aoti-debug

AOTI Debugging Guide

This skill helps diagnose and fix common AOTInductor issues.

Error Pattern Routing

Check the error message and route to the appropriate sub-guide:

Triton Index Out of Bounds

If the error matches this pattern:

Assertion `index out of bounds: 0 <= tmpN < ksM` failed

→ Follow the guide in triton-index-out-of-bounds.md

All Other Errors

Continue with the sections below.

---

First Step: Always Check Device and Shape Matching

For ANY AOTI error (segfault, exception, crash, wrong output), ALWAYS check these first:

1. Compile device == Load device: The model must be loaded on the same device type it was compiled on
2. Input devices match: Runtime inputs must be on the same device as the compiled model
3. Input shapes match: Runtime input shapes must match the shapes used during compilation (or satisfy dynamic shape constraints)

# During compilation - note the device and shapes
model = MyModel().eval()           # What device? CPU or .cuda()?
inp = torch.randn(2, 10)           # What device? What shape?
compiled_so = torch._inductor.aot_compile(model, (inp,))

# During loading - device type MUST match compilation
loaded = torch._export.aot_load(compiled_so, "???")  # Must match model/input device above

# During inference - device and shapes MUST match
out = loaded(inp.to("???"))  # Must match compile device, shape must match

If any of these don't match, you will get errors ranging from segfaults to exceptions to wrong outputs.

Key Constraint: Device Type Matching

AOTI requires compile and load to use the same device type.

• If you compile on CUDA, you must load on CUDA (device index can differ)
• If you compile on CPU, you must load on CPU
• Cross-device loading (e.g., compile on GPU, load on CPU) is NOT supported

Common Error Patterns

1. Device Mismatch Segfault

Symptom: Segfault, exception, or crash during aot_load() or model execution.

Example error messages:

• The specified pointer resides on host memory and is not registered with any CUDA device
• Crash during constant loading in AOTInductorModelBase
• Expected out tensor to have device cuda:0, but got cpu instead