Build DynamicSlice and DynamicUpdateSlice ops with MlirHloBuilder

Whitelist XlaDynamicSlice and XlaDynamicUpdateSlice for testing

PiperOrigin-RevId: 311642899
Change-Id: Icbf009cf69d3b183d0c83c10925a5fbaa3c49f1f

tensorflow/compiler/mlir/xla/ir/mlir_hlo_builder.cc

@@ -282,6 +282,28 @@ StatusOr<XlaOp> MlirHloBuilder::SliceInternal(
       GetI64ElementsAttr(strides, &builder_)));
 }
 
+StatusOr<XlaOp> MlirHloBuilder::DynamicSliceInternal(
+    const Shape& shape, XlaOp operand, absl::Span<const XlaOp> start_indices,
+    absl::Span<const int64> slice_sizes) {
+  TF_ASSIGN_OR_RETURN(
+      mlir::Type result_ty,
+      ConvertShapeToType<mlir::RankedTensorType>(shape, builder_));
+  return MakeXlaOp(builder_.create<mlir::xla_hlo::DynamicSliceOp>(
+      loc_, result_ty, GetValue(operand), GetValues(start_indices),
+      GetI64ElementsAttr(slice_sizes, &builder_)));
+}
+
+StatusOr<XlaOp> MlirHloBuilder::DynamicUpdateSliceInternal(
+    const Shape& shape, XlaOp operand, XlaOp update,
+    absl::Span<const XlaOp> start_indices) {
+  TF_ASSIGN_OR_RETURN(
+      mlir::Type result_ty,
+      ConvertShapeToType<mlir::RankedTensorType>(shape, builder_));
+  return MakeXlaOp(builder_.create<mlir::xla_hlo::DynamicUpdateSliceOp>(
+      loc_, result_ty, GetValue(operand), GetValue(update),
+      GetValues(start_indices)));
+}
+
 StatusOr<XlaOp> MlirHloBuilder::PadInternal(
     const Shape& shape, XlaOp operand, XlaOp padding_value,
     const PaddingConfig& padding_config) {

tensorflow/compiler/mlir/xla/ir/mlir_hlo_builder.h

@@ -175,6 +175,14 @@ class MlirHloBuilder : public XlaBuilder {
                                 absl::Span<const int64> limit_indices,
                                 absl::Span<const int64> strides) override;
 
+  StatusOr<XlaOp> DynamicSliceInternal(
+      const Shape& shape, XlaOp operand, absl::Span<const XlaOp> start_indices,
+      absl::Span<const int64> slice_sizes) override;
+
+  StatusOr<XlaOp> DynamicUpdateSliceInternal(
+      const Shape& shape, XlaOp operand, XlaOp update,
+      absl::Span<const XlaOp> start_indices) override;
+
   StatusOr<XlaOp> PadInternal(const Shape& shape, XlaOp operand,
                               XlaOp padding_value,
                               const PaddingConfig& padding_config) override;

tensorflow/compiler/mlir/xla/tests/legalize-tf-with-tf2xla.mlir

@@ -163,6 +163,30 @@ func @truncated_normal() -> tensor<2x2xf32> {
   return %1 : tensor<2x2xf32>
 }
 
+// CHECK-LABEL: dynamic_update_slice
+// CHECK-SAME: (%[[ARG0:.*]]: tensor<3x4xi32>, %[[ARG1:.*]]: tensor<2x2xi32>, %[[ARG2:.*]]: tensor<2xi32>
+func @dynamic_update_slice(%arg0: tensor<3x4xi32>, %arg1: tensor<2x2xi32>, %arg2: tensor<2xi32>) -> tensor<3x4xi32> {
+
+  // CHECK: %[[SLICE0:.*]] = "xla_hlo.slice"(%[[ARG2]])
+  // CHECK-DAG-SAME: start_indices = dense<0> : tensor<1xi64>
+  // CHECK-DAG-SAME: limit_indices = dense<1> : tensor<1xi64>
+  // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>
+  // CHECK-SAME: (tensor<2xi32>) -> tensor<1xi32>
+  // CHECK: %[[DIM0:.*]] = "xla_hlo.reshape"(%[[SLICE0]]) : (tensor<1xi32>) -> tensor<i32>
+
+  // CHECK: %[[SLICE1:.*]] = "xla_hlo.slice"(%[[ARG2]])
+  // CHECK-DAG-SAME: start_indices = dense<1> : tensor<1xi64>
+  // CHECK-DAG-SAME: limit_indices = dense<2> : tensor<1xi64>
+  // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>
+  // CHECK-SAME: (tensor<2xi32>) -> tensor<1xi32>
+  // CHECK: %[[DIM1:.*]] = "xla_hlo.reshape"(%[[SLICE1]]) : (tensor<1xi32>) -> tensor<i32>
+
+  // CHECK: "xla_hlo.dynamic-update-slice"(%[[ARG0]], %[[ARG1]], %[[DIM0]], %[[DIM1]])
+
+  %0 = "tf.XlaDynamicUpdateSlice"(%arg0, %arg1, %arg2) : (tensor<3x4xi32>, tensor<2x2xi32>, tensor<2xi32>) -> tensor<3x4xi32>
+  return %0: tensor<3x4xi32>
+}
+
 // TODO(hinsu): Add a test with a valid TF op for which tf2xla kernel is
 // available but doesn't support this instance.
 }

tensorflow/compiler/mlir/xla/transforms/legalize_tf_with_tf2xla.cc

@@ -168,6 +168,8 @@ static bool IsOpWhitelisted(Operation* op) {
     TypeID::get<TF::XlaBroadcastHelperOp>(),
     TypeID::get<TF::XlaConvOp>(),
     TypeID::get<TF::XlaDotOp>(),
+    TypeID::get<TF::XlaDynamicSliceOp>(),
+    TypeID::get<TF::XlaDynamicUpdateSliceOp>(),
     TypeID::get<TF::XlaPadOp>(),
     TypeID::get<TF::Xlog1pyOp>(),
     TypeID::get<TF::XlogyOp>()

tensorflow/compiler/tests/xla_ops_test.py

@@ -304,7 +304,6 @@ class XlaOpsNumericalTest(xla_test.XLATestCase, parameterized.TestCase):
       self._assertOpOutputMatchesExpected(
           lambda x: xla.transpose(x, [1, 0]), args=(v,), expected=v.T)
 
-  @test_util.disable_mlir_bridge('Not supported yet')
   def testDynamicSlice(self):
     for dtype in self.numeric_types:
       self._assertOpOutputMatchesExpected(
@@ -317,7 +316,7 @@ class XlaOpsNumericalTest(xla_test.XLATestCase, parameterized.TestCase):
                         [[673, 674], [683, 684], [693, 694]]]),
               dtype=dtype))
 
-  @test_util.disable_mlir_bridge('Not supported yet')
+  @test_util.disable_mlir_bridge('Error handling')
   def testDynamicSliceWithIncorrectStartIndicesShape(self):
     with self.session() as session:
       with self.test_scope():
@@ -331,7 +330,7 @@ class XlaOpsNumericalTest(xla_test.XLATestCase, parameterized.TestCase):
           (r'start_indices must be a vector with length equal to input rank, '
            r'but input rank is 3 and start_indices has shape \[2\].*'))
 
-  @test_util.disable_mlir_bridge('Not supported yet')
+  @test_util.disable_mlir_bridge('Error handling')
   def testDynamicSliceWithIncorrectSizeIndicesShape(self):
     with self.session() as session:
       with self.test_scope():

tensorflow/compiler/xla/client/xla_builder.cc

@@ -864,8 +864,6 @@ XlaOp XlaBuilder::DynamicSlice(XlaOp operand,
                                absl::Span<const XlaOp> start_indices,
                                absl::Span<const int64> slice_sizes) {
   return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> {
-    HloInstructionProto instr;
-
     TF_ASSIGN_OR_RETURN(const Shape* operand_shape, GetShapePtr(operand));
     std::vector<const Shape*> start_indices_shape_ptrs;
     TF_ASSIGN_OR_RETURN(const auto& start_indices_shapes,
@@ -876,6 +874,14 @@ XlaOp XlaBuilder::DynamicSlice(XlaOp operand,
     TF_ASSIGN_OR_RETURN(Shape shape,
                         ShapeInference::InferDynamicSliceShape(
                             *operand_shape, start_indices_shapes, slice_sizes));
+    return DynamicSliceInternal(shape, operand, start_indices, slice_sizes);
+  });
+}
+
+StatusOr<XlaOp> XlaBuilder::DynamicSliceInternal(
+    const Shape& shape, XlaOp operand, absl::Span<const XlaOp> start_indices,
+    absl::Span<const int64> slice_sizes) {
+  HloInstructionProto instr;
   *instr.mutable_shape() = shape.ToProto();
 
   for (int64 size : slice_sizes) {
@@ -885,14 +891,11 @@ XlaOp XlaBuilder::DynamicSlice(XlaOp operand,
   std::vector<XlaOp> operands = {operand};
   operands.insert(operands.end(), start_indices.begin(), start_indices.end());
   return AddInstruction(std::move(instr), HloOpcode::kDynamicSlice, operands);
-  });
 }
 
 XlaOp XlaBuilder::DynamicUpdateSlice(XlaOp operand, XlaOp update,
                                      absl::Span<const XlaOp> start_indices) {
   return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> {
-    HloInstructionProto instr;
-
     TF_ASSIGN_OR_RETURN(const Shape* operand_shape, GetShapePtr(operand));
     TF_ASSIGN_OR_RETURN(const Shape* update_shape, GetShapePtr(update));
     std::vector<const Shape*> start_indices_shape_ptrs;
@@ -904,13 +907,20 @@ XlaOp XlaBuilder::DynamicUpdateSlice(XlaOp operand, XlaOp update,
     TF_ASSIGN_OR_RETURN(
         Shape shape, ShapeInference::InferDynamicUpdateSliceShape(
                          *operand_shape, *update_shape, start_indices_shapes));
+    return DynamicUpdateSliceInternal(shape, operand, update, start_indices);
+  });
+}
+
+StatusOr<XlaOp> XlaBuilder::DynamicUpdateSliceInternal(
+    const Shape& shape, XlaOp operand, XlaOp update,
+    absl::Span<const XlaOp> start_indices) {
+  HloInstructionProto instr;
   *instr.mutable_shape() = shape.ToProto();
 
   std::vector<XlaOp> operands = {operand, update};
   operands.insert(operands.end(), start_indices.begin(), start_indices.end());
   return AddInstruction(std::move(instr), HloOpcode::kDynamicUpdateSlice,
                         operands);
-  });
 }
 
 XlaOp XlaBuilder::ConcatInDim(absl::Span<const XlaOp> operands,

tensorflow/compiler/xla/client/xla_builder.h

@@ -423,9 +423,15 @@ class XlaBuilder {
 
   XlaOp DynamicSlice(XlaOp operand, absl::Span<const XlaOp> start_indices,
                      absl::Span<const int64> slice_sizes);
+  virtual StatusOr<XlaOp> DynamicSliceInternal(
+      const Shape& shape, XlaOp operand, absl::Span<const XlaOp> start_indices,
+      absl::Span<const int64> slice_sizes);
 
   XlaOp DynamicUpdateSlice(XlaOp operand, XlaOp update,
                            absl::Span<const XlaOp> start_indices);
+  virtual StatusOr<XlaOp> DynamicUpdateSliceInternal(
+      const Shape& shape, XlaOp operand, XlaOp update,
+      absl::Span<const XlaOp> start_indices);
 
   XlaOp ConcatInDim(absl::Span<const XlaOp> operands, int64 dimension);
   virtual StatusOr<XlaOp> ConcatInDimInternal(const Shape& shape,