A practical implementation of the Linformer paper. This is attention with only linear complexity in n, allowing for very long sequence lengths (1mil+) to be attended to on modern hardware.
This repo is an Attention Is All You Need style transformer, complete with an encoder and decoder (WIP) module. The novelty here is that now, one can make the attention heads linear. Check out how to use it below.
Visualization of the heads is also possible. To see more information, check out the Visualization section below.
I am not the author of the paper.
pip install linformer-pytorch
Alternatively,
git clone https://github.com/tatp22/linformer-pytorch.git
cd linformer-pytorch
Linformer Language Model
from linformer_pytorch import LinformerLM
import torch
model = LinformerLM(
num_tokens=10000, # Number of tokens in the LM
input_size=512, # Dimension 1 of the input
channels=64, # Dimension 2 of the input
dim_d=None, # Overwrites the inner dim of the attention heads. If None, sticks with the recommended channels // nhead, as in the "Attention is all you need" paper
dim_k=128, # The second dimension of the P_bar matrix from the paper
dim_ff=128, # Dimension in the feed forward network
dropout_ff=0.15, # Dropout for feed forward network
nhead=4, # Number of attention heads
depth=2, # How many times to run the model
dropout=0.1, # How much dropout to apply to P_bar after softmax
activation="gelu", # What activation to use. Currently, only gelu and relu supported, and only on ff network.
use_pos_emb=True, # Whether or not to use positional embeddings
checkpoint_level="C0", # What checkpoint level to use. For more information, see below.
parameter_sharing="layerwise", # What level of parameter sharing to use. For more information, see below.
k_reduce_by_layer=0, # Going down `depth`, how much to reduce `dim_k` by, for the `E` and `F` matrices. Will have a minimum value of 1.
full_attention=False, # Use full attention instead, for O(n^2) time and space complexity. Included here just for comparison
include_ff=True, # Whether or not to include the Feed Forward layer
w_o_intermediate_dim=None, # If not None, have 2 w_o matrices, such that instead of `dim*nead,channels`, you have `dim*nhead,w_o_int`, and `w_o_int,channels`
emb_dim=128, # If you want the embedding dimension to be different than the channels for the Linformer
).cuda()
x = torch.randint(1,10000,(1,512)).cuda()
y = model(x)
print(y) # (1, 512, 10000)Linformer self attention, stacks of MHAttention and FeedForwards
from linformer_pytorch import Linformer
import torch
model = Linformer(
input_size=262144, # Dimension 1 of the input
channels=64, # Dimension 2 of the input
dim_d=None, # Overwrites the inner dim of the attention heads. If None, sticks with the recommended channels // nhead, as in the "Attention is all you need" paper
dim_k=128, # The second dimension of the P_bar matrix from the paper
dim_ff=128, # Dimension in the feed forward network
dropout_ff=0.15, # Dropout for feed forward network
nhead=4, # Number of attention heads
depth=2, # How many times to run the model
dropout=0.1, # How much dropout to apply to P_bar after softmax
activation="gelu", # What activation to use. Currently, only gelu and relu supported, and only on ff network.
checkpoint_level="C0", # What checkpoint level to use. For more information, see below.
parameter_sharing="layerwise", # What level of parameter sharing to use. For more information, see below.
k_reduce_by_layer=0, # Going down `depth`, how much to reduce `dim_k` by, for the `E` and `F` matrices. Will have a minimum value of 1.
full_attention=False, # Use full attention instead, for O(n^2) time and space complexity. Included here just for comparison
include_ff=True, # Whether or not to include the Feed Forward layer
w_o_intermediate_dim=None, # If not None, have 2 w_o matrices, such that instead of `dim*nead,channels`, you have `dim*nhead,w_o_int`, and `w_o_int,channels`
).cuda()
x = torch.randn(1, 262144, 64).cuda()
y = model(x)
print(y) # (1, 262144, 64)Linformer Multihead attention
from linformer_pytorch import MHAttention
import torch
model = MHAttention(
input_size=512, # Dimension 1 of the input
channels=64, # Dimension 2 of the input
dim=8, # Dim of each attn head
dim_k=128, # What to sample the input length down to
nhead=8, # Number of heads
dropout=0, # Dropout for each of the heads
activation="gelu", # Activation after attention has been concat'd
checkpoint_level="C2", # If C2, checkpoint each of the heads
parameter_sharing="layerwise", # What level of parameter sharing to do
E_proj, F_proj, # The E and F projection matrices
full_attention=False, # Use full attention instead
w_o_intermediate_dim=None, # If not None, have 2 w_o matrices, such that instead of `dim*nead,channels`, you have `dim*nhead,w_o_int`, and `w_o_int,channels`
)
x = torch.randn(1, 512, 64)
y = model(x)
print(y) # (1, 512, 64)The Linear attention head, the novelty of the paper
from linformer_pytorch import LinearAttentionHead
import torch
model = LinearAttentionHead(
dim=64, # Dim 2 of the input
dropout=0.1, # Dropout of the P matrix
E_proj, F_proj, # The E and F layers
full_attention=False, # Use Full Attention instead
)
x = torch.randn(1, 512, 64)
y = model(x, x, x)
print(y) # (1, 512, 64)An encoder/decoder module
import torch
from linformer_pytorch import LinformerLM
encoder = LinformerLM(
num_tokens=10000,
input_size=512,
channels=16,
dim_k=16,
dim_ff=32,
nhead=4,
depth=3,
activation="relu",
k_reduce_by_layer=1,
return_emb=True,
)
decoder = LinformerLM(
num_tokens=10000,
input_size=512,
channels=16,
dim_k=16,
dim_ff=32,
nhead=4,
depth=3,
activation="relu",
decoder_mode=True,
)
x = torch.randint(1,10000,(1,512))
y = torch.randint(1,10000,(1,512))
enc_output = encoder(x)
print(enc_output.shape) # (1, 512, 16)
dec_output = decoder(y, embeddings=enc_output)
print(dec_output.shape) # (1, 512, 10000)An easy way to get the E and F matrices can be done by calling the get_EF function. As an example, for an n of 1000 and a k of 100:
from linfromer_pytorch import get_EF
import torch
E = get_EF(1000, 100)As an attempt to further introduce memory savings, the concept of checkpoint levels have been introduced. The current three checkpoint levels are C0, C1, and C2. When going up checkpoint levels, one sacrifices speed for memory savings. That is, checkpoint level C0 is the fastest, but takes up the most space on the GPU, while C2 is the slowest, but takes up the least space on the GPU. The details of each checkpoint level are as follows:
C0: No checkpointing. The models runs while keeping all of the attention heads and ff layers in the GPU memory.C1: Checkpoint each MultiHead attention as well as each ff layer. With this, increasingdepthshould have minimal impact on the memory.C2: Along with the optimizations at theC1level, checkpoint each head in each MultiHead Attention layer. With this, increasingnheadshould have less of an impact on memory. However, concating the heads together withtorch.catstill takes up a lot of memory, and this will hopefully be optimized out in the future.
Performance details are still unknown, but the option exists for users that want to try.
Another attempt to introduce memory savings in the paper was to introduce parameter sharing between projections. This is mentioned in section 4 of the paper; in particular, there were 4 different types of parameter sharing that the authors discussed, and all have been implemented in this repo. The first option takes up the most memory, and each further option reduces the necessary memory requirements.
none: This is no parameter sharing. For every head and for every layer, a newEand a newFmatrix is calculated for every head at each layer.headwise: Each layer has a uniqueEandFmatrix. All heads in the layer share this matrix.kv: Each layer has a unique projection matrixP, andE = F = Pfor each layer. All heads share this projection matrixP.layerwise: There is one projection matrixP, and every head in every layer usesE = F = P.
As started in the paper, this means that for a 12 layer, 12 head network, there would be 288, 24, 12 and 1 different projection matrices, respectively.
Note that with the k_reduce_by_layer option, the layerwise option will not be effective, since it will use the dimension of k for the first layer. Therefore, if the value of k_reduce_by_layer value is greater than 0, one should most likely not use the layerwise sharing option.
Also, note that according to the authors, in figure 3, this parameter sharing doesn't really affect the end result too much. So it may be best to just stick with layerwise sharing for everything, but the option exists for users to try it out.
One slight problem with the current implementation of the Linformer is that your sequence length has to match the input_size flag of the model. The Padder pads the input size such that the tensor can be fed into the network. An example:
from linformer_pytorch import Linformer, Padder
import torch
model = Linformer(
input_size=512,
channels=16,
dim_d=32,
dim_k=16,
dim_ff=32,
nhead=6,
depth=3,
checkpoint_level="C1",
)
model = Padder(model)
x = torch.randn(1, 500, 16) # This does not match the input size!
y = model(x)
print(y) # (1, 500, 16)
Starting with version 0.8.0, one can now visualize the attention heads of the linformer! To see this in action, simply import the Visualizer class, and run the plot_all_heads() function to see a picture of all the attention heads at each level, of size (n,k). Make sure that you specify visualize=True in the forward pass, as this saves the P_bar matrix so that the Visualizer class can properly visualize the head.
A working example of the code can be found below, and the same code can be found in ./examples/example_vis.py:
import torch
from linformer_pytorch import Linformer, Visualizer
model = Linformer(
input_size=512,
channels=16,
dim_k=128,
dim_ff=32,
nhead=4,
depth=3,
activation="relu",
checkpoint_level="C0",
parameter_sharing="layerwise",
k_reduce_by_layer=1,
)
# One can load the model weights here
x = torch.randn(1, 512, 16) # What input you want to visualize
y = model(x, visualize=True)
vis = Visualizer(model)
vis.plot_all_heads(title="All P_bar matrices", # Change the title if you'd like
show=True, # Show the picture
save_file="./heads.png", # If not None, save the picture to a file
figsize=(8,6), # How big the figure should be
n_limit=None # If not None, limit how much from the `n` dimension to show
)- Note that the Linformer has O(nk) time and space complexity. So, while it may be linear in n, make sure that your k is not too large as well. These are editable with
input_sizeanddim_k, respectively. - Speaking about k, the authors found that empirical evidence supports the fact that "the performance of Linformer model is mainly determined by the projected dimension k instead of the ratio n/k". Therefore, even when increasing sequence lengths, it may be fine to keep a relatively low, constant k (the authors showed with k=256, that it still performed almost as good as a vanilla transformer).
- One more tip for k: The authors recommend that k = O(d/eps^2), if self attention wants to be approximated by full attention, with eps error.
- This code, so far, is pretty much only linear layers as well as matrix multiplications. So, libraries like
apexshould work with this, however, in practice, it has not been tested. - In practice, I found that the memory and time requirements are more on the order of O(nkd), with n=
input_size, k=dim_k, and d=dim_d.
- Run some benchmark tests to see what the performance is
- Instead of matrix multiplication to bring the dimensions down to k (With EKW and FVW), try to do convolution, as mentioned in the paper, with a stride length and kernel size of n/k.
Right now, all that is implemented is the encoder. Add the decoder at a future point in time.
This is the first time that I am reproducing a result from a paper, so some things may be wrong. If you see a problem, please open up an issue, and I will attempt to work on it.
Thank you to lucidrains, whose other sparse attention repositories helped me in designing this Linformer Repo.
@misc{wang2020linformer,
title={Linformer: Self-Attention with Linear Complexity},
author={Sinong Wang and Belinda Z. Li and Madian Khabsa and Han Fang and Hao Ma},
year={2020},
eprint={2006.04768},
archivePrefix={arXiv},
primaryClass={cs.LG}
}@inproceedings{vaswani2017attention,
title={Attention is all you need},
author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
booktitle={Advances in neural information processing systems},
pages={5998--6008},
year={2017}
}