[Mlir-commits] [mlir] [MLIR] TosaToLinalg: Prefer to emit identity maps (PR #123295)

[Matthias Gehre]

https://github.com/mgehre-amd updated https://github.com/llvm/llvm-project/pull/123295

>From 07bd346541893ba5306c1e7d1cd84831888bd4a6 Mon Sep 17 00:00:00 2001
From: Matthias Gehre <matthias.gehre at amd.com>
Date: Fri, 18 Oct 2024 08:05:58 -0700
Subject: [PATCH] TosaToLinalg: Prefer to emit identity maps (#386)

When deciding whether to emit a map like
`#map = affine_map<(d0, d1, d2, d3) -> (0, d1, d2, d3)>`
or `#map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>`
for an operand of a `linalg.generic` when lowering element-wise TOSA ops,
prefer the latter unless broadcasting of the operand is really needed.

This helps later transformations which often require the affine map to be
a projected permuatation.
---
 .../Conversion/TosaToLinalg/TosaToLinalg.cpp  | 10 ++++--
 .../TosaToLinalg/tosa-to-linalg.mlir          | 32 +++++++++++++++----
 2 files changed, 33 insertions(+), 9 deletions(-)

diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 9295afd36e3ab1..a183c27abf62ae 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -882,8 +882,14 @@ emitElementwiseComputation(ConversionPatternRewriter &rewriter, Location loc,
     auto shape = cast<ShapedType>(operand.getType()).getShape();
     SmallVector<AffineExpr> affineExprs;
     for (auto it : llvm::enumerate(shape)) {
-      auto affineExpr = it.value() == 1 ? rewriter.getAffineConstantExpr(0)
-                                        : rewriter.getAffineDimExpr(it.index());
+      // Prefer producting identity maps whenever possible (i.e. no broadcasting
+      // needed) because some transforms (like reshape folding)
+      // do not support affine constant exprs.
+      bool requiresBroadcast =
+          (it.value() == 1 && resultType.getDimSize(it.index()) != 1);
+      auto affineExpr = requiresBroadcast
+                            ? rewriter.getAffineConstantExpr(0)
+                            : rewriter.getAffineDimExpr(it.index());
       affineExprs.push_back(affineExpr);
     }
     return AffineMap::get(rank, 0, affineExprs, rewriter.getContext());
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 1d235092b71d55..e2c95ba7a0c6b9 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -100,16 +100,15 @@ func.func @test_add_0d(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
 
 // -----
 
-// CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> (d0, 0)>
-// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (0, 0)>
-// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (0, d1)>
 
 // CHECK-LABEL:   func.func @test_add_0d_broadcast(
 // CHECK-SAME:                                     %[[ARG0:.*]]: tensor<2x1xf32>,
 // CHECK-SAME:                                     %[[ARG1:.*]]: tensor<f32>) -> tensor<2x1xf32> {
 // CHECK:           %[[EXPANDED:.*]] = tensor.expand_shape %[[ARG1]] [] output_shape [1, 1] : tensor<f32> into tensor<1x1xf32>
 // CHECK:           %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<2x1xf32>
-// CHECK:           %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]], %[[EXPANDED]] : tensor<2x1xf32>, tensor<1x1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<2x1xf32>) {
+// CHECK:           %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]], %[[EXPANDED]] : tensor<2x1xf32>, tensor<1x1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<2x1xf32>) {
 // CHECK:           ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
 // CHECK:             %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]] : f32
 // CHECK:             linalg.yield %[[ADD]] : f32
@@ -253,6 +252,26 @@ func.func @test_add_1d_broadcast_static_to_static(%arg0: tensor<1xf32>, %arg1: t
 
 // -----
 
+// CHECK: #[[$MAP:.+]] = affine_map<(d0) -> (d0)>
+// CHECK-LABEL: @test_add_1d_matching_no_broadcast
+// CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]:
+// CHECK-SAME: %[[ARG1:[0-9a-zA-Z_]*]]:
+func.func @test_add_1d_matching_no_broadcast(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
+
+  // CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<1xf32>
+  // CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP]], #[[$MAP]], #[[$MAP]]], iterator_types = ["parallel"]} ins(%[[ARG0]], %[[ARG1]] : tensor<1xf32>, tensor<1xf32>) outs(%[[VAL_0]] : tensor<1xf32>) {
+  // CHECK: ^bb0(%[[VAL_1:.*]]: f32, %[[VAL_2:.*]]: f32, %[[VAL_3:.*]]: f32):
+  // CHECK:   %[[VAL_4:.*]] = arith.addf %[[VAL_1]], %[[VAL_2]] : f32
+  // CHECK:   linalg.yield %[[VAL_4]] : f32
+  // CHECK: } -> tensor<1xf32>
+  %0 = tosa.add %arg0, %arg1 : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
+
+  // CHECK: return %[[RESULT]] : tensor<1xf32>
+  return %0 : tensor<1xf32>
+}
+
+// -----
+
 // CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (d0)>
 // CHECK-LABEL: @test_add_1d_matching_static
 // CHECK-SAME: %[[ARG0:[0-9a-zA-Z_]*]]:
@@ -1969,13 +1988,12 @@ func.func @test_dynamic_fft2d(%arg0: tensor<?x?x?xf32>, %arg1: tensor<?x?x?xf32>
 
 // -----
 
-// CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (0)>
-// CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (d0)>
+// CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (d0)>
 
 // CHECK-LABEL:   func.func @test_cast_fp32_i64(
 // CHECK-SAME:                                  %[[ARG0:.*]]: tensor<1xf32>) -> tensor<1xi64> {
 // CHECK:           %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<1xi64>
-// CHECK:           %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<1xi64>) {
+// CHECK:           %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[ARG0]] : tensor<1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<1xi64>) {
 // CHECK:           ^bb0(%[[IN:.*]]: f32, %[[OUT:.*]]: i64):
 // CHECK:             %[[ROUND_EVEN:.*]] = math.roundeven %[[IN]] : f32
 // CHECK:             %[[FP_INT_MIN:.*]] = arith.constant -9.22337203E+18 : f32