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Sink standard dialect constants in sink_constants_to_control_flow pass

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This is required before exporting HLO dialect ops with standard dialect constant to XLA.

Also, sink constants for sort op as well. Added a TODO to generalize this pass to handle more ops and non-const values defined outside.

PiperOrigin-RevId: 324301911
Change-Id: I2a67a2cc5d1f58dc5fad11a319a2f4ca63a8f434
This commit is contained in:
Smit Hinsu 2020-07-31 16:01:37 -07:00 committed by TensorFlower Gardener
parent 489a765aac
commit cea9f19ebf
3 changed files with 33 additions and 8 deletions
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1
tensorflow/compiler/mlir/hlo/BUILD
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@ -341,6 +341,7 @@ cc_library(
"@llvm-project//llvm:Support",
"@llvm-project//mlir:IR",
"@llvm-project//mlir:Pass",
"@llvm-project//mlir:StandardOps",
"@llvm-project//mlir:Support",
"@llvm-project//mlir:Transforms",
],

26
tensorflow/compiler/mlir/hlo/lib/Dialect/mhlo/transforms/sink_constants_to_control_flow.cc
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@ -21,6 +21,7 @@ limitations under the License.
#include "mlir/Pass/PassManager.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/RegionUtils.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h" // from @llvm-project
namespace mlir {
namespace mhlo {
@ -29,6 +30,13 @@ namespace {
// A pass that sinks constants implicitly captured in control flow regions. This
// is necessary to export to XLA.
// TODO(hinsu): Generalize this pass to handle all the ops with regions. Any
// value used within the region that is defined outside of op's region should be
// sank to the regions and not just the constants. Ops such as If and While
// whose computations doesn't require fixed signature like Sort or Reduce have
// an option to pass outside values as operands of the op to avoid recomputing
// those within internally. Note that doing so is the only option in case of
// BlockArguments.
class SinkConstantsToControlFlowPass
: public mlir::PassWrapper<SinkConstantsToControlFlowPass, FunctionPass> {
void runOnFunction() override {
@ -39,6 +47,8 @@ class SinkConstantsToControlFlowPass
} else if (auto if_op = llvm::dyn_cast<IfOp>(op)) {
SinkToRegion(&if_op.true_branch());
SinkToRegion(&if_op.false_branch());
} else if (auto sort_op = llvm::dyn_cast<SortOp>(op)) {
SinkToRegion(&sort_op.comparator());
}
});
}
@ -46,26 +56,26 @@ class SinkConstantsToControlFlowPass
private:
// Performs constant sinking into a region.
static void SinkToRegion(Region* region) {
llvm::DenseMap<Value, ConstOp> sunk_constant;
llvm::DenseMap<Value, Operation*> sunk_constant;
visitUsedValuesDefinedAbove({*region}, [&](OpOperand* use) {
Value constant = use->get();
auto const_op = dyn_cast_or_null<ConstOp>(constant.getDefiningOp());
if (!const_op) return;
auto op = constant.getDefiningOp();
if (!op || !isa<ConstOp, ConstantOp>(op)) return;
auto map_entry = sunk_constant.try_emplace(constant, nullptr);
if (!map_entry.second) {
// This constant has already been cloned into the region, reuse it.
use->set(map_entry.first->getSecond().getResult());
if (constant.use_empty()) const_op.erase();
use->set(map_entry.first->getSecond()->getResult(0));
if (op->use_empty()) op->erase();
return;
}
if (constant.hasOneUse()) {
const_op.getOperation()->moveBefore(&region->front().front());
op->moveBefore(&region->front().front());
return;
}
map_entry.first->getSecond() = const_op.clone();
map_entry.first->getSecond() = op->clone();
region->front().getOperations().insert(region->front().begin(),
map_entry.first->getSecond());
use->set(map_entry.first->getSecond().getResult());
use->set(map_entry.first->getSecond()->getResult(0));
});
}
};

14
tensorflow/compiler/mlir/hlo/tests/sink-constants-to-control-flow.mlir
View File

@ -58,3 +58,17 @@ func @sink_const_to_conditional(%arg0: tensor<i64>) -> tensor<i64> {
%9 = "mhlo.get_tuple_element"(%2) {index = 0 : i32} : (tuple<tensor<i64>>) -> tensor<i64>
return %9 : tensor<i64>
}
func @sink_const_to_sort(%arg0: tensor<16xf32>) {
%c0 = constant dense<1.0> : tensor<f32>
// CHECK: "mhlo.sort"
%0 = "mhlo.sort"(%arg0) ( {
^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
// CHECK: constant dense<1.000000e+00>
%1 = "mhlo.divide"(%arg1, %c0) : (tensor<f32>, tensor<f32>) -> tensor<f32>
%2 = "mhlo.divide"(%arg2, %c0) : (tensor<f32>, tensor<f32>) -> tensor<f32>
%3 = "mhlo.compare"(%1, %2) {comparison_direction = "GT"} : (tensor<f32>, tensor<f32>) -> tensor<i1>
"mhlo.return"(%3) : (tensor<i1>) -> ()
}) {is_stable = true} : (tensor<16xf32>) -> tensor<16xi32>
return
}