[MLIR][CAPI][python] expose the python binding for linalgOp.getIndexingMaps

mlir/include/mlir-c/Dialect/Linalg.h

@@ -50,6 +50,9 @@ typedef struct MlirLinalgConvolutionDimensions {
 MLIR_CAPI_EXPORTED MlirLinalgConvolutionDimensions
 mlirLinalgInferConvolutionDimensions(MlirOperation op);
 
+MLIR_CAPI_EXPORTED MlirAttribute
+mlirLinalgGetIndexingMapsAttribute(MlirOperation op);
+
 MLIR_DECLARE_CAPI_DIALECT_REGISTRATION(Linalg, linalg);
 
 #ifdef __cplusplus

mlir/lib/Bindings/Python/DialectLinalg.cpp

@@ -120,6 +120,16 @@ static void populateDialectLinalgSubmodule(nb::module_ m) {
 
   m.def("infer_convolution_dimensions", &InferConvolutionDimensions,
         "Infers convolution dimensions", nb::arg("op"));
+
+  m.def(
+      "get_indexing_maps",
+      [](MlirOperation op) -> std::optional<MlirAttribute> {
+        MlirAttribute attr = mlirLinalgGetIndexingMapsAttribute(op);
+        if (mlirAttributeIsNull(attr))
+          return std::nullopt;
+        return attr;
+      },
+      "Returns the indexing_maps attribute for a linalg op.");
 }
 
 NB_MODULE(_mlirDialectsLinalg, m) {

mlir/lib/CAPI/Dialect/Linalg.cpp

@@ -120,4 +120,14 @@ mlirLinalgInferConvolutionDimensions(MlirOperation op) {
   return result;
 }
 
+MLIR_CAPI_EXPORTED MlirAttribute
+mlirLinalgGetIndexingMapsAttribute(MlirOperation op) {
+  auto linalgOp = llvm::dyn_cast<mlir::linalg::LinalgOp>(unwrap(op));
+  if (!linalgOp)
+    return MlirAttribute{nullptr};
+
+  ArrayAttr attr = linalgOp.getIndexingMaps();
+  return wrap(attr);
+}
+
 MLIR_DEFINE_CAPI_DIALECT_REGISTRATION(Linalg, linalg, LinalgDialect)

mlir/test/python/dialects/linalg/utils.py

@@ -159,3 +159,52 @@ def dyn_conv_fn(input, filter, output):
                 assert list(dims.depth) == []
                 assert list(dims.strides) == [1, 1]
                 assert list(dims.dilations) == [1, 1]
+
+
+@run
+def test_get_indexing_maps_attr():
+    with Context(), Location.unknown():
+        module = Module.create()
+        f32 = F32Type.get()
+        with InsertionPoint(module.body):
+            a_type = RankedTensorType.get((4, 8), f32)
+            b_type = RankedTensorType.get((8, 16), f32)
+            c_type = RankedTensorType.get((4, 16), f32)
+
+            dim_m = AffineDimExpr.get(0)
+            dim_n = AffineDimExpr.get(1)
+            dim_k = AffineDimExpr.get(2)
+
+            a_map = AffineMap.get(3, 0, [dim_m, dim_k])
+            b_map = AffineMap.get(3, 0, [dim_k, dim_n])
+            c_map = AffineMap.get(3, 0, [dim_m, dim_n])
+
+            @func.FuncOp.from_py_func(a_type, b_type, c_type)
+            def matmul_func(a, b, c):
+                zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.0), result=f32)
+                assert not linalg.get_indexing_maps(
+                    zero.operation
+                ), "Expected no indexing_maps on non-linalg op"
+
+                init = linalg.fill(zero, outs=[c])
+                fill_op = init.owner
+                fill_maps = linalg.get_indexing_maps(fill_op)
+                assert fill_maps is not None
+                assert len(fill_maps) == 2
+
+                # The fill op should have maps like (d0, d1) -> () and (d0, d1).
+                fill_input_map = fill_maps[0].value
+                fill_output_map = fill_maps[1].value
+                assert fill_input_map == AffineMap.get(2, 0, [])
+                assert fill_output_map == AffineMap.get(2, 0, [dim_m, dim_n])
+
+                result = linalg.matmul(a, b, outs=(init,))
+                matmul_op = result.owner
+                matmul_maps = linalg.get_indexing_maps(matmul_op)
+                assert matmul_maps is not None
+                assert len(matmul_maps) == 3
+
+                maps = [map_attr.value for map_attr in matmul_maps]
+                assert maps[0] == a_map
+                assert maps[1] == b_map
+                assert maps[2] == c_map