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gguf : single pass for writing tensors + refactoring writer

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M. Yusuf Sarıgöz
2023-08-17 15:19:30 +03:00
parent 42f8fe1927
commit f31e9230ad
2 changed files with 225 additions and 192 deletions
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gguf.py
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@@ -1,11 +1,7 @@
"""TODOs
1. Implement writers for known architectures, LLaMA in particular.
2. Add docstrings from the format specs.
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.
"""
import shutil
import sys
import struct
import tempfile
import numpy as np
from enum import IntEnum
@@ -15,152 +11,153 @@ from typing import Any, IO, List
# constants
#
GGUF_MAGIC = 0x47475546
GGUF_VERSION = 1
GGUF_MAGIC = 0x47475546
GGUF_VERSION = 1
GGUF_DEFAULT_ALIGNMENT = 32
# general
KEY_GENERAL_ARCHITECTURE = "general.architecture"
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_FILE_TYPE = "general.file_type"
KEY_GENERAL_LICENSE = "general.license"
KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
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_FILE_TYPE = "general.file_type"
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 = "{llm}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{llm}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{llm}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{llm}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{llm}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{llm}.tensor_data_layout"
KEY_LLM_CONTEXT_LENGTH = "{llm}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{llm}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{llm}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{llm}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{llm}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{llm}.tensor_data_layout"
# attention
KEY_ATTENTION_HEAD_COUNT = "{llm}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{llm}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{llm}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{llm}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{llm}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{llm}.attention.layer_norm_rms_epsilon"
KEY_ATTENTION_HEAD_COUNT = "{llm}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{llm}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{llm}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{llm}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{llm}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{llm}.attention.layer_norm_rms_epsilon"
# RoPE
KEY_ROPE_DIMENSION_COUNT = "{llm}.rope.dimension_count"
KEY_ROPE_SCALE = "{llm}.rope.scale"
KEY_ROPE_DIMENSION_COUNT = "{llm}.rope.dimension_count"
KEY_ROPE_SCALE = "{llm}.rope.scale"
# tokenization
KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
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"
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
#
def get_tensor_name_map(n_blocks : int):
def get_tensor_name_map(n_blocks: int):
tensor_map = {}
# Token embeddings
mapped_to = "token_embd"
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
tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
tensor_map["transformer.word_embeddings"] = mapped_to # falcon
tensor_map["model.embed_tokens"] = mapped_to # llama-hf
tensor_map["tok_embeddings"] = mapped_to # llama-pth
# Position embeddings
mapped_to = "pos_embd"
tensor_map["transformer.wpe"] = mapped_to # gpt2
tensor_map["transformer.wpe"] = mapped_to # gpt2
# Output norm
mapped_to = "output_norm"
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
tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
tensor_map["transformer.norm_f"] = mapped_to # mpt
tensor_map["model.norm"] = mapped_to # llama-hf
tensor_map["norm"] = mapped_to # llama-pth
# Output
mapped_to = "output"
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
# Attention and fee-forward layer blocks
for i in range(0,n_blocks):
for i in range(0, n_blocks):
# Attention norm
mapped_to = "blk."+str(i)+".attn_norm"
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
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 = "blk."+str(i)+".attn_norm_2"
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
# Attention query-key-value
mapped_to = "blk."+str(i)+".attn_qkv"
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
tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
# Attention query
mapped_to = "blk."+str(i)+".attn_q"
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
tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
# Attention key
mapped_to = "blk."+str(i)+".attn_k"
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
tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
# Attention value
mapped_to = "blk."+str(i)+".attn_v"
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
tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
# Attention output
mapped_to = "blk."+str(i)+".attn_output"
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
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
# Feed-forward norm
mapped_to = "blk."+str(i)+".ffn_norm"
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
tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
# Feed-forward up
mapped_to = "blk."+str(i)+".ffn_up"
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
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
# Feed-forward gate
mapped_to = "blk."+str(i)+".ffn_gate"
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
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
# Feed-forward down
mapped_to = "blk."+str(i)+".ffn_down"
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
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
@@ -168,22 +165,23 @@ def get_tensor_name_map(n_blocks : int):
# implementation
#
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
class GGUFValueType(IntEnum):
UINT8 = 0
INT8 = 1
UINT16 = 2
INT16 = 3
UINT32 = 4
INT32 = 5
UINT8 = 0
INT8 = 1
UINT16 = 2
INT16 = 3
UINT32 = 4
INT32 = 5
FLOAT32 = 6
BOOL = 7
STRING = 8
ARRAY = 9
BOOL = 7
STRING = 8
ARRAY = 9
@staticmethod
def get_type(val):
@@ -203,8 +201,9 @@ class GGUFValueType(IntEnum):
class GGUFWriter:
def __init__(self, fout: IO):
self.fout = fout
def __init__(self, path: str, architecture: str):
self.fout = open(path, "wb")
self.arch = architecture
self.offset_tensor = 0
self.data_alignment = GGUF_DEFAULT_ALIGNMENT
self.kv_data = b""
@@ -228,11 +227,6 @@ class GGUFWriter:
self.fout.write(self.ti_data)
self.flush()
@classmethod
def open(cls, path: str) -> "GGUFWriter":
f = open(path, "wb")
return cls(f)
def add_key(self, key: str):
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
@@ -269,7 +263,8 @@ class GGUFWriter:
self.add_val(val, GGUFValueType.BOOL)
def add_string(self, key: str, val: str):
if len(val) == 0: return
if len(val) == 0:
return
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
@@ -323,6 +318,8 @@ class GGUFWriter:
return ((x + n - 1) // n) * n
def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int):
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
encoded_name = name.encode("utf8")
self.ti_data += struct.pack("<I", len(encoded_name))
self.ti_data += encoded_name
@@ -331,13 +328,25 @@ class GGUFWriter:
for i in range(n_dims):
self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
self.ti_data += struct.pack("<I", dtype)
self.ti_data += struct.pack("<Q", self.offset_tensor)
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def 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_to_file(self, tensor: np.ndarray):
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
@@ -349,21 +358,33 @@ class GGUFWriter:
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, architecture: str):
self.add_string(KEY_GENERAL_ARCHITECTURE,
architecture)
def add_architecture(self):
self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
def add_author(self, author: str):
self.add_string(KEY_GENERAL_AUTHOR, author)
def add_tensor_data_layout(self, layout: str):
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT , layout)
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=self.arch), layout)
def add_url(self, url: str):
self.add_string(KEY_GENERAL_URL, url)
@@ -391,60 +412,60 @@ class GGUFWriter:
self.data_alignment = alignment
self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
def add_context_length(self, llm: str, length: int):
def add_context_length(self, length: int):
self.add_uint32(
KEY_LLM_CONTEXT_LENGTH.format(llm=llm), length)
KEY_LLM_CONTEXT_LENGTH.format(llm=self.arch), length)
def add_embedding_length(self, llm: str, length: int):
def add_embedding_length(self, length: int):
self.add_uint32(
KEY_LLM_EMBEDDING_LENGTH.format(llm=llm), length)
KEY_LLM_EMBEDDING_LENGTH.format(llm=self.arch), length)
def add_block_count(self, llm: str, length: int):
def add_block_count(self, length: int):
self.add_uint32(
KEY_LLM_BLOCK_COUNT.format(llm=llm), length)
KEY_LLM_BLOCK_COUNT.format(llm=self.arch), length)
def add_feed_forward_length(self, llm: str, length: int):
def add_feed_forward_length(self, length: int):
self.add_uint32(
KEY_LLM_FEED_FORWARD_LENGTH.format(llm=llm), length)
KEY_LLM_FEED_FORWARD_LENGTH.format(llm=self.arch), length)
def add_parallel_residual(self, llm: str, use: bool):
def add_parallel_residual(self, use: bool):
self.add_bool(
KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=llm), use)
KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=self.arch), use)
def add_tensor_data_layout(self, llm: str, layout: str):
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=llm), layout)
KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=self.arch), layout)
def add_head_count(self, llm: str, count: int):
def add_head_count(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT.format(llm=llm), count)
KEY_ATTENTION_HEAD_COUNT.format(llm=self.arch), count)
def add_head_count_kv(self, llm: str, count: int):
def add_head_count_kv(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT_KV.format(llm=llm), count)
KEY_ATTENTION_HEAD_COUNT_KV.format(llm=self.arch), count)
def add_max_alibi_bias(self, llm: str, bias: float):
def add_max_alibi_bias(self, bias: float):
self.add_float32(
KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=llm), bias)
KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=self.arch), bias)
def add_clamp_kqv(self, llm: str, value: float):
def add_clamp_kqv(self, value: float):
self.add_float32(
KEY_ATTENTION_CLAMP_KQV.format(llm=llm), value)
KEY_ATTENTION_CLAMP_KQV.format(llm=self.arch), value)
def add_layer_norm_eps(self, llm: str, value: float):
def add_layer_norm_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_EPS.format(llm=llm), value)
KEY_ATTENTION_LAYERNORM_EPS.format(llm=self.arch), value)
def add_layer_norm_rms_eps(self, llm: str, value: float):
def add_layer_norm_rms_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=llm), value)
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=self.arch), value)
def add_rope_dimension_count(self, llm: str, count: int):
def add_rope_dimension_count(self, count: int):
self.add_uint32(
KEY_ROPE_DIMENSION_COUNT.format(llm=llm), count)
KEY_ROPE_DIMENSION_COUNT.format(llm=self.arch), count)
def add_rope_scale(self, llm: str, value: float):
self.add_float32(KEY_ROPE_SCALE.format(llm=llm), value)
def add_rope_scale(self, value: float):
self.add_float32(KEY_ROPE_SCALE.format(llm=self.arch), value)
def add_tokenizer_model(self, model: str):
self.add_string(KEY_TOKENIZER_MODEL, model)
@@ -476,24 +497,27 @@ class GGUFWriter:
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.open("example.gguf")
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture("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((32,), dtype=np.float32) * 101.0
gguf_writer.add_tensor_info("tensor0", tensor1)
gguf_writer.add_tensor_info("tensor1", tensor2)
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_ti_data_to_file()
gguf_writer.write_tensor_to_file(tensor1)
gguf_writer.write_tensor_to_file(tensor2)
gguf_writer.write_tensors_to_file()
gguf_writer.close()