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Update ROCm and OpenVINO to Torch 2.8

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Disty0
2025-08-06 21:33:50 +03:00
parent 992a41b0f1
commit 6207b6d841
3 changed files with 12 additions and 9 deletions
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CHANGELOG.md
+2
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@@ -31,6 +31,8 @@ And (*as always*) many bugfixes and improvements to existing features!
great model based on FLUX.1 and then redesigned and retrained by *lodestones*
update with latest **v48**, **v48 Detail Calibrated** and **v46 Flash** variants
available via *networks -> models -> reference*
**Torch**
- Set default for ROCm and OpenVINO to `torch==2.8.0`
- **UI**
- new embedded docs/wiki search!
**Docs** search: fully-local and works in real-time on all document pages
installer.py
+8 -7
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@@ -738,13 +738,13 @@ def install_rocm_zluda():
if args.use_nightly:
if rocm.version is None or float(rocm.version) >= 6.4: # assume the latest if version check fails
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.4')
elif rocm.version == "6.3":
else: # oldest rocm version on nightly is 6.3
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.3')
else: # oldest rocm version on nightly is 6.2.4
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4')
else:
if rocm.version is None or float(rocm.version) >= 6.3: # assume the latest if version check fails
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+rocm6.3 torchvision==0.22.1+rocm6.3 --index-url https://download.pytorch.org/whl/rocm6.3')
if rocm.version is None or float(rocm.version) >= 6.4: # assume the latest if version check fails
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.8.0+rocm6.4 torchvision==0.23.0+rocm6.4 --index-url https://download.pytorch.org/whl/rocm6.4')
elif rocm.version == "6.3":
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.8.0+rocm6.3 torchvision==0.23.0+rocm6.3 --index-url https://download.pytorch.org/whl/rocm6.3')
elif rocm.version == "6.2":
# use rocm 6.2.4 instead of 6.2 as torch==2.7.1+rocm6.2 doesn't exists
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+rocm6.2.4 torchvision==0.22.1+rocm6.2.4 --index-url https://download.pytorch.org/whl/rocm6.2.4')
@@ -804,6 +804,7 @@ def install_ipex():
if args.use_nightly:
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/xpu')
else:
# torch 2.8 segfaults with torch.compile: https://github.com/pytorch/pytorch/issues/159974
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+xpu torchvision==0.22.1+xpu --index-url https://download.pytorch.org/whl/xpu')
ts('ipex', t_start)
@@ -815,9 +816,9 @@ def install_openvino():
#check_python(supported_minors=[9, 10, 11, 12, 13], reason='OpenVINO backend requires a Python version between 3.9 and 3.13')
log.info('OpenVINO: selected')
if sys.platform == 'darwin':
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1 torchvision==0.22.1')
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.8.0 torchvision==0.23.0')
else:
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.7.1+cpu torchvision==0.22.1+cpu --index-url https://download.pytorch.org/whl/cpu')
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.8.0+cpu torchvision==0.23.0+cpu --index-url https://download.pytorch.org/whl/cpu')
install(os.environ.get('OPENVINO_COMMAND', 'openvino==2025.2.0'), 'openvino')
install(os.environ.get('NNCF_COMMAND', 'nncf==2.17.0'), 'nncf')
modules/intel/ipex/int_mm.py
+2 -2
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@@ -49,7 +49,7 @@ def qlinear_unary(
# GEMM template needs 2D input, normalize input shape here
x = view(x, [-1, x_size[-1]])
if not isinstance(x_scale, ir.TensorBox):
assert type(x_scale) == float
assert isinstance(x_scale, float)
x_scale = V.graph.add_tensor_constant(
torch.tensor(x_scale, dtype=torch.float32), name="x_scale"
)
@@ -71,7 +71,7 @@ def qlinear_unary(
torch.tensor(0, dtype=torch.int32), name="x_zp"
)
if not isinstance(x_zp, ir.TensorBox):
assert type(x_zp) == int
assert isinstance(x_zp, int)
x_zp = V.graph.add_tensor_constant(
torch.tensor(x_zp, dtype=torch.int32), name="x_zp"
)