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Support fused_sgd_kernel support for CPU. ## Bench result: 32 core/sockets ICX Test Scripts: https://gist.github.com/zhuhaozhe/688763e17e93e4c5e12f25f676ec90d9 https://gist.github.com/zhuhaozhe/ad9938694bc7fae8b66d376f4dffc6c9 ``` Tensor Size: 262144, Num Tensor 4, Num Threads: 1 _single_tensor_sgd time: 0.2301 seconds _fused_sgd time: 0.0925 seconds Tensor Size: 4194304, Num Tensor 32, Num Threads: 32 _single_tensor_sgd time: 2.6195 seconds _fused_sgd time: 1.7543 seconds ``` ## Test Plan: ``` python test_optim.py -k test_fused_matches_forloop python test_optim.py -k test_fused_large_tensor python test_optim.py -k test_can_load_older_state_dict python test_optim.py -k test_grad_scaling_autocast_fused_optimizers python test_torch.py -k test_grad_scaling_autocast_fused python test_torch.py -k test_params_invalidated_with_grads_invalidated_between_unscale_and_step ``` Looks like we already have some PRs under this issue https://github.com/pytorch/pytorch/issues/123451 to unified the UTs, I did not modified UT in this PR. Co-authored-by: Jane Xu <janeyx@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/123629 Approved by: https://github.com/jgong5, https://github.com/janeyx99
48 lines
2.3 KiB
Python
48 lines
2.3 KiB
Python
from typing import List, Dict, Tuple, 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 ["cuda", "xpu", "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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# 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 {
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(device, getattr(torch, str_dtype)): value
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for (device, str_dtype), value in
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torch._C._group_tensors_by_device_and_dtype(tensorlistlist, with_indices).items()
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}
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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) == torch.Tensor for t in tensors)
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