Transfer tensor data from GPU memory back to host (CPU) memory. If the tensor is already in host (CPU) memory, the operation does nothing and gives a warning. Note that this operation only changes the storage of the tensor, and the tensor id will not change.
Both global tensor and local tensor of oneflow are applicable to this operation.
oneflow.Tensor.is_offloaded(). The behavior of load() is the opposite of offload(), is_offloaded() returns a boolean indicating whether the tensor has been moved to CPU memory.
In addition, support for offloading elements of
>>> import oneflow as flow >>> import numpy as np >>> # local tensor >>> x = flow.tensor(np.random.randn(1024, 1024, 100), dtype=flow.float32, device=flow.device("cuda"), ) >>> before_id = id(x) >>> x.offload() # Move the Tensor from the GPU to the CPU >>> after_id = id(x) >>> after_id == before_id True >>> x.is_offloaded() True >>> x.load() # Move the Tensor from the cpu to the gpu >>> x.is_offloaded() False
>>> import oneflow as flow >>> # global tensor >>> # Run on 2 ranks respectively >>> placement = flow.placement("cuda", ranks=[0, 1]) >>> sbp = flow.sbp.broadcast >>> x = flow.randn(1024, 1024, 100, dtype=flow.float32, placement=placement, sbp=sbp) >>> before_id = id(x) >>> x.offload() >>> after_id = id(x) >>> print(after_id == before_id) >>> print(x.is_offloaded()) >>> x.load() >>> print(x.is_offloaded())