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Quickstart tutorial to adding MLIR graph rewrite

This document will present a quickstart to adding graph rewrites. We shall start by defining an operation, showing multiple ways to define the rewrite using patterns, as well as defining the rewrite using a graph walker (note: using patterns and the rewrite engine is preferred, showing the walker is for demonstration purposes).

See MLIR specification for more information about MLIR, the structure of the IR, operations, etc.

Adding operation

An operation in MLIR is specified using a definition in TableGen file. TableGen is a modeling tool to specify the ops and the C++ code to interact with these operations are generated from. To define an operation one needs to specify:

The operation name. This name is a unique identifier of the operation within MLIR. Most operations are within a dialect, so for example one could have tfl.add to represent the add operation in the TensorFlow Lite dialect. Instead of repeating the dialect in the op definition, a base class for the op dialect is commonly created that prepends the dialect namespace given an op name.
The traits of the operation. These allow you to specify traits of the operation, such as whether it has side effects or whether it should be verified that the operands and result types are the same. These are backed by C++ traits that perform the verification.
The arguments of the operation. These are the input operands (values at runtime produced by other ops) and attributes (compile time known constant values that affect the behavior of the op) that are the inputs of/define the behavior of the operation. The input operands may be named, the attributes must be named.
The result(s) of the operation. These may again named or not.
Documentation of the operation. This includes a one-line summary as well as a longer human-readable description of the operation.
Derived attributes. These are accessors used to compute attributes from already known information. For example, the shape attribute for reshape where that information is already captured in the type of the operation.
Dialect specific information. Additional information could be added to the operation definition that are only used by dialect specific drivers. These are ignored by the main op and doc generators, but could be used in, say, the translation from a dialect to another representation.

def TFL_LeakyReluOp: TFL_Op<"leaky_relu", [NoSideEffect, SameValueType]>,
                     Results<(outs Tensor)> {
  let arguments = (
    ins F32Tensor:$x,
    // Slope of the activation function at x < 0.
    F32Attr:$alpha
  );

  let summary = "Leaky ReLU operator";
  let description = [{
    Element-wise Leaky ReLU operator
      x -> x >= 0 ? x : (alpha * x)
  }];

  // TFLite specific attribute that is used when generating the output
  // flatbuffer.
  let hasOptions = 0b1;
}

Note in the above the result types and inputs are specified in different ways, one by way of trait and the other by way of let. It is possible to specify both in either way.

Operations can also have custom parser, printer, builder, verifier, constant folder, or canonicalizer. These require specifying additional C++ methods to invoke for additional functionality. For example, if an operation is marked to have a constant folder, the constant folder also needs to be added, e.g.,:

Attribute SpecificOp::constantFold(ArrayRef<Attribute> operands,
                                   MLIRContext *context) {
  if (unable_to_fold)
    return {};
  ....
  return val;
}

Adding patterns

There are multiple forms of graph rewrite that can be performed in MLIR. One of the most common is DAG tile to DAG tile rewrite. Patterns provide a concise way to express this transformation as a pair of source pattern to match and resultant pattern. There is both the C++ classes to represent this transformation, as well as the patterns in TableGen from which these can be generated.

TableGen patterns

Let us continue with LeakyRelu. To map from TensorFlow's LeakyRelu to TensorFlow Lite's LeakyRelu:

def : Pat<(TF_LeakyReluOp $arg, F32Attr:$a), (TFL_LeakyReluOp $arg, $a)>

The pattern is specified by instantiating a Pat with a source and result DAG. The arguments in the from pattern is captured and can be used in the to pattern. This is a simple pattern as we have a 1:1 mapping and the attribute does not need to be transformed (e.g., both have a floating point attribute for alpha). The names of the attributes specified in the pattern is for matching/referencing and need not match the original attribute name in the op definition but the order of arguments of the dags do need to match.

To specify a pattern, both the source and results ops need to be defined using TableGen. For the above case the TensorFlow LeakyRelu was not defined yet in TableGen and instead a shortened definition was added in the legalize patterns file:

def TF_LeakyReluOp : Op<"tf.LeakyRelu">,
                     Arguments<(ins Tensor:$arg, F32Attr:$alpha)>;

If this were a more advance pattern that the current framework could not express as destination then one could use a general native code fallback method. This consists of defining a pattern as well as adding a C++ function to perform the replacement:

def : Pat<(TF_LeakyReluOp $arg, F32Attr:$a),
          (cOp<"createTFLLeakyRelu"> $arg, $a)>;

void createTFLLeakyRelu(OperationInst *op, ArrayRef<Value *> operands,
                        ArrayRef<Attribute> attrs, PatternRewriter &rewriter) {
  rewriter.replaceOpWithNewOp<mlir::TFL::LeakyReluOp>(
      op, operands[0]->getType(), /*arg=*/operands[0],
      /*alpha=*/attrs[0].cast<FloatAttr>());
}

This allows for arbitrarily complex builders. Input pattern side one can express multi-op patterns with constraints on input operands and attributes. But input patterns cannot yet express constraints across multiple operands/attributes.

C++ rewrite specification

In case patterns are not sufficient there is also the fully C++ way of expressing a rewrite:

struct ConvertTFLeakyRelu : public RewritePattern {
  ConvertTFLeakyRelu(MLIRContext *context)
      : RewritePattern("tf.LeakyRelu", 1, context) {}
  PatternMatchResult match(OperationInst *op) const override {
    return matchSuccess();
  }

  void rewrite(OperationInst *op, PatternRewriter &rewriter) const override {
    rewriter.replaceOpWithNewOp<TFL::LeakyReluOp>(
        op, op->getResult(0)->getType(), op->getOperand(0),
        /*alpha=*/op->getAttrOfType<FloatAttr>("alpha"));
  }
};

In the C++ rewrite the static benefit of the rewrite pattern is specified at construction. While in the pattern generator a simple heuristic is currently employed based around the number of ops matched and replaced.

The above rule did not capture the matching operands/attributes, but in general match function may populate and return a PatternState (or class derived from one) to pass information extracted during matching to the rewrite.

Testing

MLIR uses lit (LLVM Integrated Testing) tool for performing testing. Testing is performed by way of creating the input IR file, running a transformation and then verifying the output IR. C++ unit tests are the exception, with the IR transformation serving as the core testing mechanism. This results in fewer binaries that need to be built (and linked) and forces to focus on the representation as an important piece.

For the legalization transform above we would have a test (probably as part of the legalization pass test in TensorFlow Lite) such as:

// RUN: mlir-opt -tfl-legalize-tf %s | FileCheck %s

func @LeakyRelu(%arg0: tensor<1xf32>) -> tensor<1xf32> {
  %2 = "tf.LeakyRelu"(%arg0) {alpha: 0.1} : (tensor<1xf32>) -> tensor<1xf32>
  return %2: tensor<1xf32>

// CHECK-LABEL: LeakyRelu
// CHECK:  %0 = "tfl.leaky_relu"(%arg0) {alpha: 1.000000e-01} : (tensor<1xf32>) -> tensor<1xf32>
}

The RUN command at the top results in running the mlir-opt binary (which is compiler writer tool to exercise different registered passes) to invoke the optimization pass this transform was added as part of on the current file and to verify its output using FileCheck. FileCheck is textual output verifier. In particular it uses the CHECK expressions to verify the given output is produced.

There can be multiple RUN commands with different corresponding CHECK prefixes. And in addition multiple independent tests separated by // ----- and mlir-opt invoked with -split-input-file flag. This is especially useful for error testing.

This results in very simple, directed testing without need to work around constant propagation or other, unrelated, optimization passes.

Adding optimization pass

Optimization passes that do not fit/are difficult to specify in the above structure can be specified as general iterations across modules/functions. See Writing a Pass for a general overview and introduction to optimization passes in MLIR.

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