Using Torch-TensorRT TorchScript Frontend Directly From PyTorch

You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature is very similar to the compilation workflow described in Using Torch-TensorRT in Python

Start by loading torch_tensorrt into your application.

import torch
import torch_tensorrt

Then given a TorchScript module, you can compile it with TensorRT using the torch._C._jit_to_backend("tensorrt", ...) API.

import torchvision.models as models

model = models.mobilenet_v2(pretrained=True)
script_model = torch.jit.script(model)

Unlike the compile API in Torch-TensorRT which assumes you are trying to compile the forward function of a module or the convert_method_to_trt_engine which converts a specified function to a TensorRT engine, the backend API will take a dictionary which maps names of functions to compile to Compilation Spec objects which wrap the same sort of dictionary you would provide to compile. For more information on the compile spec dictionary take a look at the documentation for the Torch-TensorRT TensorRTCompileSpec API.

spec = {
    "forward": torch_tensorrt.ts.TensorRTCompileSpec(
        **{
            "inputs": [torch_tensorrt.Input([1, 3, 300, 300])],
            "enabled_precisions": {torch.float, torch.half},
            "refit": False,
            "debug": False,
            "device": {
                "device_type": torch_tensorrt.DeviceType.GPU,
                "gpu_id": 0,
                "dla_core": 0,
                "allow_gpu_fallback": True,
            },
            "capability": torch_tensorrt.EngineCapability.default,
            "num_avg_timing_iters": 1,
        }
    )
}

Now to compile with Torch-TensorRT, provide the target module objects and the spec dictionary to torch._C._jit_to_backend("tensorrt", ...)

trt_model = torch._C._jit_to_backend("tensorrt", script_model, spec)

To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)

input = torch.randn((1, 3, 300, 300)).to("cuda").to(torch.half)
print(trt_model.forward(input))