/
gguf_to_torch.py
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/
gguf_to_torch.py
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import json
import os
import torch
import gguf
from sentencepiece import sentencepiece_model_pb2
from safetensors.torch import save_file as safe_save_file
from transformers.modeling_utils import shard_checkpoint
from transformers.utils import (WEIGHTS_NAME, WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME)
def convert_to_state_dict(checkpoint, save_dir, max_shard_size,
safe_serialization):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
state_dict = {}
result = gguf.GGUFReader(checkpoint)
architecture = result.fields['general.architecture']
architecture = str(bytes(architecture.parts[architecture.data[0]]),
encoding='utf-8')
if architecture != "llama":
print(f"Unsupported architecture {architecture}")
return
# write vocab
vocab = sentencepiece_model_pb2.ModelProto()
vocab_size = len(result.fields['tokenizer.ggml.token_type'].data)
vocab.trainer_spec.model_type = 2 # BPE
vocab.trainer_spec.vocab_size = vocab_size
vocab.trainer_spec.byte_fallback = True
vocab.normalizer_spec.remove_extra_whitespaces = False
tokens = result.fields['tokenizer.ggml.tokens']
scores = result.fields['tokenizer.ggml.scores']
types = result.fields['tokenizer.ggml.token_type']
for i in range(vocab_size):
new_token = vocab.SentencePiece()
new_token.piece = str(bytes(tokens.parts[tokens.data[i]]),
encoding='utf-8')
new_token.score = scores.parts[scores.data[i]]
# llama.cpp tokentype is the same with sentencepiece token type
new_token.type = int(types.parts[types.data[i]])
vocab.pieces.append(new_token)
with open(os.path.join(save_dir, "tokenizer.model"), 'wb') as f:
f.write(vocab.SerializeToString())
tokenizer_config = {
"tokenizer_class": "LlamaTokenizer",
"legacy": False,
"clean_up_tokenization_spaces": False,
}
if 'tokenizer.ggml.bos_token_id' in result.fields:
tokenizer_config["bos_token"] = vocab.pieces[int(
result.fields['tokenizer.ggml.bos_token_id'].parts[-1])].piece
if 'tokenizer.ggml.eos_token_id' in result.fields:
tokenizer_config["eos_token"] = vocab.pieces[int(
result.fields['tokenizer.ggml.eos_token_id'].parts[-1])].piece
if 'tokenizer.ggml.padding_token_id' in result.fields:
tokenizer_config["pad_token"] = vocab.pieces[int(
result.fields['tokenizer.ggml.padding_token_id'].parts[-1])].piece
if 'tokenizer.ggml.unknown_token_id' in result.fields:
tokenizer_config["unk_token"] = vocab.pieces[int(
result.fields['tokenizer.ggml.unknown_token_id'].parts[-1])].piece
if 'tokenizer.ggml.add_bos_token' in result.fields:
tokenizer_config["add_bos_token"] = bool(
result.fields['tokenizer.ggml.add_bos_token'].parts[-1])
if 'tokenizer.ggml.add_eos_token' in result.fields:
tokenizer_config["add_eos_token"] = bool(
result.fields['tokenizer.ggml.add_eos_token'].parts[-1])
if 'tokenizer.chat_template' in result.fields:
tokenizer_config["chat_template"] = str(bytes(
result.fields['tokenizer.chat_template'].parts[-1]),
encoding="utf-8")
with open(os.path.join(save_dir, "tokenizer_config.json"), 'w') as f:
json.dump(tokenizer_config, f, indent=2)
# write config
context_length = int(result.fields['llama.context_length'].parts[-1])
n_layer = int(result.fields['llama.block_count'].parts[-1])
n_head = int(result.fields['llama.attention.head_count'].parts[-1])
n_local_heads = int(
result.fields['llama.attention.head_count_kv'].parts[-1])
intermediate_size = int(
result.fields['llama.feed_forward_length'].parts[-1])
norm_eps = float(
result.fields['llama.attention.layer_norm_rms_epsilon'].parts[-1])
dim = int(result.fields['llama.embedding_length'].parts[-1])
kv_dim = dim // n_head * n_local_heads
arch = "MixtralForCausalLM"
if 'llama.expert_count' in result.fields:
arch = "MixtralForCausalLM"
name = "mixtral"
else:
arch = "LlamaForCausalLM"
name = "llama"
model_config = {
"architectures": [arch],
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": dim,
"intermediate_size": intermediate_size,
"max_position_embeddings": context_length,
"model_type": name,
"num_attention_heads": n_head,
"num_hidden_layers": n_layer,
"num_key_value_heads": n_local_heads,
"rms_norm_eps": norm_eps,
"torch_dtype": "float16",
"vocab_size": vocab_size
}
if 'llama.rope.freq_base' in result.fields:
model_config['rope_theta'] = float(
result.fields['llama.rope.freq_base'].parts[-1])
if 'llama.expert_count' in result.fields:
model_config['num_local_experts'] = int(
result.fields['llama.expert_count'].parts[-1])
model_config['num_experts_per_tok'] = int(
result.fields['llama.expert_used_count'].parts[-1])
with open(os.path.join(save_dir, "config.json"), 'w') as f:
json.dump(model_config, f, indent=2)
# write tensor
tensor_mapping = {
"token_embd": ("model.embed_tokens", vocab_size),
"output": ("lm_head", vocab_size),
"output_norm": ("model.norm", -1),
"blk.{bid}.attn_norm": ("model.layers.{bid}.input_layernorm", -1),
"blk.{bid}.attn_q": ("model.layers.{bid}.self_attn.q_proj", dim),
"blk.{bid}.attn_k": ("model.layers.{bid}.self_attn.k_proj", kv_dim),
"blk.{bid}.attn_v": ("model.layers.{bid}.self_attn.v_proj", kv_dim),
"blk.{bid}.attn_output": ("model.layers.{bid}.self_attn.o_proj", dim),
"blk.{bid}.attn_rot_embd":
("model.layers.{bid}.self_attn.rotary_emb.inv_freq", -1),
"blk.{bid}.ffn_norm": ("model.layers.{bid}.post_attention_layernorm",
-1),
"blk.{bid}.ffn_up": ("model.layers.{bid}.mlp.up_proj",
intermediate_size),
"blk.{bid}.ffn_down": ("model.layers.{bid}.mlp.down_proj", dim),
"blk.{bid}.ffn_gate": ("model.layers.{bid}.mlp.gate_proj",
intermediate_size),
"blk.{bid}.ffn_up.{xid}":
("model.layers.{bid}.block_sparse_moe.experts.{xid}.w3",
intermediate_size),
"blk.{bid}.ffn_down.{xid}":
("model.layers.{bid}.block_sparse_moe.experts.{xid}.w2", dim),
"blk.{bid}.ffn_gate.{xid}":
("model.layers.{bid}.block_sparse_moe.experts.{xid}.w1",
intermediate_size),
"blk.{bid}.ffn_gate_inp": ("model.layers.{bid}.block_sparse_moe.gate",
model_config.get('num_local_experts', 1)),
}
mapping = {}
max_block_num = 200
max_expert_num = 8
for k, v in tensor_mapping.items():
for i in range(max_block_num):
for j in range(max_expert_num):
fk = k.format(bid=i, xid=j)
fv = v[0].format(bid=i, xid=j)
if k not in mapping:
mapping[fk] = (fv, v[1])
for ts in result.tensors:
weight_type = torch.tensor(int(ts.tensor_type), dtype=torch.int)
layer, suffix = ts.name.rsplit(".", 1)
new_key, output_dim = mapping[layer]
new_key += f".{suffix}"
data = torch.tensor(ts.data)
if output_dim != -1:
data = data.view(output_dim, -1)
if weight_type > 1:
state_dict[new_key.replace("weight", "weight_type")] = weight_type
state_dict[new_key] = data
if max_shard_size == "0":
if safe_serialization:
safe_save_file(state_dict,
os.path.join(save_dir, SAFE_WEIGHTS_NAME),
metadata={"format": "pt"})
else:
torch.save(state_dict, os.path.join(save_dir, WEIGHTS_NAME))
else:
shards, index = shard_checkpoint(
state_dict, max_shard_size,
SAFE_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME)
for shard_file, shard in shards.items():
if safe_serialization:
safe_save_file(shard,
os.path.join(save_dir, shard_file),
metadata={"format": "pt"})
else:
torch.save(shard, os.path.join(save_dir, shard_file))
if index is not None:
if safe_serialization:
save_index_file = SAFE_WEIGHTS_INDEX_NAME
else:
save_index_file = WEIGHTS_INDEX_NAME
save_index_file = os.path.join(save_dir, save_index_file)
# Save the index as well
with open(save_index_file, "w", encoding="utf-8") as f:
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
f.write(content)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(
description='Convert GGUF checkpoints to torch')
parser.add_argument('--input', type=str, help='The path to GGUF file')
parser.add_argument('--output',
type=str,
help='The path to output directory')
parser.add_argument(
'--max-shard-size',
default="0",
type=str,
help='Shard the model in specified shard size, e.g. 10GB. 0 to disable'
)
parser.add_argument('--safetensors',
action='store_true',
help='Save in .safetensors format')
args = parser.parse_args()
convert_to_state_dict(args.input, args.output, args.max_shard_size,
args.safetensors)