Mistral Spelled Out: Prefill and Chunking : Part 9
Summary
TLDRThe 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
😀 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.
😀 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.
Mindmap
Keywords
💡prefill
💡chunking
💡KV cache
💡attention matrix
💡query matrix
💡key matrix
💡masking
💡context
💡inference
💡performance
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
video so I hope you like this content
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