[mlir] Rename ShapeTypeConversion to ShapeBufferize

Once we have tensor_to_memref ops suitable for type materializations,
this pass can be split into a generic type conversion pattern.

Part of the refactor discussed in:
https://llvm.discourse.group/t/what-is-the-strategy-for-tensor-memref-conversion-bufferization/1938/17

Differential Revision: https://reviews.llvm.org/D89258

mlir/include/mlir/Dialect/Shape/Transforms/Passes.h

@@ -43,9 +43,12 @@ std::unique_ptr<FunctionPass> createRemoveShapeConstraintsPass();
 void populateShapeTypeConversionPatterns(
     MLIRContext *ctx, BufferAssignmentTypeConverter &converter,
     OwningRewritePatternList &patterns);
-// Collects a set of patterns to replace tensors as inputs and outputs to shape
-// operations with buffers. This only modifies the shape operations.
-std::unique_ptr<FunctionPass> createShapeTensorToMemrefPass();
+// Bufferizes shape dialect ops.
+//
+// Note that most shape dialect ops must be converted to std before
+// bufferization happens, as they are intended to be bufferized at the std
+// level.
+std::unique_ptr<FunctionPass> createShapeBufferizePass();
 
 //===----------------------------------------------------------------------===//
 // Registration

mlir/include/mlir/Dialect/Shape/Transforms/Passes.td

@@ -22,8 +22,8 @@ def ShapeToShapeLowering : FunctionPass<"shape-to-shape-lowering"> {
 }
 
 // TODO: Generalize this to allow any type conversions desired.
-def ShapeTensorToMemref : FunctionPass<"shape-tensor-to-memref"> {
-  let summary = "Replace tensors involving shape operations with memrefs";
-  let constructor = "mlir::createShapeTensorToMemrefPass()";
+def ShapeBufferize : FunctionPass<"shape-bufferize"> {
+  let summary = "Bufferize the shape dialect.";
+  let constructor = "mlir::createShapeBufferizePass()";
 }
 #endif // MLIR_DIALECT_SHAPE_TRANSFORMS_PASSES

mlir/lib/Dialect/Shape/Transforms/Bufferize.cpp

@@ -1,16 +1,12 @@
-//====----- ShapeTypeConversion.cpp - Shape Type Conversions ----*- C++-*--===//
+//====----- Bufferize.cpp - Bufferization of shape ops  ---------*- C++-*--===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
-//
-// This file defines patterns to convert types of inputs and outputs to shape
-// operations to be memrefs instead of tensors.
-//
-//===----------------------------------------------------------------------===//
 
+#include "mlir/Transforms/Bufferize.h"
 #include "PassDetail.h"
 #include "mlir/Dialect/Shape/IR/Shape.h"
 #include "mlir/Dialect/Shape/Transforms/Passes.h"
@@ -18,7 +14,6 @@
 #include "mlir/IR/Operation.h"
 #include "mlir/IR/StandardTypes.h"
 #include "mlir/Pass/Pass.h"
-#include "mlir/Transforms/Bufferize.h"
 
 using namespace mlir;
 using namespace mlir::shape;
@@ -53,8 +48,7 @@ public:
   }
 };
 
-struct ShapeTensorToMemrefPass
-    : public ShapeTensorToMemrefBase<ShapeTensorToMemrefPass> {
+struct ShapeBufferizePass : public ShapeBufferizeBase<ShapeBufferizePass> {
   void runOnFunction() override {
     MLIRContext &ctx = getContext();
 
@@ -87,9 +81,9 @@ void mlir::populateShapeTypeConversionPatterns(
 }
 
 //===----------------------------------------------------------------------===//
-// ShapeTensorToMemrefPass construction
+// ShapeBufferizePass construction
 //===----------------------------------------------------------------------===//
 
-std::unique_ptr<FunctionPass> mlir::createShapeTensorToMemrefPass() {
-  return std::make_unique<ShapeTensorToMemrefPass>();
+std::unique_ptr<FunctionPass> mlir::createShapeBufferizePass() {
+  return std::make_unique<ShapeBufferizePass>();
 }

mlir/lib/Dialect/Shape/Transforms/CMakeLists.txt

@@ -1,6 +1,6 @@
 add_mlir_dialect_library(MLIRShapeOpsTransforms
+  Bufferize.cpp
   RemoveShapeConstraints.cpp
-  ShapeTypeConversion.cpp
   ShapeToShapeLowering.cpp
 
   ADDITIONAL_HEADER_DIRS

mlir/test/Dialect/Shape/bufferize.mlir

@@ -1,4 +1,4 @@
-// RUN: mlir-opt -split-input-file -shape-tensor-to-memref <%s | FileCheck %s
+// RUN: mlir-opt -split-input-file -shape-bufferize <%s | FileCheck %s
 
 // -----
 // Check that shape.assuming returns a memref.
@@ -14,5 +14,3 @@ func @shape_assuming_returns_memref() {
   "test.sink"(%1) : (tensor<2xf16>) -> ()
   return
 }
-
-