trailofbits.python.automatic-memory-pinning.automatic-memory-pinning

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If possible, it is better to rely on automatic pinning in PyTorch to avoid undefined behavior and for efficiency

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Defintion

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rules:
  - id: automatic-memory-pinning
    message: If possible, it is better to rely on automatic pinning in PyTorch to
      avoid undefined behavior and for efficiency
    languages:
      - python
    severity: WARNING
    metadata:
      category: security
      cwe: "CWE-676: Use of Potentially Dangerous Function"
      subcategory:
        - audit
      confidence: HIGH
      likelihood: LOW
      impact: LOW
      technology:
        - pytorch
      description: "`PyTorch` memory not automatically pinned"
      references:
        - https://pytorch.org/docs/stable/data.html#memory-pinning
      license: AGPL-3.0 license
      vulnerability_class:
        - Dangerous Method or Function
    pattern-either:
      - patterns:
          - pattern: torch.utils.data.DataLoader(...)
          - pattern-not: torch.utils.data.DataLoader(..., pin_memory=$VALUE, ...)
      - pattern: torch.utils.data.DataLoader(..., pin_memory=False, ...)

Examples

automatic-memory-pinning.py

import torch

# ok: automatic-memory-pinning
loader = torch.utils.data.DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper,
                    pin_memory=True)

# ok: automatic-memory-pinning
torch.utils.data.DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper,
                    pin_memory=1)

# ok: automatic-memory-pinning
loader = torch.utils.data.DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper,
                    pin_memory=3)

# ruleid: automatic-memory-pinning
loader = torch.utils.data.DataLoader(dataset, batch_size=2, collate_fn=collate_wrapper)

# ruleid: automatic-memory-pinning
loader = torch.utils.data.DataLoader(dataset, pin_memory=False, batch_size=2, collate_fn=collate_wrapper)