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Merge pull request #7455 from brkirch/put-fix-back

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Refactor MPS PyTorch fixes, add fix still required for PyTorch nightly builds back
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AUTOMATIC1111 2023-02-01 16:11:40 +03:00 committed by GitHub
commit b1873dbb77
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1 changed files with 15 additions and 34 deletions
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49
modules/devices.py
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@ -2,6 +2,7 @@ import sys, os, shlex
import contextlib import contextlib
import torch import torch
from modules import errors from modules import errors
from modules.sd_hijack_utils import CondFunc
from packaging import version from packaging import version
@ -156,36 +157,7 @@ def test_for_nans(x, where):
raise NansException(message) 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 # 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): def cumsum_fix(input, cumsum_func, *args, **kwargs):
if input.device.type == 'mps': if input.device.type == 'mps':
output_dtype = kwargs.get('dtype', input.dtype) output_dtype = kwargs.get('dtype', input.dtype)
@ -199,11 +171,20 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
if has_mps(): if has_mps():
if version.parse(torch.__version__) < version.parse("1.13"): 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 # 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 # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
torch.Tensor.numpy = numpy_fix 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"): 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_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)) 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) ) cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *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)