reduce duplicated code from gguf_writer
This commit is contained in:
Christian Zhou-Zheng
parent
c8ecbc67e2
commit
3e9430df33
1 changed files with 24 additions and 286 deletions
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@ -16,11 +16,7 @@ if TYPE_CHECKING:
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from .constants import (
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GGMLQuantizationType,
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GGUFEndian,
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GGUFValueType,
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Keys,
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RopeScalingType,
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PoolingType,
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TokenType,
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GGUFValueType
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)
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from .gguf_writer import GGUFWriter
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@ -33,7 +29,7 @@ LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count"
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SplitTensorsPerFile: TypeAlias = deque[tuple[os.PathLike[str], deque[tuple[str, Any]], GGUFWriter]] # [(outfile name, [(tensor name, tensor data)] for each tensor in file, filewriter)]
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KVTempData: TypeAlias = dict[str, tuple[Any, GGUFValueType]] # {key: (value, type)}
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TensorTempData: TypeAlias = tuple[str, np.ndarray[Any, Any], GGMLQuantizationType] # (tensor name, tensor data, tensor dtype), aka LazyModel
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TensorTempData: TypeAlias = tuple[str, np.ndarray[Any, Any], GGMLQuantizationType] # (tensor name, tensor data, tensor dtype)
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class SplitStyle(IntEnum):
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@ -43,13 +39,6 @@ class SplitStyle(IntEnum):
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class SplitArguments:
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split: bool
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dry_run: bool
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small_first_shard: bool
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split_max_tensors: int
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split_max_size: int
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split_style: SplitStyle
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def __init__(self, args: Namespace = None) -> None:
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self.split = args.split if args else False
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self.split_max_tensors = args.split_max_tensors if args else 0
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@ -107,7 +96,7 @@ class SplitStrategy(deque):
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for i, shard in enumerate(shards):
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outname = fname_out.with_name(SHARD_NAME_FORMAT.format(fname_out.stem, i + shard_offset, total_shards))
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self.append((outname, deque(shard), GGUFWriter(outname, arch, use_temp_file=use_temp_file, endianess=endianess)))
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self.append((outname, shard, GGUFWriter(outname, arch, use_temp_file=use_temp_file, endianess=endianess)))
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@staticmethod
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def get_tensor_size(tensor) -> int:
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@ -146,35 +135,34 @@ class SplitStrategy(deque):
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num /= 1024.0
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return f"{num:.1f}T - over 1TB, --split recommended"
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# ideally this has most of the same signatures as GGUFWriter so it's nearly a drop-in replacement
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class GGUFManager:
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# TODO fall back to normal GGUFWriter in convert-hf-to-gguf.py if no --split
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class GGUFManager(GGUFWriter):
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kv_data: KVTempData
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tensors: deque[TensorTempData]
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tensors: list[TensorTempData]
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split_arguments: SplitArguments
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split_strategy: SplitStrategy
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dtype: GGMLQuantizationType
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def __init__(self, path: os.PathLike[str] | str, arch: str, split_arguments: SplitArguments,
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use_temp_file: bool = True, endianess: GGUFEndian = GGUFEndian.LITTLE
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) -> None:
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# TODO be able to use superclass constructor
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# super().__init__(path, arch, use_temp_file=use_temp_file, endianess=endianess)
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self.arch = arch
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self.path = path
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self.endianess = endianess
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self.offset_tensor = 0
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self.kv_data = {}
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self.tensors = deque()
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self.tensors = []
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# TODO how many of these do you need
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self.split_strategy = None
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self.total_shards = None
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self.total_tensors = None
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self.use_temp_file = use_temp_file
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self.split_arguments = split_arguments
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self.recent_key = None
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self.add_architecture()
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# have to consolidate because we need to know kv data count and tensor count before we can write the header
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# and we need to write tensor info before we can write metadata
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# these all kinda show up around the same places anyway so it's not a huge deal?
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# TODO split back into write_header_to_file, write_kv_data_to_file, write_ti_data_to_file
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def write_to_file(self, meta_only: bool = False) -> None:
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# here is the first place you can assume you have all tensors written and you can establish the size of the file - so logic goes here
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@ -232,11 +220,12 @@ class GGUFManager:
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while True:
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try:
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(_, tensors, writer) = self.split_strategy.popleft()
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tensors = deque(tensors) if tensors else None
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except IndexError:
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break
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shard_num_tensors = len(tensors) if tensors else 0
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if tensors:
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while True:
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try:
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@ -254,44 +243,16 @@ class GGUFManager:
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ct = ct + 1
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del tensors
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def add_uint8(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT8)
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def add_int8(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT8)
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def add_uint16(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT16)
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def add_int16(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT16)
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def add_uint32(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT32)
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def add_int32(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT32)
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def add_float32(self, key: str, val: float) -> None:
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self.kv_data[key] = (val, GGUFValueType.FLOAT32)
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def add_uint64(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.UINT64)
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def add_int64(self, key: str, val: int) -> None:
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self.kv_data[key] = (val, GGUFValueType.INT64)
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def add_float64(self, key: str, val: float) -> None:
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self.kv_data[key] = (val, GGUFValueType.FLOAT64)
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def add_bool(self, key: str, val: bool) -> None:
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self.kv_data[key] = (val, GGUFValueType.BOOL)
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def add_string(self, key: str, val: str) -> None:
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if not val:
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return
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self.kv_data[key] = (val, GGUFValueType.STRING)
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# override add_key, add_val to handle kv data separately
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def add_key(self, key: str) -> None:
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self.recent_key = key
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def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None:
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if self.recent_key is None:
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raise ValueError("No key set for value")
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self.kv_data[self.recent_key] = (val, vtype)
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# need to handle arrays separately
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def add_array(self, key: str, val: Sequence[Any]) -> None:
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if not isinstance(val, Sequence):
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raise ValueError(f'Expected a sequence for {key}, got {type(val)}')
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@ -303,231 +264,8 @@ class GGUFManager:
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) -> None:
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if self.endianess == GGUFEndian.BIG:
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tensor.byteswap(inplace=True)
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# TODO reimplement temp file
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# I'm pretty sure it gets handled per shard?
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self.tensors.append((name, tensor, raw_dtype))
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def close(self) -> None:
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for _, _, writer in self.split_strategy:
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writer.close()
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def add_architecture(self) -> None:
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self.add_string(Keys.General.ARCHITECTURE, self.arch)
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def add_author(self, author: str) -> None:
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self.add_string(Keys.General.AUTHOR, author)
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def add_version(self, version: str) -> None:
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self.add_string(Keys.General.VERSION, version)
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def add_tensor_data_layout(self, layout: str) -> None:
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self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
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def add_url(self, url: str) -> None:
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self.add_string(Keys.General.URL, url)
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def add_description(self, description: str) -> None:
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self.add_string(Keys.General.DESCRIPTION, description)
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def add_licence(self, licence: str) -> None:
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self.add_string(Keys.General.LICENSE, licence)
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def add_source_url(self, url: str) -> None:
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self.add_string(Keys.General.SOURCE_URL, url)
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def add_source_hf_repo(self, repo: str) -> None:
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self.add_string(Keys.General.SOURCE_HF_REPO, repo)
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def add_file_type(self, ftype: int) -> None:
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self.add_uint32(Keys.General.FILE_TYPE, ftype)
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def add_name(self, name: str) -> None:
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self.add_string(Keys.General.NAME, name)
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def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
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self.add_uint32(Keys.General.QUANTIZATION_VERSION, quantization_version)
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def add_custom_alignment(self, alignment: int) -> None:
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self.data_alignment = alignment
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self.add_uint32(Keys.General.ALIGNMENT, alignment)
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def add_vocab_size(self, size: int) -> None:
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self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
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def add_context_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
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def add_embedding_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
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def add_block_count(self, length: int) -> None:
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self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
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def add_feed_forward_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
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def add_parallel_residual(self, use: bool) -> None:
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self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
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def add_head_count(self, count: int) -> None:
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self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
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def add_head_count_kv(self, count: int) -> None:
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self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
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def add_key_length(self, length: int) -> None:
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self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length)
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def add_value_length(self, length: int) -> None:
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self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length)
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def add_max_alibi_bias(self, bias: float) -> None:
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self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
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def add_clamp_kqv(self, value: float) -> None:
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self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
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def add_logit_scale(self, value: float) -> None:
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self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value)
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def add_expert_count(self, count: int) -> None:
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self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
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def add_expert_used_count(self, count: int) -> None:
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self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
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def add_layer_norm_eps(self, value: float) -> None:
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self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
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def add_layer_norm_rms_eps(self, value: float) -> None:
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self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
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def add_causal_attention(self, value: bool) -> None:
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self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
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def add_pooling_type(self, value: PoolingType) -> None:
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self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
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def add_rope_dimension_count(self, count: int) -> None:
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self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
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def add_rope_freq_base(self, value: float) -> None:
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self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
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def add_rope_scaling_type(self, value: RopeScalingType) -> None:
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self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
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def add_rope_scaling_factor(self, value: float) -> None:
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self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
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def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
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self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
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def add_rope_scaling_finetuned(self, value: bool) -> None:
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self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
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def add_ssm_conv_kernel(self, value: int) -> None:
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self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value)
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def add_ssm_inner_size(self, value: int) -> None:
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self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value)
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def add_ssm_state_size(self, value: int) -> None:
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self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value)
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def add_ssm_time_step_rank(self, value: int) -> None:
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self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value)
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def add_tokenizer_model(self, model: str) -> None:
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self.add_string(Keys.Tokenizer.MODEL, model)
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def add_tokenizer_pre(self, pre: str) -> None:
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self.add_string(Keys.Tokenizer.PRE, pre)
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def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
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self.add_array(Keys.Tokenizer.LIST, tokens)
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def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
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self.add_array(Keys.Tokenizer.MERGES, merges)
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def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
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self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
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def add_token_type_count(self, value: int) -> None:
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self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value)
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def add_token_scores(self, scores: Sequence[float]) -> None:
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self.add_array(Keys.Tokenizer.SCORES, scores)
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def add_bos_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.BOS_ID, id)
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def add_eos_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.EOS_ID, id)
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def add_unk_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.UNK_ID, id)
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def add_sep_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.SEP_ID, id)
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def add_pad_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.PAD_ID, id)
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def add_cls_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.CLS_ID, id)
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def add_mask_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.MASK_ID, id)
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def add_add_bos_token(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_BOS, value)
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def add_add_eos_token(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_EOS, value)
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def add_add_space_prefix(self, value: bool) -> None:
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self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
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def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
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if isinstance(value, list):
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template_default = None
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template_names = set()
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for choice in value:
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name = choice.get('name', '')
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template = choice.get('template')
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# Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it
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name = ''.join((c if c in ascii_letters + digits else '_' for c in name))
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if name and template is not None:
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if name == 'default':
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template_default = template
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else:
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template_names.add(name)
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self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template)
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if template_names:
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self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names))
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if template_default is None:
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return
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value = template_default
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self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
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def add_prefix_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.PREFIX_ID, id)
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def add_suffix_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id)
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def add_middle_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id)
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def add_eot_token_id(self, id: int) -> None:
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self.add_uint32(Keys.Tokenizer.EOT_ID, id)
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writer.close()
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