[Mlir-commits] [mlir] [mlir][tensor] Add canonicalization to fold consecutive tensor.pad ops (PR #107302)
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llvmlistbot at llvm.org
Wed Sep 4 13:20:29 PDT 2024
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir
Author: Quinn Dawkins (qedawkins)
<details>
<summary>Changes</summary>
`tensor.pad(tensor.pad)` with the same constant padding value can be combined into a single pad that pads to the sum of the high and low padding amounts.
---
Full diff: https://github.com/llvm/llvm-project/pull/107302.diff
2 Files Affected:
- (modified) mlir/lib/Dialect/Tensor/IR/TensorOps.cpp (+84-1)
- (modified) mlir/test/Dialect/Tensor/canonicalize.mlir (+82)
``````````diff
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
index 996de530c255d4..48f9aa0d0664a1 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -3397,12 +3397,95 @@ struct FoldStaticPadding : public OpRewritePattern<PadOp> {
}
};
+/// Folds a chain of `tensor.pad` ops with the same constant padding value.
+///
+/// Example:
+///
+/// ```mlir
+/// %1 = tensor.pad %0 low[0, 1] high[0, 2] {
+/// tensor.yield %val
+/// } : tensor<1x2xf32> to tensor<2x5xf32>
+/// %res = tensor.pad %1 low[0, 2] high[3, 0] {
+/// tensor.yield %val
+/// } : tensor<1x5xf32> to tensor<5x7xf32>
+/// ```
+///
+/// folds into:
+///
+/// ```mlir
+/// %res = tensor.pad %0 low[0, 3] high[3, 2] {
+/// tensor.yield %val
+/// } : tensor<1x2xf32> to tensor<5x7xf32>
+/// ```
+struct FoldConsecutiveConstantPadding : public OpRewritePattern<tensor::PadOp> {
+ using OpRewritePattern<tensor::PadOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(tensor::PadOp padOp,
+ PatternRewriter &rewriter) const override {
+ auto producerPad = padOp.getSource().getDefiningOp<tensor::PadOp>();
+ if (!producerPad || producerPad.getNofold()) {
+ return rewriter.notifyMatchFailure(
+ padOp, "producer is not a foldable tensor.pad op");
+ }
+
+ // Fail if the tensor::PadOps padding values do not match.
+ Value consumerPadValue = padOp.getConstantPaddingValue();
+ Value producerPadValue = producerPad.getConstantPaddingValue();
+ if (!consumerPadValue || !producerPadValue ||
+ consumerPadValue != producerPadValue) {
+ return rewriter.notifyMatchFailure(
+ padOp,
+ "cannot fold PadOps with different or non-constant padding values");
+ }
+
+ Location loc = padOp.getLoc();
+
+ // Combine the low/high paddings of the two tensor::PadOps.
+ auto addPaddings = [&](ArrayRef<OpFoldResult> consumerPaddings,
+ ArrayRef<OpFoldResult> producerPaddings) {
+ SmallVector<OpFoldResult> sumPaddings;
+ for (auto [consumerIndex, producerIndex] :
+ llvm::zip_equal(consumerPaddings, producerPaddings)) {
+ Value consumerIndexVal =
+ getValueOrCreateConstantIndexOp(rewriter, loc, consumerIndex);
+ Value producerIndexVal =
+ getValueOrCreateConstantIndexOp(rewriter, loc, producerIndex);
+ Value sum = rewriter.createOrFold<arith::AddIOp>(loc, consumerIndexVal,
+ producerIndexVal);
+ APInt constantSum;
+ if (matchPattern(sum, m_ConstantInt(&constantSum))) {
+ sumPaddings.push_back(
+ rewriter.getIndexAttr(constantSum.getSExtValue()));
+ } else {
+ sumPaddings.push_back(sum);
+ }
+ }
+ return sumPaddings;
+ };
+
+ SmallVector<OpFoldResult> newHighPad =
+ addPaddings(padOp.getMixedHighPad(), producerPad.getMixedHighPad());
+ SmallVector<OpFoldResult> newLowPad =
+ addPaddings(padOp.getMixedLowPad(), producerPad.getMixedLowPad());
+
+ auto newPadOp = rewriter.create<tensor::PadOp>(
+ padOp.getLoc(), padOp.getResultType(), producerPad.getSource(),
+ newLowPad, newHighPad, padOp.getNofold(),
+ getPrunedAttributeList(padOp, tensor::PadOp::getAttributeNames()));
+ rewriter.inlineRegionBefore(padOp.getRegion(), newPadOp.getRegion(),
+ newPadOp.getRegion().begin());
+ rewriter.replaceOp(padOp, newPadOp.getResult());
+ return success();
+ }
+};
+
} // namespace
void PadOp::getCanonicalizationPatterns(RewritePatternSet &results,
MLIRContext *context) {
results.add<FoldStaticZeroPadding, FoldSourceTensorCast, FoldTargetTensorCast,
- FoldOrthogonalPaddings, FoldStaticPadding>(context);
+ FoldOrthogonalPaddings, FoldStaticPadding,
+ FoldConsecutiveConstantPadding>(context);
}
/// Return the padding value of the PadOp if it constant. In this context,
diff --git a/mlir/test/Dialect/Tensor/canonicalize.mlir b/mlir/test/Dialect/Tensor/canonicalize.mlir
index 735790e5bd6c5e..fb9e6c96b09ef6 100644
--- a/mlir/test/Dialect/Tensor/canonicalize.mlir
+++ b/mlir/test/Dialect/Tensor/canonicalize.mlir
@@ -1964,6 +1964,88 @@ func.func @dont_fold_pad_chains(%arg0: tensor<64x64xf32>,
// -----
+// CHECK-LABEL: func @merge_constant_padding
+// CHECK-SAME: %[[ARG0:[A-Za-z0-9]+]]: tensor<2x3xf32>
+// CHECK-SAME: %[[PADVAL:[A-Za-z0-9]+]]: f32
+// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]] low[1, 3] high[4, 2]
+// CHECK: tensor.yield %[[PADVAL]]
+// CHECK: return %[[PAD]]
+func.func @merge_constant_padding(%arg0: tensor<2x3xf32>, %pad_value: f32) -> tensor<7x8xf32> {
+ %pad0 = tensor.pad %arg0 low[1, 1] high[1, 0] {
+ ^bb0(%b0: index, %b1 : index):
+ tensor.yield %pad_value : f32
+ } : tensor<2x3xf32> to tensor<4x4xf32>
+ %pad1 = tensor.pad %pad0 low[0, 2] high[3, 2] {
+ ^bb0(%b2: index, %b3 : index):
+ tensor.yield %pad_value : f32
+ } : tensor<4x4xf32> to tensor<7x8xf32>
+ return %pad1 : tensor<7x8xf32>
+}
+
+// -----
+
+// CHECK-LABEL: func @merge_constant_padding_dynamic
+// CHECK-SAME: %[[ARG0:[A-Za-z0-9]+]]: tensor<?x?xf32>
+// CHECK-SAME: %[[IDX:[A-Za-z0-9]+]]: index
+// CHECK-SAME: %[[PADVAL:[A-Za-z0-9]+]]: f32
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[HIGH:.+]] = arith.addi %[[IDX]], %[[C1]] : index
+// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]] low[%[[IDX]], 3] high[%[[HIGH]], 2]
+// CHECK: tensor.yield %[[PADVAL]]
+// CHECK: return %[[PAD]]
+func.func @merge_constant_padding_dynamic(%arg0: tensor<?x?xf32>, %idx: index, %pad_value: f32) -> tensor<?x?xf32> {
+ %pad0 = tensor.pad %arg0 low[%idx, 1] high[1, 0] {
+ ^bb0(%b0: index, %b1 : index):
+ tensor.yield %pad_value : f32
+ } : tensor<?x?xf32> to tensor<?x?xf32>
+ %pad1 = tensor.pad %pad0 low[0, 2] high[%idx, 2] {
+ ^bb0(%b2: index, %b3 : index):
+ tensor.yield %pad_value : f32
+ } : tensor<?x?xf32> to tensor<?x?xf32>
+ return %pad1 : tensor<?x?xf32>
+}
+
+// -----
+
+// Verify that folding does not happen if it would drop a nofold attribute
+// CHECK-LABEL: func @dont_merge_constant_padding_nofold
+// CHECK: tensor.pad {{.*}} nofold
+// CHECK: tensor.pad
+func.func @dont_merge_constant_padding_nofold(%arg0: tensor<2x3xf32>, %pad_value: f32) -> tensor<7x8xf32> {
+ %pad0 = tensor.pad %arg0 nofold low[1, 1] high[1, 0] {
+ ^bb0(%b0: index, %b1 : index):
+ tensor.yield %pad_value : f32
+ } : tensor<2x3xf32> to tensor<4x4xf32>
+ %pad1 = tensor.pad %pad0 low[0, 2] high[3, 2] {
+ ^bb0(%b2: index, %b3 : index):
+ tensor.yield %pad_value : f32
+ } : tensor<4x4xf32> to tensor<7x8xf32>
+ return %pad1 : tensor<7x8xf32>
+}
+
+// -----
+
+// Verify that folding does not happen if it would drop a nofold attribute
+// CHECK-LABEL: func @dont_merge_constant_padding_different_vals
+// CHECK: tensor.pad
+// CHECK: tensor.pad
+func.func @dont_merge_constant_padding_different_vals(
+ %arg0: tensor<2x3xf32>,
+ %pad_value0: f32,
+ %pad_value1: f32) -> tensor<7x8xf32> {
+ %pad0 = tensor.pad %arg0 low[1, 1] high[1, 0] {
+ ^bb0(%b0: index, %b1 : index):
+ tensor.yield %pad_value0 : f32
+ } : tensor<2x3xf32> to tensor<4x4xf32>
+ %pad1 = tensor.pad %pad0 low[0, 2] high[3, 2] {
+ ^bb0(%b2: index, %b3 : index):
+ tensor.yield %pad_value1 : f32
+ } : tensor<4x4xf32> to tensor<7x8xf32>
+ return %pad1 : tensor<7x8xf32>
+}
+
+// -----
+
// CHECK-LABEL: func @fold_collapse_shape_from_elements
func.func @fold_collapse_shape_from_elements(%arg0: i32) -> tensor<i32> {
// CHECK: %[[FROM:.+]] = tensor.from_elements %arg0 : tensor<i32>
``````````
</details>
https://github.com/llvm/llvm-project/pull/107302
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