Add Scalarization Patterns for AtenToDtypeOp, AtenNegOp, AtenRemainderTensorOp

lib/Conversion/TorchToArith/TorchToArith.cpp

@@ -82,6 +82,25 @@ class ConvertAtenBinaryOp : public OpConversionPattern<AtenOp> {
 };
 } // namespace
 
+namespace {
+class ConvertAtenNegIntOp : public OpConversionPattern<AtenNegIntOp> {
+public:
+  using OpConversionPattern<AtenNegIntOp>::OpConversionPattern;
+  LogicalResult
+  matchAndRewrite(AtenNegIntOp op,
+                  typename OpConversionPattern<AtenNegIntOp>::OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    Value a = adaptor.getA();
+    rewriter.replaceOpWithNewOp<arith::SubIOp>(
+        op,
+        rewriter.create<arith::ConstantIntOp>(op.getLoc(), /*value=*/0,
+                                              /*bitwidth=*/64),
+        a);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 template <typename AtenOp, typename UnaryOp>
 class ConvertAtenUnaryOpToFloatMathOp : public OpConversionPattern<AtenOp> {
@@ -465,11 +484,14 @@ class ConvertTorchToArith
 
     target.addIllegalOp<AtenAddOp>();
     patterns.add<ConvertAtenAddOp>(typeConverter, context);
-
+    target.addIllegalOp<AtenNegIntOp>();
+    patterns.add<ConvertAtenNegIntOp>(typeConverter, context);
     target.addIllegalOp<AtenAddIntOp, AtenAddFloatIntOp, AtenSubIntOp,
-                        AtenMulIntOp>();
+                        AtenMulIntOp, AtenRemainderIntOp>();
     patterns.add<ConvertAtenBinaryOp<AtenAddIntOp, arith::AddIOp>>(
         typeConverter, context);
+    patterns.add<ConvertAtenBinaryOp<AtenRemainderIntOp, arith::RemSIOp>>(
+        typeConverter, context);
     patterns.add<ConvertAtenBinaryOp<AtenAddFloatIntOp, arith::AddFOp>>(
         typeConverter, context);
     patterns.add<ConvertAtenBinaryOp<AtenSubIntOp, arith::SubIOp>>(

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -4068,6 +4068,10 @@ OpFoldResult AtenMulIntOp::fold(FoldAdaptor adaptor) {
   int64_t lhs, rhs;
   bool lConstant = matchPattern(getOperand(0), m_TorchConstantInt(&lhs));
   bool rConstant = matchPattern(getOperand(1), m_TorchConstantInt(&rhs));
+  if (lConstant && lhs == 1)
+    return getOperand(1);
+  if (rConstant && rhs == 1)
+    return getOperand(0);
   if ((lConstant && lhs == 0) || (rConstant && rhs == 0))
     return getI64IntegerAttr(getContext(), 0);
   if (lConstant && rConstant)

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -4587,6 +4587,11 @@ class DecomposeAtenUnflattenIntOp
     if (!isValidDim(dimInt, inputRank))
       return rewriter.notifyMatchFailure(op, "dim is not a valid dim");
 
+    if (inputShape[dimInt] == Torch::kUnknownSize &&
+        llvm::count(sizesInts, -1) > 0)
+      return rewriter.notifyMatchFailure(
+          op, "Unimplemented: dynamic unflatten dim with an inferred size.");
+
     SmallVector<Value> sizesTorchInt;
     if (!getListConstructElements(op.getSizes(), sizesTorchInt))
       return rewriter.notifyMatchFailure(

lib/Dialect/Torch/Transforms/ScalarizeShapes.cpp

@@ -714,7 +714,7 @@ class PropagateAtenItemPattern : public OpRewritePattern<AtenItemOp> {
     ImplicitLocOpBuilder b(op.getLoc(), rewriter);
 
     // Rank 0 item op prop
-    if (selfTy.getSizes().size() == 0) {
+    if (selfTy.getSizes().empty()) {
       auto numToTensor = self.getDefiningOp<Torch::PrimNumToTensorScalarOp>();
       auto squeezeDim = self.getDefiningOp<AtenSqueezeDimOp>();
       if (!squeezeDim && !numToTensor)
@@ -746,6 +746,109 @@ class PropagateAtenItemPattern : public OpRewritePattern<AtenItemOp> {
 };
 } // namespace
 
+namespace {
+
+LogicalResult convertOpFoldResults(ImplicitLocOpBuilder &b,
+                                   SmallVector<OpFoldResult> &converted,
+                                   SmallVector<OpFoldResult> &elements,
+                                   Type inputDtype, Type resultDtype) {
+  auto inputIsInt = dyn_cast<mlir::IntegerType>(inputDtype);
+  auto resultIsInt = dyn_cast<mlir::IntegerType>(resultDtype);
+  if (!inputIsInt && !isa<mlir::FloatType>(inputDtype))
+    return failure();
+  if (!resultIsInt && !isa<mlir::FloatType>(resultDtype))
+    return failure();
+
+  // if dtypes are both int or both float, no conversion needed
+  if (static_cast<bool>(inputIsInt) == static_cast<bool>(resultIsInt)) {
+    converted = elements;
+    return success();
+  }
+
+  if (resultIsInt) {
+    for (auto &e : elements) {
+      auto eValue = dyn_cast<Value>(e);
+      if (eValue) {
+        converted.push_back(b.createOrFold<AtenIntScalarOp>(eValue));
+        continue;
+      }
+      auto eAttr = dyn_cast<Attribute>(e);
+      auto eFloatAttr = dyn_cast_or_null<FloatAttr>(eAttr);
+      if (!eFloatAttr)
+        return failure();
+
+      converted.push_back(IntegerAttr::get(
+          resultDtype, static_cast<int64_t>(eFloatAttr.getValueAsDouble())));
+    }
+    return success();
+  }
+
+  // result is float
+  for (auto &e : elements) {
+    auto eValue = dyn_cast<Value>(e);
+    if (eValue) {
+      converted.push_back(b.createOrFold<AtenFloatScalarOp>(eValue));
+      continue;
+    }
+    auto eAttr = dyn_cast<Attribute>(e);
+    auto eIntAttr = dyn_cast<IntegerAttr>(eAttr);
+    if (!eIntAttr)
+      return failure();
+
+    auto eInt = (inputIsInt.isSigned()) ? eIntAttr.getValue().getSExtValue()
+                                        : eIntAttr.getValue().getZExtValue();
+    converted.push_back(FloatAttr::get(resultDtype, static_cast<double>(eInt)));
+  }
+  return success();
+}
+
+class PropagateAtenToDtypePattern : public OpRewritePattern<AtenToDtypeOp> {
+public:
+  using OpRewritePattern<AtenToDtypeOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenToDtypeOp op,
+                                PatternRewriter &rewriter) const override {
+    bool nonBlocking, copyArg;
+    // The non_blocking arg must be `False`.
+    if (!matchPattern(op.getNonBlocking(), m_TorchConstantBool(&nonBlocking)) ||
+        nonBlocking)
+      return failure();
+    // The copy arg must be `False`.
+    if (!matchPattern(op.getCopy(), m_TorchConstantBool(&copyArg)) || copyArg)
+      return failure();
+    // The memory_format arg must be `none`.
+    if (!isa<Torch::NoneType>(op.getMemoryFormat().getType()))
+      return failure();
+
+    auto inputType = dyn_cast<ValueTensorType>(op.getSelf().getType());
+    auto resultType = dyn_cast<ValueTensorType>(op.getType());
+    if (!inputType || !resultType || !inputType.hasDtype() ||
+        !resultType.hasDtype())
+      return failure();
+    auto inputDtype = inputType.getDtype();
+    auto resultDtype = resultType.getDtype();
+
+    SmallVector<OpFoldResult> elements;
+    if (failed(getListFromTensor(op.getSelf(), elements)))
+      return failure();
+
+    ImplicitLocOpBuilder b(op.getLoc(), rewriter);
+    SmallVector<OpFoldResult> converted;
+    if (failed(convertOpFoldResults(b, converted, elements, inputDtype,
+                                    resultDtype)))
+      return rewriter.notifyMatchFailure(
+          op, "Unhandled attribute type encountered.");
+
+    SmallVector<Value> vals;
+    if (failed(materializeFolds(b, converted, vals)))
+      return failure();
+
+    Value result = constructAtenTensorOpFromList(b, op.getType(), vals);
+    rewriter.replaceOp(op, result);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 template <typename AtenViewLikeOp>
 class PropagateAtenViewLikePattern : public OpRewritePattern<AtenViewLikeOp> {
@@ -828,7 +931,7 @@ class PropagateAtenArithmeticPattern : public OpRewritePattern<OpTy> {
     if (failed(materializeFolds(b, resultFolds, resultVals)))
       return failure();
 
-    if (resultTy.getSizes().size() == 0) {
+    if (resultTy.getSizes().empty()) {
       rewriter.replaceOpWithNewOp<Torch::PrimNumToTensorScalarOp>(
           op, resultTy, resultVals.front());
       return success();
@@ -841,6 +944,48 @@ class PropagateAtenArithmeticPattern : public OpRewritePattern<OpTy> {
 };
 } // namespace
 
+namespace {
+template <typename OpTy, typename ScalarOpTy>
+class PropagateAtenUnaryPattern : public OpRewritePattern<OpTy> {
+public:
+  using OpRewritePattern<OpTy>::OpRewritePattern;
+  LogicalResult matchAndRewrite(OpTy op,
+                                PatternRewriter &rewriter) const override {
+    // Check type
+    auto resultTy = cast<ValueTensorType>(op.getType());
+    if (resultTy.getSizes().size() > 1)
+      return rewriter.notifyMatchFailure(op, "unsupported: rank > 1");
+    if (!resultTy.hasDtype() || !isa<mlir::IntegerType>(resultTy.getDtype()))
+      return rewriter.notifyMatchFailure(op, "not an int type");
+
+    ImplicitLocOpBuilder b(op.getLoc(), rewriter);
+    SmallVector<OpFoldResult> selfFold;
+    if (failed(getListFromTensor(op.getSelf(), selfFold)))
+      return failure();
+    SmallVector<Value> selfVals;
+    if (failed(materializeFolds(b, selfFold, selfVals)))
+      return failure();
+    SmallVector<OpFoldResult> resultFolds;
+    for (uint64_t i = 0; i < selfVals.size(); i++) {
+      resultFolds.push_back(
+          b.createOrFold<ScalarOpTy>(selfVals[i].getType(), selfVals[i]));
+    }
+    SmallVector<Value> resultVals;
+    if (failed(materializeFolds(b, resultFolds, resultVals)))
+      return failure();
+
+    if (resultTy.getSizes().size() == 0) {
+      rewriter.replaceOpWithNewOp<Torch::PrimNumToTensorScalarOp>(
+          op, resultTy, resultVals.front());
+      return success();
+    }
+
+    Value result = constructAtenTensorOpFromList(b, resultTy, resultVals);
+    rewriter.replaceOp(op, result);
+    return success();
+  }
+};
+} // namespace
 /// ------ Fold Patterns ------ ///
 // These are shape-specific folding patterns
 
@@ -915,19 +1060,22 @@ class FoldAtenTensorSplatPattern : public OpRewritePattern<AtenTensorOp> {
     auto resultTy = cast<BaseTensorType>(op.getType());
     if (!resultTy.hasSizes() || !resultTy.areAllSizesKnown())
       return rewriter.notifyMatchFailure(op, "dynamic output shape");
+    if (resultTy.getSizes().size() == 0) {
+      rewriter.replaceOpWithNewOp<Torch::PrimNumToTensorScalarOp>(
+          op, op.getType(), elements.front());
+      return success();
+    }
 
     auto loc = op.getLoc();
     SmallVector<Value> sizes;
     for (auto size : resultTy.getSizes())
       sizes.push_back(rewriter.create<Torch::ConstantIntOp>(
           loc, rewriter.getI64IntegerAttr(size)));
 
-    Value one = rewriter.create<Torch::ConstantIntOp>(
-        loc, rewriter.getType<Torch::IntType>(), 1);
     Value sizeList = rewriter.create<Torch::PrimListConstructOp>(
         loc,
         rewriter.getType<Torch::ListType>(rewriter.getType<Torch::IntType>()),
-        one);
+        sizes);
 
     Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
     Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
@@ -1031,6 +1179,24 @@ class FoldAtenWhereSelf : public OpRewritePattern<AtenWhereSelfOp> {
 };
 } // namespace
 
+namespace {
+// fold ridiculous patterns like size.int -> float.scalar -> int.scalar
+class FoldAtenIntScalarPattern : public OpRewritePattern<AtenIntScalarOp> {
+public:
+  using OpRewritePattern<AtenIntScalarOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenIntScalarOp op,
+                                PatternRewriter &rewriter) const override {
+    auto floatScalarOp = op.getA().getDefiningOp<AtenFloatScalarOp>();
+    if (!floatScalarOp)
+      return failure();
+    auto sizeOp = floatScalarOp.getA().getDefiningOp<AtenSizeIntOp>();
+    if (!sizeOp)
+      return failure();
+    rewriter.replaceOp(op, floatScalarOp.getA());
+    return success();
+  }
+};
+} // namespace
 namespace {
 class FoldAtenUnsqueezePattern : public OpRewritePattern<AtenUnsqueezeOp> {
 public:
@@ -1182,8 +1348,29 @@ class CanonicalizeAtenViewPattern : public OpRewritePattern<AtenViewOp> {
     if (inputUnmatched == 1 && outputUnmatched > 1) {
       Value dimVal =
           rewriter.create<Torch::ConstantIntOp>(op.getLoc(), leftMatchEnd);
-      ArrayRef<Value> unflattenSizes(viewSizes.begin() + leftMatchEnd,
-                                     viewSizes.end() - rightMatchEnd);
+      SmallVector<Value> unflattenSizes(viewSizes.begin() + leftMatchEnd,
+                                        viewSizes.end() - rightMatchEnd);
+      // try to convert a single dynamic size input to -1
+      int64_t dynCount = 0;
+      int64_t dynIdx = 0;
+      for (auto [i, v] : llvm::enumerate(unflattenSizes)) {
+        int64_t szeInt;
+        if (!matchPattern(v, m_TorchConstantInt(&szeInt))) {
+          dynCount++;
+          dynIdx = i;
+          continue;
+        }
+        // if we have a -1 already, make dynCount invalid and break
+        if (szeInt == -1) {
+          dynCount = -1;
+          break;
+        }
+      }
+      // if only one size is dynamic, make it -1
+      if (dynCount == 1)
+        unflattenSizes[dynIdx] =
+            rewriter.create<Torch::ConstantIntOp>(op.getLoc(), -1);
+
       Value unflattenList = rewriter.create<Torch::PrimListConstructOp>(
           op.getLoc(), op.getSize().getType(), unflattenSizes);
       rewriter.replaceOpWithNewOp<AtenUnflattenIntOp>(
@@ -1227,6 +1414,18 @@ template <typename T> class RemoveUnusedPattern : public OpRewritePattern<T> {
 
 namespace {
 
+bool isItemForSliceOp(Operation *op) {
+  auto itemOp = dyn_cast_or_null<AtenItemOp>(op);
+  if (!itemOp)
+    return false;
+  for (OpOperand &use : op->getUses()) {
+    Operation *userOp = use.getOwner();
+    if (isa<AtenSliceTensorOp>(userOp))
+      return true;
+  }
+  return false;
+}
+
 bool isSourceOpForShapeScalarization(Operation *op) {
   return llvm::isa<AtenSizeIntOp, Torch::ConstantIntOp, Torch::ConstantBoolOp,
                    Aten_ShapeAsTensorOp, Torch::ValueTensorLiteralOp>(op);
@@ -1244,7 +1443,7 @@ bool isPrimListOfInts(Operation *op) {
 
 bool isAnchorOp(Operation *op) {
   return isa<Torch::RuntimeAssertOp>(op) || isa<AtenArangeStartStepOp>(op) ||
-         isPrimListOfInts(op);
+         isPrimListOfInts(op) || isItemForSliceOp(op);
 }
 
 // The argument to this function, op, is the use of some source op, srcOp. If
@@ -1278,9 +1477,9 @@ bool isInvalidValidViewConsumer(Operation *op,
 void populateScalarizationFoldPatterns(RewritePatternSet &patterns) {
   patterns.insert<FoldAtenSqueezePattern<AtenSqueezeOp>,
                   FoldAtenSqueezePattern<AtenSqueezeDimOp>,
-                  FoldAtenUnsqueezePattern, FoldAtenWhereSelf,
-                  FoldAtenTensorSplatPattern, FoldAtenEqIntPattern>(
-      patterns.getContext());
+                  FoldAtenIntScalarPattern, FoldAtenUnsqueezePattern,
+                  FoldAtenWhereSelf, FoldAtenTensorSplatPattern,
+                  FoldAtenEqIntPattern>(patterns.getContext());
 }
 
 void populateScalarizationCanonicalizePatterns(RewritePatternSet &patterns) {
@@ -1303,24 +1502,29 @@ void populateScalarizationPropagationPatterns(RewritePatternSet &patterns) {
       PropagateAtenItemPattern, PropagateAtenShapeToTensorPattern,
       PropagateAtenSliceTensorPattern, PropagateAtenEqTensorPattern,
       PropagateAtenWhereSelfPattern, PropagateAtenBroadcastToPattern,
-      PropagateAtenTransposeIntPattern,
+      PropagateAtenTransposeIntPattern, PropagateAtenToDtypePattern,
+      PropagateAtenUnaryPattern<AtenNegOp, AtenNegIntOp>,
       PropagateAtenArithmeticPattern<AtenAddTensorOp, AtenAddIntOp>,
       PropagateAtenArithmeticPattern<AtenSubTensorOp, AtenSubIntOp>,
       PropagateAtenArithmeticPattern<AtenMulTensorOp, AtenMulIntOp>,
+      PropagateAtenArithmeticPattern<AtenRemainderTensorOp, AtenRemainderIntOp>,
       PropagateAtenArithmeticPattern<AtenDivTensorOp, AtenFloordivIntOp>>(
       patterns.getContext());
 }
 
 void populateScalarizationRemovePatterns(RewritePatternSet &patterns) {
   patterns.insert<RemoveUnusedPattern<Torch::AtenIntBoolOp>,
                   RemoveUnusedPattern<Torch::AtenEqIntOp>,
+                  RemoveUnusedPattern<Torch::AtenToDtypeOp>,
                   RemoveUnusedPattern<Torch::PrimNumToTensorScalarOp>,
                   RemoveUnusedPattern<Torch::AtenFullOp>,
                   RemoveUnusedPattern<Torch::AtenUnsqueezeOp>,
                   RemoveUnusedPattern<Torch::AtenSqueezeDimOp>,
                   RemoveUnusedPattern<Torch::AtenSizeIntOp>,
                   RemoveUnusedPattern<Torch::AtenSliceTensorOp>,
                   RemoveUnusedPattern<Torch::AtenTensorOp>,
+                  RemoveUnusedPattern<Torch::AtenFloatScalarOp>,
+                  RemoveUnusedPattern<Torch::AtenIntScalarOp>,
                   RemoveUnusedPattern<Torch::PrimListConstructOp>>(
       patterns.getContext());
 }