[mlir][NFC] Move SubTensorOp and SubTensorInsertOp to TensorDialect

The main goal of this commit is to remove the dependency of Standard dialect on the Tensor dialect.

* Rename SubTensorOp -> tensor.extract_slice, SubTensorInsertOp -> tensor.insert_slice.
* Some helper functions are (already) duplicated between the Tensor dialect and the MemRef dialect. To keep this commit smaller, this will be cleaned up in a separate commit.
* Additional dialect dependencies: Shape --> Tensor, Tensor --> Standard
* Remove dialect dependencies: Standard --> Tensor
* Move canonicalization test cases to correct dialect (Tensor/MemRef).

Note: This is a fixed version of https://reviews.llvm.org/D104499, which was reverted due to a missing update to two CMakeFile.txt.

Differential Revision: https://reviews.llvm.org/D104676

Author: matthias-springer

mlir/include/mlir/Conversion/Passes.td

@@ -367,7 +367,8 @@ def ConvertShapeToStandard : Pass<"convert-shape-to-std", "ModuleOp"> {
   let dependentDialects = [
     "memref::MemRefDialect",
     "StandardOpsDialect",
-    "scf::SCFDialect"
+    "scf::SCFDialect",
+    "tensor::TensorDialect"
   ];
 }
 
@@ -504,7 +505,7 @@ def TosaToSCF : Pass<"tosa-to-scf"> {
 
 def TosaToStandard : Pass<"tosa-to-standard"> {
   let summary = "Lower TOSA to the Standard dialect";
-  let dependentDialects = ["StandardOpsDialect"];
+  let dependentDialects = ["StandardOpsDialect", "tensor::TensorDialect"];
   let description = [{
     Pass that converts TOSA operations to the equivalent operations using the
     operations in the Standard dialect.

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

@@ -579,12 +579,11 @@ def Linalg_TiledLoopOp : Linalg_Op<"tiled_loop", [
 
     Tensor-based version:
 
-    The body region of the loop contains `subtensor` operations applied to
+    The body region of the loop contains `extract_slice` operations applied to
     every tensor argument of TiledLoopOp.
 
     The body region must contain exactly one block that terminates with
-    `linalg.yield` with the operands resulting from `subtensor_insert`
-    operations.
+    `linalg.yield` with the operands resulting from `insert_slice` operations.
 
     Example:
 
@@ -594,16 +593,16 @@ def Linalg_TiledLoopOp : Linalg_Op<"tiled_loop", [
         outs(%out : tensor<24x64xi8>)
         iterators("parallel")
         distribution("block_x") {
-      %lhs_sub = subtensor %lhs[%i, 0] [%c4, %c64] [1, 1]
+      %lhs_sub = tensor.extract_slice %lhs[%i, 0] [%c4, %c64] [1, 1]
           : tensor<24x64xi8> to tensor<?x?xi8>
-      %rhs_sub = subtensor %rhs[%i, 0] [%c4, %c64] [1, 1]
+      %rhs_sub = tensor.extract_slice %rhs[%i, 0] [%c4, %c64] [1, 1]
           : tensor<24x64xi8> to tensor<?x?xi8>
-      %out_sub = subtensor %out[%i, 0] [%c4, %c64] [1, 1]
+      %out_sub = tensor.extract_slice %out[%i, 0] [%c4, %c64] [1, 1]
           : tensor<24x64xi8> to tensor<?x?xi8>
 
       %result_sub = linalg.generic ...
 
-      %result = subtensor_insert %result_sub into %out[%i, 0][%c4, %c64][1, 1]
+      %result = tensor.insert_slice %result_sub into %out[%i, 0][%c4, %c64][1, 1]
         : tensor<?x?xi8> into tensor<24x64xi8>
       linalg.yield %result : tensor<24x64xi8>
     }

mlir/include/mlir/Dialect/Linalg/Transforms/Hoisting.h

@@ -47,7 +47,7 @@ void hoistRedundantVectorTransfersOnTensor(FuncOp func);
 /// If hoistPaddingOnTensors is called with `nLoops` = 2 on the following IR.
 /// ```
 ///    scf.for (%i, %j, %k)
-///      %st0 = subtensor f(%i, %k) : ... to tensor<?x?xf32>
+///      %st0 = tensor.extract_slice f(%i, %k) : ... to tensor<?x?xf32>
 ///      %0 = linalg.pad_tensor %st0 low[0, 0] high[...] {
 ///      ^bb0( ... ):
 ///        linalg.yield %pad
@@ -61,16 +61,17 @@ void hoistRedundantVectorTransfersOnTensor(FuncOp func);
 ///    scf.for (%i) {
 ///      %packed_init = linalg.init_tensor range(%j) : tensor<?x4x8xf32>
 ///      %packed = scf.for (%k) iter_args(%p : %packed_init) {
-///        %st0 = subtensor f(%i, %k) : ... to tensor<?x?xf32>
+///        %st0 = tensor.extract_slice f(%i, %k) : ... to tensor<?x?xf32>
 ///        %0 = linalg.pad_tensor %st0 low[0, 0] high[...] {
 ///        ^bb0( ... ):
 ///          linalg.yield %pad
 ///        } : tensor<?x?xf32> to tensor<4x8xf32>
-///        %1 = subtensor_insert %0 ... : tensor<4x8xf32> to tensor<?x4x8xf32>
+///        %1 = tensor.insert_slice %0 ...
+///            : tensor<4x8xf32> to tensor<?x4x8xf32>
 ///        scf.yield %1: tensor<?x4x8xf32>
 ///      } -> tensor<?x4x8xf32>
 ///      scf.for (%j, %k) {
-///        %st0 = subtensor %packed [%k, 0, 0][1, 4, 8][1, 1, 1] :
+///        %st0 = tensor.extract_slice %packed [%k, 0, 0][1, 4, 8][1, 1, 1] :
 ///                 tensor<?x4x8xf32> to tensor<4x8xf32>
 ///        compute(%st0)
 ///      }

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

@@ -12,6 +12,7 @@
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
 #include "mlir/Dialect/SCF/Utils.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Vector/VectorOps.h"
 #include "mlir/IR/Identifier.h"
 #include "mlir/IR/PatternMatch.h"
@@ -1077,12 +1078,12 @@ LogicalResult applyStagedPatterns(
     const FrozenRewritePatternSet &stage2Patterns,
     function_ref<LogicalResult(Operation *)> stage3Lambda = nullptr);
 
-/// Rewrite subtensor(pad_tensor(x)) into pad_tensor(subtensor(x)).
-struct SubTensorOfPadTensorSwapPattern
-    : public OpRewritePattern<SubTensorOp> {
-  using OpRewritePattern<SubTensorOp>::OpRewritePattern;
+/// Rewrite extract_slice(pad_tensor(x)) into pad_tensor(extract_slice(x)).
+struct ExtractSliceOfPadTensorSwapPattern
+    : public OpRewritePattern<tensor::ExtractSliceOp> {
+  using OpRewritePattern<tensor::ExtractSliceOp>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(SubTensorOp subTensorOp,
+  LogicalResult matchAndRewrite(tensor::ExtractSliceOp sliceOp,
                                 PatternRewriter &rewriter) const override;
 };
 

mlir/include/mlir/Dialect/Linalg/Utils/Utils.h

@@ -78,7 +78,7 @@ bool isProducerLastWriteOfView(const LinalgDependenceGraph &graph,
 bool isFusableInto(const LinalgDependenceGraph &graph, LinalgOp consumer,
                    Value consumedView, LinalgOp producer);
 
-/// Creates subtensor/subview ops for all `tiledOperands` of the given
+/// Creates extract_slice/subview ops for all `tiledOperands` of the given
 /// `linalgOp` with `builder`, assuming `linalgOp` is being fused into a loop
 /// nest for tiling with the given induction variables `ivs` and tile sizes
 /// `tileSizes`. `sizeBounds` are the iteration space bounds for *all* the
@@ -118,15 +118,17 @@ Optional<FusionInfo> fuseProducerOfBuffer(OpBuilder &b,
                                           const LinalgDependenceGraph &graph);
 /// Tensor counterpart of `fuseProducerOfBuffer`.
 /// This implements the fusion part of the "tileAndFuse on tensors"
-/// transformation and thus requires the `consumerOpOperand` to be a `subtensor`
-/// op (generally obtained by applying the tiling transformation).
+/// transformation and thus requires the `consumerOpOperand` to be a
+/// `extract_slice` op (generally obtained by applying the tiling
+/// transformation).
 Optional<FusionInfo> fuseProducerOfTensor(OpBuilder &b,
                                           OpOperand &consumerOpOperand);
 /// Tensor counterpart of `fuseProducerOfBuffer`.
 /// This implements the fusion part of the "tileAndFuse on tensors"
-/// transformation and thus requires the `consumerOpOperand` to be a `subtensor`
-/// op (generally obtained by applying the tiling transformation).
-/// Assumes `producerOfTensor` is a Linalg op that produces `consumerOpOperand`.
+/// transformation and thus requires the `consumerOpOperand` to be a
+/// `extract_slice` op (generally obtained by applying the tiling
+/// transformation). Assumes `producerOfTensor` is a Linalg op that produces
+/// `consumerOpOperand`.
 Optional<FusionInfo> fuseProducerOfTensor(OpBuilder &b,
                                           OpResult producerOpResult,
                                           OpOperand &consumerOpOperand);

mlir/include/mlir/Dialect/Shape/IR/Shape.h

@@ -14,6 +14,7 @@
 #ifndef MLIR_SHAPE_IR_SHAPE_H
 #define MLIR_SHAPE_IR_SHAPE_H
 
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/BuiltinOps.h"
 #include "mlir/IR/Dialect.h"
 #include "mlir/IR/OpDefinition.h"

mlir/include/mlir/Dialect/Shape/IR/ShapeBase.td

@@ -35,6 +35,7 @@ def ShapeDialect : Dialect {
   }];
 
   let cppNamespace = "::mlir::shape";
+  let dependentDialects = ["tensor::TensorDialect"];
 
   let hasConstantMaterializer = 1;
   let hasOperationAttrVerify = 1;

mlir/include/mlir/Dialect/StandardOps/IR/Ops.h

@@ -23,7 +23,6 @@
 #include "mlir/Interfaces/ControlFlowInterfaces.h"
 #include "mlir/Interfaces/SideEffectInterfaces.h"
 #include "mlir/Interfaces/VectorInterfaces.h"
-#include "mlir/Interfaces/ViewLikeInterface.h"
 
 // Pull in all enum type definitions and utility function declarations.
 #include "mlir/Dialect/StandardOps/IR/OpsEnums.h.inc"
@@ -34,12 +33,6 @@ class Builder;
 class FuncOp;
 class OpBuilder;
 class PatternRewriter;
-
-/// Return the list of Range (i.e. offset, size, stride). Each Range
-/// entry contains either the dynamic value or a ConstantIndexOp constructed
-/// with `b` at location `loc`.
-SmallVector<Range, 8> getOrCreateRanges(OffsetSizeAndStrideOpInterface op,
-                                        OpBuilder &b, Location loc);
 } // namespace mlir
 
 #define GET_OP_CLASSES