Austin Mroz
ef3f67fcb3
Rather than requesting as chunks, the stream is kept open and re-added to the queue every 32MB. This removes the need for the server to report ranges, or content length, or to recombine chunks, which greatly simplifies logic.
448 lines
18 KiB
Python
448 lines
18 KiB
Python
import torch
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import os
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import json
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import safetensors.torch
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import aiohttp
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import asyncio
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import queue
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import threading
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import logging
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import itertools
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import hashlib
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import comfy.samplers
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import execution
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import server
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import heapq
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SAMPLER_NODES = ["SamplerCustom", "KSampler", "KSamplerAdvanced", "SamplerCustomAdvanced"]
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SALAD_TOKEN = None
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async def get_header():
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if 'SALAD_API_KEY' in os.environ:
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#NOTE: Only for local testing. Do not add to container
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return {'Salad-Api-Key': os.environ['SALAD_API_KEY']}
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global SALAD_TOKEN
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if SALAD_TOKEN is None:
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async with aiohttp.ClientSession() as session:
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async with session.get('http://169.254.169.254:80/v1/token') as r:
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SALAD_TOKEN =(await r.json())['jwt']
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return {'Authorization': SALAD_TOKEN}
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class RequestLoop:
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def __init__(self):
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self.active_request = None
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self.current_priority = 0
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self.queue_high = queue.Queue()
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self.low = None
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self.mutex = threading.RLock()
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self.do_reset = False
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#main.py has already created an event loop
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event_loop = server.PromptServer.instance.loop
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self.process_loop = event_loop.create_task(self.process_requests())
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def queue(self, req, prio):
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with self.mutex:
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if prio == 2:
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self.low = None
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self.queue_high = queue.Queue()
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self.queue_high.put(req)
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if self.active_request is not None:
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pass
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#self.process_loop.cancel()
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elif prio == 1:
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self.low = None
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self.queue_high.put(req)
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if self.current_priority == 0:
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pass
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#self.process_loop.cancel()
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else:
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self.low = req
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def reset(self, uid):
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with self.mutex:
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self.low = None
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self.queue_high = queue.Queue()
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self.do_reset = uid
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async def delete_file(self, s, url, semaphore):
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async with semaphore:
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async with s.delete(url, headers=await get_header()) as r:
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await r.text()
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async def _reset(self, s, uid):
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base_url = '/organizations/' + ORGANIZATION +'/files'
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checkpoint_base = '/'.join([base_url, uid, 'checkpoint'])
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checkpoint_base = 'https://storage-api.salad.com' + checkpoint_base
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async with s.get(base_url, headers=await get_header()) as r:
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js = await r.json()
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files = js['files']
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checkpoints = list(filter(lambda x: x['url'].startswith(checkpoint_base), files))
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for cp in checkpoints:
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cp['url'] = cp['url'][29:]
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semaphore = asyncio.Semaphore(5)
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deletes = [asyncio.create_task(self.delete_file(s, f['url'], semaphore)) for f in checkpoints]
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if len(deletes) > 0:
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await asyncio.gather(*deletes)
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async def process_requests(self):
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headers = await get_header()
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async with aiohttp.ClientSession('https://storage-api.salad.com') as session:
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while True:
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if self.do_reset != False:
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await self._reset(session, self.do_reset)
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self.do_reset = False
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if self.active_request is None:
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await asyncio.sleep(.1)
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else:
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req = self.active_request
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fd = aiohttp.FormData({'file': req[1]})
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async with session.put(req[0], headers=headers, data=fd) as r:
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#We don't care about result, but must still await it
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await r.text()
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with self.mutex:
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if not self.queue_high.empty():
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self.active_request = self.queue_high.get()
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else:
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if self.low is not None:
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self.active_request = self.low
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self.low = None
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else:
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self.active_request = None
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class FetchQueue:
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"""Modified priority queue implementation that tracks inflight and allows priority modification"""
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def __init__(self):
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self.lock = threading.RLock()
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self.queue = []# queue contains priority, url, future
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self.count = 0
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self.consumed = {}
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self.new_items = asyncio.Event()
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def update_priority(self, i, priority):
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#lock must already be acquired
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future = self.queue[i][3]
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if priority < self.queue[i][0]:
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#priority is increased, invalidate old
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self.queue[i] = (self.queue[i][0], self.queue[i][1], None, None)
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heapq.heappush(self.queue, (priority, self.count, item, future))
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self.count += 1
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def requeue(self, future, item, dec_priority=1):
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with self.lock:
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priority = self.consumed[item][1] - dec_priority
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heapq.heappush(self.queue, (priority, self.count, item, future))
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self.count += 1
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self.new_items.set()
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def enqueue_checked(self, item, priority):
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with self.lock:
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if item in self.consumed:
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#TODO: Also update in queue
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self.consumed[item][0] = min(self.consumed[item[0], priority])
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return self.consumed[item][0]
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for i in range(len(self.queue)):
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if self.queue[i][2] == item:
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future = self.queue[i][3]
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self.update_priority(i, priority)
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return future
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future = asyncio.Future()
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heapq.heappush(self.queue, (priority, self.count, item, future))
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self.count += 1
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self.new_items.set()
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return future
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async def get(self):
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while True:
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await self.new_items.wait()
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with self.lock:
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priority, _, item, future = heapq.heappop(self.queue)
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if len(self.queue) == 0:
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self.new_items.clear()
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if item is not None:
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if isinstance(item, str):
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self.consumed[item] = [future, priority]
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return priority, item, future
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else:
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#item is future
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item.set_result(True)
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class FetchLoop:
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def __init__(self):
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self.queue = FetchQueue()
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self.semaphore = asyncio.Semaphore(5)
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self.cs = aiohttp.ClientSession()
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event_loop = server.PromptServer.instance.loop
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self.process_loop = event_loop.create_task(self.loop())
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os.makedirs("fetches", exist_ok=True)
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async def loop(self):
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event_loop = server.PromptServer.instance.loop
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while True:
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await self.semaphore.acquire()
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event_loop.create_task(self.fetch(*(await self.queue.get())))
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def enqueue(self, url, priority=0):
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return self.queue.enqueue_checked(url, priority)
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async def fetch(self, priority, url, future):
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chunk_size = 2**25 #32MB
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headers = {}
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if url.startswith(base_url):
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headers.update(await get_header())
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filename = os.path.join('fetches', string_hash(url))
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try:
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async with self.cs.get(url, headers=headers) as r:
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with open(filename, 'wb') as f:
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async for chunk in r.content.iter_chunked(chunk_size):
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f.write(chunk)
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if not r.content.is_eof():
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awaken = asyncio.Future()
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self.queue.enqueue_unchecked(awaken, priority-1)
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await awaken
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else:
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print("got eof")
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future.set_result(filename)
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except:
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future.set_result(None)
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raise
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finally:
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self.semaphore.release()
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return
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fetch_loop = FetchLoop()
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async def prepare_file(url, path, priority):
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hashloc = await fetch_loop.enqueue(url, priority)
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breakpoint()
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if os.path.exists(path):
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os.remove(path)
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#TODO consider if symlinking would be better
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os.link(hashloc, path)
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ORGANIZATION = os.environ.get('SALAD_ORGANIZATION', None)
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if ORGANIZATION is not None:
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base_url = 'https://storage-api.salad.com/organizations/' + ORGANIZATION +'/files'
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class NetCheckpoint:
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def __init__(self):
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self.requestloop = RequestLoop()
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self.has_warned_size = False
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assert ORGANIZATION is not None
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def store(self, unique_id, tensors, metadata, priority=0):
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return
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file = "/" + "/".join(['organizations', ORGANIZATION, 'files', self.uid,
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"checkpoint", f"{unique_id}.checkpoint"])
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data = safetensors.torch.save(tensors, metadata)
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if len(data) > 10 ** 8:
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if not self.has_warned_size:
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logging.warning("Checkpoint is too large and has been skipped")
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self.has_warned_size = True
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return
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self.requestloop.queue((file, data), priority)
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def get(self, unique_id):
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"""Returns the information previously saved
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If the request has checkpointed data, this information should
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be loaded prior to job start"""
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file = f"input/checkpoint/{unique_id}.checkpoint"
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if not os.path.exists(file):
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return None, None
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with safetensors.torch.safe_open(file, framework='pt' ) as f:
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metadata = f.metadata()
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tensors = {key:f.get_tensor(key) for key in f.keys()}
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return tensors, metadata
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def reset(self, unique_id=None):
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#TODO: filter delete requests by node uniqueid
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"""Clear all checkpoint information."""
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self.requestloop.reset(self.uid)
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if unique_id is not None:
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if os.path.exists(f"input/checkpoint/{unique_id}.checkpoint"):
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os.remove(f"input/checkpoint/{unique_id}.checkpoint")
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return
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os.makedirs("input/checkpoint", exist_ok=True)
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for file in os.listdir("input/checkpoint"):
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os.remove(os.path.join("input/checkpoint", file))
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class FileCheckpoint:
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def store(self, unique_id, tensors, metadata, priority=0):
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"""Swappable interface for saving checkpoints.
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Implementation must be transactional: Either the whole thing completes,
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or the prior checkpoint must be valid even if crash occurs mid execution"""
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file = f"checkpoint/{unique_id}.checkpoint"
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safetensors.torch.save_file(tensors, file, metadata)
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def get(self, unique_id):
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"""Returns the information previously saved"""
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file = f"checkpoint/{unique_id}.checkpoint"
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if not os.path.exists(file):
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return None, None
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with safetensors.torch.safe_open(file, framework='pt' ) as f:
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metadata = f.metadata()
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tensors = {key:f.get_tensor(key) for key in f.keys()}
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return tensors, metadata
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def reset(self, unique_id=None):
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"""Clear all checkpoint information."""
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if unique_id is not None:
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if os.path.exists(f"checkpoint/{unique_id}.checkpoint"):
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os.remove(f"checkpoint/{unique_id}.checkpoint")
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return
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for file in os.listdir("checkpoint"):
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os.remove(os.path.join("checkpoint", file))
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checkpoint = NetCheckpoint() if "SALAD_ORGANIZATION" in os.environ else FileCheckpoint()
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def file_hash(filename):
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h = hashlib.sha256()
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b = bytearray(10*1024*1024) # read 10 megabytes at a time
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with open(filename, 'rb', buffering=0) as f:
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while n := f.readinto(b):
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h.update(b)
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return h.hexdigest()
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def string_hash(s):
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h = hashlib.sha256()
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h.update(s.encode('utf-8'))
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return h.hexdigest()
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def fetch_remote_file(url, filepath, file_hash=None):
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assert filepath.find("..") == -1, "Paths may not contain .."
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return prepare_file(url, filepath, -1)
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async def fetch_remote_files(remote_files, uid=None):
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#TODO: Add requested support for zip files?
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if uid is not None:
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checkpoint_base = '/'.join([base_url, uid, 'checkpoint'])
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async with self.cs.get(base_url, headers=get_header()) as r:
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js = await r.json()
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files = js['files']
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checkpoints = list(filter(lambda x: x['url'].startswith(checkpoint_base), files))
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for cp in checkpoints:
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cp['filepath'] = os.path.join('input/checkpoint',
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cp['url'][len(checkpoint_base)+1:])
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remote_files = itertools.chain(remote_files, checkpoints)
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fetches = []
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for f in remote_files:
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fetches.append(fetch_remote_file(**f))
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if len(fetches) > 0:
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await asyncio.gather(*fetches)
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completion_futures = {}
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def add_future(json_data):
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index = max(completion_futures.keys())
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json_data['extra_data']['completion_future'] = index
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return json_data
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server.PromptServer.instance.add_on_prompt_handler(add_future)
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prompt_route = next(filter(lambda x: x.path == '/prompt' and x.method == 'POST',
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server.PromptServer.instance.routes))
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original_post_prompt = prompt_route.handler
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async def post_prompt_remote(request):
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if 'dump_req' in os.environ:
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with open('resp-dump.txt', 'wb') as f:
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f.write(await request.read())
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import sys
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sys.exit()
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json_data = await request.json()
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if "SALAD_ORGANIZATION" in os.environ:
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extra_data = json_data.get("extra_data", {})
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#NOTE: Rendered obsolete by existing infrastructure, can be pruned
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remote_files = extra_data.get("remote_files", [])
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uid = None#temporarily disable s4 for testing
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#uid = json_data.get("client_id", 'local')
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checkpoint.uid = uid
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await fetch_remote_files(remote_files, uid=uid)
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if 'prompt' not in json_data:
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return server.web.json_response("PreLoad Complete")
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f = asyncio.Future()
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index = max(completion_futures.keys(),default=0)+1
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completion_futures[index] = f
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base_res = await original_post_prompt(request)
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res = await f
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completion_futures.pop(index)
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res = base_res.text[:-1] + ', "outputs": ' + json.dumps(res) + '}'
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return server.web.Response(body=res)
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#Dangerous
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object.__setattr__(prompt_route, 'handler', post_prompt_remote)
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class CheckpointSampler(comfy.samplers.KSAMPLER):
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def sample(self, *args, **kwargs):
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args = list(args)
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self.unique_id = server.PromptServer.instance.last_node_id
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self.step = None
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data, metadata = checkpoint.get(self.unique_id)
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if metadata is not None and 'step' in metadata:
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data = data['x']
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self.step = int(metadata['step'])
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#checkpoint of execution exists
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args[5] = data.to(args[4].device)
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args[1] = args[1][self.step:]
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#disable added noise, as the checkpointed latent is already noised
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args[4][:] = 0
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original_callback = args[3]
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def callback(*args):
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self.callback(*args)
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if original_callback is not None:
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return original_callback(*args)
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args[3] = callback
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res = super().sample(*args, **kwargs)
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return res
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def callback(self, step, denoised, x, total_steps):
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if self.step is not None:
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step += self.step
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data = safetensors.torch.save
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checkpoint.store(self.unique_id, {'x':x}, {'step':str(step)})
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if self.step is None and "FORCE_CRASH_AT" in os.environ:
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if step == int(os.environ['FORCE_CRASH_AT']):
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raise Exception("Simulated Crash")
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original_recursive_execute = execution.recursive_execute
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def recursive_execute_injection(*args):
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unique_id = args[3]
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class_type = args[1][unique_id]['class_type']
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extra_data = args[4]
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prev_outputs = None
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if 'checkpoints' in extra_data:
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checkpoint.update(extra_data.pop('checkpoints'))
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if 'prompt_checked' not in args[4]:
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metadata = checkpoint.get('prompt')[1]
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if metadata is None or json.loads(metadata['prompt']) != args[1]:
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checkpoint.reset()
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checkpoint.store('prompt', {'x': torch.ones(1)},
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{'prompt': json.dumps(args[1])}, priority=2)
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args[4]['prompt_checked'] = True
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prev_outputs = {}
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os.makedirs("temp", exist_ok=True)
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#TODO: Consider subdir recursing?
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for item in itertools.chain(os.scandir("output"), os.scandir("temp")):
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if item.is_file():
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prev_outputs[item.path] = item.stat().st_mtime
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if class_type in SAMPLER_NODES:
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data, metadata = checkpoint.get(unique_id)
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if metadata is not None and 'step' in metadata:
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args[1][unique_id]['inputs']['latent_image'] = ['checkpointed'+unique_id, 0]
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args[2]['checkpointed'+unique_id] = [[{'samples': data['x']}]]
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elif metadata is not None and 'completed' in metadata:
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outputs = json.loads(metadata['completed'])
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for x in range(len(outputs)):
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if outputs[x] == 'tensor':
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outputs[x] = list(data[str(x)])
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elif outputs[x] == 'latent':
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outputs[x] = [{'samples': l} for l in data[str(x)]]
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args[2][unique_id] = outputs
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return True, None, None
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res = original_recursive_execute(*args)
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#Conditionally save node output
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#TODO: determine which non-sampler nodes are worth saving
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if class_type in SAMPLER_NODES and unique_id in args[2]:
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data = {}
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outputs = args[2][unique_id].copy()
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for x in range(len(outputs)):
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if isinstance(outputs[x][0], torch.Tensor):
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data[str(x)] = torch.stack(outputs[x])
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outputs[x] = 'tensor'
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elif isinstance(outputs[x][0], dict):
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data[str(x)] = torch.stack([l['samples'] for l in outputs[x]])
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outputs[x] = 'latent'
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checkpoint.store(unique_id, data, {'completed': json.dumps(outputs)}, priority=1)
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if prev_outputs is not None:
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outputs = []
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for item in itertools.chain(os.scandir("output"), os.scandir("temp")):
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if item.is_file() and prev_outputs.get(item.path, 0) < item.stat().st_mtime:
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outputs.append(item.path)
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if 'completion_future' in extra_data:
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completion_futures[extra_data['completion_future']].set_result(outputs)
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return res
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comfy.samplers.KSAMPLER = CheckpointSampler
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execution.recursive_execute = recursive_execute_injection
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NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
|