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llama.cpp/gguf.py
Georgi Gerganov cf658adc83
llm : add Falcon support (#2717) 
* llama : refactor GGUF constants into static maps

* llama : check if model architecture is known

* llama : refactor llama_model_load_internal()

* gguf : add KV constant maps

* llm : read arch-specific KVs

* convert : add dummy scores + types

* falcon : load tensor data (CPU only)

* llama : fix loading progress bar

* llama : add arch member to llama_model

* falcon : CPU inference working

* falcon : support non-40B models

* falcon : minor

* llama : minor updates

ggml-ci

* convert-falcon-hf-to-gguf.py : fix special token mapping

* llama.cpp : llama default UNK token = id 0

* llama.cpp : fix bpe tokenizer

* llama.cpp : fix the fix of bpe tokenizer

* ggml : pass eps to ggml_norm

* metal : implement RoPE (mode = 2) + avoid ggml_repeat

* ggml : ggml_repeat always creates new tensor

* falcon : copy-paste self-attention from LLaMA

* metal : print extra compute pipeline info

* falcon : minor changes (still chasing the Metal problem)

* llama.cpp : fix linefeed token

* metal : fix GELU kernel numerical stability by using precise::tanh

* metal : temporary workaround for the concurrency optimization bug

* falcon : add CUDA offloading (#2739)

* llama : better model naming and size reporting

* llama : prep new tokenizer support

* llama : advanced BPE tokenizer based on ggllm.cpp imlpementation

* llama : remove oboslete comment

ggml-ci

* common : remove obsolete BPE API + disable test-tokenizer-1

* llama : revert BPE special-case in llama_byte_to_token()

* cuda : add TODOs for RoPE NeoX implementation

* llama : default special tokens based on vocab type

* perplexity : add log for start of tokenization

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: slaren <slarengh@gmail.com>
2023-08-23 23:08:04 +03:00

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#!/usr/bin/env python3
import shutil
import sys
import struct
import tempfile
import numpy as np
from enum import IntEnum, auto
from typing import Any, IO, List, Optional
#
# constants
#
GGUF_MAGIC = 0x46554747
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"
KEY_GENERAL_FILE_TYPE = "general.file_type"
# LLM
KEY_CONTEXT_LENGTH = "{arch}.context_length"
KEY_EMBEDDING_LENGTH = "{arch}.embedding_length"
KEY_BLOCK_COUNT = "{arch}.block_count"
KEY_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
KEY_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_LINEAR = "{arch}.rope.scale_linear"
# 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.GPTNEOX: {
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_QKV: "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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,
],
}
# TODO: the following helper functions should be removed
# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
# REMOVE
def should_skip_tensor_TMP(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_mlp"] = 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_attn"] = 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
class TokenType(IntEnum):
NORMAL = 1
UNKNOWN = 2
CONTROL = 3
USER_DEFINED = 4
UNUSED = 5
BYTE = 6
#
# implementation
#
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
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, use_temp_file = True):
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()
self.use_temp_file = use_temp_file
self.tensors = []
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, raw_dtype: Optional[GGMLQuantizationType] = None):
assert raw_dtype is not None or 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])
if raw_dtype is None:
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
else:
dtype = raw_dtype
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, raw_shape: Optional[np.ndarray] = None, raw_dtype: Optional[GGMLQuantizationType] = None):
if self.use_temp_file and 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, raw_shape if raw_shape is not None else tensor.shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if not self.use_temp_file:
self.tensors.append((tensor, pad))
return
tensor.tofile(self.temp_file)
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))
if not self.use_temp_file:
for (currtensor, currpad) in self.tensors:
currtensor.tofile(self.fout)
if currpad != 0:
self.fout.write(bytes([0] * currpad))
return
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_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_file_type(self, ftype: int):
self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype)
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_CONTEXT_LENGTH.format(arch=self.arch), length)
def add_embedding_length(self, length: int):
self.add_uint32(
KEY_EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_block_count(self, length: int):
self.add_uint32(
KEY_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int):
self.add_uint32(
KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_parallel_residual(self, use: bool):
self.add_bool(
KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_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_linear(self, value: float):
self.add_float32(KEY_ROPE_SCALE_LINEAR.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()
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