[mlir][tosa] Rename Tosa Div op to IntDiv Op (#80047)

This patch renames Tosa Div Op to IntDiv Op to align with the TOSA Spec.

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

Author: Tai78641

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

@@ -605,9 +605,9 @@ def Tosa_BitwiseXorOp : Tosa_ElementwiseOp<"bitwise_xor", [
 }
 
 //===----------------------------------------------------------------------===//
-// Operator: div
+// Operator: int_div
 //===----------------------------------------------------------------------===//
-def Tosa_DivOp : Tosa_ElementwiseOp<"div", [SameOperandsAndResultElementType]> {
+def Tosa_IntDivOp : Tosa_ElementwiseOp<"int_div", [SameOperandsAndResultElementType]> {
   let summary = "Integer divide operator";
 
   let description = [{

mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp

@@ -89,8 +89,8 @@ createLinalgBodyCalculationForElementwiseOp(Operation *op, ValueRange args,
     return rewriter.create<arith::MulFOp>(loc, resultTypes, args);
   }
 
-  // tosa::DivOp
-  if (isa<tosa::DivOp>(op) && isa<IntegerType>(elementTy))
+  // tosa::IntDivOp
+  if (isa<tosa::IntDivOp>(op) && isa<IntegerType>(elementTy))
     return rewriter.create<arith::DivSIOp>(loc, resultTypes, args);
 
   // tosa::ReciprocalOp
@@ -2567,7 +2567,7 @@ void mlir::tosa::populateTosaToLinalgConversionPatterns(
       PointwiseConverter<tosa::AddOp>,
       PointwiseConverter<tosa::SubOp>,
       PointwiseConverter<tosa::MulOp>,
-      PointwiseConverter<tosa::DivOp>,
+      PointwiseConverter<tosa::IntDivOp>,
       PointwiseConverter<tosa::NegateOp>,
       PointwiseConverter<tosa::PowOp>,
       PointwiseConverter<tosa::ReciprocalOp>,

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

@@ -79,7 +79,7 @@ void mlir::tosa::registerShardingInterfaceExternalModels(
   registry.addExtension(+[](MLIRContext *ctx, TosaDialect *dialect) {
     registerElemwiseAll<
         ClampOp, SigmoidOp, TanhOp, AddOp, ArithmeticRightShiftOp, BitwiseAndOp,
-        BitwiseOrOp, BitwiseXorOp, DivOp, LogicalAndOp, LogicalLeftShiftOp,
+        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,

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

@@ -520,7 +520,7 @@ OpFoldResult ArgMaxOp::fold(FoldAdaptor adaptor) {
   return {};
 }
 
-OpFoldResult DivOp::fold(FoldAdaptor adaptor) {
+OpFoldResult IntDivOp::fold(FoldAdaptor adaptor) {
   auto lhsTy = llvm::dyn_cast<RankedTensorType>(getInput1().getType());
   auto rhsTy = llvm::dyn_cast<RankedTensorType>(getInput2().getType());
   auto resultTy = llvm::dyn_cast<RankedTensorType>(getType());

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

@@ -139,7 +139,7 @@ void TosaDialect::initialize() {
   addInterfaces<TosaDialectBytecodeInterface, TosaInlinerInterface>();
   declarePromisedInterfaces<
       mesh::ShardingInterface, ClampOp, SigmoidOp, TanhOp, AddOp,
-      ArithmeticRightShiftOp, BitwiseAndOp, BitwiseOrOp, BitwiseXorOp, DivOp,
+      ArithmeticRightShiftOp, BitwiseAndOp, BitwiseOrOp, BitwiseXorOp, IntDivOp,
       LogicalAndOp, LogicalLeftShiftOp, LogicalRightShiftOp, LogicalOrOp,
       LogicalXorOp, MaximumOp, MinimumOp, MulOp, PowOp, SubOp, AbsOp,
       BitwiseNotOp, CeilOp, ClzOp, ExpOp, FloorOp, LogOp, LogicalNotOp,
@@ -1381,12 +1381,12 @@ NARY_SHAPE_INFER(tosa::CeilOp)
 NARY_SHAPE_INFER(tosa::ClampOp)
 NARY_SHAPE_INFER(tosa::ClzOp)
 NARY_SHAPE_INFER(tosa::CosOp)
-NARY_SHAPE_INFER(tosa::DivOp)
 NARY_SHAPE_INFER(tosa::ExpOp)
 NARY_SHAPE_INFER(tosa::FloorOp)
 NARY_SHAPE_INFER(tosa::GreaterEqualOp)
 NARY_SHAPE_INFER(tosa::GreaterOp)
 NARY_SHAPE_INFER(tosa::IdentityOp)
+NARY_SHAPE_INFER(tosa::IntDivOp)
 NARY_SHAPE_INFER(tosa::LogOp)
 NARY_SHAPE_INFER(tosa::LogicalAndOp)
 NARY_SHAPE_INFER(tosa::LogicalLeftShiftOp)

mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp

@@ -232,7 +232,7 @@ struct TosaMakeBroadcastable
     patterns.add<ConvertTosaOp<tosa::AddOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::SubOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::MulOp>>(ctx);
-    patterns.add<ConvertTosaOp<tosa::DivOp>>(ctx);
+    patterns.add<ConvertTosaOp<tosa::IntDivOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::MaximumOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::MinimumOp>>(ctx);
     patterns.add<ConvertTosaOp<tosa::EqualOp>>(ctx);

mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp

@@ -200,7 +200,7 @@ struct TosaValidation : public tosa::impl::TosaValidationBase<TosaValidation> {
     CHECK_RANKS_FOR(BitwiseAnd);
     CHECK_RANKS_FOR(BitwiseOr);
     CHECK_RANKS_FOR(BitwiseXor);
-    CHECK_RANKS_FOR(Div);
+    CHECK_RANKS_FOR(IntDiv);
     CHECK_RANKS_FOR(LogicalAnd);
     CHECK_RANKS_FOR(LogicalLeftShift);
     CHECK_RANKS_FOR(LogicalRightShift);

mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir

@@ -626,7 +626,7 @@ func.func @test_simple_i32(%arg0: tensor<1xi32>) -> () {
 
   // CHECK: linalg.generic
   // CHECK: arith.divsi
-  %4 = tosa.div %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+  %40 = tosa.int_div %arg0, %arg0 : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
 
   // CHECK: linalg.generic
   // CHECK: ^bb0(%[[ARG1:.*]]: i32, %[[ARG2:.*]]: i32):

mlir/test/Dialect/Tosa/constant-op-fold.mlir

@@ -213,7 +213,7 @@ func.func @fold_add_splat_f32() -> tensor<10xf32> {
 func.func @fold_div_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
   %zero = "tosa.const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
   // CHECK: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0>
-  %div = tosa.div %zero, %arg0 : (tensor<i32>, tensor<i32>) -> tensor<i32>
+  %div = tosa.int_div %zero, %arg0 : (tensor<i32>, tensor<i32>) -> tensor<i32>
   // CHECK: return %[[ZERO]]
   return %div : tensor<i32>
 }
@@ -223,7 +223,7 @@ func.func @fold_div_zero_lhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
 // CHECK-LABEL: @fold_div_one_rhs_i32
 func.func @fold_div_one_rhs_i32(%arg0: tensor<i32>) -> tensor<i32> {
   %one = "tosa.const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
-  %div = tosa.div %arg0, %one : (tensor<i32>, tensor<i32>) -> tensor<i32>
+  %div = tosa.int_div %arg0, %one : (tensor<i32>, tensor<i32>) -> tensor<i32>
   // CHECK: return %arg0
   return %div : tensor<i32>
 }
@@ -235,7 +235,7 @@ func.func @fold_div_splat_i32() -> tensor<i32> {
   %lhs = "tosa.const"() {value = dense<10> : tensor<i32>} : () -> tensor<i32>
   %rhs = "tosa.const"() {value = dense<-3> : tensor<i32>} : () -> tensor<i32>
   // CHECK: %[[SPLAT:.+]] = "tosa.const"() <{value = dense<-3>
-  %div = tosa.div %lhs, %rhs : (tensor<i32>, tensor<i32>) -> tensor<i32>
+  %div = tosa.int_div %lhs, %rhs : (tensor<i32>, tensor<i32>) -> tensor<i32>
   // CHECK: return %[[SPLAT]]
   return %div : tensor<i32>
 }
@@ -1092,7 +1092,7 @@ func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
   // AGGRESIVE-LABEL: func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
   // AGGRESIVE:       %[[VAL_0:.*]] = "tosa.const"() <{value = dense<4> : tensor<1x3xi32>}> : () -> tensor<1x3xi32>
   // AGGRESIVE:       return %[[VAL_0:.*]] : tensor<1x3xi32>
-  
+
   // CHECK-LABEL:     func.func @reduce_sum_constant_aggressive() -> tensor<1x3xi32> {
   // CHECK:           %[[VAL_0:.*]] = "tosa.const"() <{value = dense<1> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
   // CHECK:           %[[VAL_1:.*]] = tosa.reduce_sum %[[VAL_0]] {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>

mlir/test/Dialect/Tosa/ops.mlir

@@ -257,9 +257,9 @@ func.func @test_bitwise_xor(%arg0: tensor<13x21x1xi32>, %arg1: tensor<13x21x3xi3
 }
 
 // -----
-// CHECK-LABEL: div
-func.func @test_div(%arg0: tensor<13x21x1xi32>, %arg1: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
-  %0 = tosa.div %arg0, %arg1 : (tensor<13x21x1xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
+// CHECK-LABEL: int_div
+func.func @test_int_div(%arg0: tensor<13x21x1xi32>, %arg1: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
+  %0 = tosa.int_div %arg0, %arg1 : (tensor<13x21x1xi32>, tensor<13x21x3xi32>) -> tensor<13x21x3xi32>
   return %0 : tensor<13x21x3xi32>
 }