Remove deprecated apis

core/conversion/conversionctx/ConversionCtx.cpp

@@ -48,20 +48,17 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
         cudaSetDevice(settings.device.gpu_id) == cudaSuccess, "Unable to set gpu id: " << settings.device.gpu_id);
   }
 
-  builder = make_trt(nvinfer1::createInferBuilder(logger));
-  net = make_trt(
-      builder->createNetworkV2(1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)));
+  builder = nvinfer1::make_trt(nvinfer1::createInferBuilder(logger));
+  net = nvinfer1::make_trt(builder->createNetworkV2(0U));
 
   LOG_INFO(settings);
-  cfg = make_trt(builder->createBuilderConfig());
+  cfg = nvinfer1::make_trt(builder->createBuilderConfig());
 
   for (auto p = settings.enabled_precisions.begin(); p != settings.enabled_precisions.end(); ++p) {
     switch (*p) {
       case nvinfer1::DataType::kHALF:
 // tensorrt_rtx is strong typed, cannot set fp16 by builder config, only do this for tensorrt build
 #ifndef TRT_MAJOR_RTX
-        TORCHTRT_CHECK(
-            builder->platformHasFastFp16(), "Requested inference in FP16 but platform does not support FP16");
         cfg->setFlag(nvinfer1::BuilderFlag::kFP16);
         break;
 #endif
@@ -158,7 +155,7 @@ void ConversionCtx::RecordNewITensor(const torch::jit::Value* value, nvinfer1::I
 
 std::string ConversionCtx::SerializeEngine() {
 #if defined(TRT_MAJOR_RTX) || (NV_TENSORRT_MAJOR > 7)
-  auto serialized_network = make_trt(builder->buildSerializedNetwork(*net, *cfg));
+  auto serialized_network = nvinfer1::make_trt(builder->buildSerializedNetwork(*net, *cfg));
   if (!serialized_network) {
     TORCHTRT_THROW_ERROR("Building serialized network failed in TensorRT");
   }

core/conversion/converters/impl/batch_norm.cpp

@@ -189,12 +189,14 @@ bool InstanceNormConverter(ConversionCtx* ctx, const torch::jit::Node* n, args&
   fc.nbFields = f.size();
   fc.fields = f.data();
 
-  auto creator = getPluginRegistry()->getPluginCreator("InstanceNormalization_TRT", "1", "");
-  auto instance_norm_plugin = creator->createPlugin("instance_norm", &fc);
+  auto creator = static_cast<nvinfer1::IPluginCreatorV3One*>(
+      getPluginRegistry()->getCreator("InstanceNormalization_TRT", "1", ""));
+  auto instance_norm_plugin = creator->createPlugin("instance_norm", &fc, nvinfer1::TensorRTPhase::kBUILD);
 
   TORCHTRT_CHECK(instance_norm_plugin, "Unable to create instance_norm plugin from TensorRT plugin registry" << *n);
 
-  auto new_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&input), 1, *instance_norm_plugin);
+  auto new_layer =
+      ctx->net->addPluginV3(reinterpret_cast<nvinfer1::ITensor* const*>(&input), 1, nullptr, 0, *instance_norm_plugin);
 
   new_layer->setName(util::node_info(n).c_str());
   auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));

core/conversion/converters/impl/interpolate.cpp

@@ -54,10 +54,12 @@ void create_plugin(
 
   fc.nbFields = f.size();
   fc.fields = f.data();
-  auto creator = getPluginRegistry()->getPluginCreator("Interpolate", "1", "torch_tensorrt");
-  auto interpolate_plugin = creator->createPlugin(name, &fc);
+  auto creator = static_cast<nvinfer1::IPluginCreatorV3One*>(
+      getPluginRegistry()->getCreator("Interpolate", "1", "torch_tensorrt"));
+  auto interpolate_plugin = creator->createPlugin(name, &fc, nvinfer1::TensorRTPhase::kBUILD);
 
-  auto resize_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *interpolate_plugin);
+  auto resize_layer =
+      ctx->net->addPluginV3(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, nullptr, 0, *interpolate_plugin);
   TORCHTRT_CHECK(resize_layer, "Unable to create interpolation plugin from node" << *n);
 
   resize_layer->setName(util::node_info(n).c_str());

core/conversion/converters/impl/normalize.cpp

@@ -42,9 +42,11 @@ void create_plugin(
     }
   }
 
-  auto creator = getPluginRegistry()->getPluginCreator("NormalizePlugin", "1", "torch_tensorrt");
-  auto plugin = creator->createPlugin(name, &fc);
-  auto normalize_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *plugin);
+  auto creator = static_cast<nvinfer1::IPluginCreatorV3One*>(
+      getPluginRegistry()->getCreator("NormalizePlugin", "1", "torch_tensorrt"));
+  auto plugin = creator->createPlugin(name, &fc, nvinfer1::TensorRTPhase::kBUILD);
+  auto normalize_layer =
+      ctx->net->addPluginV3(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, nullptr, 0, *plugin);
   TORCHTRT_CHECK(normalize_layer, "Unable to create normalization plugin from node" << *n);
 
   normalize_layer->setName(util::node_info(n).c_str());

core/conversion/converters/impl/pooling.cpp

@@ -97,10 +97,12 @@ bool AdaptivePoolingConverter(
   LOG_WARNING(
       "Adaptive pooling layer will be using Aten library kernels in pytorch for execution. TensorRT does not support adaptive pooling natively. Consider switching to non-adaptive pooling if this is an issue");
 
-  auto creator = getPluginRegistry()->getPluginCreator("Interpolate", "1", "torch_tensorrt");
-  auto interpolate_plugin = creator->createPlugin(mode.c_str(), &fc);
+  auto creator = static_cast<nvinfer1::IPluginCreatorV3One*>(
+      getPluginRegistry()->getCreator("Interpolate", "1", "torch_tensorrt"));
+  auto interpolate_plugin = creator->createPlugin(mode.c_str(), &fc, nvinfer1::TensorRTPhase::kBUILD);
 
-  new_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *interpolate_plugin);
+  new_layer =
+      ctx->net->addPluginV3(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, nullptr, 0, *interpolate_plugin);
   TORCHTRT_CHECK(new_layer, "Unable to create pooling (interpolation) plugin from node" << *n);
 
   new_layer->setName(util::node_info(n).c_str());