Fix the MHLO to LMHLO lowering of 'gather'

The lowering assumes that the 'gather' op attributes are identical in both MHLO and LMHLO. But that's not true; some time ago the MHLO version was changed to pack 4 of its attributes into a struct. By doing the same for the LMHLO version we both fix the lowering for this op and resolve a longstanding TODO.

PiperOrigin-RevId: 337943946

include/mlir-hlo/Dialect/mhlo/IR/lhlo_ops.td

@@ -602,11 +602,8 @@ def LHLO_GatherOp: LHLO_Op<"gather", []>, BASE_HLO_GatherOp {
   let arguments = (ins
     Arg<LHLO_Buffer, "", [MemRead]>:$operand,
     Arg<LHLO_IntBuffer, "", [MemRead]>:$start_indices,
-    I64Attr:$index_vector_dim,
-    I64ElementsAttr:$offset_dims,
+    GatherDimensionNumbers:$dimension_numbers,
     I64ElementsAttr:$slice_sizes,
-    I64ElementsAttr:$collapsed_slice_dims,
-    I64ElementsAttr:$start_index_map,
     Arg<LHLO_Buffer, "", [MemWrite]>:$output
   );
 }

tests/hlo-legalize-to-lhlo.mlir

@@ -287,6 +287,28 @@ func @imag(%operand: memref<2x2xcomplex<f32>>, %result: memref<2x2xf32>) {
 
 // -----
 
+// BOTH-LABEL: func @gather
+func @gather(%operand: memref<13x7xf32>, %idxs: memref<5xi32>, %result: memref<5x7xf32>) {
+  %tensor_operand = tensor_load %operand : memref<13x7xf32>
+  %tensor_idxs = tensor_load %idxs : memref<5xi32>
+  %tensor_result =
+    "mhlo.gather"(%tensor_operand, %tensor_idxs)
+      { dimension_numbers =
+        { collapsed_slice_dims = dense<0> : tensor<1xi64>
+        , index_vector_dim = 1 : i64
+        , offset_dims = dense<1> : tensor<1xi64>
+        , start_index_map = dense<0> : tensor<1xi64> }
+      , indices_are_sorted = false
+      , name = "gather.71"
+      , slice_sizes = dense<[1, 7]> : tensor<2xi64> }
+      : (tensor<13x7xf32>, tensor<5xi32>) -> tensor<5x7xf32>
+  // BOTH: "lmhlo.gather"(%{{.*}}, %{{.*}}, %{{.*}})
+  tensor_store %tensor_result, %result : memref<5x7xf32>
+  return
+}
+
+// -----
+
 // BOTH-LABEL: func @imag_dyn
 func @imag_dyn(%operand: memref<?xcomplex<f32>>, %result: memref<?xf32>) {
   %tensor_operand = tensor_load %operand : memref<?xcomplex<f32>>