import json
import os
-import re
import struct
import sys
from typing import Any, BinaryIO, Sequence
import numpy as np
import torch
-NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
-
+from pathlib import Path
+if 'NO_LOCAL_GGUF' not in os.environ:
+ sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
-HF_SUBLAYER_TO_GGML = {
- "self_attn.q_proj": "attn_q",
- "self_attn.k_proj": "attn_k",
- "self_attn.v_proj": "attn_v",
- "self_attn.o_proj": "attn_output",
- "mlp.gate_proj": "ffn_gate",
- "mlp.down_proj": "ffn_down",
- "mlp.up_proj": "ffn_up",
- "input_layernorm": "attn_norm",
- "post_attention_layernorm": "ffn_norm",
-}
-
-
-def translate_tensor_name(t: str) -> str:
- match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
- if match:
- nn = match.group(1)
- sub_layer = match.group(2)
- lora_type = match.group(3)
-
- sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
- if sub_layer_renamed is None:
- print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
- sys.exit(1)
- output_string = (
- f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
- )
- return output_string
- else:
- print(f"Error: unrecognized tensor {t}")
- sys.exit(1)
+NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None:
fout.write(struct.pack("i", int(params["lora_alpha"])))
-def write_tensor_header(
- self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
-) -> None:
+def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
sname = name.encode("utf-8")
fout.write(
struct.pack(
fout.seek((fout.tell() + 31) & -32)
-if len(sys.argv) != 2:
- print(f"Usage: python {sys.argv[0]} <path>")
+if len(sys.argv) < 2:
+ print(f"Usage: python {sys.argv[0]} <path> [arch]")
print(
"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
)
+ print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
sys.exit(1)
input_json = os.path.join(sys.argv[1], "adapter_config.json")
output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
model = torch.load(input_model, map_location="cpu")
+arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
+
+if arch_name not in gguf.MODEL_ARCH_NAMES.values():
+ print(f"Error: unsupported architecture {arch_name}")
+ sys.exit(1)
+
+arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
+name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
with open(input_json, "r") as f:
params = json.load(f)
write_file_header(fout, params)
for k, v in model.items():
+ orig_k = k
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
v = v.float()
t = v.detach().numpy()
- tname = translate_tensor_name(k)
+
+ prefix = "base_model.model."
+ if k.startswith(prefix):
+ k = k[len(prefix) :]
+
+ lora_suffixes = (".lora_A.weight", ".lora_B.weight")
+ if k.endswith(lora_suffixes):
+ suffix = k[-len(lora_suffixes[0]):]
+ k = k[: -len(lora_suffixes[0])]
+ else:
+ print(f"Error: unrecognized tensor name {orig_k}")
+ sys.exit(1)
+
+ tname = name_map.get_name(k)
+ if tname is None:
+ print(f"Error: could not map tensor name {orig_k}")
+ print(" Note: the arch parameter must be specified if the model is not llama")
+ sys.exit(1)
+
+ if suffix == ".lora_A.weight":
+ tname += ".weight.loraA"
+ elif suffix == ".lora_B.weight":
+ tname += ".weight.loraB"
+ else:
+ assert False
+
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)
t.tofile(fout)
const int64_t t_start_lora_us = ggml_time_us();
- auto fin = std::ifstream(path_lora, std::ios::binary);
- if (!fin) {
- LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
- return 1;
- }
+ llama_file fin(path_lora, "rb");
// verify magic and version
{
- uint32_t magic;
- fin.read((char *) &magic, sizeof(magic));
- uint32_t format_version;
- fin.read((char *) &format_version, sizeof(format_version));
+ uint32_t magic = fin.read_u32();
+ if (magic != LLAMA_FILE_MAGIC_GGLA) {
+ LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
+ return 1;
+ }
+ uint32_t format_version = fin.read_u32();
if (format_version != 1) {
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
return 1;
}
}
- int32_t lora_r;
- int32_t lora_alpha;
- fin.read((char *) &lora_r, sizeof(lora_r));
- fin.read((char *) &lora_alpha, sizeof(lora_alpha));
+ int32_t lora_r = fin.read_u32();
+ int32_t lora_alpha = fin.read_u32();
float scaling = scale * (float)lora_alpha / (float)lora_r;
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
+ // create a name -> tensor map of the model to accelerate lookups
+ // find the max tensor size to estimate the required temporary buffer size
+ size_t max_tensor_size = 0;
+ std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
+ for (const auto & kv : model.tensors_by_name) {
+ model_tensors.insert(kv);
+ size_t f32_size = ggml_nelements(kv.second) * sizeof(float);
+ max_tensor_size = std::max(max_tensor_size, f32_size);
+ }
+
// create a temporary ggml context to store the lora tensors
- // todo: calculate size from biggest possible tensor
- std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
+ // TODO: use ggml-alloc
+ size_t lora_ctx_size = max_tensor_size * 3;
+ LLAMA_LOG_INFO("%s: allocating %.f MB for lora temporary buffer\n", __func__, lora_ctx_size / 1024.0 / 1024.0);
+ std::vector<uint8_t> lora_buf(lora_ctx_size);
+
struct ggml_init_params params;
params.mem_size = lora_buf.size();
params.mem_buffer = lora_buf.data();
params.no_alloc = false;
- ggml_context * lora_ctx = ggml_init(params);
- std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
+ using unique_context = std::unique_ptr<ggml_context, decltype(&ggml_free)>;
- // create a name -> tensor map of the model to accelerate lookups
- std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
- for (const auto & kv : model.tensors_by_name) {
- model_tensors.insert(kv);
- }
+ unique_context lora_ctx(nullptr, ggml_free);
+ lora_ctx.reset(ggml_init(params));
+ std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
// load base model
std::unique_ptr<llama_model_loader> ml;
- ggml_context * base_ctx = NULL;
+
+ unique_context base_ctx(nullptr, ggml_free);
std::vector<uint8_t> base_buf;
if (path_base_model) {
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(ctx_size, mmapped_size);
+
base_buf.resize(ctx_size);
ggml_init_params base_params;
base_params.mem_buffer = base_buf.data();
base_params.no_alloc = ml->use_mmap;
- base_ctx = ggml_init(base_params);
+ base_ctx.reset(ggml_init(base_params));
- // maybe this should in llama_model_loader
+ // maybe this should be in llama_model_loader
if (ml->use_mmap) {
ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
}
std::vector<uint8_t> work_buffer;
while (true) {
+ if (fin.tell() == fin.size) {
+ // eof
+ break;
+ }
+
int32_t n_dims;
- int32_t length;
+ int32_t name_len;
int32_t ftype;
- fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
- fin.read(reinterpret_cast<char *>(&length), sizeof(length));
- fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
- if (fin.eof()) {
- break;
+ fin.read_raw(&n_dims, sizeof(n_dims));
+ fin.read_raw(&name_len, sizeof(name_len));
+ fin.read_raw(&ftype, sizeof(ftype));
+
+ if (n_dims != 1 && n_dims != 2) {
+ LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
+ return 1;
}
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
- fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+ fin.read_raw(&ne[i], sizeof(ne[i]));
}
std::string name;
{
+ GGML_ASSERT(name_len <= 1024);
char buf[1024];
- fin.read(buf, length);
- name = std::string(buf, length);
+ fin.read_raw(buf, name_len);
+ name = std::string(buf, name_len);
}
// check for lora suffix and get the type of tensor
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
- // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
+ // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(), base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
return false;
}
}
- ggml_tensor * lora_tensor;
- if (n_dims == 2) {
- lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
- }
- else {
- LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
- return 1;
- }
- ggml_set_name(lora_tensor, "lora_tensor");
+ ggml_tensor * lora_tensor = ggml_new_tensor_2d(lora_ctx.get(), wtype, ne[0], ne[1]);
+ ggml_set_name(lora_tensor, name.c_str());
// load tensor data
- size_t offset = fin.tellg();
+ size_t offset = fin.tell();
size_t tensor_data_size = ggml_nbytes(lora_tensor);
offset = (offset + 31) & -32;
- fin.seekg(offset);
- fin.read((char*)lora_tensor->data, tensor_data_size);
+ fin.seek(offset, SEEK_SET);
+ fin.read_raw(lora_tensor->data, tensor_data_size);
lora_tensors[name] = lora_tensor;
// load from base model
if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
- // TODO: throw
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
- // TODO: not tested!! maybe not working!
- base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
+ base_t = ml->create_tensor(base_ctx.get(), base_name, { dest_t->ne[0], dest_t->ne[1] }, GGML_BACKEND_CPU);
ml->load_data_for(base_t);
} else {
base_t = dest_t;
}
// w = w + BA*s
- ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+ ggml_tensor * BA = ggml_mul_mat(lora_ctx.get(), loraA, loraB);
offload_func(BA);
ggml_set_name(BA, "BA");
if (scaling != 1.0f) {
- ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
+ ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx.get(), scaling);
ggml_set_name(scale_tensor, "scale_tensor");
- BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
+ BA = ggml_scale_inplace(lora_ctx.get(), BA, scale_tensor);
offload_func(BA);
ggml_set_name(BA, "BA_scaled");
}
ggml_tensor * r;
if (base_t == dest_t) {
- r = ggml_add_inplace(lora_ctx, dest_t, BA);
+ r = ggml_add_inplace(lora_ctx.get(), dest_t, BA);
offload_func_force_inplace(r);
ggml_set_name(r, "r_add_inplace");
}
else {
- r = ggml_add(lora_ctx, base_t, BA);
+ r = ggml_add(lora_ctx.get(), base_t, BA);
offload_func(r);
ggml_set_name(r, "r_add");
- r = ggml_cpy(lora_ctx, r, dest_t);
+ r = ggml_cpy(lora_ctx.get(), r, dest_t);
offload_func(r);
ggml_set_name(r, "r_cpy");
}
- struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
+ struct ggml_cgraph * gf = ggml_new_graph(lora_ctx.get());
ggml_build_forward_expand(gf, r);
ggml_graph_compute_helper(work_buffer, gf, n_threads);
+ // the tensors in the adapter must be sorted such that loraA and loraB of the same tensor are next to each other
+ GGML_ASSERT(lora_tensors.size() == 2);
+
// we won't need these tensors again, reset the context to save memory
- ggml_free(lora_ctx);
- lora_ctx = ggml_init(params);
+ lora_ctx.reset(ggml_init(params));
lora_tensors.clear();
n_tensors++;
}
}
- // TODO: this should be in a destructor, it will leak on failure
- ggml_free(lora_ctx);
- if (base_ctx) {
- ggml_free(base_ctx);
- }
-
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
overview / pkg / ggml / sources / llama.cpp / commitdiff