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Drop upstreamed canonicalization pattern #18465

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Drop upstreamed canonicalization pattern #18465

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79 changes: 0 additions & 79 deletions compiler/src/iree/compiler/Dialect/Flow/Transforms/Canonicalizer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -19,84 +19,6 @@ namespace mlir::iree_compiler::IREE::Flow {

namespace {

/// 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>
/// ```
///
/// NOTE: This wasn't sent upstream as a canonicalization due to the use of
/// the Affine dialect.
struct FoldConsecutiveConstantPadding : public OpRewritePattern<tensor::PadOp> {
using OpRewritePattern<tensor::PadOp>::OpRewritePattern;

LogicalResult matchAndRewrite(tensor::PadOp padOp,
PatternRewriter &rewriter) const override {
if (padOp.getNofold()) {
return failure();
}
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 padding values");
}

Location loc = padOp.getLoc();
AffineExpr d0, d1;
bindDims(rewriter.getContext(), d0, d1);

// 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)) {
sumPaddings.push_back(affine::makeComposedFoldedAffineApply(
rewriter, loc, d0 + d1, {consumerIndex, producerIndex}));
}
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();
}
};

/// Canonicalize operations in nested regions.
struct CanonicalizerPass
: public impl::CanonicalizerPassBase<CanonicalizerPass> {
Expand Down Expand Up @@ -124,7 +46,6 @@ struct CanonicalizerPass
// compilation phase.
tensor::populateMergeConsecutiveInsertExtractSlicePatterns(owningPatterns);
IREE::Flow::populateTensorDialectCastOpPattern(context, owningPatterns);
owningPatterns.add<FoldConsecutiveConstantPadding>(context);

patterns =
std::make_shared<FrozenRewritePatternSet>(std::move(owningPatterns));
Expand Down
85 changes: 0 additions & 85 deletions compiler/src/iree/compiler/Dialect/Flow/Transforms/test/flow_canonicalize.mlir
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@@ -1,90 +1,5 @@
// RUN: iree-opt --iree-flow-canonicalize %s --split-input-file --mlir-print-local-scope | FileCheck %s

util.func public @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>
util.return %pad1 : tensor<7x8xf32>
}
// CHECK-LABEL: util.func public @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: util.return %[[PAD]]

// -----

util.func public @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>
util.return %pad1 : tensor<?x?xf32>
}
// CHECK-LABEL: util.func public @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: %[[HIGH:.+]] = affine.apply affine_map<()[s0] -> (s0 + 1)>()[%[[IDX]]]
// CHECK: %[[PAD:.+]] = tensor.pad %[[ARG0]] low[%[[IDX]], 3] high[%[[HIGH]], 2]
// CHECK: tensor.yield %[[PADVAL]]
// CHECK: util.return %[[PAD]]

// -----

util.func public @dont_merge_constant_padding_nofold(%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 nofold low[0, 2] high[3, 2] {
^bb0(%b2: index, %b3 : index):
tensor.yield %pad_value : f32
} : tensor<4x4xf32> to tensor<7x8xf32>
util.return %pad1 : tensor<7x8xf32>
}

// Verify that folding does not happen if it would drop a nofold attribute

// CHECK-LABEL: util.func public @dont_merge_constant_padding_nofold
// CHECK: tensor.pad
// CHECK: tensor.pad {{.*}} nofold

// -----

util.func public @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 nofold low[0, 2] high[3, 2] {
^bb0(%b2: index, %b3 : index):
tensor.yield %pad_value1 : f32
} : tensor<4x4xf32> to tensor<7x8xf32>
util.return %pad1 : tensor<7x8xf32>
}

// Verify that folding does not happen if it would drop a nofold attribute

// CHECK-LABEL: util.func public @dont_merge_constant_padding_different_vals
// CHECK: tensor.pad
// CHECK: tensor.pad

// -----

util.func public @tensor_cast_to_reshape(%reshape_17 : tensor<?x?x?x?xf32>, %65 : tensor<?x12x?x64xf32>, %0 : index, %1 : index) -> tensor<?x?x?x?xf32> {
%cast = tensor.cast %reshape_17 : tensor<?x?x?x?xf32> to tensor<?x?x12x64xf32>
%66 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>,
Expand Down
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