root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-27 03:44:35 +00:00
Code Issues Actions 4 Packages Projects Releases Wiki Activity
e426b3cfc8
llama.cpp/gguf.py
Georgi Gerganov e426b3cfc8
gguf.py : fix vertical alignment
2023-08-17 21:50:01 +03:00

666 lines
25 KiB
Python
Raw Blame History

import shutil
import sys
import struct
import tempfile
import numpy as np
from enum import IntEnum, auto
from typing import Any, IO, List
#
# constants
#
GGUF_MAGIC = 0x47475546
GGUF_VERSION = 1
GGUF_DEFAULT_ALIGNMENT = 32
# general
KEY_GENERAL_ARCHITECTURE = "general.architecture"
KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
KEY_GENERAL_ALIGNMENT = "general.alignment"
KEY_GENERAL_NAME = "general.name"
KEY_GENERAL_AUTHOR = "general.author"
KEY_GENERAL_URL = "general.url"
KEY_GENERAL_DESCRIPTION = "general.description"
KEY_GENERAL_LICENSE = "general.license"
KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
# LLM
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 = "{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 = "{arch}.rope.dimension_count"
KEY_ROPE_SCALE = "{arch}.rope.scale"
# tokenization
KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores"
KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges"
KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"
KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"
KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"
KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"
KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"
KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json"
KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
#
# recommended mapping of model tensor names for storage in gguf
#
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 = 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 = 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 = 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
# 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
# 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
tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
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 = 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 = 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 = 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 = 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 = 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 = 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 = 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 = 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 = 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 = 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
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
return tensor_map
#
# implementation
#
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
class GGUFValueType(IntEnum):
UINT8 = 0
INT8 = 1
UINT16 = 2
INT16 = 3
UINT32 = 4
INT32 = 5
FLOAT32 = 6
BOOL = 7
STRING = 8
ARRAY = 9
@staticmethod
def get_type(val):
if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray):
return GGUFValueType.STRING
elif isinstance(val, list):
return GGUFValueType.ARRAY
elif isinstance(val, float):
return GGUFValueType.FLOAT32
elif isinstance(val, bool):
return GGUFValueType.BOOL
elif isinstance(val, int):
return GGUFValueType.INT32
else:
print("Unknown type: "+str(type(val)))
sys.exit()
class GGUFWriter:
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))
self.fout.write(struct.pack("<I", GGUF_VERSION))
self.fout.write(struct.pack("<I", self.ti_data_count))
self.fout.write(struct.pack("<I", self.kv_data_count))
self.flush()
# print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
def write_kv_data_to_file(self):
self.fout.write(self.kv_data)
self.flush()
def write_ti_data_to_file(self):
self.fout.write(self.ti_data)
self.flush()
def add_key(self, key: str):
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
def add_uint8(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT8)
def add_int8(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT8)
def add_uint16(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT16)
def add_int16(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT16)
def add_uint32(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT32)
def add_int32(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT32)
def add_float32(self, key: str, val: float):
self.add_key(key)
self.add_val(val, GGUFValueType.FLOAT32)
def add_bool(self, key: str, val: bool):
self.add_key(key)
self.add_val(val, GGUFValueType.BOOL)
def add_string(self, key: str, val: str):
if len(val) == 0:
return
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
def add_array(self, key: str, val: list):
if not isinstance(val, list):
raise ValueError("Value must be a list for array type")
self.add_key(key)
self.add_val(val, GGUFValueType.ARRAY)
def add_val(self: str, val: Any, vtype: GGUFValueType = None, add_vtype: bool = True):
if vtype is None:
vtype = GGUFValueType.get_type(val)
if add_vtype:
self.kv_data += struct.pack("<I", vtype)
self.kv_data_count += 1
if vtype == GGUFValueType.UINT8:
self.kv_data += struct.pack("<B", val)
elif vtype == GGUFValueType.INT8:
self.kv_data += struct.pack("<b", val)
elif vtype == GGUFValueType.UINT16:
self.kv_data += struct.pack("<H", val)
elif vtype == GGUFValueType.INT16:
self.kv_data += struct.pack("<h", val)
elif vtype == GGUFValueType.UINT32:
self.kv_data += struct.pack("<I", val)
elif vtype == GGUFValueType.INT32:
self.kv_data += struct.pack("<i", val)
elif vtype == GGUFValueType.FLOAT32:
self.kv_data += struct.pack("<f", val)
elif vtype == GGUFValueType.BOOL:
self.kv_data += struct.pack("?", val)
elif vtype == GGUFValueType.STRING:
encoded_val = val.encode("utf8") if isinstance(val, str) else val
self.kv_data += struct.pack("<I", len(encoded_val))
self.kv_data += encoded_val
elif vtype == GGUFValueType.ARRAY:
ltype = set([GGUFValueType.get_type(item) for item in val])
assert len(ltype) == 1, "All items in a GGUF array should be of the same type"
self.kv_data += struct.pack("<I", list(ltype)[0])
self.kv_data += struct.pack("<I", len(val))
for item in val:
self.add_val(item, add_vtype=False)
else:
raise ValueError("Invalid GGUF metadata value type")
@staticmethod
def ggml_pad(x: int, n: int) -> int:
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
n_dims = len(tensor_shape)
self.ti_data += struct.pack("<I", n_dims)
for i in range(n_dims):
self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
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 add_tensor(self, name: str, tensor: np.ndarray):
if not hasattr(self, "temp_file"):
self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
self.temp_file.seek(0)
self.add_tensor_info(name, tensor.shape, tensor.dtype, tensor.nbytes)
tensor.tofile(self.temp_file)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if pad != 0:
self.temp_file.write(bytes([0] * pad))
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))
tensor.tofile(self.fout)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if pad != 0:
self.fout.write(bytes([0] * pad))
def write_tensors_to_file(self):
self.write_ti_data_to_file()
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
self.fout.write(bytes([0] * pad))
self.temp_file.seek(0)
shutil.copyfileobj(self.temp_file, self.fout)
self.flush()
self.temp_file.close()
def flush(self):
self.fout.flush()
def close(self):
self.fout.close()
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.format(arch=self.arch), layout)
def add_url(self, url: str):
self.add_string(KEY_GENERAL_URL, url)
def add_description(self, description: str):
self.add_string(KEY_GENERAL_DESCRIPTION, description)
def add_source_url(self, url: str):
self.add_string(KEY_GENERAL_SOURCE_URL, url)
def add_source_hf_repo(self, repo: str):
self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
def add_name(self, name: str):
self.add_string(KEY_GENERAL_NAME, name)
def add_quantization_version(self, quantization_version: GGMLQuantizationType):
self.add_uint32(
KEY_GENERAL_QUANTIZATION_VERSION, quantization_version)
def add_custom_alignment(self, alignment: int):
self.data_alignment = alignment
self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
def add_context_length(self, length: int):
self.add_uint32(
KEY_LLM_CONTEXT_LENGTH.format(arch=self.arch), length)
def add_embedding_length(self, length: int):
self.add_uint32(
KEY_LLM_EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_block_count(self, length: int):
self.add_uint32(
KEY_LLM_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int):
self.add_uint32(
KEY_LLM_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_parallel_residual(self, use: bool):
self.add_bool(
KEY_LLM_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_head_count(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
def add_head_count_kv(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
def add_max_alibi_bias(self, bias: float):
self.add_float32(
KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
def add_clamp_kqv(self, value: float):
self.add_float32(
KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
def add_layer_norm_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
def add_layer_norm_rms_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
def add_rope_dimension_count(self, count: int):
self.add_uint32(
KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
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)
def add_token_list(self, tokens: List):
self.add_array(KEY_TOKENIZER_LIST, tokens)
def add_token_merges(self, merges: List):
self.add_array(KEY_TOKENIZER_MERGES, merges)
def add_token_types(self, types: List[int]):
self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
def add_token_scores(self, scores: List[float]):
self.add_array(KEY_TOKENIZER_SCORES, scores)
def add_bos_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_BOS_ID, id)
def add_eos_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_EOS_ID, id)
def add_unk_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_UNK_ID, id)
def add_sep_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_SEP_ID, id)
def add_pad_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
# Example usage:
if __name__ == "__main__":
# Example usage with a file
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture()
gguf_writer.add_block_count(12)
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)
tensor1 = np.ones((32,), dtype=np.float32) * 100.0
tensor2 = np.ones((64,), dtype=np.float32) * 101.0
tensor3 = np.ones((96,), dtype=np.float32) * 102.0
gguf_writer.add_tensor("tensor1", tensor1)
gguf_writer.add_tensor("tensor2", tensor2)
gguf_writer.add_tensor("tensor3", tensor3)
gguf_writer.write_header_to_file()
gguf_writer.write_kv_data_to_file()
gguf_writer.write_tensors_to_file()
gguf_writer.close()
Reference in New Issue View Git Blame Copy Permalink