## FLASH - Pytorch

Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time

## Install

`$ pip install FLASH-pytorch`

## Usage

The main novel circuit in this paper is the "Gated Attention Unit", which they claim can replace multi-headed attention while reducing it to just one head.

It uses a relu squared activation in place of the softmax, the activation of which was first seen in the Primer paper, and the use of ReLU in ReLA Transformer. The gating style seems mostly inspired by gMLPs.

```
import torch
from flash_pytorch import GAU
gau = GAU(
dim = 512,
query_key_dim = 128, # query / key dimension
causal = True, # autoregressive or not
expansion_factor = 2, # hidden dimension = dim * expansion_factor
)
x = torch.randn(1, 1024, 512)
out = gau(x) # (1, 1024, 512)
```

The authors then combine `GAU`

with Katharopoulos linear attention, using grouping of the sequences to overcome a known issue with autoregressive linear attention.

This combination of the quadratic gated attention unit with grouped linear attention they named FLASH

You can also use this quite easily

```
import torch
from flash_pytorch import FLASH
flash = FLASH(
dim = 512,
group_size = 256, # group size
causal = True, # autoregressive or not
query_key_dim = 128, # query / key dimension
expansion_factor = 2. # hidden dimension = dim * expansion_factor
)
x = torch.randn(1, 1111, 512) # sequence will be auto-padded to nearest group size
out = flash(x) # (1, 1111, 512)
```

Finally, you can use the full FLASH transformer as mentioned in the paper. This contains all the positional embeddings mentioned in the paper. Absolute positional embedding uses scaled sinusoidal. GAU quadratic attention will get one-headed T5 relative positional bias. On top of all this, both GAU attention as well as the linear attention will be rotary embedded (RoPE).

```
import torch
from flash_pytorch import FLASHTransformer
model = FLASHTransformer(
num_tokens = 20000, # number of tokens
dim = 512, # model dimension
depth = 12, # depth
causal = True, # autoregressive or not
group_size = 256, # size of the groups
query_key_dim = 128, # dimension of queries / keys
expansion_factor = 2., # hidden dimension = dim * expansion_factor
norm_type = 'scalenorm', # in the paper, they claimed scalenorm led to faster training at no performance hit. the other option is 'layernorm' (also default)
shift_tokens = True # discovered by an independent researcher in Shenzhen @BlinkDL, this simply shifts half of the feature space forward one step along the sequence dimension - greatly improved convergence even more in my local experiments
)
x = torch.randint(0, 20000, (1, 1024))
logits = model(x) # (1, 1024, 20000)
```

## Test on Autoregressive Enwik8

`$ python train.py`

## Citations

```
@article{Hua2022TransformerQI,
title = {Transformer Quality in Linear Time},
author = {Weizhe Hua and Zihang Dai and Hanxiao Liu and Quoc V. Le},
journal = {ArXiv},
year = {2022},
volume = {abs/2202.10447}
}
```

```
@software{peng_bo_2021_5196578,
author = {PENG Bo},
title = {BlinkDL/RWKV-LM: 0.01},
month = {aug},
year = {2021},
publisher = {Zenodo},
version = {0.01},
doi = {10.5281/zenodo.5196578},
url = {https://doi.org/10.5281/zenodo.5196578}
}
```