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Add new data gathering mechanic on worker and mp tractor backend

pull/26/head
Guillermo Rodriguez 2023-10-07 21:28:52 -03:00
parent e802689523
commit d3b5d56187
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7 changed files with 552 additions and 48 deletions
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]
[[package]]
name = "xformers"
version = "0.0.22"
@ -3643,4 +3912,4 @@ cffi = ["cffi (>=1.11)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.12"
content-hash = "787c1bb602457589bdfeb6252fbc725abb038234dd7576060b61dc10dc9c4035"
content-hash = "4acde551673d4ecb752a3d22cd615b78ceacca6e9e1ad73bff2894c4455886a7"

1
pyproject.toml
View File

@ -15,6 +15,7 @@ Pillow = '^10.0.1'
docker = '^6.1.3'
py-leap = {git = 'https://github.com/guilledk/py-leap.git', rev = 'v0.1a14'}
toml = "^0.10.2"
tractor = {git = "https://github.com/goodboy/tractor.git"}
[tool.poetry.group.frontend]
optional = true

7
skynet/dgpu/__init__.py
View File

@ -1,5 +1,7 @@
#!/usr/bin/python
import trio
from skynet.dgpu.compute import SkynetMM
from skynet.dgpu.daemon import SkynetDGPUDaemon
from skynet.dgpu.network import SkynetGPUConnector
@ -8,5 +10,8 @@ from skynet.dgpu.network import SkynetGPUConnector
async def open_dgpu_node(config: dict):
conn = SkynetGPUConnector(config)
mm = SkynetMM(config)
daemon = SkynetDGPUDaemon(mm, conn, config)
await SkynetDGPUDaemon(mm, conn, config).serve_forever()
async with trio.open_nursery() as n:
n.start_soon(daemon.snap_updater_task)
await daemon.serve_forever()

165
skynet/dgpu/_mp_compute.py 100644
View File

@ -0,0 +1,165 @@
#!/usr/bin/python
import gc
import json
import logging
from hashlib import sha256
from diffusers import DiffusionPipeline
import trio
import torch
from skynet.constants import DEFAULT_INITAL_MODELS, MODELS
from skynet.dgpu.errors import DGPUComputeError
from skynet.utils import convert_from_bytes_and_crop, convert_from_cv2_to_image, convert_from_image_to_cv2, convert_from_img_to_bytes, init_upscaler, pipeline_for
def prepare_params_for_diffuse(
params: dict,
binary: bytes | None = None
):
image = None
if binary:
image = convert_from_bytes_and_crop(binary, 512, 512)
_params = {}
if image:
_params['image'] = image
_params['strength'] = float(params['strength'])
else:
_params['width'] = int(params['width'])
_params['height'] = int(params['height'])
return (
params['prompt'],
float(params['guidance']),
int(params['step']),
torch.manual_seed(int(params['seed'])),
params['upscaler'] if 'upscaler' in params else None,
_params
)
_models = {}
def is_model_loaded(model_name: str, image: bool):
for model_key, model_data in _models.items():
if (model_key == model_name and
model_data['image'] == image):
return True
return False
def load_model(
model_name: str,
image: bool,
force=False
):
logging.info(f'loading model {model_name}...')
if force or len(_models.keys()) == 0:
pipe = pipeline_for(
model_name, image=image)
_models[model_name] = {
'pipe': pipe,
'generated': 0,
'image': image
}
else:
least_used = list(_models.keys())[0]
for model in _models:
if _models[
least_used]['generated'] > _models[model]['generated']:
least_used = model
del _models[least_used]
logging.info(f'swapping model {least_used} for {model_name}...')
gc.collect()
torch.cuda.empty_cache()
pipe = pipeline_for(
model_name, image=image)
_models[model_name] = {
'pipe': pipe,
'generated': 0,
'image': image
}
logging.info(f'loaded model {model_name}')
return pipe
def get_model(model_name: str, image: bool) -> DiffusionPipeline:
if model_name not in MODELS:
raise DGPUComputeError(f'Unknown model {model_name}')
if not is_model_loaded(model_name, image):
pipe = load_model(model_name, image=image)
else:
pipe = _models[model_name]['pipe']
return pipe
def _static_compute_one(kwargs: dict):
request_id: int = kwargs['request_id']
method: str = kwargs['method']
params: dict = kwargs['params']
binary: bytes | None = kwargs['binary']
def _checkpoint(*args, **kwargs):
trio.from_thread.run(trio.sleep, 0)
try:
match method:
case 'diffuse':
image = None
arguments = prepare_params_for_diffuse(params, binary)
prompt, guidance, step, seed, upscaler, extra_params = arguments
model = get_model(params['model'], 'image' in extra_params)
image = model(
prompt,
guidance_scale=guidance,
num_inference_steps=step,
generator=seed,
callback=_checkpoint,
callback_steps=1,
**extra_params
).images[0]
if upscaler == 'x4':
upscaler = init_upscaler()
input_img = image.convert('RGB')
up_img, _ = upscaler.enhance(
convert_from_image_to_cv2(input_img), outscale=4)
image = convert_from_cv2_to_image(up_img)
img_raw = convert_from_img_to_bytes(image)
img_sha = sha256(img_raw).hexdigest()
return img_sha, img_raw
case _:
raise DGPUComputeError('Unsupported compute method')
except BaseException as e:
logging.error(e)
raise DGPUComputeError(str(e))
finally:
torch.cuda.empty_cache()
async def _tractor_static_compute_one(**kwargs):
return await trio.to_thread.run_sync(
_static_compute_one, kwargs)

11
skynet/dgpu/compute.py
View File

@ -17,6 +17,7 @@ from skynet.dgpu.errors import DGPUComputeError, DGPUInferenceCancelled
from skynet.utils import convert_from_bytes_and_crop, convert_from_cv2_to_image, convert_from_image_to_cv2, convert_from_img_to_bytes, init_upscaler, pipeline_for
from ._mp_compute import _static_compute_one, _tractor_static_compute_one
def prepare_params_for_diffuse(
params: dict,
@ -133,16 +134,16 @@ class SkynetMM:
def compute_one(
self,
request_id: int,
should_cancel_work,
method: str,
params: dict,
binary: bytes | None = None
):
def maybe_cancel_work(step, *args, **kwargs):
should_raise = trio.from_thread.run(should_cancel_work, request_id)
if should_raise:
logging.warn(f'cancelling work at step {step}')
raise DGPUInferenceCancelled()
if self._should_cancel:
should_raise = trio.from_thread.run(self._should_cancel, request_id)
if should_raise:
logging.warn(f'cancelling work at step {step}')
raise DGPUInferenceCancelled()
maybe_cancel_work(0)

95
skynet/dgpu/daemon.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/python
import json
import random
import logging
import traceback
@ -8,11 +9,21 @@ from hashlib import sha256
from functools import partial
import trio
import tractor
from skynet.dgpu.compute import SkynetMM
from skynet.dgpu.errors import *
from skynet.dgpu.compute import SkynetMM, _tractor_static_compute_one
from skynet.dgpu.network import SkynetGPUConnector
def convert_reward_to_int(reward_str):
int_part, decimal_part = (
reward_str.split('.')[0],
reward_str.split('.')[1].split(' ')[0]
)
return int(int_part + decimal_part)
class SkynetDGPUDaemon:
def __init__(
@ -40,17 +51,44 @@ class SkynetDGPUDaemon:
if 'model_blacklist' in config:
self.model_blacklist = set(config['model_blacklist'])
self.backend = 'sync-on-thread'
if 'backend' in config:
self.backend = config['backend']
self._snap = {
'queue': [],
'requests': {},
'my_results': []
}
async def should_cancel_work(self, request_id: int):
competitors = await self.conn.get_competitors_for_req(request_id)
competitors = set([
status['worker']
for status in self._snap['requests'][request_id]
if status['worker'] != self.conn.account
])
return bool(self.non_compete & competitors)
async def snap_updater_task(self):
while True:
self._snap = await self.conn.get_full_queue_snapshot()
await trio.sleep(1)
async def serve_forever(self):
try:
while True:
if self.auto_withdraw:
await self.conn.maybe_withdraw_all()
queue = await self.conn.get_work_requests_last_hour()
queue = self._snap['queue']
random.shuffle(queue)
queue = sorted(
queue,
key=lambda req: convert_reward_to_int(req['reward']),
reverse=True
)
for req in queue:
rid = req['id']
@ -68,13 +106,11 @@ class SkynetDGPUDaemon:
if model in self.model_blacklist:
continue
my_results = [res['id'] for res in (await self.conn.find_my_results())]
my_results = [res['id'] for res in self._snap['my_results']]
if rid not in my_results:
statuses = await self.conn.get_status_by_request_id(rid)
statuses = self._snap['requests'][rid]
if len(statuses) == 0:
self.conn.monitor_request(rid)
binary = await self.conn.get_input_data(req['binary_data'])
hash_str = (
@ -98,14 +134,42 @@ class SkynetDGPUDaemon:
else:
try:
img_sha, img_raw = await trio.to_thread.run_sync(
partial(
self.mm.compute_one,
rid,
self.should_cancel_work,
body['method'], body['params'], binary=binary
)
)
match self.backend:
case 'sync-on-thread':
self.mm._should_cancel = self.should_cancel_work
img_sha, img_raw = await trio.to_thread.run_sync(
partial(
self.mm.compute_one,
rid,
body['method'], body['params'], binary=binary
)
)
case 'tractor':
async def _should_cancel_oracle():
while True:
await trio.sleep(1)
if (await self.should_cancel_work(rid)):
raise DGPUInferenceCancelled
async with (
trio.open_nursery() as trio_n,
tractor.open_nursery() as tractor_n
):
trio_n.start_soon(_should_cancel_oracle)
portal = await tractor_n.run_in_actor(
_tractor_static_compute_one,
name='tractor-cuda-mp',
request_id=rid,
method=body['method'],
params=body['params'],
binary=binary
)
img_sha, img_raw = await portal.result()
trio_n.cancel_scope.cancel()
case _:
raise DGPUComputeError(f'Unsupported backend {self.backend}')
ipfs_hash = await self.conn.publish_on_ipfs(img_raw)
@ -116,7 +180,6 @@ class SkynetDGPUDaemon:
await self.conn.cancel_work(rid, str(e))
finally:
self.conn.forget_request(rid)
break
else:

50
skynet/dgpu/network.py
View File

@ -110,35 +110,35 @@ class SkynetGPUConnector:
else:
return None
def monitor_request(self, request_id: int):
logging.info(f'begin monitoring request: {request_id}')
self._wip_requests[request_id] = {
'last_update': None,
'competitors': set()
async def get_competitors_for_req(self, request_id: int) -> set:
competitors = [
status['worker']
for status in
(await self.get_status_by_request_id(request_id))
if status['worker'] != self.account
]
logging.info(f'competitors: {competitors}')
return set(competitors)
async def get_full_queue_snapshot(self):
snap = {
'requests': {},
'my_results': []
}
async def maybe_update_request(self, request_id: int):
now = time.time()
stats = self._wip_requests[request_id]
if (not stats['last_update'] or
(now - stats['last_update']) > REQUEST_UPDATE_TIME):
stats['competitors'] = [
status['worker']
for status in
(await self.get_status_by_request_id(request_id))
if status['worker'] != self.account
]
stats['last_update'] = now
snap['queue'] = await self.get_work_requests_last_hour()
async def get_competitors_for_req(self, request_id: int) -> set:
await self.maybe_update_request(request_id)
competitors = set(self._wip_requests[request_id]['competitors'])
logging.info(f'competitors: {competitors}')
return competitors
async def _run_and_save(d, key: str, fn, *args, **kwargs):
d[key] = await fn(*args, **kwargs)
def forget_request(self, request_id: int):
logging.info(f'end monitoring request: {request_id}')
del self._wip_requests[request_id]
async with trio.open_nursery() as n:
n.start_soon(_run_and_save, snap, 'my_results', self.find_my_results)
for req in snap['queue']:
n.start_soon(
_run_and_save, snap['requests'], req['id'], self.get_status_by_request_id, req['id'])
return snap
async def begin_work(self, request_id: int):
logging.info('begin_work')