README.md

OptiActs

Point-wise nonlinearity functions that saves their output instead of input.

Installation

pip install git+https://github.com/PgLoLo/optiacts

How it works

Many nonlinearity functions save their input for the backward pass in order to perform automatic differentiation:

forward: X -> Y = f(X)
save:    X
backward: dL / dX = dL / dY * f'(X)   

Our implementation, instead, saves the output tensor for backward pass:

forward: X -> Y = f(X)
save:    Y
backward: dL / dX = dL / dY * f'(f^-1(Y))

In the case when there is another layer after the nonlinearity that would save its input tensor, only one tensor Y would be saved, compared to two tensors X and Y in the standard case.

For GELU and SiLU nonlinearities, f is not invertible, thus we save additional information (only one bit per element is required to make f invertible).

Examples

We implemented drop-in replacements for torch.nn.GELU and torch.nn.SiLU layers and for torch.nn.functional.gelu and torch.nn.functional.silu functions:

import optiacts
optiacts.GELU()  # torch.nn.Module
optiacts.SiLU()  # torch.nn.Module
optiacts.gelu    # function
optiacts.silu    # function

You can use them inside your torch.nn.Module-s as usual, or replace in already constructed networks. Here is, for example, how to create a Hugging Face BERT model and replace activation functions there:

import optiacts
from transformers import BertConfig, BertModel

model = BertModel(BertConfig())
for layer in model.encoder.layer:
    layer.intermediate.intermediate_act_fn = optiacts.GELU()

The exact way to replace all nonlinearities in a given model, of course, depends on the architecture and implementation of the given model.

How Much Memory is Saved?

The exact amount of memory saved, of course, varies for different use cases

For the Mistral-7b model, our method achieves a 15.7% reduction in memory for all saved activations (see Mistral example). For the BERT-standard model, it achieves 23.1% reduction (see BERT example).

Is It Slower?

Our method is indeed a little bit slower right now, but not by much: in our experiments registered slowdown is less than 1%.