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#ifndef __LORA_HPP__
#define __LORA_HPP__
#include "ggml_extend.hpp"
#define LORA_GRAPH_SIZE 10240
struct LoraModel : public GGMLRunner {
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;
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;
}
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();
dry_run = false;
model_loader.load_tensors(on_new_tensor_cb, backend);
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;
}
struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors) {
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];
size_t k_pos = k_tensor.find(".weight");
if (k_pos == std::string::npos) {
continue;
}
k_tensor = k_tensor.substr(0, k_pos);
replace_all_chars(k_tensor, '.', '_');
// LOG_DEBUG("k_tensor %s", k_tensor.c_str());
std::string lora_up_name = "lora." + k_tensor + ".lora_up.weight";
if (lora_tensors.find(lora_up_name) == lora_tensors.end()) {
if (k_tensor == "model_diffusion_model_output_blocks_2_2_conv") {
// fix for some sdxl lora, like lcm-lora-xl
k_tensor = "model_diffusion_model_output_blocks_2_1_conv";
lora_up_name = "lora." + k_tensor + ".lora_up.weight";
}
}
std::string lora_down_name = "lora." + k_tensor + ".lora_down.weight";
std::string alpha_name = "lora." + k_tensor + ".alpha";
std::string scale_name = "lora." + k_tensor + ".scale";
ggml_tensor* lora_up = NULL;
ggml_tensor* lora_down = NULL;
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];
}
if (lora_up == NULL || lora_down == NULL) {
continue;
}
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);
// calc_cale
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];
// 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);
}
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());
} 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, int n_threads) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_lora_graph(model_tensors);
};
GGMLRunner::compute(get_graph, n_threads, true);
}
};
#endif // __LORA_HPP__
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