PyTorch fixes critical unbacked binding bug affecting M2M100 and large models
A subtle bug was silently breaking training for major AI models like M2M100.
Deep Dive
PyTorch developers have patched a critical bug in the Dynamo/Inductor compiler that caused 'PendingUnbackedSymbolNotFound' errors during training. The issue specifically affected large transformer models like M2M100ForConditionalGeneration when using graph breaks. The fix adds a missing call to `compute_unbacked_binding` to properly handle unbacked symbols in view inputs, preventing crashes in realistic, larger-scale training scenarios that weren't caught by simpler unit tests.
Why It Matters
This fix stabilizes training for cutting-edge, large language models that rely on PyTorch's performance compiler, preventing hidden failures.