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- - Support XPU prefix - #75 -
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- - - Support XPU prefix - - #75 -
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-Reviewer's Guide-This PR integrates support for the Intel GPU prefix “xpu:” by expanding device availability checks, updating the device list, and extending allocation logic to include xpu alongside cuda. -Class diagram for device selection and allocation changes-
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- classDiagram
- class DeviceSelectorMultiGPU {
- +override(*args, device=None, expert_mode_allocations=None, use_other_vram=None)
- }
- class Torch {
- +cuda.is_available()
- +cuda.device_count()
- +xpu.is_available()
- +xpu.device_count()
- }
- class mm {
- CPUState
- cpu_state
- }
- class get_torch_device_patched {
- +get_torch_device_patched()
- }
- class text_encoder_device_patched {
- +text_encoder_device_patched()
- }
- class get_device_list {
- +get_device_list()
- }
- DeviceSelectorMultiGPU --|> get_device_list
- get_torch_device_patched --|> Torch
- text_encoder_device_patched --|> Torch
- get_device_list --|> Torch
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- File-Level Changes-
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- There was a problem hiding this comment.
- - - -
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- - -Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews. - -
- Hey @Nuitari - I've reviewed your changes - here's some feedback:
--
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- Wrap direct torch.xpu calls in hasattr or try/except blocks to avoid runtime errors on PyTorch builds without XPU support. -
- Extract the repeated (cuda or xpu) availability check and device-list construction into a shared helper to reduce duplication. -
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-Prompt for AI Agents
-Please address the comments from this code review:
-## Overall Comments
-- Wrap direct torch.xpu calls in hasattr or try/except blocks to avoid runtime errors on PyTorch builds without XPU support.
-- Extract the repeated (cuda or xpu) availability check and device-list construction into a shared helper to reduce duplication.
-
-## Individual Comments
-
-### Comment 1
-<location> `__init__.py:40` </location>
-<code_context>
- def get_torch_device_patched():
- device = None
-- if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()):
-+ if (not (torch.cuda.is_available() or torch.xpu.is_available()) or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()):
- device = torch.device("cpu")
- else:
-</code_context>
-
-<issue_to_address>
-Potential AttributeError if torch.xpu is not available in all environments.
-
-To avoid errors, use hasattr(torch, 'xpu') before accessing torch.xpu methods.
-</issue_to_address>
-
-### Comment 2
-<location> `__init__.py:48` </location>
-<code_context>
- def text_encoder_device_patched():
- device = None
-- if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_text_encoder_device).lower()):
-+ if (not (torch.cuda.is_available() or torch.xpu.is_available()) or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_text_encoder_device).lower()):
- device = torch.device("cpu")
- else:
-</code_context>
-
-<issue_to_address>
-torch.xpu usage may not be safe on all platforms.
-
-Add a check like hasattr(torch, 'xpu') before using torch.xpu to prevent errors on systems where it is unavailable.
-</issue_to_address>
-
-### Comment 3
-<location> `__init__.py:328` </location>
-<code_context>
- def get_device_list():
- import torch
-- return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
-+ return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())] + [f"xpu:{i}" for i in range(torch.xpu.device_count())]
-
- class DeviceSelectorMultiGPU:
-</code_context>
-
-<issue_to_address>
-Unconditional torch.xpu.device_count() may cause errors if xpu is not present.
-
-Check for 'xpu' in torch with hasattr(torch, 'xpu') before calling torch.xpu.device_count() to avoid AttributeError on systems without xpu support.
-</issue_to_address>- - -Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews. - -
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| - - | - - | - if (not (torch.cuda.is_available() or torch.xpu.is_available()) or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()): - - | -|
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- - - -
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- issue (bug_risk): Potential AttributeError if torch.xpu is not available in all environments.
-To avoid errors, use hasattr(torch, 'xpu') before accessing torch.xpu methods.
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| - - | - - | - device = None - - | -
| - - | - - | - if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_text_encoder_device).lower()): - - | -
| - - | - - | - if (not (torch.cuda.is_available() or torch.xpu.is_available()) or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_text_encoder_device).lower()): - - | -
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- - - -
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- issue (bug_risk): torch.xpu usage may not be safe on all platforms.
-Add a check like hasattr(torch, 'xpu') before using torch.xpu to prevent errors on systems where it is unavailable.
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| - - | - - | - def get_device_list(): - - | -|
| - - | - - | - import torch - - | -|
| - - | - - | - return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())] - - | -|
| - - | - - | - return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())] + [f"xpu:{i}" for i in range(torch.xpu.device_count())] - - | -|
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- - - -
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- issue (bug_risk): Unconditional torch.xpu.device_count() may cause errors if xpu is not present.
-Check for 'xpu' in torch with hasattr(torch, 'xpu') before calling torch.xpu.device_count() to avoid AttributeError on systems without xpu support.
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Intel GPU's are detected with the prefix of xpu: instead of cuda:
--This PR allows the xpu: entries to show up in the selector.
Summary by Sourcery
-Enable detection and selection of Intel XPU devices by adding torch.xpu availability checks, listing xpu devices, and updating allocation routines to include XPU alongside CUDA
-New Features:
--- Add support for torch.xpu device availability alongside CUDA for main and text encoder device selection
-- Include xpu:{i} entries in the global device list
-- Extend VRAM allocation logic to consider XPU devices when building available device selections
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