[MLIR] Introduce RemarkEngine + pluggable remark streaming (YAML/Bitstream) (#152474)
This PR implements structured, tooling-friendly optimization remarks
with zero cost unless enabled. It implements:
- `RemarkEngine` collects finalized remarks within `MLIRContext`.
- `MLIRRemarkStreamerBase` abstract class streams them to a backend.
- Backends: `MLIRLLVMRemarkStreamer` (bridges to llvm::remarks →
YAML/Bitstream) or your own custom streamer.
- Optional mirroring to DiagnosticEngine (printAsEmitRemarks +
categories).
- Off by default; no behavior change unless enabled. Thread-safe;
ordering best-effort.
## Overview
```
Passes (reportOptimization*)
│
▼
+-------------------+
| RemarkEngine | collects
+-------------------+
│ │
│ mirror │ stream
▼ ▼
emitRemark MLIRRemarkStreamerBase (abstract)
│
├── MLIRLLVMRemarkStreamer → llvm::remarks → YAML | Bitstream
└── CustomStreamer → your sink
```
## Enable Remark engine and Plug LLVM's Remark streamer
```
// Enable once per MLIRContext. This uses `MLIRLLVMRemarkStreamer`
mlir::remark::enableOptimizationRemarksToFile(
ctx, path, llvm::remarks::Format::YAML, cats);
```
## API to emit remark
```
// Emit from a pass
remark::passed(loc, categoryVectorizer, myPassname1)
<< "vectorized loop";
remark::missed(loc, categoryUnroll, "MyPass")
<< remark::reason("not profitable at this size") // Creates structured reason arg
<< remark::suggest("increase unroll factor to >=4"); // Creates structured suggestion arg
remark::passed(loc, categoryVectorizer, myPassname1)
<< "vectorized loop"
<< remark::metric("tripCount", 128); // Create structured metric on-the-fly
```
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