this fixes some issues that were being seen on installed windows builds of 2.5.0
only load dlls that actually might be model impl dlls, otherwise we pull all sorts of random junk into the process before it might expect to be
Signed-off-by: Aaron Miller <apage43@ninjawhale.com>
so far my from-scratch mat*mats are still slower than just running more
invocations of the existing Metal ported mat*vec shaders - it should be
theoretically possible to make a mat*mat that's faster (for actual
mat*mat cases) than an optimal mat*vec, but it will need to be at
*least* as fast as the mat*vec op and then take special care to be
cache-friendly and save memory bandwidth, as the # of compute ops is the
same
* disable llama.cpp logging unless GPT4ALL_VERBOSE_LLAMACPP envvar is
nonempty
* make verbose flag for retrieve_model default false (but also be
overridable via gpt4all constructor)
should be able to run a basic test:
```python
import gpt4all
model = gpt4all.GPT4All('/Users/aaron/Downloads/rift-coder-v0-7b-q4_0.gguf')
print(model.generate('def fib(n):'))
```
and see no non-model output when successful
Use the $<CONFIG> generator expression instead of CMAKE_BUILD_TYPE. This
is needed because Visual Studio is a multi-configuration generator, so
we do not know what the build type will be until `cmake --build` is
called.
Fixes#1470
- Use F16 KV cache
- Store transposed V in the cache
- Avoid unnecessary Q copy
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ggml upstream commit 0265f0813492602fec0e1159fe61de1bf0ccaf78
* Handle edge cases when generating embeddings
* Improve Python handling & add llmodel_c.h note
- In the Python bindings fail fast with a ValueError when text is empty
- Advice other bindings authors to do likewise in llmodel_c.h
* Makefiles, black, isort
* Black and isort
* unit tests and generation method
* chat context provider
* context does not reset
* Current state
* Fixup
* Python bindings with unit tests
* GPT4All Python Bindings: chat contexts, tests
* New python bindings and backend fixes
* Black and Isort
* Documentation error
* preserved n_predict for backwords compat with langchain
---------
Co-authored-by: Adam Treat <treat.adam@gmail.com>
* backend/gptj: use scratch buffers
reduces total memory required and makes eval buf not grow with n_past
* backend/mpt: use scratch bufs
* fix format-related compile warnings
* backend: factor out common structs in model code
prepping to hack on these by hopefully making there be fewer places to fix the same bug
rename
* use common buffer wrapper instead of manual malloc
* fix replit compile warnings
most of these can just shortcut out of the model loading logic llama is a bit worse to deal with because we submodule it so I have to at least parse the hparams, and then I just use the size on disk as an estimate for the mem size (which seems reasonable since we mmap() the llama files anyway)
use RTLD_LOCAL so that symbols are *only* exposed via dlsym
without this all symbols exported by the libs are available for symbol
resolution, resulting in different lib versions potentially resolving
*each other's* symbols, causing incredibly cursed behavior such as
https://gist.github.com/apage43/085c1ff69f6dd05387793ebc301840f6
* Initial Library Loader
* Load library as part of Model factory
* Dynamically search and find the dlls
* Update tests to use locally built runtimes
* Fix dylib loading, add macos runtime support for sample/tests
* Bypass automatic loading by default.
* Only set CMAKE_OSX_ARCHITECTURES if not already set, allow cross-compile
* Switch Loading again
* Update build scripts for mac/linux
* Update bindings to support newest breaking changes
* Fix build
* Use llmodel for Windows
* Actually, it does need to be libllmodel
* Name
* Remove TFMs, bypass loading by default
* Fix script
* Delete mac script
---------
Co-authored-by: Tim Miller <innerlogic4321@ghmail.com>
fixes a definite use-after-free and likely avoids some other
potential ones - std::string will convert to a std::string_view
automatically but as soon as the std::string in question goes out of
scope it is already freed and the string_view is pointing at freed
memory - this is *mostly* fine if its returning a reference to the
tokenizer's internal vocab table but it's, imo, too easy to return a
reference to a dynamically constructed string with this as replit is
doing (and unfortunately needs to do to convert the internal whitespace
replacement symbol back to a space)
- bug introduced in 0cb2b86730
- currently getting: `warning C5102: ignoring invalid command-line macro definition '/arch:AVX2'`
- solution is to use `_options(...)` not `_definitions(...)`
no effect, but avoids a *potential* bug later if we use
actualVocabSize - which is for when a model has a larger
embedding tensor/# of output logits than actually trained token
to allow room for adding extras in finetuning - presently all of our
models have had "placeholder" tokens in the vocab so this hasn't broken
anything, but if the sizes did differ we want the equivalent of
`logits[actualVocabSize:]` (the start point is unchanged), not
`logits[-actualVocabSize:]` (this.)
* porting over replit code model to gpt4all
* replaced memory with kv_self struct
* continuing debug
* welp it built but lot of sus things
* working model loading and somewhat working generate.. need to format response?
* revert back to semi working version
* finally got rid of weird formatting
* figured out problem is with python bindings - this is good to go for testing
* addressing PR feedback
* output refactor
* fixed prompt reponse collection
* cleanup
* addressing PR comments
* building replit backend with new ggmlver code
* chatllm replit and clean python files
* cleanup
* updated replit to match new llmodel api
* match llmodel api and change size_t to Token
* resolve PR comments
* replit model commit comment
Major change to the backend that allows for pluggable versions of llama.cpp/ggml. This was squashed merged from dlopen_backend_5 where the history is preserved.
