[mlir][tosa] Add folder for multiply like reduce_prod operation

This commit uses mulBinaryFolder for reduce_prod operations that have
a constant 1D input of two values.

Change-Id: Icb234282c70898189083231506ed38a3ab40efb2
Signed-off-by: Luke Hutton <[email protected]>

Author: lhutton1

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

@@ -902,10 +902,38 @@ REDUCE_FOLDER(ReduceAllOp)
 REDUCE_FOLDER(ReduceAnyOp)
 REDUCE_FOLDER(ReduceMaxOp)
 REDUCE_FOLDER(ReduceMinOp)
-REDUCE_FOLDER(ReduceProductOp)
 REDUCE_FOLDER(ReduceSumOp)
 #undef REDUCE_FOLDER
 
+OpFoldResult ReduceProductOp::fold(FoldAdaptor adaptor) {
+  auto inputTy = llvm::cast<ShapedType>(getInput().getType());
+  if (!inputTy.hasRank())
+    return {};
+
+  if (inputTy == getType() &&
+      (inputTy.getRank() == 0 || inputTy.getDimSize(getAxis()) == 1))
+    return getInput();
+
+  if (inputTy.getRank() != 1 || inputTy.getDimSize(0) != 2)
+    return {};
+
+  // inputTy has shape { 2 } : try folding reduce_product using mulBinaryFolder
+  const auto resultTy = llvm::dyn_cast<RankedTensorType>(getType());
+  if (!resultTy)
+    return {};
+
+  const auto elements =
+      llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput());
+  if (!elements)
+    return {};
+
+  const auto lhsAttr =
+      DenseElementsAttr::get(resultTy, {elements.getValues<Attribute>()[0]});
+  const auto rhsAttr =
+      DenseElementsAttr::get(resultTy, {elements.getValues<Attribute>()[1]});
+  return mulBinaryFolder(lhsAttr, rhsAttr, resultTy, 0);
+}
+
 OpFoldResult ReshapeOp::fold(FoldAdaptor adaptor) {
   auto inputTy = llvm::dyn_cast<RankedTensorType>(getInput1().getType());
   auto outputTy = llvm::dyn_cast<RankedTensorType>(getType());

mlir/test/Dialect/Tosa/canonicalize.mlir

@@ -1040,3 +1040,36 @@ func.func @do_not_fold_int_div_division_by_0() -> tensor<1x24x2xi32> {
   %16 = tosa.int_div %4, %1 : (tensor<1x24x2xi32>, tensor<1x24x2xi32>) -> tensor<1x24x2xi32>
   return %16 : tensor<1x24x2xi32>
 }
+
+// -----
+
+// CHECK-LABEL: @fold_reduce_prod_is_mul
+func.func @fold_reduce_prod_is_mul() -> tensor<1xi32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.const"() <{values = dense<77> : tensor<1xi32>}> : () -> tensor<1xi32>
+  // CHECK: return %[[VAL_0]] : tensor<1xi32>
+  %0 = "tosa.const"() <{values = dense<[1, 77]> : tensor<2xi32>}> : () -> tensor<2xi32>
+  %1 = "tosa.reduce_product"(%0) <{axis = 0 : i32}> : (tensor<2xi32>) -> tensor<1xi32>
+  return %1 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @no_fold_reduce_prod_rank_2
+func.func @no_fold_reduce_prod_rank_2() -> tensor<1x1xi32> {
+  // check that reduce_product folding does not happen for input with rank > 1
+  // CHECK: tosa.reduce_product
+  %0 = "tosa.const"() <{values = dense<[[1, 77]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32>
+  %1 = "tosa.reduce_product"(%0) <{axis = 1 : i32}> : (tensor<1x2xi32>) -> tensor<1x1xi32>
+  return %1 : tensor<1x1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @no_fold_reduce_prod_dim_3
+func.func @no_fold_reduce_prod_dim_3() -> tensor<1xi32> {
+  // check that reduce_product folding does not happen for input with dim[0] != 2
+  // CHECK: tosa.reduce_product
+  %0 = "tosa.const"() <{values = dense<[1, 77, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
+  %1 = "tosa.reduce_product"(%0) <{axis = 0 : i32}> : (tensor<3xi32>) -> tensor<1xi32>
+  return %1 : tensor<1xi32>
+}