[mlir][tosa] Rename ReduceProd to ReduceProduct (#128751)

This patch renames TOSA ReduceProd operator to ReduceProduct to align with the TOSA Spec 1.0 Signed-off-by: Tai Ly <[email protected]>
llvm · Feb 26, 2025 · 177ede2 · 177ede2
1 parent ca5bb23
commit 177ede2
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Showing 15 changed files with 40 additions and 40 deletions.
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -1740,8 +1740,8 @@ def Tosa_ReduceMinOp : Tosa_InferTensorTypeOp<"reduce_min"> {
 //===----------------------------------------------------------------------===//
 // Operator: reduce_prod
 //===----------------------------------------------------------------------===//
-def Tosa_ReduceProdOp : Tosa_InferTensorTypeOp<"reduce_prod"> {
-  let summary = "Reduce Prod operator";
+def Tosa_ReduceProductOp : Tosa_InferTensorTypeOp<"reduce_product"> {
+  let summary = "Reduce Product operator";
 
   let description = [{
     Reduce a tensor along the given axis by computing the product of the axis.

diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -1055,10 +1055,10 @@ static TypedAttr createInitialValueForReduceOp(Operation *op, Type elementTy,
   if (isa<tosa::ReduceSumOp>(op) && isa<IntegerType>(elementTy))
     return rewriter.getIntegerAttr(elementTy, 0);
 
-  if (isa<tosa::ReduceProdOp>(op) && isa<FloatType>(elementTy))
+  if (isa<tosa::ReduceProductOp>(op) && isa<FloatType>(elementTy))
     return rewriter.getFloatAttr(elementTy, 1.0);
 
-  if (isa<tosa::ReduceProdOp>(op) && isa<IntegerType>(elementTy))
+  if (isa<tosa::ReduceProductOp>(op) && isa<IntegerType>(elementTy))
     return rewriter.getIntegerAttr(elementTy, 1);
 
   if (isa<tosa::ReduceMinOp>(op) && isa<FloatType>(elementTy))
@@ -1112,11 +1112,11 @@ static Value createLinalgBodyCalculationForReduceOp(Operation *op,
     return rewriter.create<arith::AddIOp>(loc, args);
   }
 
-  if (isa<tosa::ReduceProdOp>(op) && isa<FloatType>(elementTy)) {
+  if (isa<tosa::ReduceProductOp>(op) && isa<FloatType>(elementTy)) {
     return rewriter.create<arith::MulFOp>(loc, args);
   }
 
-  if (isa<tosa::ReduceProdOp>(op) && isa<IntegerType>(elementTy)) {
+  if (isa<tosa::ReduceProductOp>(op) && isa<IntegerType>(elementTy)) {
     return rewriter.create<arith::MulIOp>(loc, args);
   }
 
@@ -2874,7 +2874,7 @@ void mlir::tosa::populateTosaToLinalgConversionPatterns(
       ReduceConverter<tosa::ReduceMinOp>,
       ReduceConverter<tosa::ReduceMaxOp>,
       ReduceConverter<tosa::ReduceSumOp>,
-      ReduceConverter<tosa::ReduceProdOp>,
+      ReduceConverter<tosa::ReduceProductOp>,
       ArgMaxConverter,
       GatherConverter,
       RescaleConverter,

diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -949,7 +949,7 @@ REDUCE_FOLDER(ReduceAllOp)
 REDUCE_FOLDER(ReduceAnyOp)
 REDUCE_FOLDER(ReduceMaxOp)
 REDUCE_FOLDER(ReduceMinOp)
-REDUCE_FOLDER(ReduceProdOp)
+REDUCE_FOLDER(ReduceProductOp)
 REDUCE_FOLDER(ReduceSumOp)
 #undef REDUCE_FOLDER
 

diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -1832,7 +1832,7 @@ REDUCE_SHAPE_INFER(tosa::ReduceAllOp)
 REDUCE_SHAPE_INFER(tosa::ReduceAnyOp)
 REDUCE_SHAPE_INFER(tosa::ReduceMaxOp)
 REDUCE_SHAPE_INFER(tosa::ReduceMinOp)
-REDUCE_SHAPE_INFER(tosa::ReduceProdOp)
+REDUCE_SHAPE_INFER(tosa::ReduceProductOp)
 REDUCE_SHAPE_INFER(tosa::ReduceSumOp)
 #undef REDUCE_SHAPE_INFER
 COMPATIBLE_RETURN_TYPES(tosa::ConcatOp)
@@ -1892,7 +1892,7 @@ LogicalResult tosa::ReduceAllOp::verify() { return verifyReduceOp(*this); }
 LogicalResult tosa::ReduceAnyOp::verify() { return verifyReduceOp(*this); }
 LogicalResult tosa::ReduceMaxOp::verify() { return verifyReduceOp(*this); }
 LogicalResult tosa::ReduceMinOp::verify() { return verifyReduceOp(*this); }
-LogicalResult tosa::ReduceProdOp::verify() { return verifyReduceOp(*this); }
+LogicalResult tosa::ReduceProductOp::verify() { return verifyReduceOp(*this); }
 LogicalResult tosa::ReduceSumOp::verify() { return verifyReduceOp(*this); }
 
 static LogicalResult NAryInferReturnTypes(

diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
@@ -408,7 +408,7 @@ void mlir::tosa::populateTosaConstantReduction(MLIRContext *ctx,
       ctx, aggressiveReduceConstant);
   patterns.add<ReduceConstantOptimization<ReduceMinOp>>(
       ctx, aggressiveReduceConstant);
-  patterns.add<ReduceConstantOptimization<ReduceProdOp>>(
+  patterns.add<ReduceConstantOptimization<ReduceProductOp>>(
       ctx, aggressiveReduceConstant);
   patterns.add<ReduceConstantOptimization<ReduceSumOp>>(
       ctx, aggressiveReduceConstant);

diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaProfileCompliance.cpp
@@ -272,7 +272,7 @@ LogicalResult ProfileInfoDepot::populatationDispatch(Operation *op) {
   POPULATE_PROFILE_INFO_COMMON(ReduceAny)
   POPULATE_PROFILE_INFO_COMMON(ReduceMax)
   POPULATE_PROFILE_INFO_COMMON(ReduceMin)
-  POPULATE_PROFILE_INFO_COMMON(ReduceProd)
+  POPULATE_PROFILE_INFO_COMMON(ReduceProduct)
   POPULATE_PROFILE_INFO_COMMON(ReduceSum)
   POPULATE_PROFILE_INFO_COMMON(Equal)
   POPULATE_PROFILE_INFO_COMMON(GreaterEqual)

diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp
@@ -220,7 +220,7 @@ struct TosaValidation : public tosa::impl::TosaValidationBase<TosaValidation> {
     CHECK_RANKS_FOR(ReduceAny);
     CHECK_RANKS_FOR(ReduceMax);
     CHECK_RANKS_FOR(ReduceMin);
-    CHECK_RANKS_FOR(ReduceProd);
+    CHECK_RANKS_FOR(ReduceProduct);
     CHECK_RANKS_FOR(ReduceSum);
     // all data layout operators:
     CHECK_RANKS_FOR(Concat);

diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -951,7 +951,7 @@ func.func @reduce_float(%arg0: tensor<5x4xf32>) -> () {
   // CHECK: linalg.fill
   // CHECK: linalg.reduce
   // CHECK: arith.mulf
-  %2 = tosa.reduce_prod %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
+  %2 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<5x4xf32>) -> tensor<1x4xf32>
 
   // CHECK: arith.constant 3.40282347E+38 : f32
   // CHECK: linalg.fill
@@ -1027,7 +1027,7 @@ func.func @reduce_float_dyn_nonzero_batch(%arg0: tensor<5x?x4xf32>) -> () {
   // CHECK: %[[DIM_1:.+]] = tensor.dim %[[REDUCE]], %[[C1_0]] : tensor<5x?xf32>
   // CHECK: %[[C1_2:.+]] = arith.constant 1 : index
   // CHECK: tensor.expand_shape %[[REDUCE]] {{\[}}[0], [1, 2]] output_shape [5, %[[DIM_1]], 1] : tensor<5x?xf32> into tensor<5x?x1xf32>
-  %0 = tosa.reduce_prod %arg0 {axis = 2 : i32} : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32>
+  %0 = tosa.reduce_product %arg0 {axis = 2 : i32} : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32>
   return
 }
 
@@ -1085,7 +1085,7 @@ func.func @reduce_int(%arg0: tensor<5x4xi32>) -> () {
   // CHECK: linalg.fill
   // CHECK: linalg.reduce
   // CHECK: arith.muli
-  %2 = tosa.reduce_prod %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
+  %2 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<5x4xi32>) -> tensor<1x4xi32>
 
   // CHECK: arith.constant 2147483647 : i32
   // CHECK: linalg.fill

diff --git a/mlir/test/Dialect/Tosa/availability.mlir b/mlir/test/Dialect/Tosa/availability.mlir
@@ -486,7 +486,7 @@ func.func @test_reduce_min(%arg0: tensor<13x21x3xf32>) -> tensor<1x21x3xf32> {
 func.func @test_reduce_product(%arg0: tensor<13x21x3xf32>) -> tensor<1x21x3xf32> {
   // CHECK: profiles: [ [pro_fp] ]
   // CHECK: extensions: [ [bf16] ]
-  %0 = tosa.reduce_prod %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+  %0 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
   return %0 : tensor<1x21x3xf32>
 }
 

diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -501,19 +501,19 @@ func.func @reduce_min_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
 
 // -----
 
-// CHECK-LABEL: @reduce_prod_fold
-func.func @reduce_prod_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
+// CHECK-LABEL: @reduce_product_fold
+func.func @reduce_product_fold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
   // CHECK: return %arg0
-  %0 = tosa.reduce_prod %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+  %0 = tosa.reduce_product %arg0 {axis = 1 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
   return %0 : tensor<?x1xf32>
 }
 
 // -----
 
-// CHECK-LABEL: @reduce_prod_nofold
-func.func @reduce_prod_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
-  // CHECK: tosa.reduce_prod
-  %0 = tosa.reduce_prod %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
+// CHECK-LABEL: @reduce_product_nofold
+func.func @reduce_product_nofold(%arg0: tensor<?x1xf32>) -> tensor<?x1xf32> {
+  // CHECK: tosa.reduce_product
+  %0 = tosa.reduce_product %arg0 {axis = 0 : i32}: (tensor<?x1xf32>) -> tensor<?x1xf32>
   return %0 : tensor<?x1xf32>
 }
 

diff --git a/mlir/test/Dialect/Tosa/constant-op-fold.mlir b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
@@ -721,7 +721,7 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
     // CHECK:         return %[[VAL_0]] : tensor<1x3xi32>
 
     %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
-    %0 = tosa.reduce_prod %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
+    %0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<2x3xi32>) -> tensor<1x3xi32>
     return %0 : tensor<1x3xi32>
   }
 
@@ -734,7 +734,7 @@ func.func @reduce_sum_constant() -> tensor<2x3x1x5xi32> {
   // CHECK:         }
 
     %const = "tosa.const"() <{value = dense<[[1,2,3], [4,5,6]]> : tensor<2x3xi32>}> : () -> tensor<2x3xi32>
-    %0 = tosa.reduce_prod %const {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x1xi32>
+    %0 = tosa.reduce_product %const {axis = 1 : i32} : (tensor<2x3xi32>) -> tensor<2x1xi32>
     return %0 : tensor<2x1xi32>
   }
 
@@ -746,7 +746,7 @@ func.func @reduce_prod_constant() -> tensor<3x1xi32> {
   // CHECK:           return %[[VAL_0]] : tensor<3x1xi32>
   // CHECK:         }
   %const = "tosa.const"() <{value = dense<[[1, 2, 3], [4, 5, 6], [7, 8, 9]]> : tensor<3x3xi32>}> : () -> tensor<3x3xi32>
-  %0 = tosa.reduce_prod %const {axis = 1 : i32} : (tensor<3x3xi32>) -> tensor<3x1xi32>
+  %0 = tosa.reduce_product %const {axis = 1 : i32} : (tensor<3x3xi32>) -> tensor<3x1xi32>
   return %0 : tensor<3x1xi32>
 }
 
@@ -758,7 +758,7 @@ func.func @reduce_prod_constant() -> tensor<2x1x4xi32> {
   // CHECK:           return %[[VAL_0]] : tensor<2x1x4xi32>
   // CHECK:         }
   %const = "tosa.const"() <{value = dense<[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]> : tensor<2x3x4xi32>}> : () -> tensor<2x3x4xi32>
-  %0 = tosa.reduce_prod %const {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
+  %0 = tosa.reduce_product %const {axis = 1 : i32} : (tensor<2x3x4xi32>) -> tensor<2x1x4xi32>
   return %0 : tensor<2x1x4xi32>
 }
 
@@ -770,7 +770,7 @@ func.func @reduce_prod_constant() -> tensor<1x3x3xi32> {
   // CHECK:           return %[[VAL_0]] : tensor<1x3x3xi32>
   // CHECK:         }
   %const = "tosa.const"() <{value = dense<[[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]> : tensor<3x3x3xi32>}> : () -> tensor<3x3x3xi32>
-  %0 = tosa.reduce_prod %const {axis = 0 : i32} : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
+  %0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<3x3x3xi32>) -> tensor<1x3x3xi32>
   return %0 : tensor<1x3x3xi32>
 }
 
@@ -782,7 +782,7 @@ func.func @reduce_prod_constant() -> tensor<2x2x2x1xi32> {
   // CHECK:           return %[[VAL_0]] : tensor<2x2x2x1xi32>
   // CHECK:         }
   %const = "tosa.const"() <{value = dense<[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]], [[[9, 10], [11, 12]], [[13, 14], [15, 16]]]]> : tensor<2x2x2x2xi32>}> : () -> tensor<2x2x2x2xi32>
-  %0 = tosa.reduce_prod %const {axis = 3 : i32} : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
+  %0 = tosa.reduce_product %const {axis = 3 : i32} : (tensor<2x2x2x2xi32>) -> tensor<2x2x2x1xi32>
   return %0 : tensor<2x2x2x1xi32>
 }
 
@@ -794,7 +794,7 @@ func.func @reduce_prod_constant() -> tensor<1x1x1xi32> {
   // CHECK:           return %[[VAL_0]] : tensor<1x1x1xi32>
   // CHECK:         }
   %const = "tosa.const"() <{value = dense<[[[42]]]> : tensor<1x1x1xi32>}> : () -> tensor<1x1x1xi32>
-  %0 = tosa.reduce_prod %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
+  %0 = tosa.reduce_product %const {axis = 0 : i32} : (tensor<1x1x1xi32>) -> tensor<1x1x1xi32>
   return %0 : tensor<1x1x1xi32>
 }
 

diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -365,8 +365,8 @@ func.func @test_reduce_min_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
 // -----
 
 func.func @test_reduce_prod_type_mismatch(%arg0 : tensor<2x3x4x5xf32>) -> () {
-  // expected-error@+1 {{'tosa.reduce_prod' op expect reduced dimension size to be 1, got 3}}
-  %0 = tosa.reduce_prod %arg0 {axis = 1 : i32} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32>
+  // expected-error@+1 {{'tosa.reduce_product' op expect reduced dimension size to be 1, got 3}}
+  %0 = tosa.reduce_product %arg0 {axis = 1 : i32} : (tensor<2x3x4x5xf32>) -> tensor<2x3x4x5xf32>
   return
 }
 
@@ -405,8 +405,8 @@ func.func @test_reduce_min_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
 // -----
 
 func.func @test_reduce_prod_invalid_axis(%arg0 : tensor<2x3x4xf32>) -> () {
-  // expected-error@+1 {{'tosa.reduce_prod' op expect input tensor rank (3) to be larger than reduce axis (3)}}
-  %0 = tosa.reduce_prod %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
+  // expected-error@+1 {{'tosa.reduce_product' op expect input tensor rank (3) to be larger than reduce axis (3)}}
+  %0 = tosa.reduce_product %arg0 {axis = 3 : i32} : (tensor<2x3x4xf32>) -> tensor<2x3x1xf32>
   return
 }
 

diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -45,8 +45,8 @@ func.func @test_reduce_min(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1
 // -----
 
 func.func @test_reduce_prod(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32> {
-  // expected-error@+1 {{'tosa.reduce_prod' op failed level check: operand rank(shape) <= MAX_RANK}}
-  %0 = "tosa.reduce_prod"(%arg0) {axis = 0 : i32} : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
+  // expected-error@+1 {{'tosa.reduce_product' op failed level check: operand rank(shape) <= MAX_RANK}}
+  %0 = "tosa.reduce_product"(%arg0) {axis = 0 : i32} : (tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x13x21x3xf32>
   return %0 : tensor<1x1x1x1x13x21x3xf32>
 }
 

diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
@@ -558,7 +558,7 @@ func.func @test_reduce_min(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
 // -----
 // CHECK-LABEL: reduce_product
 func.func @test_reduce_product(%arg0: tensor<13x21x3xf32>) -> tensor<21x3xf32> {
-  %0 = tosa.reduce_prod %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
+  %0 = tosa.reduce_product %arg0 {axis = 0 : i32} : (tensor<13x21x3xf32>) -> tensor<1x21x3xf32>
   %2 = tosa.const_shape {value = dense<[21, 3]> : tensor<2xindex>} : () -> !tosa.shape<2>
   %1 = tosa.reshape %0, %2 : (tensor<1x21x3xf32>, !tosa.shape<2>) -> tensor<21x3xf32>
   return %1 : tensor<21x3xf32>

diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -417,8 +417,8 @@ func.func @test_reduce_float(%arg0 : tensor<2x3x?x?xf32>) -> () {
   // CHECK: tosa.reduce_min %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
   %5 = tosa.reduce_min %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
 
-  // CHECK: tosa.reduce_prod %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
-  %6 = tosa.reduce_prod %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
+  // CHECK: tosa.reduce_product %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<2x3x?x1xf32>
+  %6 = tosa.reduce_product %arg0 {axis = 3 : i32} : (tensor<2x3x?x?xf32>) -> tensor<?x?x?x?xf32>
 
   return
 }