[Mlir-commits] [mlir] [mlir][tensor] Fix bug in `ConcatOpInterface` (PR #168676)

[Matthias Springer]

================
@@ -721,6 +718,35 @@ func.func @tensor.concat_dynamic_nonconcat_dim(%f: tensor<?x?xf32>, %g: tensor<?
 
 // -----
 
+// CHECK:  #[[$sum_map:.+]] = affine_map<()[s0, s1] -> (s0 + s1)>
+
+// CHECK-LABEL:   func @tensor.concat_mixed_dynamic_static(
+// CHECK-SAME:        %[[F:.*]]: tensor<8x?xf32>, %[[G:.*]]: tensor<8x?xf32>,
+// CHECK-SAME:        %[[H:.*]]: tensor<8x2xf32>)
+// CHECK-DAG:       %[[F_MEMREF:.*]] = bufferization.to_buffer %[[F]]
+// CHECK-DAG:       %[[G_MEMREF:.*]] = bufferization.to_buffer %[[G]]
+// CHECK-DAG:       %[[H_MEMREF:.*]] = bufferization.to_buffer %[[H]]
+// CHECK-DAG:       %[[ALLOC:.*]] = memref.alloc() {alignment = 64 : i64} : memref<8x10xf32>
+// CHECK-DAG:       %[[c1:.*]] = arith.constant 1 : index
+// CHECK:           %[[F_DIM:.*]] = memref.dim %[[F_MEMREF]], %[[c1]]
+// CHECK:           %[[SUBVIEW1:.*]] = memref.subview %[[ALLOC]][0, 0] [8, %[[F_DIM]]] [1, 1]
+// CHECK:           memref.copy %[[F_MEMREF]], %[[SUBVIEW1]]
+// CHECK:           %[[G_DIM:.*]] = memref.dim %[[G_MEMREF]], %[[c1]]
+// CHECK:           %[[SUBVIEW2:.*]] = memref.subview %[[ALLOC]][0, %[[F_DIM]]] [8, %[[G_DIM]]] [1, 1]
+// CHECK:           memref.copy %[[G_MEMREF]], %[[SUBVIEW2]]
+// CHECK:           %[[OFFSET:.*]] = affine.apply #[[$sum_map]]()[%[[F_DIM]], %[[G_DIM]]]
+// CHECK:           %[[SUBVIEW3:.*]] = memref.subview %[[ALLOC]][0, %[[OFFSET]]] [8, 2] [1, 1]
+// CHECK:           memref.copy %[[H_MEMREF]], %[[SUBVIEW3]]
+// CHECK:           %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]]
+// CHECK:           return %[[RET]]
+// CHECK:         }
+func.func @tensor.concat_mixed_dynamic_static(%f: tensor<8x?xf32>, %g: tensor<8x?xf32>, %h: tensor<8x2xf32>) -> tensor<8x10xf32> {
+  %0 = tensor.concat dim(1) %f, %g, %h : (tensor<8x?xf32>, tensor<8x?xf32>, tensor<8x2xf32>) -> tensor<8x10xf32>
----------------
matthias-springer wrote:

Is there a problem with an explicit `tensor.cast`?

It would be nice to have a consistent op design across the tensor dialect. I believe one reason why we chose `input dynamicity == output dynamicity` for `collapse_shape`/`expand_shape` is that we can print better error messages: if there's only one allowable output type, you can print it during verification errors.


https://github.com/llvm/llvm-project/pull/168676