The Era of 1-bit LLMs-All Large Language Models are in 1.58 Bits

Summary

The video introduces the concept of a 1-bit large language model called BitNet that uses only -1, 0 or 1 as weight values, instead of 32-bit or 16-bit values typically used. This simplifies computations, reduces memory and power needs, while maintaining performance. The video explains how the quantization formula works to convert full-precision weights to 1-bit values. It highlights BitNet's advantages like improved feature filtering and matching baseline model performance. Comparative analysis shows BitNet requires lower memory and has lower latency than regular LLMs like LaMDA, especially for larger model sizes, making 1-bit LLMs promising for cost-effective and broad deployment.

Takeaways

• 😊 Introducing BitNet, a new 1-bit LLM model that matches performance of 32-bit models while being more efficient
• 😮 BitNet uses ternary weights of just -1, 0 or 1 to replace full precision weights
• 👍 This simplifies computations to just integer additions, reducing memory and energy needs
• 🔌 Can enable LLMs to run on low-resource devices while maintaining perplexity
• ⚡ Drastically reduces latency, memory usage and energy consumption for inference
• 🔬 Uses a quantization function called Absolute Mean Quantization to convert weights
• 😀 Replaces nn.linear with bitlinear for training 1-bit weights and 8-bit activations
• 📈 Matches performance of baseline LLMs like LLaMA in terms of perplexity
• 💡 Explicitly supports feature filtering via 0 weights to improve 1-bit LLM performance
• 🔜 This architecture calls for new hardware optimizations to fully utilize 1-bit LLMs

Q & A

What is a one bit LLN model?

A one bit LLN model is a large language model where every parameter or weight is ternary, meaning it has only three possible values: -1, 0 or 1. This allows the model to match the performance of full precision models while being more cost effective in terms of memory, throughput and energy consumption.

How does the one bit LLN model save computation resources?

The one bit LLN model saves computation resources because the weights are restricted to only -1, 0 or 1. This means multiplication is not needed during matrix multiplications, only integer addition is required which saves significant GPU resources.

What is the quantization function used to convert weights to ternary values?

The quantization function used is called the absolute mean quantization function. It applies a formula to convert the floating point weights into one of three possible ternary values: -1, 0 or 1.

What are the two main advantages of the one bit LLN model?

The two main advantages are: 1) Stronger modeling capacity due to explicit support for feature filtering made possible by the zero weights, and 2) Matching full precision model performance in terms of end-to-end task accuracy, starting from a 3B parameter size.

How does the one bit LLN model memory usage compare to the vanilla LLama?

Experiments show that the one bit LLN model uses significantly less memory than the vanilla LLama model. For example, a 7B parameter LLama requires 20.8GB RAM versus only 8.96GB for the equivalent one bit model.

What hardware optimizations are suggested for the one bit LLN model?

The paper calls for new hardware optimizations to take advantage of the computation savings from the one bit architecture, such as more efficient integer arithmetic units specialized for this model structure.

How is the one bit LLN model beneficial for deployments?

The one bit LLN model allows large language models to be deployed even with limited resources. Its lower memory footprint and computational requirements make it viable to deploy on resource constrained devices.

What is perplexity in the context of this research?

Perplexity measures how well an LLN model predicts sample text. The experiments showed the one bit LLN matched vanilla models in terms of perplexity, indicating its ability to model language is equivalent.

What is the BitLinear layer in the one bit architecture?

BitLinear replaces the standard linear layer in the Transformer architecture. It is specialized to work with the 1.5 bit weights and 8-bit activations used during training of the model.

How might the one bit architecture impact the accessibility of LLMs?

The drastic efficiency improvements may allow very large LLMs to run on common consumer devices, greatly improving public access and enabling more widespread applications.

Outlines

00:00

🎤 Introducing One Bit LLMs

The narrator introduces the concept of One Bit LLMs, which use only -1, 0 or 1 as model weights instead of 32 or 16 bits. This allows simplified math operations, reducing compute requirements while maintaining performance. The specific model discussed is called BitNet.

05:01

📊 Comparing BitNet to Regular LLMs

BitNet matches regular full precision LLMs in perplexity and task performance with significantly lower memory, latency and energy needs. This is because BitNet only requires integer addition instead of more complex float math operations.

10:03

🔢 How BitNet Works

BitNet uses a quantization function called Absolute Mean Quantization to convert regular model weights to -1, 0 or 1. This enables skipping multiplication operations and only requiring addition. BitNet also replaces nn.linear with bitlinear for 1 bit weights and 8 bit activations.

15:05

📈 BitNet Performance Statistics

Quantitative results show BitNet reduces model size and latency significantly compared to baseline LLMs like LAMA while maintaining competitive perplexity.

Keywords

💡Llm models

Llm stands for language learning models. These are AI models trained on large amounts of text data to generate or summarize text. The video discusses recent advancements in llm models like 1 bit llm models which are more efficient.

💡Quantization

It is the process of converting floating point values of model weights to low precision integer values like int8 or int4. This reduces model size and improves efficiency. The video talks about quantizing weights to 1 bit values.

💡Bitnet

Bitnet is the name of the 1 bit llm model architecture introduced in the paper. It uses ternary values of -1, 0 and 1 for its weights instead of high precision floating points.

💡Ternary values

Ternary means having three possible values. Bitnet uses weights values of either -1, 0 or 1 instead of floating point values. This is more efficient since it replaces multiply with add.

💡Pareto improvement

Replacing floating point weights with 1 bit improves efficiency and reduces cost without losing performance. This concept of improving one factor without hurting others is called Pareto improvement.

💡Perplexity

It is a common metric to evaluate language models. Lower perplexity indicates better predictive performance. The video states Bitnet matches baseline perplexity.

💡Feature filtering

Zero weights can filter unnecessary features leading to better performance. Bitnet supports this by having 0 as one of the weight values.

💡Absolute mean quantization

It is the quantization function used to convert floating point weights to 1 bit ternary values of -1, 0 and 1. The video shows the formula.

💡Bit linear

Instead of standard linear layer, Bitnet uses a bit linear layer with 1 bit weights and 8 bit activations. This maintains performance while being efficient.

💡Memory usage

Key benefit shown is Bitnet reduces memory usage compared to baseline transformer model while maintaining competitive performance.

Highlights

Introducing a one-bit LLM variant called BitNet where every parameter is ternary (-1, 0, or 1)

BitNet matches the performance of full precision Transformers in perplexity and end-to-end task performance

BitNet is significantly more cost-effective in latent memory throughput and energy consumption

Using ternary values allows skipping multiplication operations, requiring only addition for forward/backward propagation

Skipping multiplication operations reduces GPU requirements for fine-tuning and training

BitNet provides solutions to reduce inferencing cost, latency, throughput and energy of LLMs

Calls for new hardware optimizations specifically for 1-bit LLMs

BitNet includes 0 values which allow explicit feature filtering to improve 1-bit LLM performance

Energy savings from BitNet can be translated into faster computation

BitNet trains from scratch with 1.58-bit weights and 8-bit activations

BitNet matches full precision baseline in end-to-end task performance with only 3B parameters

BitNet reduces memory consumption and inferencing latency

Huge difference in model size and latency between BitNet and standard LLMs

Weights are converted to ternary values using an absolute mean quantization formula

Replaces nn.linear with bit_linear for training 1-bit models

Transcripts

hello all my name is krishak and welcome

to my YouTube channel so guys uh one of

the most interesting thing in the field

of data science or generative AI is that

the kind of research that is currently

happening right every day you'll be

seeing some new things that are actually

happening which is very much beneficial

for the entire Community who are working

with llm models uh specifically today I

saw this amazing research paper where it

is written as era of 1 bit llm so I'll

be going to talk about this particular

research paper and what exactly one bit

llm is and how it is far more

advantageous when compared to those

32bit or 16bit llms models okay so

everything I'll be discussing about one

important thing that I also want to make

sure that you learn from this particular

video is that how do you read a research

paper what are the important points that

you should definitely highlight while

reading a research paper and how you

should definit and one thing is that you

cannot directly understand just by

reading it you really need to have some

basic knowledge and without that

particular basic knowledge it will be

very diff difficult to understand so if

you're following my tutorials I always

make sure that whenever I make my videos

right I definitely watch or see all the

research papers and then with respect to

that I simplify the those Concepts and

try to explain it to you so let's go

ahead and understand about this one bit

llm now guys uh if you remember in my

previous video we have already discussed

about quation right so quation was

covered now with respect to quantisation

what we were doing is that let's say I

have a model which is called as Lama 2

which is an open source model let's say

this model is 7 billion having 7 billion

parameters when we say 7 billion

parameters I'm talking about weights

okay now obviously if I have a system

where I don't have very high

configuration not I have resource

constraint I have limited amount of Ram

or gpus what we specifically do we

perform quantisation and we convert this

Lama 2 model which is probably in FP

32bit and we try to convert this into

int 8bit okay

int 8 which is nothing but 8 Bits right

now when we are once we are doing this

specific process what is basically

happening is that the model size is

getting decreased right and because of

that we will be able to load it and

we'll be able to perform any task along

with this we can also perform fine

tuning with the help of Laura and CLA

right so I hope you know this Laura and

CLA I've already discussed in my

previous video please just go click on

my uh click on my channel otherwise just

go ahead and see in the description I've

been providing that particular links

with respect to fine tuning now with the

help of LA and cl we can perform the

fine tuning okay now the question is

that what is this one bit llm right as I

said that with the help of quantisation

we will try to convert this into 32 to

16 bit or it can be 8 bit right but

converting this into a one bit that can

be again uh if you're trying if you now

just by seeing this right if you are

able to convert this into one bit that

basically means we will never be having

any resource constraint right resource

constraint yes with limited Ram with

limited GPU with limited storage we can

probably perform everything from fine

tuning to inferencing right so

inferencing can also be performed right

and this is what is so amazing about

this and this is I I don't know like

what is going to happen just in some

days because once this is probably gone

right now we just have the research

paper once this implementation gets

started trust me it will be quite

amazing for the entire Community who are

working with llm models okay so this was

just a brief idea about this one now

let's go ahead and discuss what is 1 bit

llm okay and when we say to be precise

when we say that all large language

models it is basically in 1.58 bits okay

why it is 1.58 we'll discuss about it

and there are many points that needs to

be discussed uh along with me please

make sure that you watch this video till

the end because I'm going to read over

here because this will also give you an

idea that how you should probably go

ahead and read the research paper so let

me quickly uh go ahead and clear this

let's see whether it'll getting cleared

or not okay so over here okay clear is

basically

happening um okay I will just rub it

okay now let's go ahead and discuss

about this and let's read some of the

important information that is present

over here okay and trust me guys read

along with me then only you'll be able

to understand how you can read the

research paper okay now what exactly

this one bit llm model is um in this

work we introduce a onebit llm variant

namely bit net okay so bit net is the

llm model name one bit llm model name

and then where every single parameter or

weight of the llm is Turner right now it

is not floating 62 bit or sorry 32bit or

16 bit it is Turner Turner basically

means it has only three values it can

have only three values weights it can be

minus1 0 or 1 one okay it matches the

full Precision Transformer M with the

same model size and training tokens in

terms of perplexity perplexity basically

means so with respect to any query that

I ask and endtoend task performance

right while being significantly more

coste effective in performance of latent

memory throughput and energy consumption

so obviously at the end of the day all

the llm model will specifically have

this kind of constraint right which are

specifically with huge uh number of

parameters let's say 7 billion 170

billion right right and if you're just

using this three numbers Min -1 0 1

you'll be able to understand why I'm

saying that because of this tary values

right you'll be seeing how abundance the

performance improves okay so furthermore

uh so here you can probably see all this

points uh Laten memory throughput and

energy consump uh consumption uh energy

consumption can be with respect to

inferencing with respect to fine tuning

and all okay now let's understand how

this

operators how this values will be

basically used okay this is also

important so with respect to this what I

am actually going to do I am going to

make sure that to explain you I take the

right thing okay so let's understand

this okay understand guys whenever we

talk about parameters these are my

weights okay these are my

weights let's see so these are my

weights

okay and these are my weights so let's

consider that my initial Transformer llm

weights is this one okay now by when we

say 1 bit

llm we are going to convert all these

values and replace them with either of

these three values minus one 0 comma 1

okay so that is the reason that you see

over here all these weights is being

getting converted into something like

this okay minus1 0 or 1 only that three

parameters is is there okay and this is

what we basically say as bitnet B

1.58 okay and this is also called as

parito Improvement how this is basically

happening I will talk about it okay just

give me some time there will be some

kind of quantization getting applied

here also okay quantization getting

applied over here okay to convert these

values to this okay now let's understand

one very important thing okay and this

is the most important thing what will

happen if you convert this values to

this see with respect to any fine-tuning

or forward propagation backward

propagation what exactly happens the

model weights the model weights over

here is basically getting multiplied by

the inputs and then we get the output

right yes additionally we add a bias so

it's okay we don't include a bias right

now over here just to show it to you so

over here this let's consider that this

is my floating Point 16 number so every

number will get multiplied by the input

right and then what will happen is

that after that all the it's it's just

like this right summation of I equal to

1 to n w of x + B right so this is what

is the operation that is basically

happening whenever we do the forward

propagation Whenever there is an

updation of weight that basically means

we are doing the summation of weights

and the input right so once we are doing

this and then we are doing the sumission

okay but if we have all these weights in

the form of -1 1 0 then what will happen

is that over here you'll be seeing that

multiplication operation will not be you

know that much valuable right so over

here first of all we are doing

multiplication then addition but over

here we are just doing addition no

multiplication because any number it

will be multiplied by 0 is z only any

number that is multiplied by one is one

only any number that is multiplied by

minus1 is minus1 only so over here the

main thing is that your add addition

operation is only

happening addition operation is only

Happening Now obviously if you only need

to do addition operation then what will

happen your GPU will not be requiring

that much GPU also so your GPU will also

get reduced why why this operation takes

more GPU because multiple multiplication

needs to happen right with respect to

different different weights right then

addition of all those values needs to

happen because in the forward

propagation this is what is the equation

that specifically happens right we

multiply

the weights with the inputs and then we

do the sumission and then finally we add

the bias right so this is the most

important thing so here you'll be able

to understand with floating 16 right all

the numbers is first of all multiplied

by the inputs and then the sumission is

done but here your values are with

respect to Turner that is min -1 0 1 so

here multiplication is already skipped

because 1 into x0 is x0 only right it is

a simple multiplication right and that

much resources will not be required for

simplistic multiplication so here

maximum to maximum only addition will be

required right so I hope you're able to

understand because of this technique of

Paro Improvement because of this

technique of Paro Improvement you'll be

able to see that what we are able to

achieve right and obviously when we are

able to achieve this the GPU will be

required less when we are doing the

fine-tuning or training right so I hope

you have got this as an complete idea

and you have understood right why we

specifically do this how it is done how

this transformation is done so here you

can probably see that it provides a Paro

solutions to reduce inferencing cost

latency throughput and energy of llm

while maintaining the model performance

the new computation Paradigm of Paradigm

of bitnet 1.58 calls for Action to

design new hardware optimization for

1bit llm right I know guys this is more

of a research paper so I'm reading and

I'm telling you each and everything and

also explaining you the concept I know

this can be a little bit of boring but

trust me you need to understand in this

specific way okay now let's talk more

about this and we will have highlighted

main main things in this green color

okay these models have demonstrated

remarkable performance in a wide range

of natural language processing tasks

like llm models but their increasing

size has posed challenges for deployment

and raised concern about the

environmental and economic impact due to

high energy consumption obviously this

is the problem with llms that are

already available one approach to

address the challenges to use post

trining quantization to create low bit

models for inferencing I've already

discussed about this quantization Laura

clora everything this technique reduces

the Precision of weights and activation

significantly reducing the memory and

computational requirement of llm the

trend has been to move from 16 bit to

lower bit such as 4bit variant this is

what is basically happening with respect

to llm models right this is with llm

okay this is with

llm so here I'll write llm models now

let's see with the help of one bit

architecture one bit model architecture

what we can solve so recent work on one

bit model architecture such as bitnet

presents a promising direction from

reducing the cost of llm while

maintaining the performance vanina llms

vanila llms are in 16bit flow flating

values and the bulk of L LM is matrix

multiplication therefore the major

computation cost comes from floating

Point addition and multiplication

operation I said you just now on top of

it right in contract the matrix

multiplication of bit net only involves

integer addition because anything

multiplied by one is one uh anything

multiplied by one is that same number

anything multiplied by minus one is that

same number with a negative sign

anything multiplied by 0 is obviously

zero right so as the fun FAL limit to

compute performance in many chips is

power this energy saving can be

translated into faster computation now

this is the most important thing right

and here you can clearly see the things

that I've highlighted right I hope you

get an idea how good this one bit llm

can be okay then you can still read

about it here we are going to just use

terally values like Min -1 0 1 and

obviously because of this zero your 58

bit is basically increasing there are

two major advantages of using this also

it is written over here

see further more bitnet oh my God why

this is getting highlighted like okay

furthermore bitnet offers two additional

Advantage first its modeling capacity is

stronger due to explicit support for

feature filtering how feature filtering

happen because anything multiplied by

Zer will be zero onina right made

possible by the inclusion of zero in the

model weight which can significantly

improve the performance of 1 bit llm

secondly our experiment shows can match

full Precision Baseline in terms of this

end to end task performance starting

from a 3B size okay now most of the

things that you are able to see right

now let's discuss about one more

important thing uh that is how this

transformation is happening how this

number are getting converted to this it

is just by using the simple mathematical

equation or this quantization function

okay Quant qu ization

function quantization function okay and

this quantization function is called as

absolute mean quation and this is the

formula that is basically used by which

all the numbers are basically getting

converted to only this three values okay

0 1

okay 1 0 1 okay just by applying this

particular formula okay so in uh and

there is also one more change with

respect to the Transformer it replaces

nn. linear with bit linear okay so this

bit linear I think uh you'll be able to

see that it trained from scratch with

1.58 bit weights and 8 bit Activation so

this is what it is basically done with

respect to the initial training okay so

most of the thing I have actually

discussed over here uh let's talk about

the performance so over here you'll be

able to see that uh the Llama model of

700 million parameters bitnet will also

have 7 million parameters but here you

see the memory is in decreasing right

over here 2.08 1.18 12.33 is getting

reduced to

8.96 and then this PPL is basically

12.87 so over here you can see that how

it is getting reduced now similarly when

the billion of parameters are basically

increasing right let's say with Lama is

1.3 billion right the parameter will be

same but memory again 1.14 is required

97 11.29 right and similarly over here

also you'll be able to see the same

thing is basically happening so memory

is decreasing latency is also decreasing

for the inferencing purpose perfect and

uh one more parameter that you'll be

able to see with respect to model size

and latency right model size so the the

blue color is basically the Llama model

OKAY the orange color is basically one

bit llm models you'll be able to see how

much huge latency difference is there

similarly with respect to this how much

huge memory differences there right to

save this kind of models so uh this is

just the research paper that has come up

recently but uh I'm really really happy

to see this because in the future many

things is going to happen so again I

would like to welcome you all to the era

of 1 bit llm models and now you'll also

be able to use this onebit llm model

soon I think first of all hugging face

will only come and try to implement all

these things where you can also easily

create your application using gender AI

so I hope you like this part video if

you like it please make sure that you

subscribe my channel press the Bell

notification icon I'll see you in the

next video have a great day thank you

one all take care bye-bye