From 2217331cd1245d0bdda786a5dcaf4f7b843bd7e4 Mon Sep 17 00:00:00 2001 From: brkirch Date: Wed, 1 Feb 2023 06:20:19 -0500 Subject: [PATCH] Refactor MPS fixes to CondFunc --- modules/devices.py | 50 +++++++++++++--------------------------------- 1 file changed, 14 insertions(+), 36 deletions(-) diff --git a/modules/devices.py b/modules/devices.py index f4afb897..919048d0 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -2,6 +2,7 @@ import sys, os, shlex import contextlib import torch from modules import errors +from modules.sd_hijack_utils import CondFunc from packaging import version @@ -156,36 +157,7 @@ def test_for_nans(x, where): raise NansException(message) -# MPS workaround for https://github.com/pytorch/pytorch/issues/79383 -orig_tensor_to = torch.Tensor.to -def tensor_to_fix(self, *args, **kwargs): - if self.device.type != 'mps' and \ - ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \ - (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')): - self = self.contiguous() - return orig_tensor_to(self, *args, **kwargs) - - -# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 -orig_layer_norm = torch.nn.functional.layer_norm -def layer_norm_fix(*args, **kwargs): - if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps': - args = list(args) - args[0] = args[0].contiguous() - return orig_layer_norm(*args, **kwargs) - - -# MPS workaround for https://github.com/pytorch/pytorch/issues/90532 -orig_tensor_numpy = torch.Tensor.numpy -def numpy_fix(self, *args, **kwargs): - if self.requires_grad: - self = self.detach() - return orig_tensor_numpy(self, *args, **kwargs) - - # MPS workaround for https://github.com/pytorch/pytorch/issues/89784 -orig_cumsum = torch.cumsum -orig_Tensor_cumsum = torch.Tensor.cumsum def cumsum_fix(input, cumsum_func, *args, **kwargs): if input.device.type == 'mps': output_dtype = kwargs.get('dtype', input.dtype) @@ -199,14 +171,20 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs): if has_mps(): if version.parse(torch.__version__) < version.parse("1.13"): # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working - torch.Tensor.to = tensor_to_fix - torch.nn.functional.layer_norm = layer_norm_fix - torch.Tensor.numpy = numpy_fix + + # MPS workaround for https://github.com/pytorch/pytorch/issues/79383 + CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs), + lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')) + # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 + CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs), + lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps') + # MPS workaround for https://github.com/pytorch/pytorch/issues/90532 + CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad) elif version.parse(torch.__version__) > version.parse("1.13.1"): cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0)) cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0)) - torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) ) - torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) ) - orig_narrow = torch.narrow - torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() ) + cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs) + CondFunc('torch.cumsum', cumsum_fix_func, None) + CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None) + CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)