lucidrains/x-transformers
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license: MIT
Language: Python .
A concise but complete full-attention transformer with a set of promising experimental features from various papers
最后发布版本： 1.35.0 ( 2024-09-11 22:07:32)
x-transformers
A concise but fully-featured transformer, complete with a set of promising experimental features from various papers.
Install
$ pip install x-transformers
Usage
Full 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))
loss = model(src, tgt, mask = src_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 (SimpleViT)
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)
PaLI, state of the art language-vision model
import torch
from x_transformers import ViTransformerWrapper, XTransformer, Encoder
# PaLI composes of
# 1. vision transformer (ViTransformerWrapper) +
# 2. encoder-decoder transformer (XTransformer)
vit = ViTransformerWrapper(
image_size = 256,
patch_size = 32,
attn_layers = Encoder(
dim = 512,
depth = 6,
heads = 8
)
)
pali = 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
)
# training data
img = torch.randn(1, 3, 256, 256) # images
prompt = torch.randint(0, 256, (1, 1024)) # prompt
prompt_mask = torch.ones(1, 1024).bool() # prompt text mask
output_text = torch.randint(0, 256, (1, 1024)) # target output text
# train
img_embeds = vit(
img,
return_embeddings = True
)
loss = pali(
prompt,
output_text,
mask = prompt_mask,
src_prepend_embeds = img_embeds # will preprend image embeddings to encoder text embeddings before attention
)
loss.backward()
# do the above for many steps on a 17B parameter model
# attention is all you need
Dropouts
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,
layer_dropout = 0.1, # stochastic depth - dropout entire layer
attn_dropout = 0.1, # dropout post-attention
ff_dropout = 0.1 # feedforward dropout
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)
Features
Flash Attention
What originally started off as a short paper from Markus Rabe culminated as a practical fused attention CUDA kernel, named Flash Attention by Tri Dao.
The technique processes the attention matrix in tiles, only keeping track of the running softmax and exponentiated weighted sums. By recomputing on the backwards pass in a tiled fashion, one is able to keep the memory linear with respect to sequence length. This allows a lot of recent models to be able to reach for longer context lengths without worrying about the memory bottleneck.
Other engineering decisions made by Tri Dao led to its enormous success, namely minimizing HBM accesses so that both the forwards and backwards outperform naive attention. In other words, flash attention is not only more memory efficient, but faster as well, making it a necessity for training transformers.
MetaAI has recently added the ability to use Tri Dao's CUDA kernel through the scaled_dot_product_attention function in Pytorch 2.0. (They also have a mem_efficient attention, which is identical to flash attention design, just that the tiles are traversed differently)
Llama was trained using Flash Attention. The only reason to avoid it is if you require operating on the attention matrix (dynamic positional bias, talking heads, residual attention).
You can use it in this repository by setting attn_flash to True and enjoy the immediate memory savings and increase in speed.
ex.
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,
attn_flash = True # just set this to True if you have pytorch 2.0 installed
)
)
Augmenting Self-attention with Persistent Memory
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
)
Memory Transformers
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. This setting is compatible with both encoder and decoder training.
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
)
)
Update: MetaAI researchers have found that adding memory tokens (they call them register tokens), alleviates outliers (which is suspected now to be a pathology of attention networks unable to attend to nothing).
Transformers Without Tears
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
)
)
Along the same lines of l2 normalized embeddings, Huggingface's 175B parameter BLOOM also places a layernorm right after the embeddings and just before the tokens enter the attention layers. This was corroborated by Yandex's 100B parameter YaLM to stabilize training.
It is recommended you either have either l2norm_embed or post_emb_norm set to True but not both, as they probably serve the same purpose.
import torch
from x_transformers import TransformerWrapper, Decoder, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
post_emb_norm = True, # set this to True to layernorm summed token + pos embeddings
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8
)
)
Root Mean Square Layer Normalization
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
)
)
July 2023 A linear attention paper has experiments to show that removing the learned multiplicative gamma led to no performance degradation. This simplifies the RMS normalization to a satisfying l2norm(x) * sqrt(dim).
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_simple_rmsnorm = True # set to true to use for all layers
)
)
GLU Variants Improve Transformer
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 chose to use 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
)
)
No Bias in Feedforward
Starting with PaLM, there begun a trend to remove biases from the transformer all together. Boris Dayma has run a number of experiments that showed removing biases from feedforwards led to increased throughput without any loss of accuracy. This was corroborated by yet another paper investigating transformer architecture variants.
You can turn off the feedforward bias as follows
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_no_bias = True # set this to True
)
)
ReLU²
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
)
)
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
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)
attn_sparse_topk_straight_through = True # straight through the original gradients
)
)
An extreme case of topk value of 1, you can use the following
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 6,
heads = 8,
attn_hard = True # will only propagate the single value of the argmax of qk logit. offered in the case it addresses https://arxiv.org/abs/2410.01104
)
)
Talking-Heads Attention
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_pre_talking_heads = True, # linear combination across pre-softmax attn logits across heads
attn_post_talking_heads = True # linear combination across post-softmax attn across heads
)
)
One Write-Head Is All You Need
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
)
)
This has been further generalized in a recent paper to allow for groups of query heads to attend to a single key / value head. You can use this by specifying the attn_kv_heads
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,
attn_kv_heads = 2 # say you want 4 query heads to attend to 1 key / value head
)
)
Attention on Attention for Image Captioning
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
)
)
Intra-attention Gating on Values
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
)
)
Improving Transformer Models by Reordering their Sublayers
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
)
)
Weight-tied Layers
In the early days of the cambrian explosion of BERT, a paper explored weight tying all the layers, the model named ALBERT. You can use it by setting weight_tie_layers = True
import torch
from x_transformers import TransformerWrapper, Encoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Encoder(
dim = 512,
depth = 12,
weight_tie_layers = True # set this to True to weight tie all the layers
)
)
If you wish to do something more sophisticated, say 3 layers, with each layer recurrent 4 times before onto the next (similar to this paper), that is possible as well. Be aware the layers_execute_order is 0-indexed
import torch
from x_transformers import TransformerWrapper, Decoder
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
custom_layers = (
'a', 'f', # 3 sets of attention and feedforward
'a', 'f',
'a', 'f'
),
layers_execute_order = (
*((0, 1) * 4), # each done 4 times before sequentially passed forward, but you can probably imagine some more interesting configurations...
*((2, 3) * 4),
*((4, 5) * 4),
)
)
)
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
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
)
)
T5's Simplified Relative Positional Encoding
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
)
)
Residual 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
)
Transformer-XL recurrence
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 (or rotary_pos_emb) 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)
Setting up the logic for training and sampling from transformer xl can be a bit overwhelming. This repository offers a simple wrapper that should make this easy, with the XLAutoregressiveWrapper.
# pass in the above model_xl
xl_wrapper = XLAutoregressiveWrapper(model_xl)
seg = torch.randint(0, 20000, (1, 4096)).cuda() # sequence exceeding max length, automatically segmented and memory managed
loss = xl_wrapper(seg)
loss.backward()
# then, after much training
prime = seg[:, :1024] # if prime exceeds max length, memory will be caught up before generating
generated = xl_wrapper.generate(prime, 4096) # (1, 4096)
Enhanced recurrence
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
Gated residual
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
)
)
Rotary Positional Embeddings
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
)
)
Update (12/2022): Rotary embedding has since been hugely successful, widely adopted in many large language models, including the largest in the world, PaLM. However, it has been uncovered in the ALiBi paper that rotary embeddings cannot length extrapolate well. This was recently addressed in a Microsoft research paper. They propose a way to unobtrusively add the same decay as in ALiBi, and found that this resolves the extrapolation problem. You can use it in this repository by setting rotary_xpos = True. Like ALiBi, it would enforce the attention to be local. You can set the receptive field with rotary_xpos_scale_base value, which defaults to 512
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_xpos = True # modified rotary to extrapolate well beyond length at which it was trained
)
)
Dynamic Positional Bias
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.
Update: First place RNA folding using dynamic positional bias
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
)
)
ALiBi Positional Embedding
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. However, this is untested. If you need bidirectional length extrapolation, the safest option would be Dynamic Position Bias
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.
Update: New paper shows that no positional embedding can length extrapolate even than explicit ones
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
)
)
Shifted Tokens
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
)
)
Sandwich Norm
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)
ResiDual
This Microsoft paper proposes yet another normalization configuration, combining both pre and post layernorm. They claim this hybridization reduces representation collapse (known to be an issue with pre-layernorm with increasing depth), while maintaining stability and reducing vanishing gradients (issues with post-layernorm). Initial experiments on my end show it to work no worse than pre-layernorm or sandwich norm. More study needed by the public to see if this is actually a winning technique.
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,
resi_dual = True, # set this to True
resi_dual_scale = 0.1 # in appendix, they said on fp16 the prenorm residual is prone to overflow. they claim by scaling it at each layer by a factor, it would prevent the overflow, and keep results the same (as layernorms are invariant to scaling of the input)
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)
Normformer
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.
Cosine Sim Attention
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, and removes any need for numerical stability measures prior to softmax. Both are perennial problems 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]
Update: I have discovered a way to remove the learned temperature altogether, by grouping the feature dimension and doing l2-normalization on each group. This allows the queries and keys to have a similarity that is upper bounded by the number of groups. A group size of 8 or 16 was sufficient in my tests. Decided to name this technique "Grouped QK Normalization". The drawback is that I believe an attention head dimension 32 is too small to use this tactic (a dimension often used in vision)
Update 2: Tero Karras has successfully used cosine sim attention in a new paper.
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,
attn_qk_norm = True, # set this to True
attn_qk_norm_groups = 8 # number of groups in the feature dimension for l2norm, similarity scores will be bounded between [-group, group]. determines how sharp the attention can be
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)
Another update: Simply scaling the cosine similarity (group of 1) with a fixed constant (10) may work too
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_qk_norm = True, # set to True
attn_qk_norm_scale = 10 # new scale on the similarity, with groups of 1
)
)
x = torch.randint(0, 20000, (1, 1024))
model(x)
QK RMSNorm
Update: Google Brain has proven out something similar to cosine sim attention in a 22B parameter model. In their papers, they have analysis showing that the normalization resulted in not only extra stability, but also better results in the end (due to less need to adjust learning rate when increasing parameter count).
We are nearing the point of wiping out a source of transformer training instability with one simple intervention, in my opinion. The only slight difference in the paper is that they still have a learned scale across the feature dimension (per use of rmsnorm). Not sure how critical this is, but just to make sure we don't miss anything, I will include this here. You can use this by setting qk_norm_dim_scale = True
Update: Counterpoint from Tim Dettmers
Update 2: Counter to Tim's assertion that outliers are needed, and potentially even some solutions
Update 3: Used by 8B parameter LLM successfully
Update 4: a MetaAI group found that they can alleviate outliers by adding register tokens, also known as memory tokens from earlier literature (Burtsev et al). Perhaps what should be tried next is see if qk norm can be improved in the presence of memory tokens.
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,
attn_qk_norm = True,
attn_qk_norm_dim_scale = True # set this to True, in addition to `attn_qk_norm = True`
)
)
x = torch.randint(0, 256, (1, 1024))
model(x)
Turning off absolute positional embedding
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.
Update: This paper shows that in the absence of any engineered absolute or relative positional embeddings, decoders can generate implicit positions, and even length generalize better than solutions of the past. They were unaware of dynamic positional bias, however.
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)
Forgetful Causal Mask
This paper shows convincing results that one can combine masking (from masked language modeling) with autoregressive training, leading to significantly better results.
You can use this by setting the mask_prob on the AutoregressiveWrapper class
import torch
from x_transformers import TransformerWrapper, Decoder, AutoregressiveWrapper
model = TransformerWrapper(
num_tokens = 20000,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 12,
heads = 8
)
)
model = AutoregressiveWrapper(
model,
mask_prob = 0.15 # in paper, they use 15%, same as BERT
).cuda()
# mock data
x = torch.randint(0, 20000, (1, 1024)).cuda()
# derive cross entropy loss, masking all taken care of
loss = model(x)
loss.backward()
Miscellaneous
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)
Continuous Embeddings
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)
xVal - Continuous and Discrete
This is promising work that resulted from the collaboration across many institutes (collectively known as Polymathic AI). They found that by offering a continuously scaled number token to the transformer, the transformer was able to generalize arithmetic and forecasting tasks better than the alternative encoding schemes.
This is corroborated by some prior work
import torch
from x_transformers import (
Decoder,
XValTransformerWrapper,
XValAutoregressiveWrapper
)
model = XValTransformerWrapper(
num_tokens = 4,
numerical_token_id = 3,
max_seq_len = 1024,
attn_layers = Decoder(
dim = 512,
depth = 12,
heads = 8
)
)
# wrap it with the xval autoregressive wrapper
model = XValAutoregressiveWrapper(model)
# mock data
ids = torch.randint(0, 4, (1, 777))
nums = torch.randn(1, 777)
# train on a lot of data above
loss = model(ids, nums)
loss.backward()
# then generate
start_ids = torch.randint(0, 4, (1, 1))
start_nums = torch.randn(1, 1)
ids_out, num_out, is_number_mask = model.generate(start_ids, start_nums, 17)
# (1, 17), (1, 17), (1, 17)
# discrete, continuous, mask for discrete / continuous
Citations
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}
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}
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}
@misc{zhao2019explicit,
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@misc{shazeer2020talkingheads,
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@misc{press2020improving,
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archivePrefix = {arXiv},
primaryClass = {cs.CL}
}
@misc{lu2019understanding,
title = {Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View},
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year = {2019},
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archivePrefix = {arXiv},
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}
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}
@misc{dosovitskiy2020image,
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}
@misc{huang2019attention,
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year = {2019},
eprint = {1908.06954},
archivePrefix = {arXiv},
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}
@misc{raffel2020exploring,
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
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archivePrefix = {arXiv},
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}
@inproceedings{martins-etal-2020-sparse,
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Marinho, Zita and
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year = "2020",
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}
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}
@misc{carion2020endtoend,
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}
@misc{press2021ALiBi,
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}
@misc{parisotto2019stabilizing,
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@misc{narang2021transformer,
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@misc{su2021roformer,
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@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,
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}
@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},
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year = {2021},
eprint = {2111.09883},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
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}
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}
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}
@misc{schlag2020enhancing,
title = {Enhancing the Transformer with explicit relational encoding for math problem solving},
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}
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}
@misc{https://doi.org/10.48550/arxiv.2302.01327,
doi = {10.48550/ARXIV.2302.01327},
url = {https://arxiv.org/abs/2302.01327},
author = {Kumar, Manoj and Dehghani, Mostafa and Houlsby, Neil},
title = {Dual PatchNorm},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}
@inproceedings{dao2022flashattention,
title = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
author = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
booktitle = {Advances in Neural Information Processing Systems},
year = {2022}
}
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title = {ResiDual: Transformer with Dual Residual Connections},
author = {Shufang Xie and Huishuai Zhang and Junliang Guo and Xu Tan and Jiang Bian and Hany Hassan Awadalla and Arul Menezes and Tao Qin and Rui Yan},
journal = {ArXiv},
year = {2023},
volume = {abs/2304.14802}
}
@inproceedings{Dehghani2023ScalingVT,
title = {Scaling Vision Transformers to 22 Billion Parameters},
author = {Mostafa Dehghani and Josip Djolonga and Basil Mustafa and Piotr Padlewski and Jonathan Heek and Justin Gilmer and Andreas Steiner and Mathilde Caron and Robert Geirhos and Ibrahim M. Alabdulmohsin and Rodolphe Jenatton and Lucas Beyer and Michael Tschannen and Anurag Arnab and Xiao Wang and Carlos Riquelme and Matthias Minderer and Joan Puigcerver and Utku Evci and Manoj Kumar and Sjoerd van Steenkiste and Gamaleldin F. Elsayed and Aravindh Mahendran and Fisher Yu and Avital Oliver and Fantine Huot and Jasmijn Bastings and Mark Collier and Alexey A. Gritsenko and Vighnesh Birodkar and Cristina Nader Vasconcelos and Yi Tay and Thomas Mensink and Alexander Kolesnikov and Filip Paveti'c and Dustin Tran and Thomas Kipf and Mario Luvci'c and Xiaohua Zhai and Daniel Keysers and Jeremiah Harmsen and Neil Houlsby},
year = {2023}
}
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title = {Better plain ViT baselines for ImageNet-1k},
author = {Lucas Beyer and Xiaohua Zhai and Alexander Kolesnikov},
journal = {ArXiv},
year = {2022},
volume = {abs/2205.01580}
}
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title = {The Impact of Positional Encoding on Length Generalization in Transformers},
author = {Amirhossein Kazemnejad and Inkit Padhi and Karthikeyan Natesan Ramamurthy and Payel Das and Siva Reddy},
journal = {ArXiv},
year = {2023},
volume = {abs/2305.19466}
}
@misc{bloc97-2023
title = {NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation.},
author = {/u/bloc97},
url = {https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/}
}
@inproceedings{Zoph2022STMoEDS,
title = {ST-MoE: Designing Stable and Transferable Sparse Expert Models},
author = {Barret Zoph and Irwan Bello and Sameer Kumar and Nan Du and Yanping Huang and Jeff Dean and Noam M. Shazeer and William Fedus},
year = {2022}
}
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title = {ALBERT: A Lite BERT for Self-supervised Learning of Language Representations},
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journal = {ArXiv},
year = {2019},
volume = {abs/1909.11942},
url = {https://api.semanticscholar.org/CorpusID:202888986}
}
@inproceedings{Li2022ContrastiveDO,
title = {Contrastive Decoding: Open-ended Text Generation as Optimization},
author = {Xiang Lisa Li and Ari Holtzman and Daniel Fried and Percy Liang and Jason Eisner and Tatsunori Hashimoto and Luke Zettlemoyer and Mike Lewis},
booktitle = {Annual Meeting of the Association for Computational Linguistics},
year = {2022},
url = {https://api.semanticscholar.org/CorpusID:253157949}
}
@inproceedings{OBrien2023ContrastiveDI,
title = {Contrastive Decoding Improves Reasoning in Large Language Models},
author = {Sean O'Brien and Mike Lewis},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:261884427}
}
@inproceedings{Darcet2023VisionTN,
title = {Vision Transformers Need Registers},
author = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:263134283}
}
@article{Bondarenko2023QuantizableTR,
title = {Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing},
author = {Yelysei Bondarenko and Markus Nagel and Tijmen Blankevoort},
journal = {ArXiv},
year = {2023},
volume = {abs/2306.12929},
url = {https://api.semanticscholar.org/CorpusID:259224568}
}
@inproceedings{Golkar2023xValAC,
title = {xVal: A Continuous Number Encoding for Large Language Models},
author = {Siavash Golkar and Mariel Pettee and Michael Eickenberg and Alberto Bietti and M. Cranmer and G{\'e}raud Krawezik and Francois Lanusse and Michael McCabe and Ruben Ohana and Liam Parker and Bruno R{\'e}galdo-Saint Blancard and Tiberiu Teşileanu and Kyunghyun Cho and Shirley Ho},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:263622222}
}
@article{Wang2022DeepNetST,
title = {DeepNet: Scaling Transformers to 1, 000 Layers},
author = {Hongyu Wang and Shuming Ma and Li Dong and Shaohan Huang and Dongdong Zhang and Furu Wei},
journal = {ArXiv},
year = {2022},
volume = {abs/2203.00555},
url = {https://api.semanticscholar.org/CorpusID:247187905}
}
@article{Rafailov2023DirectPO,
title = {Direct Preference Optimization: Your Language Model is Secretly a Reward Model},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Stefano Ermon and Christopher D. Manning and Chelsea Finn},
journal = {ArXiv},
year = {2023},
volume = {abs/2305.18290},
url = {https://api.semanticscholar.org/CorpusID:258959321}
}
@misc{xAI2024Grok,
author = {xAI},
title = {Grok},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/xai-org/grok-1}},
}
@inproceedings{Golovneva2024ContextualPE,
title = {Contextual Position Encoding: Learning to Count What's Important},
author = {Olga Golovneva and Tianlu Wang and Jason Weston and Sainbayar Sukhbaatar},
year = {2024},
url = {https://api.semanticscholar.org/CorpusID:270094992}
}
@article{Peebles2022ScalableDM,
title = {Scalable Diffusion Models with Transformers},
author = {William S. Peebles and Saining Xie},
journal = {2023 IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2022},
pages = {4172-4182},
url = {https://api.semanticscholar.org/CorpusID:254854389}
}
@misc{Rubin2024,
author = {Ohad Rubin},
url = {https://medium.com/@ohadrubin/exploring-weight-decay-in-layer-normalization-challenges-and-a-reparameterization-solution-ad4d12c24950}
}
@article{Mesnard2024GemmaOM,
title = {Gemma: Open Models Based on Gemini Research and Technology},
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@inproceedings{anonymous2024forgetting,
title = {Forgetting Transformer: Softmax Attention with a Forget Gate},
author = {Anonymous},
booktitle = {Submitted to The Thirteenth International Conference on Learning Representations},
year = {2024},
url = {https://openreview.net/forum?id=q2Lnyegkr8},
note = {under review}
}
@inproceedings{anonymous2024from,
title = {From {MLP} to Neo{MLP}: Leveraging Self-Attention for Neural Fields},
author = {Anonymous},
booktitle = {Submitted to The Thirteenth International Conference on Learning Representations},
year = {2024},
url = {https://openreview.net/forum?id=A8Vuf2e8y6},
note = {under review}
}
solve intelligence... then use that to solve everything else. - Demis Hassabis
最近版本更新:(数据更新于 2024-09-11 23:01:59)
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