vrr/koboldcpp
2
0
Fork 0
mirror of https://github.com/LostRuins/koboldcpp.git synced 2025-09-10 17:14:36 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

try to fix lora naming issues

Browse source
This commit is contained in:
Concedo 2024-05-15 23:49:01 +08:00
parent 08993696c3
commit 7a5983399b
5 changed files with 109 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

108
otherarch/sdcpp/model.cpp
View file

@ -108,14 +108,14 @@ std::unordered_map<std::string, std::string> open_clip_to_hf_clip_model = {
{"model.positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"},
{"model.token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"},
{"model.text_projection", "transformer.text_model.text_projection"},
{"model.visual.class_embedding", "transformer.visual_model.embeddings.class_embedding"},
{"model.visual.conv1.weight", "transformer.visual_model.embeddings.patch_embedding.weight"},
{"model.visual.ln_post.bias", "transformer.visual_model.post_layernorm.bias"},
{"model.visual.ln_post.weight", "transformer.visual_model.post_layernorm.weight"},
{"model.visual.ln_pre.bias", "transformer.visual_model.pre_layernorm.bias"},
{"model.visual.ln_pre.weight", "transformer.visual_model.pre_layernorm.weight"},
{"model.visual.positional_embedding", "transformer.visual_model.embeddings.position_embedding.weight"},
{"model.visual.proj", "transformer.visual_model.visual_projection"},
{"model.visual.class_embedding", "transformer.vision_model.embeddings.class_embedding"},
{"model.visual.conv1.weight", "transformer.vision_model.embeddings.patch_embedding.weight"},
{"model.visual.ln_post.bias", "transformer.vision_model.post_layernorm.bias"},
{"model.visual.ln_post.weight", "transformer.vision_model.post_layernorm.weight"},
{"model.visual.ln_pre.bias", "transformer.vision_model.pre_layernorm.bias"},
{"model.visual.ln_pre.weight", "transformer.vision_model.pre_layernorm.weight"},
{"model.visual.positional_embedding", "transformer.vision_model.embeddings.position_embedding.weight"},
{"model.visual.proj", "transformer.visual_projection.weight"},
};
std::unordered_map<std::string, std::string> open_clip_to_hk_clip_resblock = {
@ -157,6 +157,10 @@ std::string convert_open_clip_to_hf_clip(const std::string& name) {
} else if (starts_with(new_name, "cond_stage_model.")) {
prefix = "cond_stage_model.";
new_name = new_name.substr(strlen("cond_stage_model."));
} else if (ends_with(new_name, "vision_model.visual_projection.weight")) {
prefix = new_name.substr(0, new_name.size() - strlen("vision_model.visual_projection.weight"));
new_name = prefix + "visual_projection.weight";
return new_name;
} else {
return new_name;
}
@ -186,7 +190,7 @@ std::string convert_open_clip_to_hf_clip(const std::string& name) {
replace_suffix();
open_clip_resblock_prefix = "model.visual.transformer.resblocks.";
hf_clip_resblock_prefix = "transformer.visual_model.encoder.layers.";
hf_clip_resblock_prefix = "transformer.vision_model.encoder.layers.";
replace_suffix();
@ -200,6 +204,25 @@ std::string convert_vae_decoder_name(const std::string& name) {
return name;
}
/* If not a SDXL LoRA the unet" prefix will have already been replaced by this
* point and "te2" and "te1" don't seem to appear in non-SDXL only "te_" */
std::string convert_sdxl_lora_name(std::string tensor_name) {
const std::pair<std::string, std::string> sdxl_lora_name_lookup[] = {
{"unet", "model_diffusion_model"},
{"te2", "cond_stage_model_1_transformer"},
{"te1", "cond_stage_model_transformer"},
{"text_encoder_2", "cond_stage_model_1_transformer"},
{"text_encoder", "cond_stage_model_transformer"},
};
for (auto& pair_i : sdxl_lora_name_lookup) {
if (tensor_name.compare(0, pair_i.first.length(), pair_i.first) == 0) {
tensor_name = std::regex_replace(tensor_name, std::regex(pair_i.first), pair_i.second);
break;
}
}
return tensor_name;
}
std::unordered_map<std::string, std::unordered_map<std::string, std::string>> suffix_conversion_underline = {
{
"attentions",
@ -248,7 +271,7 @@ std::unordered_map<std::string, std::unordered_map<std::string, std::string>> su
},
};
std::string convert_diffusers_name_to_compvis(const std::string& key, char seq) {
std::string convert_diffusers_name_to_compvis(std::string key, char seq) {
std::vector<std::string> m;
auto match = [](std::vector<std::string>& match_list, const std::regex& regex, const std::string& key) {
@ -282,6 +305,11 @@ std::string convert_diffusers_name_to_compvis(const std::string& key, char seq)
return inner_key;
};
// convert attn to out
if (ends_with(key, "to_out")) {
key += format("%c0", seq);
}
// unet
if (match(m, std::regex(format("unet%cconv_in(.*)", seq)), key)) {
return format("model%cdiffusion_model%cinput_blocks%c0%c0", seq, seq, seq, seq) + m[0];
@ -391,8 +419,8 @@ std::string convert_diffusers_name_to_compvis(const std::string& key, char seq)
}
std::string convert_tensor_name(const std::string& name) {
std::string new_name;
if (starts_with(name, "cond_stage_model.") || starts_with(name, "conditioner.embedders.")) {
std::string new_name = name;
if (starts_with(name, "cond_stage_model.") || starts_with(name, "conditioner.embedders.") || ends_with(name, ".vision_model.visual_projection.weight")) {
new_name = convert_open_clip_to_hf_clip(name);
} else if (starts_with(name, "first_stage_model.decoder")) {
new_name = convert_vae_decoder_name(name);
@ -406,8 +434,12 @@ std::string convert_tensor_name(const std::string& name) {
if (pos != std::string::npos) {
std::string name_without_network_parts = name.substr(5, pos - 5);
std::string network_part = name.substr(pos + 1);
// LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str());
std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '_');
/* For dealing with the new SDXL LoRA tensor naming convention */
new_key = convert_sdxl_lora_name(new_key);
if (new_key.empty()) {
new_name = name;
} else {
@ -416,6 +448,33 @@ std::string convert_tensor_name(const std::string& name) {
} else {
new_name = name;
}
} else if (contains(name, "lora_up") || contains(name, "lora_down") ||
contains(name, "lora.up") || contains(name, "lora.down") ||
contains(name, "lora_linear")) {
size_t pos = new_name.find(".processor");
if (pos != std::string::npos) {
new_name.replace(pos, strlen(".processor"), "");
}
pos = new_name.rfind("lora");
if (pos != std::string::npos) {
std::string name_without_network_parts = new_name.substr(0, pos - 1);
std::string network_part = new_name.substr(pos);
// LOG_DEBUG("%s %s", name_without_network_parts.c_str(), network_part.c_str());
std::string new_key = convert_diffusers_name_to_compvis(name_without_network_parts, '.');
new_key = convert_sdxl_lora_name(new_key);
replace_all_chars(new_key, '.', '_');
size_t npos = network_part.rfind("_linear_layer");
if (npos != std::string::npos) {
network_part.replace(npos, strlen("_linear_layer"), "");
}
if (starts_with(network_part, "lora.")) {
network_part = "lora_" + network_part.substr(5);
}
if (new_key.size() > 0) {
new_name = "lora." + new_key + "." + network_part;
}
// LOG_DEBUG("new name: %s", new_name.c_str());
}
} else if (starts_with(name, "unet") || starts_with(name, "vae") || starts_with(name, "te")) { // for diffuser
size_t pos = name.find_last_of('.');
if (pos != std::string::npos) {
@ -832,8 +891,12 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
}
}
TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
// ggml_n_dims returns 1 for scalars
if (n_dims == 0) {
n_dims = 1;
}
TensorStorage tensor_storage(prefix + 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;
@ -1172,7 +1235,9 @@ bool ModelLoader::parse_data_pkl(uint8_t* buffer,
if (reader.phase == PickleTensorReader::READ_DIMENS) {
reader.tensor_storage.reverse_ne();
reader.tensor_storage.file_index = file_index;
reader.tensor_storage.name = prefix + reader.tensor_storage.name;
// if(strcmp(prefix.c_str(), "scarlett") == 0)
// printf(" got tensor %s \n ", reader.tensor_storage.name.c_str());
reader.tensor_storage.name = prefix + reader.tensor_storage.name;
tensor_storages.push_back(reader.tensor_storage);
// LOG_DEBUG("%s", reader.tensor_storage.name.c_str());
// reset
@ -1275,7 +1340,8 @@ std::string ModelLoader::load_merges() {
return merges_utf8_str;
}
void remove_duplicates(std::vector<TensorStorage>& vec) {
std::vector<TensorStorage> remove_duplicates(const std::vector<TensorStorage>& vec) {
std::vector<TensorStorage> res;
std::unordered_map<std::string, size_t> name_to_index_map;
for (size_t i = 0; i < vec.size(); ++i) {
@ -1283,13 +1349,16 @@ void remove_duplicates(std::vector<TensorStorage>& vec) {
auto it = name_to_index_map.find(current_name);
if (it != name_to_index_map.end()) {
vec[it->second] = vec[i];
res[it->second] = vec[i];
} else {
name_to_index_map[current_name] = i;
res.push_back(vec[i]);
}
}
vec.resize(name_to_index_map.size());
// vec.resize(name_to_index_map.size());
return res;
}
bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend_t backend) {
@ -1303,7 +1372,9 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
preprocess_tensor(tensor_storage, processed_tensor_storages);
}
remove_duplicates(processed_tensor_storages);
std::vector<TensorStorage> dedup = remove_duplicates(processed_tensor_storages);
processed_tensor_storages = dedup;
bool success = true;
for (size_t file_index = 0; file_index < file_paths_.size(); file_index++) {
std::string file_path = file_paths_[file_index];
@ -1365,7 +1436,6 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
if (tensor_storage.file_index != file_index) {
continue;
}
ggml_tensor* dst_tensor = NULL;
success = on_new_tensor_cb(tensor_storage, &dst_tensor);