[MLIR][tensor] Improve tensor.pack verifier to catch more cases with unconditional runtime errors

mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td

@@ -1943,11 +1943,12 @@ def Tensor_PackOp : Tensor_RelayoutOp<"pack", [
 
     // Returns true if we have enough static information to catch undefined
     // behavior when the tile size does not divide perfectly the dimension of
-    // the input tensor. If a given dimension or a tile associated with it is
-    // dynamic, the dimension is not considered as we don't have enough static
-    // information to understand if the tile perfectly divides that dimension.
+    // the input tensor. Detecting UB requires that the input size and either
+    // corresponding tile or output size are static.
     static bool requirePaddingValue(ArrayRef<int64_t> inputShape,
                                     ArrayRef<int64_t> innerDimsPos,
+                                    ArrayRef<int64_t> outputShape,
+                                    ArrayRef<int64_t> outerDimsPerm,
                                     ArrayRef<OpFoldResult> innerTiles);
 
     static Value createDestinationTensor(OpBuilder &b, Location loc,

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

@@ -563,8 +563,10 @@ FailureOr<PackResult> linalg::pack(RewriterBase &rewriter,
             return getConstantIntValue(tile).has_value();
           });
       if (areConstantTiles && operandType.hasStaticShape() &&
-          !tensor::PackOp::requirePaddingValue(operandType.getShape(), innerPos,
-                                               innerPackSizes)) {
+          !tensor::PackOp::requirePaddingValue(
+              operandType.getShape(), innerPos,
+              dest.getType().cast<ShapedType>().getShape(), {},
+              innerPackSizes)) {
         packOps.push_back(rewriter.create<tensor::PackOp>(
             loc, operand, dest, innerPos, innerPackSizes));
       } else {

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

@@ -3746,15 +3746,29 @@ SmallVector<int64_t> PackOp::getStaticTiles() {
 
 bool PackOp::requirePaddingValue(ArrayRef<int64_t> inputShape,
                                  ArrayRef<int64_t> innerDimsPos,
+                                 ArrayRef<int64_t> outputShape,
+                                 ArrayRef<int64_t> outerDimsPerm,
                                  ArrayRef<OpFoldResult> innerTiles) {
+  SmallVector<int64_t> outputTileSizes(
+      outputShape.take_front(inputShape.size()));
+  if (!outerDimsPerm.empty()) {
+    assert(outerDimsPerm.size() == outputTileSizes.size() &&
+           "expected output and outer_dims_perm to have same size");
+    applyPermutationToVector(outputTileSizes,
+                             invertPermutationVector(outerDimsPerm));
+  }
   for (auto [pos, tileSize] : llvm::zip_equal(innerDimsPos, innerTiles)) {
     if (ShapedType::isDynamic(inputShape[pos]))
       continue;
     std::optional<int64_t> constantTile = getConstantIntValue(tileSize);
-    if (!constantTile)
-      continue;
-    if (inputShape[pos] % (*constantTile) != 0)
+
+    if (!constantTile) {
+      if (!ShapedType::isDynamic(outputTileSizes[pos]) &&
+          (inputShape[pos] % outputTileSizes[pos] != 0))
+        return true;
+    } else if (inputShape[pos] % (*constantTile) != 0) {
       return true;
+    }
   }
   return false;
 }
@@ -3776,9 +3790,11 @@ LogicalResult PackOp::verify() {
 
   if (!paddingValue &&
       requirePaddingValue(getSourceType().getShape(), getInnerDimsPos(),
+                          getDestType().getShape(), getOuterDimsPerm(),
                           getMixedTiles())) {
-    return emitOpError("invalid tile factor provided. Only full tiles are "
-                       "supported when padding_value is not set");
+    return emitOpError(
+        "invalid tile factor or output size provided. Only full tiles are "
+        "supported when padding_value is not set");
   }
   return success();
 }
@@ -3979,8 +3995,9 @@ static bool paddingIsNotNeeded(PackOp op) {
     return false;
   if (ShapedType::isDynamicShape(op.getStaticInnerTiles()))
     return false;
-  return !PackOp::requirePaddingValue(srcType.getShape(), op.getInnerDimsPos(),
-                                      op.getMixedTiles());
+  return !PackOp::requirePaddingValue(
+      srcType.getShape(), op.getInnerDimsPos(), op.getDestType().getShape(),
+      op.getOuterDimsPerm(), op.getMixedTiles());
 }
 
 LogicalResult PackOp::canonicalize(PackOp packOp, PatternRewriter &rewriter) {

mlir/test/Dialect/Tensor/invalid.mlir

@@ -597,13 +597,29 @@ func.func @empty_wrong_number_of_operands(%sz : index) {
 // -----
 
 func.func @pack_invalid_no_padding_no_full_tiles(%input: tensor<256x128xf32>, %output: tensor<8x8x16x33xf32>) -> tensor<8x8x16x33xf32> {
-  // expected-error@+1 {{invalid tile factor provided. Only full tiles are supported when padding_value is not set}}
+  // expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}}
   %0 = tensor.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 33] into %output : tensor<256x128xf32>  -> tensor<8x8x16x33xf32>
   return %0 : tensor<8x8x16x33xf32>
 }
 
 // -----
 
+func.func @pack_invalid_no_padding_no_full_tiles_dyn_tiles(%input: tensor<256x128xf32>, %output: tensor<10x8x?x?xf32>, %tile_size_0: index, %tile_size_1: index) -> tensor<10x8x?x?xf32> {
+  // expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}}
+  %0 = tensor.pack %input inner_dims_pos = [1, 0] inner_tiles = [%tile_size_0, %tile_size_1] into %output : tensor<256x128xf32>  -> tensor<10x8x?x?xf32>
+  return %0 : tensor<10x8x?x?xf32>
+} 
+
+// -----
+
+func.func @pack_invalid_no_padding_no_full_tiles_dyn_tiles_outperm(%input: tensor<256x128xf32>, %output: tensor<8x10x?x?xf32>, %tile_size_0: index, %tile_size_1: index) -> tensor<8x10x?x?xf32> {
+  // expected-error@+1 {{invalid tile factor or output size provided. Only full tiles are supported when padding_value is not set}}
+  %0 = tensor.pack %input outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [%tile_size_0, %tile_size_1] into %output : tensor<256x128xf32>  -> tensor<8x10x?x?xf32>
+  return %0 : tensor<8x10x?x?xf32>
+} 
+
+// -----
+
 func.func @pad_and_pack_invalid_type(%input: tensor<13x15xf32>, %output: tensor<2x8x8x2xf32>, %pad: i32) -> tensor<2x8x8x2xf32> {
   // expected-error@+1 {{expected padding_value has 'f32' but got: 'i32'}}
   %0 = tensor.pack %input padding_value(%pad: i32) inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<13x15xf32> -> tensor<2x8x8x2xf32>