Getting Started

System Dependencies

tt-torch requires the python 3.10 dev package, as well as the venv package. If not already installed, please run the following:

sudo apt-get install python3.10-dev python3.10-venv

Creating a Virtual Environment (skip if you already have one)

Create a virtual environment if you do not already have one in your project:

python3.10 -m venv myvenv

This will create a virtual environemnt in the folder myvenv in the current directory.

Activate the environemnt:

source myvenv/bin/activate

Installing tt-torch

Installation Notes

• tt-torch requires a pytorch installation that ships with their ABI.
  • The tt-torch wheel lists the following version of torch as an installation requirement: torch@https://download.pytorch.org/whl/cpu-cxx11-abi/torch-2.5.0%2Bcpu.cxx11.abi-cp310-cp310-linux_x86_64.whl
  • This will be installed by pip upon installing the tt-torch wheel
• The tt-torch wheel contains a fork of torch-mlir. Please ensure that torch-mlir has not been installed in your venv before installing the tt-torch wheel.

Torchvision Install (Required if you need to install torchvision)

If you intend to use torchvision in your project then this step must be done before installing the tt-torch wheel

You will need to build the torchvision wheel yourself with certain build flags. This is because torchvision does not publish a wheel which uses the PyTorch CXX11 ABI.

To install torchvision:

git clone https://github.com/pytorch/vision.git
cd vision
git checkout v0.20.0 # tt-torch requires PyTorch 2.5.0. torchvision 0.20 is the latest version of torchvision that is compatible with PyTorch 2.5.0
pip uninstall -y torchvision # Ensure torchvision is not in your virtual environment
pip install wheel
pip install torch@https://download.pytorch.org/whl/cpu-cxx11-abi/torch-2.5.0%2Bcpu.cxx11.abi-cp310-cp310-linux_x86_64.whl
TORCHVISION_USE_VIDEO_CODEC=0 TORCHVISION_USE_FFMPEG=0 _GLIBCXX_USE_CXX11_ABI=1 USE_CUDA=OFF python setup.py bdist_wheel
pip install dist/torchvision*.whl --force-reinstall

If the install was successful then there's no need to keep the torchvision source around:

cd ..
rm -rf vision

Installing the tt-torch wheel

Download a tt-torch wheel from here

Install the wheel:

pip install <PATH_TO_TT_TORCH_WHEEL>.whl

Updating PYTHONPATH

In addition to the tt-torch python library that gets installed in <YOUR_ENV_ROOT>/lib/python3.x/site-packages, some binaries will be installed in <YOUR_ENV_ROOT>/lib, and some files from tt-metal will be installed under <YOUR_ENV_ROOT>/tt-metal. Python needs to see these installations and so you should update your PYTHONPATH environment variable to include them:

export PYTHONPATH=$PYTHONPATH:<YOUR_ENV_ROOT>:<YOUR_ENV_ROOT>/lib

Compiling and Running a Model

Once you have your torch.nn.Module compile the model:

from tt_torch.dynamo.backend import backend
import torch

class MyModel(torch.nn.Module):
    def __init__(self):
        ...

    def foward(self, ...):
        ...

model = MyModel()

model = torch.compile(model, backend=backend)

inputs = ...

outputs = model(inputs)

Example - Add Two Tensors

Here is an exampe of a small model which adds its inputs running through tt-torch. Try it out!

from tt_torch.dynamo.backend import backend
import torch

class AddTensors(torch.nn.Module):
  def forward(self, x, y):
    return x + y


model = AddTensors()
tt_model = torch.compile(model, backend=backend)

x = torch.ones(5, 5)
y = torch.ones(5, 5)
print(tt_model(x, y))