Okay, so I’ve been messing around with this thing called “TRT ViT,” and let me tell you, it’s been a bit of a journey. I wanted to get my Vision Transformer model running faster, and everyone kept saying TensorRT was the way to go. So, I dove in.
First, I had to get all the prerequisites sorted. You know, the usual stuff. I made sure I had the right NVIDIA driver version. This is important; otherwise, things just won’t work, trust me.
Then, I installed CUDA and cuDNN – can’t do anything without those. Make sure the versions match what TensorRT supports. I had issues initially, and it took checking the version of them.
The Conversion Process
Next up, the actual conversion. I started by exporting my PyTorch model to an ONNX format. This was relatively straightforward. I used the `*` function. There are some parameters to tweak, like input shapes and the opset version, but nothing too crazy.
Then I tried it with trtexec tool. This is where I did encounter lots of errors.
I used the `trtexec` command-line tool that comes with TensorRT. It’s pretty handy for converting ONNX models to TensorRT engines. I spent a good chunk of time fiddling with the command-line arguments, like specifying the precision (FP16 in my case, ’cause who needs full precision, right?).
- FP16: I wanted to see how much faster it would be.
- Batch Size: I experimented with different batch sizes to see what worked best.
I ran into a few hiccups along the way. There was some operation in my model that TensorRT didn’t like at first. I can’t even remember what it was now, but, I had to dig through some forums and documentation to figure out how to rewrite that part of the model to be TensorRT-friendly.
The Results
Finally, I got the TensorRT engine built! It was a pretty good feeling, seeing that thing get created after all the tinkering. I then wrote a simple script to load the engine and run inference on some test images.
And… it was faster! Definitely faster than the original PyTorch model. It wasn’t, like, a mind-blowing difference, but enough to make it worth the effort. I’m still playing around with different optimization settings to squeeze out every last drop of performance.
So yeah, that’s my TRT ViT adventure so far. It’s been a bit of a learning curve, but definitely rewarding. If you’re thinking about doing it, just be prepared to get your hands dirty and do some debugging!
