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chat: major UI redesign for v3.0.0 (#2396)

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Signed-off-by: Adam Treat <treat.adam@gmail.com>
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
This commit is contained in:
AT
2024-06-24 18:49:23 -04:00
committed by GitHub
parent 1272b694ae
commit 9273b49b62
111 changed files with 8540 additions and 7879 deletions
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48
gpt4all-backend/llmodel.cpp
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@@ -92,17 +92,20 @@ LLModel::Implementation::Implementation(Implementation &&o)
o.m_dlhandle = nullptr;
}
LLModel::Implementation::~Implementation() {
LLModel::Implementation::~Implementation()
{
delete m_dlhandle;
}
static bool isImplementation(const Dlhandle &dl) {
static bool isImplementation(const Dlhandle &dl)
{
return dl.get<bool(uint32_t)>("is_g4a_backend_model_implementation");
}
// Add the CUDA Toolkit to the DLL search path on Windows.
// This is necessary for chat.exe to find CUDA when started from Qt Creator.
static void addCudaSearchPath() {
static void addCudaSearchPath()
{
#ifdef _WIN32
if (const auto *cudaPath = _wgetenv(L"CUDA_PATH")) {
auto libDir = std::wstring(cudaPath) + L"\\bin";
@@ -114,7 +117,8 @@ static void addCudaSearchPath() {
#endif
}
const std::vector<LLModel::Implementation> &LLModel::Implementation::implementationList() {
const std::vector<LLModel::Implementation> &LLModel::Implementation::implementationList()
{
if (cpu_supports_avx() == 0) {
throw std::runtime_error("CPU does not support AVX");
}
@@ -169,14 +173,16 @@ const std::vector<LLModel::Implementation> &LLModel::Implementation::implementat
return *libs;
}
static std::string applyCPUVariant(const std::string &buildVariant) {
static std::string applyCPUVariant(const std::string &buildVariant)
{
if (buildVariant != "metal" && cpu_supports_avx2() == 0) {
return buildVariant + "-avxonly";
}
return buildVariant;
}
const LLModel::Implementation* LLModel::Implementation::implementation(const char *fname, const std::string& buildVariant) {
const LLModel::Implementation* LLModel::Implementation::implementation(const char *fname, const std::string& buildVariant)
{
bool buildVariantMatched = false;
std::optional<std::string> archName;
for (const auto& i : implementationList()) {
@@ -200,7 +206,8 @@ const LLModel::Implementation* LLModel::Implementation::implementation(const cha
throw BadArchError(std::move(*archName));
}
LLModel *LLModel::Implementation::construct(const std::string &modelPath, const std::string &backend, int n_ctx) {
LLModel *LLModel::Implementation::construct(const std::string &modelPath, const std::string &backend, int n_ctx)
{
std::vector<std::string> desiredBackends;
if (backend != "auto") {
desiredBackends.push_back(backend);
@@ -240,7 +247,8 @@ LLModel *LLModel::Implementation::construct(const std::string &modelPath, const
throw MissingImplementationError("Could not find any implementations for backend: " + backend);
}
LLModel *LLModel::Implementation::constructGlobalLlama(const std::optional<std::string> &backend) {
LLModel *LLModel::Implementation::constructGlobalLlama(const std::optional<std::string> &backend)
{
static std::unordered_map<std::string, std::unique_ptr<LLModel>> implCache;
const std::vector<Implementation> *impls;
@@ -284,7 +292,8 @@ LLModel *LLModel::Implementation::constructGlobalLlama(const std::optional<std::
return nullptr;
}
std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices(size_t memoryRequired) {
std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices(size_t memoryRequired)
{
std::vector<LLModel::GPUDevice> devices;
#ifndef __APPLE__
static const std::string backends[] = {"kompute", "cuda"};
@@ -299,33 +308,40 @@ std::vector<LLModel::GPUDevice> LLModel::Implementation::availableGPUDevices(siz
return devices;
}
int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath) {
int32_t LLModel::Implementation::maxContextLength(const std::string &modelPath)
{
auto *llama = constructGlobalLlama();
return llama ? llama->maxContextLength(modelPath) : -1;
}
int32_t LLModel::Implementation::layerCount(const std::string &modelPath) {
int32_t LLModel::Implementation::layerCount(const std::string &modelPath)
{
auto *llama = constructGlobalLlama();
return llama ? llama->layerCount(modelPath) : -1;
}
bool LLModel::Implementation::isEmbeddingModel(const std::string &modelPath) {
bool LLModel::Implementation::isEmbeddingModel(const std::string &modelPath)
{
auto *llama = constructGlobalLlama();
return llama && llama->isEmbeddingModel(modelPath);
}
void LLModel::Implementation::setImplementationsSearchPath(const std::string& path) {
void LLModel::Implementation::setImplementationsSearchPath(const std::string& path)
{
s_implementations_search_path = path;
}
const std::string& LLModel::Implementation::implementationsSearchPath() {
const std::string& LLModel::Implementation::implementationsSearchPath()
{
return s_implementations_search_path;
}
bool LLModel::Implementation::hasSupportedCPU() {
bool LLModel::Implementation::hasSupportedCPU()
{
return cpu_supports_avx() != 0;
}
int LLModel::Implementation::cpuSupportsAVX2() {
int LLModel::Implementation::cpuSupportsAVX2()
{
return cpu_supports_avx2();
}