torch_ttnn Module

The torch_ttnn module has a backend function, which can used with the torch.compile() function.

import torch
import torch_ttnn

# A torch Module
class FooModule(torch.Module):
    ...
# Create a module
module = FooModule()
# Compile the module, with ttnn backend
device: ttnn.Device = ttnn.open_device(device_id=0)
option = torch_ttnn.TenstorrentBackendOption(device=self.device)
ttnn_module = torch.compile(module, backend='ttnn', option=option)
# Running inference with ttnn device
ttnn_module(input_data)

Tracer

The tracer dump the information of fx graph such as node's op_name and shape.

For example, you can run this script to parse the information

PYTHONPATH=$(pwd) python3 tools/stat_models.py --trace_orig --backward --profile
ls stat/raw

By default, the raw result will be stored at stat/raw, and you can run this script to generate the report

python3 tools/generate_report.py
ls stat/

Now the stat/ folder have these report

• fw_node_count.csv
• bw_node_count.csv
• fw_total_input_size_dist/
• bw_total_input_size_dist/
• fw_total_output_size_dist/
• bw_total_output_size_dist/
• profile/

The node_count.csv show the node with op_type appear in the fx graph. This report can help analyze the frequency of op type appear in the graph.

The *_total_*_size_dist/ statistics the op_type's input/output_size distribution from all fx graph recored in stat/raw. This report can help analyze the memory footprint durning the calculation of op_type.

• Notice: the default input_shapes in tools/stat_torchvision.py is [1,3,224,224], which has dependency with *_total_*_size_dist/ report.

• Notice: the aten ir interface is in there

The profile/ is the tools provided by pytorch, you can open it by the url: chrome://tracing

For developers

Install torch-ttnn with editable mode

During development, you may want to use the torch-ttnn package for testing. In order to do that, you can install the torch-ttnn package in "editable" mode with

pip install -e .

Now, you can utilize torch_ttnn in your Python code. Any modifications you make to the torch_ttnn package will take effect immediately, eliminating the need for constant reinstallation via pip.

Build wheel file

For developers want to deploy the wheel, you can build the wheel file with

python -m build

Then you can upload the .whl file to the PyPI (Python Package Index).