commit 5a48912c52f65ead960bec9bfde5a836d1b02ab2
author Max191 <44243577+Max191@users.noreply.github.com> Fri Aug 09 10:39:13 2024 -0700
committer GitHub <noreply@github.com> Fri Aug 09 17:39:13 2024 +0000
tree cfca41174c07993cd43731d31f566ee918121f9e
parent ab12a4effa1c59c41a8422daac198b8e34eed65e

[GPU] Add check for contractionOpInterface in setMatmulLoweringConfig (#18178)

The `setMatmulLoweringConfig` function only checks that
`linalg::inferContractionDims` returns contraction dimensions, but not
that the operation is a contraction. The inferContractionDims function
does not check that maps are projected permutations, so it can infer
contraction dims on non-pure contraction ops, like convolutions. This
causes a segmentation fault because the assumption of
`setMatmulLoweringConfig` is that there the op is a pure contraction.
This fixes the bug by checking for `linalg::isaContractionOpInterface`
as well.

---------

Signed-off-by: Max Dawkins <max.dawkins@gmail.com>

compiler/src/iree/compiler/Codegen/Dialect/GPU/TargetUtils/ConfigUtils.cpp

diff --git a/compiler/src/iree/compiler/Codegen/Dialect/GPU/TargetUtils/ConfigUtils.cpp b/compiler/src/iree/compiler/Codegen/Dialect/GPU/TargetUtils/ConfigUtils.cpp
index b10e605..2f5a48b 100644
--- a/compiler/src/iree/compiler/Codegen/Dialect/GPU/TargetUtils/ConfigUtils.cpp
+++ b/compiler/src/iree/compiler/Codegen/Dialect/GPU/TargetUtils/ConfigUtils.cpp
@@ -29,7 +29,7 @@
                                       mlir::FunctionOpInterface entryPoint,
                                       Operation *op) {
   auto linalgOp = dyn_cast<linalg::LinalgOp>(op);
-  if (!linalgOp) {
+  if (!linalgOp || !linalg::isaContractionOpInterface(linalgOp)) {
     return failure();
   }
 
@@ -39,23 +39,20 @@
   const int64_t targetSubgroupSize = target.getPreferredSubgroupSize();
 
   SmallVector<int64_t, 4> bounds = linalgOp.getStaticLoopRanges();
-  FailureOr<mlir::linalg::ContractionDimensions> contractionDims =
-      mlir::linalg::inferContractionDims(linalgOp);
-  if (failed(contractionDims)) {
-    return failure();
-  }
+  mlir::linalg::ContractionDimensions contractionDims =
+      mlir::linalg::inferContractionDims(linalgOp).value();
 
-  if (contractionDims->k.empty() || contractionDims->m.empty() ||
-      contractionDims->n.empty()) {
+  if (contractionDims.k.empty() || contractionDims.m.empty() ||
+      contractionDims.n.empty()) {
     return failure();
   }
 
   // For now we are not being smart and trying to reshape dimensions to allow
   // for better usage of intrinsics, and instead are tiling all dimensions
   // except the inner most m, n, and k dimensions to 1.
-  int64_t mDim = contractionDims->m.back();
-  int64_t nDim = contractionDims->n.back();
-  int64_t kDim = contractionDims->k.back();
+  int64_t mDim = contractionDims.m.back();
+  int64_t nDim = contractionDims.n.back();
+  int64_t kDim = contractionDims.k.back();
 
   // Dynamic dims are expected to be taken care of earlier in the pipeline.
   if (ShapedType::isDynamic(bounds[mDim]) ||
@@ -159,19 +156,19 @@
   SmallVector<int64_t> reductionTileSizes(linalgOp.getNumLoops(), 0);
   SmallVector<int64_t> subgroupTileSizes(linalgOp.getNumLoops(), 0);
   // Tile all batch dimensions with unit size.
-  for (int64_t batch : contractionDims->batch) {
+  for (int64_t batch : contractionDims.batch) {
     workgroupTileSizes[batch] = 1;
   }
 
   // Tile all m, n, and k dimensions to 1 except the innermost. Unit dims
   // from this tiling are folded before vectorization.
-  for (int64_t m : llvm::drop_end(contractionDims->m)) {
+  for (int64_t m : llvm::drop_end(contractionDims.m)) {
     workgroupTileSizes[m] = 1;
   }
-  for (int64_t n : llvm::drop_end(contractionDims->n)) {
+  for (int64_t n : llvm::drop_end(contractionDims.n)) {
     workgroupTileSizes[n] = 1;
   }
-  for (int64_t k : llvm::drop_end(contractionDims->k)) {
+  for (int64_t k : llvm::drop_end(contractionDims.k)) {
     reductionTileSizes[k] = 1;
   }