Automerge: [mlir][linalg] Fix Linalg runtime verification pass to handle tensors with dimensions of size 0 (#163791)

Runtime verification on Linalg structured ops unconditionally computed
`end - 1` to determine the last iteration index before composing
indexing maps. This caused spurious "negative index" assertion failures
while operating on empty tensors (tensors with a dimension of size 0).

The issue occurs because:

1. Empty tensors create loop ranges [0, 0) with zero trip count

2. Computing end - 1 = 0 - 1 = -1 creates a fictitious negative index

3. The negative index check triggers even though no loop iterations
occur

The fix is to guard all runtime verification with a check that ensures
all loop ranges are non-empty (start < end) before performing any index
arithmetic.

Example MLIR that previously failed:
```mlir
func.func @fill_empty() -> tensor<0xi32> {
  %c0 = arith.constant 0 : i32
  %empty = tensor.empty() : tensor<0xi32>
  %filled = linalg.fill ins(%c0 : i32) outs(%empty : tensor<0xi32>) -> tensor<0xi32>
  return %filled : tensor<0xi32>
}
```

---------

Co-authored-by: Hanumanth Hanumantharayappa <[email protected]>

Author: 2 people

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

@@ -17,6 +17,7 @@
 #include "mlir/Dialect/Index/IR/IndexOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/MemRef/IR/MemRef.h"
+#include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Interfaces/RuntimeVerifiableOpInterface.h"
 
@@ -43,6 +44,32 @@ struct StructuredOpInterface
     auto zero = arith::ConstantIndexOp::create(builder, loc, 0);
     auto one = arith::ConstantIndexOp::create(builder, loc, 1);
 
+    Value iterationDomainIsNonDegenerate;
+    for (auto [start, end] : llvm::zip(starts, ends)) {
+      auto startValue = getValueOrCreateConstantIndexOp(builder, loc, start);
+      auto endValue = getValueOrCreateConstantIndexOp(builder, loc, end);
+
+      // Loop Trip count > 0 iff start < end
+      Value dimensionHasNonZeroTripCount = builder.create<index::CmpOp>(
+          loc, index::IndexCmpPredicate::SLT, startValue, endValue);
+
+      if (!iterationDomainIsNonDegenerate) {
+        iterationDomainIsNonDegenerate = dimensionHasNonZeroTripCount;
+      } else {
+        // Iteration domain is non-degenerate iff all dimensions have loop trip
+        // count > 0
+        iterationDomainIsNonDegenerate = builder.create<arith::AndIOp>(
+            loc, iterationDomainIsNonDegenerate, dimensionHasNonZeroTripCount);
+      }
+    }
+
+    if (!iterationDomainIsNonDegenerate)
+      return;
+
+    auto ifOp = builder.create<scf::IfOp>(loc, iterationDomainIsNonDegenerate,
+                                          /*withElseRegion=*/false);
+    builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
+
     // Subtract one from the loop ends before composing with the indexing map
     transform(ends, ends.begin(), [&](OpFoldResult end) {
       auto endValue = getValueOrCreateConstantIndexOp(builder, loc, end);
@@ -110,6 +137,7 @@ struct StructuredOpInterface
         builder.createOrFold<cf::AssertOp>(loc, cmpOp, msg);
       }
     }
+    builder.setInsertionPointAfter(ifOp);
   }
 };
 

mlir/test/Integration/Dialect/Linalg/CPU/runtime-verification.mlir

@@ -103,6 +103,17 @@ func.func @main() {
   // CHECK: unexpected negative result on dimension #0 of input/output operand #0
   func.call @reverse_from_3(%d5x) : (tensor<?xf32>) -> (tensor<?xf32>)
 
+  %c0x = arith.constant dense<1.0> : tensor<0xf32>
+  %d0x = tensor.cast %c0x : tensor<0xf32> to tensor<?xf32>
+  // CHECK-NOT: ERROR: Runtime op verification failed
+  func.call @fill_empty_1d(%d0x) : (tensor<?xf32>) -> (tensor<?xf32>)
+
+  %c0x5 = arith.constant dense<0.0> : tensor<0x5xf32>
+  %d0x5 = tensor.cast %c0x5 : tensor<0x5xf32> to tensor<?x?xf32>
+
+  // CHECK-NOT: ERROR: Runtime op verification failed
+  func.call @fill_empty_2d(%d0x5) : (tensor<?x?xf32>) -> (tensor<?x?xf32>)
+
   return
 }
 
@@ -297,3 +308,15 @@ func.func @reverse_from_3(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
   } -> tensor<?xf32>
   return %result : tensor<?xf32>
 }
+
+func.func @fill_empty_1d(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
+  %c0 = arith.constant 0.0 : f32
+  %0 = linalg.fill ins(%c0 : f32) outs(%arg0 : tensor<?xf32>) -> tensor<?xf32>
+  return %0 : tensor<?xf32>
+}
+
+func.func @fill_empty_2d(%arg0: tensor<?x?xf32>) -> (tensor<?x?xf32>) {
+  %c0 = arith.constant 0.0 : f32
+  %0 = linalg.fill ins(%c0 : f32) outs(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %0 : tensor<?x?xf32>
+}