Using TRTorch Directly From PyTorch

Starting in TRTorch 0.1.0, 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 Getting Started

Start by loading trtorch into your application.

import torch
import trtorch

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 TRTorch 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 TRTorch TensorRTCompileSpec API.

spec = {
    "forward":
        trtorch.TensorRTCompileSpec({
            "inputs": [trtorch.Input([1, 3, 300, 300])],
            "enabled_precisions": {torch.float, torch.half},
            "refit": False,
            "debug": False,
            "strict_types": False,
            "device": {
                "device_type": trtorch.DeviceType.GPU,
                "gpu_id": 0,
                "dla_core": 0,
                "allow_gpu_fallback": True
            },
            "capability": trtorch.EngineCapability.default,
            "num_min_timing_iters": 2,
            "num_avg_timing_iters": 1,
            "max_batch_size": 0,
        })
    }

Now to compile with TRTorch, 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))