[TF:MLIR] Fold constant cond in IfOp

PiperOrigin-RevId: 321499354
Change-Id: Ie644b7f25d1c05d43c43f90983c7f5ac00866c47

tensorflow/compiler/mlir/lite/tf_tfl_passes.cc

@@ -175,6 +175,11 @@ void AddTFToTFLConversionPasses(const mlir::TFL::PassConfig& pass_config,
       // Add a shape inference pass to optimize away the unnecessary casts.
       pass_manager->addPass(mlir::TF::CreateTFShapeInferencePass());
     }
+
+    // Inline function calls that left in the graph after folding functional
+    // control flow ops (IfOp, CaseOp).
+    pass_manager->addPass(mlir::createInlinerPass());
+
     pass_manager->addPass(
         mlir::TFL::CreateLegalizeTFPass(pass_config.runtime_verification));
     pass_manager->addPass(mlir::TFL::CreateOptimizePass());

tensorflow/compiler/mlir/lite/transforms/quantize_patterns.td

@@ -33,7 +33,7 @@ def : Pat<(TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), (replaceWithValue $in)>;
 // point constant.
 def : Pat<(TFL_DequantizeOp
              (TFL_QuantizeOp (ConstantOp F32ElementsAttr:$cst), $qt)),
-          (ConstantOp $cst)>;
+          (TFL_ConstOp $cst)>;
 
 // Quantize the value of a constant op if the quantization parameters have been
 // propagated to the output.

tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td

@@ -229,6 +229,8 @@ else_branch: A function that takes 'inputs' and returns a list of
   let verifier = [{
     return Verify(*this);
   }];
+
+  let hasCanonicalizer = 1;
 }
 
 def TF_YieldOp : TF_Op<"Yield",

tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc

@@ -463,7 +463,7 @@ LogicalResult FoldConstantCaseOp::matchAndRewrite(
   auto call_op = rewriter.create<PartitionedCallOp>(
       op.getLoc(), op.getResultTypes(), op.getOperands().drop_front(), func,
       /*config=*/empty, /*config_proto=*/empty, /*executor_type=*/empty);
-  PropagateAttributes(op.getOperation(), call_op);
+  PropagateDeviceAndInternalAttrs(op.getOperation(), call_op);
   rewriter.replaceOp(op, call_op.getResults());
   return success();
 }
@@ -1615,6 +1615,56 @@ static LogicalResult Verify(IfOp op) {
   return success();
 }
 
+class FoldConstantIfOp : public OpRewritePattern<TF::IfOp> {
+ public:
+  explicit FoldConstantIfOp(MLIRContext *context)
+      : OpRewritePattern<TF::IfOp>(context) {}
+  LogicalResult matchAndRewrite(TF::IfOp op,
+                                PatternRewriter &rewriter) const override;
+
+ private:
+  template <typename T>
+  struct CallOpType {
+    using CallOp = T;
+  };
+};
+
+LogicalResult FoldConstantIfOp::matchAndRewrite(
+    TF::IfOp op, PatternRewriter &rewriter) const {
+  // Extract the constant cond value.
+  DenseIntElementsAttr cond_attr;
+  if (!matchPattern(op.cond(), m_Constant(&cond_attr))) return failure();
+
+  // Cond value must be a scalar.
+  if (cond_attr.getNumElements() != 1) return failure();
+
+  // Select a branch function.
+  bool cond = cond_attr.getSplatValue<BoolAttr>().getValue();
+  FlatSymbolRefAttr func = cond ? op.then_branchAttr() : op.else_branchAttr();
+
+  // Replace IfOp with PartitionedCallOp or StatefulPartitionedCallOp.
+  auto rewrite = [&](auto op_type) {
+    auto empty = rewriter.getStringAttr("");
+    auto call_op = rewriter.create<typename decltype(op_type)::CallOp>(
+        op.getLoc(), op.getResultTypes(), op.getOperands().drop_front(), func,
+        /*config=*/empty, /*config_proto=*/empty, /*executor_type=*/empty);
+    PropagateDeviceAndInternalAttrs(op.getOperation(), call_op);
+    rewriter.replaceOp(op, call_op.getResults());
+  };
+
+  if (op.is_stateless())
+    rewrite(CallOpType<PartitionedCallOp>{});
+  else
+    rewrite(CallOpType<StatefulPartitionedCallOp>{});
+
+  return success();
+}
+
+void IfOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
+                                       MLIRContext *context) {
+  results.insert<FoldConstantIfOp>(context);
+}
+
 //===----------------------------------------------------------------------===//
 // IfRegionOp
 //===----------------------------------------------------------------------===//

tensorflow/compiler/mlir/tensorflow/ir/tf_ops_helpers.inc

@@ -21,7 +21,7 @@ limitations under the License.
 // Propagates underscore and device attributes from src to dst.
 // TODO(b/158769932): This should be a general feature instead post some policy
 // discussion.
-static void PropagateAttributes(Operation *src, Operation *dst) {
+static void PropagateDeviceAndInternalAttrs(Operation *src, Operation *dst) {
   auto device = mlir::Identifier::get("device", src->getContext());
   for (auto named_attr : src->getAttrs()) {
     if (*named_attr.first.begin() == '_' || named_attr.first == device)

tensorflow/compiler/mlir/tensorflow/tests/canonicalize.mlir

@@ -755,6 +755,27 @@ func @foldFill() -> (tensor<3x2x1xf32>, tensor<*xf32>, tensor<*xcomplex<f32>>) {
   return %2, %3, %4 : tensor<3x2x1xf32>, tensor<*xf32>, tensor<*xcomplex<f32>>
 }
 
+// CHECK-LABEL: foldIf
+func @foldIf(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<i1>) -> (tensor<f32>) {
+  %0 = "tf.Const"() {value = dense<false> : tensor<i1>} : () -> tensor<i1>
+  %1 = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>
+
+  // CHECK: %0 = "tf.PartitionedCall"(%arg0, %arg1)
+  // CHECK-SAME: device = "noodle"
+  // CHECK-SAME: f = @sub
+  %2 = "tf.If"(%0, %arg0, %arg1) {then_branch = @add, else_branch = @sub, output_shapes = [#tf.shape<>], device = "noodle", is_stateless = true} : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
+  // CHECK: %1 = "tf.StatefulPartitionedCall"(%0, %arg1)
+  // CHECK-SAME: _underscore_attr = "something"
+  // CHECK-SAME: f = @add
+  %3 = "tf.If"(%1, %2, %arg1) {then_branch = @add, else_branch = @sub, output_shapes = [#tf.shape<>], _underscore_attr = "something", is_stateless = false} : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
+
+  // CHECK: %2 = "tf.If"
+  %4 = "tf.If"(%arg2, %3, %arg1) {then_branch = @add, else_branch = @sub, is_stateless = false} : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
+
+  // CHECK: return %2
+  return %4 : tensor<f32>
+}
+
 // CHECK-LABEL: foldCase
 func @foldCase(%arg0: tensor<f32>, %arg1: tensor<f32>) -> (tensor<f32>) {
   %2 = constant dense<1> : tensor<i32>

tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/control_flow_upgrade_legacy_v1.py

@@ -33,9 +33,10 @@ from tensorflow.python.ops import control_flow_ops
 
 def Test():
   data = tf.constant([1, 2, 3, 4, 5, 6])
-  zero = tf.convert_to_tensor(0)
+  # Create placeholders to prevent constant folding.
-  one = tf.convert_to_tensor(1)
+  x_op = tf.placeholder(dtype=tf.int32)
-  less_op = tf.less(zero, one)
+  y_op = tf.placeholder(dtype=tf.int32)
+  less_op = tf.less(x_op, y_op)
   switch_op = control_flow_ops.switch(data, less_op)
   merge_op = control_flow_ops.merge(switch_op)[0]
   result = tf.transpose(merge_op)