[Mlir-commits] [mlir] feat(linalg): add a way to pass controlFn to `foldIntoPackUnpackPatterns` (PR #143685)

[llvmlistbot at llvm.org]

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@@ -550,6 +596,55 @@ func.func @linalg_transpose_linalg.unpack_fold(%arg0: tensor<1x1x4x16xi32>) -> t
 
 // -----
 
+func.func @linalg_transpose_linalg.unpack_fold_multi_result(%arg0: tensor<1x1x4x16xi32>) -> (tensor<1x1x16x4xi32>, tensor<16x4xi32>) {
+  %0 = tensor.empty() : tensor<1x1x16x4xi32>
+  %transposed = linalg.transpose ins(%arg0 : tensor<1x1x4x16xi32>)
+                outs(%0 : tensor<1x1x16x4xi32>)
+                permutation = [1, 0, 3, 2]
+  %1 = tensor.empty() : tensor<16x4xi32>
+  %unpack = linalg.unpack %transposed
+            outer_dims_perm = [0, 1]
+            inner_dims_pos = [0, 1]
+            inner_tiles = [16, 4] into
+            %1 : tensor<1x1x16x4xi32> -> tensor<16x4xi32>
+  return %transposed, %unpack : tensor<1x1x16x4xi32>, tensor<16x4xi32>
+}
+//CHECK-LABEL:  func.func @linalg_transpose_linalg.unpack_fold_multi_result(
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Max191 wrote:

These tests can be less verbose in the checks. The main thing this is testing is whether or not the fusion happened with mutli-result, so you don't need to check all the shapes/op metadata. You could just check the operands and results of the operations to verify the producers/consumers.

https://github.com/llvm/llvm-project/pull/143685