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convert-new.py : output gguf (#2635)

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* convert-new.py : output gguf (WIP)

* convert-new.py : add gguf key-value pairs

* llama : add hparams.ctx_train + no longer print ftype

* convert-new.py : minor fixes

* convert-new.py : vocab-only option should work now

* llama : fix tokenizer to use llama_char_to_byte

* tests : add new ggml-vocab-llama.gguf

* convert-new.py : tensor name mapping

* convert-new.py : add map for skipping tensor serialization

* convert-new.py : convert script now works

* gguf.py : pick some of the refactoring from #2644

* convert-new.py : minor fixes
This commit is contained in:
Georgi Gerganov 2023-08-17 17:19:52 +03:00 committed by GitHub
parent d6fd53afd6
commit e0429d38e4
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9 changed files with 526 additions and 327 deletions
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2
convert-llama-7b-pth-to-gguf.py
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@ -298,7 +298,7 @@ for part_name in part_names:
print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.write_tensor_to_file(data)
gguf_writer.write_tensor_data(data)
gguf_writer.close()

443
convert-new.py
View File

@ -1,5 +1,6 @@
#!/usr/bin/env python
import gguf
import argparse
import concurrent.futures
import copy
@ -33,6 +34,13 @@ if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
ARCH=gguf.MODEL_ARCH.LLAMA
NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
#
# data types
#
@dataclass(frozen=True)
class UnquantizedDataType:
name: str
@ -44,14 +52,6 @@ DT_BF16 = UnquantizedDataType('BF16')
DataType = Union[UnquantizedDataType]
DATA_TYPE_TO_FTYPE: Dict[DataType, int] = {
DT_F32: 0,
DT_F16: 1,
}
FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \
{ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()}
DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
DT_BF16: np.dtype(np.uint16),
DT_F16: np.dtype(np.float16),
@ -62,6 +62,13 @@ DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
{dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
'BF16': DT_BF16,
'F16': DT_F16,
'F32': DT_F32,
'I32': DT_I32,
}
class GGMLFileType(enum.Enum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
@ -77,48 +84,31 @@ class GGMLFileType(enum.Enum):
else:
raise ValueError(self)
# TODO: this is LLaMA specific
def make_tensors_list() -> List[str]:
ret = [
'tok_embeddings.weight',
'norm.weight',
'output.weight',
]
for i in range(80): # maximum number of layer
ret += [
f'layers.{i}.attention.wq.weight',
f'layers.{i}.attention.wk.weight',
f'layers.{i}.attention.wv.weight',
f'layers.{i}.attention.wo.weight',
f'layers.{i}.attention_norm.weight',
f'layers.{i}.feed_forward.w1.weight',
f'layers.{i}.feed_forward.w2.weight',
f'layers.{i}.feed_forward.w3.weight',
f'layers.{i}.ffn_norm.weight',
]
return ret
# TODO: this should be generalized for non-LLaMA models
TENSORS_LIST = make_tensors_list()
TENSORS_SET = set(TENSORS_LIST)
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
for n_mult in range(8192, 1, -1):
calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff:
return n_mult
raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
#
# hparams loading
#
@dataclass
class Params:
n_vocab: int
n_embd: int
n_mult: int
n_head: int
n_layer: int
n_kv_head: Optional[int] # This parameter is only used for Llama 2
n_vocab: int
n_embd: int
n_mult: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
f_norm_eps: float
@staticmethod
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
for n_mult in range(8192, 1, -1):
calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff:
return n_mult
raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
@staticmethod
def guessed(model: 'LazyModel') -> 'Params':
@ -137,37 +127,57 @@ class Params:
raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
n_head=n_embd // 128 # guessed
n_head = n_embd // 128 # guessed
n_mult = 256 # guessed
# TODO: verify this
n_ff = int(2 * (4 * n_embd) / 3)
n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = 256,
n_head = n_head,
n_layer = n_layer,
n_kv_head = None,
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_layer = n_layer,
n_ctx = -1,
n_ff = n_ff,
n_head = n_head,
n_head_kv = n_head,
f_norm_eps = 1e-5,
)
@staticmethod
def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
config = json.load(open(config_path))
n_vocab = config["vocab_size"];
n_embd = config["hidden_size"];
n_head = config["num_attention_heads"];
n_layer = config["num_hidden_layers"];
n_ff = config["intermediate_size"];
n_kv_head = config.get("num_key_value_heads")
n_vocab = config["vocab_size"];
n_embd = config["hidden_size"];
n_layer = config["num_hidden_layers"];
n_ff = config["intermediate_size"];
n_head = config["num_attention_heads"];
n_head_kv = config["num_key_value_heads"];
f_norm_eps = config["rms_norm_eps"];
n_mult = find_n_mult(n_ff, n_embd);
n_mult = Params.find_n_mult(n_ff, n_embd);
if "max_sequence_length" in config:
n_ctx = config["max_sequence_length"]
elif "max_position_embeddings" in config:
n_ctx = config["max_position_embeddings"]
else:
raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_head = n_head,
n_layer = n_layer,
n_kv_head = n_kv_head,
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_layer = n_layer,
n_ctx = n_ctx,
n_ff = n_ff,
n_head = n_head,
n_head_kv = n_head_kv,
f_norm_eps = f_norm_eps,
)
# LLaMA v2 70B params.json
@ -176,22 +186,32 @@ class Params:
def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
config = json.load(open(config_path))
n_vocab = config["vocab_size"];
n_embd = config["dim"];
n_head = config["n_heads"];
n_layer = config["n_layers"];
n_mult = config["multiple_of"];
n_vocab = config["vocab_size"];
n_embd = config["dim"];
n_layer = config["n_layers"];
n_mult = config["multiple_of"];
n_ctx = 2048 if config["norm_eps"] == 1e-06 else 4096 # hack to determine LLaMA v1 vs v2
n_ff = -1;
n_head = config["n_heads"];
n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head;
f_norm_eps = config["norm_eps"];
if n_vocab == -1:
n_vocab = model["tok_embeddings.weight"].shape[0]
if n_ff == -1:
n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_head = n_head,
n_layer = n_layer,
n_kv_head = None,
n_vocab = n_vocab,
n_embd = n_embd,
n_mult = n_mult,
n_layer = n_layer,
n_ctx = n_ctx,
n_ff = n_ff,
n_head = n_head,
n_head_kv = n_head_kv,
f_norm_eps = f_norm_eps,
)
@staticmethod
@ -206,10 +226,13 @@ class Params:
else:
params = Params.guessed(model_plus.model)
print(f'params: n_vocab:{params.n_vocab} n_embd:{params.n_embd} n_mult:{params.n_mult} n_head:{params.n_head} n_layer:{params.n_layer}')
return params
#
# vocab
#
class BpeVocab:
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
@ -294,13 +317,17 @@ class SentencePieceVocab:
def __repr__(self) -> str:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
Vocab = Union[BpeVocab, SentencePieceVocab]
def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
#
# data loading
# TODO: reuse (probably move to gguf.py?)
#
def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
if n_head_kv is not None and n_head != n_head_kv:
n_head //= n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
@ -312,7 +339,7 @@ class Tensor(metaclass=ABCMeta):
@abstractmethod
def astype(self, data_type: DataType) -> 'Tensor': ...
@abstractmethod
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ...
def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ...
@abstractmethod
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
@abstractmethod
@ -350,8 +377,8 @@ class UnquantizedTensor(Tensor):
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head))
def permute(self, n_head: int, n_head_kv: int) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv))
def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
@ -374,18 +401,18 @@ GGMLCompatibleTensor = Union[UnquantizedTensor]
class DeferredPermutedTensor(Tensor):
def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None:
def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
self.base = base
self.n_head = n_head
self.data_type = self.base.data_type
def astype(self, data_type: DataType) -> Tensor:
return self.base.astype(data_type).permute(self.n_head, self.n_kv_head)
return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)
def to_ggml(self) -> GGMLCompatibleTensor:
return self.base.to_ggml().permute(self.n_head, self.n_kv_head)
return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
def permute(self, n_head: int, n_head_kv: int) -> Tensor:
raise Exception("shouldn't permute twice")
@ -481,10 +508,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus:
return ModelPlus(model, paths, format, vocab)
def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor:
def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().permute(n_head, n_kv_head)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description)
return lazy_tensor.load().permute(n_head, n_head_kv)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
def load() -> Tensor:
@ -500,34 +527,6 @@ def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
s[0] = s[0] // 3
return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
out: LazyModel = {}
out["tok_embeddings.weight"] = model["model.embed_tokens.weight"]
out["norm.weight"] = model["model.norm.weight"]
out["output.weight"] = model["lm_head.weight"]
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head)
out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head)
out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
else:
break
out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"]
out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"]
out[f"layers.{i}.feed_forward.w2.weight"] = model[f"model.layers.{i}.mlp.down_proj.weight"]
out[f"layers.{i}.feed_forward.w3.weight"] = model[f"model.layers.{i}.mlp.up_proj.weight"]
out[f"layers.{i}.attention_norm.weight"] = model[f"model.layers.{i}.input_layernorm.weight"]
out[f"layers.{i}.ffn_norm.weight"] = model[f"model.layers.{i}.post_attention_layernorm.weight"]
return out
# Functionality that simulates `torch.load` but where individual tensors are
# only loaded into memory on demand, not all at once.
@ -621,14 +620,6 @@ def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus:
return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None)
SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
'BF16': DT_BF16,
'F16': DT_F16,
'F32': DT_F32,
'I32': DT_I32,
}
def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
header_size, = struct.unpack('<Q', fp.read(8))
header: Dict[str, Dict[str, Any]] = json.loads(fp.read(header_size))
@ -678,7 +669,6 @@ def lazy_load_file(path: Path) -> ModelPlus:
In = TypeVar('In')
Out = TypeVar('Out')
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
'''Parallel map, but with backpressure. If the caller doesn't call `next`
fast enough, this will stop calling `func` at some point rather than
@ -715,88 +705,133 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
class OutputFile:
def __init__(self, fname_out: Path) -> None:
self.fout = open(fname_out, "wb")
self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
def write_file_header(self, params: Params, file_type: GGMLFileType) -> None:
self.fout.write(b"ggjt"[::-1]) # magic
values = [
1, # file version
params.n_vocab,
params.n_embd,
params.n_mult,
params.n_head,
params.n_layer,
params.n_embd // params.n_head, # rot (obsolete)
file_type.value,
]
self.fout.write(struct.pack("i" * len(values), *values))
def add_meta_arch(self, params: Params) -> None:
self.gguf.add_context_length (params.n_ctx)
self.gguf.add_embedding_length (params.n_embd)
self.gguf.add_block_count (params.n_layer)
self.gguf.add_feed_forward_length (params.n_ff)
self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
self.gguf.add_head_count (params.n_head)
self.gguf.add_head_count_kv (params.n_head_kv)
self.gguf.add_layer_norm_rms_eps (params.f_norm_eps)
def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None:
sname = name.encode('utf-8')
self.fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[data_type]))
self.fout.write(struct.pack("i" * len(shape), *shape[::-1]))
self.fout.write(sname)
self.fout.seek((self.fout.tell() + 31) & -32)
def write_vocab(self, vocab: Vocab) -> None:
def add_meta_vocab(self, vocab: Vocab) -> None:
tokens = []
scores = []
for text, score in vocab.all_tokens():
self.fout.write(struct.pack("i", len(text)))
self.fout.write(text)
self.fout.write(struct.pack("f", score))
tokens.append(text)
scores.append(score)
self.gguf.add_tokenizer_model("llama")
self.gguf.add_token_list(tokens)
self.gguf.add_token_scores(scores)
#self.gguf.add_token_types(toktypes) # TODO: add this
# TODO: added / special tokens
def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
n_elements = 1
for dim in tensor.shape:
n_elements *= dim
data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
data_nbytes = n_elements * data_type.itemsize
self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
def write_meta(self) -> None:
self.gguf.write_header_to_file()
self.gguf.write_kv_data_to_file()
def write_tensor_info(self) -> None:
self.gguf.write_ti_data_to_file()
def close(self) -> None:
self.gguf.close()
@staticmethod
def write_vocab_only(fname_out: Path, vocab: Vocab) -> None:
of = OutputFile(fname_out)
params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, n_head=1, n_layer=0)
of = OutputFile(fname_out)
of.write_file_header(params, file_type=GGMLFileType.AllF32)
of.write_vocab(vocab)
of.fout.close()
@staticmethod
def write_all(fname_out: Path, params: Params, file_type: GGMLFileType, model: LazyModel, vocab: Vocab) -> None:
def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
of.write_file_header(params, file_type)
print("Writing vocab...")
of.write_vocab(vocab)
# meta data
of.add_meta_arch(params)
of.add_meta_vocab(vocab)
of.write_meta()
of.close()
@staticmethod
def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
# meta data
of.add_meta_arch(params)
of.add_meta_vocab(vocab)
# tensor info
for name, lazy_tensor in model.items():
of.add_tensor_info(name, lazy_tensor)
of.write_meta()
of.write_tensor_info()
def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
name, lazy_tensor = item
return lazy_tensor.load().to_ggml().ndarray
# tensor data
ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
padi = len(str(len(model)))
print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type)
ndarray.tofile(of.fout)
of.fout.close()
of.gguf.write_tensor_data(ndarray)
of.close()
def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType:
wq_type = model["layers.0.attention.wq.weight"].data_type
if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
wq_type = model[NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type
if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
return GGMLFileType.AllF32
if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
return GGMLFileType.MostlyF16
name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
raise Exception(f"Unexpected combination of types: {name_to_type}")
def do_necessary_conversions(model: LazyModel, params: Params) -> LazyModel:
if "lm_head.weight" in model:
model = convert_transformers_to_orig(model, params)
model = filter_and_sort_tensors(model)
return model
def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
return {name: tensor.astype(output_type.type_for_tensor(name, tensor))
for (name, tensor) in model.items()}
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
tmap = gguf.get_tensor_name_map(ARCH, params.n_layer)
out: LazyModel = {}
for name, lazy_tensor in model.items():
name_new = name
if name in tmap:
name_new = tmap[name]
elif name.endswith(".weight") and name[:-7] in tmap:
name_new = tmap[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tmap:
name_new = tmap[name[:-5]] + ".bias"
else:
raise Exception(f"Unexpected tensor name: {name}")
if gguf.should_skip_tensor(ARCH, params.n_layer, name_new):
print(f"skipping tensor {name_new}")
else:
print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
out[name_new] = lazy_tensor
return out
def nth_multifile_path(path: Path, n: int) -> Optional[Path]:
'''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
@ -847,11 +882,6 @@ def load_some_model(path: Path) -> ModelPlus:
# Try the PyTorch patterns too, with lower priority
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
files = [file for glob in globs for file in path.glob(glob)]
if not files:
# Try GGML too, but with lower priority, since if both a non-GGML
# model and a GGML model exist in the same directory, we assume the
# latter was converted from the former.
files = list(path.glob("ggml-model*.bin*"))
if not files:
raise Exception(f"Can't find model in directory {path}")
if len(files) > 1:
@ -868,12 +898,7 @@ def load_some_model(path: Path) -> ModelPlus:
return model_plus
def filter_and_sort_tensors(model: LazyModel) -> LazyModel:
return {name: model[name] for name in TENSORS_LIST if name in model}
def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, SentencePieceVocab]:
print(f"vocabtype: {vocabtype}")
# Be extra-friendly and accept either a file or a directory. Also, if it's
# a directory, it might be the model directory, and tokenizer.model might
# be in the parent of that.
@ -892,8 +917,10 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence
raise FileNotFoundError(
f"Could not find tokenizer.model in {path} or its parent; "
"if it's in another directory, pass the directory as --vocab-dir")
print(f"Loading vocab file '{path}', type '{vocabtype}'")
added_tokens_path = path.parent / "added_tokens.json"
print(f"Loading vocab file {path}")
if vocabtype == "bpe":
return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None)
elif vocabtype == "spm":
@ -933,38 +960,52 @@ def main(args_in: Optional[List[str]] = None) -> None:
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
args = parser.parse_args(args_in)
vocab: Vocab
if args.dump_single:
model_plus = lazy_load_file(args.model)
do_dump_model(model_plus)
elif args.vocab_only:
model_plus = load_some_model(args.model)
params = Params.load(model_plus)
if params.n_ctx == -1:
if args.ctx is None:
raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
"Please specify one with --ctx:\n"
" - LLaMA v1: --ctx 2048\n"
" - LLaMA v2: --ctx 4096\n")
params.n_ctx = args.ctx
print(f"params = {params}")
vocab: Vocab
if args.vocab_only:
vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
assert args.outfile, "need --outfile if using --vocab-only"
outfile = args.outfile
OutputFile.write_vocab_only(outfile, vocab)
OutputFile.write_vocab_only(outfile, params, vocab)
print(f"Wrote {outfile}")
else:
model_plus = load_some_model(args.model)
if args.dump:
do_dump_model(model_plus)
return
if model_plus.vocab is not None and args.vocab_dir is None:
vocab = model_plus.vocab
else:
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(vocab_dir, args.vocabtype)
params = Params.load(model_plus)
model = model_plus.model
model = do_necessary_conversions(model, params)
model = convert_model_names(model, params)
output_type = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, output_type)
outfile = args.outfile or default_outfile(model_plus.paths, output_type)
OutputFile.write_all(outfile, params, output_type, model, vocab)
OutputFile.write_all(outfile, params, model, vocab)
print(f"Wrote {outfile}")

301
gguf.py
View File

@ -8,7 +8,7 @@ import sys
import struct
import numpy as np
from enum import IntEnum
from enum import IntEnum, auto
from typing import Any, IO, List
#
@ -33,24 +33,24 @@ KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
# LLM
KEY_LLM_CONTEXT_LENGTH = "{llm}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{llm}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{llm}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{llm}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{llm}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{llm}.tensor_data_layout"
KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
# attention
KEY_ATTENTION_HEAD_COUNT = "{llm}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{llm}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{llm}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{llm}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{llm}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{llm}.attention.layer_norm_rms_epsilon"
KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
# RoPE
KEY_ROPE_DIMENSION_COUNT = "{llm}.rope.dimension_count"
KEY_ROPE_SCALE = "{llm}.rope.scale"
KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
KEY_ROPE_SCALE = "{arch}.rope.scale"
# tokenization
KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
@ -70,34 +70,137 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
# recommended mapping of model tensor names for storage in gguf
#
def get_tensor_name_map(n_blocks : int):
class MODEL_ARCH(IntEnum):
LLAMA = auto()
FALCON = auto()
GPT2 = auto()
GPTJ = auto()
GPTNEOX = auto()
MPT = auto()
class MODEL_TENSOR(IntEnum):
TOKEN_EMBD = auto()
POS_EMBD = auto()
OUTPUT = auto()
OUTPUT_NORM = auto()
ROPE_FREQS = auto()
ATTN_Q = auto()
ATTN_K = auto()
ATTN_V = auto()
ATTN_QKV = auto()
ATTN_OUT = auto()
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
MODEL_ARCH_NAMES = {
MODEL_ARCH.LLAMA : "llama",
MODEL_ARCH.FALCON : "falcon",
MODEL_ARCH.GPT2 : "gpt2",
MODEL_ARCH.GPTJ : "gptj",
MODEL_ARCH.GPTNEOX : "gptneox",
MODEL_ARCH.MPT : "mpt",
}
MODEL_TENSOR_NAMES = {
MODEL_ARCH.LLAMA : {
MODEL_TENSOR.TOKEN_EMBD : "token_embd",
MODEL_TENSOR.OUTPUT_NORM : "output_norm",
MODEL_TENSOR.OUTPUT : "output",
MODEL_TENSOR.ROPE_FREQS : "rope_freqs",
MODEL_TENSOR.ATTN_NORM : "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_Q : "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K : "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V : "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT : "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD : "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.FFN_NORM : "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE : "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN : "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP : "blk.{bid}.ffn_up",
},
MODEL_ARCH.FALCON : {
MODEL_TENSOR.TOKEN_EMBD : "token_embd",
MODEL_TENSOR.OUTPUT_NORM : "output_norm",
MODEL_TENSOR.OUTPUT : "output",
MODEL_TENSOR.ATTN_NORM : "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2 : "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV : "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_OUT : "blk.{bid}.attn_output",
MODEL_TENSOR.FFN_DOWN : "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP : "blk.{bid}.ffn_up",
},
MODEL_ARCH.GPT2 : {
# TODO
},
# TODO
}
# tensors that will not be serialized
MODEL_TENSOR_SKIP = {
MODEL_ARCH.LLAMA : [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}
def should_skip_tensor(arch : MODEL_ARCH, n_blocks : int, name : str) -> bool:
for skip in MODEL_TENSOR_SKIP.get(arch, []):
for i in range(n_blocks):
if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
return True
return False
def get_tensor_name_map(arch : MODEL_ARCH, n_blocks : int) -> dict:
tensor_map = {}
# Token embeddings
mapped_to = "token_embd"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
tensor_map["transformer.word_embeddings"] = mapped_to # falcon
tensor_map["model.embed_tokens"] = mapped_to # llama-hf
tensor_map["tok_embeddings"] = mapped_to # llama-pth
# Position embeddings
mapped_to = "pos_embd"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
tensor_map["transformer.wpe"] = mapped_to # gpt2
# Output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
# Output norm
mapped_to = "output_norm"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
tensor_map["transformer.norm_f"] = mapped_to # mpt
tensor_map["model.norm"] = mapped_to # llama-hf
tensor_map["norm"] = mapped_to # llama-pth
# Output
mapped_to = "output"
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
# Attention and fee-forward layer blocks
# Rope frequencies
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
tensor_map["rope.freqs"] = mapped_to # llama-pth
# Attention and feed-forward blocks
for i in range(0,n_blocks):
# Attention norm
mapped_to = "blk."+str(i)+".attn_norm"
# TODO: is there are simpler way to write these 2 lines in Python?
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to else None
tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
@ -105,56 +208,93 @@ def get_tensor_name_map(n_blocks : int):
tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
# Attention norm 2
mapped_to = "blk."+str(i)+".attn_norm_2"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
# Attention query-key-value
mapped_to = "blk."+str(i)+".attn_qkv"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
# Attention query
mapped_to = "blk."+str(i)+".attn_q"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
# Attention key
mapped_to = "blk."+str(i)+".attn_k"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
# Attention value
mapped_to = "blk."+str(i)+".attn_v"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
# Attention output
mapped_to = "blk."+str(i)+".attn_output"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
# Rotary embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
# Feed-forward norm
mapped_to = "blk."+str(i)+".ffn_norm"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
# Feed-forward up
mapped_to = "blk."+str(i)+".ffn_up"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
# Feed-forward gate
mapped_to = "blk."+str(i)+".ffn_gate"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
# Feed-forward down
mapped_to = "blk."+str(i)+".ffn_down"
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
@ -203,14 +343,16 @@ class GGUFValueType(IntEnum):
class GGUFWriter:
def __init__(self, fout: IO):
self.fout = fout
def __init__(self, path: str, arch: str):
self.fout = open(path, "wb")
self.arch = arch
self.offset_tensor = 0
self.data_alignment = GGUF_DEFAULT_ALIGNMENT
self.kv_data = b""
self.kv_data_count = 0
self.ti_data = b""
self.ti_data_count = 0
self.add_architecture()
def write_header_to_file(self):
self.fout.write(struct.pack("<I", GGUF_MAGIC))
@ -228,11 +370,6 @@ class GGUFWriter:
self.fout.write(self.ti_data)
self.flush()
@classmethod
def open(cls, path: str) -> "GGUFWriter":
f = open(path, "wb")
return cls(f)
def add_key(self, key: str):
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
@ -269,7 +406,8 @@ class GGUFWriter:
self.add_val(val, GGUFValueType.BOOL)
def add_string(self, key: str, val: str):
if len(val) == 0: return
if len(val) == 0:
return
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
@ -323,6 +461,8 @@ class GGUFWriter:
return ((x + n - 1) // n) * n
def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int):
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
encoded_name = name.encode("utf8")
self.ti_data += struct.pack("<I", len(encoded_name))
self.ti_data += encoded_name
@ -331,14 +471,13 @@ class GGUFWriter:
for i in range(n_dims):
self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
self.ti_data += struct.pack("<I", dtype)
self.ti_data += struct.pack("<Q", self.offset_tensor)
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def write_tensor_to_file(self, tensor: np.ndarray):
def write_tensor_data(self, tensor: np.ndarray):
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
self.fout.write(bytes([0] * pad))
@ -355,15 +494,14 @@ class GGUFWriter:
def close(self):
self.fout.close()
def add_architecture(self, architecture: str):
self.add_string(KEY_GENERAL_ARCHITECTURE,
architecture)
def add_architecture(self):
self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
def add_author(self, author: str):
self.add_string(KEY_GENERAL_AUTHOR, author)
def add_tensor_data_layout(self, layout: str):
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT , layout)
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_url(self, url: str):
self.add_string(KEY_GENERAL_URL, url)
@ -391,60 +529,60 @@ class GGUFWriter:
self.data_alignment = alignment
self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
def add_context_length(self, llm: str, length: int):
def add_context_length(self, length: int):
self.add_uint32(
KEY_LLM_CONTEXT_LENGTH.format(llm=llm), length)
KEY_LLM_CONTEXT_LENGTH.format(arch=self.arch), length)
def add_embedding_length(self, llm: str, length: int):
def add_embedding_length(self, length: int):
self.add_uint32(
KEY_LLM_EMBEDDING_LENGTH.format(llm=llm), length)
KEY_LLM_EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_block_count(self, llm: str, length: int):
def add_block_count(self, length: int):
self.add_uint32(
KEY_LLM_BLOCK_COUNT.format(llm=llm), length)
KEY_LLM_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, llm: str, length: int):
def add_feed_forward_length(self, length: int):
self.add_uint32(
KEY_LLM_FEED_FORWARD_LENGTH.format(llm=llm), length)
KEY_LLM_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_parallel_residual(self, llm: str, use: bool):
def add_parallel_residual(self, use: bool):
self.add_bool(
KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=llm), use)
KEY_LLM_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_tensor_data_layout(self, llm: str, layout: str):
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=llm), layout)
KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_head_count(self, llm: str, count: int):
def add_head_count(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT.format(llm=llm), count)
KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
def add_head_count_kv(self, llm: str, count: int):
def add_head_count_kv(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT_KV.format(llm=llm), count)
KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
def add_max_alibi_bias(self, llm: str, bias: float):
def add_max_alibi_bias(self, bias: float):
self.add_float32(
KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=llm), bias)
KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
def add_clamp_kqv(self, llm: str, value: float):
def add_clamp_kqv(self, value: float):
self.add_float32(
KEY_ATTENTION_CLAMP_KQV.format(llm=llm), value)
KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
def add_layer_norm_eps(self, llm: str, value: float):
def add_layer_norm_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_EPS.format(llm=llm), value)
KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
def add_layer_norm_rms_eps(self, llm: str, value: float):
def add_layer_norm_rms_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=llm), value)
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
def add_rope_dimension_count(self, llm: str, count: int):
def add_rope_dimension_count(self, count: int):
self.add_uint32(
KEY_ROPE_DIMENSION_COUNT.format(llm=llm), count)
KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
def add_rope_scale(self, llm: str, value: float):
self.add_float32(KEY_ROPE_SCALE.format(llm=llm), value)
def add_rope_scale(self, value: float):
self.add_float32(KEY_ROPE_SCALE.format(arch=self.arch), value)
def add_tokenizer_model(self, model: str):
self.add_string(KEY_TOKENIZER_MODEL, model)
@ -479,9 +617,8 @@ class GGUFWriter:
# Example usage:
if __name__ == "__main__":
# Example usage with a file
gguf_writer = GGUFWriter.open("example.gguf")
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture("llama")
gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer
gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float
gguf_writer.add_custom_alignment(64)
@ -493,7 +630,7 @@ if __name__ == "__main__":
gguf_writer.write_header_to_file()
gguf_writer.write_kv_data_to_file()
gguf_writer.write_ti_data_to_file()
gguf_writer.write_tensor_to_file(tensor1)
gguf_writer.write_tensor_to_file(tensor2)
gguf_writer.write_tensor_data(tensor1)
gguf_writer.write_tensor_data(tensor2)
gguf_writer.close()

90
llama.cpp
View File

@ -676,22 +676,21 @@ static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
// default hparams (LLaMA 7B)
struct llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512;
uint32_t n_embd = 4096;
uint32_t n_head = 32;
uint32_t n_head_kv = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t n_ff = 11008;
uint32_t n_vocab = 32000;
uint32_t n_ctx_train = 2048; // the context size used during training
uint32_t n_ctx = 512; // the context size used during inference
uint32_t n_embd = 4096;
uint32_t n_head = 32;
uint32_t n_head_kv = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t n_ff = 11008;
float f_norm_rms_eps = 1e-5;
float rope_freq_base = 10000.0f;
float rope_freq_scale = 1.0f;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const {
return static_cast<bool>(memcmp(this, &other, sizeof(llama_hparams))); // NOLINT
}
@ -1023,7 +1022,8 @@ struct llama_model_loader {
int n_kv = 0;
int n_tensors = 0;
int n_created = 0;
size_t n_tot_elements = 0;
int64_t n_elements = 0;
bool use_mmap = false;
@ -1051,9 +1051,9 @@ struct llama_model_loader {
for (int i = 0; i < n_tensors; i++) {
const char * name = gguf_get_tensor_name(ctx_gguf, i);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
n_tot_elements += ggml_nelements(t);
n_elements += ggml_nelements(t);
}
// print meta data
// TODO: make optional
{
@ -1123,6 +1123,10 @@ struct llama_model_loader {
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
if (cur == NULL) {
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
}
{
bool is_ok = true;
for (size_t i = 0; i < ne.size(); ++i) {
@ -1332,7 +1336,7 @@ static void llama_model_load_internal(
}
GGUF_GET(hparams.n_vocab, gguf_get_arr_n, GGUF_TYPE_ARRAY, true, "tokenizer.ggml.tokens");
GGUF_GET(hparams.n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, true, "llama.context_length");
GGUF_GET(hparams.n_ctx_train, gguf_get_val_u32, GGUF_TYPE_UINT32, true, "llama.context_length");
GGUF_GET(hparams.n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, "llama.embedding_length");
GGUF_GET(hparams.n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, "llama.feed_forward_length");
GGUF_GET(hparams.n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, "llama.attention.head_count");
@ -1406,22 +1410,24 @@ static void llama_model_load_internal(
}
{
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->file_version));
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, hparams.n_ctx);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
LLAMA_LOG_INFO("%s: model size = %.2f B\n", __func__, ml->n_tot_elements*1e-9);
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml->file_version));
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, hparams.n_ctx);
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
LLAMA_LOG_INFO("%s: model size = %.2fB\n", __func__, ml->n_elements*1e-9);
// TODO: print number of tensors for each quantization
}
if (vocab_only) {
@ -2310,6 +2316,18 @@ static uint8_t llama_byte_to_char(const llama_vocab & vocab, uint8_t byte) {
return false;
}
static uint8_t llama_char_to_byte(const llama_vocab & vocab, uint8_t ch) {
if (llama_vocab_type(vocab) == "spm") {
return ch + 3;
}
if (llama_vocab_type(vocab) == "bpe") {
return ch - 32;
}
return false;
}
static std::string llama_escape_whitespace(const std::string& text) {
std::string result;
bool escaping = false;
@ -2446,7 +2464,7 @@ private:
if (p == rev_merge.end()) {
// output any symbols that did not form tokens as bytes.
for (int j = 0; j < (int)symbol.n; ++j) {
llama_vocab::id token_id = llama_byte_to_char(vocab_, symbol.text[j]);
llama_vocab::id token_id = llama_char_to_byte(vocab_, symbol.text[j]);
output.push_back(token_id);
}
return;
@ -3373,7 +3391,6 @@ static void llama_convert_tensor_internal(struct ggml_tensor * tensor, std::vect
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type quantized_type;
llama_ftype ftype = params->ftype;
int nthread = params->nthread;
switch (params->ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
@ -3399,6 +3416,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
int nthread = params->nthread;
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
@ -3669,6 +3688,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
}
// TODO: after the GGUF PR, this likely won't work and needs to be updated
int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
@ -4876,8 +4896,8 @@ int llama_token_to_str_with_model(const struct llama_model * model, llama_token
return 0;
}
int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * str, int length) {
return llama_token_to_str_with_model(&ctx->model, token, str, length);
int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) {
return llama_token_to_str_with_model(&ctx->model, token, buf, length);
}
std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
@ -4894,13 +4914,13 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok
return std::string(result.data(), result.size());
}
int llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token, char * str, int length) {
int llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token, char * buf, int length) {
if (0 <= token && token < llama_n_vocab_from_model(&ctx->model)) {
std::string result = ctx->model.vocab.id_to_token[token].tok;
if (length < (int) result.length()) {
return -result.length();
}
memcpy(str, result.c_str(), result.length());
memcpy(buf, result.c_str(), result.length());
return result.length();
}
return 0;

BIN
models/ggml-vocab-aquila.bin
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BIN
models/ggml-vocab-llama.bin
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BIN
models/ggml-vocab-llama.gguf Normal file
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6
tests/CMakeLists.txt
View File

@ -26,10 +26,10 @@ llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0.cpp)
llama_test_executable(test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.bin)
llama_test_executable(test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-1.cpp)
llama_test_executable(test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.bin)
llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.bin)
llama_test_executable(test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
#llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_build_and_test_executable(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
# llama_build_and_test_executable(test-opt.cpp) # SLOW

11
tests/test-tokenizer-0.cpp
View File

@ -89,6 +89,8 @@ int main(int argc, char **argv) {
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, true);
fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
@ -103,7 +105,8 @@ int main(int argc, char **argv) {
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s'\n", __func__, unescape_whitespace(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);
@ -115,9 +118,7 @@ int main(int argc, char **argv) {
}
fprintf(stderr, "\n");
llama_free_model(model);
llama_free(ctx);
return 3;
success = false;
}
}
@ -126,5 +127,5 @@ int main(int argc, char **argv) {
llama_backend_free();
return 0;
return success ? 0 : 3;
}