An interactive web-based tool for exploring intermediate representations (IRs) of PyTorch and Triton models. Designed to help developers, researchers, and students visualize and understand compilation pipelines by tracing models through various IR stages and transformations.

• Live editing of PyTorch, Triton models and raw IR input
• Pre-defined lowering IR support:
  • TorchScript Graph IR
  • Torch MLIR (and TOSA, Linalg, StableHLO dialects)
  • LLVM MLIR and LLVM IR
  • Triton IRs (TTIR, TTGIR, LLVM IR, NVPTX)
• Customizable compiler pipelines with toolchain steps like:
  • torch-mlir-opt
  • mlir-opt
  • mlir-translate
  • opt, llc, or any external tool via $PATH
• Visual pipeline builder to control and inspect transformation flow
• IR viewer with syntax highlighting
• Side-by-side IR windows
• "Print after all opts" toggle to inspect intermediate outputs

• (PyTorch) The model and input tensor must be initialized in the provided code. If multiple models are defined, it is recommended to explicitly pair each model and its input tensor using the internal __explore__(model, input) function.

• (PyTorch) The current version does not recognize or capture user attempts to dump IR inside the input PyTorch module. It is planned that, in the future, if the user manually calls fx.export_and_import() (or similar IR-producing APIs), the app will use that IR as the base and apply the user-defined custom toolchain.

• (Triton) The current implementation runs Triton kernels and retrieves IR dumps from the Triton cache directory. Timeout is set to 20s.

• Python 3.11+
• Node.js + npm
• PyTorch
• Torch-MLIR
• Triton
• LLVM with mlir-opt

Current version is tested on Ubuntu 22.04 windows subsystem using LLVM 21 dev.

In case of missing prerequisites here are some scripts to help set them up.

git clone https://github.com/MrSidims/PytorchExplorer.git
cd PytorchExplorer
source setup_frontend.sh

When you have venv suitable for torch-mlir work, install fastapi, uvicorn etc in venv like this:

pip install fastapi uvicorn pytest httpx

Otherwise here is the script to setup torch, llvm etc:

source setup_backend.sh

If you want to use your builds of the tools like torch-mlir-opt, mlir-opt etc without placing them in PATH please setup TORCH_MLIR_OPT_PATH and LLVM_BIN_PATH environment variables.

npm run start:all

With the application (or just backend) started, run:

pytest tests -v

The app uses fx.export_and_import to inpect IR output for PyTorch. Lowering to LLVM IR goes through:

module = fx.export_and_import(model, example_input, output_type=OutputType.LINALG_ON_TENSORS)
mlir-opt --one-shot-bufferize="bufferize-function-boundaries"
         -convert-linalg-to-loops
         -convert-scf-to-cf
         -convert-cf-to-llvm
         -lower-affine
         -finalize-memref-to-llvm
         -convert-math-to-llvm
         -convert-math-to-llvm
         -convert-func-to-llvm
         -reconcile-unrealized-casts
         str(module) -o output.mlir
mlir-translate --mlir-to-llvmir output.mlir