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Running on Zero
File size: 2,588 Bytes
b347817 | 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 | import torch
from .memory_buffer import BaseBufferPool
class OffloaderMixin:
def onload(self):
pass
def offload(self):
pass
def offload_grad(self):
pass
class BaseParamOffloader(OffloaderMixin):
def __init__(self, param: torch.nn.Parameter, target_device: torch.device):
self.param = param
self.target_device = target_device
class StaticParamOffloader(BaseParamOffloader):
def __init__(self, param: torch.nn.Parameter, target_device: torch.device, memory_buffer: BaseBufferPool = None):
super().__init__(param, target_device)
cpu_data = param.data.cpu().detach().contiguous()
self.cpu_copy = memory_buffer.allocate_like(cpu_data) if memory_buffer is not None else cpu_data.pin_memory()
self._placeholder = torch.empty(0, device=target_device, dtype=param.dtype)
param.data = self._placeholder
def onload(self):
self.param.data = self.cpu_copy.to(self.target_device, non_blocking=True)
def offload(self):
self.param.data = self._placeholder
class TrainableParamOffloader(BaseParamOffloader):
def __init__(self, param: torch.nn.Parameter, target_device: torch.device):
super().__init__(param, target_device)
assert param.requires_grad, "TrainableParamOffloader can only be used with trainable parameters"
def onload(self):
self.param.data = self.param.data.to(self.target_device, non_blocking=True)
def offload(self):
self.param.data = self.param.data.to('cpu', non_blocking=True)
def offload_grad(self):
if self.param.grad is not None:
self.param.grad = self.param.grad.to('cpu', non_blocking=True)
class AlwaysOnGPUParamOffloader(BaseParamOffloader):
def __init__(self, param, target_device):
super().__init__(param, target_device)
self.param.data = self.param.data.to(self.target_device)
class BufferOffloader(OffloaderMixin):
def __init__(self, module: torch.nn.Module, buf_name: str, buf: torch.Tensor, target_device: torch.device, memory_buffer: BaseBufferPool = None):
self.module = module
self.buf_name = buf_name
self.target_device = target_device
cpu_data = buf.data.cpu().contiguous()
self.cpu_copy = memory_buffer.allocate_like(cpu_data) if memory_buffer is not None else cpu_data.pin_memory()
def onload(self):
self.module._buffers[self.buf_name] = self.cpu_copy.to(self.target_device, non_blocking=True)
def offload(self):
self.module._buffers[self.buf_name] = self.cpu_copy
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