[Mlir-commits] [mlir] [MLIR] Add pattern to bubble up tensor.extract_slice (PR #126898)

[Andrzej Warzyński]

================
@@ -210,6 +214,200 @@ struct BubbleUpExpandThroughParallelCollapse
   }
 };
 
+/// Converts `tensor.extract_slice(tensor.expand_shape)` to
+/// `tensor.expand_shape(tensor.extract_slice)`.
+/// For this transformation to be possible, the slice must be fully contiguous
+/// within each reassociation group of the expand_shape. If the transformation
+/// is not possible, or if the slice is rank reducing, the function returns
+/// failure.
+///
+/// Example:
+/// ```
+/// %reshape = tensor.expand_shape %in [[0, 1], [2, 3], [4, 5, 6]]
+///     tensor<8x16x32xf32> to tensor<2x4x2x8x4x2x4xf32>
+/// %slice = tensor.extract_slice %reshape ...
+///     tensor<2x4x2x8x4x2x4xf32> to tensor<2x4x1x5x1x1x4xf32>
+///
+/// // The transformation is possible because each reassociation group has a
+/// // contiguous slice. (i.e., [2x4->2x4], [2x8->1x5], [4x2x4->1x1x4])
+/// // After the transformation:
+///
+/// %slice = tensor.extract_slice %in ...
+///     tensor<8x16x32xf32> to tensor<8x5x4xf32>
+/// %reshape = tensor.expand_shape %slice [[0, 1], [2, 3], [4, 5, 6]]
+///     tensor<8x5x4xf32> to tensor<2x4x1x5x1x1x4xf32>
+/// ```
+///
+/// Note - this pattern could be reworked to be a swap pattern between
+/// `tensor.expand_shape` and `tensor.extract_slice`, but is currently
+/// implemented only as a bubble up pattern for `tensor.extract_slice`.
+struct BubbleUpExpandShapeThroughExtractSlice
+    : public OpRewritePattern<tensor::ExtractSliceOp> {
+  using OpRewritePattern<tensor::ExtractSliceOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tensor::ExtractSliceOp sliceOp,
+                                PatternRewriter &rewriter) const override {
+    auto expandShapeOp =
+        sliceOp.getSource().getDefiningOp<tensor::ExpandShapeOp>();
+
+    if (checkPreconditionForBubbleUpExtractSlice(sliceOp, expandShapeOp,
+                                                 rewriter)
+            .failed())
+      return failure();
+
+    SmallVector<OpFoldResult> offsets = sliceOp.getMixedOffsets();
+    SmallVector<OpFoldResult> sizes = sliceOp.getMixedSizes();
+    SmallVector<OpFoldResult> outputShape =
+        getMixedValues(expandShapeOp.getStaticOutputShape(),
+                       expandShapeOp.getOutputShape(), rewriter);
+
+    // Helper variables and function for accumulating the new offset and length
+    // values.
+    Location loc = expandShapeOp->getLoc();
+    AffineExpr d0, d1, d2;
+    bindDims(rewriter.getContext(), d0, d1, d2);
+    // Multiply two integers.
+    auto mul = [&](OpFoldResult v1, OpFoldResult v2) {
+      auto mulMap = AffineMap::get(2, 0, {d0 * d1});
+      return affine::makeComposedFoldedAffineApply(rewriter, loc, mulMap,
+                                                   {v1, v2});
+    };
+
+    // Compute new offsets, lengths, and strides.
+    SmallVector<OpFoldResult> newOffsets, newLengths, newStrides;
+    for (const ReassociationIndices &indices :
+         expandShapeOp.getReassociationIndices()) {
+      OpFoldResult newSize = rewriter.getIndexAttr(1);
+      SmallVector<OpFoldResult> basis, delinOffsets;
+
+      int64_t i = 0;
+      int64_t e = indices.size();
+      // Offset = cumulative product of leading unit extracted dims.
+      for (; i < e; ++i) {
+        int64_t expandedDim = indices[i];
+        if (!isConstantIntValue(sizes[expandedDim], 1))
+          break;
+
+        basis.push_back(outputShape[expandedDim]);
+        delinOffsets.push_back(offsets[expandedDim]);
+      }
+
+      if (i != e) {
+        int64_t expandedDim = indices[i];
+        basis.push_back(outputShape[expandedDim]);
+        delinOffsets.push_back(offsets[expandedDim]);
+        newSize = sizes[expandedDim];
+        i++;
+      }
+
+      for (; i < e; ++i) {
+        OpFoldResult fullSize = outputShape[indices[i]];
+        basis.push_back(fullSize);
+        delinOffsets.push_back(rewriter.getIndexAttr(0));
+        newSize = mul(newSize, fullSize);
+      }
+      SmallVector<Value> offsetVals =
+          llvm::map_to_vector(delinOffsets, [&](OpFoldResult ofr) {
+            return getValueOrCreateConstantIndexOp(rewriter, loc, ofr);
+          });
+      OpFoldResult newOffset =
+          rewriter
+              .create<affine::AffineLinearizeIndexOp>(loc, offsetVals, basis,
+                                                      /*disjoint=*/true)
+              .getResult();
+      newOffsets.push_back(newOffset);
+      newLengths.push_back(newSize);
+
+      // Only unit stride supported.
+      newStrides.push_back(rewriter.getIndexAttr(1));
+    }
+
+    // The shape of the result can be obtained from the sizes passed in.
+    SmallVector<Value> dynDims;
+    SmallVector<int64_t> shape;
+    dispatchIndexOpFoldResults(sizes, dynDims, shape);
+    RankedTensorType resultType = RankedTensorType::get(
+        shape, expandShapeOp.getResultType().getElementType());
+
+    // Create a new ExtractSliceOp and ExpandShapeOp.
+    Value newSliceOp = rewriter.create<tensor::ExtractSliceOp>(
+        loc, expandShapeOp.getSrc(), newOffsets, newLengths, newStrides);
+    rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(
+        sliceOp, resultType, newSliceOp,
+        expandShapeOp.getReassociationIndices(), sizes);
+    return success();
+  }
+
+  LogicalResult
+  checkPreconditionForBubbleUpExtractSlice(tensor::ExtractSliceOp sliceOp,
+                                           tensor::ExpandShapeOp expandShapeOp,
+                                           PatternRewriter &rewriter) const {
+
+    if (!expandShapeOp) {
+      return rewriter.notifyMatchFailure(
+          sliceOp, "tensor.extract_slice source not produced by expand_shape");
+    }
+
+    if (!sliceOp.hasUnitStride()) {
+      return rewriter.notifyMatchFailure(
+          sliceOp, "unsupported: non-unit stride. Only contiguous slices can "
+                   "be supported in this transformation.");
+    }
+
+    SmallVector<OpFoldResult> offsets = sliceOp.getMixedOffsets();
+    SmallVector<OpFoldResult> sizes = sliceOp.getMixedSizes();
+
+    if (static_cast<size_t>(sliceOp.getResultType().getRank()) !=
+        sizes.size()) {
+      return rewriter.notifyMatchFailure(sliceOp,
+                                         "unimplemented: rank reducing slice");
+    }
+
+    SmallVector<OpFoldResult> outputShape =
+        getMixedValues(expandShapeOp.getStaticOutputShape(),
+                       expandShapeOp.getOutputShape(), rewriter);
+
+    std::function<bool(OpFoldResult, OpFoldResult, OpFoldResult)>
+        isZeroOffsetAndFullSize =
+            [](OpFoldResult offset, OpFoldResult sliceSize, OpFoldResult size) {
+              if (!isConstantIntValue(offset, 0))
+                return false;
+              FailureOr<bool> maybeEqual =
+                  ValueBoundsConstraintSet::areEqual(sliceSize, size);
+              return llvm::succeeded(maybeEqual) && maybeEqual.value();
+            };
+
+    // First verify that this is a full slice of the expanded tensor.
----------------
banach-space wrote:

>    // First verify that this is a full slice of the expanded tensor.

1. Could you define what a "full slice" means? You could just provide 2-3 examples (without using `tensor.extract_slice`)
2. This comment should be updated, "First" doesn't make sense after moving this to a dedicated hook.
3. I couldn't find any negative test for extracting a "partial" slice. It might be there and I'm failing to find it?

https://github.com/llvm/llvm-project/pull/126898