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1530 lines (1340 loc) · 58.4 KB
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#include <algorithm>
#include <atomic>
#include <chrono>
#include <cinttypes>
#include <cstdarg>
#include <cstdlib>
#include <fstream>
#include <functional>
#include <mutex>
#include <regex>
#include <set>
#include <string>
#include <thread>
#include <unordered_map>
#include <vector>
#include "core/util.h"
#include "model_io/gguf_io.h"
#include "model_io/safetensors_io.h"
#include "model_io/torch_legacy_io.h"
#include "model_io/torch_zip_io.h"
#include "model_loader.h"
#include "runtime/imatrix.h"
#include "stable-diffusion.h"
#include "core/ggml_extend_backend.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml.h"
#include "zip.h"
#include "name_conversion.h"
/*================================================= 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.",
};
bool is_unused_tensor(const 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 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,
std::vector<float> imatrix = {}) {
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 {
imatrix.resize(n_per_row, 1.0f);
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 {
imatrix.resize(n_per_row, 1.0f);
const float* im = imatrix.data();
ggml_quantize_chunk(dst_type, (float*)src_data_f32, dst, 0, nrows, n_per_row, im);
}
}
}
/*================================================= ModelLoader ==================================================*/
ModelLoader::ModelLoader()
: n_threads_(sd_get_num_physical_cores()) {
}
size_t ModelLoader::add_file_path(const std::string& file_path) {
if (model_files_processed) {
file_data.clear();
model_files_processed = false;
}
file_paths_.push_back(file_path);
return file_paths_.size() - 1;
}
void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
tensor_storage_map[tensor_storage.name] = tensor_storage;
}
void ModelLoader::set_n_threads(int n_threads) {
n_threads_ = n_threads > 0 ? n_threads : sd_get_num_physical_cores();
LOG_DEBUG("using %d threads for model loading", n_threads_);
}
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 (ends_with(file_path, ".json")) {
LOG_INFO("load %s using safetensors index format", file_path.c_str());
return init_from_safetensors_index_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_torch_zip_file(file_path)) {
LOG_INFO("load %s using torch zip format", file_path.c_str());
return init_from_torch_zip_file(file_path, prefix);
} else if (init_from_torch_legacy_file(file_path, prefix)) {
LOG_INFO("load %s using torch legacy format", file_path.c_str());
return true;
} 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());
std::vector<TensorStorage> tensor_storages;
std::string error;
if (!read_gguf_file(file_path, tensor_storages, &error)) {
LOG_ERROR("%s", error.c_str());
return false;
}
size_t file_index = add_file_path(file_path);
for (auto& tensor_storage : tensor_storages) {
// LOG_DEBUG("%s", tensor_storage.name.c_str());
if (!starts_with(tensor_storage.name, prefix)) {
tensor_storage.name = prefix + tensor_storage.name;
}
tensor_storage.file_index = file_index;
add_tensor_storage(tensor_storage);
}
return true;
}
/*================================================= SafeTensorsModelLoader ==================================================*/
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());
std::vector<TensorStorage> tensor_storages;
std::string error;
if (!read_safetensors_file(file_path, tensor_storages, &error)) {
LOG_ERROR("%s", error.c_str());
return false;
}
size_t file_index = add_file_path(file_path);
for (auto& tensor_storage : tensor_storages) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
if (!starts_with(tensor_storage.name, prefix)) {
tensor_storage.name = prefix + tensor_storage.name;
}
tensor_storage.file_index = file_index;
add_tensor_storage(tensor_storage);
// LOG_DEBUG("%s", tensor_storage.to_string().c_str());
}
return true;
}
bool ModelLoader::init_from_safetensors_index_file(const std::string& file_path, const std::string& prefix) {
LOG_DEBUG("init from safetensors index '%s', prefix = '%s'", file_path.c_str(), prefix.c_str());
std::vector<std::string> shard_paths;
std::string error;
if (!read_safetensors_index_file(file_path, shard_paths, &error)) {
LOG_ERROR("%s", error.c_str());
return false;
}
for (const std::string& shard_path : shard_paths) {
if (!init_from_file(shard_path, prefix)) {
return false;
}
}
return true;
}
/*================================================= TorchLegacyModelLoader ==================================================*/
bool ModelLoader::init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix) {
LOG_DEBUG("init from torch legacy '%s'", file_path.c_str());
std::vector<TensorStorage> tensor_storages;
std::string error;
if (!read_torch_legacy_file(file_path, tensor_storages, &error)) {
if ((!error.empty()) && (ends_with(file_path, ".pt") || ends_with(file_path, ".pth"))) {
LOG_WARN("%s", error.c_str());
}
return false;
}
size_t file_index = add_file_path(file_path);
for (auto& tensor_storage : tensor_storages) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
if (!starts_with(tensor_storage.name, prefix)) {
tensor_storage.name = prefix + tensor_storage.name;
}
tensor_storage.file_index = file_index;
add_tensor_storage(tensor_storage);
}
return true;
}
/*================================================= TorchZipModelLoader ==================================================*/
bool ModelLoader::init_from_torch_zip_file(const std::string& file_path, const std::string& prefix) {
LOG_DEBUG("init from '%s'", file_path.c_str());
std::vector<TensorStorage> tensor_storages;
std::string error;
if (!read_torch_zip_file(file_path, tensor_storages, &error)) {
LOG_ERROR("%s", error.c_str());
return false;
}
size_t file_index = add_file_path(file_path);
for (auto& tensor_storage : tensor_storages) {
if (!starts_with(tensor_storage.name, prefix)) {
tensor_storage.name = prefix + tensor_storage.name;
}
tensor_storage.file_index = file_index;
add_tensor_storage(tensor_storage);
// LOG_DEBUG("%s", tensor_storage.to_string().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");
if (!init_from_safetensors_file(unet_path, "unet.")) {
return false;
}
if (!init_from_safetensors_file(vae_path, "vae.")) {
LOG_WARN("Couldn't find working VAE in %s", file_path.c_str());
// return false;
}
if (!init_from_safetensors_file(clip_path, "te.")) {
LOG_WARN("Couldn't find working text encoder in %s", file_path.c_str());
// return false;
}
if (!init_from_safetensors_file(clip_g_path, "te.1.")) {
LOG_DEBUG("Couldn't find working second text encoder in %s", file_path.c_str());
}
return true;
}
SDVersion ModelLoader::get_sd_version() {
TensorStorage token_embedding_weight, input_block_weight, context_ebedding_weight;
bool has_multiple_encoders = false;
bool is_unet = false;
bool is_xl = false;
bool is_flux = false;
bool is_flux2 = false;
bool has_single_block_47 = false;
bool is_wan = false;
int64_t patch_embedding_channels = 0;
bool has_img_emb = false;
bool has_middle_block_1 = false;
bool has_output_block_311 = false;
bool has_output_block_71 = false;
bool has_attn_1024 = false;
for (auto& [name, tensor_storage] : tensor_storage_map) {
if (tensor_storage.name.find("model.diffusion_model.double_blocks.") != std::string::npos ||
tensor_storage.name.find("model.diffusion_model.single_transformer_blocks.") != std::string::npos) {
is_flux = true;
}
if (tensor_storage.name.find("model.diffusion_model.net.lq_proj.latent_proj.0.weight") != std::string::npos) {
return VERSION_PID;
}
if (tensor_storage.name.find("embed_image_indicator.weight") != std::string::npos) {
return VERSION_IDEOGRAM4;
}
if (tensor_storage.name.find("model.diffusion_model.txtfusion.projector.weight") != std::string::npos ||
tensor_storage.name.find("model.diffusion_model.text_fusion.projector.weight") != std::string::npos) {
return VERSION_KREA2;
}
if (tensor_storage.name.find("model.diffusion_model.nerf_final_layer_conv.") != std::string::npos) {
return VERSION_CHROMA_RADIANCE;
}
if (tensor_storage.name.find("model.diffusion_model.joint_blocks.") != std::string::npos) {
return VERSION_SD3;
}
if (tensor_storage.name.find("model.x_embedder.proj1.weight") != std::string::npos &&
tensor_storage_map.find("model.language_model.layers.0.self_attn.q_proj.weight") != tensor_storage_map.end()) {
return VERSION_HIDREAM_O1;
}
if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.attn.norm_added_q.weight") != std::string::npos &&
tensor_storage_map.find("model.diffusion_model.transformer_blocks.0.img_mlp.w1.weight") != tensor_storage_map.end()) {
return VERSION_LENS;
}
if (tensor_storage.name.find("net.img_embedder.proj1.weight") != std::string::npos) {
return VERSION_MINIT2I;
}
if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.img_mod.1.weight") != std::string::npos) {
if (tensor_storage_map.find("model.diffusion_model.time_text_embed.addition_t_embedding.weight") != tensor_storage_map.end()) {
return VERSION_QWEN_IMAGE_LAYERED;
}
return VERSION_QWEN_IMAGE;
}
if (tensor_storage.name.find("llm_adapter.blocks.0.cross_attn.q_proj.weight") != std::string::npos) {
return VERSION_ANIMA;
}
if (tensor_storage.name.find("model.diffusion_model.double_stream_modulation_img.lin.weight") != std::string::npos) {
is_flux2 = true;
}
if (tensor_storage.name.find("dual_time_embed.semantic_embedder.linear_1.weight") != std::string::npos) {
return VERSION_SEFI_IMAGE;
}
if (tensor_storage.name.find("single_blocks.47.linear1.weight") != std::string::npos) {
has_single_block_47 = true;
}
if (tensor_storage.name.find("model.diffusion_model.double_blocks.0.img_mlp.gate_proj.weight") != std::string::npos) {
return VERSION_OVIS_IMAGE;
}
if (tensor_storage.name.find("model.diffusion_model.cap_embedder.0.weight") != std::string::npos) {
return VERSION_Z_IMAGE;
}
if (tensor_storage.name.find("double_stream_layers.0.img_instruct_attn.processor.img_to_q.weight") != std::string::npos) {
return VERSION_BOOGU_IMAGE;
}
if (tensor_storage.name.find("model.diffusion_model.layers.0.adaLN_sa_ln.weight") != std::string::npos) {
return VERSION_ERNIE_IMAGE;
}
if (tensor_storage.name.find("model.diffusion_model.adaln_single.emb.timestep_embedder.linear_1.bias") != std::string::npos) {
return VERSION_LTXAV;
}
if (tensor_storage.name.find("model.diffusion_model.blocks.0.cross_attn.norm_k.weight") != std::string::npos) {
is_wan = true;
}
if (tensor_storage.name.find("model.diffusion_model.patch_embedder.weight") != std::string::npos) {
return VERSION_LINGBOT_VIDEO;
}
if (tensor_storage.name.find("model.diffusion_model.patch_embedding.weight") != std::string::npos) {
patch_embedding_channels = tensor_storage.ne[3];
}
if (tensor_storage.name.find("model.diffusion_model.img_emb") != std::string::npos) {
has_img_emb = true;
}
if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos ||
tensor_storage.name.find("unet.down_blocks.") != std::string::npos) {
is_unet = true;
if (has_multiple_encoders) {
is_xl = true;
}
}
if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos ||
tensor_storage.name.find("cond_stage_model.1") != std::string::npos ||
tensor_storage.name.find("te.1") != std::string::npos) {
has_multiple_encoders = true;
if (is_unet) {
is_xl = true;
}
}
if (tensor_storage.name.find("model.diffusion_model.input_blocks.8.0.time_mixer.mix_factor") != std::string::npos) {
return VERSION_SVD;
}
if (tensor_storage.name.find("model.diffusion_model.middle_block.1.") != std::string::npos ||
tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
has_middle_block_1 = true;
}
if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos ||
tensor_storage.name.find("unet.up_blocks.1.attentions.0.transformer_blocks.1") != std::string::npos) {
has_output_block_311 = true;
}
if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos ||
tensor_storage.name.find("unet.up_blocks.2.attentions.1") != std::string::npos) {
has_output_block_71 = true;
if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1.transformer_blocks.0.attn1.to_k.weight") != std::string::npos) {
if (tensor_storage.ne[0] == 1024)
has_attn_1024 = true;
}
}
if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||
tensor_storage.name == "cond_stage_model.model.token_embedding.weight" ||
tensor_storage.name == "text_model.embeddings.token_embedding.weight" ||
tensor_storage.name == "te.text_model.embeddings.token_embedding.weight" ||
tensor_storage.name == "conditioner.embedders.0.model.token_embedding.weight" ||
tensor_storage.name == "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight") {
token_embedding_weight = tensor_storage;
// break;
}
if (tensor_storage.name == "model.diffusion_model.input_blocks.0.0.weight" ||
tensor_storage.name == "model.diffusion_model.img_in.weight" ||
tensor_storage.name == "unet.conv_in.weight") {
input_block_weight = tensor_storage;
}
if (tensor_storage.name == "model.diffusion_model.txt_in.weight" || tensor_storage.name == "model.diffusion_model.context_embedder.weight") {
context_ebedding_weight = tensor_storage;
}
}
if (is_wan) {
LOG_DEBUG("patch_embedding_channels %d", patch_embedding_channels);
if (patch_embedding_channels == 184320 && !has_img_emb) {
return VERSION_WAN2_2_I2V;
}
if (patch_embedding_channels == 147456 && !has_img_emb) {
return VERSION_WAN2_2_TI2V;
}
return VERSION_WAN2;
}
bool is_inpaint = input_block_weight.ne[2] == 9;
bool is_ip2p = input_block_weight.ne[2] == 8;
if (is_xl) {
if (is_inpaint) {
return VERSION_SDXL_INPAINT;
}
if (is_ip2p) {
return VERSION_SDXL_PIX2PIX;
}
if (!has_middle_block_1) {
if (!has_output_block_311) {
return VERSION_SDXL_VEGA;
}
return VERSION_SDXL_SSD1B;
}
return VERSION_SDXL;
}
if (is_flux && !is_flux2) {
if (context_ebedding_weight.ne[0] == 3584) {
return VERSION_LONGCAT;
} else {
if (input_block_weight.ne[0] == 384) {
return VERSION_FLUX_FILL;
}
if (input_block_weight.ne[0] == 128) {
return VERSION_FLUX_CONTROLS;
}
if (input_block_weight.ne[0] == 196) {
return VERSION_FLEX_2;
}
return VERSION_FLUX;
}
}
if (is_flux2) {
if (has_single_block_47) {
return VERSION_FLUX2;
}
return VERSION_FLUX2_KLEIN;
}
if (token_embedding_weight.ne[0] == 768) {
if (is_inpaint) {
return VERSION_SD1_INPAINT;
}
if (is_ip2p) {
return VERSION_SD1_PIX2PIX;
}
if (!has_middle_block_1) {
if (!has_output_block_71) {
return VERSION_SDXS_512_DS;
}
return VERSION_SD1_TINY_UNET;
}
return VERSION_SD1;
} else if (token_embedding_weight.ne[0] == 1024) {
if (is_inpaint) {
return VERSION_SD2_INPAINT;
}
if (!has_middle_block_1) {
return has_attn_1024 ? VERSION_SDXS_09 : VERSION_SD2_TINY_UNET;
}
return VERSION_SD2;
}
return VERSION_COUNT;
}
std::map<ggml_type, uint32_t> ModelLoader::get_wtype_stat() {
std::map<ggml_type, uint32_t> wtype_stat;
for (auto& [name, tensor_storage] : tensor_storage_map) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
auto iter = wtype_stat.find(tensor_storage.type);
if (iter != wtype_stat.end()) {
iter->second++;
} else {
wtype_stat[tensor_storage.type] = 1;
}
}
return wtype_stat;
}
std::map<ggml_type, uint32_t> ModelLoader::get_conditioner_wtype_stat() {
std::map<ggml_type, uint32_t> wtype_stat;
for (auto& [name, tensor_storage] : tensor_storage_map) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
if ((tensor_storage.name.find("text_encoders") == std::string::npos &&
tensor_storage.name.find("cond_stage_model") == std::string::npos &&
tensor_storage.name.find("te.text_model.") == std::string::npos &&
tensor_storage.name.find("conditioner") == std::string::npos)) {
continue;
}
auto iter = wtype_stat.find(tensor_storage.type);
if (iter != wtype_stat.end()) {
iter->second++;
} else {
wtype_stat[tensor_storage.type] = 1;
}
}
return wtype_stat;
}
std::map<ggml_type, uint32_t> ModelLoader::get_diffusion_model_wtype_stat() {
std::map<ggml_type, uint32_t> wtype_stat;
for (auto& [name, tensor_storage] : tensor_storage_map) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
if (tensor_storage.name.find("model.diffusion_model.") == std::string::npos && tensor_storage.name.find("unet.") == std::string::npos) {
continue;
}
auto iter = wtype_stat.find(tensor_storage.type);
if (iter != wtype_stat.end()) {
iter->second++;
} else {
wtype_stat[tensor_storage.type] = 1;
}
}
return wtype_stat;
}
std::map<ggml_type, uint32_t> ModelLoader::get_vae_wtype_stat() {
std::map<ggml_type, uint32_t> wtype_stat;
for (auto& [name, tensor_storage] : tensor_storage_map) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
if (tensor_storage.name.find("vae.") == std::string::npos &&
tensor_storage.name.find("first_stage_model") == std::string::npos) {
continue;
}
auto iter = wtype_stat.find(tensor_storage.type);
if (iter != wtype_stat.end()) {
iter->second++;
} else {
wtype_stat[tensor_storage.type] = 1;
}
}
return wtype_stat;
}
TensorTypeRules parse_tensor_type_rules(const std::string& tensor_type_rules) {
TensorTypeRules result;
for (const auto& item : split_string(tensor_type_rules, ',')) {
if (item.size() == 0)
continue;
std::string::size_type pos = item.find('=');
if (pos == std::string::npos) {
LOG_WARN("ignoring invalid quant override \"%s\"", item.c_str());
continue;
}
std::string tensor_pattern = item.substr(0, pos);
std::string type_name = item.substr(pos + 1);
ggml_type tensor_type = GGML_TYPE_COUNT;
if (type_name == "f32") {
tensor_type = GGML_TYPE_F32;
} else {
for (size_t i = 0; i < GGML_TYPE_COUNT; i++) {
auto trait = ggml_get_type_traits((ggml_type)i);
if (trait->to_float && trait->type_size && type_name == trait->type_name) {
tensor_type = (ggml_type)i;
}
}
}
if (tensor_type != GGML_TYPE_COUNT) {
result.emplace_back(tensor_pattern, tensor_type);
} else {
LOG_WARN("ignoring invalid quant override \"%s\"", item.c_str());
}
}
return result;
}
void ModelLoader::set_wtype_override(ggml_type wtype, std::string tensor_type_rules) {
auto map_rules = parse_tensor_type_rules(tensor_type_rules);
for (auto& [name, tensor_storage] : tensor_storage_map) {
ggml_type dst_type = wtype;
for (const auto& tensor_type_rule : map_rules) {
std::regex pattern(tensor_type_rule.first);
if (std::regex_search(name, pattern)) {
dst_type = tensor_type_rule.second;
break;
}
}
if (dst_type == GGML_TYPE_COUNT) {
continue;
}
if (!tensor_should_be_converted(tensor_storage, dst_type)) {
continue;
}
tensor_storage.expected_type = dst_type;
}
}
void ModelLoader::process_model_files(bool enable_mmap, bool writable_mmap) {
if (model_files_processed) {
return;
}
std::vector<TensorStorage> processed_tensor_storages;
for (const auto& [name, tensor_storage] : tensor_storage_map) {
if (is_unused_tensor(tensor_storage.name)) {
continue;
}
processed_tensor_storages.push_back(tensor_storage);
}
for (size_t file_index = 0; file_index < file_paths_.size(); file_index++) {
std::string file_path = file_paths_[file_index];
std::vector<TensorStorage> file_tensors;
for (const auto& ts : processed_tensor_storages) {
if (ts.file_index == file_index) {
file_tensors.push_back(ts);
}
}
if (file_tensors.empty()) {
continue;
}
bool is_zip = false;
for (auto const& ts : file_tensors) {
if (ts.index_in_zip >= 0) {
is_zip = true;
break;
}
}
ModelFileData fdata = {};
fdata.path = file_path;
fdata.is_zip = is_zip;
fdata.tensors = std::move(file_tensors);
if (enable_mmap && !is_zip) {
LOG_DEBUG("using mmap for I/O");
std::unique_ptr<MmapWrapper> mmapped = MmapWrapper::create(file_path, writable_mmap);
if (mmapped) {
uint8_t* mmap_data = static_cast<uint8_t*>(mmapped->writable_data());
ggml_backend_buffer_t buf_mmap = ggml_backend_cpu_buffer_from_ptr(mmap_data, mmapped->size());
if (buf_mmap) {
LOG_INFO("using mmap for '%s'", file_path.c_str());
fdata.mmbuffer = std::shared_ptr<struct ggml_backend_buffer>(buf_mmap, ggml_backend_buffer_free);
} else {
LOG_WARN("mmap: failed to create backend buffer for file %s", fdata.path.c_str());
}
fdata.mmapped = std::shared_ptr<MmapWrapper>(std::move(mmapped));
} else {
LOG_WARN("failed to memory-map '%s' (falling back to read())", file_path.c_str());
}
}
file_data.push_back(std::move(fdata));
}
model_files_processed = true;
}
std::vector<MmapTensorStore> ModelLoader::mmap_tensors(std::map<std::string, ggml_tensor*>& tensors,
std::set<std::string> ignore_tensors,
bool writable_mmap) {
process_model_files(true, writable_mmap);
std::vector<MmapTensorStore> result;
uint64_t mapped_bytes = 0;
size_t mapped_tensors = 0;
LOG_DEBUG("memory-mapping tensors...");
int64_t t_start = ggml_time_ms();
for (auto& fdata : file_data) {
if (!fdata.mmbuffer)
continue;
const std::vector<TensorStorage>& file_tensors = fdata.tensors;
size_t file_mapped_bytes = 0;
size_t file_mapped_tensors = 0;
for (const auto& tensor_storage : file_tensors) {
const std::string& name = tensor_storage.name;
bool is_ignored = false;
for (const auto& ignore_prefix : ignore_tensors) {
if (starts_with(name, ignore_prefix)) {
is_ignored = true;
break;
}
}
if (is_ignored)
continue;
auto it = tensors.find(name);
if (it == tensors.end())
continue;
ggml_tensor* dst_tensor = it->second;
if (dst_tensor == nullptr)
continue;
if (tensor_storage.is_f8_e4m3 ||
tensor_storage.is_f8_e5m2 ||
tensor_storage.is_f64 ||
tensor_storage.is_i64 ||
tensor_storage.type != dst_tensor->type) {
continue;
}
size_t tensor_size = tensor_storage.nbytes();
size_t tensor_offset = tensor_storage.offset;
if (tensor_storage.ne[0] != dst_tensor->ne[0] ||
tensor_storage.ne[1] != dst_tensor->ne[1] ||
tensor_storage.ne[2] != dst_tensor->ne[2] ||
tensor_storage.ne[3] != dst_tensor->ne[3] ||
tensor_size != ggml_nbytes(dst_tensor)) {
// let load_tensors worry about this
continue;
}
ggml_backend_buffer_t buf_mmap = fdata.mmbuffer.get();
uint8_t* mmap_data = static_cast<uint8_t*>(ggml_backend_buffer_get_base(buf_mmap));
dst_tensor->buffer = buf_mmap;
dst_tensor->data = mmap_data + tensor_offset;
file_mapped_bytes += tensor_size;
file_mapped_tensors++;
}
if (file_mapped_bytes > 0) {
mapped_tensors += file_mapped_tensors;
mapped_bytes += file_mapped_bytes;
result.push_back({fdata.mmapped, fdata.mmbuffer});
}
}
int64_t t_end = ggml_time_ms();
int64_t duration_ms = t_end - t_start;
LOG_INFO("memory-mapped %zu tensors in %zu files (%.2f MB), taking %.2fs",
mapped_tensors,
result.size(),
mapped_bytes / (1024.0 * 1024.0),
duration_ms / 1000.0);
return result;
}
bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
bool enable_mmap,
const std::set<std::string>* target_tensor_names,
bool log_progress) {
process_model_files(enable_mmap, false);
std::atomic<int64_t> read_time_ms(0);
std::atomic<int64_t> memcpy_time_ms(0);
std::atomic<int64_t> copy_to_backend_time_ms(0);
std::atomic<int64_t> convert_time_ms(0);
std::atomic<uint64_t> bytes_processed(0);
int num_threads_to_use = n_threads_;
int64_t start_time = ggml_time_ms();
size_t total_tensors_to_process = 0;
std::vector<size_t> file_tensors_to_process_counts;
file_tensors_to_process_counts.reserve(file_data.size());
for (const auto& fdata : file_data) {
size_t file_tensors_to_process = 0;
if (target_tensor_names == nullptr) {
file_tensors_to_process = fdata.tensors.size();
} else {
for (const TensorStorage& tensor_storage : fdata.tensors) {
if (target_tensor_names->find(tensor_storage.name) != target_tensor_names->end()) {
file_tensors_to_process++;
}
}
}
file_tensors_to_process_counts.push_back(file_tensors_to_process);
total_tensors_to_process += file_tensors_to_process;