mlir/TosaToLinalg: improve debugging during conversion

[artagnon]

Summary

Make systematic use of notifyMatchFailure.

Signed-off-by: Ramkumar Ramachandra <[e-mail]>

[NatashaKnk]

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

2157

I think this can more specifically note "tosa.gather currently only supports dynamic batch dimensions", as checkHasDynamicBatchDims only returns llvm::None when it finds a dynamic dim that isn't the batch one.

[artagnon]

Address review comment by @NatashaKnk.

[artagnon]

Done, thanks :)

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

auto linalgOp = rewriter.create<linalg::GenericOp>(
         if (!opResult) {
           didEncounterError = true;
           return;
         }
         nestedBuilder.create<linalg::YieldOp>(loc, opResult);
       });
 
   if (didEncounterError)
-    return failure();
+    return rewriter.notifyMatchFailure(
+        operation, "unable to create linalg.generic body for elementwise op");
 
   rewriter.replaceOp(operation, linalgOp->getResults());
   return success();
 }
 
 // Returns the constant initial value for a given reduction operation. The
 // attribute type varies depending on the element type required.
 static Attribute createInitialValueForReduceOp(Operation *op, Type elementTy,
auto linalgOp = rewriter.create<linalg::GenericOp>(
             op, blockArgs, elementTy, rewriter);
         if (result)
           didEncounterError = true;
 
         nestedBuilder.create<linalg::YieldOp>(loc, result);
       });
 
   if (!didEncounterError)
-    return failure();
+    return rewriter.notifyMatchFailure(
+        op, "unable to create linalg.generic body for reduce op");
 
   SmallVector<ReassociationExprs, 4> reassociationMap;
   uint64_t expandInputRank =
       linalgOp.getResults()[0].getType().cast<ShapedType>().getRank();
   reassociationMap.resize(expandInputRank);
 
   for (uint64_t i = 0; i < expandInputRank; i++) {
     int32_t dimToPush = i > axis ? i + 1 : i;

 class TransposeConverter : public OpRewritePattern<tosa::TransposeOp> {
 public:
   using OpRewritePattern<tosa::TransposeOp>::OpRewritePattern;
 
   LogicalResult matchAndRewrite(tosa::TransposeOp op,
                                 PatternRewriter &rewriter) const final {
     DenseIntElementsAttr perms;
     if (!matchPattern(op.getPerms(), m_Constant(&perms))) {
-      return failure();
+      return rewriter.notifyMatchFailure(op, "unmatched permutation tensor");
     }
 
     auto loc = op.getLoc();
     auto input = op->getOperand(0);
     auto resultTy = op.getType().cast<ShapedType>();
 
     SmallVector<Value> dynDims;
     dynDims.resize(op->getResult(0).getType().cast<ShapedType>().getRank());
LogicalResult matchAndRewrite(tosa::ResizeOp op,
 
     if (imageH != 1 || imageW != 1) {
       return rewriter.notifyMatchFailure(
           op, "tosa.resize is not a pure broadcast operation");
     }
 
     // TODO(suderman): These string values should be declared the TOSA dialect.
     if (op.getMode() != "NEAREST_NEIGHBOR" && op.getMode() != "BILINEAR")
-      return failure();
+      return rewriter.notifyMatchFailure(
+          op, "tosa.resize mode should be NEAREST_NEIGHBOR or BILINEAR");
 
     const bool isBilinear = op.getMode() == "BILINEAR";
 
     SmallVector<int32_t> scale;
     getValuesFromIntArrayAttribute(op.getScale(), scale);
 
     // Collapse the 1 dimensions away.
     SmallVector<ReassociationExprs, 4> collapseMap(2);
LogicalResult matchAndRewrite(tosa::ResizeOp op,
     auto resultElementTy = resultTy.getElementType();
 
     auto imageH = inputTy.getShape()[1];
     auto imageW = inputTy.getShape()[2];
 
     auto dynamicDimsOr =
         checkHasDynamicBatchDims(rewriter, op, {input, op.getOutput()});
     if (!dynamicDimsOr.has_value())
-      return failure();
+      return rewriter.notifyMatchFailure(
+          op, "unable to get dynamic dimensions of tosa.resize");
     SmallVector<Value> dynamicDims = dynamicDimsOr.value();
 
     if (op.getMode() != "NEAREST_NEIGHBOR" && op.getMode() != "BILINEAR")
-      return failure();
+      return rewriter.notifyMatchFailure(
+          op, "tosa.resize mode should be NEAREST_NEIGHBOR or BILINEAR");
 
     auto emptyTensor = rewriter.create<tensor::EmptyOp>(
         loc, resultTy.getShape(), resultElementTy, dynamicDims);
 
     SmallVector<AffineMap, 2> affineMaps = {
         rewriter.getMultiDimIdentityMap(resultTy.getRank())};
 
     Value resize = input;
matchAndRewrite(tosa::GatherOp op, OpAdaptor adaptor,
     auto input = adaptor.getOperands()[0];
     auto indices = adaptor.getOperands()[1];
 
     auto resultTy = op.getType().cast<ShapedType>();
 
     auto dynamicDimsOr = checkHasDynamicBatchDims(
         rewriter, op, {input, indices, op.getOutput()});
     if (!dynamicDimsOr.has_value())
-      return failure();
+      return rewriter.notifyMatchFailure(
+          op, "unable to get dynamic dimensions of tosa.gather");
     SmallVector<Value> dynamicDims = dynamicDimsOr.value();
 
     auto resultElementTy = resultTy.getElementType();
 
     auto loc = op.getLoc();
 
     auto emptyTensor =
         rewriter