fmot 1.5.6

Femtosense Model Optimization Toolkit

fmot

The Femtosense Model Optimization Toolkit (fmot) quantizes neural network models for deployment on Femtosense hardware.

Installation

git clone https://github.com/femtosense/fmot.git
cd fmot
pip install -e .

Quantizing Models

You get to define your pytorch models however you want. Once your model has been trained, it can be converted to the fmot.qat format by calling fmot.convert.convert_torch_to_qat. This resulting qat model will initially not be quantized. To quantize it, provide your model, along with an iteratable of sample inputs, to fmot.qat.control.quantize. These test inputs will help the qat model to find an optimal quantization configuration. The resulting quantized model will now simulate the fixed-point integer arithmetic, exactly how it will be performed on Femtosense hardware.

import torch
import fmot

class MyModel(torch.nn.Module):
    def __init__(self, din, dout):
        super().__init__()
        weights = torch.rand(din, dout)
        self.weight = torch.nn.Parameter(weights)
        self.linear = torch.nn.Linear(dout, dout)

    def forward(self, x):
        x = torch.matmul(x, self.weight)
        x = x.relu()
        x = self.linear(x)
        x = torch.sigmoid(x)
        return x

model = MyModel(128, 256)

### TRAINING GOES HERE

# Convert the trained model to qat format
quant_model = fmot.convert.convert_torch_to_qat(model)
# Provide a set of sample inputs to choose an optimal quantization scheme
quant_model = fmot.qat.control.quantize(quant_model, [torch.randn(16, 128) for __ in range(20)])

NOTE: THE ABOVE API NEEDS A TOP-LEVEL SHORTCUT

Fine-Tuning Quantized Models

Setting Custom Bitwidths

Emitting FQIR

Once your model has been quantized,

Building and Viewing Sphinx Documentation

First, let's install sphinx. On macOs:

brew install sphinx-doc

On other platforms.

Now, let's install some dependencies with pip:

cd docs
pip install -r requirements.txt

You can now build the documentation by running

make html

This documentation can be viewed in your browser with Open File (⌘O). Navigate to

{fmot_base}/docs/_build/html/index.html

Running Tests

Pruning Weight Matrices

Sparsifying Activations

Using Custom Layers