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Merge branch 'ggerganov:master' into server-ui-pr

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Yazan Agha-Schrader 2024-05-31 14:45:35 +02:00 • committed by GitHub
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2
.devops/full-cuda.Dockerfile
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@ -31,6 +31,6 @@ ENV LLAMA_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
ENTRYPOINT ["/app/.devops/tools.sh"]

2
.devops/full-rocm.Dockerfile
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@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make
RUN make -j$(nproc)
ENTRYPOINT ["/app/.devops/tools.sh"]

2
.devops/full.Dockerfile
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@ -18,7 +18,7 @@ COPY . .
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
ENV LC_ALL=C.utf8

2
.devops/main-cuda.Dockerfile
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@ -23,7 +23,7 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable CUDA
ENV LLAMA_CUDA=1
RUN make
RUN make -j$(nproc)
FROM ${BASE_CUDA_RUN_CONTAINER} as runtime

2
.devops/main-rocm.Dockerfile
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@ -40,6 +40,6 @@ ENV LLAMA_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
RUN make
RUN make -j$(nproc)
ENTRYPOINT [ "/app/main" ]

2
.devops/main.Dockerfile
View file

@ -9,7 +9,7 @@ WORKDIR /app
COPY . .
RUN make
RUN make -j$(nproc)
FROM ubuntu:$UBUNTU_VERSION as runtime

2
.devops/server-cuda.Dockerfile
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@ -25,7 +25,7 @@ ENV LLAMA_CUDA=1
# Enable cURL
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
FROM ${BASE_CUDA_RUN_CONTAINER} as runtime

2
.devops/server-rocm.Dockerfile
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@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make
RUN make -j$(nproc)
ENTRYPOINT [ "/app/server" ]

2
.devops/server.Dockerfile
View file

@ -11,7 +11,7 @@ COPY . .
ENV LLAMA_CURL=1
RUN make
RUN make -j$(nproc)
FROM ubuntu:$UBUNTU_VERSION as runtime

2
.devops/tools.sh
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@ -8,7 +8,7 @@ arg1="$1"
shift
if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
python3 ./convert.py "$@"
python3 ./convert-hf-to-gguf.py "$@"
elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
./quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then

38
.github/ISSUE_TEMPLATE/06-question.yml vendored
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@ -1,38 +0,0 @@
name: Question
description: Used to ask questions about llama.cpp
title: "Question: "
labels: ["question"]
body:
- type: markdown
attributes:
value: |
[Please search your question first in Discussion if you got a common general question.](https://github.com/ggerganov/llama.cpp/discussions/categories/q-a)
- type: checkboxes
id: prerequisites
attributes:
label: Prerequisites
description: Please confirm the following before submitting your question.
options:
- label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
required: true
- label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new useful question to share that cannot be answered within Discussions.
required: true
- type: textarea
id: background-description
attributes:
label: Background Description
description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an question.
placeholder: Detailed description of your question
validations:
required: true
- type: textarea
id: possible-answer
attributes:
label: Possible Answer
description: If you have some idea of possible answers you want to confirm, that would also be appreciated.
placeholder: Your idea of possible answers
validations:
required: false

52
.github/ISSUE_TEMPLATE/06-research.yml vendored Normal file
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@ -0,0 +1,52 @@
name: Research
description: Track new technical research area
title: "Research: "
labels: ["research 🔬"]
body:
- type: markdown
attributes:
value: |
Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
- type: checkboxes
id: research-stage
attributes:
label: Research Stage
description: Track general state of this research ticket
options:
- label: Background Research (Let's try to avoid reinventing the wheel)
- label: Hypothesis Formed (How do you think this will work and it's effect?)
- label: Strategy / Implementation Forming
- label: Analysis of results
- label: Debrief / Documentation (So people in the future can learn from us)
- type: textarea
id: background
attributes:
label: Previous existing literature and research
description: Whats the current state of the art and whats the motivation for this research?
- type: textarea
id: hypothesis
attributes:
label: Hypothesis
description: How do you think this will work and it's effect?
- type: textarea
id: implementation
attributes:
label: Implementation
description: Got an approach? e.g. a PR ready to go?
- type: textarea
id: analysis
attributes:
label: Analysis
description: How does the proposed implementation behave?
- type: textarea
id: logs
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell

13
.github/ISSUE_TEMPLATE/config.yml vendored Normal file
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@ -0,0 +1,13 @@
blank_issues_enabled: true
contact_links:
- name: Got an idea?
url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas
about: Pop it there. It may then become an enhancement ticket.
- name: Got a question?
url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a
about: Ask a question there!
- name: Want to contribute?
url: https://github.com/ggerganov/llama.cpp/wiki/contribute
about: Head to the contribution guide page of the wiki for areas you can help with

2
CMakeLists.txt
View file

@ -1314,7 +1314,7 @@ set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}
install(TARGETS llama LIBRARY PUBLIC_HEADER)
install(
FILES convert.py
FILES convert-hf-to-gguf.py
PERMISSIONS
OWNER_READ
OWNER_WRITE

48
README.md
View file

@ -2,7 +2,9 @@
![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)
[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
@ -20,7 +22,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
### Hot topics
- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021**
- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430
- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021
- BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
- Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
@ -200,6 +203,7 @@ Unless otherwise noted these projects are open-source with permissive licensing:
- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
- [AIKit](https://github.com/sozercan/aikit) (MIT)
*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
@ -315,8 +319,6 @@ In order to build llama.cpp you have four different options.
make
```
**Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
- On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@ -328,23 +330,32 @@ In order to build llama.cpp you have four different options.
make
```
- Notes:
- For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
- For faster repeated compilation, install [ccache](https://ccache.dev/).
- For debug builds, run `make LLAMA_DEBUG=1`
- Using `CMake`:
```bash
cmake -B build
cmake --build build --config Release
```
```bash
cmake -B build
cmake --build build --config Release
```
**Note**: for `Debug` builds, there are two cases:
**Notes**:
- Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
- For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
- For faster repeated compilation, install [ccache](https://ccache.dev/).
- For debug builds, there are two cases:
1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
```bash
cmake -B build -DCMAKE_BUILD_TYPE=Debug
cmake --build build
```
- Multi-config generators (`-G` param set to Visual Studio, XCode...):
2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
```bash
cmake -B build -G "Xcode"
@ -379,6 +390,14 @@ In order to build llama.cpp you have four different options.
CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
the instructions for use and activate this options in this document below.
### Homebrew
On Mac and Linux, the homebrew package manager can be used via
```
brew install llama.cpp
```
The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
### Metal Build
On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
@ -697,7 +716,8 @@ Building the program with BLAS support may lead to some performance improvements
To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives.
It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
```bash
# obtain the official LLaMA model weights and place them in ./models
@ -714,10 +734,10 @@ ls ./models
python3 -m pip install -r requirements.txt
# convert the model to ggml FP16 format
python3 convert.py models/mymodel/
python3 convert-hf-to-gguf.py models/mymodel/
# [Optional] for models using BPE tokenizers
python convert.py models/mymodel/ --vocab-type bpe
python convert-hf-to-gguf.py models/mymodel/ --vocab-type bpe
# quantize the model to 4-bits (using Q4_K_M method)
./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M

2
ci/run.sh
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@ -287,7 +287,7 @@ function gg_run_open_llama_7b_v2 {
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"

4
convert-hf-to-gguf.py
View file

@ -25,8 +25,6 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
from convert import LlamaHfVocab
logger = logging.getLogger("hf-to-gguf")
@ -634,7 +632,7 @@ class Model:
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_llama_hf(self):
vocab = LlamaHfVocab(self.dir_model)
vocab = gguf.LlamaHfVocab(self.dir_model)
tokens = []
scores = []
toktypes = []

2
docs/HOWTO-add-model.md
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@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M
### 1. Convert the model to GGUF
This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format).
The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.

308
convert.py → examples/convert-legacy-llama.py
View file

@ -24,14 +24,16 @@ from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional
from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional
import numpy as np
from sentencepiece import SentencePieceProcessor
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
# use .parent.parent since we are in "examples" directory
sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py'))
import gguf
from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab
if TYPE_CHECKING:
from typing_extensions import Self, TypeAlias
@ -380,306 +382,6 @@ class Metadata:
return metadata
#
# vocab
#
@runtime_checkable
class BaseVocab(Protocol):
tokenizer_model: ClassVar[str]
name: ClassVar[str]
class NoVocab(BaseVocab):
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
@runtime_checkable
class Vocab(BaseVocab, Protocol):
vocab_size: int
added_tokens_dict: dict[str, int]
added_tokens_list: list[str]
fname_tokenizer: Path
def __init__(self, base_path: Path): ...
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
class BpeVocab(Vocab):
tokenizer_model = "gpt2"
name = "bpe"
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'vocab.json').exists():
# "slow" tokenizer
with open(fname_tokenizer, encoding="utf-8") as f:
self.vocab = json.load(f)
try:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
else:
# "fast" tokenizer
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding="utf-8") as f:
tokenizer_json = json.load(f)
tokenizer_model: dict[str, Any] = tokenizer_json['model']
if (
tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'ByteLevel'
):
raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
self.vocab = tokenizer_model["vocab"]
if (added := tokenizer_json.get('added_tokens')) is not None:
# Added tokens here can be duplicates of the main vocabulary.
added_tokens = {item['content']: item['id']
for item in added
if item['content'] not in self.vocab}
vocab_size = len(self.vocab)
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values())
if expected_ids != actual_ids:
expected_end_id = vocab_size + len(actual_ids) - 1
raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
f"{vocab_size} - {expected_end_id}; got {actual_ids}")
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_dict = added_tokens
self.added_tokens_list = [text for (text, idx) in items]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
for i, _ in enumerate(self.vocab):
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.bpe_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class SentencePieceVocab(Vocab):
tokenizer_model = "llama"
name = "spm"
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'tokenizer.model').exists():
# normal location
try:
with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
# not found in alternate location either
raise FileNotFoundError('Cannot find tokenizer.model')
self.sentencepiece_tokenizer = SentencePieceProcessor()
self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
vocab_size = self.sentencepiece_tokenizer.vocab_size()
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
actual_new_ids = sorted(new_tokens.keys())
if expected_new_ids != actual_new_ids:
raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
# Token pieces that were added to the base vocabulary.
self.added_tokens_dict = added_tokens
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(i)
text = piece.encode("utf-8")
score: float = tokenizer.GetScore(i)
toktype = gguf.TokenType.NORMAL
if tokenizer.IsUnknown(i):
toktype = gguf.TokenType.UNKNOWN
if tokenizer.IsControl(i):
toktype = gguf.TokenType.CONTROL
# NOTE: I think added_tokens are user defined.
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
if tokenizer.IsUnused(i):
toktype = gguf.TokenType.UNUSED
if tokenizer.IsByte(i):
toktype = gguf.TokenType.BYTE
yield text, score, toktype
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.sentencepiece_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class LlamaHfVocab(Vocab):
tokenizer_model = "llama"
name = "hfft"
def __init__(self, base_path: Path):
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding='utf-8') as f:
tokenizer_json = json.load(f)
# pre-check so we know if we need transformers
tokenizer_model: dict[str, Any] = tokenizer_json['model']
is_llama3 = (
tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
and not tokenizer_model.get('byte_fallback', True)
)
if is_llama3:
raise TypeError('Llama 3 must be converted with BpeVocab')
if not is_llama3 and (
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'Sequence'
):
raise FileNotFoundError('Cannot find Llama BPE tokenizer')
try:
from transformers import AutoTokenizer
except ImportError as e:
raise ImportError(
"To use LlamaHfVocab, please install the `transformers` package. "
"You can install it with `pip install transformers`."
) from e
# Allow the tokenizer to default to slow or fast versions.
# Explicitly set tokenizer to use local paths.
self.tokenizer = AutoTokenizer.from_pretrained(
base_path,
cache_dir=base_path,
local_files_only=True,
)
assert self.tokenizer.is_fast # assume tokenizer.json is used
# Initialize lists and dictionaries for added tokens
self.added_tokens_list = []
self.added_tokens_dict = dict()
self.added_tokens_ids = set()
# Process added tokens
for tok, tokidx in sorted(
self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
):
# Only consider added tokens that are not in the base vocabulary
if tokidx >= self.tokenizer.vocab_size:
self.added_tokens_list.append(tok)
self.added_tokens_dict[tok] = tokidx
self.added_tokens_ids.add(tokidx)
# Store special tokens and their IDs
self.specials = {
tok: self.tokenizer.get_vocab()[tok]
for tok in self.tokenizer.all_special_tokens
}
self.special_ids = set(self.tokenizer.all_special_ids)
# Set vocabulary sizes
self.vocab_size_base = self.tokenizer.vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {
id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
}
for token_id in range(self.vocab_size_base):
# Skip processing added tokens here
if token_id in self.added_tokens_ids:
continue
# Convert token text to bytes
token_text = reverse_vocab[token_id].encode("utf-8")
# Yield token text, score, and type
yield token_text, self.get_token_score(token_id), self.get_token_type(
token_id, token_text, self.special_ids # Reuse already stored special IDs
)
def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
# Special case for byte tokens
if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
return gguf.TokenType.BYTE
# Determine token type based on whether it's a special token
return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
def get_token_score(self, token_id: int) -> float:
# Placeholder for actual logic to determine the token's score
# This needs to be implemented based on specific requirements
return -1000.0 # Default score
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
if text in self.specials:
toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
score = self.get_token_score(self.specials[text])
else:
toktype = gguf.TokenType.USER_DEFINED
score = -1000.0
yield text.encode("utf-8"), score, toktype
def has_newline_token(self):
return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.hf_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
#
# data loading
# TODO: reuse (probably move to gguf.py?)

4
examples/llava/MobileVLM-README.md
View file

@ -54,10 +54,10 @@ python ./examples/llava/convert-image-encoder-to-gguf \
--projector-type ldpv2
```
4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./convert.py path/to/MobileVLM-1.7B
python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B
```
5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`

6
examples/llava/README.md
View file

@ -50,10 +50,10 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
```
5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./convert.py ../llava-v1.5-7b --skip-unknown
python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown
```
Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
@ -92,7 +92,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projecto
6) Then convert the model to gguf format:
```console
python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
```
7) And finally we can run the llava-cli using the 1.6 model version:

2
examples/llava/requirements.txt
View file

@ -1,3 +1,3 @@
-r ../../requirements/requirements-convert.txt
-r ../../requirements/requirements-convert-legacy-llama.txt
pillow~=10.2.0
torch~=2.1.1

98
examples/make-ggml.py
View file

@ -1,98 +0,0 @@
#!/usr/bin/env python3
"""
This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them.
Usage:
python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]
Arguments:
- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.
- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.
- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.
- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.
- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.
- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.
Old quant types (some base model types require these):
- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M
- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L
- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M
- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M
New quant types (recommended):
- Q2_K: smallest, extreme quality loss - not recommended
- Q3_K: alias for Q3_K_M
- Q3_K_S: very small, very high quality loss
- Q3_K_M: very small, very high quality loss
- Q3_K_L: small, substantial quality loss
- Q4_K: alias for Q4_K_M
- Q4_K_S: small, significant quality loss
- Q4_K_M: medium, balanced quality - recommended
- Q5_K: alias for Q5_K_M
- Q5_K_S: large, low quality loss - recommended
- Q5_K_M: large, very low quality loss - recommended
- Q6_K: very large, extremely low quality loss
- Q8_0: very large, extremely low quality loss - not recommended
- F16: extremely large, virtually no quality loss - not recommended
- F32: absolutely huge, lossless - not recommended
"""
import subprocess
subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True)
import argparse
import os
from huggingface_hub import snapshot_download
def main(model, model_type, outname, outdir, quants, keep_fp16):
if not os.path.isdir(model):
print(f"Model not found at {model}. Downloading...")
try:
if outname is None:
outname = model.split('/')[-1]
model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')
except Exception as e:
raise Exception(f"Could not download the model: {e}")
if outdir is None:
outdir = f'../models/{outname}'
if not os.path.isfile(f"{model}/config.json"):
raise Exception(f"Could not find config.json in {model}")
os.makedirs(outdir, exist_ok=True)
print("Building llama.cpp")
subprocess.run(f"cd .. && make quantize", shell=True, check=True)
fp16 = f"{outdir}/{outname}.gguf.fp16.bin"
print(f"Making unquantised GGUF at {fp16}")
if not os.path.isfile(fp16):
if model_type != "llama":
subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True)
else:
subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True)
else:
print(f"Unquantised GGML already exists at: {fp16}")
print("Making quants")
for type in quants:
outfile = f"{outdir}/{outname}.gguf.{type}.bin"
print(f"Making {type} : {outfile}")
subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True)
if not keep_fp16:
os.remove(fp16)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')
parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name')
parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.')
parser.add_argument('--outname', default=None, help='Output model(s) name')
parser.add_argument('--outdir', default=None, help='Output directory')
parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types')
parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)
args = parser.parse_args()
main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16)

46
ggml-quants.c
View file

@ -6088,6 +6088,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r
const uint8_t * restrict q2 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0);
const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4);
const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4);
@ -6807,6 +6808,8 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
for (int i = 0; i < nb; ++i) {
const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
const uint8_t * restrict q3 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
// Set up scales
memcpy(aux, x[i].scales, 12);
__m128i scales128 = lsx_set_w(
@ -6828,29 +6831,32 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r
int bit = 0;
int is = 0;
__m256i xvbit;
const uint8_t * restrict q3 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
for (int j = 0; j < QK_K/128; ++j) {
// load low 2 bits
const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32;
xvbit = __lasx_xvreplgr2vr_h(bit);
// prepare low and high bits
const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3);
const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2);
const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
++bit;
xvbit = __lasx_xvreplgr2vr_h(bit);
const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3);
const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2);
const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
++bit;
xvbit = __lasx_xvreplgr2vr_h(bit);
const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3);
const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2);
const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
++bit;
xvbit = __lasx_xvreplgr2vr_h(bit);
const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3);
const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2);
const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2);
++bit;
// load Q8 quants
@ -7399,6 +7405,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
*s = vec_extract(vsumf0, 0);
#elif defined __loongarch_asx
GGML_UNUSED(kmask1);
GGML_UNUSED(kmask2);
GGML_UNUSED(kmask3);
const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
@ -7411,6 +7420,11 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
memcpy(utmp, x[i].scales, 12);
utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
const uint32_t uaux = utmp[1] & kmask1;
utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
utmp[2] = uaux;
utmp[0] &= kmask1;
const uint8_t * restrict q4 = x[i].qs;
const int8_t * restrict q8 = y[i].qs;
@ -7450,16 +7464,17 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r
__m256 vd = __lasx_xvreplfr2vr_s(d);
acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc);
}
acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee));
__m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0);
acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1);
ft_union fi;
fi.i = __lsx_vpickve2gr_w(acc_m, 0);
*s = hsum_float_8(acc) + fi.f ;
#else
const uint8_t * scales = (const uint8_t*)&utmp[0];
@ -7997,6 +8012,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
*s = vec_extract(vsumf0, 0);
#elif defined __loongarch_asx
GGML_UNUSED(kmask1);
GGML_UNUSED(kmask2);
GGML_UNUSED(kmask3);
const __m256i m4 = __lasx_xvreplgr2vr_b(0xF);
const __m128i mzero = __lsx_vldi(0);
@ -8015,6 +8033,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
memcpy(utmp, x[i].scales, 12);
utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
const uint32_t uaux = utmp[1] & kmask1;
utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
utmp[2] = uaux;
utmp[0] &= kmask1;
const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0]));
@ -8033,6 +8056,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
__m256i sumi = __lasx_xvldi(0);
int bit = 0;
__m256i xvbit;
for (int j = 0; j < QK_K/64; ++j) {
@ -8041,13 +8065,15 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32;
xvbit = __lasx_xvreplgr2vr_h(bit++);
const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4);
const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4);
const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4);
const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0);
hmask = __lasx_xvslli_h(hmask, 1);
xvbit = __lasx_xvreplgr2vr_h(bit++);
const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4);
const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4);
const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4);
const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1);
hmask = __lasx_xvslli_h(hmask, 1);
@ -8061,10 +8087,12 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r
p16_1 = lasx_madd_h(scale_1, p16_1);
sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1));
}
__m256 vd = __lasx_xvreplfr2vr_s(d);
acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc);
}
*s = hsum_float_8(acc) + summs;

56
ggml.c
View file

@ -1576,11 +1576,11 @@ do { \
// F16 arithmetic is not supported by AVX, so we use F32 instead
#define GGML_F32Cx8 __m256
#define GGML_F32Cx8 __m256
#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0)
#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x))
static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) {
static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) {
float tmp[8];
for (int i = 0; i < 8; i++) {
@ -1589,13 +1589,14 @@ static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) {
return (__m256)__lasx_xvld(tmp, 0);
}
static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
float arr[8];
__lasx_xvst(y, arr, 0);
for (int i = 0; i < 8; i++)
for (int i = 0; i < 8; i++) {
x[i] = GGML_FP32_TO_FP16(arr[i]);
}
}
#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x)
#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y)
@ -1671,7 +1672,7 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) {
#define GGML_F16_STEP 32
#define GGML_F16_EPR 4
static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) {
static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) {
float tmp[4];
tmp[0] = GGML_FP16_TO_FP32(x[0]);
@ -1682,7 +1683,7 @@ static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) {
return __lsx_vld(tmp, 0);
}
static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) {
static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
float arr[4];
__lsx_vst(y, arr, 0);
@ -2315,32 +2316,27 @@ inline static __m512 ggml_v_expf(__m512 x) {
const __m512 r = _mm512_set1_ps(0x1.8p23f);
const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r);
const __m512 n = _mm512_sub_ps(z, r);
const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f),
_mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x));
const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23);
const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1))));
const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ);
const __m512 u = _mm512_mul_ps(b, b);
const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b,
_mm512_set1_ps(0x1.573e2ep-5f)), u,
_mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b,
_mm512_set1_ps(0x1.fffdb6p-2f))),
u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b));
if (_mm512_kortestz(c, c))
return _mm512_fmadd_ps(j, k, k);
const __m512i g = _mm512_and_si512(
_mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)),
_mm512_set1_epi32(0x82000000u));
const __m512 s1 =
_mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u)));
const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g));
const __m512 b =
_mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f),
_mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x));
const __mmask16 d =
_mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ);
return _mm512_mask_blend_ps(
d, _mm512_mask_blend_ps(
c, _mm512_fmadd_ps(k, j, k),
_mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)),
_mm512_mul_ps(s1, s1));
const __m512 u = _mm512_mul_ps(b, b);
const __m512 j = _mm512_fmadd_ps(
_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b,
_mm512_set1_ps(0x1.573e2ep-5f)),
u,
_mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b,
_mm512_set1_ps(0x1.fffdb6p-2f))),
u,
_mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F)));
const __m512 res = _mm512_scalef_ps(j, n);
if (_mm512_kortestz(d, d))
return res;
const __m512 zero = _mm512_setzero_ps();
const __m512 alt = _mm512_mask_blend_ps(
_mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero);
return _mm512_mask_blend_ps(d, res, alt);
}
// computes silu x/(1+exp(-x)) in single precision vector

302
gguf-py/gguf/vocab.py
View file

@ -1,10 +1,15 @@
from __future__ import annotations
import re
import logging
import json
import os
from pathlib import Path
from typing import Any, Callable, Sequence, Mapping, Iterable
from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable
from sentencepiece import SentencePieceProcessor
import gguf
from .gguf_writer import GGUFWriter
@ -163,3 +168,298 @@ class SpecialVocab:
for typ in self.special_token_types:
self._set_special_token(typ, config.get(f'{typ}_token_id'))
return True
@runtime_checkable
class BaseVocab(Protocol):
tokenizer_model: ClassVar[str]
name: ClassVar[str]
@runtime_checkable
class Vocab(BaseVocab, Protocol):
vocab_size: int
added_tokens_dict: dict[str, int]
added_tokens_list: list[str]
fname_tokenizer: Path
def __init__(self, base_path: Path): ...
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
class NoVocab(BaseVocab):
tokenizer_model = "no_vocab"
name = "no_vocab"
def __repr__(self) -> str:
return "<NoVocab for a model without integrated vocabulary>"
class BpeVocab(Vocab):
tokenizer_model = "gpt2"
name = "bpe"
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'vocab.json').exists():
# "slow" tokenizer
with open(fname_tokenizer, encoding="utf-8") as f:
self.vocab = json.load(f)
try:
# FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
with open(base_path / 'added_tokens.json', encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
else:
# "fast" tokenizer
fname_tokenizer = base_path / 'tokenizer.json'
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding="utf-8") as f:
tokenizer_json = json.load(f)
tokenizer_model: dict[str, Any] = tokenizer_json['model']
if (
tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'ByteLevel'
):
raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
self.vocab = tokenizer_model["vocab"]
if (added := tokenizer_json.get('added_tokens')) is not None:
# Added tokens here can be duplicates of the main vocabulary.
added_tokens = {item['content']: item['id']
for item in added
if item['content'] not in self.vocab}
vocab_size = len(self.vocab)
expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
actual_ids = sorted(added_tokens.values())
if expected_ids != actual_ids:
expected_end_id = vocab_size + len(actual_ids) - 1
raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
f"{vocab_size} - {expected_end_id}; got {actual_ids}")
items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
self.added_tokens_dict = added_tokens
self.added_tokens_list = [text for (text, idx) in items]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
for i, _ in enumerate(self.vocab):
yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.bpe_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class SentencePieceVocab(Vocab):
tokenizer_model = "llama"
name = "spm"
def __init__(self, base_path: Path):
added_tokens: dict[str, int] = {}
if (fname_tokenizer := base_path / 'tokenizer.model').exists():
# normal location
try:
with open(base_path / 'added_tokens.json', encoding="utf-8") as f:
added_tokens = json.load(f)
except FileNotFoundError:
pass
elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
# not found in alternate location either
raise FileNotFoundError('Cannot find tokenizer.model')
self.sentencepiece_tokenizer = SentencePieceProcessor()
self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
vocab_size = self.sentencepiece_tokenizer.vocab_size()
new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
actual_new_ids = sorted(new_tokens.keys())
if expected_new_ids != actual_new_ids:
raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
# Token pieces that were added to the base vocabulary.
self.added_tokens_dict = added_tokens
self.added_tokens_list = [new_tokens[id] for id in actual_new_ids]
self.vocab_size_base = vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.sentencepiece_tokenizer
for i in range(tokenizer.vocab_size()):
piece = tokenizer.IdToPiece(i)
text = piece.encode("utf-8")
score: float = tokenizer.GetScore(i)
toktype = gguf.TokenType.NORMAL
if tokenizer.IsUnknown(i):
toktype = gguf.TokenType.UNKNOWN
if tokenizer.IsControl(i):
toktype = gguf.TokenType.CONTROL
# NOTE: I think added_tokens are user defined.
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
if tokenizer.IsUnused(i):
toktype = gguf.TokenType.UNUSED
if tokenizer.IsByte(i):
toktype = gguf.TokenType.BYTE
yield text, score, toktype
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
score = -1000.0
yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.sentencepiece_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
class LlamaHfVocab(Vocab):
tokenizer_model = "llama"
name = "hfft"
def __init__(self, base_path: Path):
fname_tokenizer = base_path / 'tokenizer.json'
# if this fails, FileNotFoundError propagates to caller
with open(fname_tokenizer, encoding='utf-8') as f:
tokenizer_json = json.load(f)
# pre-check so we know if we need transformers
tokenizer_model: dict[str, Any] = tokenizer_json['model']
is_llama3 = (
tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
and not tokenizer_model.get('byte_fallback', True)
)
if is_llama3:
raise TypeError('Llama 3 must be converted with BpeVocab')
if not is_llama3 and (
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
or tokenizer_json['decoder']['type'] != 'Sequence'
):
raise FileNotFoundError('Cannot find Llama BPE tokenizer')
try:
from transformers import AutoTokenizer
except ImportError as e:
raise ImportError(
"To use LlamaHfVocab, please install the `transformers` package. "
"You can install it with `pip install transformers`."
) from e
# Allow the tokenizer to default to slow or fast versions.
# Explicitly set tokenizer to use local paths.
self.tokenizer = AutoTokenizer.from_pretrained(
base_path,
cache_dir=base_path,
local_files_only=True,
)
assert self.tokenizer.is_fast # assume tokenizer.json is used
# Initialize lists and dictionaries for added tokens
self.added_tokens_list = []
self.added_tokens_dict = dict()
self.added_tokens_ids = set()
# Process added tokens
for tok, tokidx in sorted(
self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
):
# Only consider added tokens that are not in the base vocabulary
if tokidx >= self.tokenizer.vocab_size:
self.added_tokens_list.append(tok)
self.added_tokens_dict[tok] = tokidx
self.added_tokens_ids.add(tokidx)
# Store special tokens and their IDs
self.specials = {
tok: self.tokenizer.get_vocab()[tok]
for tok in self.tokenizer.all_special_tokens
}
self.special_ids = set(self.tokenizer.all_special_ids)
# Set vocabulary sizes
self.vocab_size_base = self.tokenizer.vocab_size
self.vocab_size = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
reverse_vocab = {
id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
}
for token_id in range(self.vocab_size_base):
# Skip processing added tokens here
if token_id in self.added_tokens_ids:
continue
# Convert token text to bytes
token_text = reverse_vocab[token_id].encode("utf-8")
# Yield token text, score, and type
yield token_text, self.get_token_score(token_id), self.get_token_type(
token_id, token_text, self.special_ids # Reuse already stored special IDs
)
def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
# Special case for byte tokens
if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
return gguf.TokenType.BYTE
# Determine token type based on whether it's a special token
return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
def get_token_score(self, token_id: int) -> float:
# Placeholder for actual logic to determine the token's score
# This needs to be implemented based on specific requirements
return -1000.0 # Default score
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
if text in self.specials:
toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
score = self.get_token_score(self.specials[text])
else:
toktype = gguf.TokenType.USER_DEFINED
score = -1000.0
yield text.encode("utf-8"), score, toktype
def has_newline_token(self):
return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
yield from self.hf_tokens()
yield from self.added_tokens()
def __repr__(self) -> str:
return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"

199
llama.cpp
View file

@ -1702,12 +1702,13 @@ struct llama_mlock {
};
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
// NOTE: avoid ever using this except for building the token_to_piece caches
static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
int check = llama_token_to_piece(model, token, result.data(), result.size(), special);
GGML_ASSERT(check == -n_tokens);
}
else {
@ -2162,7 +2163,9 @@ struct llama_vocab {
std::unordered_map<token, id> token_to_id;
std::vector<token_data> id_to_token;
std::vector<id> special_tokens_cache;
std::vector<id> cache_special_tokens;
std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = false);
std::vector<token> cache_token_to_piece_special; // llama_token_to_piece(special = true);
std::map<std::pair<std::string, std::string>, int> bpe_ranks;
@ -4592,20 +4595,14 @@ static void llm_load_vocab(
vocab.special_cls_id = 101;
vocab.special_mask_id = 103;
vocab.add_space_prefix = false;
} else {
if (tokenizer_model == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;
} else if (tokenizer_model == "gpt2") {
vocab.type = LLAMA_VOCAB_TYPE_BPE;
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
}
} else {
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str());
LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
vocab.type = LLAMA_VOCAB_TYPE_SPM;
return;
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
if (add_space_prefix_keyidx != -1) {
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
}
// read bpe merges and populate bpe ranks
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
if (merges_keyidx == -1) {
@ -4639,6 +4636,8 @@ static void llm_load_vocab(
vocab.special_pad_id = -1;
vocab.special_cls_id = -1;
vocab.special_mask_id = -1;
} else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
}
// for now, only BPE models have pre-tokenizers
@ -4833,17 +4832,38 @@ static void llm_load_vocab(
{
for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
vocab.special_tokens_cache.push_back(id);
vocab.cache_special_tokens.push_back(id);
}
}
std::sort( vocab.special_tokens_cache.begin(), vocab.special_tokens_cache.end(),
std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
[&] (const llama_vocab::id a, const llama_vocab::id b) {
return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
}
);
LLAMA_LOG_INFO("%s: special tokens cache size = %u.\n", __func__, (uint32_t)vocab.special_tokens_cache.size());
LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
}
// build token to piece caches
{
size_t size_cache = 0;
std::vector<llama_vocab::token> cache_token_to_piece (n_vocab);
std::vector<llama_vocab::token> cache_token_to_piece_special(n_vocab);
for (uint32_t id = 0; id < n_vocab; ++id) {
cache_token_to_piece[id] = llama_token_to_piece(&model, id, false);
cache_token_to_piece_special[id] = llama_token_to_piece(&model, id, true);
size_cache += cache_token_to_piece[id].size();
size_cache += cache_token_to_piece_special[id].size();
}
std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
std::swap(vocab.cache_token_to_piece_special, cache_token_to_piece_special);
LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
}
}
@ -13233,7 +13253,7 @@ struct fragment_buffer_variant {
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
// for each special token
for (const llama_vocab::id special_id : vocab.special_tokens_cache) {
for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
const auto & special_token = vocab.id_to_token[special_id].text;
// for each text fragment
@ -14392,7 +14412,7 @@ void llama_sample_repetition_penalties(
void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
GGML_ASSERT(ctx);
const int64_t t_start_sample_us = ggml_time_us();
int64_t t_start_sample_us = ggml_time_us();
bool allow_eog = false;
for (const auto & stack : grammar->stacks) {
@ -14404,12 +14424,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
candidates_decoded.reserve(candidates->size);
std::vector<llama_grammar_candidate> candidates_grammar;
std::vector<llama_grammar_candidate> candidates_grammar;
candidates_grammar.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const std::string piece = llama_token_to_piece(ctx, id, false);
const llama_token id = candidates->data[i].id;
const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id);
if (llama_token_is_eog(&ctx->model, id)) {
if (!allow_eog) {
@ -14609,7 +14630,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
GGML_ASSERT(false);
}
const std::string piece = llama_token_to_piece(ctx, token, false);
const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token);
// Note terminating 0 in decoded string
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
@ -18292,69 +18313,83 @@ static std::string llama_decode_text(const std::string & text) {
// does not write null-terminator to buf
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
// if we have a cache - use it
{
const auto & cache = special ? model->vocab.cache_token_to_piece_special : model->vocab.cache_token_to_piece;
if (!cache.empty()) {
const auto & res = cache.at(token);
if (length < (int) res.size()) {
return -(int) res.size();
}
memcpy(buf, res.c_str(), res.size());
return res.size();
}
}
if (0 <= token && token < llama_n_vocab(model)) {
switch (llama_vocab_get_type(model->vocab)) {
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_SPM: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
llama_unescape_whitespace(result);
if (length < (int) result.length()) {
return -(int) result.length();
case LLAMA_VOCAB_TYPE_WPM:
case LLAMA_VOCAB_TYPE_SPM: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
llama_unescape_whitespace(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
if (length < 3) {
return -3;
}
memcpy(buf, "\xe2\x96\x85", 3);
return 3;
} else if (llama_is_byte_token(model->vocab, token)) {
if (length < 1) {
return -1;
}
buf[0] = llama_token_to_byte(model->vocab, token);
return 1;
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
if (length < 3) {
return -3;
}
memcpy(buf, "\xe2\x96\x85", 3);
return 3;
} else if (llama_is_byte_token(model->vocab, token)) {
if (length < 1) {
return -1;
}
buf[0] = llama_token_to_byte(model->vocab, token);
return 1;
break;
}
break;
}
case LLAMA_VOCAB_TYPE_BPE: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
result = llama_decode_text(result);
if (length < (int) result.length()) {
return -(int) result.length();
case LLAMA_VOCAB_TYPE_BPE: {
// NOTE: we accept all unsupported token types,
// suppressing them like CONTROL tokens.
if (llama_is_normal_token(model->vocab, token)) {
std::string result = model->vocab.id_to_token[token].text;
result = llama_decode_text(result);
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
} else if (
(llama_is_user_defined_token(model->vocab, token)) ||
(llama_is_control_token (model->vocab, token) && special)) {
std::string result = model->vocab.id_to_token[token].text;
if (length < (int) result.length()) {
return -(int) result.length();
}
memcpy(buf, result.c_str(), result.length());
return result.length();
break;
}
break;
}
default:
GGML_ASSERT(false);
default:
GGML_ASSERT(false);
}
}
return 0;

4
llama.h
View file

@ -424,8 +424,8 @@ extern "C" {
LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);

2
requirements.txt
View file

@ -4,7 +4,7 @@
# Package versions must stay compatible across all top-level python scripts.
#
-r ./requirements/requirements-convert.txt
-r ./requirements/requirements-convert-legacy-llama.txt
-r ./requirements/requirements-convert-hf-to-gguf.txt
-r ./requirements/requirements-convert-hf-to-gguf-update.txt

2
requirements/requirements-convert-hf-to-gguf-update.txt
View file

@ -1,2 +1,2 @@
-r ./requirements-convert.txt
-r ./requirements-convert-legacy-llama.txt
torch~=2.1.1

2
requirements/requirements-convert-hf-to-gguf.txt
View file

@ -1,2 +1,2 @@
-r ./requirements-convert.txt
-r ./requirements-convert-legacy-llama.txt
torch~=2.1.1

0
requirements/requirements-convert.txt → requirements/requirements-convert-legacy-llama.txt
View file

2
requirements/requirements-convert-llama-ggml-to-gguf.txt
View file

@ -1 +1 @@
-r ./requirements-convert.txt
-r ./requirements-convert-legacy-llama.txt

2
scripts/check-requirements.sh
View file

@ -166,7 +166,7 @@ if (( do_cleanup )); then
rm -rf -- "$all_venv"
fi
check_convert_script convert.py
check_convert_script examples/convert-legacy-llama.py
for py in convert-*.py; do
# skip convert-hf-to-gguf-update.py
# TODO: the check is failing for some reason:

20
scripts/convert-gg.sh
View file

@ -3,20 +3,20 @@
set -e
# LLaMA v1
python3 convert.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
# LLaMA v2
python3 convert.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
# Code Llama
python3 convert.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
# Falcon
python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b 1

14
scripts/pod-llama.sh
View file

@ -75,7 +75,7 @@ if [ "$1" -eq "1" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16
./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
@ -90,7 +90,7 @@ if [ "$1" -eq "2" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
@ -105,7 +105,7 @@ if [ "$1" -eq "3" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
@ -120,7 +120,7 @@ if [ "$1" -eq "4" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
@ -135,7 +135,7 @@ if [ "$1" -eq "5" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
@ -150,7 +150,7 @@ if [ "$1" -eq "6" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
@ -165,7 +165,7 @@ if [ "$1" -eq "7" ]; then
cd /workspace/llama.cpp
python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
python3 examples/convert-legacy-llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k

7
tests/CMakeLists.txt
View file

@ -129,8 +129,11 @@ llama_target_and_test(test-rope.cpp)
llama_target_and_test(test-model-load-cancel.cpp LABEL "model")
llama_target_and_test(test-autorelease.cpp LABEL "model")
llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
endif()
# dummy executable - not installed
get_filename_component(TEST_TARGET test-c.c NAME_WE)