[mlir][tensor] Fix one-shot bufferization of tensor.reshape.

[ingomueller-net]

Summary

I believe that the previous implementation did not work on any input. It
called getMemRefType with layout = {}, presumably with the intention
to create a MemrefType with identity layout. However, the implementation
of that function returns a MemrefType with *unknown* layout if it is
provided with a default-constructed layout attribute. This patch uses
getMemRefTypeWithStaticIdentityLayout instead, with has identical
behavior except for the case of a default-constructed layout, which it
passes on as-is to the MemrefType.

This problem did not surface in the test because tensor.reshape was not
tested with -one-shot-bufferize. This patch introduces a test copied
from the tests for -tesnor-bufferize adapted in as follows: since the
test is run with "bufferize-function-boundaries", a tensor that is
passed into the function is bufferized into a memref with unknown
layout, which wouldn't be a valid intput for memref.reshape, so the
tests now uses a tensor constructed with arith.constant inside of the
function.

[springerm]

A few typos in the commit message, otherwise looks good. Thx!

mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp

LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
                           const BufferizationOptions &options) const {
     auto reshapeOp = cast<tensor::ReshapeOp>(op);
     FailureOr<Value> srcBuffer =
         getBuffer(rewriter, reshapeOp.getSource(), options);
     FailureOr<Value> shapeBuffer =
         getBuffer(rewriter, reshapeOp.getShape(), options);
     if (failed(srcBuffer) || failed(shapeBuffer))
       return failure();
-    auto resultMemRefType = getMemRefType(
-        reshapeOp.getResult(), options, /*layout=*/{},
+    auto resultMemRefType = getMemRefTypeWithStaticIdentityLayout(
+        reshapeOp.getResult().getType(),
         cast<BaseMemRefType>(srcBuffer->getType()).getMemorySpace());
     replaceOpWithNewBufferizedOp<memref::ReshapeOp>(
         rewriter, op, resultMemRefType, *srcBuffer, *shapeBuffer);
     return success();
   }
 };
 
 /// Analysis of ParallelInsertSliceOp.

mlir/test/Dialect/Tensor/one-shot-bufferize.mlir

%result = scf.forall (%thread_idx) in (%num_threads) shared_outs (%o = %out) -> tensor<100xf32> {
       scf.forall.in_parallel {
         tensor.parallel_insert_slice %insert into %o[%thread_idx][1][1] :
           tensor<1xf32> into tensor<100xf32>
       }
   }
   // CHECK: }
   return
 }
+
+// -----
+
+// CHECK-LABEL: func @tensor.reshape(
+func.func @tensor.reshape() -> tensor<2x2x5xf32> {
+  // CHECK-DAG: %[[M1:.*]] = memref.cast %{{.*}} : memref<2x10xf32> to memref<?x10xf32>
+  %t1_static = arith.constant dense<0.> : tensor<2x10xf32>
+  %t1 = tensor.cast %t1_static : tensor<2x10xf32> to tensor<?x10xf32>
+
+  // CHECK: %[[SHAPE:.*]] = memref.get_global @{{.*}} : memref<3xi64>
+  %shape = arith.constant dense<[2, 2, 5]> : tensor<3xi64>
+
+  // CHECK: %[[RESHAPED:.*]] = memref.reshape %[[M1]](%[[SHAPE]]) : (memref<?x10xf32>, memref<3xi64>) -> memref<2x2x5xf32>
+  %reshaped = tensor.reshape %t1(%shape) : (tensor<?x10xf32>, tensor<3xi64>) -> tensor<2x2x5xf32>
+
+  // CHECK: return %[[RESHAPED]]
+  return %reshaped : tensor<2x2x5xf32>
+}