mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
py : type-check all Python scripts with Pyright (#8341)
* py : type-check all Python scripts with Pyright * server-tests : use trailing slash in openai base_url * server-tests : add more type annotations * server-tests : strip "chat" from base_url in oai_chat_completions * server-tests : model metadata is a dict * ci : disable pip cache in type-check workflow The cache is not shared between branches, and it's 250MB in size, so it would become quite a big part of the 10GB cache limit of the repo. * py : fix new type errors from master branch * tests : fix test-tokenizer-random.py Apparently, gcc applies optimisations even when pre-processing, which confuses pycparser. * ci : only show warnings and errors in python type-check The "information" level otherwise has entries from 'examples/pydantic_models_to_grammar.py', which could be confusing for someone trying to figure out what failed, considering that these messages can safely be ignored even though they look like errors.
This commit is contained in:
compilade
GitHub
parent
a8db2a9ce6
commit
3fd62a6b1c
@ -89,6 +89,22 @@ let
|
||||
ps.tiktoken
|
||||
ps.torchWithoutCuda
|
||||
ps.transformers
|
||||
|
||||
# server bench
|
||||
ps.matplotlib
|
||||
|
||||
# server tests
|
||||
ps.openai
|
||||
ps.behave
|
||||
ps.prometheus-client
|
||||
|
||||
# for examples/pydantic-models-to-grammar-examples.py
|
||||
ps.docstring-parser
|
||||
ps.pydantic
|
||||
|
||||
# for scripts/compare-llama-bench.py
|
||||
ps.gitpython
|
||||
ps.tabulate
|
||||
]
|
||||
);
|
||||
|
||||
|
||||
38
.github/workflows/python-type-check.yml
vendored
Normal file
38
.github/workflows/python-type-check.yml
vendored
Normal file
@ -0,0 +1,38 @@
|
||||
name: Python Type-Check
|
||||
|
||||
on:
|
||||
push:
|
||||
paths:
|
||||
- '.github/workflows/python-type-check.yml'
|
||||
- '**.py'
|
||||
- '**/requirements*.txt'
|
||||
pull_request:
|
||||
paths:
|
||||
- '.github/workflows/python-type-check.yml'
|
||||
- '**.py'
|
||||
- '**/requirements*.txt'
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
|
||||
cancel-in-progress: true
|
||||
|
||||
jobs:
|
||||
python-type-check:
|
||||
runs-on: ubuntu-latest
|
||||
name: pyright type-check
|
||||
steps:
|
||||
- name: Check out source repository
|
||||
uses: actions/checkout@v4
|
||||
- name: Set up Python environment
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.11"
|
||||
- name: Install Python dependencies
|
||||
# TODO: use a venv
|
||||
run: pip install -r requirements/requirements-all.txt
|
||||
- name: Type-check with Pyright
|
||||
uses: jakebailey/pyright-action@v2
|
||||
with:
|
||||
version: 1.1.370
|
||||
level: warning
|
||||
warnings: true
|
||||
@ -265,7 +265,7 @@ class Model:
|
||||
break
|
||||
|
||||
for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)):
|
||||
data: np.ndarray = data # type hint
|
||||
data: np.ndarray # type hint
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
data_qtype: gguf.GGMLQuantizationType | None = None
|
||||
@ -599,10 +599,6 @@ class Model:
|
||||
|
||||
tokenizer_path = self.dir_model / 'tokenizer.model'
|
||||
|
||||
tokens: list[bytes] = []
|
||||
scores: list[float] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
if not tokenizer_path.is_file():
|
||||
raise FileNotFoundError(f"File not found: {tokenizer_path}")
|
||||
|
||||
@ -2120,7 +2116,7 @@ class InternLM2Model(Model):
|
||||
logger.error(f'Error: Missing {tokenizer_path}')
|
||||
sys.exit(1)
|
||||
|
||||
sentencepiece_model = model.ModelProto()
|
||||
sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
|
||||
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
|
||||
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
|
||||
|
||||
@ -2972,16 +2968,16 @@ class T5Model(Model):
|
||||
if not tokenizer_path.is_file():
|
||||
raise FileNotFoundError(f"File not found: {tokenizer_path}")
|
||||
|
||||
sentencepiece_model = model.ModelProto()
|
||||
sentencepiece_model = model.ModelProto() # pyright: ignore[reportAttributeAccessIssue]
|
||||
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
|
||||
|
||||
# some models like Pile-T5 family use BPE tokenizer instead of Unigram
|
||||
if sentencepiece_model.trainer_spec.model_type == 2: # BPE
|
||||
if sentencepiece_model.trainer_spec.model_type == 2: # BPE
|
||||
# assure the tokenizer model file name is correct
|
||||
assert tokenizer_path.name == 'tokenizer.model'
|
||||
return self._set_vocab_sentencepiece()
|
||||
else:
|
||||
assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
|
||||
assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
|
||||
|
||||
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
|
||||
remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
|
||||
@ -3152,7 +3148,7 @@ class JaisModel(Model):
|
||||
# but Jais's PyTorch model simply precalculates the slope values and places them
|
||||
# in relative_pes.slopes
|
||||
n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
|
||||
first_val = float(data_torch._data[0])
|
||||
first_val = float(data_torch[0].item())
|
||||
self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
|
||||
|
||||
return tensors
|
||||
@ -3186,7 +3182,7 @@ class ChatGLMModel(Model):
|
||||
def set_vocab_chatglm3(self):
|
||||
dir_model = self.dir_model
|
||||
hparams = self.hparams
|
||||
tokens: list[bytearray] = []
|
||||
tokens: list[bytes] = []
|
||||
toktypes: list[int] = []
|
||||
scores: list[float] = []
|
||||
|
||||
@ -3335,7 +3331,7 @@ class ChatGLMModel(Model):
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
self.gguf_writer.add_name(self.hparams.get("_name_or_path").split("/")[1]) # THUDM/glm4-9b-chat or THUDM/chatglm3-6b
|
||||
self.gguf_writer.add_name(self.hparams["_name_or_path"].split("/")[1]) # THUDM/glm4-9b-chat or THUDM/chatglm3-6b
|
||||
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
|
||||
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
|
||||
n_head_kv = self.hparams.get("multi_query_group_num", n_head)
|
||||
|
||||
@ -354,7 +354,8 @@ class GGMLToGGUF:
|
||||
|
||||
|
||||
def handle_metadata(cfg, hp):
|
||||
import convert
|
||||
import examples.convert_legacy_llama as convert
|
||||
|
||||
assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
|
||||
hf_config_path = cfg.model_metadata_dir / "config.json"
|
||||
orig_config_path = cfg.model_metadata_dir / "params.json"
|
||||
|
||||
@ -353,7 +353,7 @@ class Metadata:
|
||||
version: Optional[str] = None
|
||||
url: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
licence: Optional[str] = None
|
||||
license: Optional[str] = None
|
||||
source_url: Optional[str] = None
|
||||
source_hf_repo: Optional[str] = None
|
||||
|
||||
@ -492,12 +492,13 @@ class LazyTensor:
|
||||
|
||||
LazyModel: TypeAlias = 'dict[str, LazyTensor]'
|
||||
|
||||
ModelFormat: TypeAlias = Literal['ggml', 'torch', 'safetensors', 'none']
|
||||
|
||||
@dataclass
|
||||
class ModelPlus:
|
||||
model: LazyModel
|
||||
paths: list[Path] # Where this was read from.
|
||||
format: Literal['ggml', 'torch', 'safetensors', 'none']
|
||||
format: ModelFormat
|
||||
vocab: BaseVocab | None # For GGML models (which have vocab built in), the vocab.
|
||||
|
||||
|
||||
@ -536,7 +537,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
|
||||
|
||||
|
||||
def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
|
||||
formats = set(mp.format for mp in models_plus)
|
||||
formats: set[ModelFormat] = set(mp.format for mp in models_plus)
|
||||
assert len(formats) == 1, "different formats?"
|
||||
format = formats.pop()
|
||||
paths = [path for mp in models_plus for path in mp.paths]
|
||||
@ -555,7 +556,7 @@ def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus:
|
||||
else:
|
||||
model = merge_sharded([mp.model for mp in models_plus])
|
||||
|
||||
return ModelPlus(model, paths, format, vocab) # pytype: disable=wrong-arg-types
|
||||
return ModelPlus(model, paths, format, vocab)
|
||||
|
||||
|
||||
def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:
|
||||
@ -805,7 +806,7 @@ class OutputFile:
|
||||
def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
|
||||
self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
|
||||
|
||||
def add_meta_model(self, params: Params, metadata: Metadata) -> None:
|
||||
def add_meta_model(self, params: Params, metadata: Metadata | None) -> None:
|
||||
# Metadata About The Model And Its Provenence
|
||||
name = "LLaMA"
|
||||
if metadata is not None and metadata.name is not None:
|
||||
@ -827,8 +828,8 @@ class OutputFile:
|
||||
self.gguf.add_url(metadata.url)
|
||||
if metadata.description is not None:
|
||||
self.gguf.add_description(metadata.description)
|
||||
if metadata.licence is not None:
|
||||
self.gguf.add_licence(metadata.licence)
|
||||
if metadata.license is not None:
|
||||
self.gguf.add_licence(metadata.license)
|
||||
if metadata.source_url is not None:
|
||||
self.gguf.add_source_url(metadata.source_url)
|
||||
if metadata.source_hf_repo is not None:
|
||||
@ -943,7 +944,7 @@ class OutputFile:
|
||||
@staticmethod
|
||||
def write_vocab_only(
|
||||
fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
|
||||
endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata = None,
|
||||
endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata | None = None,
|
||||
) -> None:
|
||||
check_vocab_size(params, vocab, pad_vocab=pad_vocab)
|
||||
|
||||
@ -977,7 +978,7 @@ class OutputFile:
|
||||
fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
|
||||
concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
|
||||
pad_vocab: bool = False,
|
||||
metadata: Metadata = None,
|
||||
metadata: Metadata | None = None,
|
||||
) -> None:
|
||||
check_vocab_size(params, vocab, pad_vocab=pad_vocab)
|
||||
|
||||
@ -1396,6 +1397,8 @@ def main(args_in: list[str] | None = None) -> None:
|
||||
if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
|
||||
vocab = model_plus.vocab
|
||||
|
||||
assert params is not None
|
||||
|
||||
logger.info(f"Vocab info: {vocab}")
|
||||
logger.info(f"Special vocab info: {special_vocab}")
|
||||
model = model_plus.model
|
||||
|
||||
@ -74,7 +74,7 @@ class Tensor:
|
||||
if len(self.ne) == 0:
|
||||
self.nbytes = 0
|
||||
else:
|
||||
self.nbytes = int(np.product(self.ne)) * 4
|
||||
self.nbytes = int(np.prod(self.ne)) * 4
|
||||
else:
|
||||
raise ValueError(f"Unhandled data type '{self.dtype}'")
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
#! pip install pydantic
|
||||
#! python json_schema_pydantic_example.py
|
||||
|
||||
from pydantic import BaseModel, Extra, TypeAdapter
|
||||
from pydantic import BaseModel, Field, TypeAdapter
|
||||
from annotated_types import MinLen
|
||||
from typing import Annotated, List, Optional
|
||||
import json, requests
|
||||
@ -17,6 +17,9 @@ if True:
|
||||
|
||||
The response_model param takes a type (+ supports Pydantic) and behaves just as w/ Instructor (see below)
|
||||
'''
|
||||
response_format = None
|
||||
type_adapter = None
|
||||
|
||||
if response_model:
|
||||
type_adapter = TypeAdapter(response_model)
|
||||
schema = type_adapter.json_schema()
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/env python3
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import itertools
|
||||
import json
|
||||
@ -188,7 +190,7 @@ def _generate_min_max_int(min_value: Optional[int], max_value: Optional[int], ou
|
||||
raise RuntimeError("At least one of min_value or max_value must be set")
|
||||
|
||||
class BuiltinRule:
|
||||
def __init__(self, content: str, deps: list = None):
|
||||
def __init__(self, content: str, deps: list | None = None):
|
||||
self.content = content
|
||||
self.deps = deps or []
|
||||
|
||||
@ -248,7 +250,7 @@ class SchemaConverter:
|
||||
|
||||
def _format_literal(self, literal):
|
||||
escaped = GRAMMAR_LITERAL_ESCAPE_RE.sub(
|
||||
lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)), literal
|
||||
lambda m: GRAMMAR_LITERAL_ESCAPES.get(m.group(0)) or m.group(0), literal
|
||||
)
|
||||
return f'"{escaped}"'
|
||||
|
||||
@ -403,11 +405,11 @@ class SchemaConverter:
|
||||
i = 0
|
||||
length = len(pattern)
|
||||
|
||||
def to_rule(s: Tuple[str, bool]) -> str:
|
||||
def to_rule(s: tuple[str, bool]) -> str:
|
||||
(txt, is_literal) = s
|
||||
return "\"" + txt + "\"" if is_literal else txt
|
||||
|
||||
def transform() -> Tuple[str, bool]:
|
||||
def transform() -> tuple[str, bool]:
|
||||
'''
|
||||
Parse a unit at index i (advancing it), and return its string representation + whether it's a literal.
|
||||
'''
|
||||
@ -420,7 +422,7 @@ class SchemaConverter:
|
||||
# We only need a flat structure here to apply repetition operators to the last item, and
|
||||
# to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
|
||||
# (GBNF's syntax is luckily very close to regular expressions!)
|
||||
seq: list[Tuple[str, bool]] = []
|
||||
seq: list[tuple[str, bool]] = []
|
||||
|
||||
def get_dot():
|
||||
if self._dotall:
|
||||
|
||||
@ -185,6 +185,8 @@ else:
|
||||
fout.add_description("two-tower CLIP model")
|
||||
|
||||
if has_text_encoder:
|
||||
assert t_hparams is not None
|
||||
assert tokens is not None
|
||||
# text_model hparams
|
||||
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
|
||||
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
|
||||
@ -259,8 +261,8 @@ if has_vision_encoder:
|
||||
|
||||
|
||||
if processor is not None:
|
||||
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
|
||||
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
|
||||
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean # pyright: ignore[reportAttributeAccessIssue]
|
||||
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std # pyright: ignore[reportAttributeAccessIssue]
|
||||
else:
|
||||
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
|
||||
image_std = args.image_std if args.image_std is not None else default_image_std
|
||||
@ -272,7 +274,7 @@ fout.add_bool("clip.use_gelu", use_gelu)
|
||||
|
||||
|
||||
if has_llava_projector:
|
||||
model.vision_model.encoder.layers.pop(-1)
|
||||
model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
|
||||
projector = torch.load(args.llava_projector)
|
||||
for name, data in projector.items():
|
||||
name = get_tensor_name(name)
|
||||
@ -286,7 +288,7 @@ if has_llava_projector:
|
||||
|
||||
print("Projector tensors added\n")
|
||||
|
||||
state_dict = model.state_dict()
|
||||
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
|
||||
for name, data in state_dict.items():
|
||||
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
|
||||
# we don't need this
|
||||
|
||||
@ -2,7 +2,9 @@ import argparse
|
||||
import glob
|
||||
import os
|
||||
import torch
|
||||
from safetensors.torch import load as safe_load, save as safe_save, safe_open, save_file
|
||||
from safetensors import safe_open
|
||||
from safetensors.torch import save_file
|
||||
from typing import Any, ContextManager, cast
|
||||
|
||||
# Function to determine if file is a SafeTensor file
|
||||
def is_safetensor_file(file_path):
|
||||
@ -13,7 +15,7 @@ def is_safetensor_file(file_path):
|
||||
def load_model(file_path):
|
||||
if is_safetensor_file(file_path):
|
||||
tensors = {}
|
||||
with safe_open(file_path, framework="pt", device="cpu") as f:
|
||||
with cast(ContextManager[Any], safe_open(file_path, framework="pt", device="cpu")) as f:
|
||||
for key in f.keys():
|
||||
tensors[key] = f.get_tensor(key).clone()
|
||||
# output shape
|
||||
@ -134,7 +136,7 @@ if len(mm_tensors) == 0:
|
||||
if last_checkpoint is not None:
|
||||
for k, v in last_checkpoint.items():
|
||||
print(k)
|
||||
print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint)} tensors.")
|
||||
print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint) if last_checkpoint is not None else 0} tensors.")
|
||||
print("No tensors found. Is this a LLaVA model?")
|
||||
exit()
|
||||
|
||||
@ -143,8 +145,10 @@ print(f"Found additional {len(first_mm_tensors)} tensors to extract.")
|
||||
# projector = {name: checkpoint.[name].float() for name in mm_tensors}
|
||||
projector = {}
|
||||
for name in mm_tensors:
|
||||
assert last_checkpoint is not None
|
||||
projector[name] = last_checkpoint[name].float()
|
||||
for name in first_mm_tensors:
|
||||
assert first_checkpoint is not None
|
||||
projector[name] = first_checkpoint[name].float()
|
||||
|
||||
if len(projector) > 0:
|
||||
|
||||
@ -6,10 +6,10 @@ import re
|
||||
from copy import copy
|
||||
from enum import Enum
|
||||
from inspect import getdoc, isclass
|
||||
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints
|
||||
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin
|
||||
|
||||
from docstring_parser import parse
|
||||
from pydantic import BaseModel, Field, create_model
|
||||
from pydantic import BaseModel, create_model
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from types import GenericAlias
|
||||
@ -17,6 +17,9 @@ else:
|
||||
# python 3.8 compat
|
||||
from typing import _GenericAlias as GenericAlias
|
||||
|
||||
# TODO: fix this
|
||||
# pyright: reportAttributeAccessIssue=information
|
||||
|
||||
|
||||
class PydanticDataType(Enum):
|
||||
"""
|
||||
@ -234,8 +237,9 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None
|
||||
|
||||
# Define the integer part rule
|
||||
integer_part_rule = (
|
||||
"integer-part" + (f"-max{max_digit}" if max_digit is not None else "") + (
|
||||
f"-min{min_digit}" if min_digit is not None else "")
|
||||
"integer-part"
|
||||
+ (f"-max{max_digit}" if max_digit is not None else "")
|
||||
+ (f"-min{min_digit}" if min_digit is not None else "")
|
||||
)
|
||||
|
||||
# Define the fractional part rule based on precision constraints
|
||||
@ -458,7 +462,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
|
||||
if not issubclass(model, BaseModel):
|
||||
# For non-Pydantic classes, generate model_fields from __annotations__ or __init__
|
||||
if hasattr(model, "__annotations__") and model.__annotations__:
|
||||
model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()}
|
||||
model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()} # pyright: ignore[reportGeneralTypeIssues]
|
||||
else:
|
||||
init_signature = inspect.signature(model.__init__)
|
||||
parameters = init_signature.parameters
|
||||
@ -680,7 +684,7 @@ def generate_markdown_documentation(
|
||||
str: Generated text documentation.
|
||||
"""
|
||||
documentation = ""
|
||||
pyd_models = [(model, True) for model in pydantic_models]
|
||||
pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models]
|
||||
for model, add_prefix in pyd_models:
|
||||
if add_prefix:
|
||||
documentation += f"{model_prefix}: {model.__name__}\n"
|
||||
@ -700,7 +704,7 @@ def generate_markdown_documentation(
|
||||
# Indenting the fields section
|
||||
documentation += f" {fields_prefix}:\n"
|
||||
else:
|
||||
documentation += f" Fields:\n"
|
||||
documentation += f" Fields:\n" # noqa: F541
|
||||
if isclass(model) and issubclass(model, BaseModel):
|
||||
for name, field_type in model.__annotations__.items():
|
||||
# if name == "markdown_code_block":
|
||||
@ -778,7 +782,7 @@ def generate_field_markdown(
|
||||
return field_text
|
||||
|
||||
if field_description != "":
|
||||
field_text += f" Description: " + field_description + "\n"
|
||||
field_text += f" Description: {field_description}\n"
|
||||
|
||||
# Check for and include field-specific examples if available
|
||||
if hasattr(model, "Config") and hasattr(model.Config,
|
||||
@ -833,7 +837,7 @@ def generate_text_documentation(
|
||||
str: Generated text documentation.
|
||||
"""
|
||||
documentation = ""
|
||||
pyd_models = [(model, True) for model in pydantic_models]
|
||||
pyd_models: list[tuple[type[BaseModel], bool]] = [(model, True) for model in pydantic_models]
|
||||
for model, add_prefix in pyd_models:
|
||||
if add_prefix:
|
||||
documentation += f"{model_prefix}: {model.__name__}\n"
|
||||
@ -1164,7 +1168,7 @@ def create_dynamic_model_from_function(func: Callable[..., Any]):
|
||||
dynamic_fields[param.name] = (
|
||||
param.annotation if param.annotation != inspect.Parameter.empty else str, default_value)
|
||||
# Creating the dynamic model
|
||||
dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) # type: ignore[call-overload]
|
||||
dynamic_model = create_model(f"{func.__name__}", **dynamic_fields)
|
||||
|
||||
for name, param_doc in param_docs:
|
||||
dynamic_model.model_fields[name].description = param_doc.description
|
||||
@ -1228,9 +1232,6 @@ def map_grammar_names_to_pydantic_model_class(pydantic_model_list):
|
||||
return output
|
||||
|
||||
|
||||
from enum import Enum
|
||||
|
||||
|
||||
def json_schema_to_python_types(schema):
|
||||
type_map = {
|
||||
"any": Any,
|
||||
@ -1275,7 +1276,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name:
|
||||
if items != {}:
|
||||
array = {"properties": items}
|
||||
array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items")
|
||||
fields[field_name] = (List[array_type], ...) # type: ignore[valid-type]
|
||||
fields[field_name] = (List[array_type], ...)
|
||||
else:
|
||||
fields[field_name] = (list, ...)
|
||||
elif field_type == "object":
|
||||
@ -1285,7 +1286,8 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name:
|
||||
required = field_data.get("enum", [])
|
||||
for key, field in fields.items():
|
||||
if key not in required:
|
||||
fields[key] = (Optional[fields[key][0]], ...)
|
||||
optional_type = fields[key][0]
|
||||
fields[key] = (Optional[optional_type], ...)
|
||||
else:
|
||||
field_type = json_schema_to_python_types(field_type)
|
||||
fields[field_name] = (field_type, ...)
|
||||
@ -1305,6 +1307,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name:
|
||||
required = dictionary.get("required", [])
|
||||
for key, field in fields.items():
|
||||
if key not in required:
|
||||
fields[key] = (Optional[fields[key][0]], ...)
|
||||
optional_type = fields[key][0]
|
||||
fields[key] = (Optional[optional_type], ...)
|
||||
custom_model = create_model(model_name, **fields)
|
||||
return custom_model
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
# Function calling example using pydantic models.
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime
|
||||
import importlib
|
||||
import json
|
||||
from enum import Enum
|
||||
from typing import Optional, Union
|
||||
@ -215,9 +216,9 @@ for call in json_data:
|
||||
if call["function"] == "Calculator":
|
||||
print(Calculator(**call["params"]).run())
|
||||
elif call["function"] == "get_current_datetime":
|
||||
print(current_datetime_model(**call["params"]).run())
|
||||
print(current_datetime_model(**call["params"]).run()) # pyright: ignore[reportAttributeAccessIssue]
|
||||
elif call["function"] == "get_current_weather":
|
||||
print(current_weather_tool_model(**call["params"]).run())
|
||||
print(current_weather_tool_model(**call["params"]).run()) # pyright: ignore[reportAttributeAccessIssue]
|
||||
# Should output something like this:
|
||||
# 2024-01-14 13:36:06
|
||||
# {"location": "London", "temperature": "42", "unit": "celsius"}
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
@ -59,10 +61,11 @@ def main(args_in: list[str] | None = None) -> None:
|
||||
sys.exit(1)
|
||||
|
||||
# start the benchmark
|
||||
iterations = 0
|
||||
data = {}
|
||||
try:
|
||||
start_benchmark(args)
|
||||
|
||||
iterations = 0
|
||||
with open("results.github.env", 'w') as github_env:
|
||||
# parse output
|
||||
with open('k6-results.json', 'r') as bench_results:
|
||||
@ -129,7 +132,7 @@ def main(args_in: list[str] | None = None) -> None:
|
||||
timestamps, metric_values = zip(*values)
|
||||
metric_values = [float(value) for value in metric_values]
|
||||
prometheus_metrics[metric] = metric_values
|
||||
timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
|
||||
timestamps_dt = [str(datetime.fromtimestamp(int(ts))) for ts in timestamps]
|
||||
plt.figure(figsize=(16, 10), dpi=80)
|
||||
plt.plot(timestamps_dt, metric_values, label=metric)
|
||||
plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
|
||||
@ -156,7 +159,7 @@ def main(args_in: list[str] | None = None) -> None:
|
||||
plt.close()
|
||||
|
||||
# Mermaid format in case images upload failed
|
||||
with (open(f"{metric}.mermaid", 'w') as mermaid_f):
|
||||
with open(f"{metric}.mermaid", 'w') as mermaid_f:
|
||||
mermaid = (
|
||||
f"""---
|
||||
config:
|
||||
@ -278,7 +281,7 @@ def start_server_background(args):
|
||||
}
|
||||
server_process = subprocess.Popen(
|
||||
args,
|
||||
**pkwargs)
|
||||
**pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue]
|
||||
|
||||
def server_log(in_stream, out_stream):
|
||||
for line in iter(in_stream.readline, b''):
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
import asyncio
|
||||
import collections
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
@ -8,19 +7,23 @@ import subprocess
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
from collections.abc import Sequence
|
||||
from contextlib import closing
|
||||
from re import RegexFlag
|
||||
from typing import Any, Literal, cast
|
||||
|
||||
import aiohttp
|
||||
import numpy as np
|
||||
import openai
|
||||
from behave import step
|
||||
from openai.types.chat import ChatCompletionChunk
|
||||
from behave import step # pyright: ignore[reportAttributeAccessIssue]
|
||||
from behave.api.async_step import async_run_until_complete
|
||||
from prometheus_client import parser
|
||||
|
||||
# pyright: reportRedeclaration=false
|
||||
|
||||
@step("a server listening on {server_fqdn}:{server_port}")
|
||||
def step_server_config(context, server_fqdn, server_port):
|
||||
def step_server_config(context, server_fqdn: str, server_port: str):
|
||||
context.server_fqdn = server_fqdn
|
||||
context.server_port = int(server_port)
|
||||
context.n_threads = None
|
||||
@ -74,34 +77,34 @@ def step_server_config(context, server_fqdn, server_port):
|
||||
|
||||
|
||||
@step('a model file {hf_file} from HF repo {hf_repo}')
|
||||
def step_download_hf_model(context, hf_file, hf_repo):
|
||||
def step_download_hf_model(context, hf_file: str, hf_repo: str):
|
||||
context.model_hf_repo = hf_repo
|
||||
context.model_hf_file = hf_file
|
||||
context.model_file = os.path.basename(hf_file)
|
||||
|
||||
|
||||
@step('a model file {model_file}')
|
||||
def step_model_file(context, model_file):
|
||||
def step_model_file(context, model_file: str):
|
||||
context.model_file = model_file
|
||||
|
||||
|
||||
@step('a model url {model_url}')
|
||||
def step_model_url(context, model_url):
|
||||
def step_model_url(context, model_url: str):
|
||||
context.model_url = model_url
|
||||
|
||||
|
||||
@step('a model alias {model_alias}')
|
||||
def step_model_alias(context, model_alias):
|
||||
def step_model_alias(context, model_alias: str):
|
||||
context.model_alias = model_alias
|
||||
|
||||
|
||||
@step('{seed:d} as server seed')
|
||||
def step_seed(context, seed):
|
||||
def step_seed(context, seed: int):
|
||||
context.server_seed = seed
|
||||
|
||||
|
||||
@step('{ngl:d} GPU offloaded layers')
|
||||
def step_n_gpu_layer(context, ngl):
|
||||
def step_n_gpu_layer(context, ngl: int):
|
||||
if 'N_GPU_LAYERS' in os.environ:
|
||||
new_ngl = int(os.environ['N_GPU_LAYERS'])
|
||||
if context.debug:
|
||||
@ -111,37 +114,37 @@ def step_n_gpu_layer(context, ngl):
|
||||
|
||||
|
||||
@step('{n_threads:d} threads')
|
||||
def step_n_threads(context, n_threads):
|
||||
def step_n_threads(context, n_threads: int):
|
||||
context.n_thread = n_threads
|
||||
|
||||
|
||||
@step('{draft:d} as draft')
|
||||
def step_draft(context, draft):
|
||||
def step_draft(context, draft: int):
|
||||
context.draft = draft
|
||||
|
||||
|
||||
@step('{n_ctx:d} KV cache size')
|
||||
def step_n_ctx(context, n_ctx):
|
||||
def step_n_ctx(context, n_ctx: int):
|
||||
context.n_ctx = n_ctx
|
||||
|
||||
|
||||
@step('{n_slots:d} slots')
|
||||
def step_n_slots(context, n_slots):
|
||||
def step_n_slots(context, n_slots: int):
|
||||
context.n_slots = n_slots
|
||||
|
||||
|
||||
@step('{n_predict:d} server max tokens to predict')
|
||||
def step_server_n_predict(context, n_predict):
|
||||
def step_server_n_predict(context, n_predict: int):
|
||||
context.n_server_predict = n_predict
|
||||
|
||||
|
||||
@step('{slot_save_path} as slot save path')
|
||||
def step_slot_save_path(context, slot_save_path):
|
||||
def step_slot_save_path(context, slot_save_path: str):
|
||||
context.slot_save_path = slot_save_path
|
||||
|
||||
|
||||
@step('using slot id {id_slot:d}')
|
||||
def step_id_slot(context, id_slot):
|
||||
def step_id_slot(context, id_slot: int):
|
||||
context.id_slot = id_slot
|
||||
|
||||
|
||||
@ -191,7 +194,7 @@ def step_start_server(context):
|
||||
|
||||
@step("the server is {expecting_status}")
|
||||
@async_run_until_complete
|
||||
async def step_wait_for_the_server_to_be_started(context, expecting_status):
|
||||
async def step_wait_for_the_server_to_be_started(context, expecting_status: Literal['healthy', 'ready', 'idle', 'busy'] | str):
|
||||
match expecting_status:
|
||||
case 'healthy':
|
||||
await wait_for_health_status(context, context.base_url, 200, 'ok',
|
||||
@ -221,7 +224,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
|
||||
|
||||
@step('all slots are {expected_slot_status_string}')
|
||||
@async_run_until_complete
|
||||
async def step_all_slots_status(context, expected_slot_status_string):
|
||||
async def step_all_slots_status(context, expected_slot_status_string: Literal['idle', 'busy'] | str):
|
||||
match expected_slot_status_string:
|
||||
case 'idle':
|
||||
expected_slot_status = 0
|
||||
@ -237,7 +240,7 @@ async def step_all_slots_status(context, expected_slot_status_string):
|
||||
|
||||
@step('a completion request with {api_error} api error')
|
||||
@async_run_until_complete
|
||||
async def step_request_completion(context, api_error):
|
||||
async def step_request_completion(context, api_error: Literal['raised'] | str):
|
||||
expect_api_error = api_error == 'raised'
|
||||
seeds = await completions_seed(context, num_seeds=1)
|
||||
completion = await request_completion(context.prompts.pop(),
|
||||
@ -777,8 +780,8 @@ def step_assert_metric_value(context, metric_name, metric_value):
|
||||
def step_available_models(context):
|
||||
# openai client always expects an api_key
|
||||
openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
|
||||
openai.api_base = f'{context.base_url}/v1'
|
||||
context.models = openai.Model.list().data
|
||||
openai.base_url = f'{context.base_url}/v1/'
|
||||
context.models = openai.models.list().data
|
||||
|
||||
|
||||
@step('{n_model:d} models are supported')
|
||||
@ -789,7 +792,7 @@ def step_supported_models(context, n_model):
|
||||
|
||||
|
||||
@step('model {i_model:d} is {param} {preposition} {param_value}')
|
||||
def step_supported_models(context, i_model, param, preposition, param_value):
|
||||
def step_supported_models(context, i_model: int, param: Literal['identified', 'trained'] | str, preposition: str, param_value: str):
|
||||
assert i_model < len(context.models)
|
||||
model = context.models[i_model]
|
||||
|
||||
@ -798,7 +801,7 @@ def step_supported_models(context, i_model, param, preposition, param_value):
|
||||
case 'identified':
|
||||
value = model.id
|
||||
case 'trained':
|
||||
value = str(model.meta.n_ctx_train)
|
||||
value = str(model.meta["n_ctx_train"])
|
||||
case _:
|
||||
assert False, "param {param} not supported"
|
||||
assert param_value == value, f"model param {param} {value} != {param_value}"
|
||||
@ -810,6 +813,7 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
|
||||
print(f"starting {context.n_prompts} concurrent completion requests...")
|
||||
assert context.n_prompts > 0
|
||||
seeds = await completions_seed(context)
|
||||
assert seeds is not None
|
||||
for prompt_no in range(context.n_prompts):
|
||||
shifted_args = [context.prompts.pop(), seeds[prompt_no], *args]
|
||||
context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
|
||||
@ -861,7 +865,7 @@ async def request_completion(prompt,
|
||||
id_slot=None,
|
||||
expect_api_error=None,
|
||||
user_api_key=None,
|
||||
temperature=None):
|
||||
temperature=None) -> int | dict[str, Any]:
|
||||
if debug:
|
||||
print(f"Sending completion request: {prompt}")
|
||||
origin = "my.super.domain"
|
||||
@ -899,8 +903,8 @@ async def request_completion(prompt,
|
||||
async def oai_chat_completions(user_prompt,
|
||||
seed,
|
||||
system_prompt,
|
||||
base_url,
|
||||
base_path,
|
||||
base_url: str,
|
||||
base_path: str,
|
||||
async_client,
|
||||
debug=False,
|
||||
temperature=None,
|
||||
@ -909,7 +913,7 @@ async def oai_chat_completions(user_prompt,
|
||||
enable_streaming=None,
|
||||
response_format=None,
|
||||
user_api_key=None,
|
||||
expect_api_error=None):
|
||||
expect_api_error=None) -> int | dict[str, Any]:
|
||||
if debug:
|
||||
print(f"Sending OAI Chat completions request: {user_prompt}")
|
||||
# openai client always expects an api key
|
||||
@ -989,32 +993,35 @@ async def oai_chat_completions(user_prompt,
|
||||
else:
|
||||
try:
|
||||
openai.api_key = user_api_key
|
||||
openai.api_base = f'{base_url}{base_path}'
|
||||
chat_completion = openai.Completion.create(
|
||||
openai.base_url = f'{base_url}{base_path.removesuffix("chat")}'
|
||||
assert model is not None
|
||||
chat_completion = openai.chat.completions.create(
|
||||
messages=payload['messages'],
|
||||
model=model,
|
||||
max_tokens=n_predict,
|
||||
stream=enable_streaming,
|
||||
response_format=payload.get('response_format'),
|
||||
response_format=payload.get('response_format') or openai.NOT_GIVEN,
|
||||
seed=seed,
|
||||
temperature=payload['temperature']
|
||||
)
|
||||
except openai.error.AuthenticationError as e:
|
||||
except openai.AuthenticationError as e:
|
||||
if expect_api_error is not None and expect_api_error:
|
||||
return 401
|
||||
else:
|
||||
assert False, f'error raised: {e}'
|
||||
|
||||
if enable_streaming:
|
||||
chat_completion = cast(openai.Stream[ChatCompletionChunk], chat_completion)
|
||||
for chunk in chat_completion:
|
||||
assert len(chunk.choices) == 1
|
||||
delta = chunk.choices[0].delta
|
||||
if 'content' in delta:
|
||||
completion_response['content'] += delta['content']
|
||||
if delta.content is not None:
|
||||
completion_response['content'] += delta.content
|
||||
completion_response['timings']['predicted_n'] += 1
|
||||
completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
|
||||
else:
|
||||
assert len(chat_completion.choices) == 1
|
||||
assert chat_completion.usage is not None
|
||||
completion_response = {
|
||||
'content': chat_completion.choices[0].message.content,
|
||||
'timings': {
|
||||
@ -1028,7 +1035,7 @@ async def oai_chat_completions(user_prompt,
|
||||
return completion_response
|
||||
|
||||
|
||||
async def request_embedding(content, seed, base_url=None):
|
||||
async def request_embedding(content, seed, base_url=None) -> list[list[float]]:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.post(f'{base_url}/embedding',
|
||||
json={
|
||||
@ -1041,7 +1048,7 @@ async def request_embedding(content, seed, base_url=None):
|
||||
|
||||
async def request_oai_embeddings(input, seed,
|
||||
base_url=None, user_api_key=None,
|
||||
model=None, async_client=False):
|
||||
model=None, async_client=False) -> list[list[float]]:
|
||||
# openai client always expects an api_key
|
||||
user_api_key = user_api_key if user_api_key is not None else 'nope'
|
||||
if async_client:
|
||||
@ -1063,7 +1070,7 @@ async def request_oai_embeddings(input, seed,
|
||||
response_json = await response.json()
|
||||
assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
|
||||
assert response_json['object'] == 'list'
|
||||
if isinstance(input, collections.abc.Sequence):
|
||||
if isinstance(input, Sequence):
|
||||
embeddings = []
|
||||
for an_oai_embeddings in response_json['data']:
|
||||
embeddings.append(an_oai_embeddings['embedding'])
|
||||
@ -1072,19 +1079,14 @@ async def request_oai_embeddings(input, seed,
|
||||
return embeddings
|
||||
else:
|
||||
openai.api_key = user_api_key
|
||||
openai.api_base = f'{base_url}/v1'
|
||||
oai_embeddings = openai.Embedding.create(
|
||||
openai.base_url = f'{base_url}/v1/'
|
||||
assert model is not None
|
||||
oai_embeddings = openai.embeddings.create(
|
||||
model=model,
|
||||
input=input,
|
||||
)
|
||||
|
||||
if isinstance(input, collections.abc.Sequence):
|
||||
embeddings = []
|
||||
for an_oai_embeddings in oai_embeddings.data:
|
||||
embeddings.append(an_oai_embeddings.embedding)
|
||||
else:
|
||||
embeddings = [oai_embeddings.data.embedding]
|
||||
return embeddings
|
||||
return [e.embedding for e in oai_embeddings.data]
|
||||
|
||||
|
||||
def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re_content=None):
|
||||
@ -1122,7 +1124,7 @@ def assert_all_predictions_equal(completion_responses):
|
||||
if i == j:
|
||||
continue
|
||||
content_j = response_j['content']
|
||||
assert content_i == content_j, "contents not equal"
|
||||
assert content_i == content_j, "contents not equal"
|
||||
|
||||
|
||||
def assert_all_predictions_different(completion_responses):
|
||||
@ -1136,7 +1138,7 @@ def assert_all_predictions_different(completion_responses):
|
||||
if i == j:
|
||||
continue
|
||||
content_j = response_j['content']
|
||||
assert content_i != content_j, "contents not different"
|
||||
assert content_i != content_j, "contents not different"
|
||||
|
||||
|
||||
def assert_all_token_probabilities_equal(completion_responses):
|
||||
@ -1153,7 +1155,7 @@ def assert_all_token_probabilities_equal(completion_responses):
|
||||
if i == j:
|
||||
continue
|
||||
probs_j = response_j['completion_probabilities'][pos]['probs']
|
||||
assert probs_i == probs_j, "contents not equal"
|
||||
assert probs_i == probs_j, "contents not equal"
|
||||
|
||||
|
||||
async def gather_tasks_results(context):
|
||||
@ -1343,7 +1345,7 @@ def start_server_background(context):
|
||||
}
|
||||
context.server_process = subprocess.Popen(
|
||||
[str(arg) for arg in [context.server_path, *server_args]],
|
||||
**pkwargs)
|
||||
**pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue]
|
||||
|
||||
def server_log(in_stream, out_stream):
|
||||
for line in iter(in_stream.readline, b''):
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
aiohttp~=3.9.3
|
||||
behave~=1.2.6
|
||||
huggingface_hub~=0.20.3
|
||||
numpy~=1.24.4
|
||||
openai~=0.25.0
|
||||
numpy~=1.26.4
|
||||
openai~=1.30.3
|
||||
prometheus-client~=0.20.0
|
||||
|
||||
@ -1,13 +1,15 @@
|
||||
import asyncio
|
||||
import asyncio.threads
|
||||
import requests
|
||||
import numpy as np
|
||||
|
||||
|
||||
n = 8
|
||||
|
||||
result = []
|
||||
|
||||
async def requests_post_async(*args, **kwargs):
|
||||
return await asyncio.to_thread(requests.post, *args, **kwargs)
|
||||
return await asyncio.threads.to_thread(requests.post, *args, **kwargs)
|
||||
|
||||
async def main():
|
||||
model_url = "http://127.0.0.1:6900"
|
||||
|
||||
@ -66,7 +66,7 @@ class Tensor:
|
||||
if len(self.ne) == 0:
|
||||
self.nbytes = 0
|
||||
else:
|
||||
self.nbytes = int(np.product(self.ne)) * 4
|
||||
self.nbytes = int(np.prod(self.ne)) * 4
|
||||
else:
|
||||
raise ValueError(f"Unhandled data type '{self.dtype}'")
|
||||
|
||||
|
||||
@ -99,6 +99,8 @@ async def main():
|
||||
|
||||
tasks = []
|
||||
|
||||
base_dict = {"FLOAT_TYPE": "float"}
|
||||
|
||||
for fp16 in (False, True):
|
||||
# MUL_MAT
|
||||
matmul_shaders(tasks, fp16, False)
|
||||
@ -106,8 +108,6 @@ async def main():
|
||||
matmul_shaders(tasks, fp16, True)
|
||||
|
||||
for tname in type_names:
|
||||
base_dict = {"FLOAT_TYPE": "float"}
|
||||
|
||||
# mul mat vec
|
||||
data_a_key = f"DATA_A_{tname.upper()}"
|
||||
shader = f"mul_mat_vec_{tname}.comp" if tname.endswith("_k") else "mul_mat_vec.comp"
|
||||
|
||||
@ -67,7 +67,7 @@ class ReaderTensor(NamedTuple):
|
||||
|
||||
class GGUFReader:
|
||||
# I - same as host, S - swapped
|
||||
byte_order: Literal['I'] | Literal['S'] = 'I'
|
||||
byte_order: Literal['I', 'S'] = 'I'
|
||||
alignment: int = GGUF_DEFAULT_ALIGNMENT
|
||||
data_offset: int
|
||||
|
||||
@ -86,7 +86,7 @@ class GGUFReader:
|
||||
GGUFValueType.BOOL: np.bool_,
|
||||
}
|
||||
|
||||
def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r+'] | Literal['c'] = 'r'):
|
||||
def __init__(self, path: os.PathLike[str] | str, mode: Literal['r', 'r+', 'c'] = 'r'):
|
||||
self.data = np.memmap(path, mode = mode)
|
||||
offs = 0
|
||||
|
||||
@ -140,7 +140,7 @@ class GGUFReader:
|
||||
return self.tensors[idx]
|
||||
|
||||
def _get(
|
||||
self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I'] | Literal['S'] | Literal['<'] = None,
|
||||
self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I', 'S', '<'] = None,
|
||||
) -> npt.NDArray[Any]:
|
||||
count = int(count)
|
||||
itemsize = int(np.empty([], dtype = dtype).itemsize)
|
||||
|
||||
@ -16,16 +16,16 @@ logger = logging.getLogger(__name__)
|
||||
class LazyMeta(ABCMeta):
|
||||
|
||||
def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs):
|
||||
def __getattr__(self, __name: str) -> Any:
|
||||
meta_attr = getattr(self._meta, __name)
|
||||
def __getattr__(self, name: str) -> Any:
|
||||
meta_attr = getattr(self._meta, name)
|
||||
if callable(meta_attr):
|
||||
return type(self)._wrap_fn(
|
||||
(lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)),
|
||||
(lambda s, *args, **kwargs: getattr(s, name)(*args, **kwargs)),
|
||||
use_self=self,
|
||||
)
|
||||
elif isinstance(meta_attr, self._tensor_type):
|
||||
# e.g. self.T with torch.Tensor should still be wrapped
|
||||
return type(self)._wrap_fn(lambda s: getattr(s, __name))(self)
|
||||
return type(self)._wrap_fn(lambda s: getattr(s, name))(self)
|
||||
else:
|
||||
# no need to wrap non-tensor properties,
|
||||
# and they likely don't depend on the actual contents of the tensor
|
||||
@ -141,19 +141,21 @@ class LazyBase(ABC, metaclass=LazyMeta):
|
||||
res = cls.meta_with_dtype_and_shape(meta_noop, res.shape)
|
||||
|
||||
if isinstance(res, cls._tensor_type):
|
||||
def collect_replace(t: LazyBase):
|
||||
if collect_replace.shared_lazy is None:
|
||||
collect_replace.shared_lazy = t._lazy
|
||||
else:
|
||||
collect_replace.shared_lazy.extend(t._lazy)
|
||||
t._lazy = collect_replace.shared_lazy
|
||||
class CollectSharedLazy:
|
||||
# emulating a static variable
|
||||
shared_lazy: None | deque[LazyBase] = None
|
||||
|
||||
# emulating a static variable
|
||||
collect_replace.shared_lazy = None
|
||||
@staticmethod
|
||||
def collect_replace(t: LazyBase):
|
||||
if CollectSharedLazy.shared_lazy is None:
|
||||
CollectSharedLazy.shared_lazy = t._lazy
|
||||
else:
|
||||
CollectSharedLazy.shared_lazy.extend(t._lazy)
|
||||
t._lazy = CollectSharedLazy.shared_lazy
|
||||
|
||||
LazyBase._recurse_apply(args, collect_replace)
|
||||
LazyBase._recurse_apply(args, CollectSharedLazy.collect_replace)
|
||||
|
||||
shared_lazy = collect_replace.shared_lazy
|
||||
shared_lazy = CollectSharedLazy.shared_lazy
|
||||
|
||||
return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs))
|
||||
else:
|
||||
@ -184,6 +186,7 @@ class LazyBase(ABC, metaclass=LazyMeta):
|
||||
lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
|
||||
lt._data = lt._func(lt._args)
|
||||
# sanity check
|
||||
assert lt._data is not None
|
||||
assert lt._data.dtype == lt._meta.dtype
|
||||
assert lt._data.shape == lt._meta.shape
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
# pyright: reportUnusedImport=false
|
||||
|
||||
from .gguf_convert_endian import main as gguf_convert_endian_entrypoint
|
||||
from .gguf_dump import main as gguf_dump_entrypoint
|
||||
from .gguf_set_metadata import main as gguf_set_metadata_entrypoint
|
||||
|
||||
@ -63,9 +63,9 @@ def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
|
||||
bar.update(sum_weights_in_tensor)
|
||||
|
||||
sha1_layer = hashlib.sha1()
|
||||
sha1_layer.update(tensor.data)
|
||||
sha1.update(tensor.data)
|
||||
uuidv5_sha1.update(tensor.data)
|
||||
sha1_layer.update(tensor.data.data)
|
||||
sha1.update(tensor.data.data)
|
||||
uuidv5_sha1.update(tensor.data.data)
|
||||
print("sha1 {0} {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
|
||||
|
||||
# Flush Hash Progress Bar
|
||||
|
||||
@ -1,4 +1,6 @@
|
||||
#!/usr/bin/env python3
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import argparse
|
||||
import os
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
import gguf # noqa: F401
|
||||
import gguf # noqa: F401 # pyright: ignore[reportUnusedImport]
|
||||
|
||||
# TODO: add tests
|
||||
|
||||
|
||||
@ -1,3 +1,21 @@
|
||||
{
|
||||
"extraPaths": ["gguf-py"],
|
||||
}
|
||||
"pythonVersion": "3.9",
|
||||
"pythonPlatform": "All",
|
||||
"reportUnusedImport": "warning",
|
||||
"reportDuplicateImport": "error",
|
||||
"reportDeprecated": "warning",
|
||||
"reportUnnecessaryTypeIgnoreComment": "warning",
|
||||
"executionEnvironments": [
|
||||
{
|
||||
// TODO: make this version override work correctly
|
||||
"root": "gguf-py",
|
||||
"pythonVersion": "3.8",
|
||||
},
|
||||
{
|
||||
// uses match expressions in steps.py
|
||||
"root": "examples/server/tests",
|
||||
"pythonVersion": "3.10",
|
||||
},
|
||||
],
|
||||
}
|
||||
|
||||
12
requirements/requirements-all.txt
Normal file
12
requirements/requirements-all.txt
Normal file
@ -0,0 +1,12 @@
|
||||
-r ../examples/llava/requirements.txt
|
||||
-r ../examples/server/bench/requirements.txt
|
||||
-r ../examples/server/tests/requirements.txt
|
||||
|
||||
-r ./requirements-compare-llama-bench.txt
|
||||
-r ./requirements-pydantic.txt
|
||||
-r ./requirements-test-tokenizer-random.txt
|
||||
|
||||
-r ./requirements-convert_hf_to_gguf.txt
|
||||
-r ./requirements-convert_hf_to_gguf_update.txt
|
||||
-r ./requirements-convert_legacy_llama.txt
|
||||
-r ./requirements-convert_llama_ggml_to_gguf.txt
|
||||
2
requirements/requirements-compare-llama-bench.txt
Normal file
2
requirements/requirements-compare-llama-bench.txt
Normal file
@ -0,0 +1,2 @@
|
||||
tabulate~=0.9.0
|
||||
GitPython~=3.1.43
|
||||
2
requirements/requirements-pydantic.txt
Normal file
2
requirements/requirements-pydantic.txt
Normal file
@ -0,0 +1,2 @@
|
||||
docstring_parser~=0.15
|
||||
pydantic~=2.6.3
|
||||
1
requirements/requirements-test-tokenizer-random.txt
Normal file
1
requirements/requirements-test-tokenizer-random.txt
Normal file
@ -0,0 +1 @@
|
||||
cffi~=1.16.0
|
||||
@ -108,6 +108,11 @@ check_convert_script() {
|
||||
fatal "$py missing requirements. Expected: $reqs"
|
||||
fi
|
||||
|
||||
# Check that all sub-requirements are added to top-level requirements.txt
|
||||
if ! grep -qF "$reqs" requirements.txt; then
|
||||
fatal "$reqs needs to be added to requirements.txt"
|
||||
fi
|
||||
|
||||
local venv="$workdir/$pyname-venv"
|
||||
python3 -m venv "$venv"
|
||||
|
||||
@ -134,12 +139,7 @@ EOF
|
||||
|
||||
readonly ignore_eq_eq='check_requirements: ignore "=="'
|
||||
|
||||
for req in "$reqs_dir"/*; do
|
||||
# Check that all sub-requirements are added to top-level requirements.txt
|
||||
if ! grep -qF "$req" requirements.txt; then
|
||||
fatal "$req needs to be added to requirements.txt"
|
||||
fi
|
||||
|
||||
for req in */**/requirements*.txt; do
|
||||
# Make sure exact release versions aren't being pinned in the requirements
|
||||
# Filters out the ignore string
|
||||
if grep -vF "$ignore_eq_eq" "$req" | grep -q '=='; then
|
||||
|
||||
@ -123,13 +123,13 @@ builds = cursor.execute("SELECT DISTINCT build_commit FROM test;").fetchall()
|
||||
|
||||
try:
|
||||
repo = git.Repo(".", search_parent_directories=True)
|
||||
except git.exc.InvalidGitRepositoryError:
|
||||
except git.InvalidGitRepositoryError:
|
||||
repo = None
|
||||
|
||||
|
||||
def find_parent_in_data(commit):
|
||||
def find_parent_in_data(commit: git.Commit):
|
||||
"""Helper function to find the most recent parent measured in number of commits for which there is data."""
|
||||
heap = [(0, commit)]
|
||||
heap: list[tuple[int, git.Commit]] = [(0, commit)]
|
||||
seen_hexsha8 = set()
|
||||
while heap:
|
||||
depth, current_commit = heapq.heappop(heap)
|
||||
@ -144,7 +144,7 @@ def find_parent_in_data(commit):
|
||||
return None
|
||||
|
||||
|
||||
def get_all_parent_hexsha8s(commit):
|
||||
def get_all_parent_hexsha8s(commit: git.Commit):
|
||||
"""Helper function to recursively get hexsha8 values for all parents of a commit."""
|
||||
unvisited = [commit]
|
||||
visited = []
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import array
|
||||
import unicodedata
|
||||
import requests
|
||||
@ -133,7 +135,7 @@ table_nfd.sort()
|
||||
|
||||
|
||||
# group ranges with same flags
|
||||
ranges_flags = [(0, codepoint_flags[0])] # start, flags
|
||||
ranges_flags: list[tuple[int, int]] = [(0, codepoint_flags[0])] # start, flags
|
||||
for codepoint, flags in enumerate(codepoint_flags):
|
||||
if flags != ranges_flags[-1][1]:
|
||||
ranges_flags.append((codepoint, flags))
|
||||
@ -141,11 +143,11 @@ ranges_flags.append((MAX_CODEPOINTS, 0x0000))
|
||||
|
||||
|
||||
# group ranges with same nfd
|
||||
ranges_nfd = [(0, 0, 0)] # start, last, nfd
|
||||
ranges_nfd: list[tuple[int, int, int]] = [(0, 0, 0)] # start, last, nfd
|
||||
for codepoint, norm in table_nfd:
|
||||
start = ranges_nfd[-1][0]
|
||||
if ranges_nfd[-1] != (start, codepoint - 1, norm):
|
||||
ranges_nfd.append(None)
|
||||
ranges_nfd.append(None) # type: ignore[arg-type] # dummy, will be replaced below
|
||||
start = codepoint
|
||||
ranges_nfd[-1] = (start, codepoint, norm)
|
||||
|
||||
@ -179,13 +181,13 @@ for codepoint in table_whitespace:
|
||||
out("};\n")
|
||||
|
||||
out("const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase = {")
|
||||
for tuple in table_lowercase:
|
||||
out("{0x%06X, 0x%06X}," % tuple)
|
||||
for tuple_lw in table_lowercase:
|
||||
out("{0x%06X, 0x%06X}," % tuple_lw)
|
||||
out("};\n")
|
||||
|
||||
out("const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase = {")
|
||||
for tuple in table_uppercase:
|
||||
out("{0x%06X, 0x%06X}," % tuple)
|
||||
for tuple_up in table_uppercase:
|
||||
out("{0x%06X, 0x%06X}," % tuple_up)
|
||||
out("};\n")
|
||||
|
||||
out("const std::vector<range_nfd> unicode_ranges_nfd = { // start, last, nfd")
|
||||
|
||||
@ -6,6 +6,8 @@
|
||||
# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
|
||||
#
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
import logging
|
||||
import argparse
|
||||
@ -13,7 +15,9 @@ import subprocess
|
||||
import random
|
||||
import unicodedata
|
||||
|
||||
from typing import Iterator
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterator, cast
|
||||
from typing_extensions import Buffer
|
||||
|
||||
import cffi
|
||||
from transformers import AutoTokenizer
|
||||
@ -28,15 +32,15 @@ class LibLlama:
|
||||
DEFAULT_PATH_INCLUDES = ["./ggml/include/", "./include/"]
|
||||
DEFAULT_PATH_LIBLLAMA = "./build/src/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON
|
||||
|
||||
def __init__(self, path_llama_h: str = None, path_includes: list[str] = [], path_libllama: str = None):
|
||||
def __init__(self, path_llama_h: str | None = None, path_includes: list[str] = [], path_libllama: str | None = None):
|
||||
path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
|
||||
path_includes = path_includes or self.DEFAULT_PATH_INCLUDES
|
||||
path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
|
||||
(self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_includes, path_libllama)
|
||||
self.lib.llama_backend_init()
|
||||
|
||||
def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str):
|
||||
cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
|
||||
def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str) -> tuple[cffi.FFI, Any]:
|
||||
cmd = ["gcc", "-O0", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
|
||||
cmd += ["-I" + path for path in path_includes] + [path_llama_h]
|
||||
res = subprocess.run(cmd, stdout=subprocess.PIPE)
|
||||
assert (res.returncode == 0)
|
||||
@ -68,7 +72,7 @@ class LibLlama:
|
||||
class LibLlamaModel:
|
||||
|
||||
def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
|
||||
self.lib = libllama.lib
|
||||
self.lib: Any = libllama.lib
|
||||
self.ffi = libllama.ffi
|
||||
if isinstance(mparams, dict):
|
||||
mparams = libllama.model_default_params(**mparams)
|
||||
@ -94,11 +98,11 @@ class LibLlamaModel:
|
||||
self.lib = None
|
||||
|
||||
def tokenize(self, text: str, add_special: bool = False, parse_special: bool = False) -> list[int]:
|
||||
text = text.encode("utf-8")
|
||||
num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special)
|
||||
encoded_text: bytes = text.encode("utf-8")
|
||||
num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
|
||||
while num < 0 and len(self.token_ids) < (16 << 20):
|
||||
self.token_ids = self.ffi.new("llama_token[]", -2 * num)
|
||||
num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special)
|
||||
num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
|
||||
return list(self.token_ids[0:num])
|
||||
|
||||
def detokenize(self, ids: list[int], remove_special: bool = False, unparse_special: bool = False) -> str:
|
||||
@ -110,7 +114,7 @@ class LibLlamaModel:
|
||||
while num < 0 and len(self.text_buff) < (16 << 20):
|
||||
self.text_buff = self.ffi.new("uint8_t[]", -2 * num)
|
||||
num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
|
||||
return str(self.ffi.buffer(self.text_buff, num), encoding="utf-8", errors="replace") # replace errors with '\uFFFD'
|
||||
return str(cast(Buffer, self.ffi.buffer(self.text_buff, num)), encoding="utf-8", errors="replace") # replace errors with '\uFFFD'
|
||||
|
||||
|
||||
class Tokenizer:
|
||||
@ -152,7 +156,7 @@ class TokenizerGroundtruth (Tokenizer):
|
||||
|
||||
class TokenizerLlamaCpp (Tokenizer):
|
||||
|
||||
libllama: LibLlama = None
|
||||
libllama: LibLlama | None = None
|
||||
|
||||
def __init__(self, vocab_file: str):
|
||||
if not self.libllama:
|
||||
@ -404,7 +408,7 @@ def generator_random_vocab_words(tokenizer: TokenizerGroundtruth, iterations=100
|
||||
|
||||
def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp, generator: Iterator[str]):
|
||||
|
||||
def find_first_mismatch(ids1: list[int], ids2: list[int]):
|
||||
def find_first_mismatch(ids1: list[int] | str, ids2: list[int] | str):
|
||||
for i, (a, b) in enumerate(zip(ids1, ids2)):
|
||||
if a != b:
|
||||
return i
|
||||
@ -433,7 +437,7 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
|
||||
decode_errors = 0
|
||||
MAX_ERRORS = 10
|
||||
|
||||
logger.info("%s: %s" % (generator.__name__, "ini"))
|
||||
logger.info("%s: %s" % (generator.__qualname__, "ini"))
|
||||
for text in generator:
|
||||
# print(repr(text), text.encode())
|
||||
# print(repr(text), hex(ord(text[0])), text.encode())
|
||||
@ -472,13 +476,13 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
|
||||
break
|
||||
|
||||
t_total = time.perf_counter() - t_start
|
||||
logger.info(f"{generator.__name__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}")
|
||||
logger.info(f"{generator.__qualname__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}")
|
||||
|
||||
|
||||
def main(argv: list[str] = None):
|
||||
def main(argv: list[str] | None = None):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
|
||||
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
|
||||
parser.add_argument("vocab_file", type=str, help="path to vocab 'gguf' file")
|
||||
parser.add_argument("dir_tokenizer", type=str, help="directory containing 'tokenizer.model' file")
|
||||
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
|
||||
args = parser.parse_args(argv)
|
||||
|
||||
@ -520,7 +524,7 @@ if __name__ == "__main__":
|
||||
format = "%(levelname)s %(message)s",
|
||||
)
|
||||
|
||||
path_tokenizers = "./models/tokenizers/"
|
||||
path_tokenizers = Path("./models/tokenizers/")
|
||||
path_vocab_format = "./models/ggml-vocab-%s.gguf"
|
||||
|
||||
tokenizers = [
|
||||
@ -556,6 +560,6 @@ if __name__ == "__main__":
|
||||
for tokenizer in tokenizers:
|
||||
logger.info("-" * 50)
|
||||
logger.info(f"TOKENIZER: '{tokenizer}'")
|
||||
vocab_file = path_vocab_format % tokenizer
|
||||
dir_tokenizer = path_tokenizers + "/" + tokenizer
|
||||
main([vocab_file, dir_tokenizer, "--verbose"])
|
||||
vocab_file = Path(path_vocab_format % tokenizer)
|
||||
dir_tokenizer = path_tokenizers / tokenizer
|
||||
main([str(vocab_file), str(dir_tokenizer), "--verbose"])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user