[Mlir-commits] [mlir] [mlir][tensor] Loosen restrictions on folding dynamic reshapes (PR #137963)

[Artem Gindinson]

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@@ -31,59 +31,70 @@ mlir::getReassociationIndicesForReshape(ShapedType sourceType,
 std::optional<SmallVector<ReassociationIndices>>
 mlir::getReassociationIndicesForCollapse(ArrayRef<int64_t> sourceShape,
                                          ArrayRef<int64_t> targetShape) {
-  if (sourceShape.size() <= targetShape.size())
+  unsigned numSourceDims = sourceShape.size(),
+           numTargetDims = targetShape.size();
+  if (numSourceDims <= numTargetDims)
     return std::nullopt;
-  unsigned sourceDim = 0;
-  SmallVector<ReassociationIndices> reassociationMap;
-  reassociationMap.reserve(targetShape.size());
-
-  ReassociationIndices currIndices;
-  int64_t prodOfCollapsedDims = 1;
-  while (sourceDim < sourceShape.size()) {
-    unsigned targetDim = reassociationMap.size();
-    // If we have mapped all the target dimensions stop and handle the remaining
-    // tail of size-1 dimensions explicitly.
-    if (targetDim == targetShape.size())
-      break;
+  SmallVector<ReassociationIndices, 4> reassociationMap;
+  reassociationMap.reserve(numTargetDims);
 
+  unsigned sourceDim = 0, targetDim = 0;
+  for (; targetDim < numTargetDims; ++targetDim) {
     int64_t currTargetShape = targetShape[targetDim];
-    while (sourceDim < (sourceShape.size() - 1) &&
-           sourceShape[sourceDim] != ShapedType::kDynamic &&
-           prodOfCollapsedDims * sourceShape[sourceDim] < currTargetShape) {
+    ReassociationIndices currIndices;
+    // 1. Target dimension is dynamic. Source shape should contain at least
+    // one dynamic dimension.
+    if (currTargetShape == ShapedType::kDynamic) {
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AGindinson wrote:

Re-checked the adjacent dynamic case, and I've clearly got lost in my own cases - it does fold right now, so I'll look for a way to disable that in the current form of the algorithm. Sorry for confusion.

Backtracking for mixed shape folds might fit in with that, so I'll try to incorporate that directly.

https://github.com/llvm/llvm-project/pull/137963