support Gather indices of type int32

Signed-off-by: Soren Lassen <[email protected]>
onnx · Sep 8, 2023 · 1a0f9fa · 1a0f9fa
1 parent e3a8a67
commit 1a0f9fa
Show file tree

Hide file tree

Showing 2 changed files with 25 additions and 4 deletions.
diff --git a/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.cpp b/src/Dialect/ONNX/ElementsAttr/ElementsAttrBuilder.cpp
@@ -629,21 +629,27 @@ ElementsAttr ElementsAttrBuilder::gather(
   ArrayRef<int64_t> inputShape = inputType.getShape();
   assert(axis < inputShape.size() && "gather axis out of range");
   auto postAxisShape = inputShape.drop_front(axis + 1);
-  ArrayRef<int64_t> indicesShape = indices.getShapedType().getShape();
+  ShapedType indicesType = indices.getShapedType();
+  assert(indicesType.getElementType().isSignlessInteger() &&
+         "gather indices must be i32 or i64");
+  ArrayRef<int64_t> indicesShape = indicesType.getShape();
   SmallVector<int64_t> outShape(inputShape.take_front(axis));
   outShape.append(indicesShape.begin(), indicesShape.end());
   outShape.append(postAxisShape.begin(), postAxisShape.end());
   auto outType = inputType.clone(outShape);
   return fromWideNums(outType, [&](MutableArrayRef<WideNum> dst) {
     size_t postAxisNumElements = ShapedType::getNumElements(postAxisShape);
     ArrayBuffer<WideNum> src = getElementsWideNums(input);
-    ArrayBuffer<int64_t> indicesArray = getElementsArray<int64_t>(indices);
+    //
+    ArrayBuffer<WideNum> indicesWideNums = getElementsWideNums(indices);
+    ArrayRef<int64_t> indicesArray =
+        castArrayRef<int64_t, WideNum>(indicesWideNums.get());
     size_t axisInputSize = inputShape[axis];
     size_t inputBlockLen = axisInputSize * postAxisNumElements;
-    size_t outBlockLen = indicesArray.get().size() * postAxisNumElements;
+    size_t outBlockLen = indicesArray.size() * postAxisNumElements;
     size_t start = 0;
     WideNum *out = dst.begin();
-    for (int64_t idx : indicesArray.get()) {
+    for (int64_t idx : indicesArray) {
       int64_t adjustedIdx = idx < 0 ? idx + axisInputSize : idx;
       const WideNum *in = src.get().begin() + adjustedIdx * postAxisNumElements;
       for (size_t offset = start; offset < dst.size(); offset += outBlockLen) {

diff --git a/test/mlir/onnx/onnx_constprop.mlir b/test/mlir/onnx/onnx_constprop.mlir
@@ -1663,6 +1663,21 @@ func.func @test_gather_negative_index() -> tensor<*xf32>{
 
 // -----
 
+func.func @test_gather_rank0_int32_indices() -> tensor<*xf32>{
+  %0 = onnx.Constant dense<[[1.0, 1.2], [2.3, 3.4], [4.5, 5.7]]> : tensor<3x2xf32>
+  %1 = onnx.Constant dense<1> : tensor<i32>
+  %2 = "onnx.Gather"(%0, %1) {axis = 0 : si64} : (tensor<3x2xf32>, tensor<i32>) -> tensor<*xf32>
+  "onnx.Return"(%2) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL:  func @test_gather_rank0_int32_indices
+  // CHECK-SAME:   () -> tensor<2xf32> {
+  // CHECK:           [[VAR_0_:%.+]] = onnx.Constant dense<[2.300000e+00, 3.400000e+00]> : tensor<2xf32>
+  // CHECK:           onnx.Return [[VAR_0_]] : tensor<2xf32>
+  // CHECK:         }
+}
+
+// -----
+
 func.func @test_reshape() -> tensor<*xf32> {
   %0 = onnx.Constant dense<[[1.0, 1.2, 1.9], [2.3, 3.4, 3.9], [4.5, 5.7, 5.9]]> : tensor<3x3xf32>
   %1 = onnx.Constant dense<[1, -1]> : tensor<2xi64>