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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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+
+ Loading
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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
+
+ File-Level Changes+
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Customizing Your Experience+Access your dashboard to: +
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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:
+-
+
- 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. +
+
+
+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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+| + | + | @@ -37,15 +37,15 @@ | +|
| + + | + + |
+ + + |
+ |
| + + | + + | + 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()): + + | +|
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+ There was a problem hiding this comment.
+ + + +
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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 = torch.device("cpu") + + | +
| + + | + + | + else: + + | +
| + + | + + | + device = torch.device(current_device) + + | +
| + + | + + | + return device + + | +
| + + | + + |
+ + + |
+
| + + | + + | + 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()): + + | +
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+ There was a problem hiding this comment.
+ + + +
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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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+ __init__.py
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+| + | + | @@ -325,7 +325,7 @@ def calculate_vvram_allocation_string(model, virtual_vram_str): | +|
| + + | + + |
+ + + |
+ |
| + + | + + | + 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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+ There was a problem hiding this comment.
+ + + +
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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
+
+