File size: 7,418 Bytes
7344bef | 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 | # Copyright (c) 2026 SandAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Dict, Tuple
import torch
class CPUOffloadWrapper:
def __init__(self, model: Any, is_cpu_offload: bool = False, is_running_on_gpu: bool = True):
object.__setattr__(self, "model", model)
object.__setattr__(self, "is_cpu_offload", is_cpu_offload)
object.__setattr__(self, "is_running_on_gpu", is_running_on_gpu)
cpu_device = torch.device("cpu")
cuda_device = torch.device("cuda")
object.__setattr__(self, "cpu_device", cpu_device)
object.__setattr__(self, "cuda_device", cuda_device)
# Initialize placement location
if is_cpu_offload:
self.model.to(cpu_device)
else:
self.model.to(cuda_device)
# Whitelist non-compute methods that shouldn't trigger device hops (pass-through only; no device switch)
object.__setattr__(
self,
"_non_compute_methods",
{
"to",
"cpu",
"cuda",
"eval",
"train",
"state_dict",
"load_state_dict",
"parameters",
"named_parameters",
"buffers",
"named_buffers",
"modules",
"named_modules",
"children",
"named_children",
"register_forward_hook",
"register_forward_pre_hook",
"register_full_backward_hook",
"zero_grad",
"share_memory",
"half",
"float",
"bfloat16",
},
)
# Get current primary device (for external reads)
@property
def device(self) -> torch.device:
if isinstance(self.model, torch.nn.Module):
return next(self.model.parameters()).device
else:
for k, v in self.model.__dict__.items():
if isinstance(v, torch.Tensor):
return v.device
elif isinstance(v, torch.nn.Module):
return next(v.parameters()).device
return self.cuda_device
def _backup_cpu_state(self) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor], Dict[str, Any]]:
# Backup module parameters and buffers
module_param_backup = {}
module_buffer_backup = {}
other_backup = {}
def save_module_state(mod: torch.nn.Module, prefix: str):
for name, param in mod.named_parameters():
if param is not None:
full_key = prefix + name
module_param_backup[full_key] = param.data
for name, buffer in mod.named_buffers():
if buffer is not None:
full_key = prefix + name
module_buffer_backup[full_key] = buffer.data
if isinstance(self.model, torch.nn.Module):
save_module_state(self.model, "")
else:
for name, attr_val in self.model.__dict__.items():
if isinstance(attr_val, torch.nn.Module):
save_module_state(attr_val, name + ".")
elif isinstance(attr_val, torch.Tensor):
other_backup[name] = attr_val
return module_param_backup, module_buffer_backup, other_backup
def _restore_cpu_state(self, backups: Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor], Dict[str, Any]]):
# Restore module parameters and buffers
module_param_backup, module_buffer_backup, other_backup = backups
def restore_module_state(mod: torch.nn.Module, prefix: str):
for name, param in mod.named_parameters():
full_key = prefix + name
if full_key in module_param_backup:
param.data = module_param_backup[full_key]
for name, buffer in mod.named_buffers():
full_key = prefix + name
if full_key in module_buffer_backup:
buffer.data = module_buffer_backup[full_key]
if isinstance(self.model, torch.nn.Module):
restore_module_state(self.model, "")
else:
for name, attr_val in self.model.__dict__.items():
if isinstance(attr_val, torch.nn.Module):
restore_module_state(attr_val, name + ".")
if not isinstance(self.model, torch.nn.Module):
for name, val in other_backup.items():
setattr(self.model, name, val)
# Unified on/offload executor
def _run_with_optional_offload(self, func: Callable[..., Any], *args, **kwargs):
if self.is_cpu_offload and self.is_running_on_gpu:
backups = self._backup_cpu_state()
self.model.to(self.cuda_device)
try:
return func(*args, **kwargs)
finally:
if torch.cuda.is_available():
torch.cuda.synchronize()
self._restore_cpu_state(backups)
else:
# Make sure model and args are on the same device
args = [
arg.to(self.device) if isinstance(arg, torch.Tensor) and arg.device != self.device else arg for arg in args
]
kwargs = {
k: v.to(self.device) if isinstance(v, torch.Tensor) and v.device != self.device else v
for k, v in kwargs.items()
}
return func(*args, **kwargs)
# Direct call (equivalent to forward)
def __call__(self, *args, **kwargs):
return self._run_with_optional_offload(self.model.__call__, *args, **kwargs)
# Explicit forward; some code calls model.forward(...)
def forward(self, *args, **kwargs):
return self._run_with_optional_offload(self.model.forward, *args, **kwargs)
# Key: passthrough all attrs/methods. For callables, wrap with on/offload; for non-compute methods, pass-through only with no device switch.
def __getattr__(self, name: str):
# Fetch attribute from the wrapped model first
attr = getattr(self.model, name)
# Wrap methods (except in whitelist)
if callable(attr) and name not in self._non_compute_methods:
def _wrapped(*args, **kwargs):
return self._run_with_optional_offload(attr, *args, **kwargs)
return _wrapped
return attr
def __dir__(self):
return sorted(set(list(super().__dir__()) + dir(self.model)))
def __setattr__(self, name: str, value: Any):
raise AttributeError("CPUOffloadWrapper is immutable")
def __repr__(self) -> str:
return f"CPUOffloadWrapper(is_cpu_offload={self.is_cpu_offload}, is_running_on_gpu={self.is_running_on_gpu}, model={repr(self.model)})"
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