[mlir][tensor] Add producer fusion for tensor.pack op.

[hanchung]

Summary

We are able to fuse the pack op only if inner tiles are not tiled or
they are fully used. Otherwise, it could generate a sequence of
non-trivial ops.

[chelini]

mlir/lib/Dialect/Tensor/IR/TensorTilingInterfaceImpl.cpp

231

Thanks, looks good, but I am not sure I understand this comment. If we want to fuse with the producer we want to iterate on the full tile (0 offest and full dim) and not a partial one, right?

[hanchung]

mlir/lib/Dialect/Tensor/IR/TensorTilingInterfaceImpl.cpp

231

Good catch. You are right, I miswrote the comment.... Sorry about that...

[hanchung]

update comments

[hanchung]

update comments

[chelini]

Nice, thanks!

mlir/lib/Dialect/Tensor/IR/TensorTilingInterfaceImpl.cpp

getResultTilePosition(Operation *op, OpBuilder &b, unsigned resultNumber,
     ReifiedRankedShapedTypeDims outputShape;
     (void)reifyResultShapes(b, packOp, outputShape);
     resultSizes.assign(sizes.begin(), sizes.end());
     for (auto dataTileDim : llvm::seq<unsigned>(inputRank, outputRank))
       resultSizes.push_back(outputShape[0][dataTileDim]);
 
     return success();
   }
+
+  FailureOr<TilingResult>
+  generateResultTileValue(Operation *op, OpBuilder &b, unsigned resultNumber,
+                          ArrayRef<OpFoldResult> offsets,
+                          ArrayRef<OpFoldResult> sizes) const {
+    auto packOp = cast<PackOp>(op);
+    int64_t numTiles = packOp.getInnerDimsPos().size();
+
+    // tensor.pack op is not fusible (as a producer) only if inner tiles are
+    // tiled or they are all used. Otherwise, it will generate a sequence of
+    // non-trivial ops.
+    for (auto offset : offsets.take_back(numTiles))
+      if (!isConstantIntValue(offset, 0))
+        return failure();
+
+    for (auto iter :
+         llvm::zip_equal(packOp.getMixedTiles(), sizes.take_back(numTiles)))
+      if (!isEqualConstantIntOrValue(std::get<0>(iter), std::get<1>(iter)))
+        return failure();
+
+    FailureOr<TilingResult> tilingResult = getTiledImplementation(
+        op, b, offsets.drop_back(numTiles), sizes.drop_back(numTiles));
+    if (failed(tilingResult))
+      return failure();
+    return tilingResult.value();
+  }
 };
 
 struct UnpackTileDimInfo {
   bool isAlignedToInnerTileSize;
   OpFoldResult sourceOffset;
   OpFoldResult sourceSize;
   OpFoldResult resultOffset;
   OpFoldResult destExpandedSize;

mlir/test/Dialect/Linalg/transform-op-fuse.mlir

 }
 
 transform.sequence failures(propagate) {
 ^bb1(%arg1: !transform.any_op):
   %0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [16, 32], tile_interchange = [0, 1]}
     : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
+
+// -----
+
+// CHECK-LABEL: func.func @pack_elemwise
+// CHECK:         %[[RES:.*]] = scf.for
+// CHECK:           scf.for
+// CHECK:             tensor.pack
+// CHECK:             linalg.elemwise_unary
+// CHECK:         return %[[RES]]
+func.func @pack_elemwise(%arg0: tensor<128x384xf32>, %arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32> {
+  %0 = tensor.empty() : tensor<16x48x8x8xf32>
+  %1 = tensor.pack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %0
+      : tensor<128x384xf32> -> tensor<16x48x8x8xf32>
+  %2 = linalg.elemwise_unary ins(%1: tensor<16x48x8x8xf32>)
+                             outs(%arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32>
+  return %2 : tensor<16x48x8x8xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [3, 5, 0, 0]}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+}
+
+// -----
+
+// CHECK-LABEL: func.func @nofuse_pack_elemwise
+// CHECK:         tensor.pack
+// CHECK:         %[[RES:.*]] = scf.for
+// CHECK:           scf.for
+// CHECK:             linalg.elemwise_unary
+// CHECK:         return %[[RES]]
+func.func @nofuse_pack_elemwise(%arg0: tensor<128x384xf32>, %arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32> {
+  %0 = tensor.empty() : tensor<16x48x8x8xf32>
+  %1 = tensor.pack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %0
+      : tensor<128x384xf32> -> tensor<16x48x8x8xf32>
+  %2 = linalg.elemwise_unary ins(%1: tensor<16x48x8x8xf32>)
+                             outs(%arg1: tensor<16x48x8x8xf32>) -> tensor<16x48x8x8xf32>
+  return %2 : tensor<16x48x8x8xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1, %loops:3 = transform.structured.fuse %0 {tile_sizes = [3, 5, 2, 0]}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+}