[Mlir-commits] [mlir] [mlir][TOSA] Add folder for multiply like reduce_prod operation (PR #128067)

[Tai Ly]

https://github.com/Tai78641 updated https://github.com/llvm/llvm-project/pull/128067

>From a4b507e7b041b6517eca9fdca6908d3c54171c07 Mon Sep 17 00:00:00 2001
From: Luke Hutton <luke.hutton at arm.com>
Date: Mon, 4 Nov 2024 08:54:36 +0000
Subject: [PATCH] [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 <luke.hutton at arm.com>
---
 .../Dialect/Tosa/IR/TosaCanonicalizations.cpp | 30 ++++++++++++++++++-
 mlir/test/Dialect/Tosa/canonicalize.mlir      | 11 +++++++
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
index 9bfc2aae1d6a5..a3e01b44064d6 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -963,10 +963,38 @@ REDUCE_FOLDER(ReduceAllOp)
 REDUCE_FOLDER(ReduceAnyOp)
 REDUCE_FOLDER(ReduceMaxOp)
 REDUCE_FOLDER(ReduceMinOp)
-REDUCE_FOLDER(ReduceProdOp)
 REDUCE_FOLDER(ReduceSumOp)
 #undef REDUCE_FOLDER
 
+OpFoldResult ReduceProdOp::fold(FoldAdaptor adaptor) {
+  ShapedType inputTy = llvm::cast<ShapedType>(getInput().getType());
+  if (!inputTy.hasRank())
+    return {};
+  if (inputTy == getType() &&
+      (inputTy.getRank() == 0 || inputTy.getDimSize(getAxis()) == 1))
+    return getInput();
+
+  // Fold multiply like reduce_prod operators using mulBinaryFolder
+  if (inputTy.getRank() == 1 && inputTy.getDimSize(0) == 2) {
+    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);
+  }
+
+  return {};
+}
+
 OpFoldResult ReshapeOp::fold(FoldAdaptor adaptor) {
   auto inputTy = llvm::dyn_cast<RankedTensorType>(getInput1().getType());
   auto outputTy = llvm::dyn_cast<RankedTensorType>(getType());
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 0e177a076ee7a..316f22f88fc69 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -1012,3 +1012,14 @@ func.func nested @do_not_fold_reciprocal_int() -> tensor<3x600x1200xi32> {
   %2 = "tosa.reciprocal"(%1): (tensor<3x600x1200xi32>) -> tensor<3x600x1200xi32>
   return %2 : tensor<3x600x1200xi32>
 }
+
+// -----
+
+// CHECK-LABEL: @fold_reduce_prod_is_mul
+func.func @fold_reduce_prod_is_mul() -> tensor<1xi32> {
+  // CHECK-DAG: %[[VAL_0:.*]] = "tosa.const"() <{value = dense<77> : tensor<1xi32>}> : () -> tensor<1xi32>
+  // CHECK: return %[[VAL_0]] : tensor<1xi32>
+  %0 = "tosa.const"() <{value = dense<[1, 77]> : tensor<2xi32>}> : () -> tensor<2xi32>
+  %1 = "tosa.reduce_prod"(%0) <{axis = 0 : i32}> : (tensor<2xi32>) -> tensor<1xi32>
+  return %1 : tensor<1xi32>
+}