MLIR-AIR

https://xilinx.github.io/mlir-air/dev
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MLIR-AIR is an open-source spatial-compute compiler infrastructure for mapping structured loop and...
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### Install system dependencies

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Install required build tools including ninja, clang, and lld.

```bash
sudo apt-get install -y ninja-build clang lld unzip
```

--------------------------------

### Verify installation

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Checks the installation by verifying the version and importing required modules.

```batch
call air_env.cmd
air-opt --version
python -c "import air, pyxrt; print('ok')"
```

--------------------------------

### Configure environment and run hardware examples

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Use this batch script to set up the required runtime environment, including GNU make and Git utilities, before running the Peano programming examples.

```batch
@echo off
call C:\dev\mlir-aie\iron_env.cmd || exit /b 1
set "MLIR_AIR_INSTALL_DIR=C:\dev\mlir-air\install"
set "PATH=%MLIR_AIR_INSTALL_DIR%\bin;%PATH%"

REM GNU make, plus Git's usr\bin for the sh.exe make uses as its shell. Both
REM install per-user or machine-wide depending on how they were installed, so
REM probe rather than assume; add your own directories if either came from
REM MSYS2 or Chocolatey instead.
if exist "%LOCALAPPDATA%\Microsoft\WinGet\Links\make.exe" set "PATH=%LOCALAPPDATA%\Microsoft\WinGet\Links;%PATH%"
if exist "%ProgramFiles%\Git\usr\bin\sh.exe" set "PATH=%ProgramFiles%\Git\usr\bin;%PATH%"
if exist "%LOCALAPPDATA%\Programs\Git\usr\bin\sh.exe" set "PATH=%LOCALAPPDATA%\Programs\Git\usr\bin;%PATH%"

where make >nul 2>&1 || (echo GNU make not found -- see section 1.2. & exit /b 1)
where sh   >nul 2>&1 || (echo sh.exe not found; is Git for Windows installed? & exit /b 1)

cd /d C:\dev\mlir-air\build
ninja check-programming-examples-peano
```

--------------------------------

### Install Dependencies Without Full MLIR-AIE Clone

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Alternative setup method using standalone CMake modules and the MLIR-AIE wheel.

```bash
git clone --depth 1 https://github.com/Xilinx/cmakeModules.git C:\dev\cmakeModules
pip install mlir_aie -f https://github.com/Xilinx/mlir-aie/releases/expanded_assets/latest-wheels-4
pip install -r C:\dev\mlir-air\utils\requirements_dev.txt
```

--------------------------------

### air.channel.get Examples

Source: https://xilinx.github.io/mlir-air/dev/AIRDialect

Examples showing synchronous and asynchronous channel receive operations.

```mlir
// Receive a 4x4 tile into %dst from channel @chan_0
air.channel.get @chan_0(%dst[%c0, %c0][%c4, %c4][%c1, %c1]) : (memref<16x16xf32>)

// Asynchronous get with dependency on %t1
%t2 = air.channel.get async [%t1] @chan_1(%dst[%c8, %c0][%c4, %c4][%c1, %c1]) : (memref<16x16xf32>)
```

--------------------------------

### Run a specific example with lit

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Execute a single programming example using the lit test runner within the configured environment.

```bash
lit -sv -j1 --filter "eltwise_add_with_l2.*peano" C:/dev/mlir-air/build/programming_examples
```

--------------------------------

### Environment Setup

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Commands to configure the environment after building, with variants for pip-installed or source-built llvm-aie.

```bash
# If you have llvm-aie (PEANO) installed via pip:
source utils/env_setup.sh install-xrt/ mlir-aie/install/ $(python3 -m pip show llvm-aie | grep Location | awk '{print $2}')/llvm-aie llvm/install/

# Or if you built llvm-aie from source:
source utils/env_setup.sh install-xrt/ mlir-aie/install/ /path/to/llvm-aie/install llvm/install/
```

--------------------------------

### Setup Python virtual environment

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Configure the Python environment with necessary packages.

```bash
source utils/setup_python_packages.sh
```

--------------------------------

### Run Matrix-Scalar Addition Example on NPU2

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Executes the matrix-scalar addition example using the ELF output format supported on NPU2 devices.

```bash
cd programming_examples\matrix_scalar_add\single_core_dma
mkdir build_peano
cd build_peano
python ..\single_core_dma.py --output-format elf
```

--------------------------------

### Define XRT SDK installation path

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

The canonical installation path for the XRT SDK on Windows.

```text
C:\Xilinx\XRT
```

--------------------------------

### Verify installation

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Check that the air-opt tool is accessible.

```bash
air-opt --help
```

--------------------------------

### Install Windows development tools via winget

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Use these commands in a terminal to install the required software components for the MLIR-AIR development environment.

```batch
REM Choose one: the full IDE or the matching Build Tools package
winget install -e --id Microsoft.VisualStudio.Community
REM winget install -e --id Microsoft.VisualStudio.BuildTools

REM The package id carries the minor version; match the Python note above
winget install -e --id Python.Python.3.13
winget install -e --id Git.Git

REM Only needed to run the example test suites (section 6.5)
winget install -e --id ezwinports.make
```

--------------------------------

### Install MLIR-AIR Wheels via PowerShell

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Use this command to create a virtual environment and install the necessary MLIR-AIR and AIE packages.

```powershell
python -m venv airenv
.\airenv\Scripts\Activate.ps1
python -m pip install --upgrade pip
pip install "mlir_air[aie]" `
  -f https://github.com/Xilinx/mlir-air/releases/expanded_assets/latest-air-wheels `
  -f https://github.com/Xilinx/mlir-aie/releases/expanded_assets/latest-wheels-4 `
  -f https://github.com/Xilinx/llvm-aie/releases/expanded_assets/nightly
```

--------------------------------

### Build and install MLIR-AIR

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Commands to execute the build process using Ninja.

```batch
cd C:\dev\mlir-air\build
ninja install
```

--------------------------------

### Set up runtime environment

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

A batch script to configure the environment variables for the MLIR-AIR installation.

```batch
@echo off
call "C:\dev\mlir-aie\iron_env.cmd" || exit /b 1
set "MLIR_AIR_INSTALL_DIR=C:\dev\mlir-air\install"
set "PATH=%MLIR_AIR_INSTALL_DIR%\bin;%PATH%"
set "PYTHONPATH=%MLIR_AIR_INSTALL_DIR%\python;%PYTHONPATH%"
```

--------------------------------

### Verify Tool Installation

Source: https://xilinx.github.io/mlir-air/dev/buildingGPU

Check if required tools are available in the system path.

```bash
which air-opt mlir-opt
```

--------------------------------

### Run Element-wise Addition Example

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Executes the element-wise addition workflow using the Python builder and XRT dispatch.

```bash
call air_env.cmd
cd programming_examples\eltwise_add_with_l2
mkdir build_peano
cd build_peano
python ..\eltwise_add.py --output-format xclbin
```

--------------------------------

### Cross-rank DMA Memcpy Example

Source: https://xilinx.github.io/mlir-air/dev/AIRComputeModel

Example of reading data from a remote rank's symmetric buffer into local memory.

```MLIR
// Read 1024 floats from rank 0's symmetric buffer into local L1.
air.dma_memcpy_nd (%local[][][], %sym[][][]) {src_rank = 0 : i64}
    : (memref<1024xf32, 2>, memref<1024xf32, 0>)
```

--------------------------------

### Override MemRef Memory Space Examples

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Examples demonstrating how to override memory spaces for specific AIR hierarchy levels.

```MLIR
// Override herd allocs to L1 (memory_space 2)
%herd = transform.structured.match ops{["air.herd"]} in %arg1
  : (!transform.any_op) -> !transform.any_op
transform.air.override_memref_memory_space %herd {memory_space = 2}
  : (!transform.any_op) -> !transform.any_op

// Override func-level allocs to L2 (memory_space 1), excluding herds
%func = transform.structured.match ops{["func.func"]} in %arg1
  : (!transform.any_op) -> !transform.any_op
transform.air.override_memref_memory_space %func {memory_space = 1}
  : (!transform.any_op) -> !transform.any_op
```

--------------------------------

### Full Compile and Run Workflow

Source: https://xilinx.github.io/mlir-air/dev/buildingGPU

A complete sequence for compiling and executing the matrix multiplication example in one workflow.

```bash
# Setup environment
source utils/env_setup_gpu.sh install

# Compile
aircc --target gpu --gpu-arch gfx942 \
    -o /tmp/output.mlir \
    test/gpu/4k_4k_mul/air_sync.mlir

# Run
mlir-runner --entry-point-result=void \
    --shared-libs=$LLVM_INSTALL_DIR/lib/libmlir_rocm_runtime.so \
    --shared-libs=$MLIR_AIR_INSTALL_DIR/lib/libairgpu.so \
    /tmp/output.mlir
```

--------------------------------

### Install MLIR-AIR with AIE backend

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Installs the MLIR-AIR package with the AIE extra, pulling necessary dependencies from specified release assets.

```batch
pip install "mlir_air[aie]" ^
  -f https://github.com/Xilinx/mlir-air/releases/expanded_assets/latest-air-wheels ^
  -f https://github.com/Xilinx/mlir-aie/releases/expanded_assets/latest-wheels-4 ^
  -f https://github.com/Xilinx/llvm-aie/releases/expanded_assets/nightly
```

--------------------------------

### Examples for transform.air.remove_uninitialized_copy

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Demonstrates the removal of memref.copy and linalg.copy operations when the source is uninitialized.

```MLIR
// memref.copy case
%alloc = memref.alloc() : memref<2x16x8xi32, 1>
%subview = memref.subview %alloc[0, 0, 0] [1, 16, 8] [1, 1, 1] : ...
%target = memref.alloc() : memref<1x16x8xi32, 2>
memref.copy %subview, %target  // <- This copy will be erased

// linalg.copy case
%alloc2 = memref.alloc() : memref<16x8xi32, 1>
%target2 = memref.alloc() : memref<16x8xi32, 2>
linalg.copy ins(%alloc2 : memref<16x8xi32, 1>) outs(%target2 : memref<16x8xi32, 2>)  // <- This copy will be erased
```

--------------------------------

### Execute air-place-herds

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformPasses

Example command for placing herds onto a segment with specified grid dimensions and anchor points.

```text
-air-place-herds"num-rows=8 num-cols=10 row-anchor=0 col-anchor=0"
```

--------------------------------

### Configure herd size

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Runs matrix multiplication examples with different herd configurations.

```bash
make run2x2 COMPILE_MODE=compile-only  # 2x2 herd
make run8x4 COMPILE_MODE=compile-only  # 8x4 herd
```

--------------------------------

### Clone and Setup MLIR-AIE Environment

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Initializes the MLIR-AIE repository and configures the ironenv Python environment for development.

```bash
git clone --recurse-submodules https://github.com/Xilinx/mlir-aie.git C:\dev\mlir-aie
cd C:\dev\mlir-aie
python utils\iron_setup.py --dev
call .\iron_env.cmd
```

--------------------------------

### Linalg Elementwise Fusion Example

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Demonstrates the transformation of linalg generic operations before and after applying elementwise fusion.

```mlir
// Before fusion:
%0 = linalg.generic {
  indexing_maps = [#map0, #map1],
  iterator_types = ["parallel"]
} ins(%input : tensor<16xf32>) outs(%temp1 : tensor<16xf32>) {
  ^bb0(%arg0: f32, %arg1: f32):
    %1 = arith.mulf %arg0, %cst : f32
    linalg.yield %1 : f32
} -> tensor<16xf32>

%result = linalg.generic {
  indexing_maps = [#map0, #map2, #map1],
  iterator_types = ["parallel"]
} ins(%0, %input2 : tensor<16xf32>, tensor<16xf32>) outs(%temp2 : tensor<16xf32>) {
  ^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
    %2 = arith.addf %arg0, %arg1 : f32
    linalg.yield %2 : f32
} -> tensor<16xf32>

// After fusion:
%result = linalg.generic {
  indexing_maps = [#map0, #map2, #map1],
  iterator_types = ["parallel"]
} ins(%input, %input2 : tensor<16xf32>, tensor<16xf32>) outs(%temp2 : tensor<16xf32>) {
  ^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
    %1 = arith.mulf %arg0, %cst : f32
    %2 = arith.addf %1, %arg1 : f32
    linalg.yield %2 : f32
} -> tensor<16xf32>
```

--------------------------------

### Configure Environment Variables with air_env.ps1

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Dot-source this script to set up the required paths and environment variables for the MLIR-AIR installation.

```powershell
. .\airenv\Scripts\Activate.ps1
$SP = Join-Path $env:VIRTUAL_ENV 'Lib\site-packages'
$env:MLIR_AIR_INSTALL_DIR = Join-Path $SP 'mlir_air'
$env:MLIR_AIE_INSTALL_DIR = Join-Path $SP 'mlir_aie'
$env:PEANO_INSTALL_DIR    = Join-Path $SP 'llvm-aie'
$env:XRT_ROOT             = 'C:\Xilinx\XRT'
$env:PATH = "$env:MLIR_AIR_INSTALL_DIR\bin;$env:MLIR_AIE_INSTALL_DIR\bin;" +
            "$env:XRT_ROOT;$env:XRT_ROOT\lib;C:\Windows\System32\AMD;$env:PATH"
$env:PYTHONPATH = "$env:MLIR_AIR_INSTALL_DIR\python;$env:MLIR_AIE_INSTALL_DIR\python;$env:XRT_ROOT\python"
```

--------------------------------

### Example of transform.air.fuse_multi_op_linalg

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Demonstrates the fusion of two linalg operations where the first contains element-wise operations and the second performs a reduction.

```mlir
// Before fusion:
%0 = linalg.generic {
  indexing_maps = [#map0, #map1],
  iterator_types = ["parallel", "parallel"]
} ins(%input : tensor<16x8xf16>) outs(%temp : tensor<16x8xf32>) {
  ^bb0(%arg0: f16):
    %1 = arith.extf %arg0 : f16 to f32
    %2 = arith.mulf %1, %cst : f32
    linalg.yield %2 : f32
} -> tensor<16x8xf32>

%result = linalg.generic {
  indexing_maps = [#map2, #map3],
  iterator_types = ["parallel", "reduction"]
} ins(%0 : tensor<16x8xf32>) outs(%output : tensor<16xf32>) {
  ^bb0(%arg0: f32, %arg1: f32):
    %3 = arith.addf %arg0, %arg1 : f32
    linalg.yield %3 : f32
} -> tensor<16xf32>

// After fusion:
%result = linalg.generic {
  indexing_maps = [#map4, #map3],
  iterator_types = ["parallel", "reduction"]
} ins(%input : tensor<16x8xf16>) outs(%output : tensor<16xf32>) {
  ^bb0(%arg0: f16, %arg1: f32):
    %1 = arith.extf %arg0 : f16 to f32
    %2 = arith.mulf %1, %cst : f32
    %3 = arith.addf %2, %arg1 : f32
    linalg.yield %3 : f32
} -> tensor<16xf32>
```

--------------------------------

### Set up environment variables

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Configure paths for MLIR-AIR, MLIR-AIE, and Peano components, and optionally source XRT.

```bash
export MLIR_AIR_INSTALL_DIR=$(python3 -m pip show mlir_air | grep ^Location: | awk '{print $2}')/mlir_air
export MLIR_AIE_INSTALL_DIR=$(python3 -m pip show mlir_aie | grep ^Location: | awk '{print $2}')/mlir_aie
export PEANO_INSTALL_DIR=$(python3 -m pip show llvm-aie | grep ^Location: | awk '{print $2}')/llvm-aie

export PATH=$MLIR_AIR_INSTALL_DIR/bin:$MLIR_AIE_INSTALL_DIR/bin:$PATH
export PYTHONPATH=$MLIR_AIR_INSTALL_DIR/python:$MLIR_AIE_INSTALL_DIR/python:$PYTHONPATH
export LD_LIBRARY_PATH=$MLIR_AIR_INSTALL_DIR/lib:$MLIR_AIE_INSTALL_DIR/lib:$LD_LIBRARY_PATH

# Only if you have XRT installed (replace with your actual XRT install path):
source /path/to/xrt/setup.sh
```

--------------------------------

### Manual Build MLIR-AIR for GPU

Source: https://xilinx.github.io/mlir-air/dev/buildingGPU

Step-by-step manual configuration and build process using CMake and Ninja.

```bash
# Clone and setup
git clone https://github.com/Xilinx/mlir-air.git
cd mlir-air
source utils/setup_python_packages.sh

# Build LLVM
./utils/clone-llvm.sh
./utils/build-llvm-local.sh llvm

# Configure MLIR-AIR for GPU
mkdir -p build_gpu && cd build_gpu
cmake .. \
    -GNinja \
    -DMLIR_DIR=$(pwd)/../llvm/install/lib/cmake/mlir \
    -DLLVM_DIR=$(pwd)/../llvm/install/lib/cmake/llvm \
    -DAIR_ENABLE_AIE=OFF \
    -DAIR_ENABLE_GPU=ON \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_INSTALL_PREFIX=$(pwd)/install

# Build and install
ninja install
```

--------------------------------

### Set up environment variables

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Configure environment paths after building, including optional XRT support.

```bash
source utils/env_setup.sh [install_dir] $(python3 -m pip show mlir_aie | grep Location | awk '{print $2}')/mlir_aie $(python3 -m pip show llvm-aie | grep Location | awk '{print $2}')/llvm-aie my_install/mlir
```

```bash
source [xrt_dir]/setup.sh
```

--------------------------------

### Vectorize Herd Operations Example

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Example usage of the transform.air.herd_vectorize operation with specific configuration attributes.

```mlir
%herd = transform.structured.match ops{["air.herd"]} in %f : (!transform.any_op) -> !transform.any_op
%vectorized = transform.air.herd_vectorize %herd {
  vectorize_nd_extract = false,
  flatten_1d_depthwise_conv = false,
  vectorize_padding = true
} : (!transform.any_op) -> !transform.any_op
```

--------------------------------

### Install no-RTTI variant

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Install MLIR-AIR using the no-RTTI variant for compatibility with downstream projects built without RTTI.

```bash
pip install 'mlir_air[aie]' \
  -f https://github.com/Xilinx/mlir-air/releases/expanded_assets/latest-air-wheels-no-rtti \
  -f https://github.com/Xilinx/mlir-aie/releases/expanded_assets/latest-wheels-no-rtti-2 \
  -f https://github.com/Xilinx/llvm-aie/releases/expanded_assets/nightly
```

--------------------------------

### Manual Tiling with -air-automatic-tiling

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformPasses

Demonstrates manual loop tiling using specified tile sizes via command line arguments.

```mlir
module  {
  func.func @task(%arg0: tensor<4096xi32>, %arg1: tensor<4096xi32>) -> tensor<4096xi32> {
    %0 = memref.alloc() : memref<4096xi32>
    %1 = "aten.type_cast"(%arg0) : (tensor<4096xi32>) -> memref<4096xi32>
    %2 = "aten.type_cast"(%arg1) : (tensor<4096xi32>) -> memref<4096xi32>
    %c0 = constant 0 : index
    %c4096 = constant 4096 : index
    %c0_0 = constant 0 : index
    affine.for %arg2 = 0 to 4096 {
      %4 = affine.load %1[%arg2] : memref<4096xi32>
      %5 = affine.load %2[%arg2] : memref<4096xi32>
      %6 = muli %4, %5 : i32
      affine.store %6, %0[%arg2] : memref<4096xi32>
    } {affine_opt_label = "air.binary_op"}
    %3 = "aten.type_cast"(%0) : (memref<4096xi32>) -> tensor<4096xi32>
    return %3 : tensor<4096xi32>
  }
}
```

```mlir
#map = affine_map<(d0, d1, d2, d3) -> (d0 + d1 * 64 + d2 * 128 + d3 * 256)>
module  {
  func.func @task(%arg0: tensor<4096xi32>, %arg1: tensor<4096xi32>) -> tensor<4096xi32> {
    %0 = memref.alloc() : memref<4096xi32>
    %1 = "aten.type_cast"(%arg0) : (tensor<4096xi32>) -> memref<4096xi32>
    %2 = "aten.type_cast"(%arg1) : (tensor<4096xi32>) -> memref<4096xi32>
    affine.for %arg2 = 0 to 16 {
      affine.for %arg3 = 0 to 2 {
        affine.for %arg4 = 0 to 2 {
          affine.for %arg5 = 0 to 64 {
            %4 = affine.apply #map(%arg5, %arg4, %arg3, %arg2)
            %5 = affine.load %1[%4] : memref<4096xi32>
            %6 = affine.load %2[%4] : memref<4096xi32>
            %7 = muli %5, %6 : i32
            affine.store %7, %0[%4] : memref<4096xi32>
          }
        }
      }
    } {affine_opt_label = ""}
    %3 = "aten.type_cast"(%0) : (memref<4096xi32>) -> tensor<4096xi32>
    return %3 : tensor<4096xi32>
  }
}
```

--------------------------------

### Create a virtual environment

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Initialize a Python virtual environment and upgrade pip.

```bash
python3 -m venv airenv
source airenv/bin/activate
pip install --upgrade pip
```

--------------------------------

### Quick Build MLIR-AIR for GPU

Source: https://xilinx.github.io/mlir-air/dev/buildingGPU

Automated script sequence to clone, build LLVM, build MLIR-AIR, and set up the environment.

```bash
# Clone the repository
git clone https://github.com/Xilinx/mlir-air.git
cd mlir-air

# Setup Python environment
source utils/setup_python_packages.sh

# Build LLVM
./utils/clone-llvm.sh
./utils/build-llvm-local.sh llvm

# Build MLIR-AIR for GPU (without AIE)
./utils/build-mlir-air-gpu.sh llvm

# Setup environment
source utils/env_setup_gpu.sh install
```

--------------------------------

### Run the build script

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Execute the build process, optionally including XRT support for hardware execution.

```bash
./utils/build-mlir-air-using-wheels.sh [build_dir] [install_dir]
```

```bash
./utils/build-mlir-air-using-wheels.sh --xrt-dir <xrt_path> [build_dir] [install_dir]
```

--------------------------------

### transform.air.get_segment_for

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Gets a handle to the parent 'air.segment' of the given operation.

```APIDOC
## transform.air.get_segment_for

### Description
Produces a handle to the parent `air.segment` op for each payload IR operation associated with the operand. Fails if a segment cannot be found.

### Operands
- **target** (TransformHandleTypeInterface) - Required - The input operation handle.

### Results
- **parent** (TransformHandleTypeInterface) - The resulting handle to the parent air.segment.
```

--------------------------------

### Broadcast Before Unary Usage

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Examples of applying the broadcast_before_unary transform to functions.

```MLIR
%func = transform.structured.match ops{["func.func"]} in %arg0
  : (!transform.any_op) -> !transform.any_op
transform.air.broadcast_before_unary %func
```

```MLIR
%func = transform.structured.match ops{["func.func"]} in %arg0
  : (!transform.any_op) -> !transform.any_op
transform.air.broadcast_before_unary %func {op_name = "math.rsqrt"}
```

--------------------------------

### Get Segment Operation Syntax

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Syntax definition for the transform.air.get_segment_for operation.

```mlir
operation ::= `transform.air.get_segment_for` $target attr-dict `:` functional-type(operands, results)
```

--------------------------------

### Create and activate a virtual environment

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Initializes a Python virtual environment and upgrades pip.

```batch
python -m venv airenv
airenv\Scripts\activate.bat
python -m pip install --upgrade pip
```

--------------------------------

### Example of transform.air.fuse_truncf_linalg

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Demonstrates folding an arith.truncf operation into a producer linalg operation.

```mlir
// Before fusion:
%0 = linalg.generic {
  ^bb0(%arg0: f32, %arg1: f32):
    %1 = arith.addf %arg0, %arg1 : f32
    linalg.yield %1 : f32
} ins(%input1, %input2 : tensor<16xf32>, tensor<16xf32>) outs(%temp : tensor<16xf32>)

%result = linalg.generic {
  ^bb0(%arg0: f32):
    %2 = arith.truncf %arg0 : f32 to f16
    linalg.yield %2 : f16
} ins(%0 : tensor<16xf32>) outs(%output : tensor<16xf16>)

// After fusion:
%result = linalg.generic {
  ^bb0(%arg0: f32, %arg1: f32):
    %1 = arith.addf %arg0, %arg1 : f32
    %2 = arith.truncf %1 : f32 to f16
    linalg.yield %2 : f16
} ins(%input1, %input2 : tensor<16xf32>, tensor<16xf32>) outs(%output : tensor<16xf16>)
```

--------------------------------

### Configure Affine Loop Optimization Options

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformPasses

List of available command-line options for the -affine-loop-opt pass.

```text
-affine-opt-tile-sizes      : Affine loop tiling sizes
-affine-opt-copy-depths     : Affine loop data copy loop depths
-affine-opt-copy-fast-space : Fast memory space to use for affine data copy
-affine-opt-copy-slow-space : slow memory space to use for affine data copy
-affine-opt-tile-separate   : Affine loop tiling separates full and partial tiles
-aaffine-opt-label          : Transform loops with the given label
-affine-opt-post-label      : Label to apply to transformed loop nest
```

--------------------------------

### Size-1 vector to scalar usage example

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Applying the transformation to a function operation.

```mlir
%func = transform.structured.match ops{["func.func"]} in %arg0 
  : (!transform.any_op) -> !transform.any_op
transform.air.convert_size1_vector_to_scalar %func
```

--------------------------------

### Memref to Linalg copy transformation

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Example showing the replacement of memref.copy with linalg.copy.

```mlir
memref.copy %source, %dest : memref<...> to memref<...>
```

```mlir
linalg.copy ins(%source : memref<...>) outs(%dest : memref<...>)
```

--------------------------------

### Configure environment variables

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenWin

Sets up paths and environment variables required for MLIR-AIR execution. Save this as air_env.cmd.

```batch
@echo off
call "%~dp0airenv\Scripts\activate.bat" || exit /b 1
set "SP=%VIRTUAL_ENV%\Lib\site-packages"
set "MLIR_AIR_INSTALL_DIR=%SP%\mlir_air"
set "MLIR_AIE_INSTALL_DIR=%SP%\mlir_aie"
set "PEANO_INSTALL_DIR=%SP%\llvm-aie"
set "XRT_ROOT=C:\Xilinx\XRT"
set "PATH=%MLIR_AIR_INSTALL_DIR%\bin;%MLIR_AIE_INSTALL_DIR%\bin;%XRT_ROOT%;%XRT_ROOT%\lib;C:\Windows\System32\AMD;%PATH%"
set "PYTHONPATH=%MLIR_AIR_INSTALL_DIR%\python;%MLIR_AIE_INSTALL_DIR%\python;%XRT_ROOT%\python"
```

--------------------------------

### Perform broadcast and tree-reduction of tokens with scf.parallel

Source: https://xilinx.github.io/mlir-air/dev/AIRComputeModel

Demonstrates how to use init tokens for broadcast dependencies and scf.reduce for merging tokens from concurrent instances.

```mlir
%t_out = scf.parallel (%i, %j) = (%lb0, %lb1) to (%ub0, %ub1) step (%s0, %s1)
    init (%t_in) -> !air.token {
  // All instances may start as soon as %t_in is signaled.
  %t_instance = air.channel.put async [%t_in] @ch[%i, %j] …
  scf.reduce(%t_instance : !air.token) {
  ^bb0(%a: !air.token, %b: !air.token):
    %t_merge = air.wait_all async [%a, %b]
    scf.reduce.return %t_merge : !air.token
  }
}
// %t_out is signaled only when ALL instances have completed.
```

--------------------------------

### Configure Environment

Source: https://xilinx.github.io/mlir-air/dev/buildingGPU

Commands to set up the environment for MLIR-AIR development.

```bash
cd mlir-air
source utils/env_setup_gpu.sh install
```

```bash
export PATH=/path/to/mlir-air/install/bin:/path/to/llvm/install/bin:$PATH
export PYTHONPATH=/path/to/mlir-air/python:$PYTHONPATH
```

--------------------------------

### Size-1 vector to scalar loop example

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Transformation of loop iter_args while preserving region boundaries.

```mlir
// Before:
%result = scf.for %i = %c0 to %c4 step %c1 iter_args(%arg = %init) -> (vector<1xf32>) {
  %add = arith.addf %arg, %cst : vector<1xf32>
  scf.yield %add : vector<1xf32>
}

// After:
%result = scf.for %i = %c0 to %c4 step %c1 iter_args(%arg = %init) -> (vector<1xf32>) {
  %0 = vector.extract %arg[0] : f32 from vector<1xf32>
  %1 = arith.addf %0, %cst_scalar : f32
  %2 = vector.broadcast %1 : f32 to vector<1xf32>
  scf.yield %2 : vector<1xf32>
}
```

--------------------------------

### Convert Divf Sqrt to Rsqrt Usage

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformOps

Example of applying the convert_divf_sqrt_to_rsqrt transform to an air.herd operation.

```MLIR
%herd = transform.structured.match ops{["air.herd"]} in %arg0 
  : (!transform.any_op) -> !transform.any_op
transform.air.convert_divf_sqrt_to_rsqrt %herd
```

--------------------------------

### Run profiling workflow

Source: https://xilinx.github.io/mlir-air/dev/buildingRyzenLin

Generates xclbin and instructions for use with a custom C++ test executable.

```bash
make profile
```

--------------------------------

### Annotate Refeed Loop Structure

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformPasses

Example of a re-feed loop structure recognized by the -air-annotate-refeed pass.

```mlir
scf.for %i = %c0 to %c6 step %c1 {
  air.channel.put @xnorm[] (%buf[] [] [])
}
```

--------------------------------

### Example of -air-place-herds transformation

Source: https://xilinx.github.io/mlir-air/dev/AIRTransformPasses

This snippet demonstrates the structure of an AIR module before the herd placement pass, showing the hierarchy of launch, segment, and herd operations.

```mlir
#map0 = affine_map<()[s0] -> (s0 * 64)>
#map1 = affine_map<()[s0] -> (s0 * 512)>
#map2 = affine_map<()[s0] -> (s0 * 32)>
module attributes {torch.debug_module_name = "mmult"} {
  func.func @forward(%arg0: memref<24576x1024xbf16>, %arg1: memref<1024x1024xbf16>) -> memref<24576x1024xbf16> {
    %c16 = arith.constant 16 : index
    %c48 = arith.constant 48 : index
    %cst = arith.constant 0.000000e+00 : bf16
    %0 = memref.alloc() {alignment = 128 : i64} : memref<24576x1024xbf16>
    linalg.fill ins(%cst : bf16) outs(%0 : memref<24576x1024xbf16>)
    %1 = memref.alloc() {alignment = 128 : i64} : memref<24576x1024xbf16>
    memref.copy %0, %1 : memref<24576x1024xbf16> to memref<24576x1024xbf16>
    %2 = memref.alloc() {alignment = 128 : i64} : memref<24576x1024xbf16>
    air.launch @launch_0 (%arg2, %arg3) in (%arg4=%c48, %arg5=%c16) args(%arg6=%arg0, %arg7=%arg1, %arg8=%1, %arg9=%2) : memref<24576x1024xbf16>, memref<1024x1024xbf16>, memref<24576x1024xbf16>, memref<24576x1024xbf16> attributes {resource_type = "vckxyz", size_x = 6 : i64, size_y = 2 : i64} {
      air.segment @segment_0  args(%arg10=%arg2, %arg11=%arg3, %arg12=%arg4, %arg13=%arg5, %arg14=%arg6, %arg15=%arg7, %arg16=%arg8, %arg17=%arg9) : index, index, index, index, memref<24576x1024xbf16>, memref<1024x1024xbf16>, memref<24576x1024xbf16>, memref<24576x1024xbf16> attributes {resource_type = "vckxyz", size_x = 3 : i64, size_y = 2 : i64} {
        %c1 = arith.constant 1 : index
        %c2 = arith.constant 2 : index
        %c0 = arith.constant 0 : index
        %c1024 = arith.constant 1024 : index
        %c64 = arith.constant 64 : index
        %3 = affine.apply #map0()[%arg11]
        %4 = affine.apply #map1()[%arg10]
        scf.for %arg18 = %c0 to %c1024 step %c64 {
          %12 = memref.alloc() : memref<64x64xbf16, 1>
          %13 = memref.alloc() : memref<64x64xbf16, 1>
          %14 = memref.alloc() : memref<64x64xbf16, 1>
          air.dma_memcpy_nd (%12[] [] [], %arg14[%4, %arg18] [%c64, %c64] [%c1024, %c1]) {id = 1 : i32} : (memref<64x64xbf16, 1>, memref<24576x1024xbf16>)
          air.dma_memcpy_nd (%13[] [] [], %arg15[%arg18, %3] [%c64, %c64] [%c1024, %c1]) {id = 2 : i32} : (memref<64x64xbf16, 1>, memref<1024x1024xbf16>)
          air.dma_memcpy_nd (%14[] [] [], %arg16[%4, %3] [%c64, %c64] [%c1024, %c1]) {id = 3 : i32} : (memref<64x64xbf16, 1>, memref<24576x1024xbf16>)
          air.herd @matmul_herd_0  tile (%arg19, %arg20) in (%arg21=%c2, %arg22=%c2) args(%arg23=%12, %arg24=%13, %arg25=%14) : memref<64x64xbf16, 1>, memref<64x64xbf16, 1>, memref<64x64xbf16, 1> {
            %c1_0 = arith.constant 1 : index
            %c0_1 = arith.constant 0 : index
            %c64_2 = arith.constant 64 : index
            %c32 = arith.constant 32 : index
            %15 = affine.apply #map2()[%arg19]
            %16 = affine.apply #map2()[%arg20]
            scf.for %arg26 = %c0_1 to %c64_2 step %c32 {
              %17 = memref.alloc() : memref<32x32xbf16, 2>
              %18 = memref.alloc() : memref<32x32xbf16, 2>
              %19 = memref.alloc() : memref<32x32xbf16, 2>
              air.dma_memcpy_nd (%17[] [] [], %arg23[%15, %arg26] [%c32, %c32] [%c64_2, %c1_0]) {id = 4 : i32} : (memref<32x32xbf16, 2>, memref<64x64xbf16, 1>)
              air.dma_memcpy_nd (%18[] [] [], %arg24[%arg26, %16] [%c32, %c32] [%c64_2, %c1_0]) {id = 5 : i32} : (memref<32x32xbf16, 2>, memref<64x64xbf16, 1>)
              air.dma_memcpy_nd (%19[] [] [], %arg25[%15, %16] [%c32, %c32] [%c64_2, %c1_0]) {id = 6 : i32} : (memref<32x32xbf16, 2>, memref<64x64xbf16, 1>)
              linalg.matmul ins(%17, %18 : memref<32x32xbf16, 2>, memref<32x32xbf16, 2>) outs(%19 : memref<32x32xbf16, 2>)
              air.dma_memcpy_nd (%arg25[%15, %16] [%c32, %c32] [%c64_2, %c1_0], %19[] [] []) {id = 7 : i32} : (memref<64x64xbf16, 1>, memref<32x32xbf16, 2>)
              memref.dealloc %17 : memref<32x32xbf16, 2>
              memref.dealloc %18 : memref<32x32xbf16, 2>
              memref.dealloc %19 : memref<32x32xbf16, 2>
            }
            air.herd_terminator
          }
          air.dma_memcpy_nd (%arg16[%4, %3] [%c64, %c64] [%c1024, %c1], %14[] [] []) {id = 8 : i32} : (memref<24576x1024xbf16>, memref<64x64xbf16, 1>)
          memref.dealloc %12 : memref<64x64xbf16, 1>
          memref.dealloc %13 : memref<64x64xbf16, 1>
          memref.dealloc %14 : memref<64x64xbf16, 1>
        }
        air.segment_terminator
      }
      air.launch_terminator
    }
    return %2 : memref<24576x1024xbf16>
  }
}
```

```mlir
air.herd @matmul_herd_0  tile (%arg19, %arg20) in (%arg21=%c2, %arg22=%c2) args(%arg23=%12, %arg24=%13, %arg25=%14) : memref<64x64xbf16, 1>, memref<64x64xbf16, 1>, memref<64x64xbf16, 1> attributes {x_loc = 0 : i64, y_loc = 7: i64} {
```
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