[Cherry-pick] Deprecate torchscript frontend

cpp/include/torch_tensorrt/ptq.h

@@ -59,7 +59,7 @@ class Int8Calibrator : Algorithm {
    * calibration cache
    * @param use_cache : bool - Whether to use the cache (if it exists)
    */
-  Int8Calibrator(DataLoaderUniquePtr dataloader, const std::string& cache_file_path, bool use_cache)
+   Int8Calibrator(DataLoaderUniquePtr dataloader, const std::string& cache_file_path, bool use_cache)
       : dataloader_(dataloader.get()), cache_file_path_(cache_file_path), use_cache_(use_cache) {
     for (auto batch : *dataloader_) {
       batched_data_.push_back(batch.data);
@@ -308,8 +308,8 @@ class Int8CacheCalibrator : Algorithm {
  * @param use_cache: bool - use calibration cache
  * @return Int8Calibrator<Algorithm, DataLoader>
  */
-
 template <typename Algorithm = nvinfer1::IInt8EntropyCalibrator2, typename DataLoader>
+[[deprecated("Int8 PTQ Calibrator has been deprecated by TensorRT, please plan on porting to a NVIDIA Model Optimizer Toolkit based workflow. See: https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/vgg16_ptq.html for more details")]]
 inline Int8Calibrator<Algorithm, DataLoader> make_int8_calibrator(
     DataLoader dataloader,
     const std::string& cache_file_path,
@@ -344,6 +344,7 @@ inline Int8Calibrator<Algorithm, DataLoader> make_int8_calibrator(
  * @return Int8CacheCalibrator<Algorithm>
  */
 template <typename Algorithm = nvinfer1::IInt8EntropyCalibrator2>
+[[deprecated("Int8 PTQ Calibrator has been deprecated by TensorRT, please plan on porting to a NVIDIA Model Optimizer Toolkit based workflow. See: https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/vgg16_ptq.html for more details")]]
 inline Int8CacheCalibrator<Algorithm> make_int8_cache_calibrator(const std::string& cache_file_path) {
   return Int8CacheCalibrator<Algorithm>(cache_file_path);
 }

py/torch_tensorrt/ts/_compiler.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import warnings
 from typing import Any, List, Optional, Sequence, Set, Tuple
 
 import torch
@@ -102,6 +103,10 @@ def compile(
         torch.jit.ScriptModule: Compiled TorchScript Module, when run it will execute via TensorRT
     """
 
+    warnings.warn(
+        'The torchscript frontend for Torch-TensorRT has been deprecated, please plan on porting to the dynamo frontend (torch_tensorrt.compile(..., ir="dynamo"). Torchscript will continue to be a supported deployment format via post compilation torchscript tracing, see: https://pytorch.org/TensorRT/user_guide/saving_models.html for more details'
+    )
+
     input_list = list(inputs) if inputs is not None else []
     enabled_precisions_set = (
         enabled_precisions if enabled_precisions is not None else set()
@@ -240,6 +245,10 @@ def convert_method_to_trt_engine(
     Returns:
         bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs
     """
+    warnings.warn(
+        'The torchscript frontend for Torch-TensorRT has been deprecated, please plan on porting to the dynamo frontend (torch_tensorrt.convert_method_to_trt_engine(..., ir="dynamo"). Torchscript will continue to be a supported deployment format via post compilation torchscript tracing, see: https://pytorch.org/TensorRT/user_guide/saving_models.html for more details'
+    )
+
     input_list = list(inputs) if inputs is not None else []
     enabled_precisions_set = (
         enabled_precisions if enabled_precisions is not None else {torch.float}

py/torch_tensorrt/ts/ptq.py

@@ -13,6 +13,8 @@
 from torch_tensorrt import _C
 from torch_tensorrt.ts.logging import Level, log
 
+import warnings
+
 
 class CalibrationAlgo(Enum):
     ENTROPY_CALIBRATION = _C.CalibrationAlgo.ENTROPY_CALIBRATION
@@ -88,6 +90,9 @@ def __init__(self, **kwargs: Any):
         pass
 
     def __new__(cls, *args: Any, **kwargs: Any) -> Self:
+        warnings.warn(
+            "Int8 PTQ Calibrator has been deprecated by TensorRT, please plan on porting to a NVIDIA Model Optimizer Toolkit based workflow. See: https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/vgg16_ptq.html for more details"
+        )
         dataloader = args[0]
         algo_type = kwargs.get("algo_type", CalibrationAlgo.ENTROPY_CALIBRATION_2)
         cache_file = kwargs.get("cache_file", None)
@@ -175,6 +180,9 @@ def __init__(self, **kwargs: Any):
         pass
 
     def __new__(cls, *args: Any, **kwargs: Any) -> Self:
+        warnings.warn(
+            "Int8 PTQ Calibrator has been deprecated by TensorRT, please plan on porting to a NVIDIA Model Optimizer Toolkit based workflow. See: https://pytorch.org/TensorRT/tutorials/_rendered_examples/dynamo/vgg16_ptq.html for more details"
+        )
         cache_file = args[0]
         algo_type = kwargs.get("algo_type", CalibrationAlgo.ENTROPY_CALIBRATION_2)