoreState(const uint8_t *src) override;

2
gpt4all-backend/llama.cpp-mainline

@ -1 +1 @@
Subproject commit 28921b84e4547c42fd7d23615d92c9d894a6cc2d
Subproject commit 997a733992d38fe5851f68f4e69cf24fa983e8f1

134
gpt4all-backend/llamamodel.cpp
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@ -32,6 +32,9 @@
#include "ggml-kompute.h"
#endif
// Maximum supported GGUF version
static constexpr int GGUF_VER_MAX = 3;
namespace {
const char *modelType_ = "LLaMA";
}
@ -121,8 +124,9 @@ struct llama_file_hparams {
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
};
size_t LLamaModel::requiredMem(const std::string &modelPath, int n_ctx) {
size_t LLamaModel::requiredMem(const std::string &modelPath, int n_ctx, int ngl) {
// TODO(cebtenzzre): update to GGUF
(void)ngl; // FIXME(cetenzzre): use this value
auto fin = std::ifstream(modelPath, std::ios::binary);
fin.seekg(0, std::ios_base::end);
size_t filesize = fin.tellg();
@ -144,7 +148,7 @@ size_t LLamaModel::requiredMem(const std::string &modelPath, int n_ctx) {
return filesize + est_kvcache_size;
}
bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx)
bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx, int ngl)
{
gpt_params params;
@ -168,11 +172,14 @@ bool LLamaModel::loadModel(const std::string &modelPath, int n_ctx)
if (llama_verbose()) {
std::cerr << "llama.cpp: using Metal" << std::endl;
}
// always fully offload on Metal
// TODO(cebtenzzre): use this parameter to allow using more than 53% of system RAM to load a model
d_ptr->model_params.n_gpu_layers = 100;
#elif defined(GGML_USE_KOMPUTE)
if (d_ptr->device != -1) {
d_ptr->model_params.main_gpu = d_ptr->device;
d_ptr->model_params.n_gpu_layers = 100;
d_ptr->model_params.n_gpu_layers = ngl;
}
#endif
@ -323,13 +330,70 @@ const std::vector<LLModel::Token> &LLamaModel::endTokens() const
return d_ptr->end_tokens;
}
#if defined(GGML_USE_KOMPUTE)
#include "ggml-kompute.h"
#endif
std::string get_arch_name(gguf_context *ctx_gguf) {
std::string arch_name;
const int kid = gguf_find_key(ctx_gguf, "general.architecture");
enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
if (ktype != (GGUF_TYPE_STRING)) {
throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
}
return gguf_get_val_str(ctx_gguf, kid);
}
static gguf_context *load_gguf(const char *fname, std::string &arch) {
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ nullptr,
};
gguf_context *ctx = gguf_init_from_file(fname, params);
if (!ctx) {
std::cerr << __func__ << ": gguf_init_from_file failed\n";
return nullptr;
}
int gguf_ver = gguf_get_version(ctx);
if (gguf_ver > GGUF_VER_MAX) {
std::cerr << __func__ << ": unsupported gguf version: " << gguf_ver << "\n";
gguf_free(ctx);
return nullptr;
}
arch = get_arch_name(ctx);
return ctx;
}
static int32_t get_arch_key_u32(std::string const &modelPath, std::string const &archKey) {
std::string arch;
auto * ctx = load_gguf(modelPath.c_str(), arch);
int32_t value = -1;
if (ctx) {
auto key = arch + "." + archKey;
int keyidx = gguf_find_key(ctx, key.c_str());
if (keyidx != -1) {
value = gguf_get_val_u32(ctx, keyidx);
} else {
std::cerr << __func__ << ": " << key << "not found in " << modelPath << "\n";
}
}
std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired)
gguf_free(ctx);
return value;
}
int32_t LLamaModel::maxContextLength(std::string const &modelPath) const
{
#if defined(GGML_USE_KOMPUTE)
return get_arch_key_u32(modelPath, "context_length");
}
int32_t LLamaModel::layerCount(std::string const &modelPath) const
{
return get_arch_key_u32(modelPath, "block_count");
}
std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired) const
{
#ifdef GGML_USE_KOMPUTE
size_t count = 0;
auto * vkDevices = ggml_vk_available_devices(memoryRequired, &count);
@ -346,6 +410,7 @@ std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryReq
/* name = */ dev.name,
/* vendor = */ dev.vendor
);
ggml_vk_device_destroy(&dev);
}
free(vkDevices);
@ -356,7 +421,7 @@ std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryReq
return {};
}
bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string &name)
bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string &name) const
{
#if defined(GGML_USE_KOMPUTE)
ggml_vk_device device;
@ -372,11 +437,11 @@ bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string &n
return false;
}
bool LLamaModel::initializeGPUDevice(const LLModel::GPUDevice &device, std::string *unavail_reason)
bool LLamaModel::initializeGPUDevice(int device, std::string *unavail_reason) const
{
#if defined(GGML_USE_KOMPUTE)
(void)unavail_reason;
d_ptr->device = device.index;
d_ptr->device = device;
return true;
#else
(void)device;
@ -387,17 +452,6 @@ bool LLamaModel::initializeGPUDevice(const LLModel::GPUDevice &device, std::stri
#endif
}
bool LLamaModel::initializeGPUDevice(int device)
{
#if defined(GGML_USE_KOMPUTE)
d_ptr->device = device;
return true;
#else
(void)device;
return false;
#endif
}
bool LLamaModel::hasGPUDevice()
{
#if defined(GGML_USE_KOMPUTE)
@ -418,16 +472,6 @@ bool LLamaModel::usingGPUDevice()
#endif
}
std::string get_arch_name(gguf_context *ctx_gguf) {
std::string arch_name;
const int kid = gguf_find_key(ctx_gguf, "general.architecture");
enum gguf_type ktype = gguf_get_kv_type(ctx_gguf, kid);
if (ktype != (GGUF_TYPE_STRING)) {
throw std::runtime_error("ERROR: Can't get general architecture from gguf file.");
}
return gguf_get_val_str(ctx_gguf, kid);
}
#if defined(_WIN32)
#define DLL_EXPORT __declspec(dllexport)
#else
@ -447,35 +491,19 @@ DLL_EXPORT const char *get_build_variant() {
return GGML_BUILD_VARIANT;
}
DLL_EXPORT bool magic_match(const char * fname) {
struct ggml_context * ctx_meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
gguf_context *ctx_gguf = gguf_init_from_file(fname, params);
if (!ctx_gguf) {
std::cerr << __func__ << ": gguf_init_from_file failed\n";
return false;
}
DLL_EXPORT bool magic_match(const char *fname) {
std::string arch;
auto * ctx = load_gguf(fname, arch);
bool valid = true;
int gguf_ver = gguf_get_version(ctx_gguf);
if (valid && gguf_ver > 3) {
std::cerr << __func__ << ": unsupported gguf version: " << gguf_ver << "\n";
valid = false;
}
auto arch = get_arch_name(ctx_gguf);
if (valid && !(arch == "llama" || arch == "starcoder" || arch == "falcon" || arch == "mpt")) {
if (!(arch == "llama" || arch == "starcoder" || arch == "falcon" || arch == "mpt")) {
if (!(arch == "gptj" || arch == "bert")) { // we support these via other modules
std::cerr << __func__ << ": unsupported model architecture: " << arch << "\n";
}
valid = false;
}
gguf_free(ctx_gguf);
gguf_free(ctx);
return valid;
}

19
gpt4all-backend/llamamodel_impl.h
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@ -4,8 +4,9 @@
#ifndef LLAMAMODEL_H
#define LLAMAMODEL_H
#include <string>
#include <functional>
#include <memory>
#include <string>
#include <vector>
#include "llmodel.h"
@ -17,23 +18,22 @@ public:
bool supportsEmbedding() const override { return false; }
bool supportsCompletion() const override { return true; }
bool loadModel(const std::string &modelPath, int n_ctx) override;
bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
bool isModelLoaded() const override;
size_t requiredMem(const std::string &modelPath, int n_ctx) override;
size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
size_t stateSize() const override;
size_t saveState(uint8_t *dest) const override;
size_t restoreState(const uint8_t *src) override;
void setThreadCount(int32_t n_threads) override;
int32_t threadCount() const override;
std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) override;
bool initializeGPUDevice(size_t memoryRequired, const std::string& name) override;
bool initializeGPUDevice(const GPUDevice &device, std::string *unavail_reason) override;
bool initializeGPUDevice(int device) override;
std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) const override;
bool initializeGPUDevice(size_t memoryRequired, const std::string& name) const override;
bool initializeGPUDevice(int device, std::string *unavail_reason) const override;
bool hasGPUDevice() override;
bool usingGPUDevice() override;
private:
LLamaPrivate *d_ptr;
std::unique_ptr<LLamaPrivate> d_ptr;
protected:
std::vector<Token> tokenize(PromptContext &, const std::string&) const override;
@ -42,6 +42,9 @@ protected:
bool evalTokens(PromptContext& ctx, const std::vector<int32_t> &tokens) const override;
int32_t contextLength() const override;
const std::vector<Token>& endTokens() const override;
int32_t maxContextLength(std::string const &modelPath) const override;
int32_t layerCount(std::string const &modelPath) const override;
};
#endif // LLAMAMODEL_H

55
gpt4all-backend/llmodel.cpp
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@ -2,15 +2,17 @@
#include "dlhandle.h"
#include "sysinfo.h"
#include <iostream>
#include <string>
#include <vector>
#include <fstream>
#include <filesystem>
#include <cassert>
#include <cstdlib>
#include <sstream>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <memory>
#include <regex>
#include <sstream>
#include <string>
#include <vector>
#ifdef _MSC_VER
#include <intrin.h>
#endif
@ -158,7 +160,7 @@ LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::s
* load time, not construct time. right now n_ctx is incorrectly hardcoded 2048 in
* most (all?) places where this is called, causing underestimation of required
* memory. */
size_t req_mem = metalimpl->requiredMem(modelPath, n_ctx);
size_t req_mem = metalimpl->requiredMem(modelPath, n_ctx, 100);
float req_to_total = (float) req_mem / (float) total_mem;
// on a 16GB M2 Mac a 13B q4_0 (0.52) works for me but a 13B q4_K_M (0.55) does not
if (req_to_total >= 0.53) {
@ -193,26 +195,39 @@ LLModel *LLModel::Implementation::construct(const std::string &modelPath, std::s
}
LLModel *LLModel::Implementation::constructDefaultLlama() {
const LLModel::Implementation *impl = nullptr;
for (const auto &i : implementationList()) {
if (i.m_buildVariant == "metal" || i.m_modelType != "LLaMA") continue;
impl = &i;
}
if (!impl) {
std::cerr << "LLModel ERROR: Could not find CPU LLaMA implementation\n";
return nullptr;
}
auto fres = impl->m_construct();
fres->m_implementation = impl;
return fres;
static std::unique_ptr<LLModel> llama([]() -> LLModel * {
const LLModel::Implementation *impl = nullptr;
for (const auto &i : implementationList()) {
if (i.m_buildVariant == "metal" || i.m_modelType != "LLaMA") continue;
impl = &i;
}
if (!impl) {
std::cerr << "LLModel ERROR: Could not find CPU LLaMA implementation\n";
return nullptr;
}
auto fres = impl->m_construct();
fres->m_implementation = impl;
return fres;
}());
return llama.get();
}
std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices() {
static LLModel *llama = LLModel::Implementation::constructDefaultLlama(); // (memory leak)
auto * llama = constructDefaultLlama();
if (llama) { return llama->availableGPUDevices(0); }
return {};
}
int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath) {
auto * llama = constructDefaultLlama();
return llama ? llama->maxContextLength(modelPath) : -1;
}
int32_t LLModel::Implementation::layerCount(const std::string &modelPath) {
auto * llama = constructDefaultLlama();
return llama ? llama->layerCount(modelPath) : -1;
}
void LLModel::Implementation::setImplementationsSearchPath(const std::string& path) {
s_implementations_search_path = path;
}

25
gpt4all-backend/llmodel.h
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@ -42,6 +42,8 @@ public:
static const Implementation *implementation(const char *fname, const std::string& buildVariant);
static LLModel *construct(const std::string &modelPath, std::string buildVariant = "auto", int n_ctx = 2048);
static std::vector<GPUDevice> availableGPUDevices();
static int32_t maxContextLength(const std::string &modelPath);
static int32_t layerCount(const std::string &modelPath);
static void setImplementationsSearchPath(const std::string& path);
static const std::string& implementationsSearchPath();
@ -77,9 +79,9 @@ public:
virtual bool supportsEmbedding() const = 0;
virtual bool supportsCompletion() const = 0;
virtual bool loadModel(const std::string &modelPath, int n_ctx) = 0;
virtual bool loadModel(const std::string &modelPath, int n_ctx, int ngl) = 0;
virtual bool isModelLoaded() const = 0;
virtual size_t requiredMem(const std::string &modelPath, int n_ctx) = 0;
virtual size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) = 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; }
@ -101,18 +103,18 @@ public:
return *m_implementation;
}
virtual std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) {
virtual std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) const {
(void)memoryRequired;
return {};
}
virtual bool initializeGPUDevice(size_t memoryRequired, const std::string& name) {
virtual bool initializeGPUDevice(size_t memoryRequired, const std::string& name) const {
(void)memoryRequired;
(void)name;
return false;
}
virtual bool initializeGPUDevice(const GPUDevice & device, std::string *unavail_reason = nullptr) {
virtual bool initializeGPUDevice(int device, std::string *unavail_reason = nullptr) const {
(void)device;
if (unavail_reason) {
*unavail_reason = "model has no GPU support";
@ -120,7 +122,6 @@ public:
return false;
}
virtual bool initializeGPUDevice(int /*device*/) { return false; }
virtual bool hasGPUDevice() { return false; }
virtual bool usingGPUDevice() { return false; }
@ -134,6 +135,18 @@ protected:
virtual int32_t contextLength() const = 0;
virtual const std::vector<Token>& endTokens() const = 0;
virtual int32_t maxContextLength(std::string const &modelPath) const
{
(void)modelPath;
return -1;
}
virtual int32_t layerCount(std::string const &modelPath) const
{
(void)modelPath;
return -1;
}
// 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);

17
gpt4all-backend/llmodel_c.cpp
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@ -47,16 +47,16 @@ void llmodel_model_destroy(llmodel_model model) {
delete reinterpret_cast<LLModelWrapper*>(model);
}
size_t llmodel_required_mem(llmodel_model model, const char *model_path, int n_ctx)
size_t llmodel_required_mem(llmodel_model model, const char *model_path, int n_ctx, int ngl)
{
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
return wrapper->llModel->requiredMem(model_path, n_ctx);
return wrapper->llModel->requiredMem(model_path, n_ctx, ngl);
}
bool llmodel_loadModel(llmodel_model model, const char *model_path, int n_ctx)
bool llmodel_loadModel(llmodel_model model, const char *model_path, int n_ctx, int ngl)
{
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
return wrapper->llModel->loadModel(model_path, n_ctx);
return wrapper->llModel->loadModel(model_path, n_ctx, ngl);
}
bool llmodel_isModelLoaded(llmodel_model model)
@ -230,15 +230,8 @@ bool llmodel_gpu_init_gpu_device_by_string(llmodel_model model, size_t memoryReq
bool llmodel_gpu_init_gpu_device_by_struct(llmodel_model model, const llmodel_gpu_device *device)
{
LLModel::GPUDevice d(
/* index = */ device->index,
/* type = */ device->type,
/* heapSize = */ device->heapSize,
/* name = */ device->name,
/* vendor = */ device->vendor
);
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
return wrapper->llModel->initializeGPUDevice(d);
return wrapper->llModel->initializeGPUDevice(device->index);
}
bool llmodel_gpu_init_gpu_device_by_int(llmodel_model model, int device)

6
gpt4all-backend/llmodel_c.h
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@ -111,18 +111,20 @@ void llmodel_model_destroy(llmodel_model model);
* @param model A pointer to the llmodel_model instance.
* @param model_path A string representing the path to the model file.
* @param n_ctx Maximum size of context window
* @param ngl Number of GPU layers to use (Vulkan)
* @return size greater than 0 if the model was parsed successfully, 0 if file could not be parsed.
*/
size_t llmodel_required_mem(llmodel_model model, const char *model_path, int n_ctx);
size_t llmodel_required_mem(llmodel_model model, const char *model_path, int n_ctx, int ngl);
/**
* Load a model from a file.
* @param model A pointer to the llmodel_model instance.
* @param model_path A string representing the path to the model file.
* @param n_ctx Maximum size of context window
* @param ngl Number of GPU layers to use (Vulkan)
* @return true if the model was loaded successfully, false otherwise.
*/
bool llmodel_loadModel(llmodel_model model, const char *model_path, int n_ctx);
bool llmodel_loadModel(llmodel_model model, const char *model_path, int n_ctx, int ngl);
/**
* Check if a model is loaded.

2
gpt4all-bindings/csharp/Gpt4All/Bindings/LLModel.cs
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@ -183,7 +183,7 @@ public class LLModel : ILLModel
/// <returns>true if the model was loaded successfully, false otherwise.</returns>
public bool Load(string modelPath)
{
return NativeMethods.llmodel_loadModel(_handle, modelPath, 2048);
return NativeMethods.llmodel_loadModel(_handle, modelPath, 2048, 100);
}
protected void Destroy()

3
gpt4all-bindings/csharp/Gpt4All/Bindings/NativeMethods.cs
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@ -71,7 +71,8 @@ internal static unsafe partial class NativeMethods
public static extern bool llmodel_loadModel(
[NativeTypeName("llmodel_model")] IntPtr model,
[NativeTypeName("const char *")][MarshalAs(UnmanagedType.LPUTF8Str)] string model_path,
[NativeTypeName("int32_t")] int n_ctx);
[NativeTypeName("int32_t")] int n_ctx,
[NativeTypeName("int32_t")] int ngl);
[DllImport("libllmodel", CallingConvention = CallingConvention.Cdecl, ExactSpelling = true)]

2
gpt4all-bindings/csharp/Gpt4All/Model/Gpt4AllModelFactory.cs
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@ -43,7 +43,7 @@ public class Gpt4AllModelFactory : IGpt4AllModelFactory
}
_logger.LogDebug("Model created handle=0x{ModelHandle:X8}", handle);
_logger.LogInformation("Model loading started");
var loadedSuccessfully = NativeMethods.llmodel_loadModel(handle, modelPath, 2048);
var loadedSuccessfully = NativeMethods.llmodel_loadModel(handle, modelPath, 2048, 100);
_logger.LogInformation("Model loading completed success={ModelLoadSuccess}", loadedSuccessfully);
if (!loadedSuccessfully)
{

2
gpt4all-bindings/golang/binding.cpp
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@ -23,7 +23,7 @@ void* load_model(const char *fname, int n_threads) {
fprintf(stderr, "%s: error '%s'\n", __func__, new_error);
return nullptr;
}
if (!llmodel_loadModel(model, fname, 2048)) {
if (!llmodel_loadModel(model, fname, 2048, 100)) {
llmodel_model_destroy(model);
return nullptr;
}

2
gpt4all-bindings/java/src/main/java/com/hexadevlabs/gpt4all/LLModel.java
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@ -195,7 +195,7 @@ public class LLModel implements AutoCloseable {
if(model == null) {
throw new IllegalStateException("Could not load, gpt4all backend returned error: " + error.getValue().getString(0));
}
library.llmodel_loadModel(model, modelPathAbs, 2048);
library.llmodel_loadModel(model, modelPathAbs, 2048, 100);
if(!library.llmodel_isModelLoaded(model)){
throw new IllegalStateException("The model " + modelName + " could not be loaded");

2
gpt4all-bindings/java/src/main/java/com/hexadevlabs/gpt4all/LLModelLibrary.java
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@ -61,7 +61,7 @@ public interface LLModelLibrary {
Pointer llmodel_model_create2(String model_path, String build_variant, PointerByReference error);
void llmodel_model_destroy(Pointer model);
boolean llmodel_loadModel(Pointer model, String model_path, int n_ctx);
boolean llmodel_loadModel(Pointer model, String model_path, int n_ctx, int ngl);
boolean llmodel_isModelLoaded(Pointer model);
@u_int64_t long llmodel_get_state_size(Pointer model);
@u_int64_t long llmodel_save_state_data(Pointer model, Pointer dest);

6
gpt4all-bindings/python/gpt4all/gpt4all.py
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@ -70,6 +70,7 @@ class GPT4All:
n_threads: Optional[int] = None,
device: Optional[str] = "cpu",
n_ctx: int = 2048,
ngl: int = 100,
verbose: bool = False,
):
"""
@ -92,6 +93,7 @@ class GPT4All:
Note: If a selected GPU device does not have sufficient RAM to accommodate the model, an error will be thrown, and the GPT4All instance will be rendered invalid. It's advised to ensure the device has enough memory before initiating the model.
n_ctx: Maximum size of context window
ngl: Number of GPU layers to use (Vulkan)
verbose: If True, print debug messages.
"""
self.model_type = model_type
@ -99,8 +101,8 @@ class GPT4All:
# Retrieve model and download if allowed
self.config: ConfigType = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download, verbose=verbose)
if device is not None and device != "cpu":
self.model.init_gpu(model_path=self.config["path"], device=device, n_ctx=n_ctx)
self.model.load_model(self.config["path"], n_ctx)
self.model.init_gpu(model_path=self.config["path"], device=device, n_ctx=n_ctx, ngl=ngl)
self.model.load_model(self.config["path"], n_ctx, ngl)
# Set n_threads
if n_threads is not None:
self.model.set_thread_count(n_threads)

24
gpt4all-bindings/python/gpt4all/pyllmodel.py
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@ -182,16 +182,16 @@ class LLModel:
if self.model is not None:
self.llmodel_lib.llmodel_model_destroy(self.model)
def memory_needed(self, model_path: str, n_ctx: int) -> int:
def memory_needed(self, model_path: str, n_ctx: int, ngl: int) -> int:
self.model = None
return self._memory_needed(model_path, n_ctx)
return self._memory_needed(model_path, n_ctx, ngl)
def _memory_needed(self, model_path: str, n_ctx: int) -> int:
def _memory_needed(self, model_path: str, n_ctx: int, ngl: int) -> int:
if self.model is None:
self.model = _create_model(model_path.encode())
return llmodel.llmodel_required_mem(self.model, model_path.encode(), n_ctx)
return llmodel.llmodel_required_mem(self.model, model_path.encode(), n_ctx, ngl)
def list_gpu(self, model_path: str, n_ctx: int) -> list[LLModelGPUDevice]:
def list_gpu(self, model_path: str, n_ctx: int, ngl: int) -> list[LLModelGPUDevice]:
"""
Lists available GPU devices that satisfy the model's memory requirements.
@ -201,13 +201,15 @@ class LLModel:
Path to the model.
n_ctx : int
Maximum size of context window
ngl : int
Number of GPU layers to use (Vulkan)
Returns
-------
list
A list of LLModelGPUDevice structures representing available GPU devices.
"""
mem_required = self._memory_needed(model_path, n_ctx)
mem_required = self._memory_needed(model_path, n_ctx, ngl)
return self._list_gpu(mem_required)
def _list_gpu(self, mem_required: int) -> list[LLModelGPUDevice]:
@ -217,8 +219,8 @@ class LLModel:
raise ValueError("Unable to retrieve available GPU devices")
return devices_ptr[:num_devices.value]
def init_gpu(self, model_path: str, device: str, n_ctx: int):
mem_required = self._memory_needed(model_path, n_ctx)
def init_gpu(self, model_path: str, device: str, n_ctx: int, ngl: int):
mem_required = self._memory_needed(model_path, n_ctx, ngl)
success = self.llmodel_lib.llmodel_gpu_init_gpu_device_by_string(self.model, mem_required, device.encode())
if not success:
@ -241,7 +243,7 @@ class LLModel:
error_msg += "\nUnavailable GPUs due to insufficient memory or features: {}.".format(unavailable_gpus)
raise ValueError(error_msg)
def load_model(self, model_path: str, n_ctx: int) -> bool:
def load_model(self, model_path: str, n_ctx: int, ngl: int) -> bool:
"""
Load model from a file.
@ -251,6 +253,8 @@ class LLModel:
Model filepath
n_ctx : int
Maximum size of context window
ngl : int
Number of GPU layers to use (Vulkan)
Returns
-------
@ -258,7 +262,7 @@ class LLModel:
"""
self.model = _create_model(model_path.encode())
llmodel.llmodel_loadModel(self.model, model_path.encode(), n_ctx)
llmodel.llmodel_loadModel(self.model, model_path.encode(), n_ctx, ngl)
filename = os.path.basename(model_path)
self.model_name = os.path.splitext(filename)[0]

4
gpt4all-bindings/typescript/index.cc
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@ -28,7 +28,7 @@ Napi::Function NodeModelWrapper::GetClass(Napi::Env env) {
Napi::Value NodeModelWrapper::GetRequiredMemory(const Napi::CallbackInfo& info)
{
auto env = info.Env();
return Napi::Number::New(env, static_cast<uint32_t>( llmodel_required_mem(GetInference(), full_model_path.c_str(), 2048) ));
return Napi::Number::New(env, static_cast<uint32_t>( llmodel_required_mem(GetInference(), full_model_path.c_str(), 2048, 100) ));
}
Napi::Value NodeModelWrapper::GetGpuDevices(const Napi::CallbackInfo& info)
@ -161,7 +161,7 @@ Napi::Value NodeModelWrapper::GetRequiredMemory(const Napi::CallbackInfo& info)
}
}
auto success = llmodel_loadModel(GetInference(), full_weight_path.c_str(), 2048);
auto success = llmodel_loadModel(GetInference(), full_weight_path.c_str(), 2048, 100);
if(!success) {
Napi::Error::New(env, "Failed to load model at given path").ThrowAsJavaScriptException();
return;

6
gpt4all-chat/chatgpt.cpp
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@ -20,17 +20,19 @@ ChatGPT::ChatGPT()
{
}
size_t ChatGPT::requiredMem(const std::string &modelPath, int n_ctx)
size_t ChatGPT::requiredMem(const std::string &modelPath, int n_ctx, int ngl)
{
Q_UNUSED(modelPath);
Q_UNUSED(n_ctx);
Q_UNUSED(ngl);
return 0;
}
bool ChatGPT::loadModel(const std::string &modelPath, int n_ctx)
bool ChatGPT::loadModel(const std::string &modelPath, int n_ctx, int ngl)
{
Q_UNUSED(modelPath);
Q_UNUSED(n_ctx);
Q_UNUSED(ngl);
return true;
}

4
gpt4all-chat/chatgpt.h
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@ -48,9 +48,9 @@ public:
bool supportsEmbedding() const override { return false; }
bool supportsCompletion() const override { return true; }
bool loadModel(const std::string &modelPath, int n_ctx) override;
bool loadModel(const std::string &modelPath, int n_ctx, int ngl) override;
bool isModelLoaded() const override;
size_t requiredMem(const std::string &modelPath, int n_ctx) override;
size_t requiredMem(const std::string &modelPath, int n_ctx, int ngl) override;
size_t stateSize() const override;
size_t saveState(uint8_t *dest) const override;
size_t restoreState(const uint8_t *src) override;

7
gpt4all-chat/chatlistmodel.h
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@ -192,6 +192,13 @@ public:
int count() const { return m_chats.size(); }
void clearChats() {
m_newChat = nullptr;
m_serverChat = nullptr;
m_currentChat = nullptr;
m_chats.clear();
}
void removeChatFile(Chat *chat) const;
Q_INVOKABLE void saveChats();
void restoreChat(Chat *chat);

11
gpt4all-chat/chatllm.cpp
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@ -247,10 +247,9 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo)
model->setAPIKey(apiKey);
m_llModelInfo.model = model;
} else {
// TODO: make configurable in UI
auto n_ctx = MySettings::globalInstance()->modelContextLength(modelInfo);
m_ctx.n_ctx = n_ctx;
auto ngl = MySettings::globalInstance()->modelGpuLayers(modelInfo);
std::string buildVariant = "auto";
#if defined(Q_OS_MAC) && defined(__arm__)
@ -269,7 +268,7 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo)
if (requestedDevice == "CPU") {
emit reportFallbackReason(""); // fallback not applicable
} else {
const size_t requiredMemory = m_llModelInfo.model->requiredMem(filePath.toStdString(), n_ctx);
const size_t requiredMemory = m_llModelInfo.model->requiredMem(filePath.toStdString(), n_ctx, ngl);
std::vector<LLModel::GPUDevice> availableDevices = m_llModelInfo.model->availableGPUDevices(requiredMemory);
LLModel::GPUDevice *device = nullptr;
@ -288,7 +287,7 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo)
std::string unavail_reason;
if (!device) {
// GPU not available
} else if (!m_llModelInfo.model->initializeGPUDevice(*device, &unavail_reason)) {
} else if (!m_llModelInfo.model->initializeGPUDevice(device->index, &unavail_reason)) {
emit reportFallbackReason(QString::fromStdString("<br>" + unavail_reason));
} else {
actualDevice = QString::fromStdString(device->name);
@ -298,14 +297,14 @@ bool ChatLLM::loadModel(const ModelInfo &modelInfo)
// Report which device we're actually using
emit reportDevice(actualDevice);
bool success = m_llModelInfo.model->loadModel(filePath.toStdString(), n_ctx);
bool success = m_llModelInfo.model->loadModel(filePath.toStdString(), n_ctx, ngl);
if (actualDevice == "CPU") {
// we asked llama.cpp to use the CPU
} else if (!success) {
// llama_init_from_file returned nullptr
emit reportDevice("CPU");
emit reportFallbackReason("<br>GPU loading failed (out of VRAM?)");
success = m_llModelInfo.model->loadModel(filePath.toStdString(), n_ctx);
success = m_llModelInfo.model->loadModel(filePath.toStdString(), n_ctx, 0);
} else if (!m_llModelInfo.model->usingGPUDevice()) {
// ggml_vk_init was not called in llama.cpp
// We might have had to fallback to CPU after load if the model is not possible to accelerate

2
gpt4all-chat/embllm.cpp
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@ -30,7 +30,7 @@ bool EmbeddingLLM::loadModel()
}
m_model = LLModel::Implementation::construct(filePath.toStdString());
bool success = m_model->loadModel(filePath.toStdString(), 2048);
bool success = m_model->loadModel(filePath.toStdString(), 2048, 0);
if (!success) {
qWarning() << "WARNING: Could not load sbert";
delete m_model;

4
gpt4all-chat/main.cpp
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@ -63,5 +63,9 @@ int main(int argc, char *argv[])
}
#endif
// Make sure ChatLLM threads are joined before global destructors run.
// Otherwise, we can get a heap-use-after-free inside of llama.cpp.
ChatListModel::globalInstance()->clearChats();
return app.exec();
}

49
gpt4all-chat/modellist.cpp
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@ -1,6 +1,7 @@
#include "modellist.h"
#include "mysettings.h"
#include "network.h"
#include "../gpt4all-backend/llmodel.h"
#include <QFile>
#include <QStandardPaths>
@ -108,6 +109,41 @@ void ModelInfo::setContextLength(int l)
m_contextLength = l;
}
int ModelInfo::maxContextLength() const
{
if (m_maxContextLength != -1) return m_maxContextLength;
auto path = (dirpath + filename()).toStdString();
int layers = LLModel::Implementation::maxContextLength(path);
if (layers < 0) {
layers = 4096; // fallback value
}
m_maxContextLength = layers;
return m_maxContextLength;
}
int ModelInfo::gpuLayers() const
{
return MySettings::globalInstance()->modelGpuLayers(*this);
}
void ModelInfo::setGpuLayers(int l)
{
if (isClone) MySettings::globalInstance()->setModelGpuLayers(*this, l, isClone /*force*/);
m_gpuLayers = l;
}
int ModelInfo::maxGpuLayers() const
{
if (m_maxGpuLayers != -1) return m_maxGpuLayers;
auto path = (dirpath + filename()).toStdString();
int layers = LLModel::Implementation::layerCount(path);
if (layers < 0) {
layers = 100; // fallback value
}
m_maxGpuLayers = layers;
return m_maxGpuLayers;
}
double ModelInfo::repeatPenalty() const
{
return MySettings::globalInstance()->modelRepeatPenalty(*this);
@ -286,6 +322,7 @@ ModelList::ModelList()
connect(MySettings::globalInstance(), &MySettings::maxLengthChanged, this, &ModelList::updateDataForSettings);
connect(MySettings::globalInstance(), &MySettings::promptBatchSizeChanged, this, &ModelList::updateDataForSettings);
connect(MySettings::globalInstance(), &MySettings::contextLengthChanged, this, &ModelList::updateDataForSettings);
connect(MySettings::globalInstance(), &MySettings::gpuLayersChanged, this, &ModelList::updateDataForSettings);
connect(MySettings::globalInstance(), &MySettings::repeatPenaltyChanged, this, &ModelList::updateDataForSettings);
connect(MySettings::globalInstance(), &MySettings::repeatPenaltyTokensChanged, this, &ModelList::updateDataForSettings);;
connect(MySettings::globalInstance(), &MySettings::promptTemplateChanged, this, &ModelList::updateDataForSettings);
@ -539,6 +576,8 @@ QVariant ModelList::dataInternal(const ModelInfo *info, int role) const
return info->promptBatchSize();
case ContextLengthRole:
return info->contextLength();
case GpuLayersRole:
return info->gpuLayers();
case RepeatPenaltyRole:
return info->repeatPenalty();
case RepeatPenaltyTokensRole:
@ -664,6 +703,10 @@ void ModelList::updateData(const QString &id, int role, const QVariant &value)
info->setMaxLength(value.toInt()); break;
case PromptBatchSizeRole:
info->setPromptBatchSize(value.toInt()); break;
case ContextLengthRole:
info->setContextLength(value.toInt()); break;
case GpuLayersRole:
info->setGpuLayers(value.toInt()); break;
case RepeatPenaltyRole:
info->setRepeatPenalty(value.toDouble()); break;
case RepeatPenaltyTokensRole:
@ -755,6 +798,7 @@ QString ModelList::clone(const ModelInfo &model)
updateData(id, ModelList::MaxLengthRole, model.maxLength());
updateData(id, ModelList::PromptBatchSizeRole, model.promptBatchSize());
updateData(id, ModelList::ContextLengthRole, model.contextLength());
updateData(id, ModelList::GpuLayersRole, model.contextLength());
updateData(id, ModelList::RepeatPenaltyRole, model.repeatPenalty());
updateData(id, ModelList::RepeatPenaltyTokensRole, model.repeatPenaltyTokens());
updateData(id, ModelList::PromptTemplateRole, model.promptTemplate());
@ -1123,6 +1167,8 @@ void ModelList::parseModelsJsonFile(const QByteArray &jsonData, bool save)
updateData(id, ModelList::PromptBatchSizeRole, obj["promptBatchSize"].toInt());
if (obj.contains("contextLength"))
updateData(id, ModelList::ContextLengthRole, obj["contextLength"].toInt());
if (obj.contains("gpuLayers"))
updateData(id, ModelList::GpuLayersRole, obj["gpuLayers"].toInt());
if (obj.contains("repeatPenalty"))
updateData(id, ModelList::RepeatPenaltyRole, obj["repeatPenalty"].toDouble());
if (obj.contains("repeatPenaltyTokens"))
@ -1217,6 +1263,8 @@ void ModelList::updateModelsFromSettings()
const int promptBatchSize = settings.value(g + "/promptBatchSize").toInt();
Q_ASSERT(settings.contains(g + "/contextLength"));
const int contextLength = settings.value(g + "/contextLength").toInt();
Q_ASSERT(settings.contains(g + "/gpuLayers"));
const int gpuLayers = settings.value(g + "/gpuLayers").toInt();
Q_ASSERT(settings.contains(g + "/repeatPenalty"));
const double repeatPenalty = settings.value(g + "/repeatPenalty").toDouble();
Q_ASSERT(settings.contains(g + "/repeatPenaltyTokens"));
@ -1236,6 +1284,7 @@ void ModelList::updateModelsFromSettings()
updateData(id, ModelList::MaxLengthRole, maxLength);
updateData(id, ModelList::PromptBatchSizeRole, promptBatchSize);
updateData(id, ModelList::ContextLengthRole, contextLength);
updateData(id, ModelList::GpuLayersRole, gpuLayers);
updateData(id, ModelList::RepeatPenaltyRole, repeatPenalty);
updateData(id, ModelList::RepeatPenaltyTokensRole, repeatPenaltyTokens);
updateData(id, ModelList::PromptTemplateRole, promptTemplate);

32
gpt4all-chat/modellist.h
Unescape Escape View File

@ -40,6 +40,9 @@ struct ModelInfo {
Q_PROPERTY(int maxLength READ maxLength WRITE setMaxLength)
Q_PROPERTY(int promptBatchSize READ promptBatchSize WRITE setPromptBatchSize)
Q_PROPERTY(int contextLength READ contextLength WRITE setContextLength)
Q_PROPERTY(int maxContextLength READ maxContextLength)
Q_PROPERTY(int gpuLayers READ gpuLayers WRITE setGpuLayers)
Q_PROPERTY(int maxGpuLayers READ maxGpuLayers)
Q_PROPERTY(double repeatPenalty READ repeatPenalty WRITE setRepeatPenalty)
Q_PROPERTY(int repeatPenaltyTokens READ repeatPenaltyTokens WRITE setRepeatPenaltyTokens)
Q_PROPERTY(QString promptTemplate READ promptTemplate WRITE setPromptTemplate)
@ -97,6 +100,10 @@ public:
void setPromptBatchSize(int s);
int contextLength() const;
void setContextLength(int l);
int maxContextLength() const;
int gpuLayers() const;
void setGpuLayers(int l);
int maxGpuLayers() const;
double repeatPenalty() const;
void setRepeatPenalty(double p);
int repeatPenaltyTokens() const;
@ -110,16 +117,19 @@ private:
QString m_id;
QString m_name;
QString m_filename;
double m_temperature = 0.7;
double m_topP = 0.4;
int m_topK = 40;
int m_maxLength = 4096;
int m_promptBatchSize = 128;
int m_contextLength = 2048;
double m_repeatPenalty = 1.18;
int m_repeatPenaltyTokens = 64;
QString m_promptTemplate = "### Human:\n%1\n### Assistant:\n";
QString m_systemPrompt = "### System:\nYou are an AI assistant who gives a quality response to whatever humans ask of you.\n";
double m_temperature = 0.7;
double m_topP = 0.4;
int m_topK = 40;
int m_maxLength = 4096;
int m_promptBatchSize = 128;
int m_contextLength = 2048;
mutable int m_maxContextLength = -1;
int m_gpuLayers = 100;
mutable int m_maxGpuLayers = -1;
double m_repeatPenalty = 1.18;
int m_repeatPenaltyTokens = 64;
QString m_promptTemplate = "### Human:\n%1\n### Assistant:\n";
QString m_systemPrompt = "### System:\nYou are an AI assistant who gives a quality response to whatever humans ask of you.\n";
friend class MySettings;
};
Q_DECLARE_METATYPE(ModelInfo)
@ -232,6 +242,7 @@ public:
MaxLengthRole,
PromptBatchSizeRole,
ContextLengthRole,
GpuLayersRole,
RepeatPenaltyRole,
RepeatPenaltyTokensRole,
PromptTemplateRole,
@ -275,6 +286,7 @@ public:
roles[MaxLengthRole] = "maxLength";
roles[PromptBatchSizeRole] = "promptBatchSize";
roles[ContextLengthRole] = "contextLength";
roles[GpuLayersRole] = "gpuLayers";
roles[RepeatPenaltyRole] = "repeatPenalty";
roles[RepeatPenaltyTokensRole] = "repeatPenaltyTokens";
roles[PromptTemplateRole] = "promptTemplate";

23
gpt4all-chat/mysettings.cpp
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@ -91,6 +91,7 @@ void MySettings::restoreModelDefaults(const ModelInfo &model)
setModelMaxLength(model, model.m_maxLength);
setModelPromptBatchSize(model, model.m_promptBatchSize);
setModelContextLength(model, model.m_contextLength);
setModelGpuLayers(model, model.m_gpuLayers);
setModelRepeatPenalty(model, model.m_repeatPenalty);
setModelRepeatPenaltyTokens(model, model.m_repeatPenaltyTokens);
setModelPromptTemplate(model, model.m_promptTemplate);
@ -303,6 +304,28 @@ void MySettings::setModelContextLength(const ModelInfo &m, int l, bool force)
emit contextLengthChanged(m);
}
int MySettings::modelGpuLayers(const ModelInfo &m) const
{
QSettings setting;
setting.sync();
return setting.value(QString("model-%1").arg(m.id()) + "/gpuLayers", m.m_gpuLayers).toInt();
}
void MySettings::setModelGpuLayers(const ModelInfo &m, int l, bool force)
{
if (modelGpuLayers(m) == l && !force)
return;
QSettings setting;
if (m.m_gpuLayers == l && !m.isClone)
setting.remove(QString("model-%1").arg(m.id()) + "/gpuLayers");
else
setting.setValue(QString("model-%1").arg(m.id()) + "/gpuLayers", l);
setting.sync();
if (!force)
emit gpuLayersChanged(m);
}
double MySettings::modelRepeatPenalty(const ModelInfo &m) const
{
QSettings setting;

5
gpt4all-chat/mysettings.h
Unescape Escape View File

@ -63,6 +63,8 @@ public:
Q_INVOKABLE void setModelSystemPrompt(const ModelInfo &m, const QString &p, bool force = false);
int modelContextLength(const ModelInfo &m) const;
Q_INVOKABLE void setModelContextLength(const ModelInfo &m, int s, bool force = false);
int modelGpuLayers(const ModelInfo &m) const;
Q_INVOKABLE void setModelGpuLayers(const ModelInfo &m, int s, bool force = false);
// Application settings
int threadCount() const;
@ -85,6 +87,8 @@ public:
void setDevice(const QString &u);
int32_t contextLength() const;
void setContextLength(int32_t value);
int32_t gpuLayers() const;
void setGpuLayers(int32_t value);
// Release/Download settings
QString lastVersionStarted() const;
@ -121,6 +125,7 @@ Q_SIGNALS:
void maxLengthChanged(const ModelInfo &model);
void promptBatchSizeChanged(const ModelInfo &model);
void contextLengthChanged(const ModelInfo &model);
void gpuLayersChanged(const ModelInfo &model);
void repeatPenaltyChanged(const ModelInfo &model);
void repeatPenaltyTokensChanged(const ModelInfo &model);
void promptTemplateChanged(const ModelInfo &model);

70
gpt4all-chat/qml/ModelSettings.qml
Unescape Escape View File

@ -332,9 +332,6 @@ MySettingsTab {
ToolTip.visible: hovered
Layout.row: 0
Layout.column: 1
validator: IntValidator {
bottom: 1
}
Connections {
target: MySettings
function onContextLengthChanged() {
@ -349,11 +346,18 @@ MySettingsTab {
}
onEditingFinished: {
var val = parseInt(text)
if (!isNaN(val)) {
if (isNaN(val)) {
text = root.currentModelInfo.contextLength
} else {
if (val < 8) {
val = 8
contextLengthField.text = val
} else if (val > root.currentModelInfo.maxContextLength) {
val = root.currentModelInfo.maxContextLength
contextLengthField.text = val
}
MySettings.setModelContextLength(root.currentModelInfo, val)
focus = false
} else {
text = root.currentModelInfo.contextLength
}
}
Accessible.role: Accessible.EditableText
@ -674,6 +678,60 @@ MySettingsTab {
Accessible.name: repeatPenaltyTokensLabel.text
Accessible.description: ToolTip.text
}
MySettingsLabel {
id: gpuLayersLabel
visible: !root.currentModelInfo.isChatGPT
text: qsTr("GPU Layers")
Layout.row: 4
Layout.column: 0
}
MyTextField {
id: gpuLayersField
visible: !root.currentModelInfo.isChatGPT
text: root.currentModelInfo.gpuLayers
font.pixelSize: theme.fontSizeLarge
color: theme.textColor
ToolTip.text: qsTr("How many GPU layers to load into VRAM. Decrease this if GPT4All runs out of VRAM while loading this model.\nLower values increase CPU load and RAM usage, and make inference slower.\nNOTE: Does not take effect until you RESTART GPT4All or SWITCH MODELS.")
ToolTip.visible: hovered
Layout.row: 4
Layout.column: 1
Connections {
target: MySettings
function onGpuLayersChanged() {
gpuLayersField.text = root.currentModelInfo.gpuLayers
}
}
Connections {
target: root
function onCurrentModelInfoChanged() {
if (root.currentModelInfo.gpuLayers == 100) {
gpuLayersField.text = root.currentModelInfo.maxGpuLayers
} else {
gpuLayersField.text = root.currentModelInfo.gpuLayers
}
}
}
onEditingFinished: {
var val = parseInt(text)
if (isNaN(val)) {
gpuLayersField.text = root.currentModelInfo.gpuLayers
} else {
if (val < 1) {
val = 1
gpuLayersField.text = val
} else if (val > root.currentModelInfo.maxGpuLayers) {
val = root.currentModelInfo.maxGpuLayers
gpuLayersField.text = val
}
MySettings.setModelGpuLayers(root.currentModelInfo, val)
focus = false
}
}
Accessible.role: Accessible.EditableText
Accessible.name: gpuLayersLabel.text
Accessible.description: ToolTip.text
}
}
Rectangle {

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