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[MLIR][Linalg] Extend detensoring control flow model. 

This patch extends the PureControlFlowDetectionModel to consider
detensoring br and cond_br operands.

See: https://github.com/google/iree/issues/1159#issuecomment-884322687,
for a disccusion on the need for such extension.

Reviewed By: silvas

Differential Revision: https://reviews.llvm.org/D107358
This commit is contained in:
KareemErgawy-TomTom 2021-08-03 18:08:00 +02:00
parent f0658c7a42
commit f984a805f3
2 changed files with 60 additions and 2 deletions
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13
mlir/lib/Dialect/Linalg/Transforms/Detensorize.cpp
View File

@ -297,8 +297,17 @@ struct LinalgDetensorize : public LinalgDetensorizeBase<LinalgDetensorize> {
DenseSet<BlockArgument> &blockArgsToDetensor) override { DenseSet<BlockArgument> &blockArgsToDetensor) override {
SmallVector<Value> workList; SmallVector<Value> workList;
func.walk( func.walk([&](CondBranchOp condBr) {
[&](CondBranchOp condBr) { workList.push_back(condBr.condition()); }); for (auto operand : condBr.getOperands()) {
workList.push_back(operand);
}
});
func.walk([&](BranchOp br) {
for (auto operand : br.getOperands()) {
workList.push_back(operand);
}
});
DenseSet<Value> visitedValues; DenseSet<Value> visitedValues;
DenseSet<Operation *> visitedOps; DenseSet<Operation *> visitedOps;

49
mlir/test/Dialect/Linalg/detensorize_br_operands.mlir Normal file
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@ -0,0 +1,49 @@
// RUN: mlir-opt %s -split-input-file -allow-unregistered-dialect -linalg-detensorize | FileCheck %s
// TODO: Detensoring breaks if %arg0 or %arg1 are passed directly as tensors. Fix that.
func @if_true_test(%arg0: i1, %arg1: i32) -> tensor<i32> attributes {} {
%arg0_t = tensor.from_elements %arg0 : tensor<1xi1>
%arg0_t2 = linalg.tensor_collapse_shape %arg0_t [] : tensor<1xi1> into tensor<i1>
%arg1_t = tensor.from_elements %arg1 : tensor<1xi32>
%arg1_t2 = linalg.tensor_collapse_shape %arg1_t [] : tensor<1xi32> into tensor<i32>
%cst = constant dense<10> : tensor<i32>
%2 = linalg.init_tensor [] : tensor<i8>
%3 = linalg.generic
{indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>], iterator_types = []}
ins(%arg0_t2 : tensor<i1>)
outs(%2 : tensor<i8>) {
^bb0(%arg2: i1, %arg3: i8): // no predecessors
%10 = zexti %arg2 : i1 to i8
linalg.yield %10 : i8
} -> tensor<i8>
%4 = tensor.extract %3[] : tensor<i8>
%5 = trunci %4 : i8 to i1
cond_br %5, ^bb1, ^bb2(%arg1_t2 : tensor<i32>)
^bb1:
%6 = linalg.init_tensor [] : tensor<i32>
%7 = linalg.generic
{indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>, affine_map<() -> ()>], iterator_types = []}
ins(%arg1_t2, %cst : tensor<i32>, tensor<i32>)
outs(%6 : tensor<i32>) {
^bb0(%arg2: i32, %arg3: i32, %arg4: i32): // no predecessors
%10 = addi %arg2, %arg3 : i32
linalg.yield %10 : i32
} -> tensor<i32>
br ^bb2(%7 : tensor<i32>)
^bb2(%8: tensor<i32>):
return %8 : tensor<i32>
}
// CHECK-LABEL: func @if_true_test
// CHECK-SAME: (%[[arg0:.*]]: i1, %[[arg1:.*]]: i32)
// CHECK-NEXT: constant 10 : i32
// CHECK-NEXT: cond_br %[[arg0]], ^[[bb1:.*]], ^[[bb2:.*]](%[[arg1]] : i32)
// CHECK-NEXT: ^[[bb1]]:
// CHECK-NEXT: %[[add_res:.*]] = addi
// CHECK-NEXT: br ^[[bb2]](%[[add_res]] : i32)
// CHECK-NEXT: ^[[bb2]]
// CHECK-NEXT: tensor.from_elements
// CHECK-NEXT: %[[func_res:.*]] = linalg.tensor_collapse_shape
// CHECK-NEXT: return %[[func_res]]