fix example code and additional documentation.

Author: linuxlonelyeagle

mlir/docs/Tutorials/transform/Ch2.md

@@ -133,7 +133,7 @@ This will generate two files, `MyExtension.h.inc` and `MyExtension.cpp.inc`, tha
 ```c++
 // In MyExtension.cpp.
 
-#include "MyExtension.h."
+#include "MyExtension.h"
 
 #define GET_OP_CLASSES
 #include "MyExtension.cpp.inc"
@@ -247,8 +247,7 @@ must be modified with the provided rewriter.
       return diag;
     }
 
-    // Use rewriter to modify the callee in place.
-    rewriter.modifyOpInPlace(call, [&]() { call.setCallee(getNewTarget()); });
+    updateCallee(call, getNewTarget());
   }
 
   // If everything went well, return success.
@@ -266,7 +265,7 @@ void ChangeCallTargetOp::getEffects(
   // Indicate that the `call` handle is only read by this operation because the
   // associated operation is not erased but rather modified in-place, so the
   // reference to it remains valid.
-  onlyReadsHandle(this->getOperation()->getOpOperands().front(), effects);
+  onlyReadsHandle(getCall(), effects);
 
   // Indicate that the payload is modified by this operation.
   modifiesPayload(effects);
@@ -286,20 +285,20 @@ void registerMyExtension(::mlir::DialectRegistry &registry) {
 }
 ```
 
-After registering the extension, it becomes possible to use our new operation in the Transform dialect interpreter. The upstream testing pass can be used as is.
+After registering the extension, it becomes possible to use our new operation in the Transform dialect interpreter. The upstream testing pass can be used as is. It actually exists in `mlir/test/Examples/transform/Ch2/sequence.mlir`, which contains the `microkernel` implementation. 
 
 ```mlir
 module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(
-       %arg0: !transform.any_op,
-       %arg1: !transform.op<"linalg.matmul">,
-       %arg2: !transform.op<"linalg.elementwise">) {
+      %arg0: !transform.any_op,
+      %arg1: !transform.op<"linalg.matmul">,
+      %arg2: !transform.op<"linalg.elementwise">) {
     // Since the %arg2 handle is associated with both elementwise operations,
     // we need to split it into two handles so we can target only the second
     // elementwise operation.
     %add, %max = transform.split_handle %arg2
         : (!transform.op<"linalg.elementwise">)
-          -> (!transform.any_op, !transform.any_op)
+        -> (!transform.any_op, !transform.any_op)
 
     // The actual tiling transformation takes tile sizes as attributes. It
     // produces a handle to the loop generated during tiling.
@@ -308,63 +307,50 @@ module attributes {transform.with_named_sequence} {
         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     // We can now fuse the other operations into the loop. Here, we fuse
-    // operations one by one. This requires the operation that is being fused to
-    // define the value used within the loop, so the order of such fusions is
-    // important. We could also use "transform.merge_handles" to obtain a single
-    // handle to all operations and give it to `fuse_into_containing_op` that
-    // would take care of the ordering in this case.
-    %add_fused, %loop_0 =
-        transform.structured.fuse_into_containing_op %add into %loop
-          : (!transform.any_op, !transform.any_op)
-            -> (!transform.any_op, !transform.any_op)
-    %matmul_fused, %loop_1 =
-        transform.structured.fuse_into_containing_op %arg1 into %loop_0
-          : (!transform.op<"linalg.matmul">, !transform.any_op)
-            -> (!transform.any_op, !transform.any_op)
+    // operations one-by-one. This requires the operation that is being fused
+    // to define the value used within the loop, so the order of such fusions
+    // is important. We could also use "transform.merge_handles" to obtain
+    // a single handle to all operations and give it to
+    // `fuse_into_containing_op` that would take care of the ordering in this
+    // case.
+    %add_fused, %loop2 = transform.structured.fuse_into_containing_op %add into %loop
+        : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
+    %matmul_fused, %loop3 = transform.structured.fuse_into_containing_op %arg1
+                    into %loop2
+        : (!transform.op<"linalg.matmul">, !transform.any_op)
+       -> (!transform.any_op, !transform.any_op)
 
     // Tile again to get the desired size. Note that this time this tiles the
     // "add" operation and fuses matmul into the loop, but doesn't affect the
     // "max" operation. This illustrates the precise targeting with the
     // transform dialect. Otherwise, it is difficult to differentiate "add" and
     // "max", both of which having the same kind.
-    %tiled_2, %loop_2 =
-        transform.structured.tile_using_forall %add_fused tile_sizes [4, 4]
-          : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
-    %matmul_fused_2, %loop_3 =
-        transform.structured.fuse_into_containing_op %matmul_fused into %loop_2
-          : (!transform.any_op, !transform.any_op)
-            -> (!transform.any_op, !transform.any_op)
+    %tiled_second, %loop_second = transform.structured.tile_using_forall %add_fused
+                        tile_sizes [4, 4]
+        : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    %matmul_fused_2, %loop_second_2 = transform.structured.fuse_into_containing_op %matmul_fused
+                      into %loop_second
+        : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     // Since outlining is currently only implemented for region-holding
     // operations such as loops, use tiling to size 1 to materialize the outer
     // loop that is going to be outlined.
-    %_, %outline_target =
-        transform.structured.tile_using_forall %tiled_2 tile_sizes [1]
-          : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
-    transform.structured.fuse_into_containing_op %matmul_fused_2
-        into %outline_target
-          : (!transform.any_op, !transform.any_op)
-            -> (!transform.any_op, !transform.any_op)
+    %_0, %loop_third = transform.structured.tile_using_forall %tiled_second tile_sizes [1]
+        : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    %_1, %outline_target = transform.structured.fuse_into_containing_op %matmul_fused_2 into %loop_third
+        : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
     %func, %call = transform.loop.outline %outline_target
                    {func_name = "outlined"}
-        : (!transform.any_op) -> (!transform.any_op, !transform.op<"func.call">)
+        : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     // Rewrite the call target.
-    transform.my.change_call_target %call, "microkernel" : !transform.op<"func.call">
+    transform.my.change_call_target %call, "microkernel" : !transform.any_op
+
     transform.yield
   }
 }
 ```
 
-When you run it with the interpreter, it produces the following error.
-
-```
-sequence.mlir:7:8: error: 'func.call' op 'microkernel' does not reference a valid function
-  %1 = linalg.elementwise kind=#linalg.elementwise_kind<add> ins(%0, %arg2 : tensor<512x512xf32>, tensor<512x512xf32>) outs(%arg3 : tensor<512x512xf32>) -> tensor<512x512xf32>
-       ^
-sequence.mlir:7:8: note: see current operation: %39 = "func.call"(%32, %33, %34, %36, %37) <{callee = @microkernel}> : (tensor<4x512xf32>, tensor<512x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<4x4xf32>
-```
-
 ## Appendix: Autogenerated Documentation
 
 [include "Tutorials/transform/MyExtensionCh2.md"]