[mlir][tosa] Switch arith::ConstantOp to tosa::ConstOp for optimized Transpose perms parameter (#124945)

When consolidating transpose ops into one, use `tosa::ConstOp` for the
permutations parameter instead of `arith::ConstantOp`.

Author: Tessil

mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp

@@ -111,8 +111,8 @@ struct ConsolidateTransposeOptimization
     auto permsTy =
         RankedTensorType::get(transposePerms.size(), rewriter.getI32Type());
     auto permsAttr = DenseIntElementsAttr::get(permsTy, perms);
-    Value permsValue =
-        rewriter.create<arith::ConstantOp>(transposeOp.getLoc(), permsAttr);
+    Value permsValue = rewriter.create<tosa::ConstOp>(transposeOp.getLoc(),
+                                                      permsTy, permsAttr);
 
     rewriter.replaceOpWithNewOp<tosa::TransposeOp>(
         transposeOp, transposeOp.getResult().getType(),

mlir/test/Dialect/Tosa/transpose-fold.mlir

@@ -6,9 +6,9 @@
 // CHECK:         }
 
 func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) {
-	%0 = arith.constant dense<[1, 2, 0]> : tensor<3xi32>
+	%0 = "tosa.const"() {value = dense<[1, 2, 0]> : tensor<3xi32>} : () -> tensor<3xi32>
 	%1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>) -> tensor<2x3x1xi32>
-	%2 = arith.constant dense<[2, 0, 1]> : tensor<3xi32>
+	%2 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
 	%3 = tosa.transpose %1, %2 : (tensor<2x3x1xi32>, tensor<3xi32>) -> tensor<1x2x3xi32>
   return %3 : tensor<1x2x3xi32>
 }
@@ -21,7 +21,7 @@ func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<
 // CHECK:         }
 
 func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) {
-	%0 = arith.constant dense<[0, 1, 2]> : tensor<3xi32>
+	%0 = "tosa.const"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
 	%1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>) -> tensor<1x2x3xi32>
   return %1 : tensor<1x2x3xi32>
 }
@@ -30,15 +30,15 @@ func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1
 
 // CHECK-LABEL:   func.func @test_do_not_cancel_different_transpose(
 // CHECK-SAME:                                                      %[[VAL_0:.*]]: tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32> {
-// CHECK:           %[[VAL_1:.*]] = arith.constant dense<[3, 2, 1, 0]> : tensor<4xi32>
+// CHECK:           %[[VAL_1:.*]] = "tosa.const"() <{value = dense<[3, 2, 1, 0]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_1]] : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<5x4x3x2xi32>
 // CHECK:           return %[[VAL_2]] : tensor<5x4x3x2xi32>
 // CHECK:         }
 
 func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) -> (tensor<5x4x3x2xi32>) {
-	%0 = arith.constant dense<[1, 2, 0, 3]> : tensor<4xi32>
+	%0 = "tosa.const"() {value = dense<[1, 2, 0, 3]> : tensor<4xi32>} : () -> tensor<4xi32>
 	%1 = tosa.transpose %arg0, %0 : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<3x4x2x5xi32>
-	%2 = arith.constant dense<[3, 1, 0, 2]> : tensor<4xi32>
+	%2 = "tosa.const"() {value = dense<[3, 1, 0, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
 	%3 = tosa.transpose %1, %2 : (tensor<3x4x2x5xi32>, tensor<4xi32>) -> tensor<5x4x3x2xi32>
   return %3 : tensor<5x4x3x2xi32>
 }
@@ -47,15 +47,15 @@ func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) ->
 
 // CHECK-LABEL:   func.func @test_prefer_compose_transpose(
 // CHECK-SAME:                                                      %[[VAL_0:.*]]: tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32> {
-// CHECK:           %[[VAL_1:.*]] = arith.constant dense<[3, 2, 1, 0]> : tensor<4xi32>
+// CHECK:           %[[VAL_1:.*]] = "tosa.const"() <{value = dense<[3, 2, 1, 0]> : tensor<4xi32>}> : () -> tensor<4xi32>
 // CHECK:           %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_1]] : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<4x3x2x1xi32>
 // CHECK:           return %[[VAL_2]] : tensor<4x3x2x1xi32>
 // CHECK:         }
 
 func.func @test_prefer_compose_transpose(%arg0: tensor<1x2x3x4xi32>) -> (tensor<4x3x2x1xi32>) {
-	%0 = arith.constant dense<[1, 2, 0, 3]> : tensor<4xi32>
+	%0 = "tosa.const"() {value = dense<[1, 2, 0, 3]> : tensor<4xi32>} : () -> tensor<4xi32>
 	%1 = tosa.transpose %arg0, %0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<2x3x1x4xi32>
-	%2 = arith.constant dense<[3, 1, 0, 2]> : tensor<4xi32>
+	%2 = "tosa.const"() {value = dense<[3, 1, 0, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
 	%3 = tosa.transpose %1, %2 : (tensor<2x3x1x4xi32>, tensor<4xi32>) -> tensor<4x3x2x1xi32>
   return %3 : tensor<4x3x2x1xi32>
 }