mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
deb7dfca4b
* llama : add ftype meta info to the model ggml-ci * convert.py : add ftype when converting (does not work) * convert.py : fix Enum to IntEnum ggml-ci
723 lines
27 KiB
Python
723 lines
27 KiB
Python
import shutil
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import sys
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import struct
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import tempfile
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import numpy as np
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from enum import IntEnum, auto
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from typing import Any, IO, List, Optional
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#
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# constants
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#
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GGUF_MAGIC = 0x46554747
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GGUF_VERSION = 1
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GGUF_DEFAULT_ALIGNMENT = 32
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# general
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KEY_GENERAL_ARCHITECTURE = "general.architecture"
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KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
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KEY_GENERAL_ALIGNMENT = "general.alignment"
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KEY_GENERAL_NAME = "general.name"
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KEY_GENERAL_AUTHOR = "general.author"
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KEY_GENERAL_URL = "general.url"
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KEY_GENERAL_DESCRIPTION = "general.description"
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KEY_GENERAL_LICENSE = "general.license"
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KEY_GENERAL_SOURCE_URL = "general.source.url"
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KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
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KEY_GENERAL_FILE_TYPE = "general.file_type"
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# LLM
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KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
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KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
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KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
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KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
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KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
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KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
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# attention
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KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
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KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
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KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
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KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
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KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
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# RoPE
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KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
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KEY_ROPE_SCALE_LINEAR = "{arch}.rope.scale_linear"
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# tokenization
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KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
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KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
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KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
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KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores"
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KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges"
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KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"
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KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"
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KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"
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KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"
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KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"
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KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json"
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KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
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#
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# recommended mapping of model tensor names for storage in gguf
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#
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class MODEL_ARCH(IntEnum):
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LLAMA = auto()
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FALCON = auto()
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GPT2 = auto()
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GPTJ = auto()
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GPTNEOX = auto()
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MPT = auto()
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class MODEL_TENSOR(IntEnum):
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TOKEN_EMBD = auto()
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POS_EMBD = auto()
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OUTPUT = auto()
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OUTPUT_NORM = auto()
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ROPE_FREQS = auto()
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ATTN_Q = auto()
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ATTN_K = auto()
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ATTN_V = auto()
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ATTN_QKV = auto()
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ATTN_OUT = auto()
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ATTN_NORM = auto()
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ATTN_NORM_2 = auto()
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ATTN_ROT_EMBD = auto()
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FFN_GATE = auto()
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FFN_DOWN = auto()
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FFN_UP = auto()
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FFN_NORM = auto()
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MODEL_ARCH_NAMES = {
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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}
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MODEL_TENSOR_NAMES = {
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MODEL_ARCH.LLAMA: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
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MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
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MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
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MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.GPTNEOX: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.FALCON: {
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MODEL_TENSOR.TOKEN_EMBD: "token_embd",
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MODEL_TENSOR.OUTPUT_NORM: "output_norm",
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MODEL_TENSOR.OUTPUT: "output",
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MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
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MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
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MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
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MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
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MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
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MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
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},
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MODEL_ARCH.GPT2: {
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# TODO
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},
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# TODO
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}
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# tensors that will not be serialized
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MODEL_TENSOR_SKIP = {
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MODEL_ARCH.LLAMA: [
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MODEL_TENSOR.ROPE_FREQS,
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MODEL_TENSOR.ATTN_ROT_EMBD,
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],
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}
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# TODO: the following helper functions should be removed
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# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
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# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
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# REMOVE
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def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
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for skip in MODEL_TENSOR_SKIP.get(arch, []):
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for i in range(n_blocks):
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if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
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return True
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return False
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def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
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tensor_map = {}
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# Token embeddings
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
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tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
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tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
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tensor_map["transformer.word_embeddings"] = mapped_to # falcon
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tensor_map["model.embed_tokens"] = mapped_to # llama-hf
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tensor_map["tok_embeddings"] = mapped_to # llama-pth
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# Position embeddings
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
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tensor_map["transformer.wpe"] = mapped_to # gpt2
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# Output
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
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tensor_map["embed_out"] = mapped_to # gptneox
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tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
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tensor_map["output"] = mapped_to # llama-pth
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# Output norm
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
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tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
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tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
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tensor_map["transformer.norm_f"] = mapped_to # mpt
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tensor_map["model.norm"] = mapped_to # llama-hf
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tensor_map["norm"] = mapped_to # llama-pth
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# Rope frequencies
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
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tensor_map["rope.freqs"] = mapped_to # llama-pth
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# Attention and feed-forward blocks
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for i in range(0, n_blocks):
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# Attention norm
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# TODO: is there are simpler way to write these 2 lines in Python?
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to else None
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tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
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tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
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tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
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# Attention norm 2
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
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# Attention query-key-value
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
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# Attention query
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
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# Attention key
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
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# Attention value
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
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# Attention output
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
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# Rotary embeddings
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
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# Feed-forward norm
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
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tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
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# Feed-forward up
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
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# Feed-forward gate
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
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# Feed-forward down
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mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
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mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
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tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
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tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
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tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
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tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
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tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
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tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
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return tensor_map
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class TokenType(IntEnum):
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NORMAL = 1
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UNKNOWN = 2
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CONTROL = 3
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USER_DEFINED = 4
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UNUSED = 5
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BYTE = 6
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#
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# implementation
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#
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class GGMLQuantizationType(IntEnum):
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F32 = 0
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F16 = 1
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Q4_0 = 2
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Q4_1 = 3
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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) or isinstance(val, bytes) or isinstance(val, bytearray):
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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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elif isinstance(val, int):
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return GGUFValueType.INT32
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else:
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print("Unknown type: "+str(type(val)))
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sys.exit()
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class GGUFWriter:
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def __init__(self, path: str, arch: str, use_temp_file = True):
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self.fout = open(path, "wb")
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self.arch = arch
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self.offset_tensor = 0
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self.data_alignment = GGUF_DEFAULT_ALIGNMENT
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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_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_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_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_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()
|