feat: add TRT 11.0 API compatibility shims

core/conversion/conversionctx/ConversionCtx.cpp

@@ -49,8 +49,13 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
   }
 
   builder = make_trt(nvinfer1::createInferBuilder(logger));
+  // kEXPLICIT_BATCH was removed in TRT 11 (explicit batch is always on); pass 0 on TRT 11+.
+#if NV_TENSORRT_MAJOR >= 11
+  net = make_trt(builder->createNetworkV2(0));
+#else
   net = make_trt(
       builder->createNetworkV2(1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH)));
+#endif
 
   LOG_INFO(settings);
   cfg = make_trt(builder->createBuilderConfig());
@@ -59,7 +64,8 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
     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
+// TRT 11.0 removed platformHasFastFp16 and kFP16 (always strongly typed).
+#if !defined(TRT_MAJOR_RTX) && NV_TENSORRT_MAJOR < 11
         TORCHTRT_CHECK(
             builder->platformHasFastFp16(), "Requested inference in FP16 but platform does not support FP16");
         cfg->setFlag(nvinfer1::BuilderFlag::kFP16);

core/conversion/converters/impl/batch_norm.cpp

@@ -189,7 +189,13 @@ bool InstanceNormConverter(ConversionCtx* ctx, const torch::jit::Node* n, args&
   fc.nbFields = f.size();
   fc.fields = f.data();
 
+  // TRT 11.0 renamed getPluginCreator → getCreator with IPluginCreatorInterface return type
+#if NV_TENSORRT_MAJOR >= 11
+  auto* creator_iface = getPluginRegistry()->getCreator("InstanceNormalization_TRT", "1", "");
+  auto* creator = dynamic_cast<nvinfer1::IPluginCreator*>(creator_iface);
+#else
   auto creator = getPluginRegistry()->getPluginCreator("InstanceNormalization_TRT", "1", "");
+#endif
   auto instance_norm_plugin = creator->createPlugin("instance_norm", &fc);
 
   TORCHTRT_CHECK(instance_norm_plugin, "Unable to create instance_norm plugin from TensorRT plugin registry" << *n);

core/conversion/converters/impl/interpolate.cpp

@@ -54,7 +54,13 @@ void create_plugin(
 
   fc.nbFields = f.size();
   fc.fields = f.data();
+  // TRT 11.0 renamed getPluginCreator → getCreator with IPluginCreatorInterface return type
+#if NV_TENSORRT_MAJOR >= 11
+  auto* creator_iface = getPluginRegistry()->getCreator("Interpolate", "1", "torch_tensorrt");
+  auto* creator = dynamic_cast<nvinfer1::IPluginCreator*>(creator_iface);
+#else
   auto creator = getPluginRegistry()->getPluginCreator("Interpolate", "1", "torch_tensorrt");
+#endif
   auto interpolate_plugin = creator->createPlugin(name, &fc);
 
   auto resize_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *interpolate_plugin);

core/conversion/converters/impl/layer_norm.cpp

@@ -51,7 +51,9 @@ auto layer_norm_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
       TORCHTRT_CHECK(normalize_layer, "Unable to create layer_norm from node: " << *n);
       normalize_layer->setName(util::node_info(n).c_str());
       normalize_layer->setEpsilon(eps);
+#if NV_TENSORRT_MAJOR < 11
       normalize_layer->setComputePrecision(input->getType());
+#endif
       auto normalized = normalize_layer->getOutput(0);
 
       ctx->AssociateValueAndTensor(n->outputs()[0], normalized);

core/conversion/converters/impl/normalize.cpp

@@ -42,7 +42,13 @@ void create_plugin(
     }
   }
 
+  // TRT 11.0 renamed getPluginCreator → getCreator with IPluginCreatorInterface return type
+#if NV_TENSORRT_MAJOR >= 11
+  auto* creator_iface = getPluginRegistry()->getCreator("NormalizePlugin", "1", "torch_tensorrt");
+  auto* creator = dynamic_cast<nvinfer1::IPluginCreator*>(creator_iface);
+#else
   auto creator = getPluginRegistry()->getPluginCreator("NormalizePlugin", "1", "torch_tensorrt");
+#endif
   auto plugin = creator->createPlugin(name, &fc);
   auto normalize_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *plugin);
   TORCHTRT_CHECK(normalize_layer, "Unable to create normalization plugin from node" << *n);

core/conversion/converters/impl/pooling.cpp

@@ -97,7 +97,13 @@ 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");
 
+  // TRT 11.0 renamed getPluginCreator → getCreator with IPluginCreatorInterface return type
+#if NV_TENSORRT_MAJOR >= 11
+  auto* creator_iface = getPluginRegistry()->getCreator("Interpolate", "1", "torch_tensorrt");
+  auto* creator = dynamic_cast<nvinfer1::IPluginCreator*>(creator_iface);
+#else
   auto creator = getPluginRegistry()->getPluginCreator("Interpolate", "1", "torch_tensorrt");
+#endif
   auto interpolate_plugin = creator->createPlugin(mode.c_str(), &fc);
 
   new_layer = ctx->net->addPluginV2(reinterpret_cast<nvinfer1::ITensor* const*>(&in), 1, *interpolate_plugin);

core/conversion/converters/impl/unary.cpp

@@ -89,7 +89,9 @@ auto sqrt_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().patter
        auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kSQRT);
        TORCHTRT_CHECK(unary_layer, "Unable to create sqrt layer from node: " << *n);
        unary_layer->setName(util::node_info(n).c_str());
+#if NV_TENSORRT_MAJOR < 11
        unary_layer->setOutputType(0, in->getType());
+#endif
        auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0));
        LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
        return true;

core/plugins/register_plugins.cpp

@@ -30,6 +30,24 @@ class TorchTRTPluginRegistry {
     plugin_logger.set_reportable_log_level(util::logging::get_logger().get_reportable_log_level());
 
     int numCreators = 0;
+    // TRT 11.0 renamed getPluginCreatorList → getAllCreators with IPluginCreatorInterface return type
+#if NV_TENSORRT_MAJOR >= 11
+    auto pluginsList = getPluginRegistry()->getAllCreators(&numCreators);
+    for (int k = 0; k < numCreators; ++k) {
+      if (!pluginsList[k]) {
+        plugin_logger.log(util::logging::LogLevel::kDEBUG, "Plugin creator for plugin " + str(k) + " is a nullptr");
+        continue;
+      }
+      auto* creator_v1 = dynamic_cast<nvinfer1::IPluginCreator*>(pluginsList[k]);
+      if (creator_v1) {
+        std::string pluginNamespace = creator_v1->getPluginNamespace();
+        plugin_logger.log(
+            util::logging::LogLevel::kDEBUG,
+            "Registered plugin creator - " + std::string(creator_v1->getPluginName()) +
+                ", Namespace: " + pluginNamespace);
+      }
+    }
+#else
     auto pluginsList = getPluginRegistry()->getPluginCreatorList(&numCreators);
     for (int k = 0; k < numCreators; ++k) {
       if (!pluginsList[k]) {
@@ -42,6 +60,7 @@ class TorchTRTPluginRegistry {
           "Registered plugin creator - " + std::string(pluginsList[k]->getPluginName()) +
               ", Namespace: " + pluginNamespace);
     }
+#endif
     plugin_logger.log(util::logging::LogLevel::kDEBUG, "Total number of plugins registered: " + str(numCreators));
   }
 

py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py

@@ -935,7 +935,9 @@ def output(self, target: str, args: Any, kwargs: Any) -> List[Any]:
                     output_dtype = self.output_dtypes[i]
 
             self.ctx.net.mark_output(output)
-            if output_dtype is not dtype.unknown:
+            if output_dtype is not dtype.unknown and not self.ctx.net.get_flag(
+                trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED
+            ):
                 output.dtype = output_dtype.to(trt.DataType, use_default=True)
             output.name = name