mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
Create convert-llama-7b-pth-to-gguf.py
This commit is contained in:
klosax
302
convert-llama-7b-pth-to-gguf.py
Normal file
302
convert-llama-7b-pth-to-gguf.py
Normal file
@@ -0,0 +1,302 @@
|
|||||||
|
# 7b pth llama --> gguf conversion, GQA/70b not supported
|
||||||
|
# Only models with a single datafile are supported, like 7B
|
||||||
|
# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
|
||||||
|
|
||||||
|
import gguf
|
||||||
|
import gguf_namemap as tmap
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import struct
|
||||||
|
import json
|
||||||
|
import numpy as np
|
||||||
|
import torch
|
||||||
|
from typing import Any, List
|
||||||
|
from pathlib import Path
|
||||||
|
from sentencepiece import SentencePieceProcessor
|
||||||
|
|
||||||
|
|
||||||
|
#NDArray = np.ndarray[Any, Any]
|
||||||
|
# compatible with python < 3.9
|
||||||
|
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
|
||||||
|
|
||||||
|
def count_model_parts(dir_model: str) -> int:
|
||||||
|
num_parts = 0
|
||||||
|
for filename in os.listdir(dir_model):
|
||||||
|
if filename.startswith("consolidated."):
|
||||||
|
num_parts += 1
|
||||||
|
|
||||||
|
if num_parts > 0:
|
||||||
|
print("gguf: found " + str(num_parts) + " model parts")
|
||||||
|
return num_parts
|
||||||
|
|
||||||
|
if len(sys.argv) < 3:
|
||||||
|
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
|
||||||
|
print(" ftype == 0 -> float32")
|
||||||
|
print(" ftype == 1 -> float16")
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
|
||||||
|
# output in the same directory as the model
|
||||||
|
dir_model = sys.argv[1]
|
||||||
|
last_dir = os.path.basename(os.path.normpath(dir_model))
|
||||||
|
|
||||||
|
|
||||||
|
# possible tensor data types
|
||||||
|
# ftype == 0 -> float32
|
||||||
|
# ftype == 1 -> float16
|
||||||
|
#
|
||||||
|
# map from ftype to string
|
||||||
|
ftype_str = ["f32", "f16"]
|
||||||
|
|
||||||
|
ftype = 1
|
||||||
|
if len(sys.argv) > 2:
|
||||||
|
ftype = int(sys.argv[2])
|
||||||
|
if ftype < 0 or ftype > 1:
|
||||||
|
print("Invalid ftype: " + str(ftype))
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
|
||||||
|
|
||||||
|
print("gguf: loading model "+last_dir)
|
||||||
|
|
||||||
|
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
|
||||||
|
hparams = json.load(f)
|
||||||
|
|
||||||
|
if hparams["architectures"][0] != "LlamaForCausalLM":
|
||||||
|
print("Model architecture not supported: " + hparams["architectures"][0])
|
||||||
|
sys.exit()
|
||||||
|
|
||||||
|
# get number of model parts
|
||||||
|
num_parts = count_model_parts(dir_model)
|
||||||
|
|
||||||
|
if num_parts > 1:
|
||||||
|
print("gguf: Only models with a single datafile are supported.")
|
||||||
|
sys.exit()
|
||||||
|
|
||||||
|
gguf_writer = gguf.GGUFWriter.open(fname_out)
|
||||||
|
|
||||||
|
|
||||||
|
print("gguf: get model metadata")
|
||||||
|
|
||||||
|
llm_arch = "llama"
|
||||||
|
block_count = hparams["num_hidden_layers"]
|
||||||
|
head_count = hparams["num_attention_heads"]
|
||||||
|
|
||||||
|
if "num_key_value_heads" in hparams:
|
||||||
|
head_count_kv = hparams["num_key_value_heads"]
|
||||||
|
else:
|
||||||
|
head_count_kv = head_count
|
||||||
|
|
||||||
|
if "_name_or_path" in hparams:
|
||||||
|
hf_repo = hparams["_name_or_path"]
|
||||||
|
else:
|
||||||
|
hf_repo=""
|
||||||
|
|
||||||
|
gguf_writer.add_architecture(llm_arch)
|
||||||
|
gguf_writer.add_name(last_dir)
|
||||||
|
gguf_writer.add_file_type( "All tensors F32" if ftype == 0 else "Most tensors F16, some F32")
|
||||||
|
gguf_writer.add_source_hf_repo(hf_repo)
|
||||||
|
gguf_writer.add_context_length(llm_arch, hparams["max_position_embeddings"])
|
||||||
|
gguf_writer.add_embedding_length(llm_arch, hparams["hidden_size"])
|
||||||
|
gguf_writer.add_block_count(llm_arch, block_count)
|
||||||
|
gguf_writer.add_feed_forward_length(llm_arch, hparams["intermediate_size"])
|
||||||
|
gguf_writer.add_rope_dimension_count(llm_arch, hparams["hidden_size"] // hparams["num_attention_heads"])
|
||||||
|
gguf_writer.add_head_count(llm_arch, head_count)
|
||||||
|
gguf_writer.add_head_count_kv(llm_arch, head_count_kv)
|
||||||
|
gguf_writer.add_layer_norm_rms_eps(llm_arch, hparams["rms_norm_eps"])
|
||||||
|
|
||||||
|
|
||||||
|
# TOKENIZATION
|
||||||
|
|
||||||
|
print("gguf: get tokenizer metadata")
|
||||||
|
|
||||||
|
tokens: List[str] = []
|
||||||
|
scores: List[float] = []
|
||||||
|
|
||||||
|
if Path(dir_model + "/tokenizer.model").is_file():
|
||||||
|
# vocab type sentencepiece
|
||||||
|
print("gguf: get sentencepiece tokenizer vocab and scores")
|
||||||
|
|
||||||
|
tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
|
||||||
|
|
||||||
|
for i in range(tokenizer.vocab_size()):
|
||||||
|
text: bytes
|
||||||
|
if tokenizer.is_unknown(i):
|
||||||
|
text = " \u2047 ".encode("utf-8")
|
||||||
|
elif tokenizer.is_control(i):
|
||||||
|
text = b""
|
||||||
|
if tokenizer.is_byte(i):
|
||||||
|
piece = tokenizer.id_to_piece(i)
|
||||||
|
if len(piece) != 6:
|
||||||
|
raise Exception(f"Invalid token: {piece}")
|
||||||
|
byte_value = int(piece[3:-1], 16)
|
||||||
|
text = struct.pack("B", byte_value)
|
||||||
|
else:
|
||||||
|
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
|
||||||
|
score: float = tokenizer.get_score(i)
|
||||||
|
|
||||||
|
tokens.append(text)
|
||||||
|
scores.append(score)
|
||||||
|
|
||||||
|
gguf_writer.add_tokenizer_model("llama")
|
||||||
|
gguf_writer.add_token_list(tokens)
|
||||||
|
gguf_writer.add_token_scores(scores)
|
||||||
|
|
||||||
|
if Path(dir_model + "/tokenizer.json").is_file():
|
||||||
|
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
|
||||||
|
tokenizer = json.load(f)
|
||||||
|
|
||||||
|
if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
|
||||||
|
print("gguf: get special token ids")
|
||||||
|
|
||||||
|
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
|
||||||
|
tokenizer_config = json.load(f)
|
||||||
|
|
||||||
|
# find special token ids
|
||||||
|
|
||||||
|
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
|
||||||
|
for key in tokenizer["added_tokens"]:
|
||||||
|
if key["content"] == tokenizer_config["bos_token"]["content"]:
|
||||||
|
gguf_writer.add_bos_token_id(key["id"])
|
||||||
|
|
||||||
|
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
|
||||||
|
for key in tokenizer["added_tokens"]:
|
||||||
|
if key["content"] == tokenizer_config["eos_token"]["content"]:
|
||||||
|
gguf_writer.add_eos_token_id(key["id"])
|
||||||
|
|
||||||
|
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
|
||||||
|
for key in tokenizer["added_tokens"]:
|
||||||
|
if key["content"] == tokenizer_config["unk_token"]["content"]:
|
||||||
|
gguf_writer.add_unk_token_id(key["id"])
|
||||||
|
|
||||||
|
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
|
||||||
|
for key in tokenizer["added_tokens"]:
|
||||||
|
if key["content"] == tokenizer_config["sep_token"]["content"]:
|
||||||
|
gguf_writer.add_sep_token_id(key["id"])
|
||||||
|
|
||||||
|
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
|
||||||
|
for key in tokenizer["added_tokens"]:
|
||||||
|
if key["content"] == tokenizer_config["pad_token"]["content"]:
|
||||||
|
gguf_writer.add_pad_token_id(key["id"])
|
||||||
|
|
||||||
|
|
||||||
|
# TENSORS
|
||||||
|
|
||||||
|
tensor_map = tmap.get_tensor_namemap(block_count)
|
||||||
|
|
||||||
|
# tensor info
|
||||||
|
print("gguf: get tensor metadata")
|
||||||
|
|
||||||
|
part_names = ( f"consolidated.{n:02}.pth" for n in range(0, num_parts) )
|
||||||
|
|
||||||
|
for part_name in part_names:
|
||||||
|
print("gguf: loading model part '"+ part_name + "'")
|
||||||
|
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
|
||||||
|
|
||||||
|
for name in model_part.keys():
|
||||||
|
data = model_part[name]
|
||||||
|
|
||||||
|
# we don't need these
|
||||||
|
if name == "rope.freqs":
|
||||||
|
continue
|
||||||
|
|
||||||
|
# convert any unsupported data types to float32
|
||||||
|
if data.dtype != torch.float16 and data.dtype != torch.float32:
|
||||||
|
data = data.to(torch.float32)
|
||||||
|
|
||||||
|
data = data.squeeze().numpy()
|
||||||
|
|
||||||
|
# map tensor names
|
||||||
|
if name.endswith(".weight") and name[:-7] in tensor_map:
|
||||||
|
name = tensor_map[name[:-7]] + ".weight"
|
||||||
|
elif name.endswith(".bias") and name[:-5] in tensor_map:
|
||||||
|
name = tensor_map[name[:-5]] + ".bias"
|
||||||
|
else:
|
||||||
|
print( "Can not map tensor '" + name + "'" )
|
||||||
|
sys.exit()
|
||||||
|
|
||||||
|
n_dims = len(data.shape)
|
||||||
|
data_dtype = data.dtype
|
||||||
|
|
||||||
|
# if f32 desired, convert any float16 to float32
|
||||||
|
if ftype == 0 and data.dtype == np.float16:
|
||||||
|
data_dtype = np.float32
|
||||||
|
|
||||||
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
||||||
|
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
||||||
|
data_dtype = np.float32
|
||||||
|
|
||||||
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||||
|
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||||
|
data_dtype = np.float16
|
||||||
|
|
||||||
|
data_nbytes = data.size * 2 if data_dtype == np.float16 else data.size * 4
|
||||||
|
|
||||||
|
gguf_writer.add_tensor_info(name, data.shape, data_dtype, data_nbytes)
|
||||||
|
|
||||||
|
|
||||||
|
print("gguf: write header")
|
||||||
|
gguf_writer.write_header_to_file()
|
||||||
|
print("gguf: write metadata")
|
||||||
|
gguf_writer.write_kv_data_to_file()
|
||||||
|
print("gguf: write tensor metadata")
|
||||||
|
gguf_writer.write_ti_data_to_file()
|
||||||
|
|
||||||
|
# tensor data
|
||||||
|
print("gguf: convert and write tensor data")
|
||||||
|
|
||||||
|
part_names = ( f"consolidated.{n:02}.pth" for n in range(0, num_parts) )
|
||||||
|
|
||||||
|
for part_name in part_names:
|
||||||
|
print("gguf: loading model part '"+ part_name + "'")
|
||||||
|
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
|
||||||
|
|
||||||
|
for name in model_part.keys():
|
||||||
|
data = model_part[name]
|
||||||
|
|
||||||
|
|
||||||
|
old_dtype = data.dtype
|
||||||
|
|
||||||
|
# we don't need these
|
||||||
|
if name == "rope.freqs":
|
||||||
|
continue
|
||||||
|
|
||||||
|
# convert any unsupported data types to float32
|
||||||
|
if data.dtype != torch.float16 and data.dtype != torch.float32:
|
||||||
|
data = data.to(torch.float32)
|
||||||
|
|
||||||
|
data = data.squeeze().numpy()
|
||||||
|
|
||||||
|
# map tensor names
|
||||||
|
if name.endswith(".weight") and name[:-7] in tensor_map:
|
||||||
|
name = tensor_map[name[:-7]] + ".weight"
|
||||||
|
elif name.endswith(".bias") and name[:-5] in tensor_map:
|
||||||
|
name = tensor_map[name[:-5]] + ".bias"
|
||||||
|
else:
|
||||||
|
print( "Can not map tensor '" + name + "'" )
|
||||||
|
sys.exit()
|
||||||
|
|
||||||
|
n_dims = len(data.shape)
|
||||||
|
data_dtype = data.dtype
|
||||||
|
|
||||||
|
# if f32 desired, convert any float16 to float32
|
||||||
|
if ftype == 0 and data.dtype == np.float16:
|
||||||
|
data = data.astype(np.float32)
|
||||||
|
|
||||||
|
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
|
||||||
|
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
|
||||||
|
data = data.astype(np.float32)
|
||||||
|
|
||||||
|
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||||
|
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
|
||||||
|
data = data.astype(np.float16)
|
||||||
|
|
||||||
|
print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
|
||||||
|
|
||||||
|
gguf_writer.write_tensor_to_file(data)
|
||||||
|
|
||||||
|
gguf_writer.close()
|
||||||
|
|
||||||
|
|
||||||
|
print("gguf: model successfully exported to '" + fname_out + "'")
|
||||||
|
print("")
|
||||||
Reference in New Issue
Block a user