/aoti-debug

name: aoti-debug
description: Debug AOTInductor (AOTI) errors and crashes. Use when encountering AOTI segfaults, device mismatch errors, constant loading failures, or runtime errors from aot_compile, aot_load, aoti_compile_and_package, or aoti_load_package.

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.

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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`

**Cause**: Compile and load device types don't match (see "First Step" above).

**Solution**: Ensure compile and load use the same device type. If compiled on CPU, load on CPU. If compiled on CUDA, load on CUDA.

2. Input Device Mismatch at Runtime

**Symptom**: RuntimeError during model execution.

**Cause**: Input device doesn't match compile device (see "First Step" above).

**Better Debugging**: Run with `AOTI_RUNTIME_CHECK_INPUTS=1` for clearer errors. This flag validates all input properties including device type, dtype, sizes, and strides:

AOTI_RUNTIME_CHECK_INPUTS=1 python your_script.py

This produces actionable error messages like:

Error: input_handles[0]: unmatched device type, expected: 0(cpu), but got: 1(cuda)

Debugging CUDA Illegal Memory Access (IMA) Errors

If you encounter CUDA illegal memory access errors, follow this systematic approach:

Step 1: Sanity Checks

Before diving deep, try these debugging flags:

AOTI_RUNTIME_CHECK_INPUTS=1
TORCHINDUCTOR_NAN_ASSERTS=1

These flags take effect at compilation time (at codegen time):

• `AOTI_RUNTIME_CHECK_INPUTS=1` checks if inputs satisfy the same guards used during compilation
• `TORCHINDUCTOR_NAN_ASSERTS=1` adds codegen before and after each kernel to check for NaN

Step 2: Pinpoint the CUDA IMA

CUDA IMA errors can be non-deterministic. Use these flags to trigger the error deterministically:

PYTORCH_NO_CUDA_MEMORY_CACHING=1
CUDA_LAUNCH_BLOCKING=1

These flags take effect at runtime:

• `PYTORCH_NO_CUDA_MEMORY_CACHING=1` disables PyTorch's Caching Allocator, which allocates bigger buffers than needed immediately. This is usually why CUDA IMA errors are non-deterministic.
• `CUDA_LAUNCH_BLOCKING=1` forces kernels to launch one at a time. Without this, you get "CUDA kernel errors might be asynchronously reported" warnings since kernels launch asynchronously.

Step 3: Identify Problematic Kernels with Intermediate Value Debugger

Use the AOTI Intermediate Value Debugger to pinpoint the problematic kernel:

AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=3

This prints kernels one by one at runtime. Together with previous flags, this shows which kernel was launched right before the error.

To inspect inputs to a specific kernel:

AOT_INDUCTOR_FILTERED_KERNELS_TO_PRINT="triton_poi_fused_add_ge_logical_and_logical_or_lt_231,_add_position_embeddings_kernel_5" AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=2

If inputs to the kernel are unexpected, inspect the kernel that produces the bad input.

Additional Debugging Tools

Logging and Tracing

• **tlparse / TORCH_TRACE**: Provides complete output codes and records guards used
• **TORCH_LOGS**: Use `TORCH_LOGS="+inductor,output_code"` to see more PT2 internal logs
• **TORCH_SHOW_CPP_STACKTRACES**: Set to `1` to see more stack traces

Common Sources of Issues

• **Dynamic shapes**: Historically a source of many IMAs. Pay special attention when debugging dynamic shape scenarios.
• **Custom ops**: Especially when implemented in C++ with dynamic shapes. The meta function may need to be Symint'ified.

API Notes

Deprecated API

torch._export.aot_compile()  # Deprecated
torch._export.aot_load()     # Deprecated

Current API

torch._inductor.aoti_compile_and_package()
torch._inductor.aoti_load_package()

The new API stores device metadata in the package, so `aoti_load_package()` automatically uses the co