* Add the DRY dynamic N-gram anti-repetition sampler
The DRY (Do not Repeat Yourself) sampler is a dynamic N-gram
repetition penalty that negatively scores tokens that would extend
sequences that already appear in the context.
See this discussion for a motivation and explanation of the sampler:
https://github.com/oobabooga/text-generation-webui/pull/5677
This implementation of DRY mostly aligns with the obabooga version
with a few modifications. It uses a more efficient linear scanning
algorithm to identify repetitions. It also supports multi-token
sequence breakers. As a limitation, this implementation reuses
the rep pen range parameter, rather than introducing a new range
just for the DRY sampler.
There is a separate change to lite.koboldai.net that exposes the DRY
sampler parameters to KoboldAI Lite, so none of the embed files have
been changed as part of this commit.
* Update default DRY parameters to match lite
* Improve DRY token debug logging
* Replace `and` with `&&` to fix MSVC compile error
Little known fact: The C++98 standard defines `and` as an
alternative token for the `&&` operator (along with a bunch
of other digraphs). MSVC does not allow these without using
the /Za option or including the <iso646.h> header. Change to
the more standard operator to make this code more portable.
* Fix MSVC compile error because log is not constexpr
Replace the compile-time computation with a floating-point
approximation of log(std::numeric_limits<float>::max()).
* Remove unused llama sampler variables and clean up sequence breakers.
* Remove KCPP_SAMPLER_DRY as a separate enum entry
The DRY sampler is effectively a repetition penalty and there
are very few reasons to apply it at a different place in sampler
order than the standard single-token penalty. There are also
multiple projects that have dependencies on the existing sampler
IDs, including KoboldAI, KoboldAI Lite, and Silly Tavern. In order
to minimize the impact of the dependencies of adding the DRY sampler
to koboldcpp, it makes the most sense to not add a new ID for now,
and instead to piggyback on KCPP_SAMPLER_REP_PEN. In the future
if we find a use case for splitting the application of rep pen and DRY
we can introduce a new enum entry then.
* Add the dry_penalty_last_n to independently control DRY penalty range
This parameter follows the oobabooga semantics: it's optional, with a
default value of zero. Zero means that DRY should sample the entire
context. Otherwise, it's the number of tokens from the end of the
context that are scanned for repetitions.
* Limit sequence breaker lengths in tokens and characters
The core DRY sampler algorithm is linear in the context length, but
there are several parts of the sampler related to multi-token
sequence breakers that are potentially quadratic. Without any
restrictions, a suitably crafted context and sequence breaker could
result in a denial-of-service attack on a server running koboldcpp.
This change limits the maximum number of characters and the maximum
token length of a sequence breaker in order to limit the maximum
overhead associated with the sampler.
This change also improves some comments, adding more detail and
changing the wording to increase clarity.
* GradientAI Auto ROPE Base calculation
https://gradient.ai/blog/scaling-rotational-embeddings-for-long-context-language-models
has a formula that better fits the ideal rope scaling.
Tested with Lllama3, checked calculation is correct for llama2. Retains logic for not scaling rope if under trained CTX.
* add in solar scaling logic
Solar based models require the context values to be multiplied by 8. This is (i'm guessing) because the positions as based on a 32k context, but sliding window of 4k.
* Update model_adapter.h
adding in tensor count to identify solar models based on tensor count of 435.
* Update model_adapter.cpp
add in n_tensor count for solar identification
* refactor and cleanup GradientAI rope scaling
---------
Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
