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+# How to debug the vLLM-torch.compile integration
+
+TL;DR:
+
+- use tlparse to acquire torch.compile logs. Include these logs in bug reports and/or support asks.
+- The vLLM-torch.compile integration is multiple pieces. vLLM exposes flags to turn off each piece:
+
+| Online Flag | Offline Flag   |      Result |
+|----------|----------|-------------|
+| --enforce-eager | enforce_eager=True |  Turn off torch.compile and CUDAGraphs |
+| -O.mode=0 | mode=CompilationMode.NONE |  Turn off torch.compile only |
+| -O.cudagraph_mode=NONE | compilation_config=CompilationConfig(mode=CompilationMode.NONE) |  Turn off CUDAGraphs only |
+| -O.backend=eager | compilation_config=CompilationConfig(backend='eager') |  Turn off TorchInductor |
+
+## vLLM-torch.compile overview
+
+To improve performance, vLLM leverages torch.compile and CUDAGraphs to speed things up.
+torch.compile generates optimized kernels for PyTorch code while CUDAGraphs eliminates overhead.
+Most notably, vLLM-compile is NOT torch.compile, it is a custom compiler built using internal PyTorch Compile APIs.
+
+![vLLM-compile diagram](../assets/design/debug_vllm_compile/design_diagram.png)
+
+- Given a model, we do a full graph capture via TorchDynamo that is dynamic on the batch size (number of tokens)
+- vLLM then optionally splits and/or specializes this graph and then uses TorchInductor to compile each graph into a compiled artifact.
+This step may use vLLM custom Inductor passes to further optimize the graph.
+- The compiled artifact is saved to vLLM's compile cache so that it can be loaded in the future.
+- vLLM applies CUDAGraphs to reduce CPU overheads.
+
+Things can go wrong in each of the four steps. When something does go wrong, please try to isolate the subsystem
+that went wrong -- this will allow you to turn off the minimal number of things to keep reliability
+goals while minimizing impact to performance and also helps us (vLLM) when you open a bug report.
+
+For more details on the design, please see the following resources:
+
+- [Introduction to vLLM-torch.compile blogpost](https://blog.vllm.ai/2025/08/20/torch-compile.html)
+- [vLLM-torch.compile integration design](https://docs.vllm.ai/en/latest/design/torch_compile.html)
+- [vLLM Office Hours #26](https://www.youtube.com/live/xLyxc7hxCJc?si=Xulo9pe53C6ywf0V&t=561)
+- [Talk at PyTorch Conference 2025](https://youtu.be/1wV1ESbGrVQ?si=s1GqymUfwiwOrDTg&t=725)
+
+## Use tlparse
+
+Use [tlparse](https://github.com/meta-pytorch/tlparse) to acquire torch.compile logs. These logs show all stages of the compilation process,
+including the fused kernels that torch.compile produces.
+If you can, we recommend sending these or pieces of these along with any bug reports --
+they are very helpful.
+
+Install tlparse:
+
+```sh
+pip install tlparse
+```
+
+Usage (offline inference)
+
+```sh
+TORCH_TRACE=~/trace_dir python my_script.py
+tlparse ~/trace_dir/<the_first_log_file>
+```
+
+Usage (serving)
+
+```sh
+TORCH_TRACE=~/trace_dir vllm serve
+# ctrl-c out of the server
+tlparse ~/trace_dir/<the_first_log_file>
+```
+
+The `tlparse` command outputs some HTML files (perhaps into e.g. `./tl_out/index.html`).
+Open it to see the logs. It'll look something like the following:
+
+![tlparse example](../assets/design/debug_vllm_compile/tlparse_inductor.png)
+
+## Turn off vLLM-torch.compile integration
+
+Pass `--enforce-eager` to turn off the vLLM-torch.compile integration and run entirely
+in eager mode. This includes turning off CUDAGraphs.
+
+```sh
+# Online
+vllm serve --enforce-eager
+```
+
+```py
+# Offline
+LLM(model, enforce_eager=True)
+```
+
+To turn off just torch.compile, pass `mode = NONE` to the compilation config.
+(`-O` is short for `--compilation_config`):
+
+```sh
+# Online
+vllm serve -O.mode=0
+```
+
+```py
+# Offline
+from vllm.config.compilation import CompilationConfig, CompilationMode
+LLM(model, compilation_config=CompilationConfig(mode=CompilationMode.NONE))
+```
+
+To turn off just CUDAGraphs, pass `cudagraph_mode = NONE`:
+
+```sh
+# Online
+vllm serve -O.cudagraph_mode=NONE
+```
+
+```py
+# Offline
+from vllm.config.compilation import CompilationConfig, CUDAGraphMode
+LLM(model, compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE))
+```
+
+## Debugging TorchDynamo
+
+vLLM requires model code be capturable into a full graph via TorchDynamo (torch.compile's frontend).
+TorchDynamo does not support all of Python. It will error (in fullgraph mode) if it cannot support
+a feature (this is sometimes known as a graph break).
+
+If you encounter a graph break, please [open an issue to pytorch/pytorch](https://github.com/pytorch/pytorch) so the PyTorch devs can prioritize.
+Then, try your best to rewrite the code to avoid the graph break.
+For more information, see this [Dynamo guide](https://docs.pytorch.org/docs/stable/compile/programming_model.dynamo_core_concepts.html).
+
+## Debugging Dynamic Shape full graph capture
+
+vLLM requires that the model's forward pass be capturable into a full graph that is dynamic
+on the batch size (i.e. the number of tokens). It (by default) compiles this one graph into
+one artifact and uses this artifact for all batch sizes.
+
+If your code cannot be captured with Dynamic Shapes, you may see silent incorrectness,
+loud errors, or CUDA illegal memory accesses. For example, the following is not
+capturable into a single graph:
+
+```py
+if data.size[0] % 128 == 0:
+    foo(...)
+else:
+    bar(...)
+```
+
+This problem is easy to diagnose. Use tlparse and click on `compilation_metrics`:
+it will tell you symbolic constraints on the batch size. If there is any constraint
+that restricts the batch sizes, then we've got a problem.
+
+![Bad tlparse example](../assets/design/debug_vllm_compile/dynamic_shapes.png)
+
+To avoid this, please either:
+
+1. avoid branching on the number of tokens
+2. wrap the branching logic into a custom operator. TorchDynamo does not
+trace into custom operators.
+
+## Debugging TorchInductor
+
+TorchInductor takes a captured graph and then compiles it down to some Python code
+that may call 1+ triton kernels. On rare (but unfortunate) occasions, it may
+produce an incorrect triton kernel. This may manifest as silent incorrectness,
+CUDA illegal memory accesses, or loud errors.
+
+To debug if TorchInductor is at fault, you can disable it by passing `backend='eager'`
+to the compilation config:
+
+```sh
+# online
+vllm serve -O.backend=eager
+```
+
+```py
+# offline
+LLM(compilation_config=CompilationConfig(backend='eager'))
+```
+
+If Inductor is at fault, [file a bug to PyTorch](https://github.com/pytorch/pytorch).
+If you're feeling adventurous, you can debug the triton kernels in the Inductor output code
+(that you can locate via using tlparse).
+
+![tlparse example](../assets/design/debug_vllm_compile/tlparse_inductor.png)
+
+You can also use `TORCH_LOGS=output_code <command>` to print the Inductor output code.
+
+### Editable TorchInductor code
+
+You can edit the TorchInductor code that gets run by setting `VLLM_COMPILE_CACHE_SAVE_FORMAT=unpacked`
+or passing `-O.compile_cache_save_format=unpacked`. The default is `binary`, which means it is not editable.
+
+This is a useful technique: you can put breakpoints (e.g. `torch.distributed.breakpoint()`)
+and print statements in the output code.
+
+## Debugging vLLM-compile cache
+
+vLLM built its own cache for torch.compile artifacts. The idea is that the artifacts
+can be compiled once and then reused after they have been compiled. This
+is a layer on top of [torch.compile's compiler cache](https://docs.pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html).
+
+While torch.compile's compiler cache is rock-stable, vLLM's compiler cache is unfortunately
+not always correct. You can disable it via setting `VLLM_DISABLE_COMPILE_CACHE=1`.
+
+You can also manually remove this cache.
+
+- Remove vLLM's compile cache with `rm -rf ~/.cache/vllm` (look at logs to see if the location changed)
+- Remove torch.compile's built-in caches with `rm -rf /tmp/torchinductor_$(whoami)`
+
+vLLM's cache is a mapping from cache key to a compiled artifact. vLLM computes
+the cache key via combining multiple factors (e.g. config flags and model name).
+If vLLM's compile cache is wrong, this usually means that a factor is missing.
+Please see [this example](https://github.com/vllm-project/vllm/blob/18b39828d90413d05d770dfd2e2f48304f4ca0eb/vllm/config/model.py#L310)
+of how vLLM computes part of the cache key.
+
+## Debugging CUDAGraphs
+
+CUDAGraphs is a feature that allows one to:
+
+- Capture a callable that launches 1+ CUDA kernels into a CUDAGraph
+- Replay the CUDAGraph
+
+The captured CUDAGraph contains all of the memory used during the capture process.
+The replay of the CUDAGraph reads and writes to exactly the same regions of memory.
+
+This leads to some restrictions:
+
+1. In order to use CUDAGraphs on new data, you'll need to copy the data into a buffer
+that the CUDAGraph is reading from
+2. CUDAGraphs only capture CUDA kernels, they don't capture work done on CPU.
+
+vLLM uses the raw CUDAGraphs API, which is unsafe when used incorrectly.
+
+To turn off just CUDAGraphs, pass `cudagraph_mode = NONE`:
+
+```sh
+# Online
+vllm serve -O.cudagraph_mode=NONE
+```
+
+```py
+# Offline
+from vllm.config.compilation import CompilationConfig, CUDAGraphMode
+LLM(model, compilation_config=CompilationConfig(cudagraph_mode=CUDAGraphMode.NONE))
+```