[mlir] Add translation from tensor.reshape to memref.reshape

[ashay-github]

Summary

This patch augments the tensor-bufferize pass by adding a conversion
rule to translate ReshapeOp from the tensor dialect to the memref
dialect, in addition to adding a unit test to validate the translation.

[springerm]

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

752

If opOperand is shape, we should return true here. For the source, we can return false.

774

nit: We should return failure() if state.getBuffer fails.

I will update the remaining implementations in this file at some point....

776

same as above

[ashay-github]

fix bufferizesToMemoryRead() and bufferize()

This patch changes the bufferizesToMemoryRead() method so that it returns true
for the shape operand. It also makes bufferize() return false if
BufferizationState::getBuffer() returns a failure.

[ashay-github]

Thanks! Fixed the code as per the suggestions. PTAL.

[springerm]

thanks!

[ashay-github]

Thanks @springerm! Could you commit the change please? I don't yet have write access to the repo.

[springerm]

In D125031#3500702, @ashay-github wrote:

Thanks @springerm! Could you commit the change please? I don't yet have write access to the repo.

done!

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

LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
     auto rankOp = cast<tensor::RankOp>(op);
     Value v = *state.getBuffer(rewriter, rankOp->getOpOperand(0) /*source*/);
     replaceOpWithNewBufferizedOp<memref::RankOp>(rewriter, op, rankOp.getType(),
                                                  v);
     return success();
   }
 };
 
+/// Bufferization of tensor.reshape. Replace with memref.reshape.
+struct ReshapeOpInterface
+    : public BufferizableOpInterface::ExternalModel<ReshapeOpInterface,
+                                                    tensor::ReshapeOp> {
+  bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
+                              const AnalysisState &state) const {
+    return false;
+  }
+
+  bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
+                               const AnalysisState &state) const {
+    return false;
+  }
+
+  SmallVector<OpResult> getAliasingOpResult(Operation *op, OpOperand &opOperand,
+                                            const AnalysisState &state) const {
+    return {op->getOpResult(0)};
+  }
+
+  BufferRelation bufferRelation(Operation *op, OpResult opResult,
+                                const AnalysisState &state) const {
+    return BufferRelation::Equivalent;
+  }
+
+  LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
+                          BufferizationState &state) const {
+    auto reshapeOp = cast<tensor::ReshapeOp>(op);
+    auto &srcOperand = reshapeOp->getOpOperand(0);
+    auto srcBuffer = *state.getBuffer(rewriter, srcOperand);
+    auto &shapeOperand = reshapeOp->getOpOperand(1);
+    auto shapeBuffer = *state.getBuffer(rewriter, shapeOperand);
+
+    auto resultTensorType = reshapeOp.getResult().getType().cast<TensorType>();
+    auto resultMemRefType = getMemRefType(resultTensorType, state.getOptions());
+
+    replaceOpWithNewBufferizedOp<memref::ReshapeOp>(
+        rewriter, op, resultMemRefType, srcBuffer, shapeBuffer);
+    return success();
+  }
+};
+
 } // namespace
 } // namespace tensor
 } // namespace mlir
 
 void mlir::tensor::registerBufferizableOpInterfaceExternalModels(
     DialectRegistry &registry) {
   registry.addExtension(+[](MLIRContext *ctx, tensor::TensorDialect *dialect) {
     CastOp::attachInterface<CastOpInterface>(*ctx);
     CollapseShapeOp::attachInterface<CollapseShapeOpInterface>(*ctx);
     DimOp::attachInterface<DimOpInterface>(*ctx);
     ExpandShapeOp::attachInterface<ExpandShapeOpInterface>(*ctx);
     ExtractSliceOp::attachInterface<ExtractSliceOpInterface>(*ctx);
     ExtractOp::attachInterface<ExtractOpInterface>(*ctx);
     FromElementsOp::attachInterface<FromElementsOpInterface>(*ctx);
     GenerateOp::attachInterface<GenerateOpInterface>(*ctx);
     InsertOp::attachInterface<InsertOpInterface>(*ctx);
     InsertSliceOp::attachInterface<InsertSliceOpInterface>(*ctx);
     RankOp::attachInterface<RankOpInterface>(*ctx);
+    ReshapeOp::attachInterface<ReshapeOpInterface>(*ctx);
   });
 }

mlir/test/Dialect/Tensor/bufferize.mlir

 func.func @tensor.collapse_shape_of_slice4(%arg0: tensor<?x2x4xf32>, %offset: index, %size: index) -> tensor<8xf32> {
   // CHECK: memref.subview %{{.*}} : memref<?x2x4xf32> to memref<4x2x1xf32, #[[$MAP7]]>
   %0 = tensor.extract_slice %arg0[0, 0, %offset] [4, 2, 1] [1, 1, 1] : tensor<?x2x4xf32> to tensor<4x2x1xf32>
   // CHECK: memref.collapse_shape %{{.*}} [
   // CHECK-SAME: [0, 1, 2]] : memref<4x2x1xf32, #[[$MAP7]]> into memref<8xf32, #[[$MAP8]]>
   %ret = tensor.collapse_shape %0 [[0, 1, 2]] : tensor<4x2x1xf32> into tensor<8xf32>
   return %ret: tensor<8xf32>
 }
+
+// CHECK-LABEL: func @tensor.reshape(
+//  CHECK-SAME:     %[[t1:.*]]: tensor<?x10xf32>
+func.func @tensor.reshape(%t1: tensor<?x10xf32>) -> tensor<2x2x5xf32> {
+  // CHECK: %[[m1:.*]] = bufferization.to_memref %[[t1]] : memref<?x10xf32>
+
+  // CHECK: %[[two:.*]] = arith.constant 2 : i64
+  %two = arith.constant 2 : i64
+  // CHECK: %[[five:.*]] = arith.constant 5 : i64
+  %five = arith.constant 5 : i64
+
+  // CHECK: %[[alloc:.*]] = memref.alloc() {alignment = 128 : i64} : memref<3xi64>
+  // CHECK: %[[zero_idx:.*]] = arith.constant 0 : index
+  // CHECK: %[[one_idx:.*]] = arith.constant 1 : index
+  // CHECK: %[[two_idx:.*]] = arith.constant 2 : index
+  // CHECK: memref.store %[[two]], %[[alloc]][%[[zero_idx]]] : memref<3xi64>
+  // CHECK: memref.store %[[two]], %[[alloc]][%[[one_idx]]] : memref<3xi64>
+  // CHECK: memref.store %[[five]], %[[alloc]][%[[two_idx]]] : memref<3xi64>
+  %shape = tensor.from_elements %two, %two, %five : tensor<3xi64>
+
+  // CHECK: %[[reshaped:.*]] = memref.reshape %[[m1]](%[[alloc]]) : (memref<?x10xf32>, memref<3xi64>) -> memref<2x2x5xf32>
+  %reshaped = tensor.reshape %t1(%shape) : (tensor<?x10xf32>, tensor<3xi64>) -> tensor<2x2x5xf32>
+
+  // CHECK: %[[r:.*]] = bufferization.to_tensor %[[reshaped]]
+  // CHECK: return %[[r]]
+  return %reshaped : tensor<2x2x5xf32>
+}