Permit runtime opt-in for caching of prepacked matrices in GEMMs.

PiperOrigin-RevId: 314832291
Change-Id: Id0362e33e4d65908a4899d54989658fcfbf7f04c

tensorflow/lite/external_cpu_backend_context.h

@@ -35,6 +35,8 @@ class TfLiteInternalBackendContext {
   // TfLite computation.
   virtual void SetMaxNumThreads(int max_num_threads) = 0;
 
+  // A context may internally cache prepacked versions of constant tensors for
+  // faster computation. This function will clear any caches on the context.
   virtual void ClearCaches() = 0;
 };
 

tensorflow/lite/kernels/BUILD

@@ -10,24 +10,27 @@ package(
     licenses = ["notice"],  # Apache 2.0
 )
 
-# Enables usage of ruy in TFLite kernels.
+# Enables usage of ruy exclusively as the GEMM backend in TFLite kernels.
+# 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_explicit_true",
+    name = "tflite_with_ruy_only_explicit_true",
-    define_values = {"tflite_with_ruy": "true"},
+    define_values = {"TFLITE_WITH_RUY_ONLY": "true"},
 )
 
-# Disables usage of ruy in TFLite kernels.
+# Disables usage of ruy as the exclusive GEMM backend in TFLite kernels.
+# TFLite will be built with ruy and other GEMM libraries. Ruy will not be
+# the default GEMM option at runtime.
 # WARNING: This build flag is experimental and subject to change.
 config_setting(
-    name = "tflite_with_ruy_explicit_false",
+    name = "tflite_with_ruy_only_explicit_false",
-    define_values = {"tflite_with_ruy": "false"},
+    define_values = {"TFLITE_WITH_RUY_ONLY": "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 by default. This is surprisingly hard to do because select()
+# TFLITE_WITH_RUY_ONLY 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.
@@ -229,45 +232,45 @@ cc_test(
 )
 
 cc_library(
-    name = "tflite_with_ruy_enabled",
+    name = "tflite_with_ruy_only_enabled",
     build_for_embedded = True,
-    defines = ["TFLITE_WITH_RUY"],
+    defines = ["TFLITE_WITH_RUY_ONLY"],
     visibility = ["//visibility:private"],
 )
 
 cc_library(
-    name = "tflite_with_ruy_and_caching_enabled",
+    name = "tflite_with_ruy_only_and_caching_enabled",
     defines = [
-        "TFLITE_WITH_RUY",
+        "TFLITE_WITH_RUY_ONLY",
         "TFLITE_WITH_RUY_GEMV",
     ],
     visibility = ["//visibility:private"],
 )
 
 cc_library(
-    name = "tflite_with_ruy_default",
+    name = "tflite_with_ruy_only_default",
     build_for_embedded = True,
     select_deps = {
-        ":chromiumos_arm64": [":tflite_with_ruy_enabled"],
+        ":chromiumos_arm64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_aarch64": [":tflite_with_ruy_enabled"],
+        ":cpu_aarch64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_arm64": [":tflite_with_ruy_enabled"],
+        ":cpu_arm64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_arm64e": [":tflite_with_ruy_enabled"],
+        ":cpu_arm64e": [":tflite_with_ruy_only_enabled"],
-        ":cpu_ios_arm64": [":tflite_with_ruy_enabled"],
+        ":cpu_ios_arm64": [":tflite_with_ruy_only_enabled"],
-        ":cpu_ios_arm64e": [":tflite_with_ruy_enabled"],
+        ":cpu_ios_arm64e": [":tflite_with_ruy_only_enabled"],
-        ":cpu_arm64_v8a": [":tflite_with_ruy_enabled"],
+        ":cpu_arm64_v8a": [":tflite_with_ruy_only_enabled"],
-        "//tensorflow:android_arm": ["tflite_with_ruy_enabled"],
+        "//tensorflow:android_arm": ["tflite_with_ruy_only_enabled"],
         "//conditions:default": [],
     },
     visibility = ["//visibility:private"],
 )
 
 cc_library(
-    name = "tflite_with_ruy",
+    name = "tflite_with_ruy_only",
     build_for_embedded = True,
     select_deps = {
-        ":tflite_with_ruy_explicit_true": [":tflite_with_ruy_enabled"],
+        ":tflite_with_ruy_only_explicit_true": [":tflite_with_ruy_only_enabled"],
-        ":tflite_with_ruy_explicit_false": [],
+        ":tflite_with_ruy_only_explicit_false": [],
-        "//conditions:default": [":tflite_with_ruy_default"],
+        "//conditions:default": [":tflite_with_ruy_only_default"],
     },
 )
 
@@ -281,7 +284,7 @@ cc_library(
     ],
     copts = tflite_copts(),
     deps = [
-        ":tflite_with_ruy",
+        ":tflite_with_ruy_only",
         ":op_macros",
         # For now this unconditionally depends on both ruy and gemmlowp.
         # See the comment inside class CpuBackendContext on the
@@ -300,11 +303,11 @@ cc_library(
     copts = tflite_copts(),
     deps = [
         ":cpu_backend_context",
-        ":tflite_with_ruy",
+        ":tflite_with_ruy_only",
         "//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
+        # We only need to depend on gemmlowp when tflite_with_ruy_only
         # is false, but putting these dependencies in a select() seems to
         # defeat copybara's rewriting rules.
         "@ruy//ruy:context",
@@ -338,19 +341,19 @@ cc_library(
     ],
     copts = tflite_copts(),
     deps = [
-        ":tflite_with_ruy",
+        ":tflite_with_ruy_only",
         "//tensorflow/lite/kernels/internal:common",
         "//tensorflow/lite/kernels/internal:compatibility",
         "//tensorflow/lite/kernels/internal:types",
         ":cpu_backend_context",
         ":cpu_backend_threadpool",
-        # Depend on ruy regardless of `tflite_with_ruy`. See the comment in
+        # Depend on ruy regardless of `tflite_with_ruy_only`. 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
+        # We only need to depend on gemmlowp and Eigen when tflite_with_ruy_only
         # is false, but putting these dependencies in a select() seems to
         # defeat copybara's rewriting rules.
         "@gemmlowp",
@@ -580,7 +583,7 @@ cc_library(
     ],
     copts = tflite_copts() + tf_opts_nortti_if_android() + EXTRA_EIGEN_COPTS,
     visibility = ["//visibility:private"],
-    deps = BUILTIN_KERNEL_DEPS + ["@farmhash_archive//:farmhash"] + [":tflite_with_ruy_and_caching_enabled"],
+    deps = BUILTIN_KERNEL_DEPS + ["@farmhash_archive//:farmhash"] + [":tflite_with_ruy_only_and_caching_enabled"],
 )
 
 cc_library(

tensorflow/lite/kernels/conv.cc

@@ -28,7 +28,7 @@ limitations under the License.
 #include "tensorflow/lite/kernels/cpu_backend_context.h"
 #include "tensorflow/lite/kernels/eigen_support.h"
 // b/131835803 forces us to include multithreaded_conv.h before optimized_ops.h
-#ifndef TFLITE_WITH_RUY
+#ifndef TFLITE_WITH_RUY_ONLY
 #include "tensorflow/lite/kernels/internal/optimized/multithreaded_conv.h"
 #endif
 #include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
@@ -768,8 +768,8 @@ void EvalFloat(TfLiteContext* context, TfLiteNode* node,
       break;
     }
     case kMultithreadOptimized: {
-#ifdef TFLITE_WITH_RUY
+#ifdef TFLITE_WITH_RUY_ONLY
-      // See Register_CONV_2D: we should never be here when tflite_with_ruy
+      // See Register_CONV_2D: we should never be here when TFLITE_WITH_RUY_ONLY
       // was enabled. We #if out this code in order to get the corresponding
       // binary size benefits.
       TFLITE_DCHECK(false);
@@ -1054,8 +1054,8 @@ 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
+#elif defined TFLITE_WITH_RUY_ONLY
-  // tflite_with_ruy optimizes the generic kernel type.
+  // TFLITE_WITH_RUY_ONLY optimizes the generic kernel type.
   return Register_CONVOLUTION_GENERIC_OPT();
 #else
   return Register_CONVOLUTION_MULTITHREADED_OPT();
@@ -1066,8 +1066,8 @@ 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
+#if defined TFLITE_WITH_RUY_ONLY
-  // tflite_with_ruy optimizes the generic kernel type.
+  // TFLITE_WITH_RUY_ONLY optimizes the generic kernel type.
   return Register_CONVOLUTION_GENERIC_OPT_UINT8();
 #else
   return Register_CONV_2D();

tensorflow/lite/kernels/conv_test.cc

@@ -141,7 +141,7 @@ 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
+#ifndef TFLITE_WITH_RUY_ONLY
     {"MultithreadedOptimized",
      ops::builtin::Register_CONVOLUTION_MULTITHREADED_OPT()},
 #endif

tensorflow/lite/kernels/cpu_backend_context.cc

@@ -55,6 +55,12 @@ CpuBackendContext::CpuBackendContext()
       ruy_context_(new ruy::Context),
       gemmlowp_context_(new gemmlowp::GemmContext) {
   SetMaxNumThreads(kDefaultNumThreadpoolThreads);
+// TODO(b/148289189) Remove when clients have transitioned to runtime flag.
+#ifdef TFLITE_WITH_RUY_GEMV
+  SetUseCaching(true);
+#else
+  SetUseCaching(false);
+#endif
 }
 
 CpuBackendContext::~CpuBackendContext() {}
@@ -67,4 +73,6 @@ void CpuBackendContext::SetMaxNumThreads(int max_num_threads) {
   gemmlowp_context_->set_max_num_threads(target_num_threads);
 }
 
+void CpuBackendContext::SetUseCaching(bool flag) { use_caching_ = flag; }
+
 }  // namespace tflite

tensorflow/lite/kernels/cpu_backend_context.h

@@ -43,6 +43,10 @@ class CpuBackendContext final : public TfLiteInternalBackendContext {
 
   int max_num_threads() const { return max_num_threads_; }
 
+  void SetUseCaching(bool flag);
+
+  bool use_caching() const { return use_caching_; }
+
   void ClearCaches() override { ruy_context_->ClearPrepackedCache(); }
 
  private:
@@ -51,7 +55,7 @@ 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.
+  // elide what can be elided based on TFLITE_WITH_RUY_ONLY.
   const std::unique_ptr<ruy::Context> ruy_context_;
   const std::unique_ptr<gemmlowp::GemmContext> gemmlowp_context_;
 
@@ -65,6 +69,12 @@ class CpuBackendContext final : public TfLiteInternalBackendContext {
   // This value also gets propagated to back-ends, where it plays the same
   // information-only role.
   int max_num_threads_;
+  // For matrix muliplications with constants parameters (i.e. weights), we can
+  // sometimes provide speedups by caching the "prepacked" data, for some
+  // additional memory cost. This flag permits the user to route all
+  // CpuBackendGem operations to a library that permits such an optimization
+  // (currently the Ruy library only).
+  bool use_caching_;
 
   CpuBackendContext(const CpuBackendContext&) = delete;
 };

tensorflow/lite/kernels/cpu_backend_gemm.h

@@ -23,7 +23,7 @@ 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
+#ifndef TFLITE_WITH_RUY_ONLY
 #include "tensorflow/lite/kernels/cpu_backend_gemm_eigen.h"
 #include "tensorflow/lite/kernels/cpu_backend_gemm_gemmlowp.h"
 #endif
@@ -41,7 +41,7 @@ template <typename LhsScalar, typename RhsScalar, typename AccumScalar,
 struct GemmImpl : detail::GemmImplUsingRuy<LhsScalar, RhsScalar, AccumScalar,
                                            DstScalar, quantization_flavor> {};
 
-#ifndef TFLITE_WITH_RUY
+#ifndef TFLITE_WITH_RUY_ONLY
 
 /* Specializations using gemmlowp */
 
@@ -81,7 +81,7 @@ template <>
 struct GemmImpl<float, float, float, float, QuantizationFlavor::kFloatingPoint>
     : detail::GemmImplUsingEigen {};
 
-#endif  // not TFLITE_WITH_RUY
+#endif  // not TFLITE_WITH_RUY_ONLY
 
 /* Public entry point */
 
@@ -94,12 +94,17 @@ void Gemm(const MatrixParams<LhsScalar>& lhs_params, const LhsScalar* lhs_data,
           CpuBackendContext* context) {
   ruy::profiler::ScopeLabel label("cpu_backend_gemm::Gemm");
   ValidateParams(lhs_params, rhs_params, dst_params, params);
-  bool do_custom_gemv = dst_params.cols == 1;
+  if (context->use_caching()) {
-#ifdef TFLITE_WITH_RUY_GEMV
+    // Dispatch to backend that supports caching of prepacked weights
-  // Prefer a Ruy GEMM to Custom GEMV unless we are doing float math.
+    // matrices.
-  // TODO(b/148692500): Add float GEMV kernels to Ruy.
+    detail::GemmImplUsingRuy<LhsScalar, RhsScalar, AccumScalar, DstScalar,
-  do_custom_gemv = do_custom_gemv && std::is_floating_point<DstScalar>::value;
+                             quantization_flavor>::Run(lhs_params, lhs_data,
-#endif
+                                                       rhs_params, rhs_data,
+                                                       dst_params, dst_data,
+                                                       params, context);
+    return;
+  }
+  const bool do_custom_gemv = (dst_params.cols == 1);
   if (do_custom_gemv) {
     // GEMV case: try a custom fast GEMV path.
     if (detail::CustomGemv(lhs_params, lhs_data, rhs_params, rhs_data,

tensorflow/lite/kernels/cpu_backend_gemm_custom_gemv.h

@@ -586,10 +586,10 @@ 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 is defined. Indeed, Eigen has decently efficient
+// unless TFLITE_WITH_RUY_ONLY 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
+#ifdef TFLITE_WITH_RUY_ONLY
 
 // 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
@@ -773,7 +773,7 @@ struct CustomGemvImpl<float, float, float, float,
   }
 };
 
-#endif  // TFLITE_WITH_RUY
+#endif  // TFLITE_WITH_RUY_ONLY
 
 #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
+#ifndef TFLITE_WITH_RUY_ONLY
 
 #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
+#endif  // not TFLITE_WITH_RUY_ONLY

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
+#ifndef TFLITE_WITH_RUY_ONLY
 
 #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
+#endif  // not TFLITE_WITH_RUY_ONLY
 
 #endif  // TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_EIGEN_H_

tensorflow/lite/kernels/cpu_backend_gemm_gemmlowp.h

@@ -16,7 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_GEMMLOWP_H_
 #define TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_GEMMLOWP_H_
 
-#ifndef TFLITE_WITH_RUY
+#ifndef TFLITE_WITH_RUY_ONLY
 
 #include <cstdint>
 #include <type_traits>
@@ -188,6 +188,6 @@ struct GemmImplUsingGemmlowp<LhsScalar, RhsScalar, AccumScalar, DstScalar,
 }  // namespace cpu_backend_gemm
 }  // namespace tflite
 
-#endif  // not TFLITE_WITH_RUY
+#endif  // not TFLITE_WITH_RUY_ONLY
 
 #endif  // TENSORFLOW_LITE_KERNELS_CPU_BACKEND_GEMM_GEMMLOWP_H_

tensorflow/lite/kernels/cpu_backend_gemm_ruy.h

@@ -42,7 +42,7 @@ inline ruy::CachePolicy ToRuyCachePolicy(CachePolicy cache_policy) {
 
 template <typename Scalar, typename DataPointer>
 void MakeRuyMatrix(const MatrixParams<Scalar>& params, DataPointer data_ptr,
-                   ruy::Matrix<Scalar>* dst) {
+                   ruy::Matrix<Scalar>* dst, bool use_caching = false) {
   ruy::Order ruy_order = params.order == Order::kColMajor
                              ? ruy::Order::kColMajor
                              : ruy::Order::kRowMajor;
@@ -52,9 +52,9 @@ void MakeRuyMatrix(const MatrixParams<Scalar>& params, DataPointer data_ptr,
   // It does care whether we assign to it a Scalar* or a const Scalar*.
   dst->set_data(data_ptr);
   dst->set_zero_point(params.zero_point);
-#ifdef TFLITE_WITH_RUY_GEMV
+  if (use_caching) {
-  dst->set_cache_policy(ToRuyCachePolicy(params.cache_policy));
+    dst->set_cache_policy(ToRuyCachePolicy(params.cache_policy));
-#endif
+  }
 }
 
 template <typename GemmParamsType, typename RuySpecType>
@@ -88,8 +88,8 @@ struct GemmImplUsingRuy {
     ruy::Matrix<LhsScalar> ruy_lhs;
     ruy::Matrix<RhsScalar> ruy_rhs;
     ruy::Matrix<DstScalar> ruy_dst;
-    MakeRuyMatrix(lhs_params, lhs_data, &ruy_lhs);
+    MakeRuyMatrix(lhs_params, lhs_data, &ruy_lhs, context->use_caching());
-    MakeRuyMatrix(rhs_params, rhs_data, &ruy_rhs);
+    MakeRuyMatrix(rhs_params, rhs_data, &ruy_rhs, context->use_caching());
     MakeRuyMatrix(dst_params, dst_data, &ruy_dst);
 
     ruy::MulParams<AccumScalar, DstScalar> ruy_mul_params;

tensorflow/lite/kernels/cpu_backend_gemm_test.cc

@@ -363,7 +363,8 @@ void TestSomeGemm(int rows, int depth, int cols,
   CpuBackendContext cpu_backend_context;
   std::default_random_engine random_engine;
   cpu_backend_context.SetMaxNumThreads(1 + (random_engine() % 8));
-
+  bool use_caching = static_cast<bool>(random_engine() % 2);
+  cpu_backend_context.SetUseCaching(use_caching);
   const bool use_golden = !golden.empty();
 
   std::vector<LhsScalar> lhs_data;

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
+#ifdef TFLITE_WITH_RUY_ONLY
 #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
+#ifdef TFLITE_WITH_RUY_ONLY
 
 using Task = ruy::Task;
 
@@ -41,7 +41,7 @@ void Execute(int tasks_count, TaskType* tasks,
       tasks_count, tasks);
 }
 
-#else  // not TFLITE_WITH_RUY
+#else  // not TFLITE_WITH_RUY_ONLY
 
 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
+#ifndef TFLITE_WITH_RUY_ONLY
   // 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/benchmark/BUILD

@@ -144,13 +144,13 @@ cc_library(
         "//tensorflow/lite:string_util",
         "//tensorflow/lite/c:common",
         "//tensorflow/lite/kernels:builtin_ops",
+        "//tensorflow/lite/kernels:cpu_backend_context",
         "//tensorflow/lite/profiling:platform_profiler",
         "//tensorflow/lite/profiling:profile_summary_formatter",
         "//tensorflow/lite/profiling:profiler",
         "//tensorflow/lite/tools:logging",
         "//tensorflow/lite/tools/delegates:delegate_provider_hdr",
         "//tensorflow/lite/tools/delegates:tflite_execution_providers",
-        "//tensorflow/lite/tools/evaluation:utils",
         "@com_google_absl//absl/base:core_headers",
         "@com_google_absl//absl/strings",
         "@ruy//ruy/profiler",

tensorflow/lite/tools/benchmark/benchmark_model.cc

@@ -34,6 +34,7 @@ BenchmarkParams BenchmarkModel::DefaultParams() {
   params.AddParam("max_secs", BenchmarkParam::Create<float>(150.0f));
   params.AddParam("run_delay", BenchmarkParam::Create<float>(-1.0f));
   params.AddParam("num_threads", BenchmarkParam::Create<int32_t>(1));
+  params.AddParam("use_caching", BenchmarkParam::Create<bool>(false));
   params.AddParam("benchmark_name", BenchmarkParam::Create<std::string>(""));
   params.AddParam("output_prefix", BenchmarkParam::Create<std::string>(""));
   params.AddParam("warmup_runs", BenchmarkParam::Create<int32_t>(1));
@@ -82,6 +83,11 @@ std::vector<Flag> BenchmarkModel::GetFlags() {
           "the end of the run but will not start the next run."),
       CreateFlag<float>("run_delay", &params_, "delay between runs in seconds"),
       CreateFlag<int32_t>("num_threads", &params_, "number of threads"),
+      CreateFlag<bool>(
+          "use_caching", &params_,
+          "Enable caching of prepacked weights matrices in matrix "
+          "multiplication routines. Currently implies the use of the Ruy "
+          "library."),
       CreateFlag<std::string>("benchmark_name", &params_, "benchmark name"),
       CreateFlag<std::string>("output_prefix", &params_,
                               "benchmark output prefix"),
@@ -108,6 +114,8 @@ void BenchmarkModel::LogParams() {
                    << params_.Get<float>("run_delay") << "]";
   TFLITE_LOG(INFO) << "Num threads: [" << params_.Get<int32_t>("num_threads")
                    << "]";
+  TFLITE_LOG(INFO) << "Use caching: [" << params_.Get<bool>("use_caching")
+                   << "]";
   TFLITE_LOG(INFO) << "Benchmark name: ["
                    << params_.Get<std::string>("benchmark_name") << "]";
   TFLITE_LOG(INFO) << "Output prefix: ["

tensorflow/lite/tools/benchmark/benchmark_test.cc

@@ -53,6 +53,7 @@ BenchmarkParams CreateParams(int32_t num_runs, float min_secs, float max_secs,
   params.AddParam("max_secs", BenchmarkParam::Create<float>(max_secs));
   params.AddParam("run_delay", BenchmarkParam::Create<float>(-1.0f));
   params.AddParam("num_threads", BenchmarkParam::Create<int32_t>(1));
+  params.AddParam("use_caching", BenchmarkParam::Create<bool>(false));
   params.AddParam("benchmark_name", BenchmarkParam::Create<std::string>(""));
   params.AddParam("output_prefix", BenchmarkParam::Create<std::string>(""));
   params.AddParam("warmup_runs", BenchmarkParam::Create<int32_t>(1));
@@ -397,6 +398,14 @@ TEST(BenchmarkTest, RunWithWrongFlags) {
   EXPECT_EQ(kTfLiteError, status);
 }
 
+TEST(BenchmarkTest, RunWithUseCaching) {
+  ASSERT_THAT(g_fp32_model_path, testing::NotNull());
+  TestBenchmark benchmark(CreateFp32Params());
+  ScopedCommandlineArgs scoped_argv({"--use_caching=false"});
+  auto status = benchmark.Run(scoped_argv.argc(), scoped_argv.argv());
+  EXPECT_EQ(kTfLiteOk, status);
+}
+
 class MaxDurationWorksTestListener : public BenchmarkListener {
   void OnBenchmarkEnd(const BenchmarkResults& results) override {
     const int64_t num_actual_runs = results.inference_time_us().count();

tensorflow/lite/tools/benchmark/benchmark_tflite_model.cc

@@ -30,6 +30,7 @@ limitations under the License.
 #include "absl/strings/numbers.h"
 #include "ruy/profiler/profiler.h"  // from @ruy
 #include "tensorflow/lite/c/common.h"
+#include "tensorflow/lite/kernels/cpu_backend_context.h"
 #include "tensorflow/lite/kernels/register.h"
 #include "tensorflow/lite/model.h"
 #include "tensorflow/lite/op_resolver.h"
@@ -600,11 +601,24 @@ TfLiteStatus BenchmarkTfLiteModel::ResetInputsAndOutputs() {
 TfLiteStatus BenchmarkTfLiteModel::InitInterpreter() {
   auto resolver = GetOpResolver();
   const int32_t num_threads = params_.Get<int32_t>("num_threads");
+  const bool use_caching = params_.Get<bool>("use_caching");
   tflite::InterpreterBuilder(*model_, *resolver)(&interpreter_, num_threads);
   if (!interpreter_) {
     TFLITE_LOG(ERROR) << "Failed to initialize the interpreter";
     return kTfLiteError;
   }
+  // Manually enable caching behavior in TF Lite interpreter.
+  if (use_caching) {
+    external_context_.reset(new tflite::ExternalCpuBackendContext());
+    std::unique_ptr<tflite::CpuBackendContext> cpu_backend_context(
+        new tflite::CpuBackendContext());
+    cpu_backend_context->SetUseCaching(true);
+    external_context_->set_internal_backend_context(
+        std::move(cpu_backend_context));
+    interpreter_->SetExternalContext(kTfLiteCpuBackendContext,
+                                     external_context_.get());
+  }
+
   return kTfLiteOk;
 }
 

tensorflow/lite/tools/benchmark/benchmark_tflite_model.h

@@ -85,6 +85,7 @@ class BenchmarkTfLiteModel : public BenchmarkModel {
 
   std::unique_ptr<tflite::FlatBufferModel> model_;
   std::unique_ptr<tflite::Interpreter> interpreter_;
+  std::unique_ptr<tflite::ExternalCpuBackendContext> external_context_;
 
  private:
   // Implement type erasure with unique_ptr with custom deleter.

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
+  CXXFLAGS += -DTFLITE_WITH_RUY_ONLY
 endif
 
 BUILD_WITH_RUY_PROFILER ?= false