from __future__ import annotations import logging import os import shutil import struct import tempfile from dataclasses import dataclass from enum import Enum, auto from io import BufferedWriter from typing import IO, Any, Sequence, Mapping from string import ascii_letters, digits import numpy as np from .constants import ( GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, GGUF_VERSION, GGMLQuantizationType, GGUFEndian, GGUFValueType, Keys, RopeScalingType, PoolingType, TokenType, ) from .quants import quant_shape_from_byte_shape logger = logging.getLogger(__name__) @dataclass class TensorInfo: shape: Sequence[int] dtype: GGMLQuantizationType nbytes: int tensor: np.ndarray[Any, Any] | None = None @dataclass class GGUFValue: value: Any type: GGUFValueType class WriterState(Enum): NO_FILE = auto() EMPTY = auto() HEADER = auto() KV_DATA = auto() TI_DATA = auto() WEIGHTS = auto() class GGUFWriter: fout: BufferedWriter | None path: os.PathLike[str] | str | None temp_file: tempfile.SpooledTemporaryFile[bytes] | None tensors: dict[str, TensorInfo] kv_data: dict[str, GGUFValue] state: WriterState _simple_value_packing = { GGUFValueType.UINT8: "B", GGUFValueType.INT8: "b", GGUFValueType.UINT16: "H", GGUFValueType.INT16: "h", GGUFValueType.UINT32: "I", GGUFValueType.INT32: "i", GGUFValueType.FLOAT32: "f", GGUFValueType.UINT64: "Q", GGUFValueType.INT64: "q", GGUFValueType.FLOAT64: "d", GGUFValueType.BOOL: "?", } def __init__( self, path: os.PathLike[str] | str | None, arch: str, use_temp_file: bool = False, endianess: GGUFEndian = GGUFEndian.LITTLE, ): self.fout = None self.path = path self.arch = arch self.endianess = endianess self.data_alignment = GGUF_DEFAULT_ALIGNMENT self.use_temp_file = use_temp_file self.temp_file = None self.tensors = dict() self.kv_data = dict() logger.info("gguf: This GGUF file is for {0} Endian only".format( "Big" if self.endianess == GGUFEndian.BIG else "Little", )) self.state = WriterState.NO_FILE self.add_architecture() def open_output_file(self, path: os.PathLike[str] | str | None = None) -> None: if self.state is WriterState.EMPTY and self.fout is not None and (path is None or path == self.path): # allow calling this multiple times as long as the path is the same return if self.state is not WriterState.NO_FILE: raise ValueError(f'Expected output file to be not yet opened, got {self.state}') if path is not None: self.path = path if self.path is not None: if self.fout is not None: self.fout.close() self.fout = open(self.path, "wb") self.state = WriterState.EMPTY def write_header_to_file(self, path: os.PathLike[str] | str | None = None) -> None: self.open_output_file(path) if self.state is not WriterState.EMPTY: raise ValueError(f'Expected output file to be empty, got {self.state}') self._write_packed(" None: if self.state is not WriterState.HEADER: raise ValueError(f'Expected output file to contain the header, got {self.state}') assert self.fout is not None kv_data = bytearray() for key, val in self.kv_data.items(): kv_data += self._pack_val(key, GGUFValueType.STRING, add_vtype=False) kv_data += self._pack_val(val.value, val.type, add_vtype=True) self.fout.write(kv_data) self.flush() self.state = WriterState.KV_DATA def write_ti_data_to_file(self) -> None: if self.state is not WriterState.KV_DATA: raise ValueError(f'Expected output file to contain KV data, got {self.state}') assert self.fout is not None ti_data = bytearray() offset_tensor = 0 for name, ti in self.tensors.items(): ti_data += self._pack_val(name, GGUFValueType.STRING, add_vtype=False) n_dims = len(ti.shape) ti_data += self._pack("I", n_dims) for i in range(n_dims): ti_data += self._pack("Q", ti.shape[n_dims - 1 - i]) ti_data += self._pack("I", ti.dtype) ti_data += self._pack("Q", offset_tensor) offset_tensor += GGUFWriter.ggml_pad(ti.nbytes, self.data_alignment) self.fout.write(ti_data) self.flush() self.state = WriterState.TI_DATA def add_key_value(self, key: str, val: Any, vtype: GGUFValueType) -> None: if key in self.kv_data: raise ValueError(f'Duplicated key name {key!r}') self.kv_data[key] = GGUFValue(value=val, type=vtype) def add_uint8(self, key: str, val: int) -> None: self.add_key_value(key,val, GGUFValueType.UINT8) def add_int8(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.INT8) def add_uint16(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.UINT16) def add_int16(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.INT16) def add_uint32(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.UINT32) def add_int32(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.INT32) def add_float32(self, key: str, val: float) -> None: self.add_key_value(key, val, GGUFValueType.FLOAT32) def add_uint64(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.UINT64) def add_int64(self, key: str, val: int) -> None: self.add_key_value(key, val, GGUFValueType.INT64) def add_float64(self, key: str, val: float) -> None: self.add_key_value(key, val, GGUFValueType.FLOAT64) def add_bool(self, key: str, val: bool) -> None: self.add_key_value(key, val, GGUFValueType.BOOL) def add_string(self, key: str, val: str) -> None: if not val: return self.add_key_value(key, val, GGUFValueType.STRING) def add_array(self, key: str, val: Sequence[Any]) -> None: if not isinstance(val, Sequence): raise ValueError("Value must be a sequence for array type") self.add_key_value(key, val, GGUFValueType.ARRAY) @staticmethod def ggml_pad(x: int, n: int) -> int: return ((x + n - 1) // n) * n def add_tensor_info( self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.state is not WriterState.NO_FILE: raise ValueError(f'Expected output file to be not yet opened, got {self.state}') if name in self.tensors: raise ValueError(f'Duplicated tensor name {name!r}') if raw_dtype is None: if tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 elif tensor_dtype == np.float32: dtype = GGMLQuantizationType.F32 elif tensor_dtype == np.float64: dtype = GGMLQuantizationType.F64 elif tensor_dtype == np.int8: dtype = GGMLQuantizationType.I8 elif tensor_dtype == np.int16: dtype = GGMLQuantizationType.I16 elif tensor_dtype == np.int32: dtype = GGMLQuantizationType.I32 elif tensor_dtype == np.int64: dtype = GGMLQuantizationType.I64 else: raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") else: dtype = raw_dtype if tensor_dtype == np.uint8: tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype) self.tensors[name] = TensorInfo(shape=tensor_shape, dtype=dtype, nbytes=tensor_nbytes) def add_tensor( self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.endianess == GGUFEndian.BIG: tensor.byteswap(inplace=True) if self.use_temp_file and self.temp_file is None: fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256 * 1024 * 1024) fp.seek(0) self.temp_file = fp shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype=raw_dtype) if self.temp_file is None: self.tensors[name].tensor = tensor return tensor.tofile(self.temp_file) self.write_padding(self.temp_file, tensor.nbytes) def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None: pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n if pad != 0: fp.write(bytes([0] * pad)) def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: if self.state is not WriterState.TI_DATA and self.state is not WriterState.WEIGHTS: raise ValueError(f'Expected output file to contain tensor info or weights, got {self.state}') assert self.fout is not None if self.endianess == GGUFEndian.BIG: tensor.byteswap(inplace=True) self.write_padding(self.fout, self.fout.tell()) tensor.tofile(self.fout) self.write_padding(self.fout, tensor.nbytes) self.state = WriterState.WEIGHTS def write_tensors_to_file(self, *, progress: bool = False) -> None: self.write_ti_data_to_file() assert self.fout is not None self.write_padding(self.fout, self.fout.tell()) if self.temp_file is None: bar = None if progress: from tqdm import tqdm total_bytes = sum(t.nbytes for t in self.tensors.values()) bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) # relying on the fact that Python dicts preserve insertion order (since 3.7) for ti in self.tensors.values(): assert ti.tensor is not None # can only iterate once over the tensors assert ti.tensor.nbytes == ti.nbytes ti.tensor.tofile(self.fout) if bar is not None: bar.update(ti.nbytes) self.write_padding(self.fout, ti.nbytes) ti.tensor = None else: self.temp_file.seek(0) shutil.copyfileobj(self.temp_file, self.fout) self.flush() self.temp_file.close() self.state = WriterState.WEIGHTS def flush(self) -> None: assert self.fout is not None self.fout.flush() def close(self) -> None: if self.fout is not None: self.fout.close() self.fout = None def add_architecture(self) -> None: self.add_string(Keys.General.ARCHITECTURE, self.arch) def add_author(self, author: str) -> None: self.add_string(Keys.General.AUTHOR, author) def add_version(self, version: str) -> None: self.add_string(Keys.General.VERSION, version) def add_tensor_data_layout(self, layout: str) -> None: self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) def add_url(self, url: str) -> None: self.add_string(Keys.General.URL, url) def add_description(self, description: str) -> None: self.add_string(Keys.General.DESCRIPTION, description) def add_licence(self, licence: str) -> None: self.add_string(Keys.General.LICENSE, licence) def add_source_url(self, url: str) -> None: self.add_string(Keys.General.SOURCE_URL, url) def add_source_hf_repo(self, repo: str) -> None: self.add_string(Keys.General.SOURCE_HF_REPO, repo) def add_file_type(self, ftype: int) -> None: self.add_uint32(Keys.General.FILE_TYPE, ftype) def add_name(self, name: str) -> None: self.add_string(Keys.General.NAME, name) def add_quantization_version(self, quantization_version: int) -> None: self.add_uint32( Keys.General.QUANTIZATION_VERSION, quantization_version) def add_custom_alignment(self, alignment: int) -> None: self.data_alignment = alignment self.add_uint32(Keys.General.ALIGNMENT, alignment) def add_vocab_size(self, size: int) -> None: self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size) def add_context_length(self, length: int) -> None: self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) def add_embedding_length(self, length: int) -> None: self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) def add_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) def add_leading_dense_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) def add_feed_forward_length(self, length: int) -> None: self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) def add_expert_feed_forward_length(self, length: int) -> None: self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) def add_parallel_residual(self, use: bool) -> None: self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) def add_head_count(self, count: int) -> None: self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) def add_head_count_kv(self, count: int) -> None: self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) def add_key_length(self, length: int) -> None: self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length) def add_value_length(self, length: int) -> None: self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length) def add_max_alibi_bias(self, bias: float) -> None: self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) def add_clamp_kqv(self, value: float) -> None: self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) def add_logit_scale(self, value: float) -> None: self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value) def add_expert_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count) def add_expert_used_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) def add_expert_shared_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count) def add_expert_weights_scale(self, value: float) -> None: self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) def add_layer_norm_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) def add_layer_norm_rms_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) def add_q_lora_rank(self, length: int) -> None: self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length) def add_kv_lora_rank(self, length: int) -> None: self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length) def add_pooling_type(self, value: PoolingType) -> None: self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) def add_rope_dimension_count(self, count: int) -> None: self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) def add_rope_freq_base(self, value: float) -> None: self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) def add_rope_scaling_type(self, value: RopeScalingType) -> None: self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) def add_rope_scaling_factor(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) def add_rope_scaling_attn_factors(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) def add_rope_scaling_orig_ctx_len(self, value: int) -> None: self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) def add_rope_scaling_finetuned(self, value: bool) -> None: self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) def add_rope_scaling_yarn_log_mul(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value) def add_ssm_conv_kernel(self, value: int) -> None: self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) def add_ssm_inner_size(self, value: int) -> None: self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value) def add_ssm_state_size(self, value: int) -> None: self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value) def add_ssm_time_step_rank(self, value: int) -> None: self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value) def add_tokenizer_model(self, model: str) -> None: self.add_string(Keys.Tokenizer.MODEL, model) def add_tokenizer_pre(self, pre: str) -> None: self.add_string(Keys.Tokenizer.PRE, pre) def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: self.add_array(Keys.Tokenizer.LIST, tokens) def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: self.add_array(Keys.Tokenizer.MERGES, merges) def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) def add_token_type_count(self, value: int) -> None: self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value) def add_token_scores(self, scores: Sequence[float]) -> None: self.add_array(Keys.Tokenizer.SCORES, scores) def add_bos_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.BOS_ID, id) def add_eos_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.EOS_ID, id) def add_unk_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.UNK_ID, id) def add_sep_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.SEP_ID, id) def add_pad_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.PAD_ID, id) def add_cls_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.CLS_ID, id) def add_mask_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.MASK_ID, id) def add_add_bos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_BOS, value) def add_add_eos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_EOS, value) def add_add_space_prefix(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None: if not isinstance(value, str): template_default = None template_names = set() for choice in value: name = choice.get('name', '') template = choice.get('template') # Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it name = ''.join((c if c in ascii_letters + digits else '_' for c in name)) if name and template is not None: if name == 'default': template_default = template else: template_names.add(name) self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template) if template_names: self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names)) if template_default is None: return value = template_default self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) def add_prefix_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.PREFIX_ID, id) def add_suffix_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id) def add_middle_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id) def add_eot_token_id(self, id: int) -> None: self.add_uint32(Keys.Tokenizer.EOT_ID, id) def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '' if not skip_pack_prefix: pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' return struct.pack(f'{pack_prefix}{fmt}', value) def _pack_val(self, val: Any, vtype: GGUFValueType, add_vtype: bool) -> bytes: kv_data = bytearray() if add_vtype: kv_data += self._pack("I", vtype) pack_fmt = self._simple_value_packing.get(vtype) if pack_fmt is not None: kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) elif vtype == GGUFValueType.STRING: encoded_val = val.encode("utf-8") if isinstance(val, str) else val kv_data += self._pack("Q", len(encoded_val)) kv_data += encoded_val elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: ltype = GGUFValueType.get_type(val[0]) if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): raise ValueError("All items in a GGUF array should be of the same type") kv_data += self._pack("I", ltype) kv_data += self._pack("Q", len(val)) for item in val: kv_data += self._pack_val(item, ltype, add_vtype=False) else: raise ValueError("Invalid GGUF metadata value type or value") return kv_data def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None: assert self.fout is not None self.fout.write(self._pack(fmt, value, skip_pack_prefix))