vbatts/llama.cpp
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fix python 3.8 compat

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This commit is contained in:
Jared Van Bortel 2023-11-09 21:09:24 -05:00
parent 795dc0f048
commit a21e9e7126
3 changed files with 127 additions and 148 deletions
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185
gguf-py/gguf/constants.py
View file

@ -1,7 +1,7 @@
from __future__ import annotations from __future__ import annotations
import sys import sys
from enum import IntEnum, StrEnum, auto from enum import Enum, IntEnum, auto
from typing import Any from typing import Any
# #
@ -17,68 +17,57 @@ GGUF_DEFAULT_ALIGNMENT = 32
# #
class GeneralKeys(StrEnum):
ARCHITECTURE = "general.architecture"
QUANTIZATION_VERSION = "general.quantization_version"
ALIGNMENT = "general.alignment"
NAME = "general.name"
AUTHOR = "general.author"
URL = "general.url"
DESCRIPTION = "general.description"
LICENSE = "general.license"
SOURCE_URL = "general.source.url"
SOURCE_HF_REPO = "general.source.huggingface.repository"
FILE_TYPE = "general.file_type"
class AttentionKeys(StrEnum):
HEAD_COUNT = "{arch}.attention.head_count"
HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
CLAMP_KQV = "{arch}.attention.clamp_kqv"
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
class RopeKeys(StrEnum):
DIMENSION_COUNT = "{arch}.rope.dimension_count"
FREQ_BASE = "{arch}.rope.freq_base"
SCALING_TYPE = "{arch}.rope.scaling.type"
SCALING_FACTOR = "{arch}.rope.scaling.factor"
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
class TokenizerKeys(StrEnum):
MODEL = "tokenizer.ggml.model"
LIST = "tokenizer.ggml.tokens"
TOKEN_TYPE = "tokenizer.ggml.token_type"
SCORES = "tokenizer.ggml.scores"
MERGES = "tokenizer.ggml.merges"
BOS_ID = "tokenizer.ggml.bos_token_id"
EOS_ID = "tokenizer.ggml.eos_token_id"
UNK_ID = "tokenizer.ggml.unknown_token_id"
SEP_ID = "tokenizer.ggml.seperator_token_id"
PAD_ID = "tokenizer.ggml.padding_token_id"
HF_JSON = "tokenizer.huggingface.json"
RWKV = "tokenizer.rwkv.world"
class LLMKeys(StrEnum):
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count"
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
class Keys: class Keys:
GENERAL = GeneralKeys class General:
LLM = LLMKeys ARCHITECTURE = "general.architecture"
ATTENTION = AttentionKeys QUANTIZATION_VERSION = "general.quantization_version"
ROPE = RopeKeys ALIGNMENT = "general.alignment"
TOKENIZER = TokenizerKeys NAME = "general.name"
AUTHOR = "general.author"
URL = "general.url"
DESCRIPTION = "general.description"
LICENSE = "general.license"
SOURCE_URL = "general.source.url"
SOURCE_HF_REPO = "general.source.huggingface.repository"
FILE_TYPE = "general.file_type"
class LLM:
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count"
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
CLAMP_KQV = "{arch}.attention.clamp_kqv"
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"
FREQ_BASE = "{arch}.rope.freq_base"
SCALING_TYPE = "{arch}.rope.scaling.type"
SCALING_FACTOR = "{arch}.rope.scaling.factor"
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
class Tokenizer:
MODEL = "tokenizer.ggml.model"
LIST = "tokenizer.ggml.tokens"
TOKEN_TYPE = "tokenizer.ggml.token_type"
SCORES = "tokenizer.ggml.scores"
MERGES = "tokenizer.ggml.merges"
BOS_ID = "tokenizer.ggml.bos_token_id"
EOS_ID = "tokenizer.ggml.eos_token_id"
UNK_ID = "tokenizer.ggml.unknown_token_id"
SEP_ID = "tokenizer.ggml.seperator_token_id"
PAD_ID = "tokenizer.ggml.padding_token_id"
HF_JSON = "tokenizer.huggingface.json"
RWKV = "tokenizer.rwkv.world"
# #
@ -343,7 +332,7 @@ class TokenType(IntEnum):
BYTE = 6 BYTE = 6
class RopeScalingType(StrEnum): class RopeScalingType(Enum):
NONE = 'none' NONE = 'none'
LINEAR = 'linear' LINEAR = 'linear'
YARN = 'yarn' YARN = 'yarn'
@ -428,17 +417,17 @@ GGML_QUANT_SIZES = {
# Aliases for backward compatibility. # Aliases for backward compatibility.
# general # general
KEY_GENERAL_ARCHITECTURE = Keys.GENERAL.ARCHITECTURE KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE
KEY_GENERAL_QUANTIZATION_VERSION = Keys.GENERAL.QUANTIZATION_VERSION KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION
KEY_GENERAL_ALIGNMENT = Keys.GENERAL.ALIGNMENT KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT
KEY_GENERAL_NAME = Keys.GENERAL.NAME KEY_GENERAL_NAME = Keys.General.NAME
KEY_GENERAL_AUTHOR = Keys.GENERAL.AUTHOR KEY_GENERAL_AUTHOR = Keys.General.AUTHOR
KEY_GENERAL_URL = Keys.GENERAL.URL KEY_GENERAL_URL = Keys.General.URL
KEY_GENERAL_DESCRIPTION = Keys.GENERAL.DESCRIPTION KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION
KEY_GENERAL_LICENSE = Keys.GENERAL.LICENSE KEY_GENERAL_LICENSE = Keys.General.LICENSE
KEY_GENERAL_SOURCE_URL = Keys.GENERAL.SOURCE_URL KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL
KEY_GENERAL_SOURCE_HF_REPO = Keys.GENERAL.SOURCE_HF_REPO KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO
KEY_GENERAL_FILE_TYPE = Keys.GENERAL.FILE_TYPE KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE
# LLM # LLM
KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH
@ -449,31 +438,31 @@ KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL
KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT
# attention # attention
KEY_ATTENTION_HEAD_COUNT = Keys.ATTENTION.HEAD_COUNT KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT
KEY_ATTENTION_HEAD_COUNT_KV = Keys.ATTENTION.HEAD_COUNT_KV KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV
KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.ATTENTION.MAX_ALIBI_BIAS KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS
KEY_ATTENTION_CLAMP_KQV = Keys.ATTENTION.CLAMP_KQV KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV
KEY_ATTENTION_LAYERNORM_EPS = Keys.ATTENTION.LAYERNORM_EPS KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS
KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.ATTENTION.LAYERNORM_RMS_EPS KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
# RoPE # RoPE
KEY_ROPE_DIMENSION_COUNT = Keys.ROPE.DIMENSION_COUNT KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT
KEY_ROPE_FREQ_BASE = Keys.ROPE.FREQ_BASE KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE
KEY_ROPE_SCALING_TYPE = Keys.ROPE.SCALING_TYPE KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE
KEY_ROPE_SCALING_FACTOR = Keys.ROPE.SCALING_FACTOR KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR
KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.ROPE.SCALING_ORIG_CTX_LEN KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
KEY_ROPE_SCALING_FINETUNED = Keys.ROPE.SCALING_FINETUNED KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED
# tokenization # tokenization
KEY_TOKENIZER_MODEL = Keys.TOKENIZER.MODEL KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL
KEY_TOKENIZER_LIST = Keys.TOKENIZER.LIST KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST
KEY_TOKENIZER_TOKEN_TYPE = Keys.TOKENIZER.TOKEN_TYPE KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE
KEY_TOKENIZER_SCORES = Keys.TOKENIZER.SCORES KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES
KEY_TOKENIZER_MERGES = Keys.TOKENIZER.MERGES KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES
KEY_TOKENIZER_BOS_ID = Keys.TOKENIZER.BOS_ID KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID
KEY_TOKENIZER_EOS_ID = Keys.TOKENIZER.EOS_ID KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID
KEY_TOKENIZER_UNK_ID = Keys.TOKENIZER.UNK_ID KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID
KEY_TOKENIZER_SEP_ID = Keys.TOKENIZER.SEP_ID KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID
KEY_TOKENIZER_PAD_ID = Keys.TOKENIZER.PAD_ID KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID
KEY_TOKENIZER_HF_JSON = Keys.TOKENIZER.HF_JSON KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON
KEY_TOKENIZER_RWKV = Keys.TOKENIZER.RWKV KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV

88
gguf-py/gguf/gguf_writer.py
View file

@ -278,132 +278,120 @@ class GGUFWriter:
self.fout.close() self.fout.close()
def add_architecture(self) -> None: def add_architecture(self) -> None:
self.add_string(Keys.GENERAL.ARCHITECTURE, self.arch) self.add_string(Keys.General.ARCHITECTURE, self.arch)
def add_author(self, author: str) -> None: def add_author(self, author: str) -> None:
self.add_string(Keys.GENERAL.AUTHOR, author) self.add_string(Keys.General.AUTHOR, author)
def add_tensor_data_layout(self, layout: str) -> None: def add_tensor_data_layout(self, layout: str) -> None:
self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.value.format(arch=self.arch), layout) self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_url(self, url: str) -> None: def add_url(self, url: str) -> None:
self.add_string(Keys.GENERAL.URL, url) self.add_string(Keys.General.URL, url)
def add_description(self, description: str) -> None: def add_description(self, description: str) -> None:
self.add_string(Keys.GENERAL.DESCRIPTION, description) self.add_string(Keys.General.DESCRIPTION, description)
def add_source_url(self, url: str) -> None: def add_source_url(self, url: str) -> None:
self.add_string(Keys.GENERAL.SOURCE_URL, url) self.add_string(Keys.General.SOURCE_URL, url)
def add_source_hf_repo(self, repo: str) -> None: def add_source_hf_repo(self, repo: str) -> None:
self.add_string(Keys.GENERAL.SOURCE_HF_REPO, repo) self.add_string(Keys.General.SOURCE_HF_REPO, repo)
def add_file_type(self, ftype: int) -> None: def add_file_type(self, ftype: int) -> None:
self.add_uint32(Keys.GENERAL.FILE_TYPE, ftype) self.add_uint32(Keys.General.FILE_TYPE, ftype)
def add_name(self, name: str) -> None: def add_name(self, name: str) -> None:
self.add_string(Keys.GENERAL.NAME, name) self.add_string(Keys.General.NAME, name)
def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None: def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
self.add_uint32( self.add_uint32(
Keys.GENERAL.QUANTIZATION_VERSION, quantization_version) Keys.General.QUANTIZATION_VERSION, quantization_version)
def add_custom_alignment(self, alignment: int) -> None: def add_custom_alignment(self, alignment: int) -> None:
self.data_alignment = alignment self.data_alignment = alignment
self.add_uint32(Keys.GENERAL.ALIGNMENT, alignment) self.add_uint32(Keys.General.ALIGNMENT, alignment)
def add_context_length(self, length: int) -> None: def add_context_length(self, length: int) -> None:
self.add_uint32( self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
Keys.LLM.CONTEXT_LENGTH.value.format(arch=self.arch), length)
def add_embedding_length(self, length: int) -> None: def add_embedding_length(self, length: int) -> None:
self.add_uint32( self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
Keys.LLM.EMBEDDING_LENGTH.value.format(arch=self.arch), length)
def add_block_count(self, length: int) -> None: def add_block_count(self, length: int) -> None:
self.add_uint32( self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
Keys.LLM.BLOCK_COUNT.value.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int) -> None: def add_feed_forward_length(self, length: int) -> None:
self.add_uint32( self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
Keys.LLM.FEED_FORWARD_LENGTH.value.format(arch=self.arch), length)
def add_parallel_residual(self, use: bool) -> None: def add_parallel_residual(self, use: bool) -> None:
self.add_bool( self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
Keys.LLM.USE_PARALLEL_RESIDUAL.value.format(arch=self.arch), use)
def add_head_count(self, count: int) -> None: def add_head_count(self, count: int) -> None:
self.add_uint32( self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
Keys.ATTENTION.HEAD_COUNT.value.format(arch=self.arch), count)
def add_head_count_kv(self, count: int) -> None: def add_head_count_kv(self, count: int) -> None:
self.add_uint32( self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
Keys.ATTENTION.HEAD_COUNT_KV.value.format(arch=self.arch), count)
def add_max_alibi_bias(self, bias: float) -> None: def add_max_alibi_bias(self, bias: float) -> None:
self.add_float32( self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
Keys.ATTENTION.MAX_ALIBI_BIAS.value.format(arch=self.arch), bias)
def add_clamp_kqv(self, value: float) -> None: def add_clamp_kqv(self, value: float) -> None:
self.add_float32( self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
Keys.ATTENTION.CLAMP_KQV.value.format(arch=self.arch), value)
def add_layer_norm_eps(self, value: float) -> None: def add_layer_norm_eps(self, value: float) -> None:
self.add_float32( self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
Keys.ATTENTION.LAYERNORM_EPS.value.format(arch=self.arch), value)
def add_layer_norm_rms_eps(self, value: float) -> None: def add_layer_norm_rms_eps(self, value: float) -> None:
self.add_float32( self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
Keys.ATTENTION.LAYERNORM_RMS_EPS.value.format(arch=self.arch), value)
def add_rope_dimension_count(self, count: int) -> None: def add_rope_dimension_count(self, count: int) -> None:
self.add_uint32( self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
Keys.ROPE.DIMENSION_COUNT.value.format(arch=self.arch), count)
def add_rope_freq_base(self, value: float) -> None: def add_rope_freq_base(self, value: float) -> None:
self.add_float32(Keys.ROPE.FREQ_BASE.value.format(arch=self.arch), value) self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
def add_rope_scaling_type(self, value: RopeScalingType) -> None: def add_rope_scaling_type(self, value: RopeScalingType) -> None:
self.add_string(Keys.ROPE.SCALING_TYPE.value.format(arch=self.arch), value) self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
def add_rope_scaling_factor(self, value: float) -> None: def add_rope_scaling_factor(self, value: float) -> None:
self.add_float32(Keys.ROPE.SCALING_FACTOR.value.format(arch=self.arch), value) self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
def add_rope_scaling_orig_ctx_len(self, value: int) -> None: def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
self.add_uint32(Keys.ROPE.SCALING_ORIG_CTX_LEN.value.format(arch=self.arch), value) self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
def add_rope_scaling_finetuned(self, value: bool) -> None: def add_rope_scaling_finetuned(self, value: bool) -> None:
self.add_bool(Keys.ROPE.SCALING_FINETUNED.value.format(arch=self.arch), value) self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
def add_tokenizer_model(self, model: str) -> None: def add_tokenizer_model(self, model: str) -> None:
self.add_string(Keys.TOKENIZER.MODEL, model) self.add_string(Keys.Tokenizer.MODEL, model)
def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
self.add_array(Keys.TOKENIZER.LIST, tokens) self.add_array(Keys.Tokenizer.LIST, tokens)
def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
self.add_array(Keys.TOKENIZER.MERGES, merges) self.add_array(Keys.Tokenizer.MERGES, merges)
def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
self.add_array(Keys.TOKENIZER.TOKEN_TYPE, types) self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
def add_token_scores(self, scores: Sequence[float]) -> None: def add_token_scores(self, scores: Sequence[float]) -> None:
self.add_array(Keys.TOKENIZER.SCORES, scores) self.add_array(Keys.Tokenizer.SCORES, scores)
def add_bos_token_id(self, id: int) -> None: def add_bos_token_id(self, id: int) -> None:
self.add_uint32(Keys.TOKENIZER.BOS_ID, id) self.add_uint32(Keys.Tokenizer.BOS_ID, id)
def add_eos_token_id(self, id: int) -> None: def add_eos_token_id(self, id: int) -> None:
self.add_uint32(Keys.TOKENIZER.EOS_ID, id) self.add_uint32(Keys.Tokenizer.EOS_ID, id)
def add_unk_token_id(self, id: int) -> None: def add_unk_token_id(self, id: int) -> None:
self.add_uint32(Keys.TOKENIZER.UNK_ID, id) self.add_uint32(Keys.Tokenizer.UNK_ID, id)
def add_sep_token_id(self, id: int) -> None: def add_sep_token_id(self, id: int) -> None:
self.add_uint32(Keys.TOKENIZER.SEP_ID, id) self.add_uint32(Keys.Tokenizer.SEP_ID, id)
def add_pad_token_id(self, id: int) -> None: def add_pad_token_id(self, id: int) -> None:
self.add_uint32(Keys.TOKENIZER.PAD_ID, id) self.add_uint32(Keys.Tokenizer.PAD_ID, id)
def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
pack_prefix = '' pack_prefix = ''

2
gguf-py/scripts/gguf-dump.py
View file

@ -1,4 +1,6 @@
#!/usr/bin/env python3 #!/usr/bin/env python3
from __future__ import annotations
import argparse import argparse
import os import os
import sys import sys