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