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remove legacy pyworker

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This commit is contained in:
Lucas Armand
2025-12-11 16:55:48 -08:00
parent 405a8f1c0d
commit 067fa936fb
36 changed files with 0 additions and 4313 deletions
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workers/comfyui-json/data_types.py
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import os
import sys
import random
import dataclasses
from typing import Dict, Any
from functools import cache
from math import ceil
from pathlib import Path
import json
import logging
from lib.data_types import ApiPayload, JsonDataException
log = logging.getLogger(__file__)
def count_workload() -> float:
# Always 100.0 where there is a single instance of ComfyUI handling requests
# Results will indicate % or a job completed per second. Avoids sub 0.1 sec performance indication
return 100.0
@dataclasses.dataclass
class ComfyWorkflowData(ApiPayload):
input: dict
@classmethod
def for_test(cls):
"""
If the user has provided a benchmark workflow we can use it here to properly gauge performance.
Otherwise, use the variables available to simulate workflows of the required running time
Example: SD1.5, simple image gen 10000 steps, 512px x 512px will run for approximately 9 minutes @ ~18 it/s (RTX 4090)
"""
# Try to load benchmark.json
benchmark_file = Path("workers/comfyui-json/misc/benchmark.json")
if benchmark_file.exists():
try:
with open(benchmark_file, "r") as f:
benchmark_workflow = json.load(f)
return cls(
input={
"request_id": f"test-{random.randint(1000, 99999)}",
"workflow_json": benchmark_workflow
}
)
except (json.JSONDecodeError, IOError):
# JSON is malformed or file can't be read, fall through to default
log.error(f"Failed to benchmark using {benchmark_file}")
# Fallback: read prompts and construct payload
log.info("Using fallback method for benchmarking")
with open("workers/comfyui-json/misc/test_prompts.txt", "r") as f:
test_prompts = f.readlines()
test_prompt = random.choice(test_prompts).rstrip()
return cls(
input={
"request_id": f"test-{random.randint(1000, 99999)}",
"modifier": "Text2Image",
"modifications": {
"prompt": test_prompt,
"width": os.getenv('BENCHMARK_TEST_WIDTH', 512),
"height": os.getenv('BENCHMARK_TEST_HEIGHT', 512),
"steps": os.getenv('BENCHMARK_TEST_STEPS', 20),
"seed": random.randint(0, sys.maxsize),
}
}
)
def generate_payload_json(self) -> Dict[str, Any]:
# input is already a dict, just return it wrapped in the expected structure
return {"input": self.input}
def count_workload(self) -> float:
return count_workload()
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "ComfyWorkflowData":
# Extract required fields
if "input" not in json_msg:
raise JsonDataException({"input": "missing parameter"})
return cls(
input=json_msg["input"]
)
workers/comfyui-json/misc/benchmark.json.example
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{
"3": {
"inputs": {
"seed": "__RANDOM_INT__",
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly-fp16.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"7": {
"inputs": {
"text": "text, watermark",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}
workers/comfyui-json/misc/test_prompts.txt
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cartoon character of a person with a hoodie , in style of cytus and deemo, ork, gold chains, realistic anime cat, dripping black goo, lineage revolution style, thug life, cute anthropomorphic bunny, balrog, arknights, aliased, very buff, black and red and yellow paint, painting illustration collage style, character composition in vector with white background
stardew valley, fine details
2D Vector Illustration of a child with soccer ball Art for Sublimation, Design Art, Chrome Art, Painting and Stunning Artwork, Highly Detailed Digital Painting, Airbrush Art, Highly Detailed Digital Artwork, Dramatic Artwork, stained antique yellow copper paint, digital airbrush art, detailed by Mark Brooks, Chicano airbrush art, Swagger! snake Culture
realistic futuristic city-downtown with short buildings, sunset
seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water
inspired by realflow-cinema4d editor features, create image of a transparent luxury cup with ice fruits and mint, connected with white, yellow and pink cream, Slow - High Speed MO Photography, YouTube Video Screenshot, Abstract Clay, Transparent Cup , molecular gastronomy, wheel, 3D fluid,Simulation rendering, still video, 4k polymer clay futras photography, very surreal, Houdini Fluid Simulation, hyperrealistic CGI and FLUIDS & MULTIPHYSICS SIMULATION effect, with Somali Stain Lurex, Metallic Jacquard, Gold Thread, Mulberry Silk, Toub Saree, Warm background, a fantastic image worthy of an award.
biker with backpack on his back riding a motorcycle, Style by Ade Santora, Oilpunk, Cover photo, craig mullins style, on the cover of a magazine, Outdoor Magazine, inspired by Alex Petruk APe, image of a male biker, Cover of an award-winning magazine, the man has a backpack, photo for magazine, with a backpack, magazine cover
generate a collage-style illustration inspired by the Procreate raster graphic editor, photographic illustration with the theme, 2D vector, art for textile sublimation, containing surrealistic cartoon cat wearing a baseball cap and jeans standing in front of a poster, inspired by Sadao Watanabe, Doraemon, Japanese cartoon style, Eichiro Oda, Iconic high detail character, Director: Nakahara Nantenbō, Kastuhiro Otomo, image detailed, by Miyamoto, Hidetaka Miyazaki, Katsuhiro illustration, 8k, masterpiece, Minimize noise and grain in photo quality without lose quality and increase brightness and lighting,Symmetry and Alignment, Avoid asymmetrical shapes and out-of-focus points. Focus and Sharpness: Make sure the image is focused and sharp and encourages the viewer to see it as a work of art printed on fabric.
fantasy medieval village world inside a glass sphere , high detail, fantasy, realistic, light effect, hyper detail, volumetric lighting, cinematic, macro, depth of field, blur, red light and clouds from the back, highly detailed epic cinematic concept art cg render made in maya, blender and photoshop, octane render, excellent composition, dynamic dramatic cinematic lighting, aesthetic, very inspirational, world inside a glass sphere by james gurney by artgerm with james jean, joe fenton and tristan eaton by ross tran, fine details
Iron Man, (Arnold Tsang, Toru Nakayama), Masterpiece, Studio Quality, 6k , toa, toaair, 1boy, glowing, axe, mecha, science_fiction, solo, weapon, jungle , green_background, nature, outdoors, solo, tree, weapon, mask, dynamic lighting, detailed shading, digital texture painting
(Pope Francis) wearing leather jacket is a DJ in a nightclub, mixing live on stage, giant mixing table, a masterpiece
Pope Francis wearing biker (leather jacket), a masterpiece
Luke Skywalker ordering a burger and fries from the Death Star canteen.
I want to generate a group avatar for a Feishu group chat. The role of this group is daily software technical communication. Now the subject technology stacks that members of this group discuss daily include: algorithms, data structures, optimization, functional programming, and the programming languages often discussed are: TypeScript, Java, python, etc. I hope this avatar has a simple aesthetic, this avatar is a single person avatar
portrait Anime black girl cute-fine-face, pretty face, realistic shaded Perfect face, fine details. Anime. realistic shaded lighting by Ilya Kuvshinov Giuseppe Dangelico Pino and Michael Garmash and Rob Rey, IAMAG premiere, WLOP matte print, cute freckles, masterpiece
young Disney socialite wearing a beige miniskirt, dark brown turtleneck sweater, small neckless, cute-fine-face, anime. illustration, realistic shaded perfect face, brown hair, grey eyes, fine details, realistic shaded lighting by ilya kuvshinov giuseppe dangelico pino and michael garmash and rob rey, iamag premiere, wlop matte print, a masterpiece
Cute small cat sitting in a movie theater eating chicken wiggs watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
Cute small dog sitting in a movie theater eating popcorn watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
fox bracelet made of buckskin with fox features, rich details, fine carvings, studio lighting
crane buckskin bracelet with crane features, rich details, fine carvings, studio lighting
london luxurious interior living-room, light walls
Parisian luxurious interior penthouse bedroom, dark walls, wooden panels
cute girl, crop-top, blond hair, black glasses, stretching, with background by greg rutkowski makoto shinkai kyoto animation key art feminine mid shot
houses in front, houses background, straight houses, digital art, smooth, sharp focus, gravity falls style, doraemon style, shinchan style, anime style
Simplified technical drawing, Leonardo da Vinci, Mechanical Dinosaur Skeleton, Minimalistic annotations, Hand-drawn illustrations, Basic design and engineering, Wonder and curiosity
High quality 8K painting impressionist style of a Japanese modern city street with a girl on the foreground wearing a traditional wedding dress with a fox mask, staring at the sky, daylight
a landscape from the Moon with the Earth setting on the horizon, realistic, detailed
Isometric Atlantis city,great architecture with columns, great details, ornaments,seaweed, blue ambiance, 3D cartoon style, soft light, 45° view
A hyper realistic avatar of a guy riding on a black honda cbr 650r in leather suit,high detail, high quality,8K,photo realism
the street of amedieval fantasy town, at dawn, dark, highly detailed
overwhelmingly beautiful eagle framed with vector flowers, long shiny wavy flowing hair, polished, ultra detailed vector floral illustration mixed with hyper realism, muted pastel colors, vector floral details in background, muted colors, hyper detailed ultra intricate overwhelming realism in detailed complex scene with magical fantasy atmosphere, no signature, no watermark
a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece | hyperrealism| highly detailed| insanely detailed| intricate| cinematic lighting| depth of field
electronik robot and ofice ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
exquisitely intricately detailed illustration, of a small world with a lake and a rainbow, inside a closed glass jar.
workers/comfyui-json/server.py
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import os
import logging
import dataclasses
import base64
from typing import Optional, Union, Type
from aiohttp import web, ClientResponse
from lib.backend import Backend, LogAction
from lib.data_types import EndpointHandler
from lib.server import start_server
from .data_types import ComfyWorkflowData
MODEL_SERVER_URL = os.getenv("MODEL_SERVER_URL", "http://127.0.0.1:18288")
# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: "
MODEL_SERVER_ERROR_LOG_MSGS = [
"MetadataIncompleteBuffer", # This error is emitted when the downloaded model is corrupted
"Value not in list: ", # This error is emitted when the model file is not there at all
"[ERROR] Provisioning Script failed", # Error inserted by provisioning script if models/nodes fail to download
]
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s[%(levelname)-5s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
log = logging.getLogger(__file__)
async def generate_client_response(
client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
# Check if the response is actually streaming based on response headers/content-type
is_streaming_response = (
model_response.content_type == "text/event-stream"
or model_response.content_type == "application/x-ndjson"
or model_response.headers.get("Transfer-Encoding") == "chunked"
or "stream" in model_response.content_type.lower()
)
if is_streaming_response:
log.debug("Detected streaming response...")
res = web.StreamResponse()
res.content_type = model_response.content_type
await res.prepare(client_request)
async for chunk in model_response.content:
await res.write(chunk)
await res.write_eof()
log.debug("Done streaming response")
return res
else:
log.debug("Detected non-streaming response...")
content = await model_response.read()
return web.Response(
body=content,
status=model_response.status,
content_type=model_response.content_type
)
@dataclasses.dataclass
class ComfyWorkflowHandler(EndpointHandler[ComfyWorkflowData]):
@property
def endpoint(self) -> str:
return "/generate/sync"
@property
def healthcheck_endpoint(self) -> Optional[str]:
return f"{MODEL_SERVER_URL}/health"
@classmethod
def payload_cls(cls) -> Type[ComfyWorkflowData]:
return ComfyWorkflowData
def make_benchmark_payload(self) -> ComfyWorkflowData:
return ComfyWorkflowData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
return await generate_client_response(client_request, model_response)
backend = Backend(
model_server_url=MODEL_SERVER_URL,
model_log_file=os.environ["MODEL_LOG"],
allow_parallel_requests=False,
benchmark_handler=ComfyWorkflowHandler(
benchmark_runs=3, benchmark_words=100
),
log_actions=[
(LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
(LogAction.Info, "Downloading:"),
*[
(LogAction.ModelError, error_msg)
for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
],
],
)
async def handle_ping(_):
return web.Response(body="pong")
routes = [
web.post("/generate/sync", backend.create_handler(ComfyWorkflowHandler())),
web.get("/ping", handle_ping),
]
if __name__ == "__main__":
start_server(backend, routes)
workers/comfyui-json/test_load.py
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from lib.test_utils import test_load_cmd, test_args
from .data_types import ComfyWorkflowData
WORKER_ENDPOINT = "/generate/sync"
if __name__ == "__main__":
test_load_cmd(ComfyWorkflowData, WORKER_ENDPOINT, arg_parser=test_args)
workers/comfyui/README.md
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This is the base PyWorker for comfyui. It can be used to create PyWorker that use various models and
workflows. It provides two endpoints:
1. `/prompt`: Uses the default comfy workflow defined under `misc/default_workflows`
2. `/custom_workflow`: Allows the client to send their own comfy workflow with each API request.
To use the comfyui PyWorker, `$COMFY_MODEL` env variable must be set in the template. Current options are
`sd3` and `flux`. Each have example clients.
To add new models, a JSON with name `$COMFY_MODEL.json` must be created under `misc/default_workflows`
NOTE: default workflows follow this format:
```json
{
"input": {
"handler": "RawWorkflow",
"aws_access_key_id": "your-s3-access-key",
"aws_secret_access_key": "your-s3-secret-access-key",
"aws_endpoint_url": "https://my-endpoint.backblaze.com",
"aws_bucket_name": "your-bucket",
"webhook_url": "your-webhook-url",
"webhook_extra_params": {},
"workflow_json": {}
}
}
```
You can ignore all of these fields except for `workflow_json`.
Fields written as "{{FOO}}" will be replaced using data from a user request. For example, SD3's workflow has the
following nodes:
```json
"5": {
"inputs": {
"width": "{{WIDTH}}",
"height": "{{HEIGHT}}",
"batch_size": 1
},
"6": {
"inputs": {
"text": "{{PROMPT}}",
"clip": ["11", 0]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
...
"17": {
"inputs": {
"scheduler": "simple",
"steps": "{{STEPS}}",
"denoise": 1,
"model": ["12", 0]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
...
"25": {
"inputs": {
"noise_seed": "{{SEED}}"
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
}
```
Incoming requests have the following JSON format:
```json
{
prompt: str
width: int
height: int
steps: int
seed: int
}
```
Each value in those fields with replace the placeholder of the same name in the default workflow.
See Vast's serverless documentation for more details on how to use comfyui with autoscaler
workers/comfyui/__init__.py
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workers/comfyui/client.py
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import logging
from urllib.parse import urljoin
import requests
from lib.test_utils import print_truncate_res
from utils.endpoint_util import Endpoint
from utils.ssl import get_cert_file_path
from vastai import Serverless
ENDPOINT_NAME = "my-comfyui-endpoint"
COST = 100 # Use a constant cost for image generation
def call_default_workflow(client: Serverless) -> None:
WORKER_ENDPOINT = "/prompt"
COST = 100
route_payload = {
"endpoint": endpoint_group_name,
"api_key": api_key,
"cost": COST,
}
response = requests.post(
urljoin(server_url, "/route/"),
json=route_payload,
timeout=4,
)
response.raise_for_status()
message = response.json()
url = message["url"]
auth_data = dict(
signature=message["signature"],
cost=message["cost"],
endpoint=message["endpoint"],
reqnum=message["reqnum"],
url=message["url"],
)
payload = dict(
prompt="a fat fluffy cat", width=1024, height=1024, steps=20, seed=123456789
)
req_data = dict(payload=payload, auth_data=auth_data)
url = urljoin(url, WORKER_ENDPOINT)
print(f"url: {url}")
response = requests.post(
url,
json=req_data,
verify=get_cert_file_path(),
)
response.raise_for_status()
print_truncate_res(str(response.json()))
def call_custom_workflow_for_sd3(
endpoint_group_name: str, api_key: str, server_url: str
) -> None:
WORKER_ENDPOINT = "/custom-workflow"
COST = 100
route_payload = {
"endpoint": endpoint_group_name,
"api_key": api_key,
"cost": COST,
}
response = requests.post(
urljoin(server_url, "/route/"),
json=route_payload,
timeout=4,
)
response.raise_for_status()
message = response.json()
url = message["url"]
auth_data = dict(
signature=message["signature"],
cost=message["cost"],
endpoint=message["endpoint"],
reqnum=message["reqnum"],
url=message["url"],
request_idx=message["request_idx"],
)
workflow = {
"3": {
"inputs": {
"seed": 156680208700286,
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": ["4", 0],
"positive": ["6", 0],
"negative": ["7", 0],
"latent_image": ["5", 0],
},
"class_type": "KSampler",
},
"4": {
"inputs": {"ckpt_name": "sd3_medium_incl_clips_t5xxlfp16.safetensors"},
"class_type": "CheckpointLoaderSimple",
},
"5": {
"inputs": {"width": 512, "height": 512, "batch_size": 1},
"class_type": "EmptyLatentImage",
},
"6": {
"inputs": {
"text": "beautiful scenery nature glass bottle landscape, purple galaxy bottle",
"clip": ["4", 1],
},
"class_type": "CLIPTextEncode",
},
"7": {
"inputs": {"text": "text, watermark", "clip": ["4", 1]},
"class_type": "CLIPTextEncode",
},
"8": {
"inputs": {"samples": ["3", 0], "vae": ["4", 2]},
"class_type": "VAEDecode",
},
"9": {
"inputs": {"filename_prefix": "ComfyUI", "images": ["8", 0]},
"class_type": "SaveImage",
},
}
# these values should match the values in the custom workflow above,
# they are used to calculate workload
custom_fields = dict(
steps=20,
width=512,
height=512,
)
req_data = dict(
payload=dict(custom_fields=custom_fields, workflow=workflow),
auth_data=auth_data,
)
url = urljoin(url, WORKER_ENDPOINT)
print(f"url: {url}")
response = requests.post(
url,
json=req_data,
verify=get_cert_file_path(),
)
response.raise_for_status()
print_truncate_res(str(response.json()))
if __name__ == "__main__":
from lib.test_utils import test_args
args = test_args.parse_args()
endpoint_api_key = Endpoint.get_endpoint_api_key(
endpoint_name=args.endpoint_group_name,
account_api_key=args.api_key,
instance=args.instance,
)
if endpoint_api_key:
try:
call_default_workflow(
api_key=endpoint_api_key,
endpoint_group_name=args.endpoint_group_name,
server_url=args.server_url,
)
call_custom_workflow_for_sd3(
api_key=endpoint_api_key,
endpoint_group_name=args.endpoint_group_name,
server_url=args.server_url,
)
except Exception as e:
log.error(f"Error during API call: {e}")
else:
log.error(f"Failed to get API key for endpoint {args.endpoint_group_name} ")
workers/comfyui/data_types.py
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import sys
import os
import json
import random
import dataclasses
import inspect
from typing import Dict, Any
from functools import cache
from math import ceil
from enum import Enum
from lib.data_types import ApiPayload, JsonDataException
with open("workers/comfyui/misc/test_prompts.txt", "r") as f:
test_prompts = f.readlines()
class Model(Enum):
Flux = "flux"
Sd3 = "sd3"
def get_request_time(self) -> int:
match self:
case Model.Flux:
return 23
case Model.Sd3:
return 6
@cache
def get_model() -> Model:
match os.environ.get("COMFY_MODEL"):
case "flux":
return Model.Flux
case "sd3":
return Model.Sd3
case None:
raise Exception(
"For comfyui pyworker, $COMFY_MODEL must be set in the vast template"
)
case model:
raise Exception(f"Unsupported comfyui model: {model}")
@cache
def get_request_template() -> str:
with open(f"workers/comfyui/misc/default_workflows/{get_model().value}.json") as f:
return f.read()
def count_workload(width: int, height: int, steps: int) -> float:
"""
we want to normalize the workload is a number such that cur_perf(tokens/second) for 1024x1024 image with
28 steps is 200 tokens on a 4090.
in order get that we calculate the
A = ( absolute workload based on given data )
B = ( absolute workload for a 1024x1024 image with 28 steps )
and adjust the workload to 200 tokens by A/B.
we then adjust for difference between Flux and SD3 by multiplying this value by expected request time for a
standard image(23s for Flux, 6s for SD3).
On a 4090, this would give us a workload that would give a cur_perf(workload / request_time) of around 200
"""
def _calculate_absolute_tokens(width_: int, height_: int, steps_: int) -> float:
"""
This is based on how openai counts image generation tokens, see: https://openai.com/api/pricing/
we count how many 512x512 grids are needed to cover the image.
each tile is then counted as 175 tokens.
each image generation also has constant of 85 base tokens.
we then adjust the count based on the number of steps. The baseline number of steps is assumed to be 28.
Some testing with flux gave me this data:
steps(X) | request time(Y)
__________|_________________
07(0.25x) | 11s (0.47x)
14(0.50x) | 15s (0.65x)
21(0.75x) | 20s (0.86x)
28(1.00x) | 23s (1.00x)
35(1.25x) | 28s (1.21x)
42(1.50x) | 32s (1.39x)
49(1.75x) | 37s (1.60x)
this gives a linear regression of Y = 0.61*X + 6.57
we can use this as an adjustment_factor for token count
adjustment_factor = (0.61 * steps + 6.57)
"""
width_grids = ceil(width_ / 512)
height_grids = ceil(height_ / 512)
tokens = 85 + width_grids * height_grids * 175
adjustment_factor = 0.61 * steps_ + 6.57
return tokens * adjustment_factor
REQUEST_TIME_FOR_STANDARD_IMAGE = get_model().get_request_time()
absolute_tokens = _calculate_absolute_tokens(
width_=width, height_=height, steps_=steps
)
absolute_tokens_standard_image = _calculate_absolute_tokens(
width_=1024, height_=1024, steps_=28
)
return REQUEST_TIME_FOR_STANDARD_IMAGE * (
(absolute_tokens / absolute_tokens_standard_image) * 200
)
@dataclasses.dataclass
class DefaultComfyWorkflowData(ApiPayload):
prompt: str
width: int
height: int
steps: int
seed: int
@classmethod
def for_test(cls):
test_prompt = random.choice(test_prompts).rstrip()
return cls(
prompt=test_prompt,
width=1024,
height=1024,
steps=28,
seed=random.randint(0, sys.maxsize),
)
def generate_payload_json(
self,
) -> Dict[str, Any]:
return json.loads(
get_request_template()
.replace("{{PROMPT}}", self.prompt)
# these values should be of int type. Since "{{VAR}}" is wrapped with " in the template
# to make the JSON valid, we must replace the double quotes. i.e. "{{WIDTH}}" -> 1024 and not "1024"
.replace('"{{WIDTH}}"', str(self.width))
.replace('"{{HEIGHT}}"', str(self.height))
.replace('"{{STEPS}}"', str(self.steps))
.replace('"{{SEED}}"', str(self.seed))
)
def count_workload(self) -> float:
return count_workload(width=self.width, height=self.height, steps=self.steps)
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "DefaultComfyWorkflowData":
errors = {}
for param in inspect.signature(cls).parameters:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
return cls(
**{
k: v
for k, v in json_msg.items()
if k in inspect.signature(cls).parameters
}
)
@dataclasses.dataclass
class CustomComfyWorkflowData(ApiPayload):
custom_fields: Dict[str, int]
workflow: Dict[str, Any]
@classmethod
def for_test(cls):
raise NotImplementedError("Custom comfy workflow is not used for testing")
def count_workload(self) -> float:
return count_workload(
width=int(self.custom_fields.get("width", 1024)),
height=int(self.custom_fields.get("height", 1024)),
steps=int(self.custom_fields.get("steps", 28)),
)
def generate_payload_json(self) -> Dict[str, Any]:
template_json = json.loads(get_request_template())
template_json["input"]["workflow_json"] = self.workflow
return template_json
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "CustomComfyWorkflowData":
errors = {}
for param in inspect.signature(cls).parameters:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
return cls(
**{
k: v
for k, v in json_msg.items()
if k in inspect.signature(cls).parameters
}
)
workers/comfyui/misc/default_workflows/flux.json
-137
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@@ -1,137 +0,0 @@
{
"input": {
"handler": "RawWorkflow",
"aws_access_key_id": "your-s3-access-key",
"aws_secret_access_key": "your-s3-secret-access-key",
"aws_endpoint_url": "https://my-endpoint.backblaze.com",
"aws_bucket_name": "your-bucket",
"webhook_url": "your-webhook-url",
"webhook_extra_params": {},
"workflow_json": {
"5": {
"inputs": {
"width": "{{WIDTH}}",
"height": "{{HEIGHT}}",
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "{{PROMPT}}",
"clip": ["11", 0]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": ["13", 0],
"vae": ["10", 0]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": ["8", 0]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"10": {
"inputs": {
"vae_name": "ae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"11": {
"inputs": {
"clip_name1": "t5xxl_fp16.safetensors",
"clip_name2": "clip_l.safetensors",
"type": "flux"
},
"class_type": "DualCLIPLoader",
"_meta": {
"title": "DualCLIPLoader"
}
},
"12": {
"inputs": {
"unet_name": "flux1-dev.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"13": {
"inputs": {
"noise": ["25", 0],
"guider": ["22", 0],
"sampler": ["16", 0],
"sigmas": ["17", 0],
"latent_image": ["5", 0]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"16": {
"inputs": {
"sampler_name": "euler"
},
"class_type": "KSamplerSelect",
"_meta": {
"title": "KSamplerSelect"
}
},
"17": {
"inputs": {
"scheduler": "simple",
"steps": "{{STEPS}}",
"denoise": 1,
"model": ["12", 0]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
"22": {
"inputs": {
"model": ["12", 0],
"conditioning": ["6", 0]
},
"class_type": "BasicGuider",
"_meta": {
"title": "BasicGuider"
}
},
"25": {
"inputs": {
"noise_seed": "{{SEED}}"
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
}
}
}
}
workers/comfyui/misc/default_workflows/sd3.json
-142
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@@ -1,142 +0,0 @@
{
"input": {
"handler": "RawWorkflow",
"aws_access_key_id": "your-s3-access-key",
"aws_secret_access_key": "your-s3-secret-access-key",
"aws_endpoint_url": "https://my-endpoint.backblaze.com",
"aws_bucket_name": "your-bucket",
"webhook_url": "your-webhook-url",
"webhook_extra_params": {},
"workflow_json": {
"6": {
"inputs": {
"text": "{{PROMPT}}",
"clip": ["252", 1]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"13": {
"inputs": {
"shift": 3,
"model": ["252", 0]
},
"class_type": "ModelSamplingSD3",
"_meta": {
"title": "ModelSamplingSD3"
}
},
"67": {
"inputs": {
"conditioning": ["71", 0]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"68": {
"inputs": {
"start": 0.1,
"end": 1,
"conditioning": ["67", 0]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"69": {
"inputs": {
"conditioning_1": ["68", 0],
"conditioning_2": ["70", 0]
},
"class_type": "ConditioningCombine",
"_meta": {
"title": "Conditioning (Combine)"
}
},
"70": {
"inputs": {
"start": 0,
"end": 0.1,
"conditioning": ["71", 0]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"71": {
"inputs": {
"text": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
"clip": ["252", 1]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"135": {
"inputs": {
"width": "{{WIDTH}}",
"height": "{{HEIGHT}}",
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"231": {
"inputs": {
"samples": ["271", 0],
"vae": ["252", 2]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"233": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": ["231", 0]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"252": {
"inputs": {
"ckpt_name": "sd3_medium_incl_clips_t5xxlfp16.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"271": {
"inputs": {
"seed": "{{SEED}}",
"steps": "{{STEPS}}",
"cfg": 4.5,
"sampler_name": "dpmpp_2m",
"scheduler": "sgm_uniform",
"denoise": 1,
"model": ["13", 0],
"positive": ["6", 0],
"negative": ["69", 0],
"latent_image": ["135", 0]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
}
}
}
}
workers/comfyui/misc/test_prompts.txt
-34
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@@ -1,34 +0,0 @@
cartoon character of a person with a hoodie , in style of cytus and deemo, ork, gold chains, realistic anime cat, dripping black goo, lineage revolution style, thug life, cute anthropomorphic bunny, balrog, arknights, aliased, very buff, black and red and yellow paint, painting illustration collage style, character composition in vector with white background
stardew valley, fine details
2D Vector Illustration of a child with soccer ball Art for Sublimation, Design Art, Chrome Art, Painting and Stunning Artwork, Highly Detailed Digital Painting, Airbrush Art, Highly Detailed Digital Artwork, Dramatic Artwork, stained antique yellow copper paint, digital airbrush art, detailed by Mark Brooks, Chicano airbrush art, Swagger! snake Culture
realistic futuristic city-downtown with short buildings, sunset
seascape by Ray Collins and artgerm, front view of a perfect wave, sunny background, ultra detailed water
inspired by realflow-cinema4d editor features, create image of a transparent luxury cup with ice fruits and mint, connected with white, yellow and pink cream, Slow - High Speed MO Photography, YouTube Video Screenshot, Abstract Clay, Transparent Cup , molecular gastronomy, wheel, 3D fluid,Simulation rendering, still video, 4k polymer clay futras photography, very surreal, Houdini Fluid Simulation, hyperrealistic CGI and FLUIDS & MULTIPHYSICS SIMULATION effect, with Somali Stain Lurex, Metallic Jacquard, Gold Thread, Mulberry Silk, Toub Saree, Warm background, a fantastic image worthy of an award.
biker with backpack on his back riding a motorcycle, Style by Ade Santora, Oilpunk, Cover photo, craig mullins style, on the cover of a magazine, Outdoor Magazine, inspired by Alex Petruk APe, image of a male biker, Cover of an award-winning magazine, the man has a backpack, photo for magazine, with a backpack, magazine cover
generate a collage-style illustration inspired by the Procreate raster graphic editor, photographic illustration with the theme, 2D vector, art for textile sublimation, containing surrealistic cartoon cat wearing a baseball cap and jeans standing in front of a poster, inspired by Sadao Watanabe, Doraemon, Japanese cartoon style, Eichiro Oda, Iconic high detail character, Director: Nakahara Nantenbō, Kastuhiro Otomo, image detailed, by Miyamoto, Hidetaka Miyazaki, Katsuhiro illustration, 8k, masterpiece, Minimize noise and grain in photo quality without lose quality and increase brightness and lighting,Symmetry and Alignment, Avoid asymmetrical shapes and out-of-focus points. Focus and Sharpness: Make sure the image is focused and sharp and encourages the viewer to see it as a work of art printed on fabric.
fantasy medieval village world inside a glass sphere , high detail, fantasy, realistic, light effect, hyper detail, volumetric lighting, cinematic, macro, depth of field, blur, red light and clouds from the back, highly detailed epic cinematic concept art cg render made in maya, blender and photoshop, octane render, excellent composition, dynamic dramatic cinematic lighting, aesthetic, very inspirational, world inside a glass sphere by james gurney by artgerm with james jean, joe fenton and tristan eaton by ross tran, fine details
Iron Man, (Arnold Tsang, Toru Nakayama), Masterpiece, Studio Quality, 6k , toa, toaair, 1boy, glowing, axe, mecha, science_fiction, solo, weapon, jungle , green_background, nature, outdoors, solo, tree, weapon, mask, dynamic lighting, detailed shading, digital texture painting
(Pope Francis) wearing leather jacket is a DJ in a nightclub, mixing live on stage, giant mixing table, a masterpiece
Pope Francis wearing biker (leather jacket), a masterpiece
Luke Skywalker ordering a burger and fries from the Death Star canteen.
I want to generate a group avatar for a Feishu group chat. The role of this group is daily software technical communication. Now the subject technology stacks that members of this group discuss daily include: algorithms, data structures, optimization, functional programming, and the programming languages often discussed are: TypeScript, Java, python, etc. I hope this avatar has a simple aesthetic, this avatar is a single person avatar
portrait Anime black girl cute-fine-face, pretty face, realistic shaded Perfect face, fine details. Anime. realistic shaded lighting by Ilya Kuvshinov Giuseppe Dangelico Pino and Michael Garmash and Rob Rey, IAMAG premiere, WLOP matte print, cute freckles, masterpiece
young Disney socialite wearing a beige miniskirt, dark brown turtleneck sweater, small neckless, cute-fine-face, anime. illustration, realistic shaded perfect face, brown hair, grey eyes, fine details, realistic shaded lighting by ilya kuvshinov giuseppe dangelico pino and michael garmash and rob rey, iamag premiere, wlop matte print, a masterpiece
Cute small cat sitting in a movie theater eating chicken wiggs watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
Cute small dog sitting in a movie theater eating popcorn watching a movie ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
fox bracelet made of buckskin with fox features, rich details, fine carvings, studio lighting
crane buckskin bracelet with crane features, rich details, fine carvings, studio lighting
london luxurious interior living-room, light walls
Parisian luxurious interior penthouse bedroom, dark walls, wooden panels
cute girl, crop-top, blond hair, black glasses, stretching, with background by greg rutkowski makoto shinkai kyoto animation key art feminine mid shot
houses in front, houses background, straight houses, digital art, smooth, sharp focus, gravity falls style, doraemon style, shinchan style, anime style
Simplified technical drawing, Leonardo da Vinci, Mechanical Dinosaur Skeleton, Minimalistic annotations, Hand-drawn illustrations, Basic design and engineering, Wonder and curiosity
High quality 8K painting impressionist style of a Japanese modern city street with a girl on the foreground wearing a traditional wedding dress with a fox mask, staring at the sky, daylight
a landscape from the Moon with the Earth setting on the horizon, realistic, detailed
Isometric Atlantis city,great architecture with columns, great details, ornaments,seaweed, blue ambiance, 3D cartoon style, soft light, 45° view
A hyper realistic avatar of a guy riding on a black honda cbr 650r in leather suit,high detail, high quality,8K,photo realism
the street of amedieval fantasy town, at dawn, dark, highly detailed
overwhelmingly beautiful eagle framed with vector flowers, long shiny wavy flowing hair, polished, ultra detailed vector floral illustration mixed with hyper realism, muted pastel colors, vector floral details in background, muted colors, hyper detailed ultra intricate overwhelming realism in detailed complex scene with magical fantasy atmosphere, no signature, no watermark
a highly detailed matte painting of a man on a hill watching a rocket launch in the distance by studio ghibli, makoto shinkai, by artgerm, by wlop, by greg rutkowski, volumetric lighting, octane render, 4 k resolution, trending on artstation, masterpiece | hyperrealism| highly detailed| insanely detailed| intricate| cinematic lighting| depth of field
electronik robot and ofice ,unreal engine, cozy indoor lighting, artstation, detailed, digital painting,cinematic,character design by mark ryden and pixar and hayao miyazaki, unreal 5, daz, hyperrealistic, octane render
exquisitely intricately detailed illustration, of a small world with a lake and a rainbow, inside a closed glass jar.
workers/comfyui/server.py
-143
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@@ -1,143 +0,0 @@
import os
import logging
import dataclasses
import base64
from typing import Optional, Union, Type
from aiohttp import web, ClientResponse
from anyio import open_file
from lib.backend import Backend, LogAction
from lib.data_types import EndpointHandler
from lib.server import start_server
from .data_types import DefaultComfyWorkflowData, CustomComfyWorkflowData
MODEL_SERVER_URL = "http://127.0.0.1:18288" # API Wrapper Service
# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
MODEL_SERVER_START_LOG_MSG = "To see the GUI go to: http://127.0.0.1:18188"
MODEL_SERVER_ERROR_LOG_MSGS = [
"MetadataIncompleteBuffer", # This error is emitted when the downloaded model is corrupted
"Value not in list: unet_name", # This error is emitted when the model file is not there at all
]
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s[%(levelname)-5s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
log = logging.getLogger(__file__)
async def generate_client_response(
request: web.Request, response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
_ = request
match response.status:
case 200:
log.debug("SUCCESS")
res = await response.json()
if "output" not in res:
return web.json_response(
data=dict(error="there was an error in the workflow"),
status=422,
)
image_paths = [path["local_path"] for path in res["output"]["images"]]
if not image_paths:
return web.json_response(
data=dict(error="workflow did not produce any images"),
status=422,
)
images = []
for image_path in image_paths:
async with await open_file(image_path, mode="rb") as f:
contents = await f.read()
images.append(
f"data:image/png;base64,{base64.b64encode(contents).decode('utf-8')}"
)
return web.json_response(data=dict(images=images))
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
@dataclasses.dataclass
class DefaultComfyWorkflowHandler(EndpointHandler[DefaultComfyWorkflowData]):
@property
def endpoint(self) -> str:
return "/runsync"
@property
def healthcheck_endpoint(self) -> Optional[str]:
return None
@classmethod
def payload_cls(cls) -> Type[DefaultComfyWorkflowData]:
return DefaultComfyWorkflowData
def make_benchmark_payload(self) -> DefaultComfyWorkflowData:
return DefaultComfyWorkflowData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
return await generate_client_response(client_request, model_response)
@dataclasses.dataclass
class CustomComfyWorkflowHandler(EndpointHandler[CustomComfyWorkflowData]):
@property
def endpoint(self) -> str:
return "/runsync"
@property
def healthcheck_endpoint(self) -> Optional[str]:
return None
@classmethod
def payload_cls(cls) -> Type[CustomComfyWorkflowData]:
return CustomComfyWorkflowData
def make_benchmark_payload(self) -> CustomComfyWorkflowData:
return CustomComfyWorkflowData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
return await generate_client_response(client_request, model_response)
backend = Backend(
model_server_url=MODEL_SERVER_URL,
model_log_file=os.environ["MODEL_LOG"],
allow_parallel_requests=False,
benchmark_handler=DefaultComfyWorkflowHandler(
benchmark_runs=3, benchmark_words=100
),
log_actions=[
(LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
(LogAction.Info, "Downloading:"),
*[
(LogAction.ModelError, error_msg)
for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
],
],
)
async def handle_ping(_):
return web.Response(body="pong")
routes = [
web.post("/prompt", backend.create_handler(DefaultComfyWorkflowHandler())),
web.post("/custom-workflow", backend.create_handler(CustomComfyWorkflowHandler())),
web.get("/ping", handle_ping),
]
if __name__ == "__main__":
start_server(backend, routes)
workers/comfyui/test_load.py
-15
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@@ -1,15 +0,0 @@
from lib.test_utils import test_load_cmd, test_args
from .data_types import DefaultComfyWorkflowData, Model
WORKER_ENDPOINT = "/prompt"
if __name__ == "__main__":
test_args.add_argument(
"-m",
dest="comfy_model",
choices=list(map(lambda x: x.value, Model)),
required=True,
help="Image generation model name",
)
test_load_cmd(DefaultComfyWorkflowData, WORKER_ENDPOINT, arg_parser=test_args)
workers/hello_world/README.md
-321
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@@ -1,321 +0,0 @@
# Vast PyWorker
## Hello_world example
There is a hello_world PyWorker implementation under `workers/hello_world`. This PyWorker is
created for an LLM model server that runs on port 5001 has two API endpoints:
1. `/generate`: generates an full response to the prompt and sends a JSON response
2. `/generate_stream`: streams a response one token at a time
Both of these endpoints take the same API JSON payload:
```
{
"prompt": String,
"max_response_tokens": Number | null
}
```
We want the PyWorker to also expose two endpoints that correspond to the above endpoints.
### Structure
All PyWorkers have four files:
```
.
└── workers
└── hello_world
├── __init__.py
├── data_types.py # contains data types representing model API endpoints
├── server.py # contains endpoint handlers
└── test_load.py # script for load testing
```
All of the classes follow strict type hinting. It is recommended that you type hint all of your function.
This will allow your IDE or VSCode with `pyright` plugin to find any type errors in your implementation.
You can also install `pyright` with `sudo npm install -g pyright` and run `pyright` in the root of the project to find
any type errors.
### data_types.py: Contains data types representing model API endpoints
This file defines the structure of the data your model server expects (its API contract) and, critically, how PyWorker *interprets* that data for autoscaling purposes. You define Python data classes that mirror the JSON payloads your model's API uses.
These classes **must** inherit from `lib.data_types.ApiPayload`. This inheritance is not just for structure; it's how PyWorker knows how to:
* **Parse Incoming Requests:** Convert JSON from clients into usable Python objects.
* **Calculate Workload:** Determine the computational cost of a request.
* **Generate Test Data:** Create realistic inputs for benchmarking.
* **Format Requests for the Model Server:** Prepare data for the underlying model.
```python
import dataclasses
import random
from typing import Dict, Any
from transformers import OpenAIGPTTokenizer # used to count tokens in a prompt
import nltk # used to download a list of all words to generate a random prompt and benchmark the LLM model
from lib.data_types import ApiPayload
nltk.download("words")
WORD_LIST = nltk.corpus.words.words()
# you can use any tokenizer that fits your LLM. `openai-gpt` is free to use and is a good fit for most LLMs
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
@dataclasses.dataclass
class InputData(ApiPayload):
prompt: str
max_response_tokens: int
@classmethod
def for_test(cls) -> "ApiPayload":
"""defines how create a payload for load testing"""
prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
return cls(prompt=prompt, max_response_tokens=300)
def generate_payload_json(self) -> Dict[str, Any]:
"""defines how to convert an ApiPayload to JSON that will be sent to model API"""
return dataclasses.asdict(self)
def count_workload(self) -> float:
"""defines how to calculate workload for a payload"""
return len(tokenizer.tokenize(self.prompt))
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputData":
"""
defines how to transform JSON data to AuthData and payload type,
in this case `InputData` defined above represents the data sent to the model API.
AuthData is data generated by autoscaler in order to authenticate payloads.
In this case, the transformation is simple and 1:1. That is not always the case. See comfyui's PyWorker
for more complicated examples
"""
errors = {}
for param in inspect.signature(cls).parameters:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
return cls(
**{
k: v
for k, v in json_msg.items()
if k in inspect.signature(cls).parameters
}
)
```
### server.py: Creating Your Model's API Endpoints
This section guides you through creating the core of your custom model API: the `EndpointHandler`. Think of `EndpointHandler` as the bridge between incoming requests from users and your underlying model. It's the key to making your model accessible and scalable.
**Why use an `EndpointHandler`?**
* **Organized Request Handling:** It provides a structured way to handle different types of requests (like generating text, generating images, or performing other model-specific tasks).
* **Scalability:** By separating request handling from the model itself, you can easily scale your API to handle many concurrent users.
* **Flexibility:** You can customize how requests are processed, validated, and transformed before being sent to your model.
* **Standard Interface:** It provides a consistent interface for interacting with your model, regardless of the underlying implementation.
For every model API endpoint you want to expose (e.g., `/generate`, `/generate_stream`), you'll implement an `EndpointHandler`. This class is responsible for:
The `EndpointHandler` achieves this through several key methods:
* **Receiving and validating incoming requests (`get_data_from_request`):** This method ensures the request contains the necessary data (authentication and payload) and is in the correct format. It's the entry point for all requests.
* **Defining the endpoint (`endpoint`):** This method specifies the URL endpoint on the model API server where requests will be sent (e.g., `/generate`).
* **Specifying the payload type (`payload_cls`):** This method indicates the specific `ApiPayload` class used for this endpoint, defining the structure of the request data.
* **Creating benchmark payloads (`make_benchmark_payload`):** This method creates payloads specifically for benchmarking the model's performance.
* **Handling the model's response (`generate_client_response`):** This method takes the response from the model API server and transforms it into the format expected by the client making the request to your PyWorker. This allows you to customize the output as needed.
The `EndpointHandler` class has several abstract functions that you *must* implement to define the behavior of your specific endpoints. Here, we'll implement two common endpoints: `/generate` (for synchronous requests) and `/generate_stream` (for streaming responses):
```python
"""
AuthData is a dataclass that represents Authentication data sent from Autoscaler to client requesting a route.
When a user requests a route from autoscaler, see Vast's Serverless documentation for how routing and AuthData
work.
When a user receives a route for this PyWorker, they'll call PyWorkers API with the following JSON:
{
auth_data: AuthData,
payload : InputData # defined above
}
"""
from aiohttp import web
from lib.data_types import EndpointHandler, JsonDataException
from lib.server import start_server
from .data_types import InputData
# This class is the implementer for the '/generate' endpoint of model API
@dataclasses.dataclass
class GenerateHandler(EndpointHandler[InputData]):
@property
def endpoint(self) -> str:
# the API endpoint
return "/generate"
@classmethod
def payload_cls(cls) -> Type[InputData]:
"""this function should just return ApiPayload subclass used by this handler"""
return InputData
def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
"""
defines how to convert `InputData` defined above, to
JSON data to be sent to the model API. This function too is a simple dataclass -> JSON, but
can be more complicated, See comfyui for an example
"""
return dataclasses.asdict(payload)
def make_benchmark_payload(self) -> InputData:
"""
defines how to generate an InputData for benchmarking. This needs to be defined in only
one EndpointHandler, the one passed to the backend as the benchmark handler. Here we use the .for_test()
method on InputData. However, in some cases you might need to fine tune your InputData used for
benchmarking to closely resemble the average request users call the endpoint with in order to get best
autoscaling performance
"""
return InputData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
"""
defines how to convert a model API response to a response to PyWorker client
"""
_ = client_request
match model_response.status:
case 200:
log.debug("SUCCESS")
data = await model_response.json()
return web.json_response(data=data)
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
```
We also handle `GenerateStreamHandler` for streaming responses. It is identical to `GenerateHandler`, except for
the endpoint name and how we create a web response, as it is a streaming response:
```python
class GenerateStreamHandler(EndpointHandler[InputData]):
@property
def endpoint(self) -> str:
return "/generate_stream"
@classmethod
def payload_cls(cls) -> Type[InputData]:
return InputData
def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
return dataclasses.asdict(payload)
def make_benchmark_payload(self) -> InputData:
return InputData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
match model_response.status:
case 200:
log.debug("Streaming response...")
res = web.StreamResponse()
res.content_type = "text/event-stream"
await res.prepare(client_request)
async for chunk in model_response.content:
await res.write(chunk)
await res.write_eof()
log.debug("Done streaming response")
return res
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
```
You can now instantiate a Backend and use it to handle requests.
```python
from lib.backend import Backend, LogAction
# the url and port of model API
MODEL_SERVER_URL = "http://0.0.0.0:5001"
# This is the log line that is emitted once the server has started
MODEL_SERVER_START_LOG_MSG = "server has started"
MODEL_SERVER_ERROR_LOG_MSGS = [
"Exception: corrupted model file" # message in the logs indicating the unrecoverable error
]
backend = Backend(
model_server_url=MODEL_SERVER_URL,
# location of model log file
model_log_file=os.environ["MODEL_LOG"],
# for some model backends that can only handle one request at a time, be sure to set this to False to
# let PyWorker handling queueing requests.
allow_parallel_requests=True,
# give the backend an EndpointHandler instance that is used for benchmarking
# number of benchmark run and number of words for a random benchmark run are given
benchmark_handler=GenerateHandler(benchmark_runs=3, benchmark_words=256),
# defines how to handle specific log messages. See docstring of LogAction for details
log_actions=[
(LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
(LogAction.Info, '"message":"Download'),
*[
(LogAction.ModelError, error_msg)
for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
],
],
)
# this is a simple ping handler for PyWorker
async def handle_ping(_: web.Request):
return web.Response(body="pong")
# this is a handler for forwarding a health check to model API
async def handle_healthcheck(_: web.Request):
healthcheck_res = await backend.session.get("/healthcheck")
return web.Response(body=healthcheck_res.content, status=healthcheck_res.status)
routes = [
web.post("/generate", backend.create_handler(GenerateHandler())),
web.post("/generate_stream", backend.create_handler(GenerateStreamHandler())),
web.get("/ping", handle_ping),
web.get("/healthcheck", handle_healthcheck),
]
if __name__ == "__main__":
# start server, called from start_server.sh
start_server(backend, routes)
```
### test_load.py
Here you can create a script that allows you test an endpoint group running instances with this PyWorker
```python
from lib.test_harness import run
from .data_types import InputData
WORKER_ENDPOINT = "/generate"
if __name__ == "__main__":
run(InputData.for_test(), WORKER_ENDPOINT)
```
You can then run the following command from the root of this repo to load test endpoint group:
```sh
# sends 1000 requests at the rate of 0.5 requests per second
python3 workers.hello_world.test_load -n 1000 -rps 0.5 -k "$API_KEY" -e "$ENDPOINT_GROUP_NAME"
```
workers/hello_world/__init__.py
View File
workers/hello_world/client.py
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workers/hello_world/data_types.py
-48
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@@ -1,48 +0,0 @@
import dataclasses
import random
import inspect
from typing import Dict, Any
from transformers import OpenAIGPTTokenizer
import nltk
from lib.data_types import ApiPayload, JsonDataException
nltk.download("words")
WORD_LIST = nltk.corpus.words.words()
# used to count to count tokens and workload for LLM
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
@dataclasses.dataclass
class InputData(ApiPayload):
prompt: str
max_response_tokens: int
@classmethod
def for_test(cls) -> "InputData":
prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
return cls(prompt=prompt, max_response_tokens=300)
def generate_payload_json(self) -> Dict[str, Any]:
return dataclasses.asdict(self)
def count_workload(self) -> int:
return len(tokenizer.tokenize(self.prompt))
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputData":
errors = {}
for param in inspect.signature(cls).parameters:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
return cls(
**{
k: v
for k, v in json_msg.items()
if k in inspect.signature(cls).parameters
}
)
workers/hello_world/server.py
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@@ -1,175 +0,0 @@
"""
PyWorker works as a man-in-the-middle between the client and model API. It's function is:
1. receive request from client, update metrics such as workload of a request, number of pending requests, etc.
2a. transform the data and forward the transformed data to model API
2b. send updated metrics to autoscaler
3. transform response from model API(if needed) and forward the response to client
PyWorker forward requests to many model API endpoint. each endpoint must have an EndpointHandler. You can also
write function to just forward requests that don't generate anything with the model to model API without an
EndpointHandler. This is useful for endpoints such as healthchecks. See below for example
"""
import os
import logging
import dataclasses
from typing import Dict, Any, Optional, Union, Type
from aiohttp import web, ClientResponse
from lib.backend import Backend, LogAction
from lib.data_types import EndpointHandler
from lib.server import start_server
from .data_types import InputData
# the url and port of model API
MODEL_SERVER_URL = "http://0.0.0.0:5001"
# This is the log line that is emitted once the server has started
MODEL_SERVER_START_LOG_MSG = "infer server has started"
MODEL_SERVER_ERROR_LOG_MSGS = [
"Exception: corrupted model file" # message in the logs indicating the unrecoverable error
]
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s[%(levelname)-5s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
log = logging.getLogger(__file__)
# This class is the implementer for the '/generate' endpoint of model API
@dataclasses.dataclass
class GenerateHandler(EndpointHandler[InputData]):
@property
def endpoint(self) -> str:
# the API endpoint
return "/generate"
@property
def healthcheck_endpoint(self) -> Optional[str]:
return None
@classmethod
def payload_cls(cls) -> Type[InputData]:
return InputData
def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
"""
defines how to convert `InputData` defined above, to
json data to be sent to the model API
"""
return dataclasses.asdict(payload)
def make_benchmark_payload(self) -> InputData:
"""
defines how to generate an InputData for benchmarking. This needs to be defined in only
one EndpointHandler, the one passed to the backend as the benchmark handler
"""
return InputData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
"""
defines how to convert a model API response to a response to PyWorker client
"""
_ = client_request
match model_response.status:
case 200:
log.debug("SUCCESS")
data = await model_response.json()
return web.json_response(data=data)
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
# This is the same as GenerateHandler, except that it calls a streaming endpoint of the model API and streams the
# response, which itself is streaming, back to the client.
# it is nearly identical to handler as above, but it calls a different model API endpoint and it streams the
# streaming response from model API to client
class GenerateStreamHandler(EndpointHandler[InputData]):
@property
def endpoint(self) -> str:
return "/generate_stream"
@property
def healthcheck_endpoint(self) -> Optional[str]:
return None
@classmethod
def payload_cls(cls) -> Type[InputData]:
return InputData
def generate_payload_json(self, payload: InputData) -> Dict[str, Any]:
return dataclasses.asdict(payload)
def make_benchmark_payload(self) -> InputData:
return InputData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
match model_response.status:
case 200:
log.debug("Streaming response...")
res = web.StreamResponse()
res.content_type = "text/event-stream"
await res.prepare(client_request)
async for chunk in model_response.content:
await res.write(chunk)
await res.write_eof()
log.debug("Done streaming response")
return res
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
# This is the backend instance of pyworker. Only one must be made which uses EndpointHandlers to process
# incoming requests
backend = Backend(
model_server_url=MODEL_SERVER_URL,
model_log_file=os.environ["MODEL_LOG"],
allow_parallel_requests=True,
# give the backend a handler instance that is used for benchmarking
# number of benchmark run and number of words for a random benchmark run are given
benchmark_handler=GenerateHandler(benchmark_runs=3, benchmark_words=256),
# defines how to handle specific log messages. See docstring of LogAction for details
log_actions=[
(LogAction.ModelLoaded, MODEL_SERVER_START_LOG_MSG),
(LogAction.Info, '"message":"Download'),
*[
(LogAction.ModelError, error_msg)
for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
],
],
)
# this is a simple ping handler for pyworker
async def handle_ping(_: web.Request):
return web.Response(body="pong")
# this is a handler for forwarding a health check to modelAPI
async def handle_healthcheck(_: web.Request):
healthcheck_res = await backend.session.get("/healthcheck")
return web.Response(body=healthcheck_res.content, status=healthcheck_res.status)
routes = [
web.post("/generate", backend.create_handler(GenerateHandler())),
web.post("/generate_stream", backend.create_handler(GenerateStreamHandler())),
web.get("/ping", handle_ping),
web.get("/healthcheck", handle_healthcheck),
]
if __name__ == "__main__":
# start the PyWorker server
start_server(backend, routes)
workers/hello_world/test_load.py
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@@ -1,7 +0,0 @@
from lib.test_utils import test_load_cmd, test_args
from .data_types import InputData
WORKER_ENDPOINT = "/generate"
if __name__ == "__main__":
test_load_cmd(InputData, WORKER_ENDPOINT, arg_parser=test_args)
workers/openai/data_types/__init__.py
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workers/openai/data_types/client.py
-58
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@@ -1,58 +0,0 @@
import json
from dataclasses import dataclass, field, fields, is_dataclass
from typing import Optional, List, Dict, Any
class SerializableDataclass:
def _serialize_recursive(self, obj: Any) -> Any:
if is_dataclass(obj):
return {
field.name: self._serialize_recursive(getattr(obj, field.name))
for field in fields(obj)
}
elif isinstance(obj, dict):
return {key: self._serialize_recursive(value) for key, value in obj.items()}
elif isinstance(obj, (list, tuple)):
return [self._serialize_recursive(item) for item in obj]
elif isinstance(obj, set):
return [self._serialize_recursive(item) for item in obj]
else:
return obj
def to_dict(self) -> Dict[str, Any]:
return self._serialize_recursive(self)
def to_json(self, indent: int = 2) -> str:
return json.dumps(self.to_dict(), indent=indent)
@dataclass
class CompletionConfig(SerializableDataclass):
"""Configuration for completion requests"""
model: str
prompt: str = "Hello"
max_tokens: int = 256
temperature: float = 0.7
top_k: int = 20
top_p: float = 0.4
stream: bool = False
@dataclass
class ChatCompletionConfig(SerializableDataclass):
"""Configuration for chat completion requests"""
model: str
messages: list = field(default_factory=list)
max_tokens: int = 2096
temperature: float = 0.7
top_k: int = 20
top_p: float = 0.4
stream: bool = False
tools: Optional[List[Dict[str, Any]]] = field(default_factory=list)
tool_choice: str = "auto"
def __post_init__(self):
if self.messages is None:
self.messages = [{"role": "user", "content": "Hello"}]
workers/openai/data_types/server.py
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@@ -1,207 +0,0 @@
import os, json, random
from abc import ABC, abstractmethod
from dataclasses import dataclass
from lib.data_types import EndpointHandler, ApiPayload, JsonDataException
from typing import Union, Type, Dict, Any, Optional
from aiohttp import web, ClientResponse
import nltk
import logging
nltk.download("words")
WORD_LIST = nltk.corpus.words.words()
log = logging.getLogger(__name__)
"""
Generic dataclass accepts any dictionary in input.
"""
@dataclass
class GenericData(ApiPayload, ABC):
input: Dict[str, Any]
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "GenericData":
return cls(input=data["input"])
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "GenericData":
errors = {}
# Validate required parameters
required_params = ["input"]
for param in required_params:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
try:
# Create clean data dict and delegate to from_dict
clean_data = {"input": json_msg["input"]}
return cls.from_dict(clean_data)
except (json.JSONDecodeError, JsonDataException) as e:
errors["parameters"] = str(e)
raise JsonDataException(errors)
@classmethod
@abstractmethod
def for_test(cls) -> "GenericData":
pass
def generate_payload_json(self) -> Dict[str, Any]:
return self.input
def count_workload(self) -> int:
return self.input.get("max_tokens", 0)
@dataclass
class GenericHandler(EndpointHandler[GenericData], ABC):
@property
@abstractmethod
def endpoint(self) -> str:
pass
@property
def healthcheck_endpoint(self) -> Optional[str]:
return os.environ.get("MODEL_HEALTH_ENDPOINT")
@classmethod
def payload_cls(cls) -> Type[GenericData]:
return GenericData
@abstractmethod
def make_benchmark_payload(self) -> GenericData:
pass
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
match model_response.status:
case 200:
# Check if the response is actually streaming based on response headers/content-type
is_streaming_response = (
model_response.content_type == "text/event-stream"
or model_response.content_type == "application/x-ndjson"
or model_response.headers.get("Transfer-Encoding") == "chunked"
or "stream" in model_response.content_type.lower()
)
if is_streaming_response:
log.debug("Detected streaming response...")
res = web.StreamResponse()
res.content_type = model_response.content_type
await res.prepare(client_request)
async for chunk in model_response.content:
await res.write(chunk)
await res.write_eof()
log.debug("Done streaming response")
return res
else:
log.debug("Detected non-streaming response...")
content = await model_response.read()
return web.Response(
body=content,
status=200,
content_type=model_response.content_type,
)
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
@dataclass
class CompletionsData(GenericData):
@classmethod
def for_test(cls) -> "CompletionsData":
system_prompt = """You are a helpful AI assistant. You have access to the following knowledge base:
Zebras (US: /ˈziːbrəz/, UK: /ˈzɛbrəz, ˈziː-/)[2] (subgenus Hippotigris) are African equines
with distinctive black-and-white striped coats. There are three living species: Grévy's zebra
(Equus grevyi), the plains zebra (E. quagga), and the mountain zebra (E. zebra). Zebras share the
genus Equus with horses and asses, the three groups being the only living members of the family
Equidae. Zebra stripes come in different patterns, unique to each individual. Zebras inhabit eastern
and southern Africa and can be found in a variety of habitats such as savannahs, grasslands,
woodlands, shrublands, and mountainous areas.
Please answer the following question based on the above context."""
unique_question = " ".join(random.choices(WORD_LIST, k=int(100)))
model = os.environ.get("MODEL_NAME")
if not model:
raise ValueError("MODEL_NAME environment variable not set")
test_input = {
"model": model,
"prompt": f"{system_prompt}\n\n{unique_question}",
"temperature": 0.7,
"max_tokens": 500,
}
return cls(input=test_input)
@dataclass
class CompletionsHandler(GenericHandler):
@property
def endpoint(self) -> str:
return "/v1/completions"
@classmethod
def payload_cls(cls) -> Type[CompletionsData]:
return CompletionsData
def make_benchmark_payload(self) -> CompletionsData:
return CompletionsData.for_test()
@dataclass
class ChatCompletionsData(GenericData):
"""Chat completions-specific data implementation"""
@classmethod
def for_test(cls) -> "ChatCompletionsData":
system_prompt = """You are a helpful AI assistant. You have access to the following knowledge base:
Zebras (US: /ˈziːbrəz/, UK: /ˈzɛbrəz, ˈziː-/)[2] (subgenus Hippotigris) are African equines
with distinctive black-and-white striped coats. There are three living species: Grévy's zebra
(Equus grevyi), the plains zebra (E. quagga), and the mountain zebra (E. zebra). Zebras share the
genus Equus with horses and asses, the three groups being the only living members of the family
Equidae. Zebra stripes come in different patterns, unique to each individual. Zebras inhabit eastern
and southern Africa and can be found in a variety of habitats such as savannahs, grasslands,
woodlands, shrublands, and mountainous areas.
Please answer the following question based on the above context."""
unique_question = " ".join(random.choices(WORD_LIST, k=int(100)))
model = os.environ.get("MODEL_NAME")
if not model:
raise ValueError("MODEL_NAME environment variable not set")
# Chat completions use messages format instead of prompt
test_input = {
"model": model,
"messages": [
{"role": "system", "content": system_prompt}, # Shared prefix
{"role": "user", "content": unique_question} # Unique per request
],
"temperature": 0.7,
"max_tokens": 500,
}
return cls(input=test_input)
@dataclass
class ChatCompletionsHandler(GenericHandler):
@property
def endpoint(self) -> str:
return "/v1/chat/completions"
@classmethod
def payload_cls(cls) -> Type[ChatCompletionsData]:
return ChatCompletionsData
def make_benchmark_payload(self) -> ChatCompletionsData:
return ChatCompletionsData.for_test()
workers/openai/test_load.py
-434
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@@ -1,434 +0,0 @@
from lib.test_utils import test_args
from utils.endpoint_util import Endpoint
from utils.ssl import get_cert_file_path
from lib.data_types import AuthData
from .data_types.server import CompletionsData
import os
import time
import threading
import requests
from dataclasses import dataclass
from collections import Counter
from urllib.parse import urljoin, urlparse
import re
# Headless plotting
import matplotlib
matplotlib.use("Agg")
import logging
logging.getLogger("matplotlib.font_manager").setLevel(logging.WARNING)
import matplotlib.pyplot as plt
import numpy as np
from concurrent.futures import ThreadPoolExecutor, wait, FIRST_COMPLETED
from requests.adapters import HTTPAdapter
def get_incremented_path(path: str) -> str:
base, ext = os.path.splitext(path)
if not os.path.exists(path):
return path
i = 1
while os.path.exists(f"{base}-{i}{ext}"):
i += 1
return f"{base}-{i}{ext}"
WORKER_ENDPOINT = "/v1/completions" # This will return the full text output at once. Latency metrics reflect that (ie not measuring TTFT)
@dataclass
class ReqResult:
worker_url: str
route_ms: float
worker_ms: float
total_ms: float
ok: bool
error: str = ""
status_code: int = 0
t_start: float = 0.0
t_end: float = 0.0
workload: float = 0.0
def do_one(endpoint_name: str,
endpoint_id: int,
endpoint_api_key: str,
server_url: str,
worker_endpoint: str,
payload,
results_list,
t0,
status_samples,
route_session,
worker_session):
try:
workload = payload.count_workload()
route_payload = {"endpoint": endpoint_name, "api_key": endpoint_api_key, "cost": workload}
headers = {"Authorization": f"Bearer {endpoint_api_key}"}
start = time.time()
r0 = route_session.post(urljoin(server_url, "/route/"), json=route_payload, headers=headers, timeout=4)
t_after_route = time.time()
if r0.status_code != 200:
results_list.append(ReqResult(worker_url="",
route_ms=(t_after_route - start) * 1000.0,
worker_ms=0.0,
total_ms=(t_after_route - start) * 1000.0,
ok=False,
error=f"route error {r0.reason} {r0.text}",
status_code=r0.status_code,
t_start=start - t0,
t_end=t_after_route - t0,
workload=workload))
return
msg = r0.json()
# 1) Check if we got a worker back from route
worker_url = msg.get("url", "")
if not worker_url:
status = msg.get("status", "")
m = re.search(r"total workers:\s*(\d+).*loading workers:\s*(\d+).*standby workers:\s*(\d+).*error workers:\s*(\d+)", status, re.I | re.S)
if m:
tot, loading, standby, err = map(int, m.groups())
idle = max(tot - loading - standby - err, 0)
status_samples.append((time.time() - t0, idle))
# 2) If we got a worker, send the request
if worker_url:
req = dict(payload=payload.__dict__, auth_data=AuthData.from_json_msg(msg).__dict__)
t_before_worker = time.time()
r1 = worker_session.post(
urljoin(worker_url, worker_endpoint),
json=req,
verify=get_cert_file_path(),
timeout=(4, 120),
)
t_after_worker = time.time()
if r1.status_code != 200:
results_list.append(ReqResult(worker_url=worker_url,
route_ms=(t_after_route - start) * 1000.0,
worker_ms=(t_after_worker - t_before_worker) * 1000.0,
total_ms=(t_after_worker - start) * 1000.0,
ok=False,
error=f"worker inference error {r1.reason} {r1.text}",
status_code=r1.status_code,
t_start=start - t0,
t_end=t_after_worker - t0,
workload=workload))
return
# Success case
results_list.append(ReqResult(worker_url=worker_url,
route_ms=(t_after_route - start) * 1000.0,
worker_ms=(t_after_worker - t_before_worker) * 1000.0,
total_ms=(t_after_worker - start) * 1000.0,
ok=True,
error="",
status_code=200,
t_start=start - t0,
t_end=t_after_worker - t0,
workload=workload))
# 3) If so, sample via /get_endpoint_workers/ for eligible (idle) worker tracking
if worker_url:
try:
r_status = route_session.post(
urljoin(server_url, "/get_endpoint_workers/"),
json={"id": endpoint_id},
headers={"Authorization": f"Bearer {endpoint_api_key}"},
timeout=3,
)
if r_status.status_code == 200:
workers = r_status.json()
idle = 0
for w in workers:
st = str(w.get("status", "")).lower()
if (st in ("idle")):
idle += 1
status_samples.append((time.time() - t0, idle))
except Exception:
pass
except Exception as e:
t = time.time()
results_list.append(ReqResult(worker_url="",
route_ms=0.0,
worker_ms=0.0,
total_ms=0.0,
ok=False,
error=f"unknown error {e}",
status_code=0,
t_start=t - t0,
t_end=t - t0,
workload=0.0))
def run_load_with_metrics(num_requests: int,
requests_per_second: float,
endpoint_group_name: str,
account_api_key: str,
server_url: str,
worker_endpoint: str,
instance: str,
out_path: str):
ep_info = Endpoint.get_endpoint_info(endpoint_name=endpoint_group_name,
account_api_key=account_api_key,
instance=instance)
if not ep_info or not ep_info.get("api_key") or not ep_info.get("id"):
print(f"Endpoint {endpoint_group_name} not found for API key")
return
endpoint_id = int(ep_info["id"])
endpoint_api_key = ep_info["api_key"]
t0 = time.time()
results = []
status_samples = []
max_concurrency = int(os.environ.get("MAX_CONCURRENCY", "8192"))
submit_queue_factor = 2 # cap queued tasks to reduce memory
# Shared HTTP sessions with connection pooling (persistent connections)
def make_session(pool_connections: int, pool_maxsize: int) -> requests.Session:
sess = requests.Session()
adapter = HTTPAdapter(pool_connections=pool_connections, pool_maxsize=pool_maxsize, max_retries=0)
sess.mount("https://", adapter)
sess.mount("http://", adapter)
return sess
# Router: mostly single host, small connection pool is sufficient
route_session = make_session(pool_connections=1, pool_maxsize=max_concurrency)
# Workers: many hosts; allow many pools and per-host concurrency up to max_concurrency
worker_session = make_session(pool_connections=64, pool_maxsize=max_concurrency // 8)
# Fire requests using a thread pool, scheduling at requested RPS
inflight = set()
with ThreadPoolExecutor(max_workers=max_concurrency) as executor:
for i in range(num_requests):
# Pace submissions to RPS
target_time = t0 + i / max(requests_per_second, 1e-9)
sleep_s = target_time - time.time()
if sleep_s > 0:
time.sleep(min(sleep_s, 0.5)) # sleep in chunks to stay responsive
payload = CompletionsData.for_test()
fut = executor.submit(
do_one,
endpoint_group_name,
endpoint_id,
endpoint_api_key,
server_url,
worker_endpoint,
payload,
results,
t0,
status_samples,
route_session,
worker_session,
)
inflight.add(fut)
# Prevent unbounded queue growth
if len(inflight) >= max_concurrency * submit_queue_factor:
done, not_done = wait(inflight, return_when=FIRST_COMPLETED)
inflight = not_done
# Wait for all outstanding tasks
if inflight:
wait(inflight)
# Close sessions
try:
route_session.close()
finally:
worker_session.close()
# Aggregate results
oks = [r for r in results if r.ok]
errs = [r for r in results if not r.ok]
total_reqs = len(results)
succ = len(oks)
total_ms = np.array([r.total_ms for r in oks]) if succ else np.array([])
worker_ms = np.array([r.worker_ms for r in oks]) if succ else np.array([])
route_ms = np.array([r.route_ms for r in oks]) if succ else np.array([])
avg_total = float(np.mean(total_ms)) if succ else 0.0
avg_worker = float(np.mean(worker_ms)) if succ else 0.0
avg_route = float(np.mean(route_ms)) if succ else 0.0
p50_total, p95_total = (float(np.percentile(total_ms, 50)), float(np.percentile(total_ms, 95))) if succ else (0.0, 0.0)
# Distribution over workers (by host:port)
hosts = [urlparse(r.worker_url).netloc for r in oks if r.worker_url]
dist = Counter(hosts)
# Idle over time (mode per second)
idle_ts, idle_vals = [], []
if status_samples:
buckets = {}
for ts, idle in status_samples:
k = int(ts)
buckets.setdefault(k, []).append(idle)
keys = sorted(buckets.keys())
idle_ts = keys
# Use the most frequent sampled value per second (mode) to keep integer counts
idle_vals = []
for k in keys:
vals_k = [int(v) for v in buckets[k]]
if vals_k:
cnt = Counter(vals_k)
idle_vals.append(cnt.most_common(1)[0][0])
else:
idle_vals.append(0)
print(f"\nResults: total={total_reqs} success={succ} errors={len(errs)}")
print(f"Avg latency (ms): {avg_total:.1f} p50: {p50_total:.1f} p95: {p95_total:.1f}")
print(f"Avg route latency (ms): {avg_route:.1f} Avg worker latency (ms): {avg_worker:.1f}")
if errs:
print("Sample errors:")
for e in errs[:5]:
print(f" {e.status_code} {e.error}")
# Plot: 2x3 grid
fig, axes = plt.subplots(2, 3, figsize=(15, 8))
fig.suptitle(f"Load test: {endpoint_group_name} n={total_reqs}, rps={requests_per_second}, success={succ}")
# Dist per worker
ax0 = axes[0, 0]
if dist:
items = sorted(dist.items(), key=lambda kv: kv[1], reverse=True)
labels, counts = zip(*items)
ax0.bar(range(len(labels)), counts)
ax0.set_xticks(range(len(labels)))
ax0.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
ax0.set_title("Request distribution over workers")
ax0.set_ylabel("count")
# Latency histogram (total)
ax1 = axes[0, 1]
if succ:
ax1.hist(total_ms, bins=30)
ax1.set_title("Total latency (ms)")
ax1.set_xlabel("ms")
ax1.set_ylabel("freq")
# Eligible workers over time
ax_idle = axes[0, 2]
if idle_ts:
ax_idle.plot(idle_ts, idle_vals, "-o", ms=3)
ax_idle.set_title("Eligible workers over time")
ax_idle.set_xlabel("time (s)")
ax_idle.set_ylabel("eligible count")
# Throughput over time (completions/sec)
ax_idle = axes[1, 0]
ax_idle.clear()
if succ:
per_sec = {}
for r in oks:
s = int(r.t_end)
per_sec[s] = per_sec.get(s, 0) + 1
ts = sorted(per_sec.keys())
vals = [per_sec[t] for t in ts]
ax_idle.plot(ts, vals, "-o", ms=3)
ax_idle.set_title("Completions per second")
ax_idle.set_xlabel("time (s)")
ax_idle.set_ylabel("completions / sec")
# Summary text
ax3 = axes[1, 1]
ax3.axis("off")
text = (
f"Total requests: {total_reqs}\n"
f"Success: {succ} Errors: {len(errs)}\n"
f"Avg total latency: {avg_total:.1f} ms\n"
f"p50: {p50_total:.1f} ms p95: {p95_total:.1f} ms\n"
f"Avg route latency: {avg_route:.1f} ms\n"
f"Avg worker latency: {avg_worker:.1f} ms\n"
f"300 errors: {len([r for r in errs if r.status_code >= 300 and r.status_code < 400])}\n"
f"429 errors: {len([r for r in errs if r.status_code == 429])}\n"
f"500 errors: {len([r for r in errs if r.status_code >= 500])}\n"
f"Other errors: {len([r for r in errs if r.status_code not in [300, 429, 500]])}\n"
)
ax3.set_title("Summary")
ax3.text(0.02, 0.98, text, va="top", ha="left", fontsize=11, transform=ax3.transAxes)
# Error count over time
ax_errors = axes[1, 2]
all_end_times = [int(r.t_end) for r in results if r.t_end > 0]
if all_end_times:
min_second = min(all_end_times)
max_second = max(all_end_times)
# Count errors per second
errors_per_second = {}
for result in errs:
second = int(result.t_end)
errors_per_second[second] = errors_per_second.get(second, 0) + 1
# Create complete timeline including zeros
time_seconds = list(range(min_second, max_second + 1))
error_counts = [errors_per_second.get(sec, 0) for sec in time_seconds]
ax_errors.plot(time_seconds, error_counts, "-o", ms=3)
ax_errors.set_title("Errors per second")
ax_errors.set_xlabel("time (s)")
ax_errors.set_ylabel("errors / sec")
# Ensure unique output path and create directory if needed
final_out_path = get_incremented_path(out_path)
out_dir = os.path.dirname(final_out_path)
if out_dir:
os.makedirs(out_dir, exist_ok=True)
plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.savefig(final_out_path, dpi=120)
print(f"Saved report to: {final_out_path}")
# Per-worker latency boxplot (top 12 by volume)
groups = {}
for r in oks:
host = urlparse(r.worker_url).netloc
groups.setdefault(host, []).append(r.total_ms)
items = sorted(groups.items(), key=lambda kv: len(kv[1]), reverse=True)[:12]
if items:
labels, data = zip(*items)
fig2, axb = plt.subplots(1, 1, figsize=(12, 5))
axb.boxplot(data, showfliers=False)
axb.set_xticklabels(labels, rotation=45, ha="right", fontsize=8)
axb.set_title("Per-worker latency (ms)")
axb.set_ylabel("ms")
plt.tight_layout()
extra_out = get_incremented_path(os.path.splitext(out_path)[0] + "-workers.png")
plt.savefig(extra_out, dpi=120)
fig2.tight_layout()
fig2.savefig(extra_out, dpi=120)
print(f"Saved worker latency plot to: {extra_out}")
if __name__ == "__main__":
# Check if MODEL_NAME environment variable is set
model_name_set = os.environ.get("MODEL_NAME") is not None
# Add model argument - required only if MODEL_NAME is not set
test_args.add_argument(
"--model",
dest="model",
required=not model_name_set,
help="Model to use for completions request (required if MODEL_NAME env var not set)",
)
# Parse known args to get model early, before adding load args
known_args, _ = test_args.parse_known_args()
if hasattr(known_args, "model") and known_args.model:
os.environ["MODEL_NAME"] = known_args.model
print(f"Set MODEL_NAME environment variable to: {known_args.model}")
# Load test args
test_args.add_argument("-n", dest="num_requests", type=int, required=True, help="total number of requests")
test_args.add_argument("-rps", dest="requests_per_second", type=float, required=True, help="requests per second")
test_args.add_argument("--out", dest="out_path", type=str, default="load_test_report.png", help="path to save the report image")
args = test_args.parse_args()
server_url = {
"prod": "https://run.vast.ai",
"alpha": "https://run-alpha.vast.ai",
"candidate": "https://run-candidate.vast.ai",
"local": "http://localhost:8080"
}.get(args.instance, "http://localhost:8080")
run_load_with_metrics(
num_requests=args.num_requests,
requests_per_second=args.requests_per_second,
endpoint_group_name=args.endpoint_group_name,
account_api_key=args.api_key,
server_url=server_url,
worker_endpoint=WORKER_ENDPOINT,
instance=args.instance,
out_path=args.out_path,
)
workers/tgi/data_types.py
-73
View File
@@ -1,73 +0,0 @@
import dataclasses
import random
import inspect
from typing import Dict, Any
from transformers import OpenAIGPTTokenizer
import nltk
from lib.data_types import ApiPayload, JsonDataException
nltk.download("words")
WORD_LIST = nltk.corpus.words.words()
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
@dataclasses.dataclass
class InputParameters:
max_new_tokens: int = 256
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputParameters":
errors = {}
for param in inspect.signature(cls).parameters:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
return cls(
**{
k: v
for k, v in json_msg.items()
if k in inspect.signature(cls).parameters
}
)
@dataclasses.dataclass
class InputData(ApiPayload):
inputs: str
parameters: InputParameters
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "InputData":
return cls(
inputs=data["inputs"], parameters=InputParameters(**data["parameters"])
)
@classmethod
def for_test(cls) -> "InputData":
prompt = " ".join(random.choices(WORD_LIST, k=int(250)))
return cls(inputs=prompt, parameters=InputParameters())
def generate_payload_json(self) -> Dict[str, Any]:
return dataclasses.asdict(self)
def count_workload(self) -> int:
return self.parameters.max_new_tokens
@classmethod
def from_json_msg(cls, json_msg: Dict[str, Any]) -> "InputData":
errors = {}
for param in inspect.signature(cls).parameters:
if param not in json_msg:
errors[param] = "missing parameter"
if errors:
raise JsonDataException(errors)
try:
parameters = InputParameters.from_json_msg(json_msg["parameters"])
return cls(inputs=json_msg["inputs"], parameters=parameters)
except JsonDataException as e:
errors["parameters"] = e.message
raise JsonDataException(errors)
workers/tgi/server.py
-130
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@@ -1,130 +0,0 @@
import os
import logging
from typing import Union, Type
import dataclasses
from aiohttp import web, ClientResponse
from lib.backend import Backend, LogAction
from lib.data_types import EndpointHandler
from lib.server import start_server
from .data_types import InputData
MODEL_SERVER_URL = "http://0.0.0.0:5001"
# This is the last log line that gets emitted once comfyui+extensions have been fully loaded
MODEL_SERVER_START_LOG_MSG = [
'"message":"Connected","target":"text_generation_router"',
'"message":"Connected","target":"text_generation_router::server"',
]
MODEL_SERVER_ERROR_LOG_MSGS = [
"Error: WebserverFailed",
"Error: DownloadError",
"Error: ShardCannotStart",
]
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s[%(levelname)-5s] %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
log = logging.getLogger(__file__)
@dataclasses.dataclass
class GenerateHandler(EndpointHandler[InputData]):
@property
def endpoint(self) -> str:
return "/generate"
@property
def healthcheck_endpoint(self) -> str:
return f"{MODEL_SERVER_URL}/health"
@classmethod
def payload_cls(cls) -> Type[InputData]:
return InputData
def make_benchmark_payload(self) -> InputData:
return InputData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
_ = client_request
match model_response.status:
case 200:
log.debug("SUCCESS")
data = await model_response.json()
return web.json_response(data=data)
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
class GenerateStreamHandler(EndpointHandler[InputData]):
@property
def endpoint(self) -> str:
return "/generate_stream"
@property
def healthcheck_endpoint(self) -> str:
return f"{MODEL_SERVER_URL}/health"
@classmethod
def payload_cls(cls) -> Type[InputData]:
return InputData
def make_benchmark_payload(self) -> InputData:
return InputData.for_test()
async def generate_client_response(
self, client_request: web.Request, model_response: ClientResponse
) -> Union[web.Response, web.StreamResponse]:
match model_response.status:
case 200:
log.debug("Streaming response...")
res = web.StreamResponse()
res.content_type = "text/event-stream"
await res.prepare(client_request)
async for chunk in model_response.content:
await res.write(chunk)
await res.write_eof()
log.debug("Done streaming response")
return res
case code:
log.debug("SENDING RESPONSE: ERROR: unknown code")
return web.Response(status=code)
backend = Backend(
model_server_url=MODEL_SERVER_URL,
model_log_file=os.environ["MODEL_LOG"],
allow_parallel_requests=True,
benchmark_handler=GenerateHandler(benchmark_runs=3, benchmark_words=256),
log_actions=[
*[(LogAction.ModelLoaded, info_msg) for info_msg in MODEL_SERVER_START_LOG_MSG],
(LogAction.Info, '"message":"Download'),
*[
(LogAction.ModelError, error_msg)
for error_msg in MODEL_SERVER_ERROR_LOG_MSGS
],
],
)
async def handle_ping(_):
return web.Response(body="pong")
routes = [
web.post("/generate", backend.create_handler(GenerateHandler())),
web.post("/generate_stream", backend.create_handler(GenerateStreamHandler())),
web.get("/ping", handle_ping),
]
if __name__ == "__main__":
start_server(backend, routes)
workers/tgi/test_load.py
-7
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@@ -1,7 +0,0 @@
from lib.test_utils import test_load_cmd, test_args
from .data_types import InputData
WORKER_ENDPOINT = "/generate"
if __name__ == "__main__":
test_load_cmd(InputData, WORKER_ENDPOINT, arg_parser=test_args)