[XLA] Make HloPass an interface, NFC

This will allow inheritance from both `HloPassInterface` and `DfsHloVisitor`, so various passes which include a visitor can have handler methods overridden per backend.
Change: 145477041

tensorflow/compiler/xla/service/BUILD

@@ -576,6 +576,7 @@ cc_test(
         ":algebraic_simplifier",
         ":cpu_plugin",
         ":hlo",
+        ":hlo_pass",
         "//tensorflow/compiler/xla:literal_util",
         "//tensorflow/compiler/xla:shape_util",
         "//tensorflow/compiler/xla:test_helpers",
@@ -1006,7 +1007,10 @@ cc_test(
 
 cc_library(
     name = "hlo_pass",
-    hdrs = ["hlo_pass.h"],
+    hdrs = [
+        "hlo_pass_fix.h",
+        "hlo_pass_interface.h",
+    ],
     deps = [
         ":hlo",
         "//tensorflow/compiler/xla:status_macros",

tensorflow/compiler/xla/service/algebraic_simplifier.h

@@ -19,12 +19,12 @@ limitations under the License.
 #include <utility>
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
 // A pass which performs AlgebraicSimplications.
-class AlgebraicSimplifier : public HloPass {
+class AlgebraicSimplifier : public HloPassInterface {
  public:
   // Given two shapes, determines if it is valid to bitcast between them after
   // considering platform dependent effects on layout like alignment
@@ -39,10 +39,10 @@ class AlgebraicSimplifier : public HloPass {
   // bitcasts.
   AlgebraicSimplifier(bool is_layout_sensitive,
                       ValidBitcastCallback valid_bitcast_callback)
-      : HloPass("algsimp"),
-        is_layout_sensitive_(is_layout_sensitive),
+      : is_layout_sensitive_(is_layout_sensitive),
         valid_bitcast_callback_(std::move(valid_bitcast_callback)) {}
   ~AlgebraicSimplifier() override {}
+  tensorflow::StringPiece name() const override { return "algsimp"; }
 
   // Run algebraic simplification on the given computation. Returns whether the
   // computation was changed.

tensorflow/compiler/xla/service/algebraic_simplifier_test.cc

@@ -24,6 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/test_helpers.h"
 #include "tensorflow/compiler/xla/tests/hlo_test_base.h"

tensorflow/compiler/xla/service/copy_insertion.h

@@ -20,7 +20,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
@@ -30,10 +30,10 @@ namespace xla {
 // constant or parameter instructions will be copied.
 // Copy insertion is necessary because constant and parameter arrays have
 // different lifetimes than computation results.
-class CopyInsertion : public HloPass {
+class CopyInsertion : public HloPassInterface {
  public:
-  CopyInsertion() : HloPass("copy-insertion") {}
   ~CopyInsertion() override {}
+  tensorflow::StringPiece name() const override { return "copy-insertion"; }
 
   // Run the pass on the given module. Returns whether the module was changed
   // (copies were inserted).

tensorflow/compiler/xla/service/cpu/conv_canonicalization.h

@@ -17,7 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CONV_CANONICALIZATION_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 namespace cpu {
@@ -30,10 +30,12 @@ namespace cpu {
 // called canonical convolutions). This pass expands non-canonical convolutions
 // into reshapes and canonical convolutions, so that these non-canonical
 // convolutions can run faster.
-class ConvCanonicalization : public HloPass {
+class ConvCanonicalization : public HloPassInterface {
  public:
-  ConvCanonicalization() : HloPass("convolution-canonicalization") {}
   ~ConvCanonicalization() override {}
+  tensorflow::StringPiece name() const override {
+    return "convolution-canonicalization";
+  }
 
   StatusOr<bool> Run(HloModule* module) override;
 };

tensorflow/compiler/xla/service/cpu/cpu_compiler.cc

@@ -62,7 +62,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_dce.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_opcode.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
 #include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
 #include "tensorflow/compiler/xla/service/inliner.h"

tensorflow/compiler/xla/service/cpu/cpu_parallelization_preparation.h

@@ -17,7 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_PARALLELIZATION_PREPARATION_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 namespace cpu {
@@ -30,10 +30,12 @@ namespace cpu {
 // computations.  However, it could make sense to coarsen the parallelization to
 // improve cache locality.  Also, we will need to do something to intelligently
 // handle While constructs.
-class ParallelizationPreparation : public HloPass {
+class ParallelizationPreparation : public HloPassInterface {
  public:
-  explicit ParallelizationPreparation() : HloPass("cpu-parallel-prepare") {}
   ~ParallelizationPreparation() override {}
+  tensorflow::StringPiece name() const override {
+    return "cpu-parallel-prepare";
+  }
 
   // Run instruction fusion on the given computation. Returns whether the
   // computation was changed.

tensorflow/compiler/xla/service/gpu/convolution_folding.h

@@ -17,14 +17,17 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_CONVOLUTION_FOLDING_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 namespace gpu {
 
-class ConvolutionFolding : public HloPass {
+class ConvolutionFolding : public HloPassInterface {
  public:
-  ConvolutionFolding() : HloPass("convolution-folding") {}
+  tensorflow::StringPiece name() const override {
+    return "convolution-folding";
+  }
+
   StatusOr<bool> Run(HloModule* module) override;
 };
 

tensorflow/compiler/xla/service/gpu/fusion_merger.h

@@ -17,7 +17,7 @@ limitations under the License.
 #define THIRD_PARTY_TENSORFLOW_COMPILER_XLA_SERVICE_GPU_FUSION_MERGER_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 namespace gpu {
@@ -32,9 +32,9 @@ namespace gpu {
 // 2) The result of merging the fusion instruction into its users would not
 //    increase bytes transferred.
 //
-class FusionMerger : public HloPass {
+class FusionMerger : public HloPassInterface {
  public:
-  FusionMerger() : HloPass("fusion merger") {}
+  tensorflow::StringPiece name() const override { return "fusion merger"; }
 
   StatusOr<bool> Run(HloModule* module) override;
 

tensorflow/compiler/xla/service/gpu/gpu_compiler.cc

@@ -48,7 +48,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_cse.h"
 #include "tensorflow/compiler/xla/service/hlo_dce.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_fix.h"
 #include "tensorflow/compiler/xla/service/hlo_pass_pipeline.h"
 #include "tensorflow/compiler/xla/service/hlo_subcomputation_unification.h"
 #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h"

tensorflow/compiler/xla/service/gpu/pad_insertion.h

@@ -16,7 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_PAD_INSERTION_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_PAD_INSERTION_H_
 
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 namespace gpu {
@@ -24,9 +24,9 @@ namespace gpu {
 // An HLO pass that canonicalizes convolution instructions for GPU codegen. It
 // inserts Pad instructions before Convolution instructions with uncanonicalized
 // padding, so that they can be lowered to cuDNN convolution.
-class PadInsertion : public HloPass {
+class PadInsertion : public HloPassInterface {
  public:
-  PadInsertion() : HloPass("pad insertion") {}
+  tensorflow::StringPiece name() const override { return "pad insertion"; }
 
   StatusOr<bool> Run(HloModule* module) override;
 

tensorflow/compiler/xla/service/hlo_cse.h

@@ -17,7 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_CSE_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
@@ -25,13 +25,14 @@ namespace xla {
 // and identical instructions with the same operands are commoned. The pass
 // iterates over the instructions in topological order which enables the pass to
 // find arbitrarily large common expressions.
-class HloCSE : public HloPass {
+class HloCSE : public HloPassInterface {
  public:
   // If is_layout_sensitive is true, then the simplifier preserves layout during
   // transformation. Otherwise, layout is ignored.
   explicit HloCSE(bool is_layout_sensitive)
-      : HloPass("cse"), is_layout_sensitive_(is_layout_sensitive) {}
+      : is_layout_sensitive_(is_layout_sensitive) {}
   ~HloCSE() override {}
+  tensorflow::StringPiece name() const override { return "cse"; }
 
   // Run CSE on the given module. Returns whether the module was changed (common
   // subexpressions were found and eliminated).

tensorflow/compiler/xla/service/hlo_dce.h

@@ -19,7 +19,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 #include "tensorflow/compiler/xla/statusor.h"
 
 namespace xla {
@@ -28,10 +28,10 @@ namespace xla {
 // module. An instruction is dead if it is not reachable from the root. This
 // pass does not remove dead parameter instructions as parameter instructions
 // cannot be deleted, nor does the pass remove dead computations.
-class HloDCE : public HloPass {
+class HloDCE : public HloPassInterface {
  public:
-  HloDCE() : HloPass("dce") {}
   ~HloDCE() override {}
+  tensorflow::StringPiece name() const override { return "dce"; }
 
   // Run the pass on the given module. Returns whether the module was changed
   // (instructions were removed).

tensorflow/compiler/xla/service/hlo_pass_fix.h

@@ -13,10 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_H_
-#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_H_
-
-#include <string>
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_FIX_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_FIX_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
 #include "tensorflow/compiler/xla/status_macros.h"
@@ -26,25 +24,6 @@ limitations under the License.
 
 namespace xla {
 
-// Base class for HLO passes. These are used with the HloPassPipeline to
-// organize a sequence of passes.
-class HloPass {
- public:
-  explicit HloPass(const string& name) : name_(name) {}
-  virtual ~HloPass() {}
-
-  const string& name() const { return name_; }
-
-  // Run the pass on the given HLO module.  Return whether it modified the
-  // module.
-  virtual StatusOr<bool> Run(HloModule* module) = 0;
-
- private:
-  const string name_;
-
-  TF_DISALLOW_COPY_AND_ASSIGN(HloPass);
-};
-
 // Do an HLO pass to a fix point.
 template <typename Pass>
 class HloPassFix : public Pass {
@@ -65,4 +44,4 @@ class HloPassFix : public Pass {
 
 }  // namespace xla
 
-#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_H_
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_FIX_H_

tensorflow/compiler/xla/service/hlo_pass_interface.h

@@ -0,0 +1,41 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_INTERFACE_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_INTERFACE_H_
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/status_macros.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+
+// Base class for HLO passes. These are used with the HloPassPipeline to
+// organize a sequence of passes.
+class HloPassInterface {
+ public:
+  virtual ~HloPassInterface() = default;
+  virtual tensorflow::StringPiece name() const = 0;
+
+  // Run the pass on the given HLO module.  Return whether it modified the
+  // module.
+  virtual StatusOr<bool> Run(HloModule* module) = 0;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_HLO_PASS_INTERFACE_H_

tensorflow/compiler/xla/service/hlo_pass_pipeline.cc

@@ -35,11 +35,12 @@ StatusOr<bool> HloPassPipeline::Run(HloModule* module) {
       tensorflow::str_util::Split(flags->xla_disable_hlo_passes, ',');
   tensorflow::gtl::FlatSet<string> disabled_passes(tmp.begin(), tmp.end());
 
-  string prefix = name() + ": pipeline start";
+  string prefix = name().ToString() + ": pipeline start";
   bool changed = false;
   string message;
   for (auto& pass : passes_) {
-    if (!disabled_passes.empty() && disabled_passes.count(pass->name()) > 0) {
+    if (!disabled_passes.empty() &&
+        disabled_passes.count(pass->name().ToString()) > 0) {
       continue;
     }
 

tensorflow/compiler/xla/service/hlo_pass_pipeline.h

@@ -24,7 +24,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/ptr_util.h"
 #include "tensorflow/compiler/xla/service/compiler.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/types.h"
 #include "tensorflow/core/platform/macros.h"
@@ -32,11 +32,12 @@ limitations under the License.
 namespace xla {
 
 // Pipeline of HLO passes.
-class HloPassPipeline : public HloPass {
+class HloPassPipeline : public HloPassInterface {
  public:
   explicit HloPassPipeline(const string& name,
                            const Compiler::HloDumper& dumper)
-      : HloPass(name), dumper_(dumper) {}
+      : name_(name), dumper_(dumper) {}
+  tensorflow::StringPiece name() const override { return name_; }
 
   // Add a pass to the pipeline. It should be called with the arguments for the
   // pass constructor:
@@ -55,8 +56,9 @@ class HloPassPipeline : public HloPass {
   StatusOr<bool> Run(HloModule* module) override;
 
  private:
+  const string name_;
   Compiler::HloDumper dumper_;
-  std::vector<std::unique_ptr<HloPass>> passes_;
+  std::vector<std::unique_ptr<HloPassInterface>> passes_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(HloPassPipeline);
 };

tensorflow/compiler/xla/service/hlo_subcomputation_unification.h

@@ -16,15 +16,17 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SUBCOMPUTATION_UNIFICATION_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_HLO_SUBCOMPUTATION_UNIFICATION_H_
 
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
 // Unify subcomputations of a `HloModule`: if any computations are equal, choose
 // one arbitrarily to use and delete the others.
-class HloSubcomputationUnification : public HloPass {
+class HloSubcomputationUnification : public HloPassInterface {
  public:
-  HloSubcomputationUnification() : HloPass("subcomputation unification") {}
+  tensorflow::StringPiece name() const override {
+    return "subcomputation-unification";
+  }
 
   StatusOr<bool> Run(HloModule* module) override;
 };

tensorflow/compiler/xla/service/inliner.h

@@ -17,17 +17,17 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_INLINER_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
 // A pass which performs inlining. Which can result, for example, in functions
 // that were previously being mapped by Map instead directly applied to the
 // forwarded operands (i.e., map({X, Y}, max) -> max(X, Y)).
-class Inliner : public HloPass {
+class Inliner : public HloPassInterface {
  public:
-  Inliner() : HloPass("inline") {}
   ~Inliner() override = default;
+  tensorflow::StringPiece name() const override { return "inline"; }
 
   // Run inlining on the given computation. Returns whether the computation was
   // changed.

tensorflow/compiler/xla/service/instruction_fusion.h

@@ -19,7 +19,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 #include "tensorflow/core/platform/macros.h"
 
 namespace xla {
@@ -38,11 +38,12 @@ bool FusionWouldDuplicate(HloInstruction* producer, HloInstruction* consumer);
 // with the intent that the loops which compute their values will be fused in
 // code generation. Derived classes define ShouldFuse method to select which
 // instructions to fuse.
-class InstructionFusion : public HloPass {
+class InstructionFusion : public HloPassInterface {
  public:
   explicit InstructionFusion(bool may_duplicate = true)
-      : HloPass("fusion"), may_duplicate_(may_duplicate) {}
+      : may_duplicate_(may_duplicate) {}
   ~InstructionFusion() override {}
+  tensorflow::StringPiece name() const override { return "fusion"; }
 
   // Run instruction fusion on the given computation. Returns whether the
   // computation was changed (instructions were fused).

tensorflow/compiler/xla/service/layout_assignment.cc

@@ -638,8 +638,7 @@ Status CheckLayouts(
 }  // namespace
 
 LayoutAssignment::LayoutAssignment(ComputationLayout* entry_computation_layout)
-    : HloPass("layout-assignment"),
-      entry_computation_layout_(entry_computation_layout) {
+    : entry_computation_layout_(entry_computation_layout) {
   VLOG(1) << "entry computation layout given to layout assignment: "
           << entry_computation_layout_->ToString();
   // Layouts of all parameter instructions must be set.

tensorflow/compiler/xla/service/layout_assignment.h

@@ -29,7 +29,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/hlo_computation.h"
 #include "tensorflow/compiler/xla/service/hlo_instruction.h"
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 #include "tensorflow/compiler/xla/service/logical_buffer.h"
 #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h"
 #include "tensorflow/compiler/xla/shape_layout.h"
@@ -203,12 +203,13 @@ class LayoutConstraints {
 
 // HLO pass which assigns layouts to all instructions in the HLO module while
 // satisfying all necessary invariants and minimizing cost.
-class LayoutAssignment : public HloPass {
+class LayoutAssignment : public HloPassInterface {
  public:
   // entry_computation_layout is modified to populate a layout for the result in
   // the case that no particular layout is requested.
   explicit LayoutAssignment(ComputationLayout* entry_computation_layout);
   ~LayoutAssignment() override {}
+  tensorflow::StringPiece name() const override { return "layout-assignment"; }
 
   // Assign layouts to the given module. Returns whether the module was changed
   // (any layouts were changed).

tensorflow/compiler/xla/service/reshape_mover.h

@@ -16,7 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_RESHAPE_MOVER_H_
 #define TENSORFLOW_COMPILER_XLA_SERVICE_RESHAPE_MOVER_H_
 
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
@@ -24,9 +24,9 @@ namespace xla {
 // This now only moves them outputward across elementwise ops all whose operands
 // are equivalent Reshapes or Transposes, but in future could potentially move
 // them inputward also.
-class ReshapeMover : public HloPass {
+class ReshapeMover : public HloPassInterface {
  public:
-  ReshapeMover() : HloPass("reshape motion") {}
+  tensorflow::StringPiece name() const override { return "reshape-motion"; }
 
   StatusOr<bool> Run(HloModule* module) override;
 };

tensorflow/compiler/xla/service/transpose_folding.cc

@@ -82,8 +82,7 @@ bool FoldTransposeIntoDot(HloInstruction* dot, HloComputation* computation) {
 }  // namespace
 
 TransposeFolding::TransposeFolding(IsTransposableGemmFn is_transposable_gemm)
-    : HloPass("transpose-folding"),
-      is_transposable_gemm_(std::move(is_transposable_gemm)) {}
+    : is_transposable_gemm_(std::move(is_transposable_gemm)) {}
 
 StatusOr<bool> TransposeFolding::Run(HloModule* module) {
   // Modifying the graph while traversing is dangerous, so we find all folding

tensorflow/compiler/xla/service/transpose_folding.h

@@ -17,18 +17,19 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_TRANSPOSE_FOLDING_H_
 
 #include "tensorflow/compiler/xla/service/hlo_module.h"
-#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
 
 namespace xla {
 
 // HLO pass that folds transpose operators into Dot operators, where the Dot
 // operator is implemented by a GEMM kernel that can transpose its inputs.
-class TransposeFolding : public HloPass {
+class TransposeFolding : public HloPassInterface {
  public:
   // IsTransposableGemmFn should return true iff the instruction argument is
   // implemented as a GEMM kernel that supports transposing its arguments.
   typedef std::function<bool(const HloInstruction&)> IsTransposableGemmFn;
   explicit TransposeFolding(IsTransposableGemmFn is_transposable_gemm);
+  tensorflow::StringPiece name() const override { return "transpose-folding"; }
 
   StatusOr<bool> Run(HloModule* module) override;