Catalyst Command Line Interface

Catalyst includes a standalone command-line-interface compiler tool catalyst-cli that quantum-compiles MLIR input files into an object file, independent of the Catalyst Python frontend.

This compiler tool combines three stages of compilation:

1. quantum-opt: Performs the MLIR-level optimizations, including quantum optimizations, and translates the input dialect to the LLVM dialect.

2. mlir-translate: Translates the input LLVM dialect into LLVM IR.

3. llc: Performs lower-level optimizations on the LLVM IR input and creates the object file.

catalyst-cli runs all three stages under the hood by default, but it also has the ability to run each stage individually. For example:

# Creates both the optimized IR and an object file
catalyst-cli input.mlir -o output.o

# Only performs MLIR optimizations and translates to LLVM dialect
catalyst-cli --tool=opt input.mlir -o llvm-dialect.mlir

# Only lowers LLVM dialect input to LLVM IR
catalyst-cli --tool=translate llvm-dialect.mlir -o llvm-ir.ll

# Only performs lower-level optimizations and creates object file (object.o)
catalyst-cli --tool=llc llvm-ir.ll -o output.ll --module-name object

Note

The Catalyst CLI tool is currently only available when Catalyst is built from source, and is not included when installing Catalyst via pip or from wheels.

After building Catalyst, the catalyst-cli executable will be available in the mlir/build/bin/ directory.

Usage

catalyst-cli [options] <input file>

Calling catalyst-cli without any options runs the three compilation stages (quantum-opt, mlir-translate and llc) using all default configurations, and outputs by default an object file named catalyst_module.o. The name of the output file can be set by changing the output module name using the --module-name option (the default module name is catalyst_module).

Command line options

The complete list of options for the Catalyst CLI tool can be displayed by running catalyst-cli --help. As this list contains all available options, including those for configuring LLVM, the options most relevant to the usage of the Catalyst CLI tool are covered in more detail below.

--help

Show available command-line options and exit.

--verbose

Emit verbose messages.

-o <filename>

Output IR filename. If no output filename is provided, the resulting IR is output to stdout.

--tool=<opt|translate|llc|all>

Select the tool to run individually. The default is all.

• opt: Run quantum-opt on the MLIR input.

• translate: Run mlir-translate on the LLVM dialect input.

• llc: Run llc on the LLVM IR input.

• all: Run all of opt, translate and llc on the MLIR input.

--save-ir-after-each=<pass|pipeline>

Keep intermediate files after each pass or after each pipeline in the compilation. By default, no intermediate files are saved.

• pass: Keep intermediate files after each transformation/optimization pass.

• pipeline: Keep intermediate files after each pipeline, where a pipeline is a sequence of transformation/optimization passes.

--keep-intermediate[=<true|false>]

Keep intermediate files after each pipeline in the compilation. By default, no intermediate files are saved. Using --keep-intermediate is equivalent to using --save-ir-after-each=pipeline.

--catalyst-pipeline=<pipeline1(pass1[;pass2[;...]])[,pipeline2(...)]>

Specify the Catalyst compilation pass pipelines.

A pipeline is composed of a semicolon-delimited sequence of one or more transformation or optimization passes. Multiple pass pipelines can be specified and input as a comma-delimited sequence of pipelines.

For example, if we wanted to specify two pass pipelines, pipe1 and pipe2, where pipe1 applies the passes split-multiple-tapes and apply-transform-sequence, and where pipe2 applies the pass inline-nested-module, we would specify this pipeline configuration as:

--catalyst-pipeline=pipe1(split-multiple-tapes;apply-transform-sequence),pipe2(inline-nested-module)

--workspace=<path>

The workspace directory where intermediate files are saved. The default is the current working directory.

Note that the workspace directory must exist before running catalyst-cli with this option.

--module-name=<name>

The module name used in naming the output file(s). The default is "catalyst_module". Using the -o option to specify the output filename overrides this option.

--async-qnodes[=<true|false>]

Enable asynchronous QNodes.

--checkpoint-stage=<stage name>

Define a checkpoint stage, used to indicate that the compiler should start only after reaching the given pass.

--dump-catalyst-pipeline[=<true|false>]

Print (to stderr) the pipeline(s) that will be run.

Examples

To illustrate how to use the Catalyst CLI tool, consider the simple MLIR code, my_circuit.mlir, which defines a function my_circuit that implements a single-qubit quantum circuit that applies the sequence of gates \(R_x(\theta) \to H \to H \to R_x(\theta)\) to the input qubit for some rotation angle \(\theta\):

module {
  func.func @my_circuit(%in_qubit: !quantum.bit, %angle: f64) -> !quantum.bit {
    %0 = quantum.custom "RX"(%angle) %in_qubit : !quantum.bit
    %1 = quantum.custom "Hadamard"() %0 : !quantum.bit
    %2 = quantum.custom "Hadamard"() %1 : !quantum.bit
    %3 = quantum.custom "RX"(%angle) %2 : !quantum.bit
    return %3 : !quantum.bit
  }
}

We’ll use the Catalyst CLI tool to run the quantum-opt compiler to perform the MLIR-level optimizations and translate the input to the LLVM dialect. We’ll define a pass pipeline that applies two quantum-optimization passes:

1. remove-chained-self-inverse, which removes any operations that are applied next to their (self-)inverses or adjoint, in this case the two adjacent Hadamard gates.

2. merge-rotations, which combines rotation gates of the same type that act sequentially, in this case the two RX gates the become adjacent after the two Hadamard gates have been removed by the remove-chained-self-inverse pass.

To define the pass pipeline, we must supply the name of the function to which each pass applies using the func-name argument. The func-name argument is specific to the two passes we are applying and is not a general requirement. To apply these two passes to our my_circuit function, we can do so as follows:

pipe(remove-chained-self-inverse{func-name=my_circuit};merge-rotations{func-name=my_circuit})

Finally, we’ll use the option --mlir-print-ir-after-all to print the resulting MLIR after each pass that is applied, and the -o option to set the name of the output IR file:

catalyst-cli my_circuit.mlir \
    --tool=opt \
    --catalyst-pipeline="pipe(remove-chained-self-inverse{func-name=my_circuit};merge-rotations{func-name=my_circuit})" \
    --mlir-print-ir-after-all \
    -o my_circuit-llvm.mlir

Running this command will output the following intermediate IR to the console:

// -----// IR Dump After RemoveChainedSelfInversePass (remove-chained-self-inverse) //----- //
module {
  func.func @my_circuit(%arg0: !quantum.bit, %arg1: f64) -> !quantum.bit {
    %out_qubits = quantum.custom "RX"(%arg1) %arg0 : !quantum.bit
    %out_qubits_0 = quantum.custom "RX"(%arg1) %out_qubits : !quantum.bit
    return %out_qubits_0 : !quantum.bit
  }
}


// -----// IR Dump After MergeRotationsPass (merge-rotations) //----- //
module {
  func.func @my_circuit(%arg0: !quantum.bit, %arg1: f64) -> !quantum.bit {
    %0 = arith.addf %arg1, %arg1 : f64
    %out_qubits = quantum.custom "RX"(%0) %arg0 : !quantum.bit
    return %out_qubits : !quantum.bit
  }
}

and produce a new file my_circuit-llvm.mlir containing the resulting module in the LLVM dialect:

module {
  func.func @my_circuit(%arg0: !quantum.bit, %arg1: f64) -> !quantum.bit {
    %0 = arith.addf %arg1, %arg1 : f64
    %out_qubits = quantum.custom "RX"(%0) %arg0 : !quantum.bit
    return %out_qubits : !quantum.bit
  }
}

We can see in the intermediate IR after the remove-chained-self-inverse pass that the two adjacent Hadamard gates were removed and that the two RX gates were merged into one after the merge-rotations pass, with the input angle to the single RX gate being the sum of the two input angles to the original two gates. The result in my_circuit-llvm.mlir contains the final, optimized MLIR.

For a list of transformation passes currently available in Catalyst, see the Catalyst’s Transformation Library documentation. The available passes are also listed in the catalyst-cli --help message.

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