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fe4b88f6aa
This change adds "hpu" to the list of device types that support fused kernels in the optimizer, ensuring compatibility with HPU backend. Without this change, when `test_all_gather_extension_outer_size_stride` of `pytorch/test/distributed/_composable/fsdp/test_fully_shard_extensions.py` is run on 'hpu' backend, it fails with: RuntimeError: fused=True requires all the params to be floating point Tensors of supported devices: ['mps', 'cuda', 'xpu', 'cpu', 'privateuseone'] but torch.float32 and hpu Pull Request resolved: https://github.com/pytorch/pytorch/pull/148666 Approved by: https://github.com/albanD
45 lines
2.3 KiB
Python
45 lines
2.3 KiB
Python
from typing import Optional
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import torch
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from torch import Tensor
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from torch.autograd.grad_mode import no_grad
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from typing_extensions import TypeAlias
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def _get_foreach_kernels_supported_devices() -> list[str]:
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r"""Return the device type list that supports foreach kernels."""
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return ["cuda", "xpu", torch._C._get_privateuse1_backend_name()]
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def _get_fused_kernels_supported_devices() -> list[str]:
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r"""Return the device type list that supports fused kernels in optimizer."""
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return ["mps", "cuda", "xpu", "hpu", "cpu", torch._C._get_privateuse1_backend_name()]
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TensorListList: TypeAlias = list[list[Optional[Tensor]]]
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Indices: TypeAlias = list[int]
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_foreach_supported_types = [torch.Tensor]
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# This util function splits tensors into groups by device and dtype, which is useful before sending
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# tensors off to a foreach implementation, which requires tensors to be on one device and dtype.
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# If tensorlistlist contains more than one tensorlist, the following assumptions are made BUT NOT verified:
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# - tensorlists CAN be None
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# - all tensors in the first specified list cannot be None
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# - given an index i, all specified tensorlist[i]s match in dtype and device
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# with_indices (bool, optional): whether to track previous indices as the last list per dictionary entry.
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# It comes in handy if there are Nones or literals in the tensorlists that are getting scattered out.
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# Whereas mutating a tensor in the resulting split-up tensorlists WILL propagate changes back to the
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# original input tensorlists, changing up Nones/literals WILL NOT propagate, and manual propagation
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# may be necessary. Check out torch/optim/sgd.py for an example.
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@no_grad()
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def _group_tensors_by_device_and_dtype(
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tensorlistlist: TensorListList,
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with_indices: bool = False,
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) -> dict[tuple[torch.device, torch.dtype], tuple[TensorListList, Indices]]:
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return torch._C._group_tensors_by_device_and_dtype(tensorlistlist, with_indices)
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def _device_has_foreach_support(device: torch.device) -> bool:
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return device.type in (_get_foreach_kernels_supported_devices() + ["cpu"]) and not torch.jit.is_scripting()
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def _has_foreach_support(tensors: list[Tensor], device: torch.device) -> bool:
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return _device_has_foreach_support(device) and all(t is None or type(t) in _foreach_supported_types for t in tensors)
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