Merge branch 'main' into setup

This commit is contained in:
Andrei Betlen 2023-05-26 17:53:58 -04:00
commit 447a3d249e
22 changed files with 1095 additions and 254 deletions

2
.gitmodules vendored
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@ -1,3 +1,3 @@
[submodule "vendor/llama.cpp"]
path = vendor/llama.cpp
url = git@github.com:ggerganov/llama.cpp.git
url = https://github.com/ggerganov/llama.cpp.git

12
CHANGELOG.md Normal file
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@ -0,0 +1,12 @@
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
### Added
- Added first version of the changelog

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@ -28,4 +28,4 @@ else()
LIBRARY DESTINATION llama_cpp
RUNTIME DESTINATION llama_cpp
)
endif(UNIX)
endif()

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@ -15,6 +15,8 @@ This package provides:
- OpenAI-like API
- LangChain compatibility
Documentation is available at [https://abetlen.github.io/llama-cpp-python](https://abetlen.github.io/llama-cpp-python).
## Installation from PyPI (recommended)
Install from PyPI (requires a c compiler):
@ -26,6 +28,18 @@ pip install llama-cpp-python
The above command will attempt to install the package and build build `llama.cpp` from source.
This is the recommended installation method as it ensures that `llama.cpp` is built with the available optimizations for your system.
If you have previously installed `llama-cpp-python` through pip and want to upgrade your version or rebuild the package with different compiler options, please add the following flags to ensure that the package is rebuilt correctly:
```bash
pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir
```
Note: If you are using Apple Silicon (M1) Mac, make sure you have installed a version of Python that supports arm64 architecture. For example:
```
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh
```
Otherwise, while installing it will build the llama.ccp x86 version which will be 10x slower on Apple Silicon (M1) Mac.
### Installation with OpenBLAS / cuBLAS / CLBlast
@ -35,19 +49,19 @@ Use the `FORCE_CMAKE=1` environment variable to force the use of `cmake` and ins
To install with OpenBLAS, set the `LLAMA_OPENBLAS=1` environment variable before installing:
```bash
LLAMA_OPENBLAS=1 FORCE_CMAKE=1 pip install llama-cpp-python
CMAKE_ARGS="-DLLAMA_OPENBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python
```
To install with cuBLAS, set the `LLAMA_CUBLAS=1` environment variable before installing:
```bash
LLAMA_CUBLAS=1 FORCE_CMAKE=1 pip install llama-cpp-python
CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python
```
To install with CLBlast, set the `LLAMA_CLBLAST=1` environment variable before installing:
```bash
LLAMA_CLBLAST=1 FORCE_CMAKE=1 pip install llama-cpp-python
CMAKE_ARGS="-DLLAMA_CLBLAST=on" FORCE_CMAKE=1 pip install llama-cpp-python
```
@ -102,7 +116,7 @@ Navigate to [http://localhost:8000/docs](http://localhost:8000/docs) to see the
A Docker image is available on [GHCR](https://ghcr.io/abetlen/llama-cpp-python). To run the server:
```bash
docker run --rm -it -p8000:8000 -v /path/to/models:/models -eMODEL=/models/ggml-model-name.bin ghcr.io/abetlen/llama-cpp-python:latest
docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/ggml-model-name.bin ghcr.io/abetlen/llama-cpp-python:latest
```
## Low-level API
@ -120,7 +134,7 @@ Below is a short example demonstrating how to use the low-level API to tokenize
>>> ctx = llama_cpp.llama_init_from_file(b"./models/7b/ggml-model.bin", params)
>>> max_tokens = params.n_ctx
# use ctypes arrays for array params
>>> tokens = (llama_cppp.llama_token * int(max_tokens))()
>>> tokens = (llama_cpp.llama_token * int(max_tokens))()
>>> n_tokens = llama_cpp.llama_tokenize(ctx, b"Q: Name the planets in the solar system? A: ", tokens, max_tokens, add_bos=llama_cpp.c_bool(True))
>>> llama_cpp.llama_free(ctx)
```

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@ -0,0 +1,51 @@
# Define the image argument and provide a default value
ARG IMAGE=python:3-slim-bullseye
# Use the image as specified
FROM ${IMAGE}
# Re-declare the ARG after FROM
ARG IMAGE
# Update and upgrade the existing packages
RUN apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends \
python3 \
python3-pip \
ninja-build \
build-essential
RUN python3 -m pip install --upgrade pip pytest cmake scikit-build setuptools fastapi uvicorn sse-starlette
# Perform the conditional installations based on the image
RUN echo "Image: ${IMAGE}" && \
if [ "${IMAGE}" = "python:3-slim-bullseye" ] ; then \
echo "OpenBLAS install:" && \
apt-get install -y --no-install-recommends libopenblas-dev && \
LLAMA_OPENBLAS=1 pip install llama-cpp-python --verbose; \
else \
echo "CuBLAS install:" && \
LLAMA_CUBLAS=1 pip install llama-cpp-python --verbose; \
fi
# Clean up apt cache
RUN rm -rf /var/lib/apt/lists/*
# Set a working directory for better clarity
WORKDIR /app
# Copy files to the app directory
RUN echo "Installing model...this can take some time..."
COPY ./model.bin /app/model.bin
COPY ./start_server.sh /app/start_server.sh
# Make the server start script executable
RUN chmod +x /app/start_server.sh
# Set environment variable for the host
ENV HOST=0.0.0.0
# Expose a port for the server
EXPOSE 8000
# Run the server start script
CMD ["/bin/sh", "/app/start_server.sh"]

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@ -1,4 +1,5 @@
FROM nvidia/cuda:12.1.1-devel-ubuntu20.04
ARG CUDA_IMAGE="12.1.1-devel-ubuntu22.04"
FROM ${CUDA_IMAGE}
# We need to set the host to 0.0.0.0 to allow outside access
ENV HOST 0.0.0.0
@ -12,4 +13,4 @@ RUN python3 -m pip install --upgrade pip pytest cmake scikit-build setuptools fa
RUN LLAMA_CUBLAS=1 python3 setup.py develop
# Run the server
CMD python3 -m llama_cpp.server
CMD python3 -m llama_cpp.server

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docker/README.md Normal file
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@ -0,0 +1,46 @@
# Dockerfiles for building the llama-cpp-python server
- `Dockerfile.openblas_simple` - a simple Dockerfile for non-GPU OpenBLAS
- `Dockerfile.cuda_simple` - a simple Dockerfile for CUDA accelerated CuBLAS
- `hug_model.py` - a Python utility for interactively choosing and downloading the latest `5_1` quantized models from [huggingface.co/TheBloke]( https://huggingface.co/TheBloke)
- `Dockerfile` - a single OpenBLAS and CuBLAS combined Dockerfile that automatically installs a previously downloaded model `model.bin`
# Get model from Hugging Face
`python3 ./hug_model.py`
You should now have a model in the current directory and `model.bin` symlinked to it for the subsequent Docker build and copy step. e.g.
```
docker $ ls -lh *.bin
-rw-rw-r-- 1 user user 4.8G May 23 18:30 <downloaded-model-file>.q5_1.bin
lrwxrwxrwx 1 user user 24 May 23 18:30 model.bin -> <downloaded-model-file>.q5_1.bin
```
**Note #1:** Make sure you have enough disk space to download the model. As the model is then copied into the image you will need at least
**TWICE** as much disk space as the size of the model:
| Model | Quantized size |
|------:|----------------:|
| 7B | 5 GB |
| 13B | 10 GB |
| 30B | 25 GB |
| 65B | 50 GB |
**Note #2:** If you want to pass or tune additional parameters, customise `./start_server.sh` before running `docker build ...`
# Install Docker Server
**Note #3:** This was tested with Docker running on Linux. If you can get it working on Windows or MacOS, please update this `README.md` with a PR!
[Install Docker Engine](https://docs.docker.com/engine/install)
# Use OpenBLAS
Use if you don't have a NVidia GPU. Defaults to `python:3-slim-bullseye` Docker base image and OpenBLAS:
## Build:
`docker build --build-arg -t openblas .`
## Run:
`docker run --cap-add SYS_RESOURCE -t openblas`
# Use CuBLAS
Requires a NVidia GPU with sufficient VRAM (approximately as much as the size above) and Docker NVidia support (see [container-toolkit/install-guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
## Build:
`docker build --build-arg IMAGE=nvidia/cuda:12.1.1-devel-ubuntu22.04 -t cublas .`
## Run:
`docker run --cap-add SYS_RESOURCE -t cublas`

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@ -0,0 +1,116 @@
import requests
import json
import os
import struct
def make_request(url, params=None):
print(f"Making request to {url}...")
response = requests.get(url, params=params)
if response.status_code == 200:
return json.loads(response.text)
else:
print(f"Request failed with status code {response.status_code}")
return None
def check_magic_and_version(filename):
with open(filename, 'rb') as f:
# Read the first 6 bytes from the file
data = f.read(6)
# Unpack the binary data, interpreting the first 4 bytes as a little-endian unsigned int
# and the next 2 bytes as a little-endian unsigned short
magic, version = struct.unpack('<I H', data)
print(f"magic: 0x{magic:08x}, version: 0x{version:04x}, file: {filename}")
return magic, version
def download_file(url, destination):
print(f"Downloading {url} to {destination}...")
response = requests.get(url, stream=True)
if response.status_code == 200:
with open(destination, 'wb') as f:
total_downloaded = 0
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
total_downloaded += len(chunk)
if total_downloaded >= 10485760: # 10 MB
print('.', end='', flush=True)
total_downloaded = 0
print("\nDownload complete.")
# Creating a symbolic link from destination to "model.bin"
if os.path.isfile("model.bin"):
os.remove("model.bin") # remove the existing link if any
os.symlink(destination, "model.bin")
else:
print(f"Download failed with status code {response.status_code}")
def get_user_choice(model_list):
# Print the enumerated list
print("\n")
for i, (model_id, rfilename) in enumerate(model_list):
print(f"{i+1}: Model ID: {model_id}, RFilename: {rfilename}")
# Get user's choice
choice = input("Choose a model to download by entering the corresponding number: ")
try:
index = int(choice) - 1
if 0 <= index < len(model_list):
# Return the chosen model
return model_list[index]
else:
print("Invalid choice.")
except ValueError:
print("Invalid input. Please enter a number corresponding to a model.")
except IndexError:
print("Invalid choice. Index out of range.")
return None
import argparse
def main():
# Create an argument parser
parser = argparse.ArgumentParser(description='Process the model version.')
parser.add_argument('-v', '--version', type=int, default=0x0003,
help='an integer for the version to be used')
# Parse the arguments
args = parser.parse_args()
# Define the parameters
params = {
"author": "TheBloke", # Filter by author
"tags": "llama"
}
models = make_request('https://huggingface.co/api/models', params=params)
if models is None:
return
model_list = []
# Iterate over the models
for model in models:
model_id = model['id']
model_info = make_request(f'https://huggingface.co/api/models/{model_id}')
if model_info is None:
continue
for sibling in model_info.get('siblings', []):
rfilename = sibling.get('rfilename')
if rfilename and 'q5_1' in rfilename:
model_list.append((model_id, rfilename))
model_choice = get_user_choice(model_list)
if model_choice is not None:
model_id, rfilename = model_choice
url = f"https://huggingface.co/{model_id}/resolve/main/{rfilename}"
download_file(url, rfilename)
_, version = check_magic_and_version(rfilename)
if version != args.version:
print(f"Warning: Expected version {args.version}, but found different version in the file.")
if __name__ == '__main__':
main()

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@ -0,0 +1,11 @@
#!/bin/sh
# For mmap support
ulimit -l unlimited
if [ "$IMAGE" = "python:3-slim-bullseye" ]; then
python3 -B -m llama_cpp.server --model /app/model.bin
else
# You may have to reduce --n_gpu_layers=1000 to 20 or less if you don't have enough VRAM
python3 -B -m llama_cpp.server --model /app/model.bin --n_gpu_layers=1000
fi

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@ -112,8 +112,12 @@ python3 setup.py develop
show_root_heading: true
::: llama_cpp.LlamaCache
options:
show_root_heading: true
::: llama_cpp.LlamaState
options:
show_root_heading: true
::: llama_cpp.llama_cpp
options:

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@ -368,10 +368,10 @@ n_keep = {self.params.n_keep}
id = llama_cpp.llama_sample_token_mirostat_v2(self.ctx, candidates_p, llama_cpp.c_float(self.params.mirostat_tau), llama_cpp.c_float(self.params.mirostat_eta), llama_cpp.c_float(mirostat_mu))
else:
# Temperature sampling
llama_cpp.llama_sample_top_k(self.ctx, candidates_p, top_k)
llama_cpp.llama_sample_tail_free(self.ctx, candidates_p, llama_cpp.c_float(self.params.tfs_z))
llama_cpp.llama_sample_typical(self.ctx, candidates_p, llama_cpp.c_float(self.params.typical_p))
llama_cpp.llama_sample_top_p(self.ctx, candidates_p, llama_cpp.c_float(self.params.top_p))
llama_cpp.llama_sample_top_k(self.ctx, candidates_p, top_k, min_keep=llama_cpp.c_size_t(1))
llama_cpp.llama_sample_tail_free(self.ctx, candidates_p, llama_cpp.c_float(self.params.tfs_z), min_keep=llama_cpp.c_size_t(1))
llama_cpp.llama_sample_typical(self.ctx, candidates_p, llama_cpp.c_float(self.params.typical_p), min_keep=llama_cpp.c_size_t(1))
llama_cpp.llama_sample_top_p(self.ctx, candidates_p, llama_cpp.c_float(self.params.top_p), min_keep=llama_cpp.c_size_t(1))
llama_cpp.llama_sample_temperature(self.ctx, candidates_p, llama_cpp.c_float(self.params.temp))
id = llama_cpp.llama_sample_token(self.ctx, candidates_p)
# print("`{}`".format(candidates_p.size))
@ -382,12 +382,15 @@ n_keep = {self.params.n_keep}
# replace end of text token with newline token when in interactive mode
if (id == llama_cpp.llama_token_eos() and self.params.interactive and not self.params.instruct):
id = self.llama_token_newline[0]
self.embd.append(id)
if (self.use_antiprompt()):
# tokenize and inject first reverse prompt
self.embd_inp += self.first_antiprompt[0]
# add it to the context
self.embd.append(id)
for id in self.first_antiprompt[0]:
self.embd.append(id)
else:
# add it to the context
self.embd.append(id)
# echo this to console
self.output_echo = True
@ -493,7 +496,7 @@ n_keep = {self.params.n_keep}
# Contains multi-byte UTF8
for num, pattern in [(2, 192), (3, 224), (4, 240)]:
# Bitwise AND check
if pattern & int.from_bytes(cur_char) == pattern:
if pattern & int.from_bytes(cur_char, 'little') == pattern:
self.multibyte_fix = [cur_char] + ([None] * (num-1))
# Stop incomplete bytes from passing

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@ -4,7 +4,17 @@ import uuid
import time
import math
import multiprocessing
from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque, Tuple
from typing import (
List,
Optional,
Union,
Generator,
Sequence,
Iterator,
Deque,
Tuple,
Callable,
)
from collections import deque, OrderedDict
from . import llama_cpp
@ -15,9 +25,7 @@ class LlamaCache:
"""Cache for a llama.cpp model."""
def __init__(self, capacity_bytes: int = (2 << 30)):
self.cache_state: OrderedDict[
Tuple[llama_cpp.llama_token, ...], "LlamaState"
] = OrderedDict()
self.cache_state: OrderedDict[Tuple[int, ...], "LlamaState"] = OrderedDict()
self.capacity_bytes = capacity_bytes
@property
@ -26,8 +34,8 @@ class LlamaCache:
def _find_longest_prefix_key(
self,
key: Tuple[llama_cpp.llama_token, ...],
) -> Optional[Tuple[llama_cpp.llama_token, ...]]:
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
min_len = 0
min_key = None
keys = (
@ -39,7 +47,7 @@ class LlamaCache:
min_key = k
return min_key
def __getitem__(self, key: Sequence[llama_cpp.llama_token]) -> "LlamaState":
def __getitem__(self, key: Sequence[int]) -> "LlamaState":
key = tuple(key)
_key = self._find_longest_prefix_key(key)
if _key is None:
@ -48,10 +56,10 @@ class LlamaCache:
self.cache_state.move_to_end(_key)
return value
def __contains__(self, key: Sequence[llama_cpp.llama_token]) -> bool:
def __contains__(self, key: Sequence[int]) -> bool:
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[llama_cpp.llama_token], value: "LlamaState"):
def __setitem__(self, key: Sequence[int], value: "LlamaState"):
key = tuple(key)
if key in self.cache_state:
del self.cache_state[key]
@ -63,7 +71,7 @@ class LlamaCache:
class LlamaState:
def __init__(
self,
eval_tokens: Deque[llama_cpp.llama_token],
eval_tokens: Deque[int],
eval_logits: Deque[List[float]],
llama_state, # type: llama_cpp.Array[llama_cpp.c_uint8]
llama_state_size: int,
@ -74,6 +82,24 @@ class LlamaState:
self.llama_state_size = llama_state_size
LogitsProcessor = Callable[[List[int], List[float]], List[float]]
class LogitsProcessorList(List[LogitsProcessor]):
def __call__(self, input_ids: List[int], scores: List[float]) -> List[float]:
for processor in self:
scores = processor(input_ids, scores)
return scores
StoppingCriteria = Callable[[List[int], List[float]], bool]
class StoppingCriteriaList(List[StoppingCriteria]):
def __call__(self, input_ids: List[int], logits: List[float]) -> bool:
return any([stopping_criteria(input_ids, logits) for stopping_criteria in self])
class Llama:
"""High-level Python wrapper for a llama.cpp model."""
@ -83,6 +109,7 @@ class Llama:
# NOTE: These parameters are likely to change in the future.
n_ctx: int = 512,
n_parts: int = -1,
n_gpu_layers: int = 0,
seed: int = 1337,
f16_kv: bool = True,
logits_all: bool = False,
@ -128,7 +155,7 @@ class Llama:
self.params = llama_cpp.llama_context_default_params()
self.params.n_ctx = n_ctx
self.params.n_parts = n_parts
self.params.n_gpu_layers = n_gpu_layers
self.params.seed = seed
self.params.f16_kv = f16_kv
self.params.logits_all = logits_all
@ -139,7 +166,7 @@ class Llama:
self.last_n_tokens_size = last_n_tokens_size
self.n_batch = min(n_ctx, n_batch)
self.eval_tokens: Deque[llama_cpp.llama_token] = deque(maxlen=n_ctx)
self.eval_tokens: Deque[int] = deque(maxlen=n_ctx)
self.eval_logits: Deque[List[float]] = deque(maxlen=n_ctx if logits_all else 1)
self.cache: Optional[LlamaCache] = None
@ -149,6 +176,10 @@ class Llama:
self.lora_base = lora_base
self.lora_path = lora_path
### DEPRECATED ###
self.n_parts = n_parts
### DEPRECATED ###
if not os.path.exists(model_path):
raise ValueError(f"Model path does not exist: {model_path}")
@ -174,7 +205,30 @@ class Llama:
if self.verbose:
print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
def tokenize(self, text: bytes) -> List[llama_cpp.llama_token]:
self._n_vocab = self.n_vocab()
self._n_ctx = self.n_ctx()
data = (llama_cpp.llama_token_data * self._n_vocab)(
*[
llama_cpp.llama_token_data(
id=llama_cpp.llama_token(i),
logit=llama_cpp.c_float(0.0),
p=llama_cpp.c_float(0.0),
)
for i in range(self._n_vocab)
]
)
size = llama_cpp.c_size_t(self._n_vocab)
sorted = False
candidates = llama_cpp.llama_token_data_array(
data=data,
size=size,
sorted=sorted,
)
self._candidates = candidates
self._token_nl = Llama.token_nl()
self._token_eos = Llama.token_eos()
def tokenize(self, text: bytes, add_bos: bool = True) -> List[int]:
"""Tokenize a string.
Args:
@ -187,20 +241,32 @@ class Llama:
A list of tokens.
"""
assert self.ctx is not None
n_ctx = llama_cpp.llama_n_ctx(self.ctx)
tokens = (llama_cpp.llama_token * int(n_ctx))()
n_ctx = self._n_ctx
tokens = (llama_cpp.llama_token * n_ctx)()
n_tokens = llama_cpp.llama_tokenize(
self.ctx,
text,
tokens,
n_ctx,
llama_cpp.c_bool(True),
llama_cpp.c_int(n_ctx),
llama_cpp.c_bool(add_bos),
)
if int(n_tokens) < 0:
raise RuntimeError(f'Failed to tokenize: text="{text}" n_tokens={n_tokens}')
if n_tokens < 0:
n_tokens = abs(n_tokens)
tokens = (llama_cpp.llama_token * n_tokens)()
n_tokens = llama_cpp.llama_tokenize(
self.ctx,
text,
tokens,
llama_cpp.c_int(n_tokens),
llama_cpp.c_bool(add_bos),
)
if n_tokens < 0:
raise RuntimeError(
f'Failed to tokenize: text="{text}" n_tokens={n_tokens}'
)
return list(tokens[:n_tokens])
def detokenize(self, tokens: List[llama_cpp.llama_token]) -> bytes:
def detokenize(self, tokens: List[int]) -> bytes:
"""Detokenize a list of tokens.
Args:
@ -212,7 +278,9 @@ class Llama:
assert self.ctx is not None
output = b""
for token in tokens:
output += llama_cpp.llama_token_to_str(self.ctx, token)
output += llama_cpp.llama_token_to_str(
self.ctx, llama_cpp.llama_token(token)
)
return output
def set_cache(self, cache: Optional[LlamaCache]):
@ -228,14 +296,14 @@ class Llama:
self.eval_tokens.clear()
self.eval_logits.clear()
def eval(self, tokens: Sequence[llama_cpp.llama_token]):
def eval(self, tokens: Sequence[int]):
"""Evaluate a list of tokens.
Args:
tokens: The list of tokens to evaluate.
"""
assert self.ctx is not None
n_ctx = int(llama_cpp.llama_n_ctx(self.ctx))
n_ctx = self._n_ctx
for i in range(0, len(tokens), self.n_batch):
batch = tokens[i : min(len(tokens), i + self.n_batch)]
n_past = min(n_ctx - len(batch), len(self.eval_tokens))
@ -247,18 +315,16 @@ class Llama:
n_past=llama_cpp.c_int(n_past),
n_threads=llama_cpp.c_int(self.n_threads),
)
if int(return_code) != 0:
if return_code != 0:
raise RuntimeError(f"llama_eval returned {return_code}")
# Save tokens
self.eval_tokens.extend(batch)
# Save logits
rows = n_tokens if self.params.logits_all else 1
n_vocab = llama_cpp.llama_n_vocab(self.ctx)
cols = int(n_vocab)
n_vocab = self._n_vocab
cols = n_vocab
logits_view = llama_cpp.llama_get_logits(self.ctx)
logits: List[List[float]] = [
[logits_view[i * cols + j] for j in range(cols)] for i in range(rows)
]
logits = [logits_view[i * cols : (i + 1) * cols] for i in range(rows)]
self.eval_logits.extend(logits)
def _sample(
@ -275,28 +341,33 @@ class Llama:
mirostat_mode: llama_cpp.c_int,
mirostat_tau: llama_cpp.c_float,
mirostat_eta: llama_cpp.c_float,
penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None,
):
assert self.ctx is not None
assert len(self.eval_logits) > 0
n_vocab = int(llama_cpp.llama_n_vocab(self.ctx))
n_vocab = self._n_vocab
n_ctx = self._n_ctx
top_k = llama_cpp.c_int(n_vocab) if top_k.value <= 0 else top_k
last_n_tokens_size = (
llama_cpp.c_int(n_ctx)
if last_n_tokens_size.value < 0
else last_n_tokens_size
)
logits = self.eval_logits[-1]
data = (llama_cpp.llama_token_data * n_vocab)(
*[
llama_cpp.llama_token_data(
id=llama_cpp.llama_token(i),
logit=logits[i],
p=llama_cpp.c_float(0.0),
)
for i in range(n_vocab)
]
)
size = llama_cpp.c_size_t(n_vocab)
sorted = False
candidates = llama_cpp.llama_token_data_array(
data=data,
size=size,
sorted=sorted,
)
if logits_processor is not None:
logits = logits_processor(list(self.eval_tokens), logits)
self.eval_logits[-1] = logits
nl_logit = logits[self._token_nl]
candidates = self._candidates
for i, logit in enumerate(logits):
candidates.data[i].id = llama_cpp.llama_token(i)
candidates.data[i].logit = llama_cpp.c_float(logit)
candidates.data[i].p = llama_cpp.c_float(0.0)
candidates.sorted = llama_cpp.c_bool(False)
candidates.size = llama_cpp.c_size_t(n_vocab)
llama_cpp.llama_sample_repetition_penalty(
ctx=self.ctx,
last_tokens_data=last_n_tokens_data,
@ -312,6 +383,8 @@ class Llama:
alpha_frequency=frequency_penalty,
alpha_presence=presence_penalty,
)
if not penalize_nl:
candidates.data[self._token_nl].logit = llama_cpp.c_float(nl_logit)
if temp.value == 0.0:
return llama_cpp.llama_sample_token_greedy(
ctx=self.ctx,
@ -394,6 +467,8 @@ class Llama:
mirostat_mode: int = 0,
mirostat_eta: float = 0.1,
mirostat_tau: float = 5.0,
penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None,
):
"""Sample a token from the model.
@ -425,24 +500,27 @@ class Llama:
mirostat_mode=llama_cpp.c_int(mirostat_mode),
mirostat_tau=llama_cpp.c_float(mirostat_tau),
mirostat_eta=llama_cpp.c_float(mirostat_eta),
penalize_nl=penalize_nl,
logits_processor=logits_processor,
)
def generate(
self,
tokens: Sequence[llama_cpp.llama_token],
top_k: int,
top_p: float,
temp: float,
repeat_penalty: float,
tokens: Sequence[int],
top_k: int = 40,
top_p: float = 0.95,
temp: float = 0.80,
repeat_penalty: float = 1.1,
reset: bool = True,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
) -> Generator[
llama_cpp.llama_token, Optional[Sequence[llama_cpp.llama_token]], None
]:
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
) -> Generator[int, Optional[Sequence[int]], None]:
"""Create a generator of tokens from a prompt.
Examples:
@ -495,16 +573,24 @@ class Llama:
repeat_penalty=repeat_penalty,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
logits_processor=logits_processor,
)
if stopping_criteria is not None and stopping_criteria(
list(self.eval_tokens), self.eval_logits[-1]
):
return
tokens_or_none = yield token
tokens = [token]
if tokens_or_none is not None:
tokens.extend(tokens_or_none)
def create_embedding(self, input: str) -> Embedding:
def create_embedding(
self, input: Union[str, List[str]], model: Optional[str] = None
) -> Embedding:
"""Embed a string.
Args:
@ -514,6 +600,7 @@ class Llama:
An embedding object.
"""
assert self.ctx is not None
model_name: str = model if model is not None else self.model_path
if self.params.embedding == False:
raise RuntimeError(
@ -523,30 +610,40 @@ class Llama:
if self.verbose:
llama_cpp.llama_reset_timings(self.ctx)
tokens = self.tokenize(input.encode("utf-8"))
self.reset()
self.eval(tokens)
n_tokens = len(tokens)
embedding = llama_cpp.llama_get_embeddings(self.ctx)[
: llama_cpp.llama_n_embd(self.ctx)
]
if isinstance(input, str):
inputs = [input]
else:
inputs = input
data: List[EmbeddingData] = []
total_tokens = 0
for index, input in enumerate(inputs):
tokens = self.tokenize(input.encode("utf-8"))
self.reset()
self.eval(tokens)
n_tokens = len(tokens)
total_tokens += n_tokens
embedding = llama_cpp.llama_get_embeddings(self.ctx)[
: llama_cpp.llama_n_embd(self.ctx)
]
data.append(
{
"object": "embedding",
"embedding": embedding,
"index": index,
}
)
if self.verbose:
llama_cpp.llama_print_timings(self.ctx)
return {
"object": "list",
"data": [
{
"object": "embedding",
"embedding": embedding,
"index": 0,
}
],
"model": self.model_path,
"data": data,
"model": model_name,
"usage": {
"prompt_tokens": n_tokens,
"total_tokens": n_tokens,
"prompt_tokens": total_tokens,
"total_tokens": total_tokens,
},
}
@ -570,35 +667,39 @@ class Llama:
top_p: float = 0.95,
logprobs: Optional[int] = None,
echo: bool = False,
stop: Optional[List[str]] = [],
stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
repeat_penalty: float = 1.1,
top_k: int = 40,
stream: bool = False,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Iterator[Completion], Iterator[CompletionChunk]]:
assert self.ctx is not None
completion_id: str = f"cmpl-{str(uuid.uuid4())}"
created: int = int(time.time())
completion_tokens: List[llama_cpp.llama_token] = []
completion_tokens: List[int] = []
# Add blank space to start of prompt to match OG llama tokenizer
prompt_tokens: List[llama_cpp.llama_token] = self.tokenize(
b" " + prompt.encode("utf-8")
)
prompt_tokens: List[int] = self.tokenize(b" " + prompt.encode("utf-8"))
text: bytes = b""
returned_characters: int = 0
stop = stop if stop is not None else []
returned_tokens: int = 0
stop = (
stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
)
model_name: str = model if model is not None else self.model_path
if self.verbose:
llama_cpp.llama_reset_timings(self.ctx)
if len(prompt_tokens) + max_tokens > int(llama_cpp.llama_n_ctx(self.ctx)):
raise ValueError(
f"Requested tokens exceed context window of {llama_cpp.llama_n_ctx(self.ctx)}"
)
if len(prompt_tokens) + max_tokens > self._n_ctx:
raise ValueError(f"Requested tokens exceed context window of {self._n_ctx}")
if stop != []:
stop_sequences = [s.encode("utf-8") for s in stop]
@ -634,14 +735,17 @@ class Llama:
top_k=top_k,
top_p=top_p,
temp=temperature,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
repeat_penalty=repeat_penalty,
stopping_criteria=stopping_criteria,
logits_processor=logits_processor,
):
if token == llama_cpp.llama_token_eos():
if token == self._token_eos:
text = self.detokenize(completion_tokens)
finish_reason = "stop"
break
@ -671,63 +775,189 @@ class Llama:
break
if stream:
start = returned_characters
longest = 0
# We want to avoid yielding any characters from
# the generated text if they are part of a stop
# sequence.
first_stop_position = 0
for s in stop_sequences:
for i in range(len(s), 0, -1):
if all_text.endswith(s[:i]):
if i > longest:
longest = i
if i > first_stop_position:
first_stop_position = i
break
text = all_text[: len(all_text) - longest]
returned_characters += len(text[start:])
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": self.model_path,
"choices": [
{
"text": text[start:].decode("utf-8", errors="ignore"),
"index": 0,
"logprobs": None,
"finish_reason": None,
token_end_position = 0
remaining_tokens = completion_tokens[returned_tokens:]
remaining_length = len(self.detokenize(remaining_tokens))
for token in remaining_tokens:
token_end_position += len(self.detokenize([token]))
# Check if stop sequence is in the token
if token_end_position >= (
remaining_length - first_stop_position - 1
):
break
logprobs_or_none: Optional[CompletionLogprobs] = None
if logprobs is not None:
token_str = self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
text_offset = len(prompt) + len(
self.detokenize(completion_tokens[:returned_tokens])
)
token_offset = len(prompt_tokens) + returned_tokens
logits = self.eval_logits[token_offset - 1]
current_logprobs = Llama.logits_to_logprobs(logits)
sorted_logprobs = list(
sorted(
zip(current_logprobs, range(len(current_logprobs))),
reverse=True,
)
)
top_logprob = {
self.detokenize([i]).decode(
"utf-8", errors="ignore"
): logprob
for logprob, i in sorted_logprobs[:logprobs]
}
],
}
top_logprob.update({token_str: current_logprobs[int(token)]})
logprobs_or_none = {
"tokens": [
self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
],
"text_offset": [text_offset],
"token_logprobs": [sorted_logprobs[int(token)][0]],
"top_logprobs": [top_logprob],
}
returned_tokens += 1
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": self.detokenize([token]).decode(
"utf-8", errors="ignore"
),
"index": 0,
"logprobs": logprobs_or_none,
"finish_reason": None,
}
],
}
if len(completion_tokens) >= max_tokens:
text = self.detokenize(completion_tokens)
finish_reason = "length"
break
if stopping_criteria is not None and stopping_criteria(
list(self.eval_tokens), self.eval_logits[-1]
):
text = self.detokenize(completion_tokens)
finish_reason = "stop"
if self.verbose:
llama_cpp.llama_print_timings(self.ctx)
if stream:
remaining_tokens = completion_tokens[returned_tokens:]
all_text = self.detokenize(remaining_tokens)
any_stop = [s for s in stop_sequences if s in all_text]
if len(any_stop) > 0:
end = min(all_text.index(stop) for stop in any_stop)
else:
end = len(all_text)
token_end_position = 0
for token in remaining_tokens:
token_end_position += len(self.detokenize([token]))
logprobs_or_none: Optional[CompletionLogprobs] = None
if logprobs is not None:
token_str = self.detokenize([token]).decode(
"utf-8", errors="ignore"
)
text_offset = len(prompt) + len(
self.detokenize(completion_tokens[:returned_tokens])
)
token_offset = len(prompt_tokens) + returned_tokens - 1
logits = self.eval_logits[token_offset]
current_logprobs = Llama.logits_to_logprobs(logits)
sorted_logprobs = list(
sorted(
zip(current_logprobs, range(len(current_logprobs))),
reverse=True,
)
)
top_logprob = {
self.detokenize([i]).decode("utf-8", errors="ignore"): logprob
for logprob, i in sorted_logprobs[:logprobs]
}
top_logprob.update({token_str: current_logprobs[int(token)]})
logprobs_or_none = {
"tokens": [
self.detokenize([token]).decode("utf-8", errors="ignore")
],
"text_offset": [text_offset],
"token_logprobs": [sorted_logprobs[int(token)][0]],
"top_logprobs": [top_logprob],
}
if token_end_position >= end:
last_text = self.detokenize([token])
if token_end_position == end - 1:
break
returned_tokens += 1
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": last_text[
: len(last_text) - (token_end_position - end)
].decode("utf-8", errors="ignore"),
"index": 0,
"logprobs": logprobs_or_none,
"finish_reason": finish_reason,
}
],
}
break
returned_tokens += 1
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": model_name,
"choices": [
{
"text": self.detokenize([token]).decode(
"utf-8", errors="ignore"
),
"index": 0,
"logprobs": logprobs_or_none,
"finish_reason": finish_reason
if returned_tokens == len(completion_tokens)
else None,
}
],
}
if self.cache:
if self.verbose:
print("Llama._create_completion: cache save", file=sys.stderr)
self.cache[prompt_tokens + completion_tokens] = self.save_state()
return
if self.cache:
if self.verbose:
print("Llama._create_completion: cache save", file=sys.stderr)
self.cache[prompt_tokens + completion_tokens] = self.save_state()
if stream:
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": self.model_path,
"choices": [
{
"text": text[returned_characters:].decode(
"utf-8", errors="ignore"
),
"index": 0,
"logprobs": None,
"finish_reason": finish_reason,
}
],
}
return
text_str = text.decode("utf-8", errors="ignore")
if echo:
@ -738,13 +968,19 @@ class Llama:
logprobs_or_none: Optional[CompletionLogprobs] = None
if logprobs is not None:
text_offset = 0
text_offset = 0 if echo else len(prompt)
token_offset = 0 if echo else len(prompt_tokens[1:])
text_offsets: List[int] = []
token_logprobs: List[float] = []
token_logprobs: List[Optional[float]] = []
tokens: List[str] = []
top_logprobs: List[Dict[str, float]] = []
top_logprobs: List[Optional[Dict[str, float]]] = []
if echo:
# Remove leading BOS token
all_tokens = prompt_tokens[1:] + completion_tokens
else:
all_tokens = completion_tokens
all_tokens = prompt_tokens + completion_tokens
all_token_strs = [
self.detokenize([token]).decode("utf-8", errors="ignore")
for token in all_tokens
@ -752,7 +988,7 @@ class Llama:
all_logprobs = [
Llama.logits_to_logprobs(list(map(float, row)))
for row in self.eval_logits
]
][token_offset:]
for token, token_str, logprobs_token in zip(
all_tokens, all_token_strs, all_logprobs
):
@ -765,14 +1001,18 @@ class Llama:
)
)
token_logprobs.append(sorted_logprobs[int(token)][0])
top_logprob = {
self.detokenize([llama_cpp.llama_token(i)]).decode(
"utf-8", errors="ignore"
): logprob
top_logprob: Optional[Dict[str, float]] = {
self.detokenize([i]).decode("utf-8", errors="ignore"): logprob
for logprob, i in sorted_logprobs[:logprobs]
}
top_logprob.update({token_str: sorted_logprobs[int(token)][0]})
top_logprob.update({token_str: logprobs_token[int(token)]})
top_logprobs.append(top_logprob)
# Weird idosincracy of the OpenAI API where
# token_logprobs and top_logprobs are null for
# the first token.
if echo and len(all_tokens) > 0:
token_logprobs[0] = None
top_logprobs[0] = None
logprobs_or_none = {
"tokens": tokens,
"text_offset": text_offsets,
@ -780,14 +1020,11 @@ class Llama:
"top_logprobs": top_logprobs,
}
if self.verbose:
llama_cpp.llama_print_timings(self.ctx)
yield {
"id": completion_id,
"object": "text_completion",
"created": created,
"model": self.model_path,
"model": model_name,
"choices": [
{
"text": text_str,
@ -812,15 +1049,19 @@ class Llama:
top_p: float = 0.95,
logprobs: Optional[int] = None,
echo: bool = False,
stop: Optional[List[str]] = [],
stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
repeat_penalty: float = 1.1,
top_k: int = 40,
stream: bool = False,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Completion, Iterator[CompletionChunk]]:
"""Generate text from a prompt.
@ -858,9 +1099,13 @@ class Llama:
repeat_penalty=repeat_penalty,
top_k=top_k,
stream=stream,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
stopping_criteria=stopping_criteria,
logits_processor=logits_processor,
)
if stream:
chunks: Iterator[CompletionChunk] = completion_or_chunks
@ -877,15 +1122,19 @@ class Llama:
top_p: float = 0.95,
logprobs: Optional[int] = None,
echo: bool = False,
stop: Optional[List[str]] = [],
stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
repeat_penalty: float = 1.1,
top_k: int = 40,
stream: bool = False,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Completion, Iterator[CompletionChunk]]:
"""Generate text from a prompt.
@ -923,9 +1172,13 @@ class Llama:
repeat_penalty=repeat_penalty,
top_k=top_k,
stream=stream,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
stopping_criteria=stopping_criteria,
logits_processor=logits_processor,
)
def _convert_text_completion_to_chat(
@ -993,14 +1246,16 @@ class Llama:
top_p: float = 0.95,
top_k: int = 40,
stream: bool = False,
stop: Optional[List[str]] = [],
stop: Optional[Union[str, List[str]]] = [],
max_tokens: int = 256,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
repeat_penalty: float = 1.1,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]:
"""Generate a chat completion from a list of messages.
@ -1017,7 +1272,9 @@ class Llama:
Returns:
Generated chat completion or a stream of chat completion chunks.
"""
stop = stop if stop is not None else []
stop = (
stop if isinstance(stop, list) else [stop] if isinstance(stop, str) else []
)
chat_history = "".join(
f'### {"Human" if message["role"] == "user" else "Assistant"}:{message["content"]}'
for message in messages
@ -1035,9 +1292,11 @@ class Llama:
repeat_penalty=repeat_penalty,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
)
if stream:
chunks: Iterator[CompletionChunk] = completion_or_chunks # type: ignore
@ -1056,7 +1315,7 @@ class Llama:
verbose=self.verbose,
model_path=self.model_path,
n_ctx=self.params.n_ctx,
n_parts=self.params.n_parts,
n_gpu_layers=self.params.n_gpu_layers,
seed=self.params.seed,
f16_kv=self.params.f16_kv,
logits_all=self.params.logits_all,
@ -1069,6 +1328,9 @@ class Llama:
n_threads=self.n_threads,
lora_base=self.lora_base,
lora_path=self.lora_path,
### DEPRECATED ###
n_parts=self.n_parts,
### DEPRECATED ###
)
def __setstate__(self, state):
@ -1076,6 +1338,7 @@ class Llama:
model_path=state["model_path"],
n_ctx=state["n_ctx"],
n_parts=state["n_parts"],
n_gpu_layers=state["n_gpu_layers"],
seed=state["seed"],
f16_kv=state["f16_kv"],
logits_all=state["logits_all"],
@ -1120,16 +1383,41 @@ class Llama:
if llama_cpp.llama_set_state_data(self.ctx, state.llama_state) != state_size:
raise RuntimeError("Failed to set llama state data")
def n_ctx(self) -> int:
"""Return the context window size."""
assert self.ctx is not None
return llama_cpp.llama_n_ctx(self.ctx)
def n_embd(self) -> int:
"""Return the embedding size."""
assert self.ctx is not None
return llama_cpp.llama_n_embd(self.ctx)
def n_vocab(self) -> int:
"""Return the vocabulary size."""
assert self.ctx is not None
return llama_cpp.llama_n_vocab(self.ctx)
def tokenizer(self) -> "LlamaTokenizer":
"""Return the tokenizer for this model."""
assert self.ctx is not None
return LlamaTokenizer(self)
@staticmethod
def token_eos() -> llama_cpp.llama_token:
def token_eos() -> int:
"""Return the end-of-sequence token."""
return llama_cpp.llama_token_eos()
@staticmethod
def token_bos() -> llama_cpp.llama_token:
def token_bos() -> int:
"""Return the beginning-of-sequence token."""
return llama_cpp.llama_token_bos()
@staticmethod
def token_nl() -> int:
"""Return the newline token."""
return llama_cpp.llama_token_nl()
@staticmethod
def logits_to_logprobs(logits: List[float]) -> List[float]:
exps = [math.exp(float(x)) for x in logits]
@ -1137,9 +1425,7 @@ class Llama:
return [math.log(x / sum_exps) for x in exps]
@staticmethod
def longest_token_prefix(
a: Sequence[llama_cpp.llama_token], b: Sequence[llama_cpp.llama_token]
):
def longest_token_prefix(a: Sequence[int], b: Sequence[int]):
longest_prefix = 0
for _a, _b in zip(a, b):
if _a == _b:
@ -1147,3 +1433,20 @@ class Llama:
else:
break
return longest_prefix
class LlamaTokenizer:
def __init__(self, llama: Llama):
self.llama = llama
def encode(self, text: str, add_bos: bool = True) -> List[int]:
return self.llama.tokenize(
text.encode("utf-8", errors="ignore"), add_bos=add_bos
)
def decode(self, tokens: List[int]) -> str:
return self.llama.detokenize(tokens).decode("utf-8", errors="ignore")
@classmethod
def from_ggml_file(cls, path: str) -> "LlamaTokenizer":
return cls(Llama(model_path=path, vocab_only=True))

View file

@ -44,15 +44,20 @@ def _load_shared_library(lib_base_name: str):
_base_path = _lib.parent.resolve()
_lib_paths = [_lib.resolve()]
cdll_args = dict() # type: ignore
# Add the library directory to the DLL search path on Windows (if needed)
if sys.platform == "win32" and sys.version_info >= (3, 8):
os.add_dll_directory(str(_base_path))
if "CUDA_PATH" in os.environ:
os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "bin"))
os.add_dll_directory(os.path.join(os.environ["CUDA_PATH"], "lib"))
cdll_args["winmode"] = 0
# Try to load the shared library, handling potential errors
for _lib_path in _lib_paths:
if _lib_path.exists():
try:
return ctypes.CDLL(str(_lib_path))
return ctypes.CDLL(str(_lib_path), **cdll_args)
except Exception as e:
raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
@ -67,31 +72,61 @@ _lib_base_name = "llama"
# Load the library
_lib = _load_shared_library(_lib_base_name)
# C types
LLAMA_FILE_VERSION = c_int(1)
LLAMA_FILE_MAGIC = b"ggjt"
LLAMA_FILE_MAGIC_UNVERSIONED = b"ggml"
LLAMA_SESSION_MAGIC = b"ggsn"
# Misc
c_float_p = POINTER(c_float)
c_uint8_p = POINTER(c_uint8)
c_size_t_p = POINTER(c_size_t)
# llama.h bindings
# #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
LLAMA_FILE_MAGIC_GGJT = ctypes.c_uint(0x67676A74)
# #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
LLAMA_FILE_MAGIC_GGLA = ctypes.c_uint(0x67676C61)
# #define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
LLAMA_FILE_MAGIC_GGMF = ctypes.c_uint(0x67676D66)
# #define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
LLAMA_FILE_MAGIC_GGML = ctypes.c_uint(0x67676D6C)
# #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
LLAMA_FILE_MAGIC_GGSN = ctypes.c_uint(0x6767736E)
# #define LLAMA_FILE_VERSION 3
LLAMA_FILE_VERSION = c_int(3)
LLAMA_FILE_MAGIC = LLAMA_FILE_MAGIC_GGJT
LLAMA_FILE_MAGIC_UNVERSIONED = LLAMA_FILE_MAGIC_GGML
LLAMA_SESSION_MAGIC = LLAMA_FILE_MAGIC_GGSN
LLAMA_SESSION_VERSION = c_int(1)
# struct llama_context;
llama_context_p = c_void_p
# typedef int llama_token;
llama_token = c_int
llama_token_p = POINTER(llama_token)
# typedef struct llama_token_data {
# llama_token id; // token id
# float logit; // log-odds of the token
# float p; // probability of the token
# } llama_token_data;
class llama_token_data(Structure):
_fields_ = [
("id", llama_token), # token id
("logit", c_float), # log-odds of the token
("p", c_float), # probability of the token
("id", llama_token),
("logit", c_float),
("p", c_float),
]
llama_token_data_p = POINTER(llama_token_data)
# typedef struct llama_token_data_array {
# llama_token_data * data;
# size_t size;
# bool sorted;
# } llama_token_data_array;
class llama_token_data_array(Structure):
_fields_ = [
("data", llama_token_data_p),
@ -102,53 +137,72 @@ class llama_token_data_array(Structure):
llama_token_data_array_p = POINTER(llama_token_data_array)
# typedef void (*llama_progress_callback)(float progress, void *ctx);
llama_progress_callback = ctypes.CFUNCTYPE(None, c_float, c_void_p)
# struct llama_context_params {
# int n_ctx; // text context
# int n_gpu_layers; // number of layers to store in VRAM
# int seed; // RNG seed, -1 for random
# bool f16_kv; // use fp16 for KV cache
# bool logits_all; // the llama_eval() call computes all logits, not just the last one
# bool vocab_only; // only load the vocabulary, no weights
# bool use_mmap; // use mmap if possible
# bool use_mlock; // force system to keep model in RAM
# bool embedding; // embedding mode only
# // called with a progress value between 0 and 1, pass NULL to disable
# llama_progress_callback progress_callback;
# // context pointer passed to the progress callback
# void * progress_callback_user_data;
# };
class llama_context_params(Structure):
_fields_ = [
("n_ctx", c_int), # text context
("n_parts", c_int), # -1 for default
("seed", c_int), # RNG seed, 0 for random
("f16_kv", c_bool), # use fp16 for KV cache
("n_ctx", c_int),
("n_gpu_layers", c_int),
("seed", c_int),
("f16_kv", c_bool),
(
"logits_all",
c_bool,
), # the llama_eval() call computes all logits, not just the last one
("vocab_only", c_bool), # only load the vocabulary, no weights
("use_mmap", c_bool), # use mmap if possible
("use_mlock", c_bool), # force system to keep model in RAM
("embedding", c_bool), # embedding mode only
# called with a progress value between 0 and 1, pass NULL to disable
),
("vocab_only", c_bool),
("use_mmap", c_bool),
("use_mlock", c_bool),
("embedding", c_bool),
("progress_callback", llama_progress_callback),
# context pointer passed to the progress callback
("progress_callback_user_data", c_void_p),
]
llama_context_params_p = POINTER(llama_context_params)
# enum llama_ftype {
# LLAMA_FTYPE_ALL_F32 = 0,
# LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
# // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
# // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
# LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
# LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
# };
LLAMA_FTYPE_ALL_F32 = c_int(0)
LLAMA_FTYPE_MOSTLY_F16 = c_int(1) # except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2) # except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3) # except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int(
4
) # tok_embeddings.weight and output.weight are F16
LLAMA_FTYPE_MOSTLY_Q4_2 = c_int(5) # except 1d tensors
# LLAMA_FTYPE_MOSTYL_Q4_3 = c_int(6) # except 1d tensors
LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7) # except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8) # except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9) # except 1d tensors
# Misc
c_float_p = POINTER(c_float)
c_uint8_p = POINTER(c_uint8)
c_size_t_p = POINTER(c_size_t)
# Functions
LLAMA_FTYPE_MOSTLY_F16 = c_int(1)
LLAMA_FTYPE_MOSTLY_Q4_0 = c_int(2)
LLAMA_FTYPE_MOSTLY_Q4_1 = c_int(3)
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = c_int(4)
LLAMA_FTYPE_MOSTLY_Q8_0 = c_int(7)
LLAMA_FTYPE_MOSTLY_Q5_0 = c_int(8)
LLAMA_FTYPE_MOSTLY_Q5_1 = c_int(9)
# LLAMA_API struct llama_context_params llama_context_default_params();
def llama_context_default_params() -> llama_context_params:
return _lib.llama_context_default_params()
@ -157,6 +211,7 @@ _lib.llama_context_default_params.argtypes = []
_lib.llama_context_default_params.restype = llama_context_params
# LLAMA_API bool llama_mmap_supported();
def llama_mmap_supported() -> bool:
return _lib.llama_mmap_supported()
@ -165,6 +220,7 @@ _lib.llama_mmap_supported.argtypes = []
_lib.llama_mmap_supported.restype = c_bool
# LLAMA_API bool llama_mlock_supported();
def llama_mlock_supported() -> bool:
return _lib.llama_mlock_supported()
@ -173,9 +229,33 @@ _lib.llama_mlock_supported.argtypes = []
_lib.llama_mlock_supported.restype = c_bool
# Various functions for loading a ggml llama model.
# Allocate (almost) all memory needed for the model.
# Return NULL on failure
# // TODO: not great API - very likely to change
# // Initialize the llama + ggml backend
# // Call once at the start of the program
# LLAMA_API void llama_init_backend();
def llama_init_backend():
return _lib.llama_init_backend()
_lib.llama_init_backend.argtypes = []
_lib.llama_init_backend.restype = None
# LLAMA_API int64_t llama_time_us();
def llama_time_us() -> int:
return _lib.llama_time_us()
_lib.llama_time_us.argtypes = []
_lib.llama_time_us.restype = ctypes.c_int64
# // Various functions for loading a ggml llama model.
# // Allocate (almost) all memory needed for the model.
# // Return NULL on failure
# LLAMA_API struct llama_context * llama_init_from_file(
# const char * path_model,
# struct llama_context_params params);
def llama_init_from_file(
path_model: bytes, params: llama_context_params
) -> llama_context_p:
@ -187,8 +267,9 @@ _lib.llama_init_from_file.restype = llama_context_p
# Frees all allocated memory
# LLAMA_API void llama_free(struct llama_context * ctx);
def llama_free(ctx: llama_context_p):
_lib.llama_free(ctx)
return _lib.llama_free(ctx)
_lib.llama_free.argtypes = [llama_context_p]
@ -198,9 +279,14 @@ _lib.llama_free.restype = None
# TODO: not great API - very likely to change
# Returns 0 on success
# nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
# LLAMA_API int llama_model_quantize(
# const char * fname_inp,
# const char * fname_out,
# enum llama_ftype ftype,
# int nthread);
def llama_model_quantize(
fname_inp: bytes, fname_out: bytes, ftype: c_int, nthread: c_int
) -> c_int:
) -> int:
return _lib.llama_model_quantize(fname_inp, fname_out, ftype, nthread)
@ -214,12 +300,17 @@ _lib.llama_model_quantize.restype = c_int
# The model needs to be reloaded before applying a new adapter, otherwise the adapter
# will be applied on top of the previous one
# Returns 0 on success
# LLAMA_API int llama_apply_lora_from_file(
# struct llama_context * ctx,
# const char * path_lora,
# const char * path_base_model,
# int n_threads);
def llama_apply_lora_from_file(
ctx: llama_context_p,
path_lora: c_char_p,
path_base_model: c_char_p,
n_threads: c_int,
) -> c_int:
) -> int:
return _lib.llama_apply_lora_from_file(ctx, path_lora, path_base_model, n_threads)
@ -228,7 +319,8 @@ _lib.llama_apply_lora_from_file.restype = c_int
# Returns the number of tokens in the KV cache
def llama_get_kv_cache_token_count(ctx: llama_context_p) -> c_int:
# LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
def llama_get_kv_cache_token_count(ctx: llama_context_p) -> int:
return _lib.llama_get_kv_cache_token_count(ctx)
@ -237,6 +329,7 @@ _lib.llama_get_kv_cache_token_count.restype = c_int
# Sets the current rng seed.
# LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
def llama_set_rng_seed(ctx: llama_context_p, seed: c_int):
return _lib.llama_set_rng_seed(ctx, seed)
@ -247,7 +340,8 @@ _lib.llama_set_rng_seed.restype = None
# Returns the maximum size in bytes of the state (rng, logits, embedding
# and kv_cache) - will often be smaller after compacting tokens
def llama_get_state_size(ctx: llama_context_p) -> c_size_t:
# LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
def llama_get_state_size(ctx: llama_context_p) -> int:
return _lib.llama_get_state_size(ctx)
@ -258,10 +352,11 @@ _lib.llama_get_state_size.restype = c_size_t
# Copies the state to the specified destination address.
# Destination needs to have allocated enough memory.
# Returns the number of bytes copied
# LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
def llama_copy_state_data(
ctx: llama_context_p, dest # type: Array[c_uint8]
ctx: llama_context_p, dst # type: Array[c_uint8]
) -> int:
return _lib.llama_copy_state_data(ctx, dest)
return _lib.llama_copy_state_data(ctx, dst)
_lib.llama_copy_state_data.argtypes = [llama_context_p, c_uint8_p]
@ -270,6 +365,7 @@ _lib.llama_copy_state_data.restype = c_size_t
# Set the state reading from the specified address
# Returns the number of bytes read
# LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
def llama_set_state_data(
ctx: llama_context_p, src # type: Array[c_uint8]
) -> int:
@ -281,13 +377,14 @@ _lib.llama_set_state_data.restype = c_size_t
# Save/load session file
# LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
def llama_load_session_file(
ctx: llama_context_p,
path_session: bytes,
tokens_out, # type: Array[llama_token]
n_token_capacity: c_size_t,
n_token_count_out, # type: _Pointer[c_size_t]
) -> c_size_t:
) -> int:
return _lib.llama_load_session_file(
ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
)
@ -303,12 +400,13 @@ _lib.llama_load_session_file.argtypes = [
_lib.llama_load_session_file.restype = c_size_t
# LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
def llama_save_session_file(
ctx: llama_context_p,
path_session: bytes,
tokens, # type: Array[llama_token]
n_token_count: c_size_t,
) -> c_size_t:
) -> int:
return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
@ -325,13 +423,19 @@ _lib.llama_save_session_file.restype = c_size_t
# tokens + n_tokens is the provided batch of new tokens to process
# n_past is the number of tokens to use from previous eval calls
# Returns 0 on success
# LLAMA_API int llama_eval(
# struct llama_context * ctx,
# const llama_token * tokens,
# int n_tokens,
# int n_past,
# int n_threads);
def llama_eval(
ctx: llama_context_p,
tokens, # type: Array[llama_token]
n_tokens: c_int,
n_past: c_int,
n_threads: c_int,
) -> c_int:
) -> int:
return _lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
@ -344,13 +448,19 @@ _lib.llama_eval.restype = c_int
# Returns the number of tokens on success, no more than n_max_tokens
# Returns a negative number on failure - the number of tokens that would have been returned
# TODO: not sure if correct
# LLAMA_API int llama_tokenize(
# struct llama_context * ctx,
# const char * text,
# llama_token * tokens,
# int n_max_tokens,
# bool add_bos);
def llama_tokenize(
ctx: llama_context_p,
text: bytes,
tokens, # type: Array[llama_token]
n_max_tokens: c_int,
add_bos: c_bool,
) -> c_int:
) -> int:
return _lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
@ -358,7 +468,8 @@ _lib.llama_tokenize.argtypes = [llama_context_p, c_char_p, llama_token_p, c_int,
_lib.llama_tokenize.restype = c_int
def llama_n_vocab(ctx: llama_context_p) -> c_int:
# LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
def llama_n_vocab(ctx: llama_context_p) -> int:
return _lib.llama_n_vocab(ctx)
@ -366,7 +477,8 @@ _lib.llama_n_vocab.argtypes = [llama_context_p]
_lib.llama_n_vocab.restype = c_int
def llama_n_ctx(ctx: llama_context_p) -> c_int:
# LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
def llama_n_ctx(ctx: llama_context_p) -> int:
return _lib.llama_n_ctx(ctx)
@ -374,7 +486,8 @@ _lib.llama_n_ctx.argtypes = [llama_context_p]
_lib.llama_n_ctx.restype = c_int
def llama_n_embd(ctx: llama_context_p) -> c_int:
# LLAMA_API int llama_n_embd (const struct llama_context * ctx);
def llama_n_embd(ctx: llama_context_p) -> int:
return _lib.llama_n_embd(ctx)
@ -387,6 +500,7 @@ _lib.llama_n_embd.restype = c_int
# Can be mutated in order to change the probabilities of the next token
# Rows: n_tokens
# Cols: n_vocab
# LLAMA_API float * llama_get_logits(struct llama_context * ctx);
def llama_get_logits(
ctx: llama_context_p,
): # type: (...) -> Array[float] # type: ignore
@ -399,6 +513,7 @@ _lib.llama_get_logits.restype = c_float_p
# Get the embeddings for the input
# shape: [n_embd] (1-dimensional)
# LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
def llama_get_embeddings(
ctx: llama_context_p,
): # type: (...) -> Array[float] # type: ignore
@ -410,6 +525,7 @@ _lib.llama_get_embeddings.restype = c_float_p
# Token Id -> String. Uses the vocabulary in the provided context
# LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
def llama_token_to_str(ctx: llama_context_p, token: llama_token) -> bytes:
return _lib.llama_token_to_str(ctx, token)
@ -420,7 +536,8 @@ _lib.llama_token_to_str.restype = c_char_p
# Special tokens
def llama_token_bos() -> llama_token:
# LLAMA_API llama_token llama_token_bos();
def llama_token_bos() -> int:
return _lib.llama_token_bos()
@ -428,7 +545,8 @@ _lib.llama_token_bos.argtypes = []
_lib.llama_token_bos.restype = llama_token
def llama_token_eos() -> llama_token:
# LLAMA_API llama_token llama_token_eos();
def llama_token_eos() -> int:
return _lib.llama_token_eos()
@ -436,7 +554,8 @@ _lib.llama_token_eos.argtypes = []
_lib.llama_token_eos.restype = llama_token
def llama_token_nl() -> llama_token:
# LLAMA_API llama_token llama_token_nl();
def llama_token_nl() -> int:
return _lib.llama_token_nl()
@ -448,6 +567,7 @@ _lib.llama_token_nl.restype = llama_token
# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
# LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
def llama_sample_repetition_penalty(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -471,6 +591,7 @@ _lib.llama_sample_repetition_penalty.restype = None
# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
def llama_sample_frequency_and_presence_penalties(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -501,6 +622,7 @@ _lib.llama_sample_frequency_and_presence_penalties.restype = None
# @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
# LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
def llama_sample_softmax(
ctx: llama_context_p, candidates # type: _Pointer[llama_token_data]
):
@ -515,6 +637,7 @@ _lib.llama_sample_softmax.restype = None
# @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
# LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
def llama_sample_top_k(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -534,6 +657,7 @@ _lib.llama_sample_top_k.restype = None
# @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
# LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
def llama_sample_top_p(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -553,6 +677,7 @@ _lib.llama_sample_top_p.restype = None
# @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
# LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
def llama_sample_tail_free(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -572,6 +697,7 @@ _lib.llama_sample_tail_free.restype = None
# @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
# LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
def llama_sample_typical(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -590,6 +716,7 @@ _lib.llama_sample_typical.argtypes = [
_lib.llama_sample_typical.restype = None
# LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
def llama_sample_temperature(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -612,6 +739,7 @@ _lib.llama_sample_temperature.restype = None
# @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
# @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
# LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
def llama_sample_token_mirostat(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
@ -619,7 +747,7 @@ def llama_sample_token_mirostat(
eta: c_float,
m: c_int,
mu, # type: _Pointer[c_float]
) -> llama_token:
) -> int:
return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
@ -639,13 +767,14 @@ _lib.llama_sample_token_mirostat.restype = llama_token
# @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
# @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
# LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
def llama_sample_token_mirostat_v2(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
tau: c_float,
eta: c_float,
mu, # type: _Pointer[c_float]
) -> llama_token:
) -> int:
return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
@ -660,10 +789,11 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
# @details Selects the token with the highest probability.
# LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
def llama_sample_token_greedy(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
) -> llama_token:
) -> int:
return _lib.llama_sample_token_greedy(ctx, candidates)
@ -675,10 +805,11 @@ _lib.llama_sample_token_greedy.restype = llama_token
# @details Randomly selects a token from the candidates based on their probabilities.
# LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
def llama_sample_token(
ctx: llama_context_p,
candidates, # type: _Pointer[llama_token_data_array]
) -> llama_token:
) -> int:
return _lib.llama_sample_token(ctx, candidates)
@ -692,6 +823,7 @@ _lib.llama_sample_token.restype = llama_token
# Performance information
# LLAMA_API void llama_print_timings(struct llama_context * ctx);
def llama_print_timings(ctx: llama_context_p):
_lib.llama_print_timings(ctx)
@ -700,6 +832,7 @@ _lib.llama_print_timings.argtypes = [llama_context_p]
_lib.llama_print_timings.restype = None
# LLAMA_API void llama_reset_timings(struct llama_context * ctx);
def llama_reset_timings(ctx: llama_context_p):
_lib.llama_reset_timings(ctx)
@ -709,9 +842,19 @@ _lib.llama_reset_timings.restype = None
# Print system information
# LLAMA_API const char * llama_print_system_info(void);
def llama_print_system_info() -> bytes:
return _lib.llama_print_system_info()
_lib.llama_print_system_info.argtypes = []
_lib.llama_print_system_info.restype = c_char_p
###################################################################################################
_llama_initialized = False
if not _llama_initialized:
llama_init_backend()
_llama_initialized = True

View file

@ -1,4 +1,4 @@
from typing import List, Optional, Dict, Union
from typing import List, Optional, Dict
from typing_extensions import TypedDict, NotRequired, Literal
@ -22,9 +22,9 @@ class Embedding(TypedDict):
class CompletionLogprobs(TypedDict):
text_offset: List[int]
token_logprobs: List[float]
token_logprobs: List[Optional[float]]
tokens: List[str]
top_logprobs: List[Dict[str, float]]
top_logprobs: List[Optional[Dict[str, float]]]
class CompletionChoice(TypedDict):

View file

@ -1,4 +1,5 @@
import json
import logging
import multiprocessing
from threading import Lock
from typing import List, Optional, Union, Iterator, Dict
@ -16,7 +17,16 @@ class Settings(BaseSettings):
model: str = Field(
description="The path to the model to use for generating completions."
)
model_alias: Optional[str] = Field(
default=None,
description="The alias of the model to use for generating completions.",
)
n_ctx: int = Field(default=2048, ge=1, description="The context size.")
n_gpu_layers: int = Field(
default=0,
ge=0,
description="The number of layers to put on the GPU. The rest will be on the CPU.",
)
n_batch: int = Field(
default=512, ge=1, description="The batch size to use per eval."
)
@ -59,6 +69,7 @@ class Settings(BaseSettings):
router = APIRouter()
settings: Optional[Settings] = None
llama: Optional[llama_cpp.Llama] = None
@ -80,6 +91,7 @@ def create_app(settings: Optional[Settings] = None):
global llama
llama = llama_cpp.Llama(
model_path=settings.model,
n_gpu_layers=settings.n_gpu_layers,
f16_kv=settings.f16_kv,
use_mlock=settings.use_mlock,
use_mmap=settings.use_mmap,
@ -95,6 +107,12 @@ def create_app(settings: Optional[Settings] = None):
if settings.cache:
cache = llama_cpp.LlamaCache(capacity_bytes=settings.cache_size)
llama.set_cache(cache)
def set_settings(_settings: Settings):
global settings
settings = _settings
set_settings(settings)
return app
@ -106,6 +124,10 @@ def get_llama():
yield llama
def get_settings():
yield settings
model_field = Field(description="The model to use for generating completions.")
max_tokens_field = Field(
@ -152,9 +174,23 @@ repeat_penalty_field = Field(
+ "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.",
)
presence_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.",
)
frequency_penalty_field = Field(
default=0.0,
ge=-2.0,
le=2.0,
description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.",
)
class CreateCompletionRequest(BaseModel):
prompt: Optional[str] = Field(
prompt: Union[str, List[str]] = Field(
default="", description="The prompt to generate completions for."
)
suffix: Optional[str] = Field(
@ -168,20 +204,20 @@ class CreateCompletionRequest(BaseModel):
default=False,
description="Whether to echo the prompt in the generated text. Useful for chatbots.",
)
stop: Optional[List[str]] = stop_field
stop: Optional[Union[str, List[str]]] = stop_field
stream: bool = stream_field
logprobs: Optional[int] = Field(
default=None,
ge=0,
description="The number of logprobs to generate. If None, no logprobs are generated.",
)
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
logprobs: Optional[int] = Field(None)
presence_penalty: Optional[float] = 0
frequency_penalty: Optional[float] = 0
best_of: Optional[int] = 1
logit_bias: Optional[Dict[str, float]] = Field(None)
user: Optional[str] = Field(None)
@ -209,10 +245,13 @@ CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)
def create_completion(
request: CreateCompletionRequest, llama: llama_cpp.Llama = Depends(get_llama)
):
if isinstance(request.prompt, list):
assert len(request.prompt) <= 1
request.prompt = request.prompt[0] if len(request.prompt) > 0 else ""
completion_or_chunks = llama(
**request.dict(
exclude={
"model",
"n",
"best_of",
"logit_bias",
@ -221,15 +260,22 @@ def create_completion(
)
)
if request.stream:
async def server_sent_events(
chunks: Iterator[llama_cpp.CompletionChunk],
):
for chunk in chunks:
yield dict(data=json.dumps(chunk))
chunks: Iterator[llama_cpp.CompletionChunk] = completion_or_chunks # type: ignore
return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
return EventSourceResponse(server_sent_events(chunks))
completion: llama_cpp.Completion = completion_or_chunks # type: ignore
return completion
class CreateEmbeddingRequest(BaseModel):
model: Optional[str] = model_field
input: str = Field(description="The input to embed.")
input: Union[str, List[str]] = Field(description="The input to embed.")
user: Optional[str]
class Config:
@ -250,7 +296,7 @@ CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)
def create_embedding(
request: CreateEmbeddingRequest, llama: llama_cpp.Llama = Depends(get_llama)
):
return llama.create_embedding(**request.dict(exclude={"model", "user"}))
return llama.create_embedding(**request.dict(exclude={"user"}))
class ChatCompletionRequestMessage(BaseModel):
@ -269,12 +315,12 @@ class CreateChatCompletionRequest(BaseModel):
top_p: float = top_p_field
stop: Optional[List[str]] = stop_field
stream: bool = stream_field
presence_penalty: Optional[float] = presence_penalty_field
frequency_penalty: Optional[float] = frequency_penalty_field
# ignored or currently unsupported
model: Optional[str] = model_field
n: Optional[int] = 1
presence_penalty: Optional[float] = 0
frequency_penalty: Optional[float] = 0
logit_bias: Optional[Dict[str, float]] = Field(None)
user: Optional[str] = Field(None)
@ -311,7 +357,6 @@ def create_chat_completion(
completion_or_chunks = llama.create_chat_completion(
**request.dict(
exclude={
"model",
"n",
"logit_bias",
"user",
@ -354,13 +399,16 @@ GetModelResponse = create_model_from_typeddict(ModelList)
@router.get("/v1/models", response_model=GetModelResponse)
def get_models(
settings: Settings = Depends(get_settings),
llama: llama_cpp.Llama = Depends(get_llama),
) -> ModelList:
return {
"object": "list",
"data": [
{
"id": llama.model_path,
"id": settings.model_alias
if settings.model_alias is not None
else llama.model_path,
"object": "model",
"owned_by": "me",
"permissions": [],

12
poetry.lock generated
View file

@ -463,14 +463,14 @@ socks = ["socksio (>=1.0.0,<2.0.0)"]
[[package]]
name = "httpx"
version = "0.24.0"
version = "0.24.1"
description = "The next generation HTTP client."
category = "dev"
optional = false
python-versions = ">=3.7"
files = [
{file = "httpx-0.24.0-py3-none-any.whl", hash = "sha256:447556b50c1921c351ea54b4fe79d91b724ed2b027462ab9a329465d147d5a4e"},
{file = "httpx-0.24.0.tar.gz", hash = "sha256:507d676fc3e26110d41df7d35ebd8b3b8585052450f4097401c9be59d928c63e"},
{file = "httpx-0.24.1-py3-none-any.whl", hash = "sha256:06781eb9ac53cde990577af654bd990a4949de37a28bdb4a230d434f3a30b9bd"},
{file = "httpx-0.24.1.tar.gz", hash = "sha256:5853a43053df830c20f8110c5e69fe44d035d850b2dfe795e196f00fdb774bdd"},
]
[package.dependencies]
@ -800,14 +800,14 @@ mkdocs = ">=1.1"
[[package]]
name = "mkdocs-material"
version = "9.1.11"
version = "9.1.14"
description = "Documentation that simply works"
category = "dev"
optional = false
python-versions = ">=3.7"
files = [
{file = "mkdocs_material-9.1.11-py3-none-any.whl", hash = "sha256:fbc86d50ec2cf34d40d5c4365780f290ceedde23f1a0704323b34e7f16b0c0dd"},
{file = "mkdocs_material-9.1.11.tar.gz", hash = "sha256:f5d473eb79d6640a5e668d4b2ab5b9de5e76ae0a0e2d864112df0cfe9016dc1d"},
{file = "mkdocs_material-9.1.14-py3-none-any.whl", hash = "sha256:b56a9f955ed32d38333715cbbf68ce38f683bf38610c65094fa4ef2db9f08bcd"},
{file = "mkdocs_material-9.1.14.tar.gz", hash = "sha256:1ae74cc5464ef2f64574d4884512efed7f4db386fb9bc6af20fd427d7a702f49"},
]
[package.dependencies]

View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "llama_cpp_python"
version = "0.1.48"
version = "0.1.55"
description = "Python bindings for the llama.cpp library"
authors = ["Andrei Betlen <abetlen@gmail.com>"]
license = "MIT"
@ -24,9 +24,9 @@ black = "^23.3.0"
twine = "^4.0.2"
mkdocs = "^1.4.3"
mkdocstrings = {extras = ["python"], version = "^0.21.2"}
mkdocs-material = "^9.1.11"
mkdocs-material = "^9.1.14"
pytest = "^7.3.1"
httpx = "^0.24.0"
httpx = "^0.24.1"
scikit-build = "0.13"
[tool.poetry.extras]

View file

@ -10,7 +10,7 @@ setup(
description="A Python wrapper for llama.cpp",
long_description=long_description,
long_description_content_type="text/markdown",
version="0.1.48",
version="0.1.55",
author="Andrei Betlen",
author_email="abetlen@gmail.com",
license="MIT",

View file

@ -17,7 +17,7 @@ def test_llama():
# @pytest.mark.skip(reason="need to update sample mocking")
def test_llama_patch(monkeypatch):
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
n_vocab = int(llama_cpp.llama_n_vocab(llama.ctx))
n_vocab = llama_cpp.llama_n_vocab(llama.ctx)
## Set up mock function
def mock_eval(*args, **kwargs):
@ -107,7 +107,7 @@ def test_llama_pickle():
def test_utf8(monkeypatch):
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
n_vocab = int(llama_cpp.llama_n_vocab(llama.ctx))
n_vocab = llama_cpp.llama_n_vocab(llama.ctx)
## Set up mock function
def mock_eval(*args, **kwargs):

2
vendor/llama.cpp vendored

@ -1 +1 @@
Subproject commit 1b0fd454650ef4d68a980e3225488b79e6e9af25
Subproject commit 66874d4fbcc7866377246efbcee938e8cc9c7d76