[mlir][tosa] Switch zero point of negate to input variable type

This commit changes the zero point attribute to an input to align
with the 1.0 spec.

Change-Id: Ibc9e5959b36c182a9e0c5c23a2f9d42a572a1184
Signed-off-by: Tai Ly <[email protected]>

Author: lhutton1

mlir/include/mlir/Dialect/Tosa/IR/TosaComplianceData.h.inc

@@ -112,8 +112,12 @@ profileComplianceMap = {
     {"tosa.logical_not",
      {{{Profile::pro_int, Profile::pro_fp}, {{boolT, boolT}}}}},
     {"tosa.negate",
-     {{{Profile::pro_int}, {{i8T, i8T}, {i16T, i16T}, {i32T, i32T}}},
-      {{Profile::pro_fp}, {{fp16T, fp16T}, {fp32T, fp32T}}}}},
+     {{{Profile::pro_int},
+       {{i8T, i8T, i8T, i8T},
+        {i16T, i16T, i16T, i16T},
+        {i32T, i32T, i32T, i32T}}},
+      {{Profile::pro_fp},
+       {{fp16T, fp16T, fp16T, fp16T}, {fp32T, fp32T, fp32T, fp32T}}}}},
     {"tosa.reciprocal",
      {{{Profile::pro_fp}, {{fp16T, fp16T}, {fp32T, fp32T}}}}},
     {"tosa.rsqrt", {{{Profile::pro_fp}, {{fp16T, fp16T}, {fp32T, fp32T}}}}},
@@ -308,7 +312,7 @@ extensionComplianceMap = {
     {"tosa.exp", {{{Extension::bf16}, {{bf16T, bf16T}}}}},
     {"tosa.floor", {{{Extension::bf16}, {{bf16T, bf16T}}}}},
     {"tosa.log", {{{Extension::bf16}, {{bf16T, bf16T}}}}},
-    {"tosa.negate", {{{Extension::bf16}, {{bf16T, bf16T}}}}},
+    {"tosa.negate", {{{Extension::bf16}, {{bf16T, bf16T, bf16T, bf16T}}}}},
     {"tosa.reciprocal", {{{Extension::bf16}, {{bf16T, bf16T}}}}},
     {"tosa.rsqrt", {{{Extension::bf16}, {{bf16T, bf16T}}}}},
     {"tosa.select", {{{Extension::bf16}, {{bf16T, bf16T, bf16T}}}}},

mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td

@@ -178,13 +178,13 @@ def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilder<
                                   input, kernel, stride, pad, acc_type);
   }]>;
 
-// This builder is called on single-parameter unary operators that have a scale
+// This builder is called on single-parameter negate operators that have a scale
 // relationship between their input and output, expressed by the
 // UnaryOpQuantizationAttr.
-def Tosa_UnaryOpQuantInfoBuilder : OpBuilder<
+def Tosa_NegateOpQuantInfoBuilder : OpBuilder<
   (ins "Type":$outputType, "Value":$input),
   [{
-    buildUnaryOpWithQuantInfo($_builder, $_state, outputType, input);
+    buildNegateOpWithQuantInfo($_builder, $_state, outputType, input);
   }]>;
 
 // These builders are called on the TOSA pad operator that needs to create its

mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td

@@ -1349,7 +1349,9 @@ def Tosa_LogicalNotOp : Tosa_ElementwiseUnaryOp<"logical_not"> {
 //===----------------------------------------------------------------------===//
 // Operator: negate
 //===----------------------------------------------------------------------===//
-def Tosa_NegateOp : Tosa_ElementwiseUnaryOp<"negate"> {
+def Tosa_NegateOp : Tosa_InferShapedTypeOp<"negate", [
+    TosaElementwiseOperator,
+    Pure]> {
   let summary = "Elementwise negate op";
 
   let description = [{
@@ -1358,8 +1360,8 @@ def Tosa_NegateOp : Tosa_ElementwiseUnaryOp<"negate"> {
 
   let arguments = (ins
       Tosa_Tensor:$input1,
-      OptionalAttr<I32Attr>:$input1_zp,
-      OptionalAttr<I32Attr>:$output_zp
+      Tosa_ScalarIntOrFloatTensor:$input1_zp,
+      Tosa_ScalarIntOrFloatTensor:$output_zp
   );
 
   let results = (outs
@@ -1371,9 +1373,20 @@ def Tosa_NegateOp : Tosa_ElementwiseUnaryOp<"negate"> {
     Extension<[Tosa_EXT_BF16]>,
   ];
 
-  let builders = [Tosa_UnaryOpQuantInfoBuilder];
+  let builders = [Tosa_NegateOpQuantInfoBuilder];
+
+  let extraClassDeclaration = [{
+    FailureOr<int64_t> getInput1ZeroPoint();
+    FailureOr<int64_t> getOutputZeroPoint();
+    LogicalResult verifyInput1ZeroPoint(int64_t zp);
+    LogicalResult verifyOutputZeroPoint(int64_t zp);
+  }];
 
   let hasFolder = 1;
+  let hasVerifier = 1;
+
+  let assemblyFormat =
+      "operands attr-dict `:` functional-type(operands, results)";
 }
 
 //===----------------------------------------------------------------------===//

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

@@ -193,18 +193,29 @@ static Value createLinalgBodyCalculationForElementwiseOp(
 
   // tosa::NegateOp
   if (isa<tosa::NegateOp>(op)) {
-    if (isa<FloatType>(elementTy))
-      return rewriter.create<arith::NegFOp>(loc, resultTypes, args);
+    auto negate = cast<tosa::NegateOp>(op);
 
-    if (isa<IntegerType>(elementTy)) {
-      auto inputZpAttr = cast<tosa::NegateOp>(op).getInput1ZpAttr();
-      auto outputZpAttr = cast<tosa::NegateOp>(op).getOutputZpAttr();
+    FailureOr<int64_t> maybeInZp = negate.getInput1ZeroPoint();
+    if (failed(maybeInZp)) {
+      (void)rewriter.notifyMatchFailure(
+          op, "input1 zero point cannot be statically determined");
+      return nullptr;
+    }
+
+    FailureOr<int64_t> maybeOutZp = negate.getOutputZeroPoint();
+    if (failed(maybeOutZp)) {
+      (void)rewriter.notifyMatchFailure(
+          op, "output zero point cannot be statically determined");
+      return nullptr;
+    }
 
-      const int64_t inZp =
-          inputZpAttr ? inputZpAttr.getValue().getSExtValue() : 0;
-      const int64_t outZp =
-          outputZpAttr ? outputZpAttr.getValue().getSExtValue() : 0;
+    int64_t inZp = *maybeInZp;
+    int64_t outZp = *maybeOutZp;
 
+    if (isa<FloatType>(elementTy))
+      return rewriter.create<arith::NegFOp>(loc, resultTypes, args[0]);
+
+    if (isa<IntegerType>(elementTy)) {
       if (!inZp && !outZp) {
         auto constant = rewriter.create<arith::ConstantOp>(
             loc, IntegerAttr::get(elementTy, 0));

mlir/lib/Dialect/Tosa/IR/ShardingInterfaceImpl.cpp

@@ -60,6 +60,45 @@ struct MatMulOpSharding
   }
 };
 
+struct NegateOpSharding
+    : public ShardingInterface::ExternalModel<NegateOpSharding, NegateOp> {
+  SmallVector<utils::IteratorType> getLoopIteratorTypes(Operation *op) const {
+    Value val = op->getOperand(0);
+    auto type = dyn_cast<RankedTensorType>(val.getType());
+    if (!type)
+      return {};
+    SmallVector<utils::IteratorType> types(type.getRank(),
+                                           utils::IteratorType::parallel);
+    return types;
+  }
+
+  SmallVector<AffineMap> getIndexingMaps(Operation *op) const {
+    MLIRContext *ctx = op->getContext();
+    Value val = op->getOperand(0);
+    auto type = dyn_cast<RankedTensorType>(val.getType());
+    if (!type)
+      return {};
+    int64_t rank = type.getRank();
+    SmallVector<AffineMap> maps = {
+        AffineMap::getMultiDimIdentityMap(rank, ctx),
+        AffineMap::get(0, 0, {}, ctx), AffineMap::get(0, 0, {}, ctx),
+        AffineMap::getMultiDimIdentityMap(rank, ctx)};
+    return maps;
+  }
+
+  LogicalResult spmdize(Operation *op, ArrayRef<Value> spmdizedOperands,
+                        ArrayRef<MeshSharding> operandShardings,
+                        ArrayRef<MeshSharding> resultShardings,
+                        IRMapping &spmdizationMap,
+                        SymbolTableCollection &symbolTable,
+                        OpBuilder &builder) const {
+    spmdizeTriviallyShardableOperation(*op, spmdizedOperands, operandShardings,
+                                       resultShardings, spmdizationMap,
+                                       symbolTable, builder);
+    return success();
+  }
+};
+
 template <typename OpType>
 static void registerElemwiseOne(MLIRContext *ctx) {
   OpType::template attachInterface<ElementwiseShardingInterface<OpType>>(*ctx);
@@ -82,9 +121,10 @@ void mlir::tosa::registerShardingInterfaceExternalModels(
         BitwiseOrOp, BitwiseXorOp, IntDivOp, LogicalAndOp, LogicalLeftShiftOp,
         LogicalRightShiftOp, LogicalOrOp, LogicalXorOp, MaximumOp, MinimumOp,
         MulOp, PowOp, SubOp, AbsOp, BitwiseNotOp, CeilOp, ClzOp, ExpOp, FloorOp,
-        LogOp, LogicalNotOp, NegateOp, ReciprocalOp, RsqrtOp, SelectOp, EqualOp,
+        LogOp, LogicalNotOp, ReciprocalOp, RsqrtOp, SelectOp, EqualOp,
         GreaterOp, GreaterEqualOp>(ctx);
 
     MatMulOp::attachInterface<MatMulOpSharding>(*ctx);
+    NegateOp::attachInterface<NegateOpSharding>(*ctx);
   });
 }

mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp

@@ -1143,13 +1143,36 @@ OpFoldResult tosa::ExpOp::fold(FoldAdaptor adaptor) {
 }
 
 OpFoldResult tosa::NegateOp::fold(FoldAdaptor adaptor) {
-  auto input = getInput1();
   // Element-wise negate(negate(x)) = x
-  if (auto op = input.getDefiningOp<tosa::NegateOp>()) {
-    return op.getInput1();
+  // iff all zero points are constant 0
+  auto definingOp = getInput1().getDefiningOp<tosa::NegateOp>();
+  if (!definingOp) {
+    // defining op of input1 is not a negate, cannot fold
+    return {};
   }
 
-  return {};
+  if (FailureOr<int64_t> maybeIZp = getInput1ZeroPoint();
+      failed(maybeIZp) || *maybeIZp != 0) {
+    // input1 zero point is not constant 0, cannot fold
+    return {};
+  }
+  if (FailureOr<int64_t> maybeOZp = getOutputZeroPoint();
+      failed(maybeOZp) || *maybeOZp != 0) {
+    // output zero point is not constant 0, cannot fold
+    return {};
+  }
+  if (FailureOr<int64_t> maybeIZp = definingOp.getInput1ZeroPoint();
+      failed(maybeIZp) || *maybeIZp != 0) {
+    // definingOp's input1 zero point is not constant 0, cannot fold
+    return {};
+  }
+  if (FailureOr<int64_t> maybeOZp = definingOp.getOutputZeroPoint();
+      failed(maybeOZp) || *maybeOZp != 0) {
+    // definingOp's output zero point is not constant 0, cannot fold
+    return {};
+  }
+
+  return definingOp.getInput1();
 }
 
 OpFoldResult tosa::AbsOp::fold(FoldAdaptor adaptor) {

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

@@ -715,23 +715,43 @@ buildAvgPool2dOpWithQuantInfo(OpBuilder &builder, OperationState &result,
   result.types.push_back(outputType);
 }
 
-/// This builder is called on single-parameter unary operators that have scale
-/// relationship between their input and output, expressed by the
-/// UnaryOpQuantizationAttr.
-static void buildUnaryOpWithQuantInfo(OpBuilder &builder,
-                                      OperationState &result, Type outputType,
-                                      Value input) {
-  result.addOperands(input);
+/// This builder is called on single-parameter negate operator
+/// to construct input and output zero points based on their
+/// types.
+static void buildNegateOpWithQuantInfo(OpBuilder &builder,
+                                       OperationState &result, Type outputType,
+                                       Value input) {
+  const Location loc{result.location};
+  int64_t input1Zp{0};
+  int64_t outputZp{0};
   auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
   if (quantAttr) {
-    // note: negateOp has attributes input1_zp and output_zp
-    result.addAttribute("input1_zp",
-                        builder.getI32IntegerAttr(
-                            static_cast<int32_t>(quantAttr.getInputZp())));
-    result.addAttribute("output_zp",
-                        builder.getI32IntegerAttr(
-                            static_cast<int32_t>(quantAttr.getOutputZp())));
+    input1Zp = quantAttr.getInputZp();
+    outputZp = quantAttr.getOutputZp();
+  }
+  const std::optional<Value> input1ZpOp =
+      createZeroPointTensor(builder, loc, input.getType(), input1Zp);
+  if (!input1ZpOp) {
+    (void)emitError(
+        loc, "Failed to create input1 zero point for quantized NEGATE op");
+  }
+
+  const std::optional<Value> outputZpOp =
+      createZeroPointTensor(builder, loc, input.getType(), outputZp);
+  if (!outputZpOp) {
+    (void)emitError(
+        loc, "Failed to create output zero point for quantized NEGATE op");
   }
+
+  if (input1ZpOp && outputZpOp) {
+    result.addOperands({input, input1ZpOp.value(), outputZpOp.value()});
+  } else {
+    // failed to create one or more zero points above: just add input as
+    // operands. This will trigger error in building the op because of
+    // missing zero points
+    result.addOperands({input});
+  }
+
   result.types.push_back(outputType);
 }
 
@@ -1721,6 +1741,9 @@ ZERO_POINT_HELPER(TransposeConv2DOp, Input)
 ZERO_POINT_HELPER(TransposeConv2DOp, Weight)
 ZERO_POINT_HELPER(AvgPool2dOp, Input)
 ZERO_POINT_HELPER(AvgPool2dOp, Output)
+ZERO_POINT_HELPER(NegateOp, Input1)
+ZERO_POINT_HELPER(NegateOp, Output)
+
 #undef ZERO_POINT_HELPER
 
 LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
@@ -2223,7 +2246,6 @@ NARY_SHAPE_INFER(tosa::LogicalRightShiftOp)
 NARY_SHAPE_INFER(tosa::LogicalXorOp)
 NARY_SHAPE_INFER(tosa::MaximumOp)
 NARY_SHAPE_INFER(tosa::MinimumOp)
-NARY_SHAPE_INFER(tosa::NegateOp)
 NARY_SHAPE_INFER(tosa::PowOp)
 NARY_SHAPE_INFER(tosa::ReciprocalOp)
 NARY_SHAPE_INFER(tosa::ReverseOp)
@@ -2236,6 +2258,55 @@ NARY_SHAPE_INFER(tosa::ErfOp)
 NARY_SHAPE_INFER(tosa::SigmoidOp)
 #undef PRED_SHAPE_INFER
 
+LogicalResult tosa::NegateOp::inferReturnTypeComponents(
+    MLIRContext *context, ::std::optional<Location> location,
+    NegateOp::Adaptor adaptor,
+    SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
+  ShapeAdaptor inputShape(adaptor.getInput1().getType());
+  inferredReturnShapes.push_back(ShapedTypeComponents(inputShape));
+  return success();
+}
+
+LogicalResult tosa::NegateOp::verify() {
+  // Verify same element type
+  const Type input1Type = getInput1().getType();
+  const Type outputType = getOutput().getType();
+  if (verifySameElementTypes(*this, input1Type, outputType).failed())
+    return failure();
+
+  // Verify same shape
+  const SmallVector<Type, 2> types = {input1Type, outputType};
+  if (failed(verifyCompatibleShapes(types)))
+    return emitOpError() << "requires the same shape for input1 and output";
+
+  const Type input1EType = getStorageElementTypeOrSelf(getInput1().getType());
+  const Type input1ZpEType =
+      getStorageElementTypeOrSelf(getInput1Zp().getType());
+  if (input1EType != input1ZpEType) {
+    return emitOpError("expect both input1 and its zero point are the same "
+                       "element type, got ")
+           << input1EType << " and " << input1ZpEType;
+  }
+  const Type outputEType = getStorageElementTypeOrSelf(getOutput().getType());
+  const Type outputZpEType =
+      getStorageElementTypeOrSelf(getOutputZp().getType());
+  if (outputEType != outputZpEType) {
+    return emitOpError("expect both output and its zero point are the same "
+                       "element type, got ")
+           << outputEType << " and " << outputZpEType;
+  }
+
+  FailureOr<int64_t> maybeIZp = getInput1ZeroPoint();
+  if (succeeded(maybeIZp) && verifyInput1ZeroPoint(*maybeIZp).failed())
+    return failure();
+
+  FailureOr<int64_t> maybeOZp = getOutputZeroPoint();
+  if (succeeded(maybeOZp) && verifyOutputZeroPoint(*maybeOZp).failed())
+    return failure();
+
+  return success();
+}
+
 static LogicalResult poolingInferReturnTypes(
     ShapeAdaptor inputShape, ArrayRef<int64_t> kernel, ArrayRef<int64_t> stride,
     ArrayRef<int64_t> pad,