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lora.hpp
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#ifndef __LORA_HPP__
#define __LORA_HPP__
#include "ggml_extend.hpp"
#define LORA_GRAPH_SIZE 10240
struct LoraModel : public GGMLRunner {
enum lora_t {
REGULAR = 0,
DIFFUSERS = 1,
DIFFUSERS_2 = 2,
DIFFUSERS_3 = 3,
TRANSFORMERS = 4,
LORA_TYPE_COUNT
};
const std::string lora_ups[LORA_TYPE_COUNT] = {
".lora_up",
"_lora.up",
".lora_B",
".lora.up",
".lora_linear_layer.up",
};
const std::string lora_downs[LORA_TYPE_COUNT] = {
".lora_down",
"_lora.down",
".lora_A",
".lora.down",
".lora_linear_layer.down",
};
const std::string lora_pre[LORA_TYPE_COUNT] = {
"lora.",
"",
"",
"",
"",
};
const std::map<std::string, std::string> alt_names = {
// mmdit
{"final_layer.adaLN_modulation.1", "norm_out.linear"},
{"pos_embed", "pos_embed.proj"},
{"final_layer.linear", "proj_out"},
{"y_embedder.mlp.0", "time_text_embed.text_embedder.linear_1"},
{"y_embedder.mlp.2", "time_text_embed.text_embedder.linear_2"},
{"t_embedder.mlp.0", "time_text_embed.timestep_embedder.linear_1"},
{"t_embedder.mlp.2", "time_text_embed.timestep_embedder.linear_2"},
{"x_block.mlp.fc1", "ff.net.0.proj"},
{"x_block.mlp.fc2", "ff.net.2"},
{"context_block.mlp.fc1", "ff_context.net.0.proj"},
{"context_block.mlp.fc2", "ff_context.net.2"},
{"x_block.adaLN_modulation.1", "norm1.linear"},
{"context_block.adaLN_modulation.1", "norm1_context.linear"},
{"context_block.attn.proj", "attn.to_add_out"},
{"x_block.attn.proj", "attn.to_out.0"},
{"x_block.attn2.proj", "attn2.to_out.0"},
// flux
// singlestream
{"linear2", "proj_out"},
{"modulation.lin", "norm.linear"},
// doublestream
{"txt_attn.proj", "attn.to_add_out"},
{"img_attn.proj", "attn.to_out.0"},
{"txt_mlp.0", "ff_context.net.0.proj"},
{"txt_mlp.2", "ff_context.net.2"},
{"img_mlp.0", "ff.net.0.proj"},
{"img_mlp.2", "ff.net.2"},
{"txt_mod.lin", "norm1_context.linear"},
{"img_mod.lin", "norm1.linear"},
};
const std::map<std::string, std::string> qkv_prefixes = {
// mmdit
{"context_block.attn.qkv", "attn.add_"}, // suffix "_proj"
{"x_block.attn.qkv", "attn.to_"},
{"x_block.attn2.qkv", "attn2.to_"},
// flux
// doublestream
{"txt_attn.qkv", "attn.add_"}, // suffix "_proj"
{"img_attn.qkv", "attn.to_"},
};
const std::map<std::string, std::string> qkvm_prefixes = {
// flux
// singlestream
{"linear1", ""},
};
const std::string* type_fingerprints = lora_ups;
float multiplier = 1.0f;
std::map<std::string, struct ggml_tensor*> lora_tensors;
std::string file_path;
ModelLoader model_loader;
bool load_failed = false;
bool applied = false;
std::vector<int> zero_index_vec = {0};
ggml_tensor* zero_index = NULL;
enum lora_t type = REGULAR;
LoraModel(ggml_backend_t backend,
const std::string& file_path = "",
const std::string prefix = "")
: file_path(file_path), GGMLRunner(backend) {
if (!model_loader.init_from_file(file_path, prefix)) {
load_failed = true;
}
}
std::string get_desc() {
return "lora";
}
bool load_from_file(bool filter_tensor = false) {
LOG_INFO("loading LoRA from '%s'", file_path.c_str());
if (load_failed) {
LOG_ERROR("init lora model loader from file failed: '%s'", file_path.c_str());
return false;
}
bool dry_run = true;
auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
const std::string& name = tensor_storage.name;
if (filter_tensor && !contains(name, "lora")) {
// LOG_INFO("skipping LoRA tesnor '%s'", name.c_str());
return true;
}
// LOG_INFO("%s", name.c_str());
for (int i = 0; i < LORA_TYPE_COUNT; i++) {
if (name.find(type_fingerprints[i]) != std::string::npos) {
type = (lora_t)i;
break;
}
}
if (dry_run) {
struct ggml_tensor* real = ggml_new_tensor(params_ctx,
tensor_storage.type,
tensor_storage.n_dims,
tensor_storage.ne);
lora_tensors[name] = real;
} else {
auto real = lora_tensors[name];
*dst_tensor = real;
}
return true;
};
model_loader.load_tensors(on_new_tensor_cb, backend);
alloc_params_buffer();
// exit(0);
dry_run = false;
model_loader.load_tensors(on_new_tensor_cb, backend);
LOG_DEBUG("lora type: \"%s\"/\"%s\"", lora_downs[type].c_str(), lora_ups[type].c_str());
LOG_DEBUG("finished loaded lora");
return true;
}
ggml_tensor* to_f32(ggml_context* ctx, ggml_tensor* a) {
auto out = ggml_reshape_1d(ctx, a, ggml_nelements(a));
out = ggml_get_rows(ctx, out, zero_index);
out = ggml_reshape(ctx, out, a);
return out;
}
std::vector<std::string> to_lora_keys(std::string blk_name, SDVersion version) {
std::vector<std::string> keys;
// if (!sd_version_is_sd3(version) || blk_name != "model.diffusion_model.pos_embed") {
size_t k_pos = blk_name.find(".weight");
if (k_pos == std::string::npos) {
return keys;
}
blk_name = blk_name.substr(0, k_pos);
// }
keys.push_back(blk_name);
keys.push_back("lora." + blk_name);
if (sd_version_is_dit(version)) {
if (blk_name.find("model.diffusion_model") != std::string::npos) {
blk_name.replace(blk_name.find("model.diffusion_model"), sizeof("model.diffusion_model") - 1, "transformer");
}
if (blk_name.find(".single_blocks") != std::string::npos) {
blk_name.replace(blk_name.find(".single_blocks"), sizeof(".single_blocks") - 1, ".single_transformer_blocks");
}
if (blk_name.find(".double_blocks") != std::string::npos) {
blk_name.replace(blk_name.find(".double_blocks"), sizeof(".double_blocks") - 1, ".transformer_blocks");
}
if (blk_name.find(".joint_blocks") != std::string::npos) {
blk_name.replace(blk_name.find(".joint_blocks"), sizeof(".joint_blocks") - 1, ".transformer_blocks");
}
for (const auto& item : alt_names) {
size_t match = blk_name.find(item.first);
if (match != std::string::npos) {
blk_name = blk_name.substr(0, match) + item.second;
}
}
for (const auto& prefix : qkv_prefixes) {
size_t match = blk_name.find(prefix.first);
if (match != std::string::npos) {
std::string split_blk = "SPLIT|" + blk_name.substr(0, match) + prefix.second;
keys.push_back(split_blk);
}
}
for (const auto& prefix : qkvm_prefixes) {
size_t match = blk_name.find(prefix.first);
if (match != std::string::npos) {
std::string split_blk = "SPLIT_L|" + blk_name.substr(0, match) + prefix.second;
keys.push_back(split_blk);
}
}
}
keys.push_back(blk_name);
std::vector<std::string> ret;
for (std::string& key : keys) {
ret.push_back(key);
replace_all_chars(key, '.', '_');
ret.push_back(key);
}
return ret;
}
struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version) {
struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, LORA_GRAPH_SIZE, false);
zero_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, 1);
set_backend_tensor_data(zero_index, zero_index_vec.data());
ggml_build_forward_expand(gf, zero_index);
std::set<std::string> applied_lora_tensors;
for (auto it : model_tensors) {
std::string k_tensor = it.first;
struct ggml_tensor* weight = model_tensors[it.first];
std::vector<std::string> keys = to_lora_keys(k_tensor, version);
if (keys.size() == 0)
continue;
ggml_tensor* lora_up = NULL;
ggml_tensor* lora_down = NULL;
for (auto& key : keys) {
std::string alpha_name = "";
std::string scale_name = "";
std::string split_q_scale_name = "";
std::string lora_down_name = "";
std::string lora_up_name = "";
if (starts_with(key, "SPLIT|")) {
key = key.substr(sizeof("SPLIT|") - 1);
// TODO: Handle alphas
std::string suffix = "";
auto split_q_d_name = lora_pre[type] + key + "q" + suffix + lora_downs[type] + ".weight";
if (lora_tensors.find(split_q_d_name) == lora_tensors.end()) {
suffix = "_proj";
split_q_d_name = lora_pre[type] + key + "q" + suffix + lora_downs[type] + ".weight";
}
if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
// print_ggml_tensor(it.second, true); //[3072, 21504, 1, 1]
// find qkv and mlp up parts in LoRA model
auto split_k_d_name = lora_pre[type] + key + "k" + suffix + lora_downs[type] + ".weight";
auto split_v_d_name = lora_pre[type] + key + "v" + suffix + lora_downs[type] + ".weight";
auto split_q_u_name = lora_pre[type] + key + "q" + suffix + lora_ups[type] + ".weight";
auto split_k_u_name = lora_pre[type] + key + "k" + suffix + lora_ups[type] + ".weight";
auto split_v_u_name = lora_pre[type] + key + "v" + suffix + lora_ups[type] + ".weight";
auto split_q_scale_name = lora_pre[type] + key + "q" + suffix + ".scale";
auto split_k_scale_name = lora_pre[type] + key + "k" + suffix + ".scale";
auto split_v_scale_name = lora_pre[type] + key + "v" + suffix + ".scale";
auto split_q_alpha_name = lora_pre[type] + key + "q" + suffix + ".alpha";
auto split_k_alpha_name = lora_pre[type] + key + "k" + suffix + ".alpha";
auto split_v_alpha_name = lora_pre[type] + key + "v" + suffix + ".alpha";
ggml_tensor* lora_q_down = NULL;
ggml_tensor* lora_q_up = NULL;
ggml_tensor* lora_k_down = NULL;
ggml_tensor* lora_k_up = NULL;
ggml_tensor* lora_v_down = NULL;
ggml_tensor* lora_v_up = NULL;
lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
}
if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
}
if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
}
if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
}
if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
}
float q_rank = lora_q_up->ne[0];
float k_rank = lora_k_up->ne[0];
float v_rank = lora_v_up->ne[0];
float lora_q_scale = 1;
float lora_k_scale = 1;
float lora_v_scale = 1;
if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
applied_lora_tensors.insert(split_q_scale_name);
}
if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
applied_lora_tensors.insert(split_k_scale_name);
}
if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
applied_lora_tensors.insert(split_v_scale_name);
}
if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
applied_lora_tensors.insert(split_q_alpha_name);
lora_q_scale = lora_q_alpha / q_rank;
}
if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
applied_lora_tensors.insert(split_k_alpha_name);
lora_k_scale = lora_k_alpha / k_rank;
}
if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
applied_lora_tensors.insert(split_v_alpha_name);
lora_v_scale = lora_v_alpha / v_rank;
}
ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
// print_ggml_tensor(lora_q_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_k_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_v_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_q_up, true); //[R, 3072, 1, 1]
// print_ggml_tensor(lora_k_up, true); //[R, 3072, 1, 1]
// print_ggml_tensor(lora_v_up, true); //[R, 3072, 1, 1]
// these need to be stitched together this way:
// |q_up,0 ,0 |
// |0 ,k_up,0 |
// |0 ,0 ,v_up|
// (q_down,k_down,v_down) . (q ,k ,v)
// up_concat will be [9216, R*3, 1, 1]
// down_concat will be [R*3, 3072, 1, 1]
ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), lora_v_down, 1);
ggml_tensor* z = ggml_dup_tensor(compute_ctx, lora_q_up);
ggml_scale(compute_ctx, z, 0);
ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
ggml_tensor* q_up = ggml_concat(compute_ctx, lora_q_up, zz, 1);
ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), z, 1);
ggml_tensor* v_up = ggml_concat(compute_ctx, zz, lora_v_up, 1);
// print_ggml_tensor(q_up, true); //[R, 9216, 1, 1]
// print_ggml_tensor(k_up, true); //[R, 9216, 1, 1]
// print_ggml_tensor(v_up, true); //[R, 9216, 1, 1]
ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), v_up, 0);
// print_ggml_tensor(lora_up_concat, true); //[R*3, 9216, 1, 1]
lora_down = ggml_cont(compute_ctx, lora_down_concat);
lora_up = ggml_cont(compute_ctx, lora_up_concat);
applied_lora_tensors.insert(split_q_u_name);
applied_lora_tensors.insert(split_k_u_name);
applied_lora_tensors.insert(split_v_u_name);
applied_lora_tensors.insert(split_q_d_name);
applied_lora_tensors.insert(split_k_d_name);
applied_lora_tensors.insert(split_v_d_name);
}
}
if (starts_with(key, "SPLIT_L|")) {
key = key.substr(sizeof("SPLIT_L|") - 1);
auto split_q_d_name = lora_pre[type] + key + "attn.to_q" + lora_downs[type] + ".weight";
if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
// print_ggml_tensor(it.second, true); //[3072, 21504, 1, 1]
// find qkv and mlp up parts in LoRA model
auto split_k_d_name = lora_pre[type] + key + "attn.to_k" + lora_downs[type] + ".weight";
auto split_v_d_name = lora_pre[type] + key + "attn.to_v" + lora_downs[type] + ".weight";
auto split_q_u_name = lora_pre[type] + key + "attn.to_q" + lora_ups[type] + ".weight";
auto split_k_u_name = lora_pre[type] + key + "attn.to_k" + lora_ups[type] + ".weight";
auto split_v_u_name = lora_pre[type] + key + "attn.to_v" + lora_ups[type] + ".weight";
auto split_m_d_name = lora_pre[type] + key + "proj_mlp" + lora_downs[type] + ".weight";
auto split_m_u_name = lora_pre[type] + key + "proj_mlp" + lora_ups[type] + ".weight";
auto split_q_scale_name = lora_pre[type] + key + "attn.to_q" + ".scale";
auto split_k_scale_name = lora_pre[type] + key + "attn.to_k" + ".scale";
auto split_v_scale_name = lora_pre[type] + key + "attn.to_v" + ".scale";
auto split_m_scale_name = lora_pre[type] + key + "proj_mlp" + ".scale";
auto split_q_alpha_name = lora_pre[type] + key + "attn.to_q" + ".alpha";
auto split_k_alpha_name = lora_pre[type] + key + "attn.to_k" + ".alpha";
auto split_v_alpha_name = lora_pre[type] + key + "attn.to_v" + ".alpha";
auto split_m_alpha_name = lora_pre[type] + key + "proj_mlp" + ".alpha";
ggml_tensor* lora_q_down = NULL;
ggml_tensor* lora_q_up = NULL;
ggml_tensor* lora_k_down = NULL;
ggml_tensor* lora_k_up = NULL;
ggml_tensor* lora_v_down = NULL;
ggml_tensor* lora_v_up = NULL;
ggml_tensor* lora_m_down = NULL;
ggml_tensor* lora_m_up = NULL;
lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
}
if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
}
if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
}
if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
}
if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
}
if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
}
if (lora_tensors.find(split_m_d_name) != lora_tensors.end()) {
lora_m_down = to_f32(compute_ctx, lora_tensors[split_m_d_name]);
}
if (lora_tensors.find(split_m_u_name) != lora_tensors.end()) {
lora_m_up = to_f32(compute_ctx, lora_tensors[split_m_u_name]);
}
float q_rank = lora_q_up->ne[0];
float k_rank = lora_k_up->ne[0];
float v_rank = lora_v_up->ne[0];
float m_rank = lora_v_up->ne[0];
float lora_q_scale = 1;
float lora_k_scale = 1;
float lora_v_scale = 1;
float lora_m_scale = 1;
if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
applied_lora_tensors.insert(split_q_scale_name);
}
if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
applied_lora_tensors.insert(split_k_scale_name);
}
if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
applied_lora_tensors.insert(split_v_scale_name);
}
if (lora_tensors.find(split_m_scale_name) != lora_tensors.end()) {
lora_m_scale = ggml_backend_tensor_get_f32(lora_tensors[split_m_scale_name]);
applied_lora_tensors.insert(split_m_scale_name);
}
if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
applied_lora_tensors.insert(split_q_alpha_name);
lora_q_scale = lora_q_alpha / q_rank;
}
if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
applied_lora_tensors.insert(split_k_alpha_name);
lora_k_scale = lora_k_alpha / k_rank;
}
if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
applied_lora_tensors.insert(split_v_alpha_name);
lora_v_scale = lora_v_alpha / v_rank;
}
if (lora_tensors.find(split_m_alpha_name) != lora_tensors.end()) {
float lora_m_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_m_alpha_name]);
applied_lora_tensors.insert(split_m_alpha_name);
lora_m_scale = lora_m_alpha / m_rank;
}
ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
ggml_scale_inplace(compute_ctx, lora_m_down, lora_m_scale);
// print_ggml_tensor(lora_q_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_k_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_v_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_m_down, true); //[3072, R, 1, 1]
// print_ggml_tensor(lora_q_up, true); //[R, 3072, 1, 1]
// print_ggml_tensor(lora_k_up, true); //[R, 3072, 1, 1]
// print_ggml_tensor(lora_v_up, true); //[R, 3072, 1, 1]
// print_ggml_tensor(lora_m_up, true); //[R, 12288, 1, 1]
// these need to be stitched together this way:
// |q_up,0 ,0 ,0 |
// |0 ,k_up,0 ,0 |
// |0 ,0 ,v_up,0 |
// |0 ,0 ,0 ,m_up|
// (q_down,k_down,v_down,m_down) . (q ,k ,v ,m)
// up_concat will be [21504, R*4, 1, 1]
// down_concat will be [R*4, 3072, 1, 1]
ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), ggml_concat(compute_ctx, lora_v_down, lora_m_down, 1), 1);
// print_ggml_tensor(lora_down_concat, true); //[3072, R*4, 1, 1]
// this also means that if rank is bigger than 672, it is less memory efficient to do it this way (should be fine)
// print_ggml_tensor(lora_q_up, true); //[3072, R, 1, 1]
ggml_tensor* z = ggml_dup_tensor(compute_ctx, lora_q_up);
ggml_tensor* mlp_z = ggml_dup_tensor(compute_ctx, lora_m_up);
ggml_scale(compute_ctx, z, 0);
ggml_scale(compute_ctx, mlp_z, 0);
ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
ggml_tensor* q_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_up, zz, 1), mlp_z, 1);
ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), ggml_concat(compute_ctx, z, mlp_z, 1), 1);
ggml_tensor* v_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, lora_v_up, 1), mlp_z, 1);
ggml_tensor* m_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, z, 1), lora_m_up, 1);
// print_ggml_tensor(q_up, true); //[R, 21504, 1, 1]
// print_ggml_tensor(k_up, true); //[R, 21504, 1, 1]
// print_ggml_tensor(v_up, true); //[R, 21504, 1, 1]
// print_ggml_tensor(m_up, true); //[R, 21504, 1, 1]
ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), ggml_concat(compute_ctx, v_up, m_up, 0), 0);
// print_ggml_tensor(lora_up_concat, true); //[R*4, 21504, 1, 1]
lora_down = ggml_cont(compute_ctx, lora_down_concat);
lora_up = ggml_cont(compute_ctx, lora_up_concat);
applied_lora_tensors.insert(split_q_u_name);
applied_lora_tensors.insert(split_k_u_name);
applied_lora_tensors.insert(split_v_u_name);
applied_lora_tensors.insert(split_m_u_name);
applied_lora_tensors.insert(split_q_d_name);
applied_lora_tensors.insert(split_k_d_name);
applied_lora_tensors.insert(split_v_d_name);
applied_lora_tensors.insert(split_m_d_name);
}
}
if (lora_up == NULL || lora_down == NULL) {
lora_up_name = lora_pre[type] + key + lora_ups[type] + ".weight";
if (lora_tensors.find(lora_up_name) == lora_tensors.end()) {
if (key == "model_diffusion_model_output_blocks_2_2_conv") {
// fix for some sdxl lora, like lcm-lora-xl
key = "model_diffusion_model_output_blocks_2_1_conv";
lora_up_name = lora_pre[type] + key + lora_ups[type] + ".weight";
}
}
lora_down_name = lora_pre[type] + key + lora_downs[type] + ".weight";
alpha_name = lora_pre[type] + key + ".alpha";
scale_name = lora_pre[type] + key + ".scale";
if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
lora_up = lora_tensors[lora_up_name];
}
if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
lora_down = lora_tensors[lora_down_name];
}
applied_lora_tensors.insert(lora_up_name);
applied_lora_tensors.insert(lora_down_name);
applied_lora_tensors.insert(alpha_name);
applied_lora_tensors.insert(scale_name);
}
if (lora_up == NULL || lora_down == NULL) {
continue;
}
// calc_scale
int64_t dim = lora_down->ne[ggml_n_dims(lora_down) - 1];
float scale_value = 1.0f;
if (lora_tensors.find(scale_name) != lora_tensors.end()) {
scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
} else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
scale_value = alpha / dim;
}
scale_value *= multiplier;
// flat lora tensors to multiply it
int64_t lora_up_rows = lora_up->ne[ggml_n_dims(lora_up) - 1];
lora_up = ggml_reshape_2d(compute_ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
auto lora_down_n_dims = ggml_n_dims(lora_down);
// assume n_dims should always be a multiple of 2 (otherwise rank 1 doesn't work)
lora_down_n_dims = (lora_down_n_dims + lora_down_n_dims % 2);
int64_t lora_down_rows = lora_down->ne[lora_down_n_dims - 1];
lora_down = ggml_reshape_2d(compute_ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
// ggml_mul_mat requires tensor b transposed
lora_down = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, lora_down));
struct ggml_tensor* updown = ggml_mul_mat(compute_ctx, lora_up, lora_down);
updown = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, updown));
updown = ggml_reshape(compute_ctx, updown, weight);
GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
ggml_tensor* final_weight;
if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
// final_weight = ggml_new_tensor(compute_ctx, GGML_TYPE_F32, ggml_n_dims(weight), weight->ne);
// final_weight = ggml_cpy(compute_ctx, weight, final_weight);
final_weight = to_f32(compute_ctx, weight);
final_weight = ggml_add_inplace(compute_ctx, final_weight, updown);
final_weight = ggml_cpy(compute_ctx, final_weight, weight);
} else {
final_weight = ggml_add_inplace(compute_ctx, weight, updown);
}
// final_weight = ggml_add_inplace(compute_ctx, weight, updown); // apply directly
ggml_build_forward_expand(gf, final_weight);
break;
}
}
size_t total_lora_tensors_count = 0;
size_t applied_lora_tensors_count = 0;
for (auto& kv : lora_tensors) {
total_lora_tensors_count++;
if (applied_lora_tensors.find(kv.first) == applied_lora_tensors.end()) {
LOG_WARN("unused lora tensor |%s|", kv.first.c_str());
print_ggml_tensor(kv.second, true);
// exit(0);
} else {
applied_lora_tensors_count++;
}
}
/* Don't worry if this message shows up twice in the logs per LoRA,
* this function is called once to calculate the required buffer size
* and then again to actually generate a graph to be used */
if (applied_lora_tensors_count != total_lora_tensors_count) {
LOG_WARN("Only (%lu / %lu) LoRA tensors have been applied",
applied_lora_tensors_count, total_lora_tensors_count);
} else {
LOG_DEBUG("(%lu / %lu) LoRA tensors applied successfully",
applied_lora_tensors_count, total_lora_tensors_count);
}
return gf;
}
void apply(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_lora_graph(model_tensors, version);
};
GGMLRunner::compute(get_graph, n_threads, true);
}
};
#endif // __LORA_HPP__