# Understanding the IR Structure
The MLIR Language Reference describes the
[High Level Structure](../LangRef.md/#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](../PassManagement.md/#operation-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
`Region`s, each of which is actually a list of `Blocks`, each of which itself
wraps a list of `Operation`s. 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:
```c++
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.getName() << "' : '"
<< attr.getValue() << "'\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 `Block`s:
```c++
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 `Operation`s:
```c++
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](https://github.com/llvm/llvm-project/blob/main/mlir/test/lib/IR/TestPrintNesting.cpp)
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`:
```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 2 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:
```c++
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.
```c++
// 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:
```c++
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:
```c++
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](../LangRef.md/#high-level-structure),
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 `Operation`s, 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:
```c++
// 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 `Value`s
produced by an `Operation` and for each result will iterate the users of these
results and print informations about them:
```c++
// 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](https://github.com/llvm/llvm-project/blob/main/mlir/test/lib/IR/TestPrintDefUse.cpp)
and can be exercised with `mlir-opt -test-print-defuse`.
The chaining of `Value`s 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"):
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