[mlir][transform] Drop redundant padding_dimensions spec from pad_tiling_interface

mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td

@@ -1195,17 +1195,29 @@ def PadTilingInterfaceOp : Op<Transform_Dialect, "structured.pad_tiling_interfac
      TransformOpInterface,
      ReportTrackingListenerFailuresOpTrait]> {
   let description = [{
-    Pads the operations pointed to by the target handle using the options
-    provided as operation attributes. The operation returns a handle to the
-    padded operation and to the padding operation ("tensor.pad").
+    Pads the **iteration domain** of the operations pointed to by the target
+    handle using the options provided as operation attributes. Padding the
+    iteration domain induces a padding of the operands that is consistent
+    across the op semantics and, unlike for simple elementwise ops, may not be
+    trivially deducible or specifiable on operands only (e.g. convolutions).
+
+    The specification of `padding_sizes` follows that of `tile_sizes` during
+    tiling: the value "0" on a particular iterator encode "no padding". Like in
+    the case of tiling, an automatic completion by 0 to the operation rank
+    occurs.
+
+    This transformation returns a handle to the padded operation and to the
+    padding operation ("tensor.pad").
 
     TODO: in the future this should be moved out of a specific Linalg
     implementation file and into a more general "Structured" file.
 
     #### Return modes
 
-    This operation ignores non-Linalg ops and drops them in the return.
-    In the future, this operation will support all TilingInterfaceOps.
+    This operation ignores non-IndexingMapOpInterface ops and drops them in the
+    return. In the future, this operation will support all TilingInterfaceOps
+    for which the contract between iteration domain and operands can be 
+    reified.    
 
     This operation may produce a definite failure if the padding fails for any
     reason.
@@ -1219,7 +1231,6 @@ def PadTilingInterfaceOp : Op<Transform_Dialect, "structured.pad_tiling_interfac
   let arguments =
     (ins TransformHandleTypeInterface:$target,
          DefaultValuedAttr<ArrayAttr, "{}">:$padding_values,
-         DefaultValuedAttr<I64ArrayAttr, "{}">:$padding_dimensions,
          Variadic<TransformAnyParamTypeOrAnyHandle>:$padding_sizes,
          DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:
             $static_padding_sizes,
@@ -1245,11 +1256,9 @@ def PadTilingInterfaceOp : Op<Transform_Dialect, "structured.pad_tiling_interfac
     // add/mul ring at the moment.
     // TODO: support other operations (e.g. min, max etc).
     OpBuilder<(ins "Value":$target,
-                   "ArrayRef<int64_t>":$paddingDimensions,
                    CArg<"ArrayRef<int64_t>", "{}">:$staticPaddingSizes,
                    CArg<"bool", "false">:$padToMultipleOf)>,
     OpBuilder<(ins "Value":$target,
-                   "ArrayRef<int64_t>":$paddingDimensions,
                    "ArrayRef<OpFoldResult>":$mixedPadPaddingSizes,
                    CArg<"bool", "false">:$usePrescribedTensorShapes)>
   ];

mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp

@@ -2163,7 +2163,6 @@ LogicalResult transform::PadOp::verify() {
 void transform::PadTilingInterfaceOp::build(OpBuilder &b,
                                             OperationState &result,
                                             Value target,
-                                            ArrayRef<int64_t> paddingDimensions,
                                             ArrayRef<int64_t> paddingSizes,
                                             bool padToMultipleOf) {
   auto resultType = transform::AnyOpType::get(b.getContext());
@@ -2172,7 +2171,6 @@ void transform::PadTilingInterfaceOp::build(OpBuilder &b,
                /*types=*/TypeRange{resultType, resultType},
                /*target=*/target,
                /*paddingValues=*/ArrayAttr(), // let inference handle this
-               /*paddingDimensions=*/b.getI64ArrayAttr(paddingDimensions),
                /*paddingSizes=*/ValueRange{},
                /*paddingSizes=*/
                (paddingSizes.empty() ? DenseI64ArrayAttr()
@@ -2183,7 +2181,6 @@ void transform::PadTilingInterfaceOp::build(OpBuilder &b,
 
 void transform::PadTilingInterfaceOp::build(
     OpBuilder &b, OperationState &result, Value target,
-    ArrayRef<int64_t> paddingDimensions,
     ArrayRef<OpFoldResult> mixedPaddingSizes, bool padToMultipleOf) {
   auto resultType = transform::AnyOpType::get(b.getContext());
   SmallVector<int64_t> staticPaddingSizes;
@@ -2195,7 +2192,6 @@ void transform::PadTilingInterfaceOp::build(
                /*types=*/TypeRange{resultType, resultType},
                /*target=*/target,
                /*paddingValues=*/ArrayAttr(), // let inference handle this
-               /*paddingDimensions=*/b.getI64ArrayAttr(paddingDimensions),
                /*paddingSizes=*/dynamicPaddingSizes,
                /*paddingSizes=*/staticPaddingSizes,
                /*usePrescribedTensorShapes=*/padToMultipleOf);
@@ -2277,8 +2273,6 @@ transform::PadTilingInterfaceOp::apply(transform::TransformRewriter &rewriter,
     TilingInterface paddedOp;
     PadTilingInterfaceOptions options;
     options.setPaddingValues(paddingValues)
-        .setPaddingDimensions(
-            extractFromIntegerArrayAttr<int64_t>(getPaddingDimensions()))
         .setPaddingSizes(getMixedPaddingSizes())
         .setPadToMultipleOf(getPadToMultipleOf());
 
@@ -2303,20 +2297,7 @@ transform::PadTilingInterfaceOp::apply(transform::TransformRewriter &rewriter,
   return DiagnosedSilenceableFailure::success();
 }
 
-LogicalResult transform::PadTilingInterfaceOp::verify() {
-  SmallVector<int64_t> paddingDimensions =
-      extractFromIntegerArrayAttr<int64_t>(getPaddingDimensions());
-  if (any_of(paddingDimensions,
-             [](int64_t paddingDimension) { return paddingDimension < 0; })) {
-    return emitOpError() << "expects padding_dimensions to contain positive "
-                            "integers, found "
-                         << getPaddingDimensions();
-  }
-  if (getMixedPaddingSizes().size() != paddingDimensions.size()) {
-    return emitOpError() << "expects as many multiples as padding_dimensions";
-  }
-  return success();
-}
+LogicalResult transform::PadTilingInterfaceOp::verify() { return success(); }
 
 //===---------------------------------------------------------------------===//
 // HoistPadOp

mlir/lib/Dialect/Linalg/Transforms/PadTilingInterface.cpp

@@ -32,29 +32,27 @@ using namespace mlir::tensor;
 #define DBGSNL() (llvm::dbgs() << "\n")
 
 /// Form a "full-rank" padding specification so that the application is easy.
-static llvm::SmallDenseMap<int64_t, OpFoldResult>
-getDimsToSize(Builder &b, ArrayRef<OpFoldResult> indexingSizes,
-              const PadTilingInterfaceOptions &options) {
-  llvm::SmallDenseMap<int64_t, OpFoldResult> dimsToSize;
-  for (const auto &[paddingDim, paddingSize] :
-       llvm::zip_equal(options.paddingDimensions, options.paddingSizes)) {
-    dimsToSize[paddingDim] = paddingSize;
-  }
+static SmallVector<OpFoldResult>
+getFullRankPaddingSizes(Builder &b, ArrayRef<OpFoldResult> indexingSizes,
+                        const PadTilingInterfaceOptions &options) {
+  SmallVector<OpFoldResult> paddingSizes;
   // Complete the padding specification to specify all dimensions.
-  for (int64_t idx = 0, e = indexingSizes.size(); idx != e; ++idx) {
-    if (dimsToSize.find(idx) != dimsToSize.end())
-      continue;
-    // If a dimension is not specified, either complete with:
+  for (size_t idx = 0, e = indexingSizes.size(); idx != e; ++idx) {
+    // Complete to zero if needed.
+    paddingSizes.push_back(options.paddingSizes.size() > idx
+                               ? options.paddingSizes[idx]
+                               : b.getIndexAttr(0));
+    // If a dimension is zero (either specified or completed), replace by:
     //   - 1 if we are padding to the next multiple of.
     //   - indexingSizes[idx] otherwise
-    dimsToSize[idx] =
-        options.padToMultipleOf ? b.getIndexAttr(1) : indexingSizes[idx];
-  }
-  for (int64_t idx = 0, e = indexingSizes.size(); idx != e; ++idx) {
-    LLVM_DEBUG(DBGS() << "----idx: " << idx << " : " << dimsToSize[idx]
+    if (isZeroInteger(paddingSizes[idx])) {
+      paddingSizes[idx] =
+          options.padToMultipleOf ? b.getIndexAttr(1) : indexingSizes[idx];
+    }
+    LLVM_DEBUG(DBGS() << "----idx: " << idx << " : " << paddingSizes[idx]
                       << "\n");
   }
-  return dimsToSize;
+  return paddingSizes;
 }
 
 /// Compute the padded shape of the given value `v` of `RankedTensorType` given
@@ -80,8 +78,8 @@ SmallVector<OpFoldResult> linalg::computePaddedShape(
          "rank");
 
   // "Full-rank" padding specification.
-  llvm::SmallDenseMap<int64_t, OpFoldResult> dimsToSize =
-      getDimsToSize(rewriter, indexingSizes, options);
+  SmallVector<OpFoldResult> paddingSizes =
+      getFullRankPaddingSizes(rewriter, indexingSizes, options);
 
   // For each dimension in the operand's shape, iterate over indexingSizes and
   // add the various term contributions.
@@ -97,7 +95,9 @@ SmallVector<OpFoldResult> linalg::computePaddedShape(
     // Find all padding dimensions that contribute to this operand dimension
     // and compute the padded term contribution to the final padded shape.
     SmallVector<OpFoldResult> terms;
-    for (const auto &[paddingDim, paddingSize] : dimsToSize) {
+    for (size_t paddingDim = 0, e = paddingSizes.size(); paddingDim != e;
+         ++paddingDim) {
+      OpFoldResult paddingSize = paddingSizes[paddingDim];
       LLVM_DEBUG(DBGS() << "------try apply padding of dim: " << paddingDim
                         << " to: " << paddingSize << "\n");
       if (!enResults.value().isFunctionOfDim(paddingDim))
@@ -224,9 +224,6 @@ linalg::rewriteAsPaddedOp(RewriterBase &rewriter, TilingInterface opToPad,
                           SmallVector<tensor::PadOp> &padOps,
                           PadSizeComputationFunction computePaddingSizeFun) {
   LLVM_DEBUG(DBGS() << "Start rewriteAsPaddedOp : " << opToPad << "\n");
-  assert(constOptions.paddingSizes.size() ==
-             constOptions.paddingDimensions.size() &&
-         "invalid number of elements in padToMultipleOf");
 
   Location loc = opToPad.getLoc();
   PadTilingInterfaceOptions options(constOptions);

mlir/test/Dialect/Linalg/transform-op-pad-tiling-interface-multiple-of.mlir

@@ -36,8 +36,7 @@ module attributes {transform.with_named_sequence} {
     // Tile to 5 then pad to 8 (supposedly to better hit vector ops).
     %matmul_l1, %loops_l1 = transform.structured.tile_using_for %matmul tile_sizes [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
     %matmul_padded, %_ = transform.structured.pad_tiling_interface %matmul_l1 to padding_sizes [8] pad_to_multiple_of {
-      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
-      padding_dimensions=[0]
+      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32]
     } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     transform.yield
@@ -71,11 +70,10 @@ module {
     return %0 : tensor<7x11x12xf32>
   }
   module attributes {transform.with_named_sequence} {
-    transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {
-      %0 = transform.structured.match ops{["linalg.generic"]} in %module_op : (!transform.any_op) -> !transform.any_op
-      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [3, 5] pad_to_multiple_of {
-        padding_dimensions = [0, 2],
-        padding_values = [0.000000e+00 : f32, 0.000000e+00 : f32, 0.000000e+00 : f32]
+    transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+      %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [3, 0, 5] pad_to_multiple_of {
+        padding_values = [0.0 : f32, 0.0 : f32, 0.0 : f32]
       } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
       transform.yield
     }
@@ -126,11 +124,10 @@ module {
     return %0 : tensor<?x11x?xf32>
   }
   module attributes {transform.with_named_sequence} {
-    transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {
-      %0 = transform.structured.match ops{["linalg.generic"]} in %module_op : (!transform.any_op) -> !transform.any_op
-      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [3, 5] pad_to_multiple_of {
-        padding_dimensions = [0, 2],
-        padding_values = [0.000000e+00 : f32, 0.000000e+00 : f32, 0.000000e+00 : f32]
+    transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+      %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [3, 0, 5] pad_to_multiple_of {
+        padding_values = [0.0 : f32, 0.0 : f32, 0.0 : f32]
       } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
       transform.yield
     }
@@ -172,9 +169,8 @@ module attributes {transform.with_named_sequence} {
       : (!transform.any_op) -> !transform.any_op
 
     // Pad then tile should produce static shapes.
-    %matmul_padded, %_ = transform.structured.pad_tiling_interface %matmul to padding_sizes [8, 16] pad_to_multiple_of {
-      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
-      padding_dimensions=[0, 2]
+    %matmul_padded, %_ = transform.structured.pad_tiling_interface %matmul to padding_sizes [8, 0, 16] pad_to_multiple_of {
+      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32]
     } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     %m, %l0, %l1 = transform.structured.tile_using_for %matmul_padded tile_sizes [8, 0, 16]
@@ -234,9 +230,8 @@ module attributes {transform.with_named_sequence} {
     %m, %l0, %l1 = transform.structured.tile_using_for %matmul tile_sizes [8, 0, 16]
       : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 
-    %matmul_padded, %_ = transform.structured.pad_tiling_interface %m to padding_sizes [8, 16] pad_to_multiple_of {
-      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
-      padding_dimensions=[0, 2]
+    %matmul_padded, %_ = transform.structured.pad_tiling_interface %m to padding_sizes [8, 0, 16] pad_to_multiple_of {
+      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32]
     } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     transform.yield
@@ -269,9 +264,8 @@ module attributes {transform.with_named_sequence} {
     %m, %l0, %l1 = transform.structured.tile_using_for %matmul tile_sizes [8, 0, 16]
       : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 
-    %matmul_padded, %_ = transform.structured.pad_tiling_interface %m to padding_sizes [8, 16] pad_to_multiple_of {
-      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
-      padding_dimensions=[0, 2]
+    %matmul_padded, %_ = transform.structured.pad_tiling_interface %m to padding_sizes [8, 0, 16] pad_to_multiple_of {
+      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32]
     } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     transform.yield

mlir/test/Dialect/Linalg/transform-op-pad-tiling-interface.mlir

@@ -18,8 +18,7 @@ module attributes {transform.with_named_sequence} {
       : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     %fill_padded, %_ = transform.structured.pad_tiling_interface %fill_l1 to padding_sizes [8] {
-      padding_values=[0.0 : f32, 0.0 : f32],
-      padding_dimensions=[0]
+      padding_values=[0.0 : f32, 0.0 : f32]
     } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     transform.yield
@@ -55,8 +54,7 @@ module attributes {transform.with_named_sequence} {
     // Tile to 5 then pad to 8 (supposedly to better hit vector ops).
     %matmul_l1, %loops_l1 = transform.structured.tile_using_for %matmul tile_sizes [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
     %matmul_padded, %_ = transform.structured.pad_tiling_interface %matmul_l1 to padding_sizes [8] {
-      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
-      padding_dimensions=[0]
+      padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32]
     } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     transform.yield
@@ -91,8 +89,7 @@ module {
   module attributes {transform.with_named_sequence} {
     transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
       %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
-      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [8, 14] {
-        padding_dimensions = [0, 2], 
+      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [8, 0, 14] {
         padding_values = [0.000000e+00 : f32, 0.000000e+00 : f32, 0.000000e+00 : f32]
       } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
       transform.yield 
@@ -147,8 +144,7 @@ module {
   module attributes {transform.with_named_sequence} {
     transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
       %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
-      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [8, 14] {
-        padding_dimensions = [0, 2], 
+      %padded, %pad = transform.structured.pad_tiling_interface %0 to padding_sizes [8, 0, 14] {
         padding_values = [0.000000e+00 : f32, 0.000000e+00 : f32, 0.000000e+00 : f32]
       } : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
       transform.yield