Automated rollback of commit d68a02f6d1

PiperOrigin-RevId: 272811870

tensorflow/compiler/tf2tensorrt/BUILD

@@ -211,7 +211,6 @@ tf_cuda_library(
     hdrs = ["utils/trt_logger.h"],
     visibility = ["//visibility:public"],
     deps = [
-        ":logger_registry",
         "//tensorflow/core:lib_proto_parsing",
     ] + if_tensorrt([":tensorrt_lib"]),
 )
@@ -301,19 +300,6 @@ tf_cc_test(
     ],
 )
 
-tf_cuda_library(
-    name = "logger_registry",
-    srcs = ["convert/logger_registry.cc"],
-    hdrs = [
-        "convert/logger_registry.h",
-    ],
-    copts = tf_copts(),
-    deps = [
-        "@com_google_absl//absl/strings",
-        "//tensorflow/core:lib",
-    ] + if_tensorrt([":tensorrt_lib"]),
-)
-
 # Library for the node-level conversion portion of TensorRT operation creation
 tf_cuda_library(
     name = "trt_conversion",
@@ -328,7 +314,6 @@ tf_cuda_library(
         "convert/trt_optimization_pass.h",
     ],
     deps = [
-        ":logger_registry",
         ":segment",
         ":trt_allocator",
         ":trt_plugins",

tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc

@@ -26,7 +26,6 @@ limitations under the License.
 
 #include "absl/strings/str_cat.h"
 #include "tensorflow/compiler/tf2tensorrt/convert/convert_nodes.h"
-#include "tensorflow/compiler/tf2tensorrt/convert/logger_registry.h"
 #include "tensorflow/compiler/tf2tensorrt/convert/utils.h"
 #include "tensorflow/compiler/tf2tensorrt/segment/segment.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
@@ -418,15 +417,16 @@ Status CreateTRTNode(const ConversionParams& params,
   // Build the engine and get its serialized representation.
   string segment_string;
   if (info.engine_type == EngineInfo::EngineType::TRTStatic) {
-    auto trt_logger = GetLoggerRegistry()->LookUp(params.trt_logger_name);
     // Create static engine for fp32/fp16 mode.
+    Logger trt_logger;
     TrtUniquePtrType<nvinfer1::ICudaEngine> engine;
     // TODO(sami): What happens if 1st dim is not batch?
     TF_RETURN_IF_ERROR(ConvertGraphDefToEngine(
         info.segment_graph_def,
         calibrate_int8 ? TrtPrecisionMode::FP32 : info.precision_mode,
-        max_batch_size, info.max_workspace_size_bytes, input_shapes, trt_logger,
-        alloc, /*calibrator=*/nullptr, &engine, info.use_calibration,
+        max_batch_size, info.max_workspace_size_bytes, input_shapes,
+        &trt_logger, alloc, /*calibrator=*/nullptr, &engine,
+        info.use_calibration,
         /*convert_successfully=*/nullptr));
     TrtUniquePtrType<nvinfer1::IHostMemory> engine_data(engine->serialize());
     segment_string = string(static_cast<const char*>(engine_data->data()),

tensorflow/compiler/tf2tensorrt/convert/convert_graph.h

@@ -35,7 +35,6 @@ namespace convert {
 struct ConversionParams {
   const GraphDef* input_graph_def = nullptr;
   const std::vector<string>* output_names = nullptr;
-  string trt_logger_name;
   size_t max_batch_size = 1;
   size_t max_workspace_size_bytes = 1 << 30;
   GraphDef* output_graph_def = nullptr;

tensorflow/compiler/tf2tensorrt/convert/convert_nodes.cc

@@ -1176,13 +1176,14 @@ Status TrtNodeValidator::ConvertConstToWeights(
   return status;
 }
 
-static void InitializeTrtPlugins(nvinfer1::ILogger* trt_logger) {
+static void InitializeTrtPlugins() {
   static mutex plugin_mutex(LINKER_INITIALIZED);
   static bool plugin_initialized = false;
+  static Logger trt_logger;
   mutex_lock lock(plugin_mutex);
   if (plugin_initialized) return;
 
-  plugin_initialized = initLibNvInferPlugins(trt_logger, "");
+  plugin_initialized = initLibNvInferPlugins(&trt_logger, "");
   if (!plugin_initialized) {
     LOG(ERROR) << "Failed to initialize TensorRT plugins, and conversion may "
                   "fail later.";
@@ -1209,12 +1210,11 @@ static void InitializeTrtPlugins(nvinfer1::ILogger* trt_logger) {
 }
 
 Converter::Converter(nvinfer1::INetworkDefinition* trt_network,
-                     TrtPrecisionMode precision_mode, bool use_calibration,
-                     nvinfer1::ILogger* trt_logger)
+                     TrtPrecisionMode precision_mode, bool use_calibration)
     : trt_network_(trt_network),
       precision_mode_(precision_mode),
       use_calibration_(use_calibration) {
-  InitializeTrtPlugins(trt_logger);
+  InitializeTrtPlugins();
   this->RegisterOpConverters();
 }
 
@@ -5476,9 +5476,8 @@ void Converter::RegisterOpConverters() {
 Status ConvertGraphDefToEngine(
     const GraphDef& gdef, TrtPrecisionMode precision_mode, int max_batch_size,
     size_t max_workspace_size_bytes,
-    const std::vector<PartialTensorShape>& input_shapes,
-    nvinfer1::ILogger* trt_logger, nvinfer1::IGpuAllocator* allocator,
-    TRTInt8Calibrator* calibrator,
+    const std::vector<PartialTensorShape>& input_shapes, Logger* logger,
+    nvinfer1::IGpuAllocator* allocator, TRTInt8Calibrator* calibrator,
     TrtUniquePtrType<nvinfer1::ICudaEngine>* engine, bool use_calibration,
     bool* convert_successfully) {
   engine->reset();
@@ -5486,7 +5485,7 @@ Status ConvertGraphDefToEngine(
 
   // Create the builder.
   TrtUniquePtrType<nvinfer1::IBuilder> builder(
-      nvinfer1::createInferBuilder(*trt_logger));
+      nvinfer1::createInferBuilder(*logger));
   builder->setMaxBatchSize(max_batch_size);
   builder->setMaxWorkspaceSize(max_workspace_size_bytes);
   builder->setGpuAllocator(allocator);
@@ -5518,8 +5517,7 @@ Status ConvertGraphDefToEngine(
     TF_RETURN_IF_ERROR(TrtPrecisionModeToName(precision_mode, &mode_str));
     VLOG(1) << "Starting engine conversion, precision mode: " << mode_str;
   }
-  Converter converter(trt_network.get(), precision_mode, use_calibration,
-                      trt_logger);
+  Converter converter(trt_network.get(), precision_mode, use_calibration);
   std::vector<Converter::EngineOutputInfo> output_tensors;
   // Graph nodes are already topologically sorted during construction
   for (const auto& node_def : gdef.node()) {

tensorflow/compiler/tf2tensorrt/convert/convert_nodes.h

@@ -147,9 +147,8 @@ Status ConvertSegmentToGraphDef(
 Status ConvertGraphDefToEngine(
     const GraphDef& gdef, TrtPrecisionMode precision_mode, int max_batch_size,
     size_t max_workspace_size_bytes,
-    const std::vector<PartialTensorShape>& input_shapes,
-    nvinfer1::ILogger* logger, nvinfer1::IGpuAllocator* allocator,
-    TRTInt8Calibrator* calibrator,
+    const std::vector<PartialTensorShape>& input_shapes, Logger* logger,
+    nvinfer1::IGpuAllocator* allocator, TRTInt8Calibrator* calibrator,
     TrtUniquePtrType<nvinfer1::ICudaEngine>* engine, bool use_calibration,
     bool* convert_successfully);
 
@@ -444,8 +443,7 @@ class Converter {
   };
 
   Converter(nvinfer1::INetworkDefinition* trt_network,
-            TrtPrecisionMode precision_mode, bool use_calibration,
-            nvinfer1::ILogger* trt_logger);
+            TrtPrecisionMode precision_mode, bool use_calibration);
 
   //////////////////////////////////////////////////////////////////////////////
   // Methods used by the TRT engine builder to build a TRT network from a TF

tensorflow/compiler/tf2tensorrt/convert/logger_registry.cc

@@ -1,60 +0,0 @@
-/* Copyright 2018 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.
-==============================================================================*/
-#if GOOGLE_CUDA
-#if GOOGLE_TENSORRT
-
-#include "tensorflow/compiler/tf2tensorrt/convert/logger_registry.h"
-
-#include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/platform/mutex.h"
-
-namespace tensorflow {
-namespace tensorrt {
-
-class LoggerRegistryImpl : public LoggerRegistry {
-  Status Register(const string& name, nvinfer1::ILogger* logger) override {
-    mutex_lock lock(mu_);
-    if (!registry_.emplace(name, std::unique_ptr<nvinfer1::ILogger>(logger))
-             .second) {
-      return errors::AlreadyExists("Logger ", name, " already registered");
-    }
-    return Status::OK();
-  }
-
-  nvinfer1::ILogger* LookUp(const string& name) override {
-    mutex_lock lock(mu_);
-    const auto found = registry_.find(name);
-    if (found == registry_.end()) {
-      return nullptr;
-    }
-    return found->second.get();
-  }
-
- private:
-  mutable mutex mu_;
-  mutable std::unordered_map<string, std::unique_ptr<nvinfer1::ILogger>>
-      registry_ GUARDED_BY(mu_);
-};
-
-LoggerRegistry* GetLoggerRegistry() {
-  static LoggerRegistryImpl* registry = new LoggerRegistryImpl;
-  return registry;
-}
-
-}  // namespace tensorrt
-}  // namespace tensorflow
-
-#endif  // GOOGLE_TENSORRT
-#endif  // GOOGLE_CUDA

tensorflow/compiler/tf2tensorrt/convert/logger_registry.h

@@ -1,57 +0,0 @@
-/* Copyright 2018 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_TF2TENSORRT_CONVERT_LOGGER_REGISTRY_H_
-#define TENSORFLOW_COMPILER_TF2TENSORRT_CONVERT_LOGGER_REGISTRY_H_
-
-#include "tensorflow/core/lib/core/status.h"
-#include "tensorflow/core/platform/macros.h"
-#include "tensorflow/core/platform/types.h"
-
-#if GOOGLE_CUDA
-#include "third_party/tensorrt/NvInfer.h"
-
-namespace tensorflow {
-namespace tensorrt {
-
-class LoggerRegistry {
- public:
-  virtual Status Register(const string& name, nvinfer1::ILogger* logger) = 0;
-  virtual nvinfer1::ILogger* LookUp(const string& name) = 0;
-  virtual ~LoggerRegistry() {}
-};
-
-LoggerRegistry* GetLoggerRegistry();
-
-class RegisterLogger {
- public:
-  RegisterLogger(const string& name, nvinfer1::ILogger* logger) {
-    TF_CHECK_OK(GetLoggerRegistry()->Register(name, logger));
-  }
-};
-
-#define REGISTER_TENSORRT_LOGGER(name, logger) \
-  REGISTER_TENSORRT_LOGGER_UNIQ_HELPER(__COUNTER__, name, logger)
-#define REGISTER_TENSORRT_LOGGER_UNIQ_HELPER(ctr, name, logger) \
-  REGISTER_TENSORRT_LOGGER_UNIQ(ctr, name, logger)
-#define REGISTER_TENSORRT_LOGGER_UNIQ(ctr, name, logger)                 \
-  static ::tensorflow::tensorrt::RegisterLogger register_trt_logger##ctr \
-      TF_ATTRIBUTE_UNUSED =                                              \
-          ::tensorflow::tensorrt::RegisterLogger(name, logger)
-
-}  // namespace tensorrt
-}  // namespace tensorflow
-
-#endif  // GOOGLE_CUDA
-#endif  // TENSORFLOW_COMPILER_TF2TENSORRT_CONVERT_LOGGER_REGISTRY_H_

tensorflow/compiler/tf2tensorrt/convert/logger_registry_test.cc

@@ -1,34 +0,0 @@
-/* Copyright 2018 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.
-==============================================================================*/
-
-#include <gmock/gmock.h>
-#include <gtest/gtest.h>
-
-namespace {
-
-class TestLogger : public nvinfer1::ILogger {
-  void log(nvinfer1::ILogger::Severity severity, const char* msg) override {}
-};
-
-TestLogger test_logger;
-
-REGISTER_TENSORRT_LOGGER("test_logger", &test_logger);
-
-TEST(LoggerRegistryTest, RegistersCorrectly) {
-  auto registered_logger = GetLoggerRegistry()->LookUp("test_logger");
-  EXPECT_THAT(registered_logger, Eq(&test_logger));
-}
-
-}  // namespace

tensorflow/compiler/tf2tensorrt/convert/trt_optimization_pass.cc

@@ -67,9 +67,6 @@ Status TRTOptimizationPass::Init(
   if (params.count("use_calibration")) {
     use_calibration_ = params.at("use_calibration").b();
   }
-  if (params.count("trt_logger")) {
-    trt_logger_name_ = params.at("trt_logger").s();
-  }
   return Status::OK();
 }
 
@@ -248,7 +245,6 @@ Status TRTOptimizationPass::Optimize(grappler::Cluster* cluster,
   }
   cp.input_graph_def = &item.graph;
   cp.output_names = &nodes_to_preserve;
-  cp.trt_logger_name = trt_logger_name_;
   cp.max_batch_size = maximum_batch_size_;
   cp.max_workspace_size_bytes = max_workspace_size_bytes_;
   cp.output_graph_def = optimized_graph;

tensorflow/compiler/tf2tensorrt/convert/trt_optimization_pass.h

@@ -34,7 +34,6 @@ class TRTOptimizationPass : public grappler::CustomGraphOptimizer {
  public:
   TRTOptimizationPass(const string& name = "TRTOptimizationPass")
       : name_(name),
-        trt_logger_name_("DefaultLogger"),
         minimum_segment_size_(3),
         precision_mode_(TrtPrecisionMode::FP32),
         maximum_batch_size_(-1),
@@ -64,7 +63,6 @@ class TRTOptimizationPass : public grappler::CustomGraphOptimizer {
 
  private:
   const string name_;
-  string trt_logger_name_;
   int minimum_segment_size_;
   TrtPrecisionMode precision_mode_;
   int maximum_batch_size_;
@@ -73,6 +71,7 @@ class TRTOptimizationPass : public grappler::CustomGraphOptimizer {
   int max_cached_batches_;
   int64_t max_workspace_size_bytes_;
   bool use_calibration_;
+
 };
 
 }  // namespace convert

tensorflow/compiler/tf2tensorrt/utils/trt_logger.cc

@@ -17,7 +17,6 @@ limitations under the License.
 
 #if GOOGLE_CUDA
 #if GOOGLE_TENSORRT
-#include "tensorflow/compiler/tf2tensorrt/convert/logger_registry.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace tensorflow {
@@ -55,15 +54,6 @@ void Logger::log(Severity severity, const char* msg) {
     }
   }
 }
-
-// static
-Logger* Logger::GetLogger() {
-  static Logger* logger = new Logger("DefaultLogger");
-  return logger;
-}
-
-REGISTER_TENSORRT_LOGGER("DefaultLogger", Logger::GetLogger());
-
 }  // namespace tensorrt
 }  // namespace tensorflow
 

tensorflow/compiler/tf2tensorrt/utils/trt_logger.h

@@ -31,8 +31,6 @@ class Logger : public nvinfer1::ILogger {
   Logger(string name = "DefaultLogger") : name_(name) {}
   void log(nvinfer1::ILogger::Severity severity, const char* msg) override;
 
-  static Logger* GetLogger();
-
  private:
   string name_;
 };