stable-diffusion.cpp/src/convert.cpp at master · unslothai/stable-diffusion.cpp · GitHub

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#include <algorithm>

#include <condition_variable>

#include <cstdint>

#include <cstring>

#include <exception>

#include <fstream>

#include <memory>

#include <mutex>

#include <regex>

#include <string>

#include <thread>

#include <vector>

#include "core/util.h"

#include "model_io/gguf_io.h"

#include "model_io/safetensors_io.h"

#include "model_io/streaming_writer.h"

#include "model_loader.h"

struct TensorExportInfo {

TensorStorage storage;

ggml_type type;

};

struct TensorExportJob {

TensorExportInfo info;

std::vector<uint8_t> data;

std::string error;

bool success = false;

};

static ggml_type get_export_tensor_type(ModelLoader& model_loader,

const TensorStorage& tensor_storage,

ggml_type type,

const TensorTypeRules& tensor_type_rules) {

const std::string& name = tensor_storage.name;

ggml_type tensor_type = tensor_storage.type;

ggml_type dst_type = type;

for (const auto& tensor_type_rule : tensor_type_rules) {

std::regex pattern(tensor_type_rule.first);

if (std::regex_search(name, pattern)) {

dst_type = tensor_type_rule.second;

break;

}

if (model_loader.tensor_should_be_converted(tensor_storage, dst_type)) {

tensor_type = dst_type;

}

return tensor_type;

}

static bool collect_tensors_for_export(ModelLoader& model_loader,

ggml_type type,

const TensorTypeRules& tensor_type_rules,

std::vector<TensorExportInfo>& tensors) {

tensors.clear();

tensors.reserve(model_loader.get_tensor_storage_map().size());

for (const auto& kv : model_loader.get_tensor_storage_map()) {

const TensorStorage& tensor_storage = kv.second;

TensorExportInfo info;

info.storage = tensor_storage;

info.type = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);

tensors.push_back(std::move(info));

}

LOG_INFO("collected %zu tensors for export", tensors.size());

return true;

}

static size_t export_tensor_nbytes(const TensorExportInfo& info) {

TensorStorage output_storage = info.storage;

output_storage.type = info.type;

return static_cast<size_t>(output_storage.nbytes());

}

static TensorWritePlan tensor_write_plan_from_export_info(const TensorExportInfo& info) {

TensorWritePlan plan;

plan.name = info.storage.name;

plan.type = info.type;

plan.n_dims = info.storage.n_dims;

for (int i = 0; i < SD_MAX_DIMS; i++) {

plan.ne[i] = info.storage.ne[i];

}

return plan;

}

static std::vector<TensorWritePlan> tensor_write_plans_from_export_infos(const std::vector<TensorExportInfo>& tensors) {

std::vector<TensorWritePlan> plans;

plans.reserve(tensors.size());

for (const TensorExportInfo& info : tensors) {

plans.push_back(tensor_write_plan_from_export_info(info));

}

return plans;

}

static bool preallocate_output_file(const std::string& output_path, uint64_t file_size, std::string* error) {

if (file_size == 0) {

return true;

}

std::fstream file(output_path, std::ios::binary | std::ios::in | std::ios::out);

if (!file.is_open()) {

if (error != nullptr) {

*error = "failed to open output file '" + output_path + "' for preallocation";

}

return false;

}

// This portable fallback sets the final file size. A platform-specific

// posix_fallocate/ftruncate path can replace it later.

file.seekp(static_cast<std::streamoff>(file_size - 1), std::ios::beg);

file.put('\0');

file.flush();

if (!file) {

if (error != nullptr) {

*error = "failed to preallocate output file '" + output_path + "'";

}

return false;

}

return true;

}

static bool load_tensor_for_export(ModelLoader& model_loader, TensorExportJob& job) {

size_t mem_size = 1 * 1024 * 1024;

mem_size += ggml_tensor_overhead();

TensorStorage output_storage = job.info.storage;

output_storage.type = job.info.type;

mem_size += static_cast<size_t>(output_storage.nbytes());

ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});

if (ggml_ctx == nullptr) {

job.error = "ggml_init failed for tensor '" + job.info.storage.name + "'";

return false;

}

ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, job.info.type, job.info.storage.n_dims, job.info.storage.ne);

if (tensor == nullptr) {

ggml_free(ggml_ctx);

job.error = "ggml_new_tensor failed for tensor '" + job.info.storage.name + "'";

return false;

}

ggml_set_name(tensor, job.info.storage.name.c_str());

const size_t tensor_nbytes = ggml_nbytes(tensor);

if (tensor_nbytes > 0 && !model_loader.load_tensor(job.info.storage, tensor)) {

ggml_free(ggml_ctx);

job.error = "failed to load tensor '" + job.info.storage.name + "'";

return false;

}

job.data.resize(tensor_nbytes);

if (tensor_nbytes > 0) {

memcpy(job.data.data(), tensor->data, tensor_nbytes);

}

ggml_free(ggml_ctx);

return true;

}

static bool stream_tensor_data(ModelLoader& model_loader,

const std::string& output_path,

const std::vector<TensorExportInfo>& tensors,

const StreamingModelWriter& writer,

int n_threads,

std::string* error) {

n_threads = n_threads > 0 ? n_threads : sd_get_num_physical_cores();

n_threads = std::max(1, n_threads);

LOG_INFO("streaming convert with %d threads", n_threads);

int64_t start_time = ggml_time_ms();

uint64_t bytes_written = 0;

size_t tensors_written = 0;

size_t next_tensor_index = 0;

bool failed = false;

std::string failure;

const size_t memory_budget = 1024ull * 1024ull * 1024ull;

size_t reserved_bytes = 0;

std::mutex work_mutex;

std::mutex progress_mutex;

std::condition_variable memory_cv;

std::vector<std::thread> workers;

workers.reserve(n_threads);

auto reserve_memory = [&](size_t bytes) -> bool {

std::unique_lock<std::mutex> lock(work_mutex);

memory_cv.wait(lock, [&]() {

return failed || reserved_bytes == 0 || reserved_bytes + bytes <= memory_budget;

});

if (failed) {

return false;

}

reserved_bytes += bytes;

return true;

};

auto release_memory = [&](size_t bytes) {

{

std::lock_guard<std::mutex> lock(work_mutex);

reserved_bytes -= std::min(reserved_bytes, bytes);

}

memory_cv.notify_all();

};

auto fail = [&](const std::string& message) {

{

std::lock_guard<std::mutex> lock(work_mutex);

if (!failed) {

failed = true;

failure = message;

}

memory_cv.notify_all();

};

for (int worker = 0; worker < n_threads; worker++) {

workers.emplace_back([&]() {

std::fstream output_file(output_path, std::ios::binary | std::ios::in | std::ios::out);

if (!output_file.is_open()) {

fail("failed to open output file '" + output_path + "' for tensor writing");

return;

}

while (true) {

size_t tensor_index = 0;

{

std::lock_guard<std::mutex> lock(work_mutex);

if (failed || next_tensor_index >= tensors.size()) {

return;

}

tensor_index = next_tensor_index++;

}

const size_t tensor_bytes = export_tensor_nbytes(tensors[tensor_index]);

if (!reserve_memory(tensor_bytes)) {

return;

}

TensorExportJob job;

job.info = tensors[tensor_index];

try {

job.success = load_tensor_for_export(model_loader, job);

} catch (const std::exception& e) {

job.error = e.what();

job.success = false;

}

if (!job.success) {

release_memory(tensor_bytes);

fail(job.error.empty() ? "streaming conversion failed" : job.error);

return;

}

std::string write_error;

if (!writer.write_tensor(output_file,

tensor_index,

job.data.empty() ? nullptr : job.data.data(),

job.data.size(),

&write_error)) {

release_memory(tensor_bytes);

fail(write_error.empty() ? "streaming conversion write failed" : write_error);

return;

}

{

std::lock_guard<std::mutex> lock(progress_mutex);

bytes_written += job.data.size();

tensors_written++;

float elapsed_seconds = (ggml_time_ms() - start_time) / 1000.0f;

pretty_bytes_progress(static_cast<int>(tensors_written),

static_cast<int>(tensors.size()),

bytes_written,

elapsed_seconds);

}

release_memory(tensor_bytes);

}

});

}

for (auto& worker : workers) {

worker.join();

}

printf("\n");

if (failed) {

if (error != nullptr) {

*error = failure;

}

return false;

}

LOG_INFO("streaming conversion completed, taking %.2fs", (ggml_time_ms() - start_time) / 1000.f);

return true;

}

static bool write_model_file_streaming(ModelLoader& model_loader,

const std::string& output_path,

const std::vector<TensorExportInfo>& tensors,

StreamingModelWriter& writer,

int n_threads,

std::string* error) {

std::vector<TensorWritePlan> plans = tensor_write_plans_from_export_infos(tensors);

if (!writer.write_metadata(output_path, plans, error)) {

return false;

}

if (!preallocate_output_file(output_path, writer.file_size(), error)) {

return false;

}

model_loader.process_model_files(false, false);

return stream_tensor_data(model_loader, output_path, tensors, writer, n_threads, error);

}

static bool init_convert_path(ModelLoader& model_loader, const char* path, const char* prefix, bool& loaded_any) {

if (path == nullptr || strlen(path) == 0) {

return true;

}

if (!model_loader.init_from_file(path, prefix)) {

LOG_ERROR("init model loader from file failed: '%s'", path);

return false;

}

loaded_any = true;

return true;

}

static bool export_loaded_model(ModelLoader& model_loader,

const char* output_path,

sd_type_t output_type,

const char* tensor_type_rules,

int n_threads) {

ggml_type type = sd_type_to_ggml_type(output_type);

bool output_is_safetensors = ends_with(output_path, ".safetensors");

TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);

std::vector<TensorExportInfo> tensors;

bool success = collect_tensors_for_export(model_loader, type, type_rules, tensors);

std::string error;

if (success) {

std::unique_ptr<StreamingModelWriter> writer;

if (output_is_safetensors) {

writer = std::make_unique<SafetensorsStreamingWriter>();

} else {

writer = std::make_unique<GGUFStreamingWriter>();

}

success = write_model_file_streaming(model_loader, output_path, tensors, *writer, n_threads, &error);

}

if (!success && !error.empty()) {

LOG_ERROR("%s", error.c_str());

}

return success;

}

bool convert_with_components(const char* model_path,

const char* clip_l_path,

const char* clip_g_path,

const char* t5xxl_path,

const char* diffusion_model_path,

const char* vae_path,

const char* output_path,

sd_type_t output_type,

const char* tensor_type_rules,

bool convert_name,

int n_threads) {

ModelLoader model_loader;

bool loaded_any = false;

if (!init_convert_path(model_loader, model_path, "", loaded_any) ||

!init_convert_path(model_loader, clip_l_path, "text_encoders.clip_l.transformer.", loaded_any) ||

!init_convert_path(model_loader, clip_g_path, "text_encoders.clip_g.transformer.", loaded_any) ||

!init_convert_path(model_loader, t5xxl_path, "text_encoders.t5xxl.transformer.", loaded_any) ||

!init_convert_path(model_loader, diffusion_model_path, "model.diffusion_model.", loaded_any) ||

!init_convert_path(model_loader, vae_path, "vae.", loaded_any)) {

return false;

}

if (!loaded_any) {

LOG_ERROR("no input model path provided for convert");

return false;

}

if (convert_name) {

model_loader.convert_tensors_name();

}

return export_loaded_model(model_loader, output_path, output_type, tensor_type_rules, n_threads);

}

bool convert(const char* input_path,

const char* vae_path,

const char* output_path,

sd_type_t output_type,

const char* tensor_type_rules,

bool convert_name) {

return convert_with_components(input_path,

nullptr,

vae_path,

output_path,

output_type,

tensor_type_rules,

convert_name,

0);

}

stable-diffusion.cpp/src/convert.cpp at master · unslothai/stable-diffusion.cpp · GitHub

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