aoti-debug

name	aoti-debug
name_zh	aoti-调试
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.
description_zh	调试 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.
category	applications
tags	["ai","api","backend","evaluation","frontend"]
source	null
language	en
needs_review	false
slug	aoti-debug
version	1.0.0
created	2026-06-12
updated	2026-06-12
inputs	[{"name":"request","type":"string","required":true,"description":"User request or task description"}]
output	{"format":"markdown","description":"Generated content based on the user request"}
author	AI-SKILL
license	MIT

When to use

Use this skill when you need guidance on aoti-debug.

Inputs

User request or task description.

Output

Generated content based on the user request.

Prompt

Follow the guidelines in this skill when working on related tasks.

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

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 correct device type. You can only change the device index (e.g., cuda:0 vs cuda:1), not the device type.

Environment Variables Summary

Variable	When	Purpose
AOTI_RUNTIME_CHECK_INPUTS=1	Compile time	Validate inputs match compilation guards
TORCHINDUCTOR_NAN_ASSERTS=1	Compile time	Check for NaN before/after kernels
PYTORCH_NO_CUDA_MEMORY_CACHING=1	Runtime	Make IMA errors deterministic
CUDA_LAUNCH_BLOCKING=1	Runtime	Force synchronous kernel launches
AOT_INDUCTOR_DEBUG_INTERMEDIATE_VALUE_PRINTER=3	Compile time	Print kernels at runtime
TORCH_LOGS="+inductor,output_code"	Runtime	See PT2 internal logs
TORCH_SHOW_CPP_STACKTRACES=1	Runtime	Show C++ stack traces

When NOT to use

Do not use this skill for tasks outside its scope.

Example

See the skill content above for practical examples.