PR #181620: Quality: Data race on global cached_tensorimpls_enabled flag causes undefined behavior

• Repository: pytorch/pytorch
• Author: Nam0101
• State: open | merged: False
• Link: https://github.com/pytorch/pytorch/pull/181620

Description (problem / solution / changelog)

✨ Code Quality

Problem

cached_tensorimpls_enabled is a non-atomic global bool read from multiple threads (is_cached_tensor, add_cached_tensor, remove_cached_tensor) and written in set_cached_tensors_enabled without synchronization. This is a C++ data race (UB), which can manifest as intermittent crashes or incorrect cache-state decisions under concurrency. In practice this can also skew adjusted_use_count, affecting uniqueness/inplacing behavior.

Severity: high File: aten/src/ATen/CachedTensorUtils.cpp

Solution

Make the flag atomic (or guard all reads/writes with the same mutex). A minimal fix is: static std::atomic<bool> cached_tensorimpls_enabled{false}; then use load(std::memory_order_acquire) in readers and store(enabled, std::memory_order_release) in set_cached_tensors_enabled. If you need strict consistency with the map state, also take cached_tensorimpl_mutex in set_cached_tensors_enabled (and optionally clear the map when disabling).

Changes

• aten/src/ATen/CachedTensorUtils.cpp (modified)

Testing

• Existing tests pass
• Manual review completed
• No new warnings/errors introduced

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Closes #181619

Changed files

• aten/src/ATen/CachedTensorUtils.cpp (modified, +6/-4)