Add minimal ONNX dialect build option

- Add ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT CMake flag for minimal builds
- Decouple src/Dialect/Mlir from Compiler and Krnl dependencies
- Add test-onnx-to-mlir utility for ONNX model import testing
- Include documentation and CI workflow for minimal build
- Exclude non-essential interfaces and third-party dependencies

This enables building only the ONNX dialect and essential utilities
without the full onnx-mlir compiler stack, useful for projects that
only need ONNX dialect IR generation.

Signed-off-by: Anurag <[email protected]>

Author: anuragsingh-tt

.github/workflows/minimal-onnx-dialect.yml

@@ -0,0 +1,50 @@
+name: Minimal ONNX dialect build
+
+on:
+  pull_request:
+  push:
+
+jobs:
+  build-minimal:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          submodules: recursive
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Install prerequisites
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y ninja-build cmake gcc g++ python3-pip
+      - name: Cache MLIR build
+        id: cache-mlir
+        uses: actions/cache@v3
+        with:
+          path: llvm-project
+          key: ${{ runner.os }}-mlir-${{ hashFiles('utils/clone-mlir.sh', 'utils/build-mlir.sh') }}
+      - name: Clone + build MLIR
+        if: steps.cache-mlir.outputs.cache-hit != 'true'
+        run: |
+          bash utils/clone-mlir.sh
+          bash utils/build-mlir.sh
+      - name: Configure minimal build
+        run: |
+          cmake -S . -B build -G Ninja \
+            -DMLIR_DIR=$GITHUB_WORKSPACE/llvm-project/build/lib/cmake/mlir \
+            -DONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT=ON \
+            -DCMAKE_BUILD_TYPE=Release
+      - name: Build test-onnx-to-mlir
+        run: |
+          cmake --build build --target test-onnx-to-mlir -j2
+      - name: Smoke test
+        run: |
+          echo 'digraph G { }' > /tmp/empty.onnx || true
+          # Use a known small ONNX from torch-mlir tests if present in tree; otherwise skip.
+          if [ -f third_party/onnx/onnx/backend/test/data/node/test_relu/model.onnx ]; then
+            build/Release/bin/test-onnx-to-mlir third_party/onnx/onnx/backend/test/data/node/test_relu/model.onnx | head -n 20
+          else
+            echo 'Minimal build completed.'
+          fi
+

CMakeLists.txt

@@ -12,6 +12,7 @@ option(ONNX_MLIR_ENABLE_WERROR "Enable warnings as errors." OFF)
 option(ONNX_MLIR_SUPPRESS_THIRD_PARTY_WARNINGS "Suppress warning in third_party code." ON)
 option(ONNX_MLIR_ENABLE_JAVA "Set to ON for building the Java runtime, tools, and tests" ON)
 option(ONNX_MLIR_ENABLE_PYRUNTIME_LIGHT "Set to ON for building Python driver of running the compiled model without llvm-project." OFF)
+option(ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT "Build only ONNX dialect without lowering passes and runtime" OFF)
 
 set(CMAKE_CXX_STANDARD 17)
 
@@ -162,7 +163,10 @@ endif()
 set(CMAKE_MESSAGE_LOG_LEVEL NOTICE)
 
 # Add third party subdirectories and define options appropriate to run their cmakes.
-if (ONNX_MLIR_ENABLE_PYRUNTIME_LIGHT)
+if (ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT)
+  # Minimal build: only include ONNX protobuf library.
+  add_subdirectory(third_party/onnx)
+elseif (ONNX_MLIR_ENABLE_PYRUNTIME_LIGHT)
   add_subdirectory(third_party/onnx)
   add_subdirectory(third_party/pybind11)
 else()
@@ -195,13 +199,15 @@ endif()
 # compile flags updated via llvm_update_compile_flags, so we need to do that to
 # benchmark and rapidcheck as well, so that we can successfully link against them.
 # Otherwise, some of the flags for exceptions (among others) are not set correctly.
-llvm_update_compile_flags(benchmark)
-llvm_update_compile_flags(benchmark_main)
-llvm_update_compile_flags(rapidcheck)
+if (NOT ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT)
+  llvm_update_compile_flags(benchmark)
+  llvm_update_compile_flags(benchmark_main)
+  llvm_update_compile_flags(rapidcheck)
 
-if (ONNX_MLIR_ENABLE_STABLEHLO)
-  llvm_update_compile_flags(stablehlo-opt)
-  llvm_update_compile_flags(stablehlo-translate)
+  if (ONNX_MLIR_ENABLE_STABLEHLO)
+    llvm_update_compile_flags(stablehlo-opt)
+    llvm_update_compile_flags(stablehlo-translate)
+  endif()
 endif()
 
 # Increase the log level for onnx-mlir
@@ -216,16 +222,24 @@ if (ONNX_MLIR_SUPPRESS_THIRD_PARTY_WARNINGS)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DSUPPRESS_THIRD_PARTY_WARNINGS")
 endif()
 
-if (ONNX_MLIR_ENABLE_STABLEHLO)
+if (ONNX_MLIR_ENABLE_STABLEHLO AND NOT ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT)
   add_compile_definitions(ONNX_MLIR_ENABLE_STABLEHLO)
 endif()
 
-if (ONNX_MLIR_ENABLE_PYRUNTIME_LIGHT)
+if (ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT)
+  # Minimal build: only include headers and ONNX dialect source.
+  add_subdirectory(include)
+  add_subdirectory(src/Interface)
+  add_subdirectory(src/Support)
+  add_subdirectory(src/Builder)
+  add_subdirectory(src/Dialect/Mlir)
+  add_subdirectory(src/Dialect/ONNX)
+elseif (ONNX_MLIR_ENABLE_PYRUNTIME_LIGHT)
   add_subdirectory(src)
 else()
   add_subdirectory(utils)
   add_subdirectory(include)
   add_subdirectory(src)
   add_subdirectory(docs)
   add_subdirectory(test)
-endif()
+endif()

README.md

@@ -78,6 +78,38 @@ After installation, an `onnx-mlir` executable should appear in the `build/Debug/
 If you have difficulties building, rebuilding, or testing `onnx-mlir`, check this [page](docs/TestingHighLevel.md) for helpful hints.
 
 
+### Minimal build for ONNX dialect only
+
+For consumers that only need the ONNX dialect (without lowering passes, runtime, or non-essential dialects), configure with:
+
+```
+cmake -S . -B build -G Ninja \
+  -DMLIR_DIR=/path/to/llvm-project/build/lib/cmake/mlir \
+  -DONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT=ON \
+  -DCMAKE_BUILD_TYPE=Release
+
+cmake --build build --target test-onnx-to-mlir -j$(nproc)
+```
+
+This builds only the following components:
+- `include/`
+- `src/Interface/` (only interfaces required by ONNX)
+- `src/Support/`
+- `src/Builder/`
+- `src/Dialect/Mlir/`
+- `src/Dialect/ONNX/`
+
+And provides a small test executable `test-onnx-to-mlir` that loads an ONNX model and prints ONNX dialect IR:
+
+```
+build/Release/bin/test-onnx-to-mlir model.onnx > model.mlir
+```
+
+Notes:
+- Release builds are recommended to reduce memory usage during compilation.
+- SpecializedKernel and Krnl dependencies are excluded in this mode.
+
+
 ## Using ONNX-MLIR
 
 The usage of `onnx-mlir` is as such:

docs/MinimalONNXDialectBuild.md

@@ -0,0 +1,48 @@
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+## Minimal ONNX dialect build
+
+This document describes how to build a minimal subset of ONNX-MLIR that exposes only the ONNX dialect and the minimal helpers required to parse ONNX models and produce ONNX dialect MLIR.
+
+### Rationale
+
+Some projects want to import the ONNX dialect IR without bringing the full onnx-mlir stack (lowering passes, Krnl dialect, runtime). The CMake option `ONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT` enables such a build.
+
+### What gets built
+
+When `-DONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT=ON` is set, the following subdirectories are included:
+
+- `include/`
+- `src/Interface/` (ShapeInference, ResultTypeInference, HasOnnxSubgraph, ShapeHelper)
+- `src/Support/`
+- `src/Builder/` (for `ImportFrontendModelFile` and builders)
+- `src/Dialect/Mlir/` (utility builders only; no Krnl or Compiler dependencies)
+- `src/Dialect/ONNX/`
+
+In addition, a small utility `test-onnx-to-mlir` is built to load an ONNX model and print the ONNX dialect IR.
+
+### Configure and build
+
+```
+cmake -S . -B build -G Ninja \
+  -DMLIR_DIR=/path/to/llvm-project/build/lib/cmake/mlir \
+  -DONNX_MLIR_ENABLE_ONLY_ONNX_DIALECT=ON \
+  -DCMAKE_BUILD_TYPE=Release
+
+cmake --build build --target test-onnx-to-mlir -j$(nproc)
+```
+
+Release builds are recommended to lower memory usage during compilation.
+
+### Using the test helper
+
+```
+build/Release/bin/test-onnx-to-mlir /path/to/model.onnx > model.mlir
+```
+
+### Notes
+
+- Krnl and Compiler modules are excluded.
+- SpecializedKernel interface is excluded.
+- The minimal build still requires the upstream LLVM/MLIR toolchain and ONNX protobuf.
+

src/Builder/CMakeLists.txt

@@ -21,4 +21,4 @@ add_onnx_mlir_library(OMBuilder
 configure_file(OpBuildTable.inc.dc.in
   ${CMAKE_CURRENT_BINARY_DIR}/OpBuildTable.inc.dc
   @ONLY
-  )
+  )

src/Dialect/Mlir/CMakeLists.txt

@@ -7,12 +7,7 @@ add_onnx_mlir_library(OMMlirDialects
   DialectBuilder.cpp
   VectorMachineSupport.cpp
 
-  DEPENDS
-  OMKrnlIncGen
-  OMSpecializedKernelOpInterface
-
   LINK_LIBS PUBLIC
-  OMCompilerOptions
   MLIRMathDialect
   MLIRAffineDialect
   MLIRSCFDialect

src/Dialect/Mlir/DialectBuilder.cpp

@@ -25,7 +25,6 @@
 #include "llvm/Support/Debug.h"
 
 // Please do not add dependences on ONNX or KRNL dialects.
-#include "src/Compiler/CompilerOptions.hpp"
 #include "src/Dialect/Mlir/DialectBuilder.hpp"
 #include "src/Dialect/Mlir/VectorMachineSupport.hpp"
 
@@ -1757,16 +1756,12 @@ Value MemRefBuilder::dim(Value val, Value index) const {
 
 void MemRefBuilder::prefetch(Value memref, ValueRange indices, bool isWrite,
     unsigned locality, bool isData) {
-  if (disableMemRefPrefetch)
-    return;
   b().create<memref::PrefetchOp>(
       loc(), memref, indices, isWrite, locality, isData);
 }
 
 void MemRefBuilder::prefetchIE(Value memref, ArrayRef<IndexExpr> indices,
     bool isWrite, unsigned locality, bool isData) {
-  if (disableMemRefPrefetch)
-    return;
   SmallVector<Value, 4> indexVals;
   IndexExpr::getValues(indices, indexVals);
   prefetch(memref, indexVals, isWrite, locality, isData);

src/Dialect/Mlir/VectorMachineSupport.cpp

@@ -9,7 +9,6 @@
 // =============================================================================
 
 #include "src/Dialect/Mlir/VectorMachineSupport.hpp"
-#include "src/Compiler/CompilerOptions.hpp"
 
 #include "mlir/IR/BuiltinTypes.h"
 #include "llvm/Support/Debug.h"
@@ -22,6 +21,29 @@ using namespace mlir;
 
 namespace onnx_mlir {
 
+// Minimal version of getZArchNum to avoid dependency on Compiler module
+namespace {
+int64_t decodeZArchNum(std::string str) {
+  if (str == "arch12" || str == "z14") // Z14 and equivalents.
+    return 12;
+  if (str == "arch13" || str == "z15") // Z15 and equivalents.
+    return 13;
+  if (str == "arch14" || str == "z16") // Z16 and equivalents.
+    return 14;
+  if (str == "arch15" || str == "z17") // Z17 and equivalents.
+    return 15;
+  return -1;
+}
+
+int64_t getZArchNumLocal(const std::string &arch, const std::string cpu) {
+  // Give priority to march, use (deprecated) mcpu if march is not defined.
+  int64_t num = decodeZArchNum(arch);
+  if (num == -1)
+    num = decodeZArchNum(cpu);
+  return num;
+}
+} // anonymous namespace
+
 // =============================================================================
 // Handling of global vector machine support pointer
 
@@ -31,7 +53,7 @@ namespace onnx_mlir {
 /*static*/ void VectorMachineSupport::setGlobalVectorMachineSupport(
     const std::string &arch, const std::string &cpu, const std::string &attr) {
   // IBM Z servers use march (deprecated mcpu), process here.
-  int64_t zArchNum = getZArchNum(arch, cpu);
+  int64_t zArchNum = getZArchNumLocal(arch, cpu);
   if (zArchNum == 12) {
     globalVectorMachineSupport = new ZArch12VectorMachineSupport();
   } else if (zArchNum == 13) {

src/Dialect/ONNX/CMakeLists.txt

@@ -124,7 +124,22 @@ add_onnx_mlir_library(OMONNXOps
   MLIRMemRefTransforms
   )
 
+# Add the generated directory to the include path so interface headers can find the generated files
+target_include_directories(OMONNXOps PRIVATE ${GEN_DIR})
+
 configure_file(ONNXOps.td.inc.dc.in
   ${CMAKE_CURRENT_BINARY_DIR}/ONNXOps.td.inc.dc
   @ONLY
   )
+
+# Add test executable
+add_onnx_mlir_executable(test-onnx-to-mlir
+  test-onnx-to-mlir.cpp
+
+  LINK_LIBS PRIVATE
+  OMONNXOps
+  OMBuilder
+  MLIRFuncDialect
+  MLIRSupport
+  LLVMSupport
+  )

src/Dialect/ONNX/test-onnx-to-mlir.cpp

@@ -0,0 +1,47 @@
+// test-onnx-to-mlir.cpp
+
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/BuiltinOps.h"
+#include "mlir/IR/DialectRegistry.h"
+#include "mlir/IR/MLIRContext.h"
+
+#include "src/Builder/FrontendDialectTransformer.hpp"
+#include "src/Dialect/ONNX/ONNXDialect.hpp"
+#include "llvm/Support/InitLLVM.h"
+#include "llvm/Support/raw_ostream.h"
+
+int main(int argc, char **argv) {
+  llvm::InitLLVM y(argc, argv);
+
+  if (argc != 2) {
+    llvm::errs() << "Usage: test-onnx-to-mlir <input.onnx>\n";
+    return 1;
+  }
+
+  mlir::MLIRContext context;
+
+  // Register the ONNX dialect.
+  mlir::DialectRegistry registry;
+  registry.insert<mlir::func::FuncDialect>();
+  registry.insert<mlir::ONNXDialect>();
+  context.appendDialectRegistry(registry);
+  context.loadAllAvailableDialects();
+
+  // Import the ONNX model.
+  mlir::OwningOpRef<mlir::ModuleOp> module;
+  std::string errorMessage;
+  onnx_mlir::ImportOptions options;
+
+  int result = onnx_mlir::ImportFrontendModelFile(
+      argv[1], context, module, &errorMessage, options);
+
+  if (result != 0) {
+    llvm::errs() << "Failed to import model: " << errorMessage << "\n";
+    return 1;
+  }
+
+  llvm::outs() << "Successfully imported ONNX model!\n";
+  module->print(llvm::outs());
+
+  return 0;
+}