XTC

Links

Refer to documentation at https://corse.gitlabpages.inria.fr/XTC

Refer to installable python packages at: https://gitlab.inria.fr/corse/xtc/-/packages

Refer to tutorials here and to additionnal developers documentation here.

Overview

XTC is a domain-specific dataflow graph compiler for linear algebra operations. It provides:

• Operational DSL: Define computation graphs with tensors and operators
• Scheduling DSL: High-level transformations (tiling, parallelization, vectorization, etc.)
• Multiple backends: MLIR (linalg + transform), TVM (Tensor IR), JIR (INRIA internal)
• Autotuning: Definition and exploration of the optimization space

Build & Development

System requirements

Debian-like distributions:

sudo apt install python3 python3-dev build-essential libomp5 binutils binutils-aarch64-linux-gnu binutils-x86-64-linux-gnu

# Optionally if using PMU counters on CPU for evaluation
sudo apt install libpfm4-dev 
sudo sysctl kernel.perf_event_paranoid=1

Fedora:

sudo dnf install python3 python3-devel libomp binutils binutils-aarch64-linux-gnu binutils-x86_64-linux-gnu

# For Fedora 40+
sudo dnf group install c-development development-tools # For Fedora 40+

# Optionally if using PMU counters on CPU for evaluation
sudo dnf install libpfm-devel
sudo sysctl kernel.perf_event_paranoid=1

Installation

Python: 3.10 to 3.14 inclusive

python3 -m venv .venv && source .venv/bin/activate
pip3 install -e '.[dev]'
pip3 install -r mlir_requirements.txt  # Optional: MLIR backend
pip3 install -r tvm_requirements.txt   # Optional: TVM backend
make test                              # Run minimal unit tests

Code quality

Code quality requirements:

• Type annotations: Strict pyright mode, full annotations required
• Formatting: Ruff (line length 88)
• License headers: BSD-3-Clause required on all source files
• All checks must pass before merge

Type checking:

make check-type          # Run both pyright and mypy
pyright                  # Run pyright only
mypy                     # Run mypy only

Formatting:

make format              # Apply all formatting (license + ruff)
make check-format        # Check formatting without modifying files

Testing structure:

• tests/pytest/unit/: Core interface unit tests
• tests/pytest/{mlir,tvm}/: Backend-specific tests
• tests/filecheck/: Lit+FileCheck functional tests for code generation

Global test commands:

make test                # Run minimal unit tests
make check               # Run ALL acceptance tests (required for contributions)
make check-pytest        # Run pytest suite only
make check-lit           # Run LIT tests for LLVM IR target
make check-lit-c         # Run LIT tests for C target
pytest tests/pytest/unit # Run specific test directory

Running individual tests:

# Single pytest file
pytest tests/pytest/unit/test_specific.py -v

# Single lit test
lit -v tests/filecheck/backends/specific_test.py

# C target for lit tests
XTC_MLIR_TARGET=c lit -v tests/filecheck/backends/specific_test.py

Architecture

Core Abstractions (src/xtc/itf/)

Abstract interfaces defining the compilation pipeline:

• data/ - Tensor, DataType, ShapeType
• operator/ - Linear algebra operator interface
• graph/ - Graph, Node, Operation abstractions
• back/ - Backend interface
• schd/ - Scheduler and Schedule abstractions
• comp/ - Compiler interface
• exec/ - Executor and Evaluator interfaces
• search/ - Search space exploration interface

Backends (src/xtc/backends/)

Exposed backends:

• mlir/ - MLIR backend using linalg + transform dialects
• tvm/ - TVM backend using Tensor IR + Schedule APIs

XTC also supports multiple MLIR Targets for the code generation:

• llvmir (default)
• c

To force the use of a specific target, you can set the env variable XTC_MLIR_TARGET=<mlir-target>.

The MLIR backend can be extended using the SDist extension, which provides distribution primitives. To install SDist, follow the instructions in docs/develop/optional_backends.md, in the "MLIR development version" section.

Compilation Pipeline

1. User defines Graph with Tensors and Operators
2. Backend created from Graph
3. Scheduler applies transformations and produces Schedule
4. Compiler generates executable Module
5. Executor/Evaluator runs and measures performance

CLI Tools (src/xtc/cli/)

• mlir-loop - High-level scheduling for MLIR linalg operators
• mlir-backend - MLIR backend wrapper
• loop-explore - Autotuning and space exploration
• loop-display - Visualization of exploration results

AI assistants

To create the CLAUDE.md file required to use Claude Code: make claude