[Mlir-commits] [mlir] [MLIR] Add shape propagation through tensor.pad (PR #136681)

[llvmlistbot at llvm.org]

================
@@ -1100,6 +1100,198 @@ class FoldPadWithProducerReshapeOpByExpansion
   ControlFusionFn controlFoldingReshapes;
 };
 
+/// Pattern to fold a tensor.expand_shape op with its producer tensor.pad op
+/// by bubbling the expand_shape before the pad.
+struct FoldReshapeWithProducerPadOpByExpansion
+    : public OpRewritePattern<tensor::ExpandShapeOp> {
+
+  FoldReshapeWithProducerPadOpByExpansion(MLIRContext *context,
+                                          ControlFusionFn foldReshapes,
+                                          PatternBenefit benefit = 1)
+      : OpRewritePattern<tensor::ExpandShapeOp>(context, benefit),
+        controlFoldingReshapes(std::move(foldReshapes)) {}
+
+  LogicalResult matchAndRewrite(tensor::ExpandShapeOp expandOp,
+                                PatternRewriter &rewriter) const override {
+    tensor::PadOp padOp = expandOp.getSrc().getDefiningOp<tensor::PadOp>();
+    if (!padOp)
+      return failure();
+
+    if (!padOp->hasOneUse())
+      return failure();
+
+    if (!controlFoldingReshapes(&expandOp.getSrcMutable())) {
+      return rewriter.notifyMatchFailure(expandOp,
+                                         "fusion blocked by control function");
+    }
+
+    Value constantPaddingValue = padOp.getConstantPaddingValue();
+    if (!constantPaddingValue) {
+      return rewriter.notifyMatchFailure(
+          expandOp, "cannot fold with non-constant padding value");
+    }
+
+    SmallVector<ReassociationIndices> reassociations =
+        expandOp.getReassociationIndices();
+    SmallVector<OpFoldResult> low = padOp.getMixedLowPad();
+    SmallVector<OpFoldResult> high = padOp.getMixedHighPad();
+
+    for (auto [reInd, l, h] : llvm::zip_equal(reassociations, low, high)) {
+      if (reInd.size() > 1 &&
+          (!isConstantIntValue(l, 0) || !isConstantIntValue(h, 0)))
+        return rewriter.notifyMatchFailure(
+            expandOp, "fusion blocked by non-zero padding");
+    }
+
+    SmallVector<OpFoldResult> newLow, newHigh;
+    for (auto [idx, reInd] : llvm::enumerate(reassociations)) {
+      newLow.append(reInd.size(), low[idx]);
+      newHigh.append(reInd.size(), high[idx]);
+    }
+
+    Location loc = expandOp.getLoc();
+    ArrayRef<int64_t> finalShape = expandOp.getResultType().getShape();
+    SmallVector<OpFoldResult> expandedShape = expandOp.getMixedOutputShape();
+
+    for (auto [inDimIdx, reInd] : llvm::enumerate(reassociations)) {
+      OpFoldResult l = low[inDimIdx];
+      OpFoldResult h = high[inDimIdx];
+
+      if (!isConstantIntValue(l, 0) || !isConstantIntValue(h, 0)) {
+        assert(reInd.size() == 1 && "expected single dimension");
+        expandedShape[reInd[0]] =
+            tensor::getMixedSize(rewriter, loc, padOp.getSource(), inDimIdx);
+        ;
+      }
+    }
+
+    for (auto [outDimIdx, dimSize] : llvm::enumerate(finalShape)) {
+      if (dimSize == ShapedType::kDynamic &&
+          !isa<Value>(expandedShape[outDimIdx]) &&
+          !isa<Attribute>(expandedShape[outDimIdx])) {
+        expandedShape[outDimIdx] =
+            tensor::getMixedSize(rewriter, loc, expandOp.getSrc(), outDimIdx);
+      }
+    }
----------------
Max191 wrote:

You can delete this loop because `expandOp.getMixedOutputShape()` will already populate the expandedShape with the right dynamic values.

https://github.com/llvm/llvm-project/pull/136681