From 0e7c32325a873871f1337ade892f5e726e8db41e Mon Sep 17 00:00:00 2001
From: Matthias Springer <springerm@google.com>
Date: Tue, 5 Dec 2023 16:18:00 +0900
Subject: [PATCH] [mlir][linalg] Fix invalid IR in Linalg op fusion

Linalg op fusion (`Linalg/Transforms/Fusion.cpp`) used to generate invalid fused producer ops:
```
error: 'linalg.conv_2d_nhwc_hwcf' op expected type of operand #2 ('tensor<1x8x16x4xf32>') to match type of corresponding result ('tensor<?x?x?x?xf32>')
note: see current operation:
%24 = "linalg.conv_2d_nhwc_hwcf"(%21, %22, %23) <{dilations = dense<1> : tensor<2xi64>, operandSegmentSizes = array<i32: 2, 1>, strides = dense<2> : tensor<2xi64>}> ({
^bb0(%arg9: f32, %arg10: f32, %arg11: f32):
  %28 = "arith.mulf"(%arg9, %arg10) <{fastmath = #arith.fastmath<none>}> : (f32, f32) -> f32
  %29 = "arith.addf"(%arg11, %28) <{fastmath = #arith.fastmath<none>}> : (f32, f32) -> f32
  "linalg.yield"(%29) : (f32) -> ()
}) {linalg.memoized_indexing_maps = [affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 * 2 + d4, d2 * 2 + d5, d6)>, affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d4, d5, d6, d3)>, affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)>]} : (tensor<1x?x?x3xf32>, tensor<3x3x3x4xf32>, tensor<1x8x16x4xf32>) -> tensor<?x?x?x?xf32>
```

This is a problem because the input IR to greedy pattern rewriter during `-test-linalg-greedy-fusion` is invalid. This commit fixes tests such as `mlir/test/Dialect/Linalg/tile-and-fuse-tensors.mlir` when verifying the IR after each pattern application (#74270).
---
 mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp | 24 ++++++-------------
 1 file changed, 7 insertions(+), 17 deletions(-)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp b/mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp
index 11bd886c36e53..e48188fe516d3 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp
@@ -144,27 +144,17 @@ static LinalgOp fuse(OpBuilder &b, LinalgOp producer,
       b, loc, producer, getTiledOperands(producer), ivs, tileSizes, sizeBounds,
       /**omitPartialTileCheck=*/false));
 
-  // Iterate over the results in order.
-  // Extract the subtensor type from the linearized range.
-  // Since we do not enforce any canonicalizations on the fly, this is always
-  // fully dynamic at construction time.
+  // Take result types from the tiled init operands.
+  MutableOperandRange producerDpsInits = producer.getDpsInitsMutable();
   SmallVector<Type, 4> resultTypes;
   resultTypes.reserve(producer->getNumResults());
-  for (Value operand : producer.getDpsInits()) {
-    auto tensorType = dyn_cast<RankedTensorType>(operand.getType());
-    if (!tensorType)
-      continue;
-    unsigned rank = tensorType.getRank();
-    SmallVector<int64_t, 4> staticOffsetsVector(
-        rank, ShapedType::kDynamic);
-    SmallVector<int64_t, 4> staticSizesVector(rank, ShapedType::kDynamic);
-    SmallVector<int64_t, 4> staticStridesVector(
-        rank, ShapedType::kDynamic);
-    resultTypes.push_back(tensor::ExtractSliceOp::inferResultType(
-        tensorType, staticOffsetsVector, staticSizesVector,
-        staticStridesVector));
+  int64_t firstInitOperandIdx =
+      static_cast<OperandRange>(producerDpsInits).getBeginOperandIndex();
+  for (int64_t i = 0, e = producer->getNumResults(); i < e; ++i) {
+    resultTypes.push_back(clonedShapes[firstInitOperandIdx + i].getType());
   }
 
+  // Clone the producer with new operands and result types.
   LinalgOp clonedOp = clone(b, producer, resultTypes, clonedShapes);
 
   // Shift all IndexOp results by the tile offset.