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IPEX torch.tensor FP64 workaround

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Disty0
2024-01-30 01:52:32 +03:00
parent ccc3a481e7
commit 988dee02b9
1 changed files with 16 additions and 6 deletions
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22
library/ipex/hijacks.py
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@@ -5,6 +5,8 @@ import torch
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
import numpy as np
device_supports_fp64 = torch.xpu.has_fp64_dtype()
# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
@@ -49,6 +51,7 @@ def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corn
return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode,
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)
# Diffusers Float64 (Alchemist GPUs doesn't support 64 bit):
original_from_numpy = torch.from_numpy
@wraps(torch.from_numpy)
@@ -69,7 +72,8 @@ def as_tensor(data, dtype=None, device=None):
else:
return original_as_tensor(data, dtype=dtype, device=device)
if torch.xpu.has_fp64_dtype() and os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None) is None:
if device_supports_fp64 and os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None) is None:
original_torch_bmm = torch.bmm
original_scaled_dot_product_attention = torch.nn.functional.scaled_dot_product_attention
else:
@@ -159,11 +163,16 @@ def functional_pad(input, pad, mode='constant', value=None):
original_torch_tensor = torch.tensor
@wraps(torch.tensor)
def torch_tensor(*args, device=None, **kwargs):
def torch_tensor(data, *args, dtype=None, device=None, **kwargs):
if check_device(device):
return original_torch_tensor(*args, device=return_xpu(device), **kwargs)
else:
return original_torch_tensor(*args, device=device, **kwargs)
device = return_xpu(device)
if not device_supports_fp64:
if (isinstance(device, torch.device) and device.type == "xpu") or (isinstance(device, str) and "xpu" in device):
if dtype == torch.float64:
dtype = torch.float32
elif dtype is None and (hasattr(data, "dtype") and (data.dtype == torch.float64 or data.dtype == float)):
dtype = torch.float32
return original_torch_tensor(data, *args, dtype=dtype, device=device, **kwargs)
original_Tensor_to = torch.Tensor.to
@wraps(torch.Tensor.to)
@@ -253,6 +262,7 @@ def torch_load(f, map_location=None, pickle_module=None, *, weights_only=False,
else:
return original_torch_load(f, map_location=map_location, pickle_module=pickle_module, weights_only=weights_only, mmap=mmap, **kwargs)
# Hijack Functions:
def ipex_hijacks():
torch.tensor = torch_tensor
@@ -283,6 +293,6 @@ def ipex_hijacks():
torch.bmm = torch_bmm
torch.cat = torch_cat
if not torch.xpu.has_fp64_dtype():
if not device_supports_fp64:
torch.from_numpy = from_numpy
torch.as_tensor = as_tensor