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2168 lines (1952 loc) · 83.3 KB
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#include <algorithm>
#include <atomic>
#include <chrono>
#include <cstdarg>
#include <filesystem>
#include <fstream>
#include <functional>
#include <mutex>
#include <regex>
#include <set>
#include <string>
#include <thread>
#include <unordered_map>
#include <vector>
#include "gguf_reader.hpp"
#include "model.h"
#include "stable-diffusion.h"
#include "util.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml-cpu.h"
#include "ggml.h"
#include "name_conversion.h"
#include "stable-diffusion.h"
#if defined(SD_CUDA_THREADED_WEIGHT_LOADER) && defined(SD_USE_CUDA)
#include "loader/async_weight_loader.h"
#include "loader/loader_stats.h"
#include "loader/pinned_host_arena.h"
#include "loader/threaded_file_reader.h"
#include <cuda_runtime_api.h>
#endif
#ifdef SD_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef SD_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#ifdef SD_USE_OPENCL
#include "ggml-opencl.h"
#endif
#define ST_HEADER_SIZE_LEN 8
uint64_t read_u64(uint8_t* buffer) {
// little endian
uint64_t value = 0;
value |= static_cast<int64_t>(buffer[7]) << 56;
value |= static_cast<int64_t>(buffer[6]) << 48;
value |= static_cast<int64_t>(buffer[5]) << 40;
value |= static_cast<int64_t>(buffer[4]) << 32;
value |= static_cast<int64_t>(buffer[3]) << 24;
value |= static_cast<int64_t>(buffer[2]) << 16;
value |= static_cast<int64_t>(buffer[1]) << 8;
value |= static_cast<int64_t>(buffer[0]);
return value;
}
int32_t read_int(uint8_t* buffer) {
// little endian
int value = 0;
value |= buffer[3] << 24;
value |= buffer[2] << 16;
value |= buffer[1] << 8;
value |= buffer[0];
return value;
}
uint16_t read_short(uint8_t* buffer) {
// little endian
uint16_t value = 0;
value |= buffer[1] << 8;
value |= buffer[0];
return value;
}
#if defined(SD_CUDA_THREADED_WEIGHT_LOADER) && defined(SD_USE_CUDA)
namespace {
struct ThreadedWeightLoaderContext {
bool source_is_zip = false;
bool source_supports_direct_cuda_upload = false;
bool has_pinned_arena = false;
bool has_threaded_reader = false;
bool has_async_loader = false;
bool async_loader_available = false;
};
struct ThreadedWeightLoaderDecision {
bool use_fast_path = false;
sd::loader::LoaderFallbackReason fallback_reason = sd::loader::LoaderFallbackReason::other;
};
bool tensor_points_to_cuda_device(const ggml_tensor* tensor) {
if (tensor == nullptr || tensor->data == nullptr) {
return false;
}
cudaPointerAttributes attributes{};
cudaError_t err = cudaPointerGetAttributes(&attributes, tensor->data);
if (err != cudaSuccess) {
(void)cudaGetLastError();
return false;
}
return attributes.type == cudaMemoryTypeDevice;
}
ThreadedWeightLoaderDecision should_use_threaded_weight_loader(const TensorStorage& tensor_storage,
const ggml_tensor* dst_tensor,
const sd::loader::LoaderConfig& config,
const ThreadedWeightLoaderContext& context) {
ThreadedWeightLoaderDecision decision{};
if (!config.enable_threaded_loader) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::unsupported_backend;
return decision;
}
if (dst_tensor == nullptr) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::null_destination;
return decision;
}
if (dst_tensor->buffer == nullptr || ggml_backend_buffer_is_host(dst_tensor->buffer)) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::host_destination;
return decision;
}
if (context.source_is_zip || tensor_storage.index_in_zip >= 0) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::zip_or_indirect;
return decision;
}
if (!context.source_supports_direct_cuda_upload) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::zip_or_indirect;
return decision;
}
if (static_cast<uint64_t>(tensor_storage.nbytes_to_read()) < config.min_tensor_bytes) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::below_threshold;
return decision;
}
if (tensor_storage.is_f8_e4m3 ||
tensor_storage.is_f8_e5m2 ||
tensor_storage.has_f8_weight_scale ||
tensor_storage.is_f64 ||
tensor_storage.is_i64 ||
(tensor_storage.expected_type != GGML_TYPE_COUNT && tensor_storage.expected_type != tensor_storage.type)) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::conversion_required;
return decision;
}
if (tensor_storage.type != dst_tensor->type ||
ggml_nbytes(dst_tensor) != tensor_storage.nbytes()) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::type_mismatch;
return decision;
}
if (!config.enable_pinned_staging || !context.has_pinned_arena) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::arena_unavailable;
return decision;
}
if (!context.has_threaded_reader ||
!context.has_async_loader ||
!context.async_loader_available ||
!tensor_points_to_cuda_device(dst_tensor)) {
decision.fallback_reason = sd::loader::LoaderFallbackReason::unsupported_backend;
return decision;
}
decision.use_fast_path = true;
return decision;
}
} // namespace
#endif
/*================================================= Preprocess ==================================================*/
const char* unused_tensors[] = {
"betas",
"alphas_cumprod_prev",
"sqrt_alphas_cumprod",
"sqrt_one_minus_alphas_cumprod",
"log_one_minus_alphas_cumprod",
"sqrt_recip_alphas_cumprod",
"sqrt_recipm1_alphas_cumprod",
"posterior_variance",
"posterior_log_variance_clipped",
"posterior_mean_coef1",
"posterior_mean_coef2",
"cond_stage_model.transformer.text_model.embeddings.position_ids",
"cond_stage_model.1.model.text_model.embeddings.position_ids",
"cond_stage_model.transformer.vision_model.embeddings.position_ids",
"cond_stage_model.model.logit_scale",
"conditioner.embedders.0.transformer.text_model.embeddings.position_ids",
"conditioner.embedders.0.model.logit_scale",
"conditioner.embedders.1.model.logit_scale",
"model.diffusion_model.time_embedding.cond_proj.weight",
"unet.time_embedding.cond_proj.weight",
"model_ema.decay",
"model_ema.num_updates",
"model_ema.diffusion_model",
"embedding_manager",
"denoiser.sigmas",
"text_encoders.t5xxl.transformer.encoder.embed_tokens.weight", // only used during training
"ztsnr", // Found in some SDXL vpred models
"edm_vpred.sigma_min", // Found in CosXL
// TODO: find another way to avoid the "unknown tensor" for these two
// "edm_vpred.sigma_max", // Used to detect CosXL
// "v_pred", // Used to detect SDXL vpred models
"text_encoders.llm.output.weight",
"text_encoders.llm.lm_head.",
"first_stage_model.bn.",
};
bool is_unused_tensor(std::string name) {
for (size_t i = 0; i < sizeof(unused_tensors) / sizeof(const char*); i++) {
if (starts_with(name, unused_tensors[i])) {
return true;
}
}
return false;
}
uint16_t f8_e4m3_to_f16(uint8_t f8) {
// do we need to support uz?
const uint32_t exponent_bias = 7;
if (f8 == 0xff) {
return ggml_fp32_to_fp16(-NAN);
} else if (f8 == 0x7f) {
return ggml_fp32_to_fp16(NAN);
}
uint32_t sign = f8 & 0x80;
uint32_t exponent = (f8 & 0x78) >> 3;
uint32_t mantissa = f8 & 0x07;
uint32_t result = sign << 24;
if (exponent == 0) {
if (mantissa > 0) {
exponent = 0x7f - exponent_bias;
// yes, 2 times
if ((mantissa & 0x04) == 0) {
mantissa &= 0x03;
mantissa <<= 1;
exponent -= 1;
}
if ((mantissa & 0x04) == 0) {
mantissa &= 0x03;
mantissa <<= 1;
exponent -= 1;
}
result |= (mantissa & 0x03) << 21;
result |= exponent << 23;
}
} else {
result |= mantissa << 20;
exponent += 0x7f - exponent_bias;
result |= exponent << 23;
}
return ggml_fp32_to_fp16(*reinterpret_cast<const float*>(&result));
}
uint16_t f8_e5m2_to_f16(uint8_t fp8) {
return static_cast<uint16_t>(fp8) << 8;
}
void f8_e4m3_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
// support inplace op
for (int64_t i = n - 1; i >= 0; i--) {
dst[i] = f8_e4m3_to_f16(src[i]);
}
}
void f8_e5m2_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
// support inplace op
for (int64_t i = n - 1; i >= 0; i--) {
dst[i] = f8_e5m2_to_f16(src[i]);
}
}
void scale_f16_vec(uint16_t* dst, int64_t n, float scale) {
if (scale == 1.0f) {
return;
}
for (int64_t i = 0; i < n; i++) {
dst[i] = ggml_fp32_to_fp16(ggml_fp16_to_fp32(dst[i]) * scale);
}
}
void f64_to_f32_vec(double* src, float* dst, int64_t n) {
// support inplace op
for (int64_t i = 0; i < n; i++) {
dst[i] = (float)src[i];
}
}
void i64_to_i32_vec(int64_t* src, int32_t* dst, int64_t n) {
// support inplace op
for (int64_t i = 0; i < n; i++) {
dst[i] = (int32_t)src[i];
}
}
void convert_tensor(void* src,
ggml_type src_type,
void* dst,
ggml_type dst_type,
int nrows,
int n_per_row) {
int n = nrows * n_per_row;
if (src_type == dst_type) {
size_t nbytes = n * ggml_type_size(src_type) / ggml_blck_size(src_type);
memcpy(((char*)dst), ((char*)src), nbytes);
} else if (src_type == GGML_TYPE_F32) {
if (dst_type == GGML_TYPE_F16) {
ggml_fp32_to_fp16_row((float*)src, (ggml_fp16_t*)dst, n);
} else {
std::vector<float> imatrix(n_per_row, 1.0f); // dummy importance matrix
const float* im = imatrix.data();
ggml_quantize_chunk(dst_type, (float*)src, dst, 0, nrows, n_per_row, im);
}
} else if (dst_type == GGML_TYPE_F32) {
if (src_type == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((ggml_fp16_t*)src, (float*)dst, n);
} else {
auto qtype = ggml_get_type_traits(src_type);
if (qtype->to_float == nullptr) {
throw std::runtime_error(sd_format("type %s unsupported for integer quantization: no dequantization available",
ggml_type_name(src_type)));
}
qtype->to_float(src, (float*)dst, n);
}
} else {
// src_type == GGML_TYPE_F16 => dst_type is quantized
// src_type is quantized => dst_type == GGML_TYPE_F16 or dst_type is quantized
auto qtype = ggml_get_type_traits(src_type);
if (qtype->to_float == nullptr) {
throw std::runtime_error(sd_format("type %s unsupported for integer quantization: no dequantization available",
ggml_type_name(src_type)));
}
std::vector<char> buf;
buf.resize(sizeof(float) * n);
char* src_data_f32 = buf.data();
qtype->to_float(src, (float*)src_data_f32, n);
if (dst_type == GGML_TYPE_F16) {
ggml_fp32_to_fp16_row((float*)src_data_f32, (ggml_fp16_t*)dst, n);
} else {
std::vector<float> imatrix(n_per_row, 1.0f); // dummy importance matrix
const float* im = imatrix.data();
ggml_quantize_chunk(dst_type, (float*)src_data_f32, dst, 0, nrows, n_per_row, im);
}
}
}
/*================================================= ModelLoader ==================================================*/
void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
tensor_storage_map[tensor_storage.name] = tensor_storage;
}
bool is_zip_file(const std::string& file_path) {
zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
if (zip == nullptr) {
return false;
}
zip_close(zip);
return true;
}
bool is_gguf_file(const std::string& file_path) {
std::ifstream file(std::filesystem::u8path(file_path), std::ios::binary);
if (!file.is_open()) {
return false;
}
char magic[4];
file.read(magic, sizeof(magic));
if (!file) {
return false;
}
for (uint32_t i = 0; i < sizeof(magic); i++) {
if (magic[i] != GGUF_MAGIC[i]) {
return false;
}
}
return true;
}
bool is_safetensors_file(const std::string& file_path) {
std::ifstream file(std::filesystem::u8path(file_path), std::ios::binary);
if (!file.is_open()) {
return false;
}
// get file size
file.seekg(0, file.end);
size_t file_size_ = file.tellg();
file.seekg(0, file.beg);
// read header size
if (file_size_ <= ST_HEADER_SIZE_LEN) {
return false;
}
uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
if (!file) {
return false;
}
size_t header_size_ = read_u64(header_size_buf);
if (header_size_ >= file_size_ || header_size_ <= 2) {
return false;
}
// read header
std::vector<char> header_buf;
header_buf.resize(header_size_ + 1);
header_buf[header_size_] = '\0';
file.read(header_buf.data(), header_size_);
if (!file) {
return false;
}
try {
nlohmann::json header_ = nlohmann::json::parse(header_buf.data());
} catch (const std::exception&) {
return false;
}
return true;
}
bool ModelLoader::init_from_file(const std::string& file_path, const std::string& prefix) {
if (is_directory(file_path)) {
LOG_INFO("load %s using diffusers format", file_path.c_str());
return init_from_diffusers_file(file_path, prefix);
} else if (is_gguf_file(file_path)) {
LOG_INFO("load %s using gguf format", file_path.c_str());
return init_from_gguf_file(file_path, prefix);
} else if (is_safetensors_file(file_path)) {
LOG_INFO("load %s using safetensors format", file_path.c_str());
return init_from_safetensors_file(file_path, prefix);
} else if (is_zip_file(file_path)) {
LOG_INFO("load %s using checkpoint format", file_path.c_str());
return init_from_ckpt_file(file_path, prefix);
} else {
if (file_exists(file_path)) {
LOG_WARN("unknown format %s", file_path.c_str());
} else {
LOG_WARN("file %s not found", file_path.c_str());
}
return false;
}
}
void ModelLoader::convert_tensors_name() {
SDVersion version = (version_ == VERSION_COUNT) ? get_sd_version() : version_;
String2TensorStorage new_map;
for (auto& [_, tensor_storage] : tensor_storage_map) {
auto new_name = convert_tensor_name(tensor_storage.name, version);
// LOG_DEBUG("%s -> %s", tensor_storage.name.c_str(), new_name.c_str());
tensor_storage.name = new_name;
new_map[new_name] = std::move(tensor_storage);
}
tensor_storage_map.swap(new_map);
}
bool ModelLoader::init_from_file_and_convert_name(const std::string& file_path, const std::string& prefix, SDVersion version) {
if (version_ == VERSION_COUNT && version != VERSION_COUNT) {
version_ = version;
}
if (!init_from_file(file_path, prefix)) {
return false;
}
convert_tensors_name();
return true;
}
/*================================================= GGUFModelLoader ==================================================*/
bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::string& prefix) {
LOG_DEBUG("init from '%s'", file_path.c_str());
file_paths_.push_back(file_path);
size_t file_index = file_paths_.size() - 1;
gguf_context* ctx_gguf_ = nullptr;
ggml_context* ctx_meta_ = nullptr;
ctx_gguf_ = gguf_init_from_file(file_path.c_str(), {true, &ctx_meta_});
if (!ctx_gguf_) {
LOG_ERROR("failed to open '%s' with gguf_init_from_file. Try to open it with GGUFReader.", file_path.c_str());
GGUFReader gguf_reader;
if (!gguf_reader.load(file_path)) {
LOG_ERROR("failed to open '%s' with GGUFReader.", file_path.c_str());
return false;
}
size_t data_offset = gguf_reader.data_offset();
for (const auto& gguf_tensor_info : gguf_reader.tensors()) {
std::string name = gguf_tensor_info.name;
if (!starts_with(name, prefix)) {
name = prefix + name;
}
TensorStorage tensor_storage(
name,
gguf_tensor_info.type,
gguf_tensor_info.shape.data(),
static_cast<int>(gguf_tensor_info.shape.size()),
file_index,
data_offset + gguf_tensor_info.offset);
// LOG_DEBUG("%s %s", name.c_str(), tensor_storage.to_string().c_str());
add_tensor_storage(tensor_storage);
}
return true;
}
int n_tensors = static_cast<int>(gguf_get_n_tensors(ctx_gguf_));
size_t total_size = 0;
size_t data_offset = gguf_get_data_offset(ctx_gguf_);
for (int i = 0; i < n_tensors; i++) {
std::string name = gguf_get_tensor_name(ctx_gguf_, i);
ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
size_t offset = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);
// LOG_DEBUG("%s", name.c_str());
if (!starts_with(name, prefix)) {
name = prefix + name;
}
TensorStorage tensor_storage(name, dummy->type, dummy->ne, ggml_n_dims(dummy), file_index, offset);
GGML_ASSERT(ggml_nbytes(dummy) == tensor_storage.nbytes());
add_tensor_storage(tensor_storage);
}
gguf_free(ctx_gguf_);
ggml_free(ctx_meta_);
return true;
}
/*================================================= SafeTensorsModelLoader ==================================================*/
ggml_type str_to_ggml_type(const std::string& dtype) {
ggml_type ttype = GGML_TYPE_COUNT;
if (dtype == "F16") {
ttype = GGML_TYPE_F16;
} else if (dtype == "BF16") {
ttype = GGML_TYPE_BF16;
} else if (dtype == "F32") {
ttype = GGML_TYPE_F32;
} else if (dtype == "F64") {
ttype = GGML_TYPE_F32;
} else if (dtype == "F8_E4M3") {
ttype = GGML_TYPE_F16;
} else if (dtype == "F8_E5M2") {
ttype = GGML_TYPE_F16;
} else if (dtype == "I64") {
ttype = GGML_TYPE_I32;
} else if (dtype == "I32") {
// Lens conditioning bundles may store attention masks as safetensors I32.
ttype = GGML_TYPE_I32;
}
return ttype;
}
// https://huggingface.co/docs/safetensors/index
bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const std::string& prefix) {
LOG_DEBUG("init from '%s', prefix = '%s'", file_path.c_str(), prefix.c_str());
file_paths_.push_back(file_path);
size_t file_index = file_paths_.size() - 1;
std::ifstream file(std::filesystem::u8path(file_path), std::ios::binary);
if (!file.is_open()) {
LOG_ERROR("failed to open '%s'", file_path.c_str());
file_paths_.pop_back();
return false;
}
// get file size
file.seekg(0, file.end);
size_t file_size_ = file.tellg();
file.seekg(0, file.beg);
// read header size
if (file_size_ <= ST_HEADER_SIZE_LEN) {
LOG_ERROR("invalid safetensor file '%s'", file_path.c_str());
file_paths_.pop_back();
return false;
}
uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
if (!file) {
LOG_ERROR("read safetensors header size failed: '%s'", file_path.c_str());
return false;
}
size_t header_size_ = read_u64(header_size_buf);
if (header_size_ >= file_size_) {
LOG_ERROR("invalid safetensor file '%s'", file_path.c_str());
file_paths_.pop_back();
return false;
}
// read header
std::vector<char> header_buf;
header_buf.resize(header_size_ + 1);
header_buf[header_size_] = '\0';
file.read(header_buf.data(), header_size_);
if (!file) {
LOG_ERROR("read safetensors header failed: '%s'", file_path.c_str());
file_paths_.pop_back();
return false;
}
nlohmann::json header_;
try {
header_ = nlohmann::json::parse(header_buf.data());
} catch (const std::exception&) {
LOG_ERROR("parsing safetensors header failed", file_path.c_str());
file_paths_.pop_back();
return false;
}
std::unordered_map<std::string, float> f8_weight_scales;
for (auto& item : header_.items()) {
std::string name = item.key();
if (!ends_with(name, ".weight_scale")) {
continue;
}
nlohmann::json tensor_info = item.value();
if (tensor_info.contains("dtype") && tensor_info["dtype"].get<std::string>() == "F32") {
nlohmann::json shape = tensor_info["shape"];
bool scalar_shape = shape.empty();
if (!scalar_shape) {
scalar_shape = true;
}
for (auto& dim : shape) {
scalar_shape = scalar_shape && dim.get<int64_t>() == 1;
}
size_t begin = tensor_info["data_offsets"][0].get<size_t>();
size_t end = tensor_info["data_offsets"][1].get<size_t>();
if (scalar_shape && end - begin == sizeof(float)) {
float scale = 1.0f;
file.seekg(ST_HEADER_SIZE_LEN + header_size_ + begin);
file.read(reinterpret_cast<char*>(&scale), sizeof(float));
if (!file) {
LOG_ERROR("read fp8 weight scale failed: '%s'", name.c_str());
return false;
}
if (!starts_with(name, prefix)) {
name = prefix + name;
}
f8_weight_scales[name.substr(0, name.size() - std::string("_scale").size())] = scale;
}
}
}
for (auto& item : header_.items()) {
std::string name = item.key();
nlohmann::json tensor_info = item.value();
// LOG_DEBUG("%s %s\n", name.c_str(), tensor_info.dump().c_str());
if (name == "__metadata__") {
continue;
}
if (is_unused_tensor(name)) {
continue;
}
if (ends_with(name, ".input_scale") || ends_with(name, ".weight_scale")) {
continue;
}
std::string dtype = tensor_info["dtype"];
nlohmann::json shape = tensor_info["shape"];
if (dtype == "U8") {
continue;
}
size_t begin = tensor_info["data_offsets"][0].get<size_t>();
size_t end = tensor_info["data_offsets"][1].get<size_t>();
ggml_type type = str_to_ggml_type(dtype);
if (type == GGML_TYPE_COUNT) {
LOG_ERROR("unsupported dtype '%s' (tensor '%s')", dtype.c_str(), name.c_str());
return false;
}
if (shape.size() > SD_MAX_DIMS) {
LOG_ERROR("invalid tensor '%s'", name.c_str());
return false;
}
int n_dims = (int)shape.size();
int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
for (int i = 0; i < n_dims; i++) {
ne[i] = shape[i].get<int64_t>();
}
if (n_dims == 5) {
n_dims = 4;
ne[0] = ne[0] * ne[1];
ne[1] = ne[2];
ne[2] = ne[3];
ne[3] = ne[4];
}
// ggml_n_dims returns 1 for scalars
if (n_dims == 0) {
n_dims = 1;
}
if (!starts_with(name, prefix)) {
name = prefix + name;
}
TensorStorage tensor_storage(name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
tensor_storage.reverse_ne();
size_t tensor_data_size = end - begin;
bool tensor_size_ok;
if (dtype == "F8_E4M3") {
tensor_storage.is_f8_e4m3 = true;
// f8 -> f16
tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
} else if (dtype == "F8_E5M2") {
tensor_storage.is_f8_e5m2 = true;
// f8 -> f16
tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
} else if (dtype == "F64") {
tensor_storage.is_f64 = true;
// f64 -> f32
tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
} else if (dtype == "I64") {
tensor_storage.is_i64 = true;
// i64 -> i32
tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
} else {
tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size);
}
auto scale_it = f8_weight_scales.find(tensor_storage.name);
if ((tensor_storage.is_f8_e4m3 || tensor_storage.is_f8_e5m2) && scale_it != f8_weight_scales.end()) {
tensor_storage.has_f8_weight_scale = true;
tensor_storage.f8_weight_scale = scale_it->second;
}
if (!tensor_size_ok) {
LOG_ERROR("size mismatch for tensor '%s' (%s)\n", name.c_str(), dtype.c_str());
return false;
}
add_tensor_storage(tensor_storage);
// LOG_DEBUG("%s %s", tensor_storage.to_string().c_str(), dtype.c_str());
}
return true;
}
/*================================================= DiffusersModelLoader ==================================================*/
bool ModelLoader::init_from_diffusers_file(const std::string& file_path, const std::string& prefix) {
std::string unet_path = path_join(file_path, "unet/diffusion_pytorch_model.safetensors");
std::string vae_path = path_join(file_path, "vae/diffusion_pytorch_model.safetensors");
std::string clip_path = path_join(file_path, "text_encoder/model.safetensors");
std::string clip_g_path = path_join(file_path, "text_encoder_2/model.safetensors");
auto load_diffusers_safetensors = [&](const std::string& primary_path,
const std::string& tensor_prefix) -> bool {
if (init_from_safetensors_file(primary_path, tensor_prefix)) {
return true;
}
std::string fp16_path = primary_path;
const std::string suffix = ".safetensors";
if (ends_with(fp16_path, suffix)) {
fp16_path = fp16_path.substr(0, fp16_path.size() - suffix.size()) + ".fp16.safetensors";
if (init_from_safetensors_file(fp16_path, tensor_prefix)) {
return true;
}
}
return false;
};
if (!load_diffusers_safetensors(unet_path, "unet.")) {
return false;
}
if (!load_diffusers_safetensors(vae_path, "vae.")) {
LOG_WARN("Couldn't find working VAE in %s", file_path.c_str());
// return false;
}
if (!load_diffusers_safetensors(clip_path, "te.")) {
LOG_WARN("Couldn't find working text encoder in %s", file_path.c_str());
// return false;
}
if (!load_diffusers_safetensors(clip_g_path, "te.1.")) {
LOG_DEBUG("Couldn't find working second text encoder in %s", file_path.c_str());
}
return true;
}
/*================================================= CkptModelLoader ==================================================*/
// $ python -m pickletools sd-v1-4/archive/data.pkl | head -n 100
// 0: \x80 PROTO 2
// 2: } EMPTY_DICT
// 3: q BINPUT 0
// 5: ( MARK
// 6: X BINUNICODE 'epoch'
// 16: q BINPUT 1
// 18: K BININT1 6
// 20: X BINUNICODE 'global_step'
// 36: q BINPUT 2
// 38: J BININT 470000
// 43: X BINUNICODE 'pytorch-lightning_version'
// 73: q BINPUT 3
// 75: X BINUNICODE '1.4.2'
// 85: q BINPUT 4
// 87: X BINUNICODE 'state_dict'
// 102: q BINPUT 5
// 104: } EMPTY_DICT
// 105: q BINPUT 6
// 107: ( MARK
// 108: X BINUNICODE 'betas'
// 118: q BINPUT 7
// 120: c GLOBAL 'torch._utils _rebuild_tensor_v2'
// 153: q BINPUT 8
// 155: ( MARK
// 156: ( MARK
// 157: X BINUNICODE 'storage'
// 169: q BINPUT 9
// 171: c GLOBAL 'torch FloatStorage'
// 191: q BINPUT 10
// 193: X BINUNICODE '0'
// 199: q BINPUT 11
// 201: X BINUNICODE 'cpu'
// 209: q BINPUT 12
// 211: M BININT2 1000
// 214: t TUPLE (MARK at 156)
// 215: q BINPUT 13
// 217: Q BINPERSID
// 218: K BININT1 0
// 220: M BININT2 1000
// ...............................
// 3201: q BINPUT 250
// 3203: R REDUCE
// 3204: q BINPUT 251
// 3206: X BINUNICODE 'model.diffusion_model.input_blocks.1.1.proj_in.weight'
// 3264: q BINPUT 252
// 3266: h BINGET 8
// 3268: ( MARK
// 3269: ( MARK
// 3270: h BINGET 9
// 3272: h BINGET 10
// 3274: X BINUNICODE '30'
// 3281: q BINPUT 253
// 3283: h BINGET 12
// 3285: J BININT 102400
// 3290: t TUPLE (MARK at 3269)
// 3291: q BINPUT 254
// 3293: Q BINPERSID
// 3294: K BININT1 0
// 3296: ( MARK
// 3297: M BININT2 320
// 3300: M BININT2 320
// 3303: K BININT1 1
// 3305: K BININT1 1
// 3307: t TUPLE (MARK at 3296)
// 3308: q BINPUT 255
// 3310: ( MARK
// 3311: M BININT2 320
// 3314: K BININT1 1
// 3316: K BININT1 1
// 3318: K BININT1 1
// 3320: t TUPLE (MARK at 3310)
// 3321: r LONG_BINPUT 256
// 3326: \x89 NEWFALSE
// 3327: h BINGET 16
// 3329: ) EMPTY_TUPLE
// 3330: R REDUCE
// 3331: r LONG_BINPUT 257
// 3336: t TUPLE (MARK at 3268)
// 3337: r LONG_BINPUT 258
// 3342: R REDUCE
// 3343: r LONG_BINPUT 259
// 3348: X BINUNICODE 'model.diffusion_model.input_blocks.1.1.proj_in.bias'
// 3404: r LONG_BINPUT 260
// 3409: h BINGET 8
// 3411: ( MARK
// 3412: ( MARK
// 3413: h BINGET 9
// 3415: h BINGET 10
// 3417: X BINUNICODE '31'
struct PickleTensorReader {
enum ReadPhase {
READ_NAME,
READ_DATA,
CHECK_SIZE,
READ_DIMENS
};
ReadPhase phase = READ_NAME;
size_t entry_size = 0;
int32_t nelements = 0;
TensorStorage tensor_storage;
static ggml_type global_type; // all pickle_tensors data type
static bool read_global_type;
bool read_int_value(uint32_t value) {
if (phase == CHECK_SIZE) {
if (entry_size == value * ggml_type_size(tensor_storage.type)) {
nelements = value;
phase = READ_DIMENS;
return true;
} else {
phase = READ_NAME;
}
} else if (phase == READ_DIMENS) {
if (tensor_storage.n_dims + 1 > SD_MAX_DIMS) { // too many dimens
phase = READ_NAME;
tensor_storage.n_dims = 0;
}
if (nelements % value == 0) {
tensor_storage.ne[tensor_storage.n_dims] = value;
tensor_storage.n_dims++;
}
}
return false;
}
void read_global(const std::string& str) {
if (str == "FloatStorage") {
if (read_global_type) {
global_type = GGML_TYPE_F32;
read_global_type = false;
}
tensor_storage.type = GGML_TYPE_F32;
} else if (str == "HalfStorage") {
if (read_global_type) {
global_type = GGML_TYPE_F16;
read_global_type = false;
}
tensor_storage.type = GGML_TYPE_F16;
} else if (str == "BFloat16Storage") {
if (read_global_type) {
global_type = GGML_TYPE_BF16;
read_global_type = false;
}
tensor_storage.type = GGML_TYPE_BF16;
} else if (str == "ByteStorage") {
if (read_global_type) {
global_type = GGML_TYPE_I8;
read_global_type = false;
}
tensor_storage.type = GGML_TYPE_I8;
} else if (str == "IntStorage") {
if (read_global_type) {
global_type = GGML_TYPE_I32;
read_global_type = false;
}
tensor_storage.type = GGML_TYPE_I32;
}
}
void read_string(const std::string& str, zip_t* zip, std::string dir) {
if (str == "storage") {
read_global_type = true;
} else if (str != "state_dict") {
if (phase == READ_DATA) {
std::string entry_name = dir + "data/" + std::string(str);
size_t i, n = zip_entries_total(zip);
for (i = 0; i < n; ++i) {
zip_entry_openbyindex(zip, i);
{
std::string name = zip_entry_name(zip);
if (name == entry_name) {
tensor_storage.index_in_zip = (int)i;
entry_size = zip_entry_size(zip);
zip_entry_close(zip);