File size: 8,410 Bytes
ae88638 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 | import argparse
import hashlib
from io import BytesIO
from typing import Any, Callable, Optional
import logging
import safetensors.torch
import torch
logger = logging.getLogger(__name__)
def model_hash(filename):
"""Old model hash used by stable-diffusion-webui"""
try:
with open(filename, "rb") as file:
m = hashlib.sha256()
file.seek(0x100000)
m.update(file.read(0x10000))
return m.hexdigest()[0:8]
except FileNotFoundError:
return "NOFILE"
except IsADirectoryError: # Linux?
return "IsADirectory"
except PermissionError: # Windows
return "IsADirectory"
def calculate_sha256(filename):
"""New model hash used by stable-diffusion-webui"""
try:
hash_sha256 = hashlib.sha256()
blksize = 1024 * 1024
with open(filename, "rb") as f:
for chunk in iter(lambda: f.read(blksize), b""):
hash_sha256.update(chunk)
return hash_sha256.hexdigest()
except FileNotFoundError:
return "NOFILE"
except IsADirectoryError: # Linux?
return "IsADirectory"
except PermissionError: # Windows
return "IsADirectory"
def addnet_hash_legacy(b):
"""Old model hash used by sd-webui-additional-networks for .safetensors format files"""
m = hashlib.sha256()
b.seek(0x100000)
m.update(b.read(0x10000))
return m.hexdigest()[0:8]
def addnet_hash_safetensors(b):
"""New model hash used by sd-webui-additional-networks for .safetensors format files"""
hash_sha256 = hashlib.sha256()
blksize = 1024 * 1024
b.seek(0)
header = b.read(8)
n = int.from_bytes(header, "little")
offset = n + 8
b.seek(offset)
for chunk in iter(lambda: b.read(blksize), b""):
hash_sha256.update(chunk)
return hash_sha256.hexdigest()
def precalculate_safetensors_hashes(tensors, metadata):
"""Precalculate the model hashes needed by sd-webui-additional-networks to
save time on indexing the model later."""
# Because writing user metadata to the file can change the result of
# sd_models.model_hash(), only retain the training metadata for purposes of
# calculating the hash, as they are meant to be immutable
metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")}
bytes = safetensors.torch.save(tensors, metadata)
b = BytesIO(bytes)
model_hash = addnet_hash_safetensors(b)
legacy_hash = addnet_hash_legacy(b)
return model_hash, legacy_hash
def dtype_to_str(dtype: torch.dtype) -> str:
# get name of the dtype
dtype_name = str(dtype).split(".")[-1]
return dtype_name
def str_to_dtype(s: Optional[str], default_dtype: Optional[torch.dtype] = None) -> torch.dtype:
"""
Convert a string to a torch.dtype
Args:
s: string representation of the dtype
default_dtype: default dtype to return if s is None
Returns:
torch.dtype: the corresponding torch.dtype
Raises:
ValueError: if the dtype is not supported
Examples:
>>> str_to_dtype("float32")
torch.float32
>>> str_to_dtype("fp32")
torch.float32
>>> str_to_dtype("float16")
torch.float16
>>> str_to_dtype("fp16")
torch.float16
>>> str_to_dtype("bfloat16")
torch.bfloat16
>>> str_to_dtype("bf16")
torch.bfloat16
>>> str_to_dtype("fp8")
torch.float8_e4m3fn
>>> str_to_dtype("fp8_e4m3fn")
torch.float8_e4m3fn
>>> str_to_dtype("fp8_e4m3fnuz")
torch.float8_e4m3fnuz
>>> str_to_dtype("fp8_e5m2")
torch.float8_e5m2
>>> str_to_dtype("fp8_e5m2fnuz")
torch.float8_e5m2fnuz
"""
if s is None:
return default_dtype
if s in ["bf16", "bfloat16"]:
return torch.bfloat16
elif s in ["fp16", "float16"]:
return torch.float16
elif s in ["fp32", "float32", "float"]:
return torch.float32
elif s in ["fp8_e4m3fn", "e4m3fn", "float8_e4m3fn"]:
return torch.float8_e4m3fn
elif s in ["fp8_e4m3fnuz", "e4m3fnuz", "float8_e4m3fnuz"]:
return torch.float8_e4m3fnuz
elif s in ["fp8_e5m2", "e5m2", "float8_e5m2"]:
return torch.float8_e5m2
elif s in ["fp8_e5m2fnuz", "e5m2fnuz", "float8_e5m2fnuz"]:
return torch.float8_e5m2fnuz
elif s in ["fp8", "float8"]:
return torch.float8_e4m3fn # default fp8
else:
raise ValueError(f"Unsupported dtype: {s}")
def to_device(x: Any, device: torch.device) -> Any:
if isinstance(x, torch.Tensor):
return x.to(device)
elif isinstance(x, list):
return [to_device(elem, device) for elem in x]
elif isinstance(x, tuple):
return tuple(to_device(elem, device) for elem in x)
elif isinstance(x, dict):
return {k: to_device(v, device) for k, v in x.items()}
else:
return x
def to_cpu(x: Any) -> Any:
"""
Recursively moves torch.Tensor objects (and containers thereof) to CPU.
Args:
x: A torch.Tensor, or a (possibly nested) list, tuple, or dict containing tensors.
Returns:
The same structure as x, with all torch.Tensor objects moved to CPU.
Non-tensor objects are returned unchanged.
"""
if isinstance(x, torch.Tensor):
return x.cpu()
elif isinstance(x, list):
return [to_cpu(elem) for elem in x]
elif isinstance(x, tuple):
return tuple(to_cpu(elem) for elem in x)
elif isinstance(x, dict):
return {k: to_cpu(v) for k, v in x.items()}
else:
return x
def create_cpu_offloading_wrapper(func: Callable, device: torch.device) -> Callable:
"""
Create a wrapper function that offloads inputs to CPU before calling the original function
and moves outputs back to the specified device.
Args:
func: The original function to wrap.
device: The device to move outputs back to.
Returns:
A wrapped function that offloads inputs to CPU and moves outputs back to the specified device.
"""
def wrapper(orig_func: Callable) -> Callable:
def custom_forward(*inputs):
nonlocal device, orig_func
cuda_inputs = to_device(inputs, device)
outputs = orig_func(*cuda_inputs)
return to_cpu(outputs)
return custom_forward
return wrapper(func)
def disable_linear_from_compile(module: torch.nn.Module):
"""Monkey-patch to disable torch.compile for all Linear layers (if the class name ends with 'Linear') in the given module."""
for sub_module in module.modules():
# if isinstance(sub_module, torch.nn.Linear):
if sub_module.__class__.__name__.endswith("Linear"):
if not hasattr(sub_module, "_forward_before_disable_compile"):
sub_module._forward_before_disable_compile = sub_module.forward
sub_module._eager_forward = torch._dynamo.disable()(sub_module.forward)
sub_module.forward = sub_module._eager_forward # override forward to disable compile
def compile_transformer(
args: argparse.Namespace,
transformer: torch.nn.Module,
target_blocks: list[torch.nn.ModuleList | list[torch.nn.Module]],
disable_linear: bool,
) -> torch.nn.Module:
if disable_linear:
logger.info("Disable linear from torch.compile for swap blocks...")
for blocks in target_blocks:
for block in blocks:
disable_linear_from_compile(block)
compile_dynamic = None
if args.compile_dynamic is not None:
compile_dynamic = {"true": True, "false": False, "auto": None}[args.compile_dynamic.lower()]
logger.info(
f"Compiling DiT model with torch.compile: backend={args.compile_backend}, mode={args.compile_mode}, dynamic={compile_dynamic}, fullgraph={args.compile_fullgraph}"
)
if args.compile_cache_size_limit is not None:
torch._dynamo.config.cache_size_limit = args.compile_cache_size_limit
for blocks in target_blocks:
for i, block in enumerate(blocks):
block = torch.compile(
block,
backend=args.compile_backend,
mode=args.compile_mode,
dynamic=compile_dynamic,
fullgraph=args.compile_fullgraph,
)
blocks[i] = block
return transformer
|