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feat: support stopping words in python backend. (#32) 

* Improve python backend

* Update lockfile

* Support stop words in python backend

* Support LanguagePresets for triton

* Update pre-commit
add-more-languages
Meng Zhang 2023-03-29 20:23:11 +08:00 committed by GitHub
parent 2f31418ac6
commit be7894a5e6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 358 additions and 298 deletions
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1
.pre-commit-config.yaml
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@ -11,6 +11,7 @@ repos:
rev: 5.12.0
hooks:
- id: isort
args: ["--profile", "black"]
- repo: https://github.com/PyCQA/autoflake
rev: v2.0.2
hooks:

434
poetry.lock generated
View File

@ -547,29 +547,29 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
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@ -1191,61 +1191,61 @@ test = ["objgraph", "psutil"]
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{file = "regex-2023.3.23.tar.gz", hash = "sha256:dc80df325b43ffea5cdea2e3eaa97a44f3dd298262b1c7fe9dbb2a9522b956a7"},
]
[[package]]
@ -2628,14 +2600,14 @@ tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov",
[[package]]
name = "rich"
version = "13.3.2"
version = "13.3.3"
description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
category = "main"
optional = false
python-versions = ">=3.7.0"
files = [
{file = "rich-13.3.2-py3-none-any.whl", hash = "sha256:a104f37270bf677148d8acb07d33be1569eeee87e2d1beb286a4e9113caf6f2f"},
{file = "rich-13.3.2.tar.gz", hash = "sha256:91954fe80cfb7985727a467ca98a7618e5dd15178cc2da10f553b36a93859001"},
{file = "rich-13.3.3-py3-none-any.whl", hash = "sha256:540c7d6d26a1178e8e8b37e9ba44573a3cd1464ff6348b99ee7061b95d1c6333"},
{file = "rich-13.3.3.tar.gz", hash = "sha256:dc84400a9d842b3a9c5ff74addd8eb798d155f36c1c91303888e0a66850d2a15"},
]
[package.dependencies]
@ -2659,14 +2631,14 @@ files = [
[[package]]
name = "setuptools"
version = "67.6.0"
version = "67.6.1"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
category = "main"
optional = false
python-versions = ">=3.7"
files = [
{file = "setuptools-67.6.0-py3-none-any.whl", hash = "sha256:b78aaa36f6b90a074c1fa651168723acbf45d14cb1196b6f02c0fd07f17623b2"},
{file = "setuptools-67.6.0.tar.gz", hash = "sha256:2ee892cd5f29f3373097f5a814697e397cf3ce313616df0af11231e2ad118077"},
{file = "setuptools-67.6.1-py3-none-any.whl", hash = "sha256:e728ca814a823bf7bf60162daf9db95b93d532948c4c0bea762ce62f60189078"},
{file = "setuptools-67.6.1.tar.gz", hash = "sha256:257de92a9d50a60b8e22abfcbb771571fde0dbf3ec234463212027a4eeecbe9a"},
]
[package.extras]
@ -2959,14 +2931,14 @@ telegram = ["requests"]
[[package]]
name = "transformers"
version = "4.27.1"
version = "4.27.3"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
category = "main"
optional = false
python-versions = ">=3.7.0"
files = [
{file = "transformers-4.27.1-py3-none-any.whl", hash = "sha256:b8cf844cbeaefd24b07f7ab1550af11b5d9cf0a7d09e5f4fedede9b15a3da2c0"},
{file = "transformers-4.27.1.tar.gz", hash = "sha256:05120d91e407c0d3db364f5d0710cdacccd15f67114b075f161a71535c133695"},
{file = "transformers-4.27.3-py3-none-any.whl", hash = "sha256:d764c351b6d590952ac6104573721abe323b3eabd55e53bae0215c14750d5f13"},
{file = "transformers-4.27.3.tar.gz", hash = "sha256:26bff783ad6ed463fe3f0954406e7c30a8313758b6488677827886e48e6bd5a9"},
]
[package.dependencies]
@ -3056,15 +3028,15 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "tritonclient"
version = "2.31.0"
version = "2.32.0"
description = "Python client library and utilities for communicating with Triton Inference Server"
category = "main"
optional = false
python-versions = "*"
files = [
{file = "tritonclient-2.31.0-py3-none-any.whl", hash = "sha256:d0b6fcad61279a998e8a695e4177c09a688efba795dc4fe056a16a9a35a76aec"},
{file = "tritonclient-2.31.0-py3-none-manylinux1_x86_64.whl", hash = "sha256:a5841d88d3ddb7bc36ed8630cb8f67cbe0f4073fb01ef300ed3da4caec66c775"},
{file = "tritonclient-2.31.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d7fa63aab2b5158f3808aa3db1cf68999c19ddd5545cf3b4bdf4f0fa579db54d"},
{file = "tritonclient-2.32.0-py3-none-any.whl", hash = "sha256:2a9077a0f1424ef521ee89a519cdbe29892a058068b195641e83ba97817f150d"},
{file = "tritonclient-2.32.0-py3-none-manylinux1_x86_64.whl", hash = "sha256:0431006258c0cca6ecb840d0af5b97bd40e13ec3224005dcd31da14f9d396421"},
{file = "tritonclient-2.32.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8a1476c64c07f8873a65025bfd14d40ffc153e0966bb2b361939ad4eceec0a6a"},
]
[package.dependencies]
@ -3095,14 +3067,14 @@ files = [
[[package]]
name = "tzdata"
version = "2022.7"
version = "2023.3"
description = "Provider of IANA time zone data"
category = "main"
optional = false
python-versions = ">=2"
files = [
{file = "tzdata-2022.7-py2.py3-none-any.whl", hash = "sha256:2b88858b0e3120792a3c0635c23daf36a7d7eeeca657c323da299d2094402a0d"},
{file = "tzdata-2022.7.tar.gz", hash = "sha256:fe5f866eddd8b96e9fcba978f8e503c909b19ea7efda11e52e39494bad3a7bfa"},
{file = "tzdata-2023.3-py2.py3-none-any.whl", hash = "sha256:7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda"},
{file = "tzdata-2023.3.tar.gz", hash = "sha256:11ef1e08e54acb0d4f95bdb1be05da659673de4acbd21bf9c69e94cc5e907a3a"},
]
[[package]]
@ -3550,4 +3522,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "8fa4ae65c0f2bfedd73b2784cb2d24cd99d151d130b8a434a6e8142bd2c16e24"
content-hash = "886b7b417b646f32ba468770a8c793d50ea3253d455b684e4bfd0593ec586511"

3
pyproject.toml
View File

@ -20,11 +20,10 @@ toml = "^0.10.2"
gitpython = "^3.1.31"
peft = {git = "https://github.com/huggingface/peft.git", rev = "v0.2.0"}
duckdb = "^0.7.1"
torch = "^2.0.0"
[tool.poetry.group.dev.dependencies]
pre-commit = "^3.1.1"
torch = "^2.0.0"
[build-system]
requires = ["poetry-core"]

3
tabby/server/__init__.py
View File

@ -6,9 +6,8 @@ from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from . import events
from .backend import PythonModelService, TritonService
from .models import CompletionRequest, CompletionResponse
from .python import PythonModelService
from .triton import TritonService
app = FastAPI(
title="TabbyServer",

2
tabby/server/backend/__init__.py Normal file
View File

@ -0,0 +1,2 @@
from .python import PythonModelService
from .triton import TritonService

19
tabby/server/backend/language_presets.py Normal file
View File

@ -0,0 +1,19 @@
from typing import List
from pydantic import BaseModel, Field
class LanguagePreset(BaseModel):
max_length: int
stop_words: List[str]
PythonPreset = LanguagePreset(
max_length=128, stop_words=["\n\n", "\ndef", "\n#", "\nimport", "\nfrom", "\nclass"]
)
JavascriptPreset = LanguagePreset(
max_length=128, stop_words=["\n\n", "\nfunction", "\n//", "\nimport", "\nclass"]
)
LanguagePresets = {"python": PythonPreset, "javascript": JavascriptPreset}

105
tabby/server/backend/python.py Normal file
View File

@ -0,0 +1,105 @@
import time
from typing import List
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
StoppingCriteria,
StoppingCriteriaList,
)
from ..models import Choice, CompletionRequest, CompletionResponse
from .language_presets import LanguagePresets
from .utils import random_completion_id, trim_with_stopwords
class PythonModelService:
def __init__(
self,
model_name,
):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
else:
device = torch.device("cpu")
dtype = torch.float32
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(
model_name, local_files_only=True
)
self.model = (
AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=dtype,
device_map="auto" if torch.cuda.is_available() else None,
local_files_only=True,
)
.to(device)
.eval()
)
self.stopping_criteria_mappings = {}
def generate(self, request: CompletionRequest) -> List[Choice]:
# FIXME(meng): read preset from request.
preset_name = "python"
preset = LanguagePresets[preset_name]
stopping_criteria_list = self.stopping_criteria_for_preset(preset_name)
input_ids = self.tokenizer.encode(request.prompt, return_tensors="pt").to(
self.device
)
res = self.model.generate(
input_ids,
max_length=preset.max_length,
stopping_criteria=stopping_criteria_list,
)
output_ids = res[0][len(input_ids[0]) :]
text = trim_with_stopwords(self.tokenizer.decode(output_ids), preset.stop_words)
return [Choice(index=0, text=text)]
def stopping_criteria_for_preset(self, name: str) -> StoppingCriteriaList:
return StoppingCriteriaList(
[
StopWordsIdsCriteria(
[self.tokenizer.encode(x) for x in LanguagePresets[name].stop_words]
)
]
)
def __call__(self, request: CompletionRequest) -> CompletionResponse:
choices = self.generate(request)
return CompletionResponse(
id=random_completion_id(), created=int(time.time()), choices=choices
)
class StopWordsIdsCriteria(StoppingCriteria):
def __init__(self, stop_words_ids: List[str]):
self.stop_words_ids = stop_words_ids
def __call__(
self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
) -> bool:
if len(input_ids) != 1:
raise ValueError("Only 1-length list is handled")
# FIXME(meng): trie based lookup.
tokens = input_ids[0]
for stop_word in self.stop_words_ids:
if len(tokens) < len(stop_word):
continue
matched = True
for i in range(len(stop_word)):
if tokens[i - len(stop_word)] != stop_word[i]:
matched = False
break
if matched:
return True
return False

33
tabby/server/triton.py → tabby/server/backend/triton.py
View File

@ -1,5 +1,3 @@
import random
import string
import time
from typing import List
@ -8,7 +6,9 @@ import tritonclient.grpc as client_util
from transformers import AutoTokenizer
from tritonclient.utils import InferenceServerException, np_to_triton_dtype
from .models import Choice, CompletionRequest, CompletionResponse
from ..models import Choice, CompletionRequest, CompletionResponse
from .language_presets import LanguagePresets
from .utils import random_completion_id, trim_with_stopwords
class TritonService:
@ -25,12 +25,13 @@ class TritonService:
)
def generate(self, data: CompletionRequest) -> List[Choice]:
# FIXME(meng): Make following vars configurable
n = 1
np_type = np.uint32
max_tokens = 128
model_name = "fastertransformer"
stop_words = ["\n\n"]
# FIXME(meng): read preset from request.
preset_name = "python"
preset = LanguagePresets[preset_name]
prompt = data.prompt
input_start_ids = np.expand_dims(self.tokenizer.encode(prompt), 0)
@ -39,10 +40,10 @@ class TritonService:
input_len = prompt_len * np.ones([input_start_ids.shape[0], 1]).astype(np_type)
prompt_tokens: int = input_len[0][0]
output_len = np.ones_like(input_len).astype(np_type) * max_tokens
output_len = np.ones_like(input_len).astype(np_type) * preset.max_length
stop_word_list = np.repeat(
to_word_list_format([stop_words], self.tokenizer),
to_word_list_format([preset.stop_words], self.tokenizer),
input_start_ids.shape[0],
axis=0,
)
@ -68,7 +69,7 @@ class TritonService:
self.tokenizer.decode(out[prompt_len : prompt_len + g])
for g, out in zip(gen_len, output_data)
]
trimmed = [trim_with_stopwords(d, stop_words) for d in decoded]
trimmed = [trim_with_stopwords(d, preset.stop_words) for d in decoded]
return [Choice(index=i, text=text) for i, text in enumerate(trimmed)]
def __call__(self, data: CompletionRequest) -> CompletionResponse:
@ -86,20 +87,6 @@ def prepare_tensor(name: str, tensor_input):
return t
def random_completion_id():
return "cmpl-" + "".join(
random.choice(string.ascii_letters + string.digits) for _ in range(29)
)
def trim_with_stopwords(output: str, stopwords: list) -> str:
for w in sorted(stopwords, key=len, reverse=True):
if output.endswith(w):
output = output[: -len(w)]
break
return output
def to_word_list_format(word_dict, tokenizer):
flat_ids = []
offsets = []

16
tabby/server/backend/utils.py Normal file
View File

@ -0,0 +1,16 @@
import random
import string
def random_completion_id():
return "cmpl-" + "".join(
random.choice(string.ascii_letters + string.digits) for _ in range(29)
)
def trim_with_stopwords(output: str, stopwords: list) -> str:
for w in sorted(stopwords, key=len, reverse=True):
if output.endswith(w):
output = output[: -len(w)]
break
return output

40
tabby/server/python.py
View File

@ -1,40 +0,0 @@
import random
import string
import time
from typing import List
from transformers import AutoModelForCausalLM, AutoTokenizer
from .models import Choice, CompletionRequest, CompletionResponse
class PythonModelService:
def __init__(
self,
model_name,
):
self.tokenizer = AutoTokenizer.from_pretrained(
model_name, local_files_only=True
)
self.model = AutoModelForCausalLM.from_pretrained(
model_name, local_files_only=True
)
def generate(self, request: CompletionRequest) -> List[Choice]:
input_ids = self.tokenizer.encode(request.prompt, return_tensors="pt")
res = self.model.generate(input_ids, max_length=64)
output_ids = res[0][len(input_ids[0]) :]
text = self.tokenizer.decode(output_ids)
return [Choice(index=0, text=text)]
def __call__(self, request: CompletionRequest) -> CompletionResponse:
choices = self.generate(request)
return CompletionResponse(
id=random_completion_id(), created=int(time.time()), choices=choices
)
def random_completion_id():
return "cmpl-" + "".join(
random.choice(string.ascii_letters + string.digits) for _ in range(29)
)