Understanding the IR Structure
The MLIR Language Reference describes the High Level Structure, this document illustrates this structure through examples, and introduces at the same time the C++ APIs involved in manipulating it.
We will implement a pass that traverses any MLIR input and prints the entity inside the IR. A pass (or in general almost any piece of IR) is always rooted with an operation. Most of the time the top-level operation is a ModuleOp, the MLIR PassManager is actually limited to operation on a top-level ModuleOp. As such a pass starts with an operation, and so will our traversal:
void runOnOperation() override {
Operation *op = getOperation();
resetIndent();
printOperation(op);
}
Traversing the IR Nesting
The IR is recursively nested, an Operation can have one or multiple nested Regions, each of which is actually a list of Blocks, each of which itself wraps a list of Operations. Our traversal will follow this structure with three methods: printOperation(), printRegion(), and printBlock().
The first method inspects the properties of an operation, before iterating on the nested regions and print them individually:
void printOperation(Operation *op) { // Print the operation itself and some of its properties printIndent() << "visiting op: '" << op->getName() << "' with " << op->getNumOperands() << " operands and " << op->getNumResults() << " results\n"; // Print the operation attributes if (!op->getAttrs().empty()) { printIndent() << op->getAttrs().size() << " attributes:\n"; for (NamedAttribute attr : op->getAttrs()) printIndent() << " - '" << attr.first << "' : '" << attr.second << "'\n"; } // Recurse into each of the regions attached to the operation. printIndent() << " " << op->getNumRegions() << " nested regions:\n"; auto indent = pushIndent(); for (Region ®ion : op->getRegions()) printRegion(region); }
A Region does not hold anything other than a list of Blocks:
void printRegion(Region ®ion) { // A region does not hold anything by itself other than a list of blocks. printIndent() << "Region with " << region.getBlocks().size() << " blocks:\n"; auto indent = pushIndent(); for (Block &block : region.getBlocks()) printBlock(block); }
Finally, a Block has a list of arguments, and holds a list of Operations:
void printBlock(Block &block) { // Print the block intrinsics properties (basically: argument list) printIndent() << "Block with " << block.getNumArguments() << " arguments, " << block.getNumSuccessors() << " successors, and " // Note, this `.size()` is traversing a linked-list and is O(n). << block.getOperations().size() << " operations\n"; // A block main role is to hold a list of Operations: let's recurse into // printing each operation. auto indent = pushIndent(); for (Operation &op : block.getOperations()) printOperation(&op); }
The code for the pass is available here in the repo and can be exercised with mlir-opt -test-print-nesting.
Example
The Pass introduced in the previous section can be applied on the following IR with mlir-opt -test-print-nesting -allow-unregistered-dialect llvm-project/mlir/test/IR/print-ir-nesting.mlir:
"builtin.module"() ( { %0:4 = "dialect.op1"() {"attribute name" = 42 : i32} : () -> (i1, i16, i32, i64) "dialect.op2"() ( { "dialect.innerop1"(%0#0, %0#1) : (i1, i16) -> () }, { "dialect.innerop2"() : () -> () "dialect.innerop3"(%0#0, %0#2, %0#3)[^bb1, ^bb2] : (i1, i32, i64) -> () ^bb1(%1: i32): // pred: ^bb0 "dialect.innerop4"() : () -> () "dialect.innerop5"() : () -> () ^bb2(%2: i64): // pred: ^bb0 "dialect.innerop6"() : () -> () "dialect.innerop7"() : () -> () }) {"other attribute" = 42 : i64} : () -> () }) : () -> ()
And will yield the following output:
visiting op: 'builtin.module' with 0 operands and 0 results
1 nested regions:
Region with 1 blocks:
Block with 0 arguments, 0 successors, and 3 operations
visiting op: 'dialect.op1' with 0 operands and 4 results
1 attributes:
- 'attribute name' : '42 : i32'
0 nested regions:
visiting op: 'dialect.op2' with 0 operands and 0 results
2 nested regions:
Region with 1 blocks:
Block with 0 arguments, 0 successors, and 1 operations
visiting op: 'dialect.innerop1' with 2 operands and 0 results
0 nested regions:
Region with 3 blocks:
Block with 0 arguments, 2 successors, and 2 operations
visiting op: 'dialect.innerop2' with 0 operands and 0 results
0 nested regions:
visiting op: 'dialect.innerop3' with 3 operands and 0 results
0 nested regions:
Block with 1 arguments, 0 successors, and 2 operations
visiting op: 'dialect.innerop4' with 0 operands and 0 results
0 nested regions:
visiting op: 'dialect.innerop5' with 0 operands and 0 results
0 nested regions:
Block with 1 arguments, 0 successors, and 2 operations
visiting op: 'dialect.innerop6' with 0 operands and 0 results
0 nested regions:
visiting op: 'dialect.innerop7' with 0 operands and 0 results
0 nested regions:
0 nested regions:
Other IR Traversal Methods.
In many cases, unwrapping the recursive structure of the IR is cumbersome and you may be interested in using other helpers.
Filtered iterator: getOps<OpTy>()
For example the Block class exposes a convenient templated method getOps<OpTy>() that provided a filtered iterator. Here is an example:
auto varOps = entryBlock.getOps<spirv::GlobalVariableOp>(); for (spirv::GlobalVariableOp gvOp : varOps) { // process each GlobalVariable Operation in the block. ... }
Similarly, the Region class exposes the same getOps method that will iterate on all the blocks in the region.
Walkers
The getOps<OpTy>() is useful to iterate on some Operations immediately listed inside a single block (or a single region), however it is frequently interesting to traverse the IR in a nested fashion. To this end MLIR exposes the walk() helper on Operation, Block, and Region. This helper takes a single argument: a callback method that will be invoked for every operation recursively nested under the provided entity.
// Recursively traverse all the regions and blocks nested inside the function // and apply the callback on every single operation in post-order. getFunction().walk([&](mlir::Operation *op) { // process Operation `op`. });
The provided callback can be specialized to filter on a particular type of Operation, for example the following will apply the callback only on LinalgOp operations nested inside the function:
getFunction.walk([](LinalgOp linalgOp) { // process LinalgOp `linalgOp`. });
Finally, the callback can optionally stop the walk by returning a WalkResult::interrupt() value. For example the following walk will find all AllocOp nested inside the function and interrupt the traversal if one of them does not satisfy a criteria:
WalkResult result = getFunction().walk([&](AllocOp allocOp) { if (!isValid(allocOp)) return WalkResult::interrupt(); return WalkResult::advance(); }); if (result.wasInterrupted()) // One alloc wasn't matching. ...
Traversing the def-use chains
Another relationship in the IR is the one that links a Value with its users. As defined in the language reference, each Value is either a BlockArgument or the result of exactly one Operation (an Operation can have multiple results, each of them is a separate Value). The users of a Value are Operations, through their arguments: each Operation argument references a single Value.
Here is a code sample that inspects the operands of an Operation and prints some information about them:
// Print information about the producer of each of the operands. for (Value operand : op->getOperands()) { if (Operation *producer = operand.getDefiningOp()) { llvm::outs() << " - Operand produced by operation '" << producer->getName() << "'\n"; } else { // If there is no defining op, the Value is necessarily a Block // argument. auto blockArg = operand.cast<BlockArgument>(); llvm::outs() << " - Operand produced by Block argument, number " << blockArg.getArgNumber() << "\n"; } }
Similarly, the following code sample iterates through the result Values produced by an Operation and for each result will iterate the users of these results and print informations about them:
// Print information about the user of each of the result. llvm::outs() << "Has " << op->getNumResults() << " results:\n"; for (auto indexedResult : llvm::enumerate(op->getResults())) { Value result = indexedResult.value(); llvm::outs() << " - Result " << indexedResult.index(); if (result.use_empty()) { llvm::outs() << " has no uses\n"; continue; } if (result.hasOneUse()) { llvm::outs() << " has a single use: "; } else { llvm::outs() << " has " << std::distance(result.getUses().begin(), result.getUses().end()) << " uses:\n"; } for (Operation *userOp : result.getUsers()) { llvm::outs() << " - " << userOp->getName() << "\n"; } }
The illustrating code for this pass is available here in the repo and can be exercised with mlir-opt -test-print-defuse.
The chaining of Values and their uses can be viewed as following:
The uses of a Value (OpOperand or BlockOperand) are also chained in a doubly linked-list, which is particularly useful when replacing all uses of a Value with a new one (“RAUW”):