Mistral Spelled Out: Prefill and Chunking : Part 9

Summary

The video explains prefilling and chunking techniques used to optimize performance when prompting large language models. Rather than generating tokens one-by-one or caching the entire prompt, prefilling uses 'chunks' - splitting the prompt into segments the size of the attention window. Each chunk is cached and referenced to provide context when processing subsequent chunks. This balances loading time, memory usage and context for optimal performance. These techniques, along with others like mixture of experts models, aim to fully leverage the capabilities of large language models.

Takeaways

• 😀 The goal is to optimize model performance when using long prompts by prefilling and chunking the prompt
• 👌 Prefilling allows caching the entire prompt in the key-value cache, but this may crash with very long prompts
• 💡 Chunking splits the prompt into chunks the size of the sliding window attention length
• 📝 The key-value cache is prefilled with the first chunk before processing the next chunk
• 🔀 When processing a new chunk, contents from the cache are combined with the new chunk to provide more context
• 🔁 This cycle repeats - cache gets updated and used with each new chunk for better context
• ⚖️ Chunking balances loading the full prompt vs loading tokens one-by-one
• 🚀 Utilizing prefilling and chunking improves performance compared to no caching or full prompt caching
• 🎯 The goal is optimal performance in generating tokens conditioned on the prompt
• 📈 Additional techniques like mixture of experts further improve performance

Q & A

Why do we need prefill and chunking?

We need prefill and chunking to optimize performance when generating tokens from a long prompt. Loading the entire long prompt into the KV cache may crash it, while generating tokens one by one does not utilize the GPU optimally. Prefill and chunking strike a balance.

How does prefill work?

In prefill, we first calculate the attention matrix for the first chunk of tokens from the prompt. Then we fill the KV cache with the output of this operation before moving to the next chunk.

What is the chunk size used in chunking?

The chunk size used is the same as the sliding window size in the attention mechanism, usually around 3 tokens.

How are the key and query matrices populated when chunking?

The query matrix gets the current chunk. The key matrix gets the current chunk concatenated with contents from the KV cache to provide more context.

Why bring KV cache contents along with the current chunk for key matrix?

This provides more context to the current tokens in relation to previous tokens. For example, the token 'you' needs the context of previous tokens to understand its meaning.

What happens as we move from chunk to chunk?

The KV cache gets populated with the attention output of the previous chunk. So later chunks have access to representations of earlier chunks.

How does chunking balance prompt token generation?

By using KV cache for key matrix and current chunk for query matrix. So it utilizes the prompt better than token-by-token generation but does not overload cache like full prompt prefill.

What techniques optimize Mystal performance?

Techniques like KV cache, mixture of experts layers, prefill and chunking optimize Mystal performance for long sequence tasks like prompting.

Does chunking reduce compute compared to full prompt prefill?

Yes, chunking requires less compute per inference compared to calculating attention on the full prompt in one go for prefill.

Why is prompt optimization important?

Prompting is used heavily in AI systems today to get desired outputs from LLMs. Optimizing prompt handling improves real-world performance, latency and cost.

Outlines

00:00

😀 Understanding need for prefill & chunking in transformer models

This paragraph explains why prefill and chunking is needed in transformer models when using long prompts. It discusses issues with caching entire long prompts which can crash cache, or generating tokens one-by-one which is slow. Chunking balances these by splitting prompt into chunks using sliding window size to prefill cache.

05:02

😀 How prefill & chunking works with cache and attention calculation

This paragraph provides an example to demonstrate how prefill and chunking works. It shows how current chunk and contents from cache are used to calculate attention, then cache is updated after each chunk. This gives context to current tokens and balances prompt loading.

Keywords

💡prefill

Prefill refers to the concept of pre-loading parts of the input prompt into the key-value (KV) caches before generating the output tokens. This optimizes performance by not having to load the full, lengthy prompt into the caches all at once, which could crash them. Prefilling caches with chunks of the prompt strikes a balance between loading tokens one-by-one, or the full prompt at once.

💡chunking

Chunking refers to splitting the input prompt into smaller chunks or windows based on the sliding window size used in attention layers. These chunks are then used to prefill the KV caches. Chunking the prompt allows feeding parts of it through the model without slowing things down by processing thousands of tokens.

💡KV cache

The key-value (KV) caches store the key and value vectors during processing. Prefilling these caches with chunks of a long prompt optimizes performance. The caches also provide context from previous chunks when processing later chunks.

💡attention matrix

The attention matrix contains the attention scores between different input tokens based on multiplying the query and transpose of the key matrices. Prefilling KV caches allows creating this matrix without processing the full prompt.

💡query matrix

The query matrix contains representations of the current input chunk. It is multiplied with the key matrix to determine attention between tokens. Only the current chunk goes into this, while the KV cache provides context.

💡key matrix

The key matrix contains representations of the current chunk plus relevant context from the KV cache. Using cache context prevents losing relationships between chunks.

💡masking

Masking refers to setting certain attention scores to -infinity, like outside a sliding attention window. This focuses attention on relevant nearby tokens.

💡context

Context refers to the related tokens surrounding a chunk. Including KV cache context in processing each chunk improves understanding and performance.

💡inference

Inference refers to the model generating the output tokens. Prefilling caches optimizes this by not needing to process all prompt tokens first.

💡performance

Performance refers to the speed, efficiency and stability of processing long input prompts. The prefill and chunking techniques optimize this compared to other approaches.

Highlights

We can prefill the KV cache with prompts to optimize performance

If your prompt is very long, caching it may crash the cache

Chunking strikes a balance between loading tokens one by one and loading the full prompt

We chunk the prompt using the same window size as the sliding window attention

The first chunk is fed to the query and key matrices to create the attention matrix

After calculating the attention matrix, we fill the KV cache with the output

For next chunks, we bring content from the KV cache to provide more context

The query matrix equals the current chunk, the key matrix uses current chunk and KV cache content

This gives more context to current tokens related to chunks in the KV cache

We keep prefilling KV cache and using chunking to utilize known prompt content

Don't need to generate each prompt token or load full prompt to cache

Chunking strikes balance between loading tokens one by one and full prompt

With chunking and prefilling, the model gets optimal performance

Mixture of experts is a new ensemble model covered in the next video

Questions can be dropped in the comments section

Transcripts

in this video I want to talk about

prefill and chunking so we are

continuing the series on mystal

architecture explanation and in this

video I will talk about the prefill and

chunking right so let's first understand

why we need a prefill and chunking right

so if you remember the videos which I

talked about the KV caches so there we

kind of uh like pass on the each of the

tokens then kind of generate the next

tokens which is kind of also we kind of

cache the KV uh the key and the value

vectors and with that we are kind of

generating the uh the attention metrix

related to this each of these tokens and

then in the subsequent tokens right but

in case of like we are using in case of

like prompting right so prompt is always

we know beforehand we don't need to come

up generate the prompt in case of like

we are passing a question to a rag in

that in those cases we don't need to

like uh generate the tokens by tokens in

case of the prompt right so as we always

know the prompt beforehand so can we

prefill the KV cache with prompts to

optimize the performance and generate

the future tokens right so can we do

that let's let's talk about that right

so what we can do is we can like uh cach

everything the whole prompt into the KV

cache and then uh generate the answer

using the what is the content of KV

cache but what happens if your prompt is

a very long like 5,000 to 8,000 tokens

which is generally the case in case of

you are asking a question to a rag right

so in those cases if you are trying to

load a prompt which is very large in the

number of tokens so in that case your uh

cach a will may not optimizely work

right so your cach a may be crash right

so uh to tackle that what we can do is

we can um do is we can like generate the

tokens one by one but again that will

give you a uh not the optimal perform

performance right you will not utilize

the GPU that is already available so can

we strike a balance between filling the

cachier with one by one token at a time

or loading the KV cashier with the full

prompt right so is there a way to kind

of strike a balance between these two

approaches and load the KV cache with

the prompt right so the answer is

chunking right so in case of uh CH

chunking what we will do is we will

chunk The Prompt with the window size

that window size is uh same as the

sliding window attention we have used

and then we will pre-fill the KV cache

with those uh chunks which are which we

have created from The Prompt uh with the

help of window size right example and

try to understand what this prefill and

junking is so we will take an example of

this which is kind of an input sequence

or a prompt so this prompt is uh

attention is all you need uh stood the

test of time right so this is kind of a

sentence which I am using so I could

have used the attention is all you need

but I wanted to a longer sequence to

show you how the prefill and junking

works and let's take this as one of the

input sequence or the prompt and then

let's see how the prefill and junking

works so at this point of time your KV

cache is uh blank right before this is

this is the first uh time we are we will

create the chunk then we will kind of

create the tension metrics and and so

forth right so at this point of time you

can see the KV cacher is blank and we

are using a window size of three and we

have chunked the input token or the

input prompt in in the size of uh in the

window size of three so attention is all

is kind of your first chunk and then you

need stood is kind of the second chunk

and the uh so forth after that so what

we will do is we will have this uh we

will calculate this attention Matrix so

the first window or the first chunk

which is attention is all so that will

be Fade to your query and the key

matrices and with that we will kind of

create the tension Matrix which is kind

of a multiplication of Q multiplied by K

transpose right and we will also apply

the uh the mask which are related to the

caal masking right so after that this is

the first step and now we will kind of

we can fill the KV cach with the uh with

this output of this uh operation right

so now let's see what this is so first

we will like fill the KV cache so once

the attention Matrix is calculated then

only we will fill the KV cache right so

here we have this uh content which is

attention is all so this is kind of

presently uh available in the KV cache

and now the second chunk will come right

so the second chunk is you need stood

right so what we could have done is we

could have like use you need stood in

the query and the key matrices right but

we will not do in case of the prefill

and chunking what we will do is we will

uh kind of bring certain contents from

the KV cacher and also we will uh use

the current chunk right so the current

chunk is you need stood so if I just

take the pen so this is your uh kind of

the current chunk right and this is what

is present in the uh KV cacher so this

will come here so in the query Matrix

your current chunk will be used so this

is nothing but the current chunk

right but in case of the K metric what

we will use we will use along with the

current chunk which is this we will also

use the content of KV cache so this is

your content from KV cash right so that

we will use and with this two contents

which are which we will only use for the

K Matrix and then we will calculate the

attention score right and you can see

like uh we will also apply the sliding

window attention mask so the uh tokens

which are beyond the sliding window

those will be marks to minus infinity

right so now what we need why we need to

use this extra uh contains from the KV

cache right so if we don't use the KV

cache we will only use this part to

generate the attention uh metrics right

but this token right youu right if you

just think this token this this lacks

the context right this is actually

related to all these tokens which are

present in the KV cache so to give more

context to the current tokens or which

are related to the current Chunk we kind

of bring the content of KV Cas and then

we kind of calculate the attention uh

metrics right so this is actually the

concept of uh prefill and chunking so we

will use the contents of KV Cas along

with the current chunk and we will keep

the contents of query Matrix uh which is

same as equals to the current chunk and

then we calculate the attention Matrix

right now let's see what will happen in

the third uh chunk right now what we

will do is we we will uh pick up the

contents of KV caches so the previously

we calculate uh the attention which is

related to you need stood right so that

will be available in the KV cache and

now the new content or the new current

chunk which we will get is the test off

right so to give the more context we

will bring in the contents from KV cache

and we will also use the current Chunk

from the uh input sequence and that we

will use in case of the K matrices and

the query Matrix as I mentioned

previously also so that will be same as

only the the current chunk so this will

be used here and this is actually coming

from the KV cache and this is actually

again the current context right or the

current junk so in this way we will kind

of uh prefill uh prefill the contents of

the KV cache and also we will use the

chunking concept to kind of utilize the

prompts which we already know the

content of right so in that case we

don't need to like uh generate the token

by token each of the uh prompt content

and then kind of ask the llm to get the

answer which is related to The Prompt

right so I hope you got an understanding

of what this prefill and chunking is how

we can use it to kind of optimize the KV

cache in case of the prompt right and it

also kind of strikes a balance between

uh loading the prompt tokens one by one

and also like loading the full prompt to

the KV cache and then do the inference

right so this chunking prefill and

chunking kind of strikes a balance

between those two approaches and it

gives you the more Optimal Performance

so I hope you got an understanding of

this so with all these techniques your

mystal model is kind of getting the

Optimal Performance and getting the

optimal result and uh with this I will

end this video and in the next video I

will talk about the mixture of experts

model right so which is kind of uh the

new Ensemble kind of model which we will

talk about in detail in the next video

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