[mlir][linalg][python] Add Python Bindings for Inferring Contraction Dimensions from Affine Maps (#167587)

This PR exposes `linalg::inferContractionDims(ArrayRef<AffineMap>)` to
Python, allowing users to infer contraction dimensions (batch/m/n/k)
directly from a list of affine maps without needing an operation.

---------

Signed-off-by: Bangtian Liu <[email protected]>

Author: bangtianliu

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

@@ -10,6 +10,7 @@
 #ifndef MLIR_C_DIALECT_LINALG_H
 #define MLIR_C_DIALECT_LINALG_H
 
+#include "mlir-c/AffineMap.h"
 #include "mlir-c/IR.h"
 #include "mlir-c/Support.h"
 
@@ -34,6 +35,10 @@ typedef struct MlirLinalgContractionDimensions {
 MLIR_CAPI_EXPORTED MlirLinalgContractionDimensions
 mlirLinalgInferContractionDimensions(MlirOperation op);
 
+MLIR_CAPI_EXPORTED MlirLinalgContractionDimensions
+mlirLinalgInferContractionDimensionsFromMaps(const MlirAffineMap *indexingMaps,
+                                             size_t numMaps);
+
 MLIR_CAPI_EXPORTED bool mlirLinalgIsAConvolutionOp(MlirOperation op);
 
 typedef struct MlirLinalgConvolutionDimensions {

mlir/lib/Bindings/Python/DialectLinalg.cpp

@@ -80,6 +80,28 @@ static void populateDialectLinalgSubmodule(nb::module_ m) {
         "op.",
         nb::arg("op"));
 
+  m.def(
+      "infer_contraction_dimensions_from_maps",
+      [](std::vector<MlirAffineMap> indexingMaps)
+          -> std::optional<MlirLinalgContractionDimensions> {
+        if (indexingMaps.empty())
+          return std::nullopt;
+
+        MlirLinalgContractionDimensions dims =
+            mlirLinalgInferContractionDimensionsFromMaps(indexingMaps.data(),
+                                                         indexingMaps.size());
+
+        // Detect "empty" result from invalid input or failed inference.
+        if (mlirAttributeIsNull(dims.batch) && mlirAttributeIsNull(dims.m) &&
+            mlirAttributeIsNull(dims.n) && mlirAttributeIsNull(dims.k)) {
+          return std::nullopt;
+        }
+        return dims;
+      },
+      "Infers contraction dimensions (batch/m/n/k) from a list of affine "
+      "maps.",
+      nb::arg("indexing_maps"));
+
   m.def("isa_convolution_op", &mlirLinalgIsAConvolutionOp,
         "Checks if the given operation is a Linalg convolution operation.",
         nb::arg("op"));

mlir/lib/CAPI/Dialect/Linalg.cpp

@@ -7,6 +7,7 @@
 //===----------------------------------------------------------------------===//
 
 #include "mlir-c/Dialect/Linalg.h"
+#include "mlir/CAPI/AffineMap.h"
 #include "mlir/CAPI/Registration.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 
@@ -62,9 +63,8 @@ mlirLinalgInferContractionDimensions(MlirOperation op) {
   const linalg::ContractionDimensions &contractionDims = *maybeDims;
   MLIRContext *ctx = linalgOp.getContext();
 
-  auto toAttr = [&ctx](const SmallVector<unsigned, 2> &vals) -> MlirAttribute {
-    return wrap(
-        DenseI32ArrayAttr::get(ctx, llvm::to_vector_of<int32_t, 2>(vals)));
+  auto toAttr = [ctx](ArrayRef<unsigned> vals) -> MlirAttribute {
+    return wrap(DenseI32ArrayAttr::get(ctx, llvm::to_vector_of<int32_t>(vals)));
   };
 
   result.batch = toAttr(contractionDims.batch);
@@ -75,6 +75,38 @@ mlirLinalgInferContractionDimensions(MlirOperation op) {
   return result;
 }
 
+MLIR_CAPI_EXPORTED MlirLinalgContractionDimensions
+mlirLinalgInferContractionDimensionsFromMaps(const MlirAffineMap *indexingMaps,
+                                             size_t numMaps) {
+  MlirLinalgContractionDimensions result{};
+  if (!indexingMaps || numMaps == 0)
+    return result;
+
+  SmallVector<AffineMap, 3> maps;
+  maps.reserve(numMaps);
+  for (size_t i = 0; i < numMaps; ++i) {
+    maps.push_back(unwrap(indexingMaps[i]));
+  }
+
+  FailureOr<linalg::ContractionDimensions> maybeDims =
+      linalg::inferContractionDims(maps);
+  if (failed(maybeDims))
+    return result;
+
+  MLIRContext *ctx = maps[0].getContext();
+
+  auto toAttr = [ctx](ArrayRef<unsigned> vals) -> MlirAttribute {
+    return wrap(DenseI32ArrayAttr::get(ctx, llvm::to_vector_of<int32_t>(vals)));
+  };
+
+  result.batch = toAttr(maybeDims->batch);
+  result.m = toAttr(maybeDims->m);
+  result.n = toAttr(maybeDims->n);
+  result.k = toAttr(maybeDims->k);
+
+  return result;
+}
+
 MLIR_CAPI_EXPORTED bool mlirLinalgIsAConvolutionOp(MlirOperation op) {
   auto linalgOp = llvm::dyn_cast<mlir::linalg::LinalgOp>(unwrap(op));
   if (!linalgOp)

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

@@ -208,3 +208,43 @@ def matmul_func(a, b, c):
                 assert maps[0] == a_map
                 assert maps[1] == b_map
                 assert maps[2] == c_map
+
+
+@run
+def test_infer_contraction_dimensions_from_maps():
+    with Context(), Location.unknown():
+        module = Module.create()
+        with InsertionPoint(module.body):
+            # === Test valid contraction (matmul) ===
+            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])
+
+            dims = linalg.infer_contraction_dimensions_from_maps([a_map, b_map, c_map])
+            assert dims is not None
+
+            # Expect m=[0], n=[1], k=[2] as per standard matmul.
+            assert list(dims.m) == [0], f"Expected m=[0], got {list(dims.m)}"
+            assert list(dims.n) == [1], f"Expected n=[1], got {list(dims.n)}"
+            assert list(dims.k) == [2], f"Expected k=[2], got {list(dims.k)}"
+            assert list(dims.batch) == [], f"Expected batch=[], got {list(dims.batch)}"
+
+            # === Test invalid input (wrong number of maps) ===
+            invalid_dims = linalg.infer_contraction_dimensions_from_maps([a_map, b_map])
+            assert invalid_dims is None
+
+            # === Test element-wise operation ===
+            dim_i = AffineDimExpr.get(0)
+            dim_j = AffineDimExpr.get(1)
+            elementwise_map = AffineMap.get(2, 0, [dim_i, dim_j])
+            elementwise_dims = linalg.infer_contraction_dimensions_from_maps(
+                [elementwise_map, elementwise_map, elementwise_map]
+            )
+            assert elementwise_dims is not None
+            assert len(elementwise_dims.m) == 0
+            assert len(elementwise_dims.n) == 0
+            assert len(elementwise_dims.k) == 0
+            assert list(elementwise_dims.batch) == [0, 1]