Merge pull request #1242 from pytorch/ptq-tutorial-compile_spec-fix

Update PTQ example to fix new compile_spec requirements

Author: peri044

docsrc/tutorials/ptq.rst

@@ -167,19 +167,16 @@ a TensorRT calibrator by providing desired configuration. The following code dem
                                                   algo_type=torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
                                                   device=torch.device('cuda:0'))
 
-    compile_spec = {
-             "inputs": [torch_tensorrt.Input((1, 3, 32, 32))],
-             "enabled_precisions": {torch.float, torch.half, torch.int8},
-             "calibrator": calibrator,
-             "device": {
-                 "device_type": torch_tensorrt.DeviceType.GPU,
-                 "gpu_id": 0,
-                 "dla_core": 0,
-                 "allow_gpu_fallback": False,
-                 "disable_tf32": False
-             }
-         }
-    trt_mod = torch_tensorrt.compile(model, compile_spec)
+    trt_mod = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input((1, 3, 32, 32))],
+                                        enabled_precisions={torch.float, torch.half, torch.int8},
+                                        calibrator=calibrator,
+                                        device={
+                                             "device_type": torch_tensorrt.DeviceType.GPU,
+                                             "gpu_id": 0,
+                                             "dla_core": 0,
+                                             "allow_gpu_fallback": False,
+                                             "disable_tf32": False
+                                         })
 
 In the cases where there is a pre-existing calibration cache file that users want to use, ``CacheCalibrator`` can be used without any dataloaders. The following example demonstrates how
 to use ``CacheCalibrator`` to use in INT8 mode.
@@ -188,13 +185,9 @@ to use ``CacheCalibrator`` to use in INT8 mode.
 
   calibrator = torch_tensorrt.ptq.CacheCalibrator("./calibration.cache")
 
-  compile_settings = {
-        "inputs": [torch_tensorrt.Input([1, 3, 32, 32])],
-        "enabled_precisions": {torch.float, torch.half, torch.int8},
-        "calibrator": calibrator,
-    }
-
-  trt_mod = torch_tensorrt.compile(model, compile_settings)
+  trt_mod = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input([1, 3, 32, 32])],
+                                        enabled_precisions={torch.float, torch.half, torch.int8},
+                                        calibrator=calibrator)
 
 If you already have an existing calibrator class (implemented directly using TensorRT API), you can directly set the calibrator field to your class which can be very convenient.
 For a demo on how PTQ can be performed on a VGG network using Torch-TensorRT API, you can refer to https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py