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use memory mapping to accelerate hashing of large models. #340

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use memory mapping to accelerate hashing of large models. #340

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6a6519b
use memory mapping to accelerate hashing of large models.
rfletchr Sep 6, 2024
8dfe034
Merge branch 'pythongosssss:main' into accelerate_checkpoint_hashing
rfletchr Nov 2, 2024
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17 changes: 13 additions & 4 deletions py/model_info.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,21 @@
import hashlib
import json
import mmap
from aiohttp import web
from server import PromptServer
import folder_paths
import os


async def generate_sha256(path):
hasher = hashlib.sha256()
with open(path, "rb") as f:
with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
hasher.update(mm)

return hasher.hexdigest()


def get_metadata(filepath):
with open(filepath, "rb") as file:
# https://github.com/huggingface/safetensors#format
Expand All @@ -28,7 +38,7 @@ async def save_notes(request):
name = request.match_info["name"]
pos = name.index("/")
type = name[0:pos]
name = name[pos+1:]
name = name[pos + 1:]

file_path = None
if type == "embeddings" or type == "loras":
Expand Down Expand Up @@ -64,7 +74,7 @@ async def load_metadata(request):
name = request.match_info["name"]
pos = name.index("/")
type = name[0:pos]
name = name[pos+1:]
name = name[pos + 1:]

file_path = None
if type == "embeddings" or type == "loras":
Expand Down Expand Up @@ -107,8 +117,7 @@ async def load_metadata(request):
with open(hash_file, "rt") as f:
meta["pysssss.sha256"] = f.read()
else:
with open(file_path, "rb") as f:
meta["pysssss.sha256"] = hashlib.sha256(f.read()).hexdigest()
meta["pysssss.sha256"] = await generate_sha256(file_path)
with open(hash_file, "wt") as f:
f.write(meta["pysssss.sha256"])

Expand Down