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Serverless GPU support on banana.dev (#106)

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* serverless: implement banana backend for both audio and LLM

Related to monadical-sas/reflector-gpu-banana project

* serverless: got llm working on banana !

* tests: fixes

* serverless: fix dockerfile to use fastapi server + httpx
This commit is contained in:
Mathieu Virbel
2023-08-04 10:24:11 +02:00
committed by GitHub
parent a5ce66c299
commit d94e2911c3
17 changed files with 602 additions and 53 deletions
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4
server/Dockerfile
View File

@@ -18,7 +18,7 @@ COPY pyproject.toml poetry.lock /tmp
RUN pip install "poetry==$POETRY_VERSION" RUN pip install "poetry==$POETRY_VERSION"
RUN python -m venv /venv RUN python -m venv /venv
RUN . /venv/bin/activate && poetry config virtualenvs.create false RUN . /venv/bin/activate && poetry config virtualenvs.create false
RUN . /venv/bin/activate && poetry install --only main --no-root --no-interaction --no-ansi RUN . /venv/bin/activate && poetry install --only main,aws --no-root --no-interaction --no-ansi
# bootstrap # bootstrap
FROM base AS final FROM base AS final
@@ -26,4 +26,4 @@ COPY --from=builder /venv /venv
RUN mkdir -p /app RUN mkdir -p /app
COPY reflector /app/reflector COPY reflector /app/reflector
WORKDIR /app WORKDIR /app
CMD ["/venv/bin/python", "-m", "reflector.server"] CMD ["/venv/bin/python", "-m", "reflector.app"]

243
server/poetry.lock generated
View File

@@ -1,5 +1,45 @@
# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. # This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
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@@ -137,6 +177,17 @@ files = [
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ifaddr = ">=0.2.0" ifaddr = ">=0.2.0"
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@@ -380,6 +431,44 @@ d = ["aiohttp (>=3.7.4)"]
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crt = ["awscrt (==0.16.9)"]
[[package]] [[package]]
name = "certifi" name = "certifi"
version = "2023.7.22" version = "2023.7.22"
@@ -1127,6 +1216,17 @@ files = [
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[[package]] [[package]]
name = "loguru" name = "loguru"
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@@ -1751,6 +1851,20 @@ pytest = ">=7.0.0"
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"] docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"] testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
[[package]]
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description = "Extensions to the standard Python datetime module"
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@@ -1835,6 +1949,23 @@ urllib3 = ">=1.21.1,<3"
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use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
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[[package]] [[package]]
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@@ -1878,6 +2009,17 @@ starlette = ["starlette (>=0.19.1)"]
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tornado = ["tornado (>=5)"] tornado = ["tornado (>=5)"]
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[[package]] [[package]]
name = "yarl" name = "yarl"
version = "1.9.2" version = "1.9.2"
@@ -2370,4 +2595,4 @@ multidict = ">=4.0"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.11" python-versions = "^3.11"
content-hash = "f94162f1217c3767f792902a9a45fec81275ae3a98f2809662bf3a3d574984e2" content-hash = "1a98a080ce035b381521426c9d6f9f80e8656258beab6cdff95ea90cf6c77e85"

6
server/pyproject.toml
View File

@@ -21,6 +21,7 @@ structlog = "^23.1.0"
uvicorn = {extras = ["standard"], version = "^0.23.1"} uvicorn = {extras = ["standard"], version = "^0.23.1"}
fastapi = "^0.100.1" fastapi = "^0.100.1"
sentry-sdk = {extras = ["fastapi"], version = "^1.29.2"} sentry-sdk = {extras = ["fastapi"], version = "^1.29.2"}
httpx = "^0.24.1"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]
@@ -28,7 +29,6 @@ black = "^23.7.0"
[tool.poetry.group.client.dependencies] [tool.poetry.group.client.dependencies]
httpx = "^0.24.1"
pyaudio = "^0.2.13" pyaudio = "^0.2.13"
stamina = "^23.1.0" stamina = "^23.1.0"
@@ -38,6 +38,10 @@ pytest-aiohttp = "^1.0.4"
pytest-asyncio = "^0.21.1" pytest-asyncio = "^0.21.1"
pytest = "^7.4.0" pytest = "^7.4.0"
[tool.poetry.group.aws.dependencies]
aioboto3 = "^11.2.0"
[build-system] [build-system]
requires = ["poetry-core"] requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api" build-backend = "poetry.core.masonry.api"

2
server/reflector/llm/__init__.py
View File

@@ -1,3 +1 @@
from .base import LLM # noqa: F401 from .base import LLM # noqa: F401
from . import llm_oobagooda # noqa: F401
from . import llm_openai # noqa: F401

32
server/reflector/llm/base.py
View File

@@ -1,6 +1,7 @@
from reflector.logger import logger from reflector.logger import logger
from reflector.settings import settings from reflector.settings import settings
import asyncio from reflector.utils.retry import retry
import importlib
import json import json
import re import re
@@ -13,7 +14,7 @@ class LLM:
cls._registry[name] = klass cls._registry[name] = klass
@classmethod @classmethod
def instance(cls): def get_instance(cls, name=None):
""" """
Return an instance depending on the settings. Return an instance depending on the settings.
Settings used: Settings used:
@@ -21,22 +22,19 @@ class LLM:
- `LLM_BACKEND`: key of the backend, defaults to `oobagooda` - `LLM_BACKEND`: key of the backend, defaults to `oobagooda`
- `LLM_URL`: url of the backend - `LLM_URL`: url of the backend
""" """
return cls._registry[settings.LLM_BACKEND]() if name is None:
name = settings.LLM_BACKEND
if name not in cls._registry:
module_name = f"reflector.llm.llm_{name}"
importlib.import_module(module_name)
return cls._registry[name]()
async def generate( async def generate(self, prompt: str, **kwargs) -> dict:
self, prompt: str, retry_count: int = 5, retry_interval: int = 1, **kwargs try:
) -> dict: result = await retry(self._generate)(prompt=prompt, **kwargs)
while retry_count > 0: except Exception:
try: logger.exception("Failed to call llm after retrying")
result = await self._generate(prompt=prompt, **kwargs) raise
break
except Exception:
logger.exception("Failed to call llm")
retry_count -= 1
await asyncio.sleep(retry_interval)
if retry_count == 0:
raise Exception("Failed to call llm after retrying")
if isinstance(result, str): if isinstance(result, str):
result = self._parse_json(result) result = self._parse_json(result)

41
server/reflector/llm/llm_banana.py Normal file
View File

@@ -0,0 +1,41 @@
from reflector.llm.base import LLM
from reflector.settings import settings
from reflector.utils.retry import retry
import httpx
class BananaLLM(LLM):
def __init__(self):
super().__init__()
self.timeout = settings.LLM_TIMEOUT
self.headers = {
"X-Banana-API-Key": settings.LLM_BANANA_API_KEY,
"X-Banana-Model-Key": settings.LLM_BANANA_MODEL_KEY,
}
async def _generate(self, prompt: str, **kwargs):
async with httpx.AsyncClient() as client:
response = await retry(client.post)(
settings.LLM_URL,
headers=self.headers,
json={"prompt": prompt},
timeout=self.timeout,
)
response.raise_for_status()
text = response.json()["text"]
text = text[len(prompt) :] # remove prompt
return text
LLM.register("banana", BananaLLM)
if __name__ == "__main__":
async def main():
llm = BananaLLM()
result = await llm.generate("Hello, my name is")
print(result)
import asyncio
asyncio.run(main())

37
server/reflector/processors/audio_transcript_auto.py
View File

@@ -1,19 +1,38 @@
from reflector.processors.base import Processor from reflector.processors.base import Processor
from reflector.processors.audio_transcript import AudioTranscriptProcessor from reflector.processors.audio_transcript import AudioTranscriptProcessor
from reflector.processors.audio_transcript_whisper import (
AudioTranscriptWhisperProcessor,
)
from reflector.processors.types import AudioFile from reflector.processors.types import AudioFile
from reflector.settings import settings
import importlib
class AudioTranscriptAutoProcessor(AudioTranscriptProcessor): class AudioTranscriptAutoProcessor(AudioTranscriptProcessor):
BACKENDS = { _registry = {}
"whisper": AudioTranscriptWhisperProcessor,
}
BACKEND_DEFAULT = "whisper"
def __init__(self, backend=None, **kwargs): @classmethod
self.processor = self.BACKENDS[backend or self.BACKEND_DEFAULT]() def register(cls, name, kclass):
cls._registry[name] = kclass
@classmethod
def get_instance(cls, name):
if name not in cls._registry:
module_name = f"reflector.processors.audio_transcript_{name}"
importlib.import_module(module_name)
# gather specific configuration for the processor
# search `TRANSCRIPT_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
config = {}
name_upper = name.upper()
settings_prefix = "TRANSCRIPT_"
config_prefix = f"{settings_prefix}{name_upper}_"
for key, value in settings:
if key.startswith(config_prefix):
config_name = key[len(settings_prefix) :].lower()
config[config_name] = value
return cls._registry[name](**config)
def __init__(self, **kwargs):
self.processor = self.get_instance(settings.TRANSCRIPT_BACKEND)
super().__init__(**kwargs) super().__init__(**kwargs)
def connect(self, processor: Processor): def connect(self, processor: Processor):

85
server/reflector/processors/audio_transcript_banana.py Normal file
View File

@@ -0,0 +1,85 @@
"""
Implementation using the GPU service from banana.
API will be a POST request to TRANSCRIPT_URL:
```json
{
"audio_url": "https://...",
"audio_ext": "wav",
"timestamp": 123.456
"language": "en"
}
```
"""
from reflector.processors.audio_transcript import AudioTranscriptProcessor
from reflector.processors.audio_transcript_auto import AudioTranscriptAutoProcessor
from reflector.processors.types import AudioFile, Transcript, Word
from reflector.settings import settings
from reflector.storage import Storage
from reflector.utils.retry import retry
from pathlib import Path
import httpx
class AudioTranscriptBananaProcessor(AudioTranscriptProcessor):
def __init__(self, banana_api_key: str, banana_model_key: str):
super().__init__()
self.transcript_url = settings.TRANSCRIPT_URL
self.timeout = settings.TRANSCRIPT_TIMEOUT
self.storage = Storage.get_instance(
settings.TRANSCRIPT_STORAGE_BACKEND, "TRANSCRIPT_STORAGE_"
)
self.headers = {
"X-Banana-API-Key": banana_api_key,
"X-Banana-Model-Key": banana_model_key,
}
async def _transcript(self, data: AudioFile):
async with httpx.AsyncClient() as client:
print(f"Uploading audio {data.path.name} to S3")
url = await self._upload_file(data.path)
print(f"Try to transcribe audio {data.path.name}")
request_data = {
"audio_url": url,
"audio_ext": data.path.suffix[1:],
"timestamp": float(round(data.timestamp, 2)),
}
response = await retry(client.post)(
self.transcript_url,
json=request_data,
headers=self.headers,
timeout=self.timeout,
)
print(f"Transcript response: {response.status_code} {response.content}")
response.raise_for_status()
result = response.json()
transcript = Transcript(
text=result["text"],
words=[
Word(text=word["text"], start=word["start"], end=word["end"])
for word in result["words"]
],
)
# remove audio file from S3
await self._delete_file(data.path)
return transcript
@retry
async def _upload_file(self, path: Path) -> str:
upload_result = await self.storage.put_file(path.name, open(path, "rb"))
return upload_result.url
@retry
async def _delete_file(self, path: Path):
await self.storage.delete_file(path.name)
return True
AudioTranscriptAutoProcessor.register("banana", AudioTranscriptBananaProcessor)

4
server/reflector/processors/audio_transcript_whisper.py
View File

@@ -1,4 +1,5 @@
from reflector.processors.audio_transcript import AudioTranscriptProcessor from reflector.processors.audio_transcript import AudioTranscriptProcessor
from reflector.processors.audio_transcript_auto import AudioTranscriptAutoProcessor
from reflector.processors.types import AudioFile, Transcript, Word from reflector.processors.types import AudioFile, Transcript, Word
from faster_whisper import WhisperModel from faster_whisper import WhisperModel
@@ -40,3 +41,6 @@ class AudioTranscriptWhisperProcessor(AudioTranscriptProcessor):
) )
return transcript return transcript
AudioTranscriptAutoProcessor.register("whisper", AudioTranscriptWhisperProcessor)

2
server/reflector/processors/transcript_topic_detector.py
View File

@@ -28,7 +28,7 @@ class TranscriptTopicDetectorProcessor(Processor):
super().__init__(**kwargs) super().__init__(**kwargs)
self.transcript = None self.transcript = None
self.min_transcript_length = min_transcript_length self.min_transcript_length = min_transcript_length
self.llm = LLM.instance() self.llm = LLM.get_instance()
async def _push(self, data: Transcript): async def _push(self, data: Transcript):
if self.transcript is None: if self.transcript is None:

29
server/reflector/settings.py
View File

@@ -26,8 +26,29 @@ class Settings(BaseSettings):
AUDIO_SAMPLING_WIDTH: int = 2 AUDIO_SAMPLING_WIDTH: int = 2
AUDIO_BUFFER_SIZE: int = 256 * 960 AUDIO_BUFFER_SIZE: int = 256 * 960
# Audio Transcription
# backends: whisper, banana
TRANSCRIPT_BACKEND: str = "whisper"
TRANSCRIPT_URL: str | None = None
TRANSCRIPT_TIMEOUT: int = 90
# Audio transcription banana.dev configuration
TRANSCRIPT_BANANA_API_KEY: str | None = None
TRANSCRIPT_BANANA_MODEL_KEY: str | None = None
# Audio transcription storage
TRANSCRIPT_STORAGE_BACKEND: str = "aws"
# Storage configuration for AWS
TRANSCRIPT_STORAGE_AWS_BUCKET_NAME: str = "reflector-bucket/chunks"
TRANSCRIPT_STORAGE_AWS_REGION: str = "us-east-1"
TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
# LLM # LLM
LLM_BACKEND: str = "oobagooda" LLM_BACKEND: str = "oobagooda"
# LLM common configuration
LLM_URL: str | None = None LLM_URL: str | None = None
LLM_HOST: str = "localhost" LLM_HOST: str = "localhost"
LLM_PORT: int = 7860 LLM_PORT: int = 7860
@@ -38,11 +59,9 @@ class Settings(BaseSettings):
LLM_MAX_TOKENS: int = 1024 LLM_MAX_TOKENS: int = 1024
LLM_TEMPERATURE: float = 0.7 LLM_TEMPERATURE: float = 0.7
# Storage # LLM Banana configuration
STORAGE_BACKEND: str = "aws" LLM_BANANA_API_KEY: str | None = None
STORAGE_AWS_ACCESS_KEY: str = "" LLM_BANANA_MODEL_KEY: str | None = None
STORAGE_AWS_SECRET_KEY: str = ""
STORAGE_AWS_BUCKET: str = ""
# Sentry # Sentry
SENTRY_DSN: str | None = None SENTRY_DSN: str | None = None

1
server/reflector/storage/__init__.py Normal file
View File

@@ -0,0 +1 @@
from .base import Storage # noqa

47
server/reflector/storage/base.py Normal file
View File

@@ -0,0 +1,47 @@
from pydantic import BaseModel
from reflector.settings import settings
import importlib
class FileResult(BaseModel):
filename: str
url: str
class Storage:
_registry = {}
CONFIG_SETTINGS = []
@classmethod
def register(cls, name, kclass):
cls._registry[name] = kclass
@classmethod
def get_instance(cls, name, settings_prefix=""):
if name not in cls._registry:
module_name = f"reflector.storage.storage_{name}"
importlib.import_module(module_name)
# gather specific configuration for the processor
# search `TRANSCRIPT_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
config = {}
name_upper = name.upper()
config_prefix = f"{settings_prefix}{name_upper}_"
for key, value in settings:
if key.startswith(config_prefix):
config_name = key[len(settings_prefix) :].lower()
config[config_name] = value
return cls._registry[name](**config)
async def put_file(self, filename: str, data: bytes) -> FileResult:
return await self._put_file(filename, data)
async def _put_file(self, filename: str, data: bytes) -> FileResult:
raise NotImplementedError
async def delete_file(self, filename: str):
return await self._delete_file(filename)
async def _delete_file(self, filename: str):
raise NotImplementedError

67
server/reflector/storage/storage_aws.py Normal file
View File

@@ -0,0 +1,67 @@
import aioboto3
from reflector.storage.base import Storage, FileResult
from reflector.logger import logger
class AwsStorage(Storage):
def __init__(
self,
aws_access_key_id: str,
aws_secret_access_key: str,
aws_bucket_name: str,
aws_region: str,
):
if not aws_access_key_id:
raise ValueError("Storage `aws_storage` require `aws_access_key_id`")
if not aws_secret_access_key:
raise ValueError("Storage `aws_storage` require `aws_secret_access_key`")
if not aws_bucket_name:
raise ValueError("Storage `aws_storage` require `aws_bucket_name`")
if not aws_region:
raise ValueError("Storage `aws_storage` require `aws_region`")
super().__init__()
self.aws_bucket_name = aws_bucket_name
self.aws_folder = ""
if "/" in aws_bucket_name:
self.aws_bucket_name, self.aws_folder = aws_bucket_name.split("/", 1)
self.session = aioboto3.Session(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
region_name=aws_region,
)
self.base_url = f"https://{aws_bucket_name}.s3.amazonaws.com/"
async def _put_file(self, filename: str, data: bytes) -> FileResult:
bucket = self.aws_bucket_name
folder = self.aws_folder
logger.info(f"Uploading {filename} to S3 {bucket}/{folder}")
s3filename = f"{folder}/{filename}" if folder else filename
async with self.session.client("s3") as client:
await client.put_object(
Bucket=bucket,
Key=s3filename,
Body=data,
)
presigned_url = await client.generate_presigned_url(
"get_object",
Params={"Bucket": bucket, "Key": s3filename},
ExpiresIn=3600,
)
return FileResult(
filename=filename,
url=presigned_url,
)
async def _delete_file(self, filename: str):
bucket = self.aws_bucket_name
folder = self.aws_folder
logger.info(f"Deleting {filename} from S3 {bucket}/{folder}")
s3filename = f"{folder}/{filename}" if folder else filename
async with self.session.client("s3") as client:
await client.delete_object(Bucket=bucket, Key=s3filename)
Storage.register("aws", AwsStorage)

25
server/reflector/tools/process.py
View File

@@ -12,7 +12,7 @@ from reflector.processors import (
import asyncio import asyncio
async def process_audio_file(filename, event_callback): async def process_audio_file(filename, event_callback, only_transcript=False):
async def on_transcript(data): async def on_transcript(data):
await event_callback("transcript", data) await event_callback("transcript", data)
@@ -22,15 +22,21 @@ async def process_audio_file(filename, event_callback):
async def on_summary(data): async def on_summary(data):
await event_callback("summary", data) await event_callback("summary", data)
# transcription output # build pipeline for audio processing
pipeline = Pipeline( processors = [
AudioChunkerProcessor(), AudioChunkerProcessor(),
AudioMergeProcessor(), AudioMergeProcessor(),
AudioTranscriptAutoProcessor.as_threaded(), AudioTranscriptAutoProcessor.as_threaded(),
TranscriptLinerProcessor(callback=on_transcript), TranscriptLinerProcessor(callback=on_transcript),
TranscriptTopicDetectorProcessor.as_threaded(callback=on_topic), ]
TranscriptFinalSummaryProcessor.as_threaded(callback=on_summary), if not only_transcript:
) processors += [
TranscriptTopicDetectorProcessor.as_threaded(callback=on_topic),
TranscriptFinalSummaryProcessor.as_threaded(callback=on_summary),
]
# transcription output
pipeline = Pipeline(*processors)
pipeline.describe() pipeline.describe()
# start processing audio # start processing audio
@@ -52,6 +58,7 @@ if __name__ == "__main__":
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("source", help="Source file (mp3, wav, mp4...)") parser.add_argument("source", help="Source file (mp3, wav, mp4...)")
parser.add_argument("--only-transcript", "-t", action="store_true")
args = parser.parse_args() args = parser.parse_args()
async def event_callback(event, data): async def event_callback(event, data):
@@ -62,4 +69,8 @@ if __name__ == "__main__":
elif event == "summary": elif event == "summary":
print(f"Summary: {data}") print(f"Summary: {data}")
asyncio.run(process_audio_file(args.source, event_callback)) asyncio.run(
process_audio_file(
args.source, event_callback, only_transcript=args.only_transcript
)
)

29
server/reflector/utils/retry.py Normal file
View File

@@ -0,0 +1,29 @@
from reflector.logger import logger
import asyncio
def retry(fn):
async def decorated(*args, **kwargs):
retry_max = kwargs.pop("retry_max", 5)
retry_delay = kwargs.pop("retry_delay", 2)
retry_ignore_exc_types = kwargs.pop("retry_ignore_exc_types", ())
result = None
attempt = 0
last_exception = None
for attempt in range(retry_max):
try:
result = await fn(*args, **kwargs)
if result:
return result
except retry_ignore_exc_types as e:
last_exception = e
logger.debug(
f"Retrying {fn} - in {retry_delay} seconds "
f"- attempt {attempt + 1}/{retry_max}"
)
await asyncio.sleep(retry_delay)
if last_exception is not None:
raise type(last_exception) from last_exception
return result
return decorated

1
server/tests/test_processors_pipeline.py
View File

@@ -12,6 +12,7 @@ async def test_basic_process(event_loop):
# use an LLM test backend # use an LLM test backend
settings.LLM_BACKEND = "test" settings.LLM_BACKEND = "test"
settings.TRANSCRIPT_BACKEND = "whisper"
class LLMTest(LLM): class LLMTest(LLM):
async def _generate(self, prompt: str, **kwargs) -> str: async def _generate(self, prompt: str, **kwargs) -> str: