[mlir][linalg] Add pattern to clean unused results after fusion

In some cases, elementwise fusion can produce ops with multiple
results, but only one of them is used in the IR. This makes the
IR less readable and prevents additional fusions from being triggered.

This patch adds the `DropRedundantResultsFromGenericOps` pattern
to find these outputs and convert them into inputs.

Signed-off-by: Pavel Lipskiy <[email protected]>

Author: Pavel Lipskiy

mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp

@@ -2200,6 +2200,63 @@ struct RemoveOutsDependency : public OpRewritePattern<GenericOp> {
   }
 };
 
+/// Drops an unused result from an elementwise `linalg.generic` by
+/// reclassifying its tied `outs` operand as an extra input operand.
+struct DropRedundantResultsFromGenericOps
+    : public OpRewritePattern<linalg::GenericOp> {
+  using OpRewritePattern<linalg::GenericOp>::OpRewritePattern;
+  LogicalResult matchAndRewrite(linalg::GenericOp op,
+                                PatternRewriter &rewriter) const override {
+    if (!linalg::isElementwise(op) || op.getNumResults() < 2U)
+      return failure();
+
+    // Given that the op has no reductions, there is no need to preserve an
+    // unused result: transform it into an input instead.
+    auto maybeUnusedRes = llvm::find_if(
+        op.getResults(), [](OpResult res) { return res.use_empty(); });
+    if (maybeUnusedRes == op.getResults().end())
+      return failure();
+
+    OpResult unusedRes = *maybeUnusedRes;
+    const unsigned resIdx = unusedRes.getResultNumber();
+    auto resTypes = llvm::to_vector(op.getResultTypes());
+    resTypes.erase(resTypes.begin() + resIdx);
+    SmallVector<Value> resValues = llvm::to_vector_of<Value>(op.getResults());
+    resValues.erase(resValues.begin() + resIdx);
+    const int64_t numInputs = op.getNumDpsInputs();
+    OpOperand *resOperand = op.getTiedOpOperand(unusedRes);
+    AffineMap map = op.getIndexingMapMatchingResult(unusedRes);
+    const unsigned operandIdx = resOperand->getOperandNumber();
+    
+    // Remove the output operand and add it as an input operand with the same
+    // map.
+    SmallVector<Value> outs(op.getOutputs());
+    outs.erase(outs.begin() + resIdx);
+    SmallVector<Value> ins(op.getInputs());
+    ins.insert(ins.begin() + numInputs, resOperand->get());
+    SmallVector<AffineMap> maps = op.getIndexingMapsArray();
+    maps.erase(maps.begin() + operandIdx);
+    maps.insert(maps.begin() + numInputs, map);
+    rewriter.setInsertionPoint(op);
+
+    auto newGenericOp = rewriter.create<linalg::GenericOp>(
+        op.getLoc(), TypeRange(resTypes), ins, outs, maps,
+        op.getIteratorTypesArray());
+
+    op->setDiscardableAttrs(op->getDiscardableAttrDictionary());
+    op.getBody()->getTerminator()->eraseOperands(resIdx);
+    newGenericOp.getRegion().takeBody(op.getBodyRegion());
+
+    // Replace the remaining results of the old op with the results of the new
+    // op.
+    rewriter.replaceAllUsesWith(resValues, newGenericOp.getResults());
+    
+    // Remove the old op.
+    rewriter.eraseOp(op);
+    return success();
+  }
+};
+
 /// Fold linalg.fill into linalg.generic
 struct FoldFillWithGenericOp : public OpRewritePattern<GenericOp> {
   using OpRewritePattern<GenericOp>::OpRewritePattern;
@@ -2262,6 +2319,7 @@ void mlir::linalg::populateElementwiseOpsFusionPatterns(
                RemoveOutsDependency>(context);
   // Add the patterns that clean up dead operands and results.
   populateEraseUnusedOperandsAndResultsPatterns(patterns);
+  patterns.add<DropRedundantResultsFromGenericOps>(context);
 }
 
 void mlir::linalg::populateCollapseDimensions(

mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir

@@ -1079,4 +1079,49 @@ module {
 // CHECK-NOT:     linalg.generic
 // CHECK:         tensor.expand_shape
 // CHECK:         linalg.generic {{.*}}, iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "parallel", "reduction"]}
-// CHECK-SAME:     ins(%[[ARG0]], %[[FUSED]]#1 : tensor<1x1x2x1xf32>, tensor<4x1x1x1xf32>)
+// CHECK-SAME:     ins(%[[ARG0]], %[[FUSED]]#1 : tensor<1x1x2x1xf32>, tensor<4x1x1x1xf32>)
+
+// -----
+
+// CHECK-LABEL: @drop_unused_results
+// CHECK-SAME:   [[ARG0:%[a-zA-Z0-9]+]]: tensor<64xf32>, [[ARG1:%[a-zA-Z0-9]+]]: tensor<1x56x56x64xf32>
+func.func @drop_unused_results(%arg0: tensor<64xf32>, %arg1: tensor<1x56x56x64xf32>) -> tensor<1x56x56x64xf32> {
+  %cst = arith.constant 3.40282347E+38 : f32
+  %cst_0 = arith.constant 0.000000e+00 : f32
+  // CHECK: [[OUT:%[a-zA-Z0-9]+]] = tensor.empty() : tensor<1x56x56x64xf32>
+  %0 = tensor.empty() : tensor<1x56x56x64xf32>
+  // CHECK: [[RES:%[0-9]+]] = linalg.generic {{.*}} ins([[ARG0]], [[ARG1]] : tensor<64xf32>, tensor<1x56x56x64xf32>) outs([[OUT]] : tensor<1x56x56x64xf32>)
+  %1:2 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<64xf32>) outs(%arg1, %0 : tensor<1x56x56x64xf32>, tensor<1x56x56x64xf32>) {
+  ^bb0(%in: f32, %out: f32, %out_1: f32):
+    %2 = arith.addf %in, %out : f32
+    %3 = arith.minimumf %2, %cst : f32
+    %4 = arith.maximumf %3, %cst_0 : f32
+    linalg.yield %2, %4 : f32, f32
+  } -> (tensor<1x56x56x64xf32>, tensor<1x56x56x64xf32>)
+  // CHECK: -> tensor<1x56x56x64xf32>
+  // CHECK: return [[RES]] : tensor<1x56x56x64xf32>
+  return %1#1 : tensor<1x56x56x64xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @swap_drop_unused_results
+// CHECK-SAME:   [[ARG0:%[a-zA-Z0-9]+]]: tensor<64xf32>, [[ARG1:%[a-zA-Z0-9]+]]: tensor<1x56x56x64xf32>
+func.func @swap_drop_unused_results(%arg0: tensor<64xf32>, %arg1: tensor<1x56x56x64xf32>) -> tensor<1x56x56x64xf32> {
+  %cst = arith.constant 3.40282347E+38 : f32
+  %cst_0 = arith.constant 0.000000e+00 : f32
+  // CHECK: [[OUT:%[a-zA-Z0-9]+]] = tensor.empty() : tensor<1x56x56x64xf32>
+  %0 = tensor.empty() : tensor<1x56x56x64xf32>
+  // CHECK: [[RES:%[0-9]+]] = linalg.generic {{.*}} ins([[ARG0]] : tensor<64xf32>) outs([[OUT]] : tensor<1x56x56x64xf32>)
+  %1:2 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<64xf32>) outs(%arg1, %0 : tensor<1x56x56x64xf32>, tensor<1x56x56x64xf32>) {
+  ^bb0(%in: f32, %out_1: f32, %out: f32):
+    %2 = arith.addf %in, %out : f32
+    %3 = arith.minimumf %2, %cst : f32
+    %4 = arith.maximumf %3, %cst_0 : f32
+    linalg.yield %2, %4 : f32, f32
+  } -> (tensor<1x56x56x64xf32>, tensor<1x56x56x64xf32>)
+  // CHECK: -> tensor<1x56x56x64xf32>
+  // CHECK: return [[RES]] : tensor<1x56x56x64xf32>
+  return %1#0 : tensor<1x56x56x64xf32>
+}
+