mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
284 lines
9.8 KiB
Python
284 lines
9.8 KiB
Python
"""TODOs
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1. Implement writers for known architectures, LLaMA in particular.
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2. Add docstrings from the format specs.
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3. After development is done, Convert it to a proper pip-installable Python package, and possibly move it to its own repo under ggml-org.
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"""
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import struct
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import constants
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from enum import IntEnum
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from typing import Any, IO, List
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import numpy as np
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class GGMLQuantizationType(IntEnum):
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F32 = 0
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F16 = 1
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QR_0 = 2
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Q4_1 = 3
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# Q4_2 = 4 # support has been removed
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# Q4_3 = 5 # support has been removed
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Q5_0 = 6
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Q5_1 = 7
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Q8_0 = 8
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Q8_1 = 9
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Q2_K = 10
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Q3_K = 11
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Q4_K = 12
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Q5_K = 13
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Q6_K = 14
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Q8_K = 15
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class GGUFValueType(IntEnum):
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UINT8 = 0
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INT8 = 1
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UINT16 = 2
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INT16 = 3
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UINT32 = 4
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INT32 = 5
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FLOAT32 = 6
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BOOL = 7
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STRING = 8
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ARRAY = 9
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@staticmethod
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def get_type(val):
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if isinstance(val, str):
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return GGUFValueType.STRING
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elif isinstance(val, list):
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return GGUFValueType.ARRAY
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elif isinstance(val, float):
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return GGUFValueType.FLOAT32
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elif isinstance(val, bool):
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return GGUFValueType.BOOL
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else:
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return GGUFValueType.INT32
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class GGUFWriter:
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def __init__(self, fout: IO):
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self.fout = fout
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self.offset_tensor = 0
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self.tensors: List[np.ndarray] = []
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def write_header(self, tensor_count: int, metadata_kv_count: int):
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self.fout.write(struct.pack("<I", constants.GGUF_MAGIC))
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self.fout.write(struct.pack("<I", constants.GGUF_VERSION))
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self.fout.write(struct.pack("<I", tensor_count))
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self.fout.write(struct.pack("<I", metadata_kv_count))
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@classmethod
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def open(cls, path: str) -> "GGUFWriter":
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f = open(path, "wb")
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return cls(f)
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def write_key(self, key: str):
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self.write_val(key, GGUFValueType.STRING)
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def write_uint8(self, key: str, val: int):
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self.write_key(key)
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self.write_val(val, GGUFValueType.UINT8)
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def write_int8(self, key: str, val: int):
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self.write_key(key)
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self.write_val(val, GGUFValueType.INT8)
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def write_uint16(self, key: str, val: int):
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self.write_key(key)
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self.write_val(val, GGUFValueType.UINT16)
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def write_int16(self, key: str, val: int):
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self.write_key(key)
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self.write_val(val, GGUFValueType.INT16)
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def write_uint32(self, key: str, val: int):
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self.write_key(key)
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self.write_val(val, GGUFValueType.UINT32)
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def write_int32(self, key: str, val: int):
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self.write_key(key)
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self.write_val(val, GGUFValueType.INT32)
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def write_float32(self, key: str, val: float):
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self.write_key(key)
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self.write_val(val, GGUFValueType.FLOAT32)
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def write_bool(self, key: str, val: bool):
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self.write_key(key)
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self.write_val(val, GGUFValueType.BOOL)
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def write_string(self, key: str, val: str):
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self.write_key(key)
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self.write_val(val, GGUFValueType.STRING)
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def write_array(self, key: str, val: list):
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if not isinstance(val, list):
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raise ValueError("Value must be a list for array type")
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self.write_key(key)
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self.write_val(val, GGUFValueType.ARRAY)
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def write_val(self: str, val: Any, vtype: GGUFValueType = None):
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if vtype is None:
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vtype = GGUFValueType.get_type(val)
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self.fout.write(struct.pack("<I", vtype))
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if vtype == GGUFValueType.UINT8:
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self.fout.write(struct.pack("<B", val))
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elif vtype == GGUFValueType.INT8:
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self.fout.write(struct.pack("<b", val))
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elif vtype == GGUFValueType.UINT16:
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self.fout.write(struct.pack("<H", val))
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elif vtype == GGUFValueType.INT16:
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self.fout.write(struct.pack("<h", val))
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elif vtype == GGUFValueType.UINT32:
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self.fout.write(struct.pack("<I", val))
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elif vtype == GGUFValueType.INT32:
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self.fout.write(struct.pack("<i", val))
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elif vtype == GGUFValueType.FLOAT32:
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self.fout.write(struct.pack("<f", val))
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elif vtype == GGUFValueType.BOOL:
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self.fout.write(struct.pack("?", val))
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elif vtype == GGUFValueType.STRING:
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encoded_val = val.encode("utf8")
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self.fout.write(struct.pack("<I", len(encoded_val)))
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self.fout.write(encoded_val)
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elif vtype == GGUFValueType.ARRAY:
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self.fout.write(struct.pack("<I", len(val)))
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for item in val:
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self.write_val(item)
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else:
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raise ValueError("Invalid GGUF metadata value type")
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@staticmethod
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def ggml_pad(x: int, n: int) -> int:
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return ((x + n - 1) // n) * n
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def write_tensor_info(self, name: str, tensor: np.ndarray):
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self.write_val(name, GGUFValueType.STRING)
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n_dims = len(tensor.shape)
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self.write_val(n_dims, GGUFValueType.INT32)
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for i in range(n_dims):
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self.write_val(tensor.shape[n_dims - 1 - i], GGUFValueType.INT32)
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assert tensor.dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
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dtype = GGMLQuantizationType.F32 if tensor.dtype == np.float32 else GGMLQuantizationType.F16
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self.write_val(dtype, GGUFValueType.INT32)
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self.fout.write(struct.pack("<Q", self.offset_tensor))
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self.offset_tensor += GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT)
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offset_data = GGUFWriter.ggml_pad(self.fout.tell(), constants.GGUF_DEFAULT_ALIGNMENT)
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pad = offset_data - self.fout.tell()
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self.fout.write(bytes([0] * pad))
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self.tensors.append(tensor)
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def write_tensors(self):
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for tensor in self.tensors:
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tensor.tofile(self.fout)
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pad = GGUFWriter.ggml_pad(tensor.nbytes, constants.GGUF_DEFAULT_ALIGNMENT) - tensor.nbytes
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self.fout.write(bytes([0] * pad))
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def flush(self):
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self.fout.flush()
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def close(self):
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self.fout.close()
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def write_architecture(self, architecture: str):
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self.write_string(constants.KEY_GENERAL_ARCHITECTURE,
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architecture)
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def write_author(self, author: str):
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self.write_string(constants.KEY_GENERAL_AUTHOR, author)
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def write_url(self, url: str):
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self.write_string(constants.KEY_GENERAL_URL, url)
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def write_description(self, description: str):
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self.write_string(constants.KEY_GENERAL_DESCRIPTION, description)
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def write_file_type(self, file_type: str):
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self.write_string(constants.KEY_GENERAL_FILE_TYPE, file_type)
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def write_source_url(self, url: str):
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self.write_string(constants.KEY_GENERAL_SOURCE_URL, url)
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def write_source_hf_repo(self, repo: str):
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self.write_string(constants.KEY_GENERAL_SOURCE_HF_REPO, repo)
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def write_name(self, name: str):
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self.write_string(constants.KEY_GENERAL_NAME, name)
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def write_quantization_version(self, quantization_version: GGMLQuantizationType):
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self.write_uint32(
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constants.KEY_GENERAL_QUANTIZATION_VERSION, quantization_version)
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def write_context_length(self, llm: str, length: int):
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self.write_uint32(
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constants.KEY_LLM_CONTEXT_LENGTH.format(llm=llm), length)
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def write_embedding_length(self, llm: str, length: int):
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self.write_uint32(
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constants.KEY_LLM_EMBEDDING_LENGTH.format(llm=llm), length)
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def write_layer_count(self, llm: str, length: int):
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self.write_uint32(
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constants.KEY_LLM_LAYER_COUNT.format(llm=llm), length)
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def write_feed_forward_length(self, llm: str, length: int):
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self.write_uint32(
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constants.KEY_LLM_FEED_FORWARD_LENGTH.format(llm=llm), length)
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def write_parallel_residual(self, llm: str, use: bool):
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self.write_bool(
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constants.KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=llm), use)
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def write_tensor_data_layout(self, llm: str, layout: str):
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self.write_string(
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constants.KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=llm), layout)
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def write_head_count(self, llm: str, count: int):
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self.write_uint32(
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constants.KEY_ATTENTION_HEAD_COUNT.format(llm=llm), count)
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def write_head_count_kv(self, llm: str, count: int):
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self.write_uint32(
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constants.KEY_ATTENTION_HEAD_COUNT_KV.format(llm=llm), count)
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def write_max_alibi_bias(self, llm: str, bias: float):
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self.write_float32(
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constants.KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=llm), bias)
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def write_clamp_kqv(self, llm: str, value: float):
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self.write_float32(
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constants.KEY_ATTENTION_CLAMP_KQV.format(llm=llm), value)
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def write_rope_dimension_count(self, llm: str, count: int):
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self.write_uint32(
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constants.KEY_ROPE_DIMENSION_COUNT.format(llm=llm), count)
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def write_rope_scale(self, llm: str, value: float):
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self.write_float32(constants.KEY_ROPE_SCALE.format(llm=llm), value)
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# Example usage:
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if __name__ == "__main__":
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# Example usage with a file
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gguf_writer = GGUFWriter.open("example.gguf")
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gguf_writer.write_header(2, 3)
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gguf_writer.write_architecture("llama")
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gguf_writer.write_uint32("answer", 42) # Write a 32-bit integer
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gguf_writer.write_float32("answer_in_float", 42.0) # Write a 32-bit float
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tensor1 = np.random.random(size=(7, 10)).astype(np.float32)
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tensor2 = np.random.random(size=(16, 12)).astype(np.float16)
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gguf_writer.write_tensor_info("tensor1", tensor1)
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gguf_writer.write_tensor_info("tensor2", tensor2)
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gguf_writer.write_tensors()
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gguf_writer.close()
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