This change makes it possible to use flags like `--grammar` when using
the `llava-cli` program. The rest is just code cleanup deleting a long
standing TODO comment.
This change also ensures that logging information is emitted to stderr
which helps the `llava-cli` command be more friendly to shell scripts.
See Mozilla-Ocho/llamafile@1cd334f
The server currently schedules tasks using a sleep(5ms) busy loop. This
adds unnecessary latency since most sleep implementations do a round up
to the system scheduling quantum (usually 10ms). Other libc sleep impls
spin for smaller time intervals which results in the server's busy loop
consuming all available cpu. Having the explicit notify() / wait() code
also helps aid in the readability of the server code.
See mozilla-Ocho/llamafile@711344b
The default values for tfs_z and typical_p were being set to zero, which
caused the token candidates array to get shrunk down to one element thus
preventing any sampling. Note this only applies to OpenAI API compatible
HTTP server requests.
The solution is to use the default values that OpenAI documents, as well
as ensuring we use the llama.cpp defaults for the rest. I've tested this
change still ensures deterministic output by default. If a "temperature"
greater than 0 is explicitly passed, then output is unique each time. If
"seed" is specified in addition to "temperature" then the output becomes
deterministic once more.
See mozilla-Ocho/llamafile#117
See mozilla-Ocho/llamafile@9e4bf29
* initial commit, going through initializations
* main loop finished, starting to debug
* BUG: generates gibberish/repeating tokens after a while
* kv_cache management
* Added colors to distinguish drafted tokens (--color). Updated README
* lookup : fix token positions in the draft batch
* lookup : use n_draft from CLI params
* lookup : final touches
---------
Co-authored-by: Leon Ericsson <leon.ericsson@icloud.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add API key authentication for enhanced server-client security
* server : to snake_case
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fixes "Not enough space in the context's memory pool" encountered on certain models, which seems to be caused by some imprecision related to the automatic casting of floating point values
* do not cast to size_t, instead just use doubles
* ggml : add ggml_row_size(), deprecate ggml_type_sizef()
* ggml : fix row size compute to avoid overflows
* tests : fix sizey -> sizez
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
On commit b1108 (44c117f4) xaedes added
ggml_allocr * alloc = NULL;
... (many lines in between)
if (alloc) {
ggml_allocr_free(alloc);
}
Which is correct, but it's easy to lose context after many lines in between.
On commit b1287 (0e76a899) xaedes made a big change. From here on, alloc is freed eagerly.
alloc = ggml_allocr_new(...)
... (short lines of code)
ggml_allocr_free(alloc)
This happens a few times, but alloc is never set to NULL, and many lines below,
we still have
if (alloc) {
ggml_allocr_free(alloc);
}
which causes a double-free.
* Samplers sequence order w parameter
* Cleaned commented code
* Fixed formatting
* Rewrote with unordered_map
* Revert and rewrite, too many problems and safeguards would be needed
* Fixed code style
* Code style fixes according to review
* More readable samplers input string, fixed help
* Style fix in sampler_queue
* Formatting fixes
* Fixing whitespaces
This commit updates the error message that is printed when the
KV cache is not big enough to hold all the prompt and generated
tokens. Specifically it removes the reference to n_parallel and
replaces it with n_len.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* metal : implement soft_max_ext
* cuda : implement soft_max_ext
* ggml : implement soft_max_ext (CPU)
* batched-bench : print threads
ggml-ci
* metal : simplify soft_max encoding
ggml-ci
* cuda : use 512 threads for soft_max instead of 32
* ggml : update soft max cpu
* cuda : do warp-based block reduce
* cuda : increase max block size to 1024
* cuda : fix warp reduction initialization of shared mem
* metal : warp-based reduction for soft max kernel
* metal : warp-based reduce for rms_norm
* metal : simplify soft max kernel
ggml-ci
* alloc : fix build with debug
* * add multiprompt support
* * cleanup
* * more cleanup
* * remove atomicity of id_gen, and change lock_guard to unique_lock on completion requests
* * remove all references to mutex_multitasks
* Update examples/server/server.cpp
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update examples/server/server.cpp
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update examples/server/server.cpp
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update examples/server/server.cpp
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* * change to set
---------
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* ShareGPT4 compatibility (vision encoder only loading)
Load only a CLIP vision encoder (as supplied by ShareGPT finetunes)
Corrects the argument parsing for --img_mean and --img_std (which were previously not parsed but attempted to access)
Defines defaults for img_mean and img_std which are equal to the llava 1.5 CLIP encoder, so you do not have to provide them
* Update convert-image-encoder-to-gguf.py
* fix oai proxy
fix generation not stoped while bot stop talking in chat mode
fix possible `slot_id` not exist
response for cors (and pre flight)
* oai proxy: workaround for some client (such as Chatbox)
* use stop as separator to replace hardcoded `\n`
* copy to llama.cpp as subdir
* attempt enabling metal, fails
* ggml metal compiles!
* Update README.md
* initial conversion to new format, utf8 errors?
* bug fixes, but now has an invalid memory access :(
* added O3, now has insufficient memory access
* begin sync with master
* update to match latest code, new errors
* fixed it!
* fix for loop conditionals, increase result size
* fix current workflow errors
* attempt a llama.swiftui workflow
* Update .github/workflows/build.yml
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add openai-compatible POST /v1/chat/completions API endpoint to server example
* fix code style
* Update server README.md
* Improve server README.md
* Fix server.cpp code style according to review
* server : some style changes
* server : indentation
* server : enable special tokens during tokenization by default
* server : minor code style
* server : change random string generator
* straightforward /v1/models endpoint
---------
Co-authored-by: kir-gadjello <111190790+kir-gadjello@users.noreply.github.com>
Co-authored-by: Tobi Lütke <tobi@Tobis-MacBook-Pro.local>
* llama : keep track of used KV cells + better KV cache management
* llama : zero KV cache used upon clear
ggml-ci
* llama : allow exporting a view of the KV cache (#4180)
* Allow exporting a view of the KV cache
* Allow dumping the sequences per cell in common
* Track max contiguous cells value and position as well
* Fix max contiguous empty cells index calculation
Make dump functions deal with lengths or sequences counts > 10 better
* Fix off by one error in dump_kv_cache_view
* Add doc comments for KV cache view functions
Eliminate cell sequence struct; use llama_seq_id directly
Minor cleanups
* common : add -dkvc arg for enabling kv cache dumps
---------
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
* Support special tokens and not adding BOS to prompt in speculative
* Adapt to new should_add_bos function
* Ensure tgt and dft have same add_bos setting
- introduces help entry for the argument
- cuts '--gpu-layers' form in order to simplify usage and documentation.
Signed-off-by: Jiri Podivin <jpodivin@gmail.com>
Co-authored-by: Jiri Podivin <jpodivin@redhat.com>
* finetune : zero the loraB initial vectors
Without this, the first iteration is starting out far from the base model, instead of exactly on it.
Zeroing loraB is what the paper recommends. loralib also zeroes at least one of the init vector pairs
(though it departs from the paper in using a different distribution for the other vector, in some cases).
* tabs to spaces
* Use ggml_set_zero instead of adding a new function
* gguf-py: gguf-dump: Respect --no-tensor flag in JSON mode.
* Respect add_bos_token GGUF metadata value
* gguf-py: Try to fix SpecialVocab giving up too easily for the Nth time
* gguf-py: Refactor and add file reading support
* Replay changes from #3871
Credit to @cebtenzzre for that pull
* Various type annotation fixes.
* sort imports with isort (again)
* Fix missing return statement in add_tensor
* style cleanup with flake8
* fix NamedTuple and Enum usage
* Fix an issue with state init in GGUFReader
Move examples to an examples/ directory
Clean up examples
Add an example of modifying keys in a GGUF file
Update documentation with info on examples
Try to support people importing gguf/gguf.py directly
* Damagage is not a word.
* Clean up gguf-py/examples/modify_gguf.py whitespace
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update gguf-py/examples/modify_gguf.py formatting
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update gguf-py/gguf/gguf_reader.py type hint
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Make examples executable, formatting changes
* Add more information to GGUFReader and examples comments
* Include a gguf Python package version bump
* Add convert-gguf-endian.py script
* cleanup
* gguf-py : bump minor version
* Reorganize scripts
* Make GGUFReader endian detection less arbitrary
* Add JSON dumping support to gguf-dump.py
Which I kind of regret now
* A few for gguf-dump.py cleanups
* Murder accidental tuple in gguf-py/scripts/gguf-dump.py
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* cleanup
* constants : remove unneeded type annotations
* fix python 3.8 compat
* Set up gguf- scripts in pyproject.toml
* And include scripts/__init__.py, derp
* convert.py: We can't currently support Q8_0 on big endian.
* gguf-py: SpecialVocab: Always try available sources for special token ids
gguf-py: SpecialVocab: Try to load merges from merges.txt if not in tokenizer.json
gguf-py: SpecialVocab: Add 'add_bos_token' type bools to GGUF metadata
u
* cleanup
* Promote add_X_token to GGUF metadata for BOS and EOS
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Update server.cpp with min_p after it was introduced in https://github.com/ggerganov/llama.cpp/pull/3841
* Use spaces instead of tabs
* Update index.html.hpp after running deps.sh
* Fix test - fix line ending
* fix backward process of rope
rope backward process was broken after YaRN RoPE (#2268) implementation, due to missing changes in backward functions.
the code for the backward process is nearly identically to the forward process:
the only difference is the sign of the sin-values.
to avoid future regressions remove the near-duplicate backward functions and reuse the forward code:
for this a new function argument `bool forward` was added to `ggml_compute_forward_rope_f32` and `ggml_compute_forward_rope_f16`.
the sin-values will be negated when forward is false.
* fix finetune rope call to use correct default attn_factor of 1.0f
* remove unused `ggml_rope_xpos_back`
it is better to have only one `ggml_rope_back` function that accepts all rope parameters, so that `ggml_compute_backward` can propagate all parameters without having to switch between different rope_back variants.
* fix comments explaining the sinus sign in ggml_forward_rope
* add missing function arguments in declaration
* fix function argument type in declaration
llava-cli was loading models with default params and ignoring settings
from the cli. This switches to a generic function to load the params
from the cli options.
* wip llava python bindings compatibility
* add external llava API
* add base64 in-prompt image support
* wip refactor image loading
* refactor image load out of llava init
* cleanup
* further cleanup; move llava-cli into its own file and rename
* move base64.hpp into common/
* collapse clip and llava libraries
* move llava into its own subdir
* wip
* fix bug where base64 string was not removed from the prompt
* get libllava to output in the right place
* expose llava methods in libllama.dylib
* cleanup memory usage around clip_image_*
* cleanup and refactor *again*
* update headerdoc
* build with cmake, not tested (WIP)
* Editorconfig
* Editorconfig
* Build with make
* Build with make
* Fix cyclical depts on Windows
* attempt to fix build on Windows
* attempt to fix build on Windows
* Upd TODOs
* attempt to fix build on Windows+CUDA
* Revert changes in cmake
* Fix according to review comments
* Support building as a shared library
* address review comments
---------
Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* cmake : fix build when .git does not exist
* cmake : simplify BUILD_INFO target
* cmake : add missing dependencies on BUILD_INFO
* build : link against build info instead of compiling against it
* zig : make build info a .cpp source instead of a header
Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>
* cmake : revert change to CMP0115
---------
Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>
* Add '-ngl' support to finetune.cpp
* Add fprintf in ggml_cuda_op_add
When I tried CUDA offloading during finetuning following the readme, I got an assert here.
This probably isn't an important case because inference later gives a warning saying you should use f16 or f32 instead when using lora
* Add 'finetune.sh', which currently fails when using GPU
"error: operator (): Finetuning on tensors with type 'f16' is not yet supported"
* tweak finetune.sh
* Suppress some warnings in ggml.c
* Add f16 implementation to ggml_compute_forward_add_f16_f32
* Add an f16 case to ggml_add_cast_impl and llama_build_lora_finetune_graphs
* finetune.sh: Edit comments
* Add "add_f16_f32_f32_cuda"
* Tweak an error message
* finetune.sh: Add an optional LLAMA_MODEL_DIR variable
* finetune.sh: Add an optional LLAMA_TRAINING_DIR variable
* train : minor
* tabs to spaces
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
* Introduce the new Min-P sampler by @kalomaze
The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token.
* Min-P enabled and set to 0.05 default
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
* Extend llama_kv_cache_seq_rm to allow matichng any sequence
* Replace llama_kv_cache_tokens_rm with llama_kv_cache_clear
Use llama_kv_cache_clear for cache clearing
Change calls to llama_kv_cache_tokens_rm that want to delete by position to use llama_kv_cache_seq_rm functionality
* cmake : add helper for faster CUDA builds
* batched : add NGL arg
* ggml : skip nops in compute_forward
* cuda : minor indentation
* cuda : batched cuBLAS GEMMs for src0 F16 and src1 F32 (attention ops)
* Apply suggestions from code review
These changes plus:
```c++
#define cublasGemmBatchedEx hipblasGemmBatchedEx
```
are needed to compile with ROCM. I haven't done performance testing, but it seems to work.
I couldn't figure out how to propose a change for lines outside what the pull changed, also this is the first time trying to create a multi-part review so please forgive me if I mess something up.
* cuda : add ROCm / hipBLAS cublasGemmBatchedEx define
* cuda : add cublasGemmStridedBatchedEx for non-broadcasted cases
* cuda : reduce mallocs in cublasGemmBatchedEx branch
* cuda : add TODO for calling cublas from kernel + using mem pool
---------
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
