A concise but fully-featured transformer, complete with a set of promising experimental features from various papers.
$ pip install x-transformersFull encoder / decoder
import torch
from x_transformers import XTransformer
model = XTransformer(
dim = 512,
enc_num_tokens = 256,
enc_depth = 6,
enc_heads = 8,
enc_max_seq_len = 1024,
dec_num_tokens = 256,
dec_depth = 6,
dec_heads = 8,
dec_max_seq_len = 1024,
tie_token_emb = True # tie embeddings of encoder and decoder
)
src = torch.randint(0, 256, (1, 1024))
src_mask = torch.ones_like(src).bool()
tgt = torch.randint(0, 256, (1, 1024))
tgt_mask = torch.ones_like(tgt).bool()
loss = model(src, tgt, src_mask = src_mask, tgt_mask = tgt_mask) # (1, 1024, 512)
loss.backward()Decoder-only (GPT-like)
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 12,
heads = 8
)
).cuda()
x = torch.randint(0, 256, (1, 1024)).cuda()
model(x) # (1, 1024, 20000)GPT3 would be approximately the following (but you wouldn't be able to run it anyways)
gpt3 = TransformerWrapper(
num_tokens = 50000,
max_seq_len = 2048,
attn_layers = Decoder(
dim = 12288,
depth = 96,
heads = 96,
attn_dim_head = 128
)
).cuda()Encoder-only (BERT-like)
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 12,
heads = 8
)
).cuda()
x = torch.randint(0, 256, (1, 1024)).cuda()
mask = torch.ones_like(x).bool()
model(x, mask = mask) # (1, 1024, 20000)State of the art image classification
import torch
from x_transformers import ViTransformerWrapper, Encoder
model = ViTransformerWrapper(
image_size = 256,
patch_size = 32,
num_classes = 1000,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8,
)
)
img = torch.randn(1, 3, 256, 256)
model(img) # (1, 1000)Image -> caption
import torch
from x_transformers import ViTransformerWrapper, TransformerWrapper, Encoder, Decoder
encoder = ViTransformerWrapper(
image_size = 256,
patch_size = 32,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8
)
)
decoder = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
cross_attend = True
)
)
img = torch.randn(1, 3, 256, 256)
caption = torch.randint(0, 20000, (1, 1024))
encoded = encoder(img, return_embeddings = True)
decoder(caption, context = encoded) # (1, 1024, 20000)import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
emb_dropout = 0.1, # dropout after embedding
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_dropout = 0.1, # dropout post-attention
ff_dropout = 0.1 # feedforward dropout
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)
https://arxiv.org/abs/1907.01470
Proposes adding learned memory key / values prior to attention. They were able to remove feedforwards altogether and attain similar performance to the original transformers. I have found that keeping the feedforwards and adding the memory key / values leads to even better performance.
from x_transformers import Decoder, Encoder
enc = Encoder(
dim = 512,
depth = 6,
heads = 8,
attn_num_mem_kv = 16 # 16 memory key / values
)
https://arxiv.org/abs/2006.11527
Proposes adding learned tokens, akin to CLS tokens, named memory tokens, that is passed through the attention layers alongside the input tokens.
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
num_memory_tokens = 20, # 20 memory tokens
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8
)
)
https://arxiv.org/abs/1910.05895
They experiment with alternatives to Layer normalization and found one that is both effective and simpler. Researchers have shared with me this leads to faster convergence.
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
use_scalenorm = True # set to True to use for all layers
)
)You can also use the l2 normalized embeddings proposed as part of fixnorm. I have found it leads to improved convergence, when paired with small initialization (proposed by BlinkDL). The small initialization will be taken care of as long as l2norm_embed is set to True
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
l2norm_embed = True, # set this to True for l2 normalized embedding + small init
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8
)
)https://arxiv.org/abs/1910.07467
The authors propose to replace layer normalization with a simpler alternative, without mean centering and the learned bias. An investigative paper found this to be the best performing normalization variant. It was also used in Deepmind's latest large language models, Retro and Gopher.
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
use_rmsnorm = True # set to true to use for all layers
)
)
https://arxiv.org/abs/2002.05202
Noam Shazeer paper that explores gating in the feedforward, finding that simple gating with GELU leads to significant improvements. This variant also showed up in the latest mT5 architecture. You should always turn this on (I may eventually turn it on by default).
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
ff_glu = True # set to true to use for all feedforwards
)
)The PaLM language model also reported even better results than GEGLU using the Swish GLU variant. You can turn this on by setting two flags
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
ff_swish = True, # set this to True
ff_glu = True # set to true to use for all feedforwards
)
)https://arxiv.org/abs/2109.08668
This paper used neural architecture search and found an activation, Relu Squared, that is both simpler and performs better than GELU, in the autoregressive language model setting. I have confirmed this in my independent experiments. However, if one were using the GLU variant from above, GELU still performs better. Pending further corroboration.
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
ff_relu_squared = True
)
)
https://arxiv.org/abs/1912.11637
This paper proposes an efficient way to sparsify attention by zeroing all dot-product query/key values not within the top k values. The show that this cheap method was as effective as other more expensive operations like sparsemax or entmax15. This technique comes with the cost of an extra hyperparameter (the top k values to keep). The paper recommends a value of k = 8
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_sparse_topk = 8 # keep only the top 8 values before attention (softmax)
)
)Alternatively, if you would like to use entmax15, you can also do so with one setting as shown below.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_use_entmax15 = True # use entmax15 for attention step
)
)
https://arxiv.org/abs/2003.02436
A Noam Shazeer paper that proposes mixing information between heads pre and post attention (softmax). This comes with the cost of extra memory and compute.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_talking_heads = True # turn on information exchange between attention heads
)
)https://arxiv.org/abs/1911.02150
Yet another Noam Shazeer paper (he's a legend) that proposes to only have one head for the key / values, but multi-headed queries. This paper was largely ignored for a while, but recently validated at scale in AlphaCode as well as PaLM. It has the property of being memory efficient when decoding extremely large language models. You can use it with one keyword argument as shown below.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_one_kv_head = True
)
)
https://arxiv.org/abs/1908.06954
This paper proposes to add a gated linear unit at the end of the attention layer, further gated by the original queries. Although this is not widely used outside of visual question / answering, I suspect it should lead to improvements after seeing the success of the feedforward GLU variant.
Update: After some experimentation, I found this variant actually performs worse, but if it were to be modified to not concatenate the queries before gating, it performs much better. That is what we will be using in this repository.
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8,
attn_on_attn = True # gate output of attention layer, by queries
)
)
Alphafold2 had a peculiar variant of attention where they gate the aggregated values with the input, presumably to have the block have more control over the update.
A quick test shows a small but noticeable improvement, on about the same order as attention on attention.
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8,
attn_gate_values = True # gate aggregated values with the input
)
)
https://arxiv.org/abs/1911.03864
This paper proposes to break from the normal fixed pattern of alternating attention and feedforwards, but to have blocks of only attention at the beginning followed by blocks of feedforwards at the end. This was further corroborated by a paper by Nvidia that reduces the number of attention layers to be 1/3rd of the feedforwards without loss in performance.
The amount of interleaving is controlled by a "sandwich coefficient", which they found to be optimal at a value of 6.
You can experiment with this feature as shown below
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8,
sandwich_coef = 6 # interleave attention and feedforwards with sandwich coefficient of 6
)
)
https://arxiv.org/abs/1906.02762
The authors propose to view the success of transformers from a dynamical systems point of view, and then proposes an improvement based on mathematics of that POV. Specifically, they propose to place the attention layer in between two feedforward layers. This was adopted by a paper using transformers for speech recognition, the Conformer.
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8,
macaron = True # use macaron configuration
)
)https://arxiv.org/abs/1910.10683
T5 is one of the most successful encoder / decoder transformer architectures trained to date. They invented a new simplified relative positional encoding based on learned bias values that are added to the attention matrix pre-softmax. This bias is shared and injected into each attention layer. I have decided to include this because it offers a cheap way to have relative positional encoding (superior to absolute positional), and I have read papers that suggest having positional encoding added to each layer (vs only before the first) is beneficial.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
rel_pos_bias = True # adds relative positional bias to all attention layers, a la T5
)
)
https://arxiv.org/abs/2005.12872
https://ofir.io/shortformer.pdf
In these two papers, the authors independently figured out a new technique where fixed sinusoidal positional embeddings are injected into the input prior to the queries and keys projection for all layers, leading to "position infused" attention, but leaving the actual tokens (values) uncolored by positional embedding. The Shortformer paper uses this property to cache the tokens for simplified recurrent type of transformer that bested Transformer-XL.
I have tested this, and found that it produces better results than plain absolute positional encoding, even in the absence of recurrence. However, I have found that the T5 relative positional bias (also injected into all layers and has the same properties as PIA) performs even better. So given the option, you should just go with T5's rel_pos_bias above.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
position_infused_attn = True # turns on position infused attention
)
)
https://arxiv.org/abs/2012.11747
This paper from Google proposes residualizing the pre-attention scores across all layers. At the cost of no extra parameters, they show improvement on top of regular attention networks. If you turn on this setting, be aware that the best results in the paper used post-normalization, in which case a learning warmup will be needed. The authors also reported that they could use a higher learning rate and get even better gains in the same amount of steps. (In the paper they use 2e-4 vs 1e-4 for vanilla transformer)
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8,
pre_norm = False, # in the paper, residual attention had best results with post-layernorm
residual_attn = True # add residual attention
)
)I also tried residualizing cross attention and may have noticed an improvement in convergence. You can try it by setting the cross_residual_attn keyword to True
import torch
from x_transformers import XTransformer
model = XTransformer(
dim = 512,
enc_num_tokens = 256,
enc_depth = 6,
enc_heads = 8,
enc_max_seq_len = 1024,
dec_num_tokens = 256,
dec_depth = 6,
dec_heads = 8,
dec_max_seq_len = 1024,
dec_cross_residual_attn = True # residualize cross attention
)You can also do Transformer-XL recurrence, by simply passing in a max_mem_len in the TransformerWrapper class, and then making sure your Decoder has rel_pos_bias set to True.
Then, you can retrieve the memories at each step with the return_mems keyword and pass it to the next iteration.
import torch
from x_transformers import TransformerWrapper, Decoder
model_xl = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 512,
max_mem_len = 2048,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
rel_pos_bias = True
)
)
seg1 = torch.randint(0, 20000, (1, 512))
seg2 = torch.randint(0, 20000, (1, 512))
seg3 = torch.randint(0, 20000, (1, 512))
logits1, mems1 = model_xl(seg1, return_mems = True)
logits2, mems2 = model_xl(seg2, mems = mems1, return_mems = True)
logits3, mems3 = model_xl(seg3, mems = mems2, return_mems = True)
This paper proposes a simple technique to enhance the range of Transformer-XL. They simply route the memory segment of a layer to the layer below it, for the next recurrent step. You can enable this by setting shift_mem_down = 1. You can also shift down arbitrary number of layers by setting this value to > 1.
import torch
from x_transformers import TransformerWrapper, Decoder
model_xl = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 512,
max_mem_len = 2048,
shift_mem_down = 1,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
rotary_pos_emb = True
)
)
seg1 = torch.randint(0, 20000, (1, 512))
seg2 = torch.randint(0, 20000, (1, 512))
seg3 = torch.randint(0, 20000, (1, 512))
logits1, mems1 = model_xl(seg1, return_mems = True)
logits2, mems2 = model_xl(seg2, mems = mems1, return_mems = True) # mems1 of layer N are automatically routed to the layer N-1
https://arxiv.org/abs/1910.06764
The authors propose gating the residual connections in the transformer network and demonstrate increased stability and performance for Transformer-XL in a variety of reinforcement learning tasks.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
max_mem_len = 2048,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 16,
gate_residual = True
)
)
Developed in Beijing, this new technique quickly gained interest in the NLP circles. In short, it allows you to endow the transformer with relative positional embeddings at the cost of no learned parameters. You apply a rotary operation to the queries and keys prior to their dot product in attention. The big idea is injecting positions through rotations.
Highly recommend that you have this turned on whenever you are working on an ordered sequence.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
rotary_pos_emb = True # turns on rotary positional embeddings
)
)
This technique bears roots from the field of vision transformers, where researchers are trying to have relative positions generalize to larger resolutions (without having to retrain the entire network). It was used in two recent papers, CrossFormer, as well as SwinV2.
Charles Foster first tried this for a language model, and found that it works. Later on Eric Engelhart produced experimental results that show the same type of extrapolation holds, even for 1d sequences.
Eric trained at sequence lengths of 128, and showed that it generalized well to 1024. In addition, he showed that linear positions was better than log (used in SwinV2), for language.
Linear distances
Log distances
Negative control - Sinusoidal
More of Eric's experimental results can be found here
You can use this type of relative position if you wish to train at smaller sequence lengths and have it generalize to longer ones, for both autoregressive and bidirectional models.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 256,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
dynamic_pos_bias = True, # set this to True
dynamic_pos_bias_log_distance = False # whether to use log distance, as in SwinV2
)
)This paper proposes to simply apply a static linear bias to the attention matrix. The authors show this is not only effective as a relative positional encoding, but also allows the attention net to extrapolate to greater sequences length than what it was trained on, for autoregressive language models.
This repository also offers a bidirectional variant (nonsymmetric), proposed by the authors here.
Update: It may be that ALiBi enforces a strong local attention across the heads, and may hinder it from attending at distances greater than 1k. To avoid any issues with global message passing, I've decided to introduce another hyperparameter alibi_num_heads, so one can specify less heads for the ALiBi bias
Update: There are reports that ALiBi outperform Rotary embeddings for pretraining and downstream fine-tuning.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
alibi_pos_bias = True, # turns on ALiBi positional embedding
alibi_num_heads = 4 # only use ALiBi for 4 out of the 8 heads, so other 4 heads can still attend far distances
)
)An independent researcher has found that shifting a subset of the feature dimension along the sequence dimension by 1 token helps with convergence (Time-mixing). I have tested this for the autoregressive case and can confirm that it leads to greatly improved convergence. This also lines up with the results of some papers in the vision domain.
To use it, simply set shift_tokens = 1 (or to whatever number of shifts you desire). The feature dimension will be divided by shift_tokens + 1 and then each chunk will be shifted [0, shift_tokens] respectively
Update: new experiments by @sdtblck suggests this may only work for character-level training
Update: after more experiments, it seems that in the context of BPE encoding, with rotary turned on, there is no benefit to shifting. for character-level training, shifting may still improve a tiny bit
Update: When doing BPE encoded tokens, it seems that shift of 2 will bottleneck the dimensions (divided by 5). It is recommended you always do a shift of 1, unless if you are working with character level.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
shift_tokens = 1
)
)If you want finer control over how much is shifted per block (whether attention or feedforward), simply pass in a tuple of size that is equal to the number of layers.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
shift_tokens = (1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0) # 12 blocks, attention and feedforward alternating, with progressively less shifting
)
)
This technique first made an appearance in the CoqView paper, a Chinese version of the famous text-to-image transformer DALL-E. They propose, when using pre-layernorm, to add an extra layernorm to all the branch outputs. I have found this to be very effective for a number of projects, when facing instability during training.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
sandwich_norm = True # set this to True
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)
This paper uncovers an issue with pre-norm transformers where gradients are mismatched between the early and later layers. They propose 4 changes, of which I will be offering 3.
The first change is to offer per head scaling after aggregating the values in attention. My experiments show a slight improvement in convergence.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_head_scale = True # set this to True
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)The second change is an extra layernorm right after the activation in the feedforward. I have also verified a slight improvement, at the cost of extra compute.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
ff_post_act_ln = True # set this to True
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)For the residual scaling, you simply have to set scale_residual = True. I have noticed slight improvements, but occasional instability as well, so use with caution.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
scale_residual = True # set this to True
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)The last change is a layernorm right after the outwards projection in attention. This is actually identical to the sandwich norm proposed by the Coqview paper, so you can use this by simply setting sandwich_norm = True, although it would also add it to the feedforward layer.
This paper proposes to l2 normalize the queries and keys along the head dimension before the dot product (cosine similarity), with the additional change of the scale being learned rather than static. The normalization prevents the attention operation from overflowing, a perennial problem when training transformers.
This was validated at scale recently by the training of a 3B parameter vision transformer. The SwinV2 paper also proposes to change the pre-layernorm to a post-layernorm for further stability.
I have validated that this works just as well as dot product attention in an autoregressive setting, if one were to initialize the temperature as proposed in the QK-norm paper (as a function of the sequence length).
This flavor of attention also has a connection to sparse distributed memory. [youtube talk]
You can use it as follows
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
use_qk_norm_attn = True, # set this to True
qk_norm_attn_seq_len = 1024 # set this to max_seq_len from above
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)A number of papers have hinted that causal transformers (Decoder) can learn absolute positions in the absence of added embeddings of any sort. This was recently thoroughly investigated here. You can turn off the absolute positional embedding by setting use_abs_pos_emb = False in the TransformerWrapper
Given PaLM, the trend going forward may be to forgo absolute positional embedding (again, for causal transformers only), and add relative positional embeddings with RoPE, ALiBi, etc.
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
use_abs_pos_emb = False, # set this to False
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)Cross Attention
import torch
from x_transformers import Encoder, CrossAttender
enc = Encoder(dim = 512, depth = 6)
model = CrossAttender(dim = 512, depth = 6)
nodes = torch.randn(1, 1, 512)
node_masks = torch.ones(1, 1).bool()
neighbors = torch.randn(1, 5, 512)
neighbor_masks = torch.ones(1, 5).bool()
encoded_neighbors = enc(neighbors, mask = neighbor_masks)
model(nodes, context = encoded_neighbors, mask = node_masks, context_mask = neighbor_masks) # (1, 1, 512)Pass in continuous values
import torch
from x_transformers import ContinuousTransformerWrapper, Decoder
model = ContinuousTransformerWrapper(
dim_in = 32,
dim_out = 100,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 12,
heads = 8
)
)
x = torch.randn((1, 1024, 32))
mask = torch.ones(1, 1024).bool()
model(x, mask = mask) # (1, 1024, 100)You can also train a transformer that accepts continuous values autoregressively easily, in the same scheme as done successfully in this paper
import torch
from x_transformers import ContinuousTransformerWrapper, Decoder
from x_transformers import ContinuousAutoregressiveWrapper
model = ContinuousTransformerWrapper(
dim_in = 777,
dim_out = 777,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 12,
heads = 8
)
)
# wrap it with the continuous autoregressive wrapper
model = ContinuousAutoregressiveWrapper(model)
# mock data
x = torch.randn((1, 1024, 777))
mask = torch.ones(1, 1024).bool()
# train on a lot of data above
loss = model(x, mask = mask)
loss.backward
# then generate
start_emb = torch.randn(1, 777)
generated = model.generate(start_emb, 17) # (17, 777)@misc{vaswani2017attention,
title = {Attention Is All You Need},
author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
year = {2017},
eprint = {1706.03762},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@article{DBLP:journals/corr/abs-1907-01470,
author = {Sainbayar Sukhbaatar and
Edouard Grave and
Guillaume Lample and
Herv{\'{e}} J{\'{e}}gou and
Armand Joulin},
title = {Augmenting Self-attention with Persistent Memory},
journal = {CoRR},
volume = {abs/1907.01470},
year = {2019},
url = {http://arxiv.org/abs/1907.01470}
}@article{1910.05895,
author = {Toan Q. Nguyen and Julian Salazar},
title = {Transformers without Tears: Improving the Normalization of Self-Attention},
year = {2019},
eprint = {arXiv:1910.05895},
doi = {10.5281/zenodo.3525484},
}@misc{shazeer2020glu,
title = {GLU Variants Improve Transformer},
author = {Noam Shazeer},
year = {2020},
url = {https://arxiv.org/abs/2002.05202}
}@misc{bhojanapalli2020lowrank,
title = {Low-Rank Bottleneck in Multi-head Attention Models},
author = {Srinadh Bhojanapalli and Chulhee Yun and Ankit Singh Rawat and Sashank J. Reddi and Sanjiv Kumar},
year = {2020},
eprint = {2002.07028}
}@misc{burtsev2020memory,
title = {Memory Transformer},
author = {Mikhail S. Burtsev and Grigory V. Sapunov},
year = {2020},
eprint = {2006.11527},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{zhao2019explicit,
title = {Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection},
author = {Guangxiang Zhao and Junyang Lin and Zhiyuan Zhang and Xuancheng Ren and Qi Su and Xu Sun},
year = {2019},
eprint = {1912.11637},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{correia2019adaptively,
title = {Adaptively Sparse Transformers},
author = {Gonçalo M. Correia and Vlad Niculae and André F. T. Martins},
year = {2019},
eprint = {1909.00015},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{shazeer2020talkingheads,
title = {Talking-Heads Attention},
author = {Noam Shazeer and Zhenzhong Lan and Youlong Cheng and Nan Ding and Le Hou},
year = {2020},
eprint = {2003.02436},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{press2020improving,
title = {Improving Transformer Models by Reordering their Sublayers},
author = {Ofir Press and Noah A. Smith and Omer Levy},
year = {2020},
eprint = {1911.03864},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{lu2019understanding,
title = {Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View},
author = {Yiping Lu and Zhuohan Li and Di He and Zhiqing Sun and Bin Dong and Tao Qin and Liwei Wang and Tie-Yan Liu},
year = {2019},
eprint = {1906.02762},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{ke2020rethinking,
title = {Rethinking Positional Encoding in Language Pre-training},
author = {Guolin Ke and Di He and Tie-Yan Liu},
year = {2020},
eprint = {2006.15595},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{dosovitskiy2020image,
title = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
year = {2020},
eprint = {2010.11929},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@misc{huang2019attention,
title = {Attention on Attention for Image Captioning},
author = {Lun Huang and Wenmin Wang and Jie Chen and Xiao-Yong Wei},
year = {2019},
eprint = {1908.06954},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@misc{raffel2020exploring,
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
year = {2020},
eprint = {1910.10683},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@inproceedings{martins-etal-2020-sparse,
title = "Sparse Text Generation",
author = "Martins, Pedro Henrique and
Marinho, Zita and
Martins, Andr{\'e} F. T.",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.348"
}@misc{he2020realformer,
title = {RealFormer: Transformer Likes Residual Attention},
author = {Ruining He and Anirudh Ravula and Bhargav Kanagal and Joshua Ainslie},
year = {2020},
eprint = {2012.11747},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{carion2020endtoend,
title = {End-to-End Object Detection with Transformers},
author = {Nicolas Carion and Francisco Massa and Gabriel Synnaeve and Nicolas Usunier and Alexander Kirillov and Sergey Zagoruyko},
year = {2020},
eprint = {2005.12872},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@misc{press2020shortformer,
title = {Shortformer: Better Language Modeling using Shorter Inputs},
author = {Ofir Press and Noah A. Smith and Mike Lewis},
year = {2020}
}@misc{press2021ALiBi,
title = {Train Short, Test Long: Attention with Linear Biases Enable Input Length Extrapolation},
author = {Ofir Press and Noah A. Smith and Mike Lewis},
year = {2021},
url = {https://ofir.io/train_short_test_long.pdf}
}@misc{parisotto2019stabilizing,
title = {Stabilizing Transformers for Reinforcement Learning},
author = {Emilio Parisotto and H. Francis Song and Jack W. Rae and Razvan Pascanu and Caglar Gulcehre and Siddhant M. Jayakumar and Max Jaderberg and Raphael Lopez Kaufman and Aidan Clark and Seb Noury and Matthew M. Botvinick and Nicolas Heess and Raia Hadsell},
year = {2019},
eprint = {1910.06764},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{narang2021transformer,
title = {Do Transformer Modifications Transfer Across Implementations and Applications?},
author = {Sharan Narang and Hyung Won Chung and Yi Tay and William Fedus and Thibault Fevry and Michael Matena and Karishma Malkan and Noah Fiedel and Noam Shazeer and Zhenzhong Lan and Yanqi Zhou and Wei Li and Nan Ding and Jake Marcus and Adam Roberts and Colin Raffel},
year = {2021},
eprint = {2102.11972},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{zhang2019root,
title = {Root Mean Square Layer Normalization},
author = {Biao Zhang and Rico Sennrich},
year = {2019},
eprint = {1910.07467},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{su2021roformer,
title = {RoFormer: Enhanced Transformer with Rotary Position Embedding},
author = {Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
year = {2021},
eprint = {2104.09864},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@Article{AlphaFold2021,
author = {Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and Bridgland, Alex and Meyer, Clemens and Kohl, Simon A A and Ballard, Andrew J and Cowie, Andrew and Romera-Paredes, Bernardino and Nikolov, Stanislav and Jain, Rishub and Adler, Jonas and Back, Trevor and Petersen, Stig and Reiman, David and Clancy, Ellen and Zielinski, Michal and Steinegger, Martin and Pacholska, Michalina and Berghammer, Tamas and Bodenstein, Sebastian and Silver, David and Vinyals, Oriol and Senior, Andrew W and Kavukcuoglu, Koray and Kohli, Pushmeet and Hassabis, Demis},
journal = {Nature},
title = {Highly accurate protein structure prediction with {AlphaFold}},
year = {2021},
doi = {10.1038/s41586-021-03819-2},
note = {(Accelerated article preview)},
}@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}
}@misc{csordás2021devil,
title = {The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers},
author = {Róbert Csordás and Kazuki Irie and Jürgen Schmidhuber},
year = {2021},
eprint = {2108.12284},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{so2021primer,
title = {Primer: Searching for Efficient Transformers for Language Modeling},
author = {David R. So and Wojciech Mańke and Hanxiao Liu and Zihang Dai and Noam Shazeer and Quoc V. Le},
year = {2021},
eprint = {2109.08668},
archivePrefix = {arXiv},
primaryClass = {cs.LG}
}@misc{ding2021erniedoc,
title = {ERNIE-Doc: A Retrospective Long-Document Modeling Transformer},
author = {Siyu Ding and Junyuan Shang and Shuohuan Wang and Yu Sun and Hao Tian and Hua Wu and Haifeng Wang},
year = {2021},
eprint = {2012.15688},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{ding2021cogview,
title = {CogView: Mastering Text-to-Image Generation via Transformers},
author = {Ming Ding and Zhuoyi Yang and Wenyi Hong and Wendi Zheng and Chang Zhou and Da Yin and Junyang Lin and Xu Zou and Zhou Shao and Hongxia Yang and Jie Tang},
year = {2021},
eprint = {2105.13290},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@inproceedings{anonymous2022normformer,
title = {NormFormer: Improved Transformer Pretraining with Extra Normalization},
author = {Anonymous},
booktitle = {Submitted to The Tenth International Conference on Learning Representations },
year = {2022},
url = {https://openreview.net/forum?id=GMYWzWztDx5},
note = {under review}
}@misc{henry2020querykey,
title = {Query-Key Normalization for Transformers},
author = {Alex Henry and Prudhvi Raj Dachapally and Shubham Pawar and Yuxuan Chen},
year = {2020},
eprint = {2010.04245},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}@misc{liu2021swin,
title = {Swin Transformer V2: Scaling Up Capacity and Resolution},
author = {Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
year = {2021},
eprint = {2111.09883},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}@article{Haviv2022TransformerLM,
title = {Transformer Language Models without Positional Encodings Still Learn Positional Information},
author = {Adi Haviv and Ori Ram and Ofir Press and Peter Izsak and Omer Levy},
journal = {ArXiv},
year = {2022},
volume = {abs/2203.16634}
}@article{chowdhery2022PaLM,
title = {PaLM: Scaling Language Modeling with Pathways},
author = {Chowdhery, Aakanksha et al},
year = {2022}
}@article{Shazeer2019FastTD,
title = {Fast Transformer Decoding: One Write-Head is All You Need},
author = {Noam M. Shazeer},
journal = {ArXiv},
year = {2019},
volume = {abs/1911.02150}
}solve intelligence... then use that to solve everything else. - Demis Hassabis