Restore functionality of --define=tflite_with_ruy=true

PiperOrigin-RevId: 317677225
Change-Id: If6533fdfeb21f676dd4b77a536b1aca894a03003

tensorflow/lite/kernels/BUILD

@@ -14,8 +14,8 @@ package(
 # This will cause TFLite to build with ruy only, providing a smaller binary.
 # WARNING: This build flag is experimental and subject to change.
 config_setting(
-    name = "tflite_with_ruy_only_explicit_true",
-    define_values = {"TFLITE_WITH_RUY_ONLY": "true"},
+    name = "tflite_with_ruy_explicit_true",
+    define_values = {"tflite_with_ruy": "true"},
 )
 
 # Disables usage of ruy as the exclusive GEMM backend in TFLite kernels.
@@ -23,14 +23,14 @@ config_setting(
 # the default GEMM option at runtime.
 # WARNING: This build flag is experimental and subject to change.
 config_setting(
-    name = "tflite_with_ruy_only_explicit_false",
-    define_values = {"TFLITE_WITH_RUY_ONLY": "false"},
+    name = "tflite_with_ruy_explicit_false",
+    define_values = {"tflite_with_ruy": "false"},
 )
 
 ###### Beginning of config_setting's to match aarch64 ######
 #
 # We need to identify the aarch64 instruction set to decide whether to enable
-# TFLITE_WITH_RUY_ONLY by default. This is surprisingly hard to do because select()
+# TFLITE_WITH_RUY by default. This is surprisingly hard to do because select()
 # can only consume config_setting's, these config_settings are not centralized,
 # and the "cpu" value which they define are free-form strings and there is no
 # standardization of the strings that we need to match for the aarch64 architecture.
@@ -239,45 +239,45 @@ cc_test(
 )
 
 cc_library(
-    name = "tflite_with_ruy_only_enabled",
+    name = "tflite_with_ruy_enabled",
     build_for_embedded = True,
-    defines = ["TFLITE_WITH_RUY_ONLY"],
+    defines = ["TFLITE_WITH_RUY"],
     visibility = ["//visibility:private"],
 )
 
 cc_library(
-    name = "tflite_with_ruy_only_and_caching_enabled",
+    name = "tflite_with_ruy_and_caching_enabled",
     defines = [
-        "TFLITE_WITH_RUY_ONLY",
+        "TFLITE_WITH_RUY",
         "TFLITE_WITH_RUY_GEMV",
     ],
     visibility = ["//visibility:private"],
 )
 
 cc_library(
-    name = "tflite_with_ruy_only_default",
+    name = "tflite_with_ruy_default",
     build_for_embedded = True,
     select_deps = {
-        ":chromiumos_arm64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_aarch64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_arm64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_arm64e": [":tflite_with_ruy_only_enabled"],
-        ":cpu_ios_arm64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_ios_arm64e": [":tflite_with_ruy_only_enabled"],
-        ":cpu_arm64_v8a": [":tflite_with_ruy_only_enabled"],
-        "//tensorflow:android_arm": ["tflite_with_ruy_only_enabled"],
+        ":chromiumos_arm64": [":tflite_with_ruy_enabled"],
+        ":cpu_aarch64": [":tflite_with_ruy_enabled"],
+        ":cpu_arm64": [":tflite_with_ruy_enabled"],
+        ":cpu_arm64e": [":tflite_with_ruy_enabled"],
+        ":cpu_ios_arm64": [":tflite_with_ruy_enabled"],
+        ":cpu_ios_arm64e": [":tflite_with_ruy_enabled"],
+        ":cpu_arm64_v8a": [":tflite_with_ruy_enabled"],
+        "//tensorflow:android_arm": ["tflite_with_ruy_enabled"],
         "//conditions:default": [],
     },
     visibility = ["//visibility:private"],
 )
 
 cc_library(
-    name = "tflite_with_ruy_only",
+    name = "tflite_with_ruy",
     build_for_embedded = True,
     select_deps = {
-        ":tflite_with_ruy_only_explicit_true": [":tflite_with_ruy_only_enabled"],
-        ":tflite_with_ruy_only_explicit_false": [],
-        "//conditions:default": [":tflite_with_ruy_only_default"],
+        ":tflite_with_ruy_explicit_true": [":tflite_with_ruy_enabled"],
+        ":tflite_with_ruy_explicit_false": [],
+        "//conditions:default": [":tflite_with_ruy_default"],
     },
 )
 
@@ -291,7 +291,7 @@ cc_library(
     ],
     copts = tflite_copts(),
     deps = [
-        ":tflite_with_ruy_only",
+        ":tflite_with_ruy",
         ":op_macros",
         # For now this unconditionally depends on both ruy and gemmlowp.
         # See the comment inside class CpuBackendContext on the
@@ -311,11 +311,11 @@ cc_library(
     copts = tflite_copts(),
     deps = [
         ":cpu_backend_context",
-        ":tflite_with_ruy_only",
+        ":tflite_with_ruy",
         "//tensorflow/lite/kernels/internal:compatibility",
         "//tensorflow/lite/kernels/internal:types",
         # For now this unconditionally depends on both ruy and gemmlowp.
-        # We only need to depend on gemmlowp when tflite_with_ruy_only
+        # We only need to depend on gemmlowp when tflite_with_ruy
         # is false, but putting these dependencies in a select() seems to
         # defeat copybara's rewriting rules.
         "@ruy//ruy:context",
@@ -349,20 +349,20 @@ cc_library(
     ],
     copts = tflite_copts(),
     deps = [
-        ":tflite_with_ruy_only",
+        ":tflite_with_ruy",
         "//tensorflow/lite/kernels/internal:common",
         "//tensorflow/lite/kernels/internal:compatibility",
         "//tensorflow/lite/kernels/internal:cpu_check",
         "//tensorflow/lite/kernels/internal:types",
         ":cpu_backend_context",
         ":cpu_backend_threadpool",
-        # Depend on ruy regardless of `tflite_with_ruy_only`. See the comment in
+        # Depend on ruy regardless of `tflite_with_ruy`. See the comment in
         # cpu_backend_gemm.h about why ruy is the generic path.
         "@ruy//ruy",
         "@ruy//ruy:matrix",
         "@ruy//ruy:path",
         "@ruy//ruy/profiler:instrumentation",
-        # We only need to depend on gemmlowp and Eigen when tflite_with_ruy_only
+        # We only need to depend on gemmlowp and Eigen when tflite_with_ruy
         # is false, but putting these dependencies in a select() seems to
         # defeat copybara's rewriting rules.
         "@gemmlowp",
@@ -605,7 +605,7 @@ cc_library(
         "//tensorflow/lite/kernels/internal:cppmath",
         "//tensorflow/lite:string",
         "@farmhash_archive//:farmhash",
-    ] + [":tflite_with_ruy_only_and_caching_enabled"],
+    ] + [":tflite_with_ruy_and_caching_enabled"],
 )
 
 cc_library(

tensorflow/lite/kernels/conv.cc

@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/lite/kernels/internal/compatibility.h"
 #include "tensorflow/lite/kernels/internal/types.h"
 // b/131835803 forces us to include multithreaded_conv.h before optimized_ops.h
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
 #include "tensorflow/lite/kernels/internal/optimized/multithreaded_conv.h"
 #endif
 #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
@@ -765,8 +765,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
       break;
     }
     case kMultithreadOptimized: {
-#ifdef TFLITE_WITH_RUY_ONLY
-      // See Register_CONV_2D: we should never be here when TFLITE_WITH_RUY_ONLY
+#ifdef TFLITE_WITH_RUY
+      // See Register_CONV_2D: we should never be here when TFLITE_WITH_RUY
       // was enabled. We #if out this code in order to get the corresponding
       // binary size benefits.
       TFLITE_DCHECK(false);
@@ -1051,8 +1051,8 @@ TfLiteRegistration* Register_CONVOLUTION_CBLAS_OPT() {
 TfLiteRegistration* Register_CONV_2D() {
 #if defined TFLITE_USE_APPLE_ACCELERATE_FOR_CONV
   return Register_CONVOLUTION_CBLAS_OPT();
-#elif defined TFLITE_WITH_RUY_ONLY
-  // TFLITE_WITH_RUY_ONLY optimizes the generic kernel type.
+#elif defined TFLITE_WITH_RUY
+  // TFLITE_WITH_RUY optimizes the generic kernel type.
   return Register_CONVOLUTION_GENERIC_OPT();
 #else
   return Register_CONVOLUTION_MULTITHREADED_OPT();
@@ -1063,8 +1063,8 @@ TfLiteRegistration* Register_CONV_2D() {
 // models only need the UINT8 type. TFLite's op registration mechanism doesn't
 // yet allow for more nuanced registration mechanisms.
 TfLiteRegistration* Register_CONV_2D_UINT8() {
-#if defined TFLITE_WITH_RUY_ONLY
-  // TFLITE_WITH_RUY_ONLY optimizes the generic kernel type.
+#if defined TFLITE_WITH_RUY
+  // TFLITE_WITH_RUY optimizes the generic kernel type.
   return Register_CONVOLUTION_GENERIC_OPT_UINT8();
 #else
   return Register_CONV_2D();

tensorflow/lite/kernels/conv_test.cc

@@ -148,7 +148,7 @@ class ConvolutionOpModel : public BaseConvolutionOpModel<float> {
 const auto kKernelMap = new std::map<string, TfLiteRegistration*>({
     {"Reference", ops::builtin::Register_CONVOLUTION_REF()},
     {"GenericOptimized", ops::builtin::Register_CONVOLUTION_GENERIC_OPT()},
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
     {"MultithreadedOptimized",
      ops::builtin::Register_CONVOLUTION_MULTITHREADED_OPT()},
 #endif

tensorflow/lite/kernels/cpu_backend_context.h

@@ -56,7 +56,7 @@ class CpuBackendContext final : public TfLiteInternalBackendContext {
   // (see :cpu_backend_gemm), for now a CpuBackendContext always
   // stores both a gemmlowp context and a ruy context.
   // TODO(b/131416458): Once call sites all go through abstractions,
-  // elide what can be elided based on TFLITE_WITH_RUY_ONLY.
+  // elide what can be elided based on TFLITE_WITH_RUY.
   const std::unique_ptr<ruy::Context> ruy_context_;
   const std::unique_ptr<gemmlowp::GemmContext> gemmlowp_context_;
 

tensorflow/lite/kernels/cpu_backend_gemm.h

@@ -24,7 +24,7 @@ limitations under the License.
 #include "tensorflow/lite/kernels/cpu_backend_gemm_params.h"
 #include "tensorflow/lite/kernels/cpu_backend_gemm_ruy.h"
 
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
 #include "tensorflow/lite/kernels/cpu_backend_gemm_eigen.h"
 #include "tensorflow/lite/kernels/cpu_backend_gemm_gemmlowp.h"
 #endif
@@ -42,7 +42,7 @@ template <typename LhsScalar, typename RhsScalar, typename AccumScalar,
 struct GemmImpl : detail::GemmImplUsingRuy<LhsScalar, RhsScalar, AccumScalar,
                                            DstScalar, quantization_flavor> {};
 
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
 
 /* Specializations using gemmlowp */
 
@@ -82,7 +82,7 @@ template <>
 struct GemmImpl<float, float, float, float, QuantizationFlavor::kFloatingPoint>
     : detail::GemmImplUsingEigen {};
 
-#endif  // not TFLITE_WITH_RUY_ONLY
+#endif  // not TFLITE_WITH_RUY
 
 /* Public entry point */
 

tensorflow/lite/kernels/cpu_backend_gemm_custom_gemv.h

@@ -591,10 +591,10 @@ struct CustomGemvImpl<LhsScalar, RhsScalar, std::int32_t, DstScalar,
 // The float specialization below is unconditionally faster than ruy
 // because ruy does not currently have any Gemv path.
 // But it is not unconditionally faster than Eigen, which is what is used
-// unless TFLITE_WITH_RUY_ONLY is defined. Indeed, Eigen has decently efficient
+// unless TFLITE_WITH_RUY is defined. Indeed, Eigen has decently efficient
 // Gemv paths, and they may use AVX instructions, while the present
 // NEON intrinsics code maps at best to SSE4 on x86.
-#ifdef TFLITE_WITH_RUY_ONLY
+#ifdef TFLITE_WITH_RUY
 
 // We want to use fused multiply-add when it's available (that is, on A64
 // unconditionally and on A32 with VFPv4) because it's often faster, and
@@ -778,7 +778,7 @@ struct CustomGemvImpl<float, float, float, float,
   }
 };
 
-#endif  // TFLITE_WITH_RUY_ONLY
+#endif  // TFLITE_WITH_RUY
 
 #endif  // USE_NEON
 

tensorflow/lite/kernels/cpu_backend_gemm_eigen.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
 
 #include "tensorflow/lite/kernels/cpu_backend_gemm_eigen.h"
 
@@ -78,4 +78,4 @@ void GemmImplUsingEigen::Run(
 }  // namespace cpu_backend_gemm
 }  // namespace tflite
 
-#endif  // not TFLITE_WITH_RUY_ONLY
+#endif  // not TFLITE_WITH_RUY

tensorflow/lite/kernels/cpu_backend_gemm_eigen.h

@@ -16,7 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_EIGEN_H_
 #define TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_EIGEN_H_
 
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
 
 #include "tensorflow/lite/kernels/cpu_backend_context.h"
 #include "tensorflow/lite/kernels/cpu_backend_gemm_params.h"
@@ -37,6 +37,6 @@ struct GemmImplUsingEigen {
 }  // namespace cpu_backend_gemm
 }  // namespace tflite
 
-#endif  // not TFLITE_WITH_RUY_ONLY
+#endif  // not TFLITE_WITH_RUY
 
 #endif  // TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_EIGEN_H_

tensorflow/lite/kernels/cpu_backend_gemm_gemmlowp.h

@@ -19,7 +19,7 @@ limitations under the License.
 #include <tuple>
 
 #include "tensorflow/lite/kernels/internal/compatibility.h"
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
 
 #include <cstdint>
 #include <type_traits>
@@ -190,6 +190,6 @@ struct GemmImplUsingGemmlowp<LhsScalar, RhsScalar, AccumScalar, DstScalar,
 }  // namespace cpu_backend_gemm
 }  // namespace tflite
 
-#endif  // not TFLITE_WITH_RUY_ONLY
+#endif  // not TFLITE_WITH_RUY
 
 #endif  // TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_GEMMLOWP_H_

tensorflow/lite/kernels/cpu_backend_threadpool.h

@@ -19,7 +19,7 @@ limitations under the License.
 #include "tensorflow/lite/kernels/cpu_backend_context.h"
 #include "tensorflow/lite/kernels/internal/compatibility.h"
 
-#ifdef TFLITE_WITH_RUY_ONLY
+#ifdef TFLITE_WITH_RUY
 #include "ruy/context.h"  // from @ruy
 #include "ruy/thread_pool.h"  // from @ruy
 #else
@@ -29,7 +29,7 @@ limitations under the License.
 namespace tflite {
 namespace cpu_backend_threadpool {
 
-#ifdef TFLITE_WITH_RUY_ONLY
+#ifdef TFLITE_WITH_RUY
 
 using Task = ruy::Task;
 
@@ -41,7 +41,7 @@ void Execute(int tasks_count, TaskType* tasks,
       tasks_count, tasks);
 }
 
-#else  // not TFLITE_WITH_RUY_ONLY
+#else  // not TFLITE_WITH_RUY
 
 using Task = gemmlowp::Task;
 

tensorflow/lite/kernels/internal/optimized/depthwiseconv_multithread.h

@@ -132,7 +132,7 @@ inline void DepthwiseConv(const DepthwiseParams& params,
   int thread_count = HowManyConvThreads(output_shape, filter_shape);
   const int max_threads = cpu_backend_context->max_num_threads();
   thread_count = std::max(1, std::min(thread_count, max_threads));
-#ifndef TFLITE_WITH_RUY_ONLY
+#ifndef TFLITE_WITH_RUY
   // Cap the number of threads to 2 for float path to avoid regression in
   // performance (b/132294857).
   if (std::is_floating_point<T>::value) {

tensorflow/lite/tools/make/Makefile

@@ -187,7 +187,7 @@ ifeq ($(TARGET_ARCH),aarch64)
 	BUILD_WITH_RUY=true
 endif
 ifeq ($(BUILD_WITH_RUY),true)
-  CXXFLAGS += -DTFLITE_WITH_RUY_ONLY
+  CXXFLAGS += -DTFLITE_WITH_RUY
 endif
 
 BUILD_WITH_RUY_PROFILER ?= false