[Mlir-commits] [mlir] [mlir][linalg] Use the tile size for untransformed Winograd axes (PR #226119)

[llvmlistbot at llvm.org]

llvmorg-github-actions[bot] wrote:


<!--LLVM PR SUMMARY COMMENT-->

@llvm/pr-subscribers-mlir

Author: Samarth Narang (snarang181)

<details>
<summary>Changes</summary>

When a Winograd input or output transform leaves one spatial axis untransformed (alpha == 1), the tiled implementation already maps the offset one to one, but hard-coded the corresponding slice size to 1. A tile of size 2 on that axis therefore paired a two-position result tile with a one-row operand slice. Use the requested tile size instead, in both `WinogradInputTransformOp::getTiledImplementation` and `WinogradOutputTransformOp::getResultTilePosition`.

Doing so exposes the same gap in `WinogradInputTransformOp::verify`: for a dynamic input extent it expected the transformed alpha unconditionally, so a partial tile with a dynamic size on an untransformed axis was rejected. Derive the expected alpha from leftTransform/rightTransform there just like how the static branch already does.

Both are leftovers of #<!-- -->123675, which switched the offsets and the static verifier branch to the identity mapping but not the sizes.

Fixes #<!-- -->225251

---
Full diff: https://github.com/llvm/llvm-project/pull/226119.diff


2 Files Affected:

- (modified) mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp (+10-11) 
- (modified) mlir/test/Dialect/Linalg/transform-tile-winograd.mlir (+51) 


``````````diff
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 87b14439597b43..29b9f6bdeec4b3 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -3550,7 +3550,7 @@ LogicalResult WinogradInputTransformOp::verify() {
 
   SmallVector<int64_t> expectedOutputShape(6, inputH);
   if (ShapedType::isDynamic(inputH)) {
-    expectedOutputShape[getOutputAlphaHDim()] = tileSize;
+    expectedOutputShape[getOutputAlphaHDim()] = leftTransform ? tileSize : 1;
     expectedOutputShape[getOutputTileHDim()] = ShapedType::kDynamic;
   } else {
     expectedOutputShape[getOutputAlphaHDim()] = leftTransform ? tileSize : 1;
@@ -3558,7 +3558,7 @@ LogicalResult WinogradInputTransformOp::verify() {
         leftTransform ? (inputH - (r - 1)) / m : inputH;
   }
   if (ShapedType::isDynamic(inputW)) {
-    expectedOutputShape[getOutputAlphaWDim()] = tileSize;
+    expectedOutputShape[getOutputAlphaWDim()] = rightTransform ? tileSize : 1;
     expectedOutputShape[getOutputTileWDim()] = ShapedType::kDynamic;
   } else {
     expectedOutputShape[getOutputAlphaWDim()] = rightTransform ? tileSize : 1;
@@ -3684,10 +3684,10 @@ WinogradInputTransformOp::getTiledImplementation(OpBuilder &builder,
   OpFoldResult offsetW = OpFoldResult(mappedOffsetW);
   sliceOffsets.append(
       {offsets[getOutputNDim()], offsetH, offsetW, offsets[getOutputCDim()]});
-  OpFoldResult sizeH =
-      alphaH != 1 ? OpFoldResult(mappedSizeH) : OpFoldResult(oneAttr);
-  OpFoldResult sizeW =
-      alphaW != 1 ? OpFoldResult(mappedSizeW) : OpFoldResult(oneAttr);
+  OpFoldResult sizeH = alphaH != 1 ? OpFoldResult(mappedSizeH)
+                                   : sizes[getOutputTileHDim()];
+  OpFoldResult sizeW = alphaW != 1 ? OpFoldResult(mappedSizeW)
+                                   : sizes[getOutputTileWDim()];
   sliceSizes.append(
       {sizes[getOutputNDim()], sizeH, sizeW, sizes[getOutputCDim()]});
   int64_t inputRank = getInputOperandRank();
@@ -3818,13 +3818,12 @@ LogicalResult WinogradOutputTransformOp::getResultTilePosition(
   Value mappedSizeW = affine::makeComposedAffineApply(
       builder, loc, affineMap, sizes[getValueTileWDim()]);
 
-  IntegerAttr oneAttr = builder.getI64IntegerAttr(1);
   OpFoldResult offsetH = OpFoldResult(mappedOffsetH);
   OpFoldResult offsetW = OpFoldResult(mappedOffsetW);
-  OpFoldResult sizeH =
-      valueH != 1 ? OpFoldResult(mappedSizeH) : OpFoldResult(oneAttr);
-  OpFoldResult sizeW =
-      valueW != 1 ? OpFoldResult(mappedSizeW) : OpFoldResult(oneAttr);
+  OpFoldResult sizeH = valueH != 1 ? OpFoldResult(mappedSizeH)
+                                   : sizes[getValueTileHDim()];
+  OpFoldResult sizeW = valueW != 1 ? OpFoldResult(mappedSizeW)
+                                   : sizes[getValueTileWDim()];
 
   resultOffsets.append(
       {offsets[getValueNDim()], offsetH, offsetW, offsets[getValueFDim()]});
diff --git a/mlir/test/Dialect/Linalg/transform-tile-winograd.mlir b/mlir/test/Dialect/Linalg/transform-tile-winograd.mlir
index beb8d0b1257384..03a42a4f74ce1b 100644
--- a/mlir/test/Dialect/Linalg/transform-tile-winograd.mlir
+++ b/mlir/test/Dialect/Linalg/transform-tile-winograd.mlir
@@ -386,3 +386,54 @@ module attributes {transform.with_named_sequence} {
 // CHECK:           %[[S8:.*]] = affine.apply #[[$MAP2]]()
 // CHECK:           %[[EXTRACTED_SLICE_9:.*]] = tensor.extract_slice %[[ARG12]][%[[ARG6]], %[[S5]], %[[S6]], %[[ARG8]]] [1, %[[S7]], 1, 1] [1, 1, 1, 1] : tensor<3x8x1x5xf32> to tensor<1x?x1x1xf32>
 // CHECK:           %[[S9:.*]] = linalg.winograd_output_transform fmr(F_4_3) ins(%[[EXTRACTED_SLICE]] : tensor<6x1x1x1x1x1xf32>) outs(%[[EXTRACTED_SLICE_9]] : tensor<1x?x1x1xf32>) -> tensor<1x?x1x1xf32>
+
+// -----
+
+func.func @tile_winograd_input_alpha_h_one(%arg0: tensor<1x3x10x2xf32>, %arg1: tensor<1x6x3x2x1x2xf32>) -> tensor<1x6x3x2x1x2xf32> {
+  %0 = linalg.winograd_input_transform fmr(F_4_3) ins(%arg0 : tensor<1x3x10x2xf32>) outs(%arg1 : tensor<1x6x3x2x1x2xf32>) -> tensor<1x6x3x2x1x2xf32>
+  return %0 : tensor<1x6x3x2x1x2xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.winograd_input_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loop:2 = transform.structured.tile_using_for %0 tile_sizes [0, 0, 2, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+    transform.yield
+  }
+}
+
+// The untransformed H axis maps one to one, so the input slice must use the
+// tile size rather than a single row.
+// CHECK-LABEL: func.func @tile_winograd_input_alpha_h_one(
+// CHECK-SAME:  %[[ARG0:.*]]: tensor<1x3x10x2xf32>, %[[ARG1:.*]]: tensor<1x6x3x2x1x2xf32>)
+// CHECK: scf.for %[[ARG2:.*]] = {{.*}} iter_args(%[[ARG3:.*]] = %[[ARG1]])
+// CHECK:   scf.for %[[ARG4:.*]] = {{.*}} iter_args(%[[ARG5:.*]] = %[[ARG3]])
+// CHECK:     %[[SIZE_H:.*]] = affine.min
+// CHECK:     %[[IN:.*]] = tensor.extract_slice %[[ARG0]][0, %{{.*}}, %{{.*}}, 0] [1, %[[SIZE_H]], %{{.*}}, 2] [1, 1, 1, 1] : tensor<1x3x10x2xf32> to tensor<1x?x?x2xf32>
+// CHECK:     %[[OUT:.*]] = tensor.extract_slice %[[ARG5]][0, 0, %[[ARG2]], %[[ARG4]], 0, 0] [1, 6, %[[SIZE_H]], 1, 1, 2] [1, 1, 1, 1, 1, 1] : tensor<1x6x3x2x1x2xf32> to tensor<1x6x?x1x1x2xf32>
+// CHECK:     linalg.winograd_input_transform fmr(F_4_3) ins(%[[IN]] : tensor<1x?x?x2xf32>) outs(%[[OUT]] : tensor<1x6x?x1x1x2xf32>)
+
+// -----
+
+func.func @tile_winograd_output_alpha_h_one(%arg0: tensor<1x6x3x2x1x2xf32>, %arg1: tensor<1x3x8x2xf32>) -> tensor<1x3x8x2xf32> {
+  %0 = linalg.winograd_output_transform fmr(F_4_3) ins(%arg0 : tensor<1x6x3x2x1x2xf32>) outs(%arg1 : tensor<1x3x8x2xf32>) -> tensor<1x3x8x2xf32>
+  return %0 : tensor<1x3x8x2xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.winograd_output_transform"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loop:2 = transform.structured.tile_using_for %0 tile_sizes [0, 0, 2, 1, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+    transform.yield
+  }
+}
+
+// CHECK-LABEL: func.func @tile_winograd_output_alpha_h_one(
+// CHECK-SAME:  %[[ARG0:.*]]: tensor<1x6x3x2x1x2xf32>, %[[ARG1:.*]]: tensor<1x3x8x2xf32>)
+// CHECK: scf.for %[[ARG2:.*]] = {{.*}} iter_args(%[[ARG3:.*]] = %[[ARG1]])
+// CHECK:   scf.for %[[ARG4:.*]] = {{.*}} iter_args(%[[ARG5:.*]] = %[[ARG3]])
+// CHECK:     %[[SIZE_H:.*]] = affine.min
+// CHECK:     %[[IN:.*]] = tensor.extract_slice %[[ARG0]][0, 0, %[[ARG2]], %[[ARG4]], 0, 0] [1, 6, %[[SIZE_H]], 1, 1, 2] [1, 1, 1, 1, 1, 1] : tensor<1x6x3x2x1x2xf32> to tensor<1x6x?x1x1x2xf32>
+// CHECK:     %[[OUT:.*]] = tensor.extract_slice %[[ARG5]][0, %{{.*}}, %{{.*}}, 0] [1, %[[SIZE_H]], %{{.*}}, 2] [1, 1, 1, 1] : tensor<1x3x8x2xf32> to tensor<1x?x?x2xf32>
+// CHECK:     %[[RES:.*]] = linalg.winograd_output_transform fmr(F_4_3) ins(%[[IN]] : tensor<1x6x?x1x1x2xf32>) outs(%[[OUT]] : tensor<1x?x?x2xf32>)
+// CHECK:     tensor.insert_slice %[[RES]] into %[[ARG5]][0, %{{.*}}, %{{.*}}, 0] [1, %[[SIZE_H]], %{{.*}}, 2] [1, 1, 1, 1]

``````````

</details>


https://github.com/llvm/llvm-project/pull/226119