Raptor/README.md

# Raptor

Raptor is a domain-specific MLIR compiler for neural networks in ONNX format,
targeting in-memory computing / processing-in-memory (PIM) architectures. It
extends ONNX-MLIR with a PIM accelerator and progressively lowers ONNX-MLIR
through custom MLIR dialects to simulator artifacts.

The current target is the PIM simulator stack under `backend-simulators/pim`.
Raptor emits binary per-core `.pim` instruction files by default, plus
`memory.bin`, `config.json`, and weight binaries. It can also emit per-core JSON
instruction files with `--pim-emit-json`.

## Overview

PIM architectures perform most computation directly in memory. The supported
target models a chip with:
- shared host memory,
- multiple PIM cores,
- ReRAM crossbars for vector-matrix / matrix-vector work,
- explicit communication between cores,
- no hardware branch or loop support in emitted simulator code.

Because repeated work such as convolutions is eventually made explicit, emitted
instruction counts can grow quickly. Most compiler work therefore focuses on
lowering, scheduling, memory layout, and code-generation optimizations.

### Targets and simulators

- `backend-simulators/pim/pim-simulator` is the in-tree Rust functional
  simulator used by validation. It reads Raptor's `pim/` artifact directory and
  compares simulator output against native ONNX-MLIR execution.
- `backend-simulators/pim/pimsim-nn` is the non-functional simulator submodule
  used internally by validation for latency, power, and energy.
  The helper scripts in `pimcomp_utils/` are for comparison with PIMCOMP-NN and
  contain local paths; treat them as local utilities, not portable workflows.

## Compilation pipeline

The PIM sources live under `src/PIM` and tests under `test/PIM`. CMake exposes
them to ONNX-MLIR through generated shim directories under
`onnx-mlir/src/Accelerators/PIM` and `onnx-mlir/test/accelerators/PIM`.

High-level lowering flow:

```
ONNX-MLIR -> Spatial -> Pim (tensor) -> Pim (bufferized) -> PIM artifacts
```

1. **ONNX -> Spatial** (`src/PIM/Conversion/ONNXToSpatial`).
   Lowers supported ONNX ops into the `spat` dialect
   (`src/PIM/Dialect/Spatial`). Conversion patterns are split by op family under
   `Patterns/{Math,NN,Tensor}` and currently cover Conv, Gemm, MatMul,
   elementwise Add/Mul/Div, ReduceMean, pooling, Relu, Sigmoid, Softmax,
   Concat, Gather, Reshape, Resize, and Split.
   The compiler-layer target adapter supplies the target-neutral
   `SpatialTargetInfo`. Layout-aware plan ops advertise typed alternatives
   through the Spatial layout interface; the layout planner records the
   selected layout and explicit materialization edges. `LowerSpatialPlans`
   then pattern-lowers those selected plans. Contraction and Conv lowering
   keep semantic problems, target-dependent plans, and IR materializers in
   separate layers.

2. **Merge, schedule, and realize Spatial communication**
   (`src/PIM/Dialect/Spatial/Transforms/MergeComputeNodes`).
   `TrivialGraphComputeMerge` performs local graph merging, then
   `ScheduleSpatialGraph` materializes scheduled computes and explicit deferred
   communication. `VerifyScheduledSpatial` checks that intermediate contract;
   `RealizeSpatialCommunication` resolves transfers and forwarding; and
   `VerifyRealizedSpatial` checks the final scheduled graph. Supporting
   scheduling code lives under `MergeComputeNodes/Scheduling`.

3. **Spatial -> Pim** (`src/PIM/Conversion/SpatialToPim`).
   Lowers Spatial operations to the `pim` dialect (`src/PIM/Dialect/Pim`),
   including `pim.core`, `pim.core_batch`, communication, tensor packing, global
   tensor materialization, and return-path normalization.

4. **Bufferization** (`src/PIM/Dialect/Pim/Transforms/Bufferization`).
   `PimBufferizationPreparation` establishes writable destinations without
   duplicating the one-shot copy analysis, `PimOneShotBufferization` runs
   MLIR's one-shot analysis,
   `PimMemoryNormalization` forwards/removes redundant copies and normalizes
   addressable accesses, and `PimBufferizationVerification` checks tensor
   absence, contiguity, and copy address spaces.

5. **PIM local-memory planning**
   (`src/PIM/Dialect/Pim/Transforms/LocalMemoryPlanning`).
   Computes whole-core lifetimes, reuses addresses for non-overlapping
   allocations, and records the explicit plan in PIM IR.
6. **PIM verification and code generation** (`src/PIM/Pass/PimCodegen` and
   `src/PIM/Compiler`).
   Verifies the memory plan and other PIM invariants, then emits `.pim` core
   files, weights, and `memory.bin` / `config.json` without rerunning liveness.

Supporting pieces:
- `src/PIM/Common` - shared IR, filesystem, diagnostics, reports, and utility
  helpers.
- `src/PIM/Compiler` - PIM compiler options, planned-address materialization, binary
  instruction format, artifact writing, weight emission, and codegen entry
  points.
- `src/PIM/Conversion/SpatialToGraphviz` - optional Spatial graphviz conversion
  pass.
- `src/PIM/Pass` - pass registration and auxiliary passes.
- `src/PIM/PimAccelerator.{cpp,hpp}` - ONNX-MLIR accelerator entry point.

## PIM compiler options

Pass these to `onnx-mlir` when compiling for PIM. These are all Raptor/PIM-specific
options; `onnx-mlir --help` lists the inherited ONNX-MLIR options.

- `--maccel=PIM` - select the PIM accelerator.
- `--EmitSpatial`, `--EmitPim`, `--EmitPimBufferized`,
  `--EmitPimCodegen` - stop the PIM pipeline at the requested stage. The PIM
  default is `--EmitPimCodegen`.
- `--core-count=<N>` - required positive core count for PIM compilation.
- `--crossbar-size=<N>` - crossbar width/height. Default in code is `128`.
- `--crossbar-count=<N>` - crossbars per core. Default in code is `64`.
- `--pim-target-config=<PATH>` - optional PIM target configuration used by the
  target adapter to construct the target-neutral Spatial scheduling cost and
  topology model. Resource values must match the explicit core/crossbar flags.
- `--pim-memory-report=<summary|none>` - emit the concise combined memory report
  under `reports/memory_report.txt`, or disable it. Default is `summary`.
- `--pim-only-codegen` - assume input is already bufferized PIM IR and only run
  the codegen tail.
- `--pim-emit-json` - also emit `core_*.json` instruction files alongside
  `core_*.pim`.
- `--pim-export-spatial-dataflow=<none|spatial1|spatial2|spatial3|spatial4|all>` -
  control Spatial dataflow CSV reports for the graph, trivially merged graph,
  scheduled, and realized snapshots under `reports/`. Default is `none`.
- `--pim-conv-lowering=<auto|legacy|depthwise|packed-im2col|streamed-patch|streamed-packed|output-channel-tiled|input-k-tiled|tiled-2d>` -
  select the convolution lowering strategy. Default is `auto`.
- `--pim-conv-im2col-max-elements=<N>` - maximum globally materialized im2col
  elements per convolution before streaming. Default is `1048576`.
- `--pim-conv-stream-chunk-positions=<N>` - maximum output positions per
  streamed convolution chunk. Default is `1024`.
- `--use-experimental-conv-impl` - use the alternate convolution lowering.
- `--pim-detect-communication-deadlock` - statically simulate expanded
  send/receive ordering and reject blocking deadlocks. Default is off.
- `--pim-materialize-scalar-fanout-global-order` - use the experimental,
  expensive globally ordered scalar-fanout materializer. Default is off.
- `--pim-trace-communication-materialization` - emit verbose communication
  materialization diagnostics and provenance attributes. Default is off.
- `--ignore-concat-error` - soft-fail a ConcatOp corner case.

## Standard PIM hardware profile

Raptor's standard development and YOLO validation profile is:

| Parameter | Value |
| --- | ---: |
| Cores | 144 |
| Crossbars per core | 64 |
| Crossbar size | 128 × 128 |

Canonical compiler flags:

`--crossbar-count=64 --crossbar-size=128 --core-count=144`

`--core-count` remains mandatory and must be passed explicitly to the compiler.

Example:

```bash
./build_release/Release/bin/onnx-mlir model.onnx -o /tmp/raptor/model \
  --maccel=PIM --EmitPimCodegen \
  --crossbar-count=64 --crossbar-size=128 --core-count=144
```

This writes PIM artifacts under `/tmp/raptor/pim/`.

## Validation

Functional validation compiles ONNX models, compares native ONNX-MLIR and PIM
simulator outputs, and optionally reports latency, power, and energy. See
[`validation/README.md`](validation/README.md) for prerequisites, usage,
options, artifacts, and results.

## Build

Initialize submodules first:

```bash
git submodule update --init --recursive
```

The project follows ONNX-MLIR's build requirements. The CI workflow documents
the currently used versions and setup:
- CMake 4.3.0 in CI,
- LLVM/MLIR checked out under `onnx-mlir/llvm-project`,
- Protobuf `v34.0`,
- Rust stable for `pim-simulator`,
- Python packages `numpy`, `onnx`, `colorama` for validation.

### Protobuf

Install Protobuf if your system does not already provide a compatible version:

```bash
git clone --depth 1 --branch v34.0 https://github.com/protocolbuffers/protobuf
cmake -S protobuf -B protobuf/build -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -Dprotobuf_BUILD_TESTS=OFF
cmake --build protobuf/build
sudo cmake --install protobuf/build
```

You can then remove the temporary checkout:

```bash
rm -rf protobuf
```

### MLIR

Follow the ONNX-MLIR instructions in
`onnx-mlir/docs/BuildOnLinuxOSX.md` to build LLVM/MLIR. The local Raptor build
expects `MLIR_DIR` to point at the MLIR CMake package, for example:

```bash
MLIR_DIR=$(pwd)/onnx-mlir/llvm-project/build_release/lib/cmake/mlir
```

If your LLVM build directory is named `build` instead of `build_release`, adjust
the path accordingly.

### Raptor

Configure a release build:

```bash
MLIR_DIR=$(pwd)/onnx-mlir/llvm-project/build_release/lib/cmake/mlir
cmake -S . -B build_release -G Ninja \
  -DCMAKE_BUILD_TYPE=Release \
  -DONNX_MLIR_ACCELERATORS=PIM \
  -DLLVM_ENABLE_ASSERTIONS=ON \
  -DMLIR_DIR=${MLIR_DIR}
```

Configure a debug build similarly:

```bash
MLIR_DIR=$(pwd)/onnx-mlir/llvm-project/build_debug/lib/cmake/mlir
cmake -S . -B build_debug -G Ninja \
  -DCMAKE_BUILD_TYPE=Debug \
  -DONNX_MLIR_ACCELERATORS=PIM \
  -DLLVM_ENABLE_ASSERTIONS=ON \
  -DMLIR_DIR=${MLIR_DIR}
```

For debug development, using `mold` can reduce link time and memory use:

```bash
cmake -S . -B build_debug -G Ninja \
  -DCMAKE_BUILD_TYPE=Debug \
  -DONNX_MLIR_ACCELERATORS=PIM \
  -DLLVM_ENABLE_ASSERTIONS=ON \
  -DMLIR_DIR=${MLIR_DIR} \
  -DCMAKE_EXE_LINKER_FLAGS="-fuse-ld=mold" \
  -DCMAKE_SHARED_LINKER_FLAGS="-fuse-ld=mold" \
  -DCMAKE_MODULE_LINKER_FLAGS="-fuse-ld=mold"
```

Build the compiler with CMake:

```bash
cmake --build ./build_release
cmake --build ./build_debug
```

Do not invoke `ninja` directly for this project; use `cmake --build` so CMake's
configuration and generated shims stay consistent.

If a build fails because Protobuf headers are missing fixed-width integer
definitions, patch the affected Protobuf-generated files by adding
`#include <cstdint>`.

## Tests

The Rust simulator has its own tests:

```bash
cd backend-simulators/pim/pim-simulator
cargo test
```

## Repository Layout

- `src/PIM/` - PIM accelerator implementation.
- `test/PIM/` - PIM C++ unit tests.
- `validation/` - functional validation scripts, ONNX operation tests, network
  slices, and pimsim config generation.
- `backend-simulators/pim/pim-simulator/` - in-tree Rust functional simulator.
- `backend-simulators/pim/pimsim-nn/` - non-functional simulator submodule.
- `pimcomp_utils/` - local comparison helpers for PIMCOMP-NN.
- `.github/actions/` and `.github/workflows/validate_operations.yml` - CI setup
  for MLIR/Protobuf caching, building Raptor, and validation.