* server : add --no-context-shift option
* small fix
* Update examples/server/tests/features/embeddings.feature
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* tests : minor fix
* revert usage of GGML_ASSERT
* update server documentation
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* added cli arg to disable context shift
* reverted precommit
* updated README.md for main
* white space
* allow disabling context shift in the server
* Update common/arg.cpp
no-context-shift only works for main example
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* added server example to --no-context-shift args
* removed server changes
* white space
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Adding loading page for '/' server requests
* set content when model is loading
* removed loading html file
* updated cmakelist
* updated makefile
* cleaned up whitespace
* cleanup for PR removed error
* updated server test to handle 503 HTML
* updated server test to handle 503 HTML
* ca†ch 503 before parsing json
* revert test
* account for both api and web browser requests
* precommit corrections
* eol fix
* revert changes to pre-commit
* removed print statement
* made loading message more descriptive
* also support .html files
---------
Co-authored-by: VJHack <flymyplane21@gmail.com>
Co-authored-by: Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com>
* llama : llama_perf + option to disable timings during decode
ggml-ci
* common : add llama_arg
* Update src/llama.cpp
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* perf : separate functions in the API
ggml-ci
* perf : safer pointer handling + naming update
ggml-ci
* minor : better local var name
* perf : abort on invalid sampler pointer
ggml-ci
---------
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* server : added with_pieces functionality to /tokenize endpoint
* server : Add tokenize with pieces tests to server.feature
* Handle case if tokenizer splits along utf8 continuation bytes
* Add example of token splitting
* Remove trailing ws
* Fix trailing ws
* Maybe fix ci
* maybe this fix windows ci?
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Arm AArch64: Documentation updates
* Update docs/build.md to include information on how to enable the Arm optimized gemm/gemv kernels
* Update examples/quantize/README.md with information on the Q4_0_4_4, Q4_0_4_8 and Q4_0_8_8 formats
* Add newline to the end of docs/build.md
* common : do not add null tokens during warmup
ggml-ci
* llama : check that the input tokens are valid
ggml-ci
* tests : fix batch size of bert model
ggml-ci
- Add `struct llama_sampler` and `struct llama_sampler_i`
- Add `llama_sampler_` API
- Add `llama_sampler_chain_` API for chaining multiple samplers
- Remove `LLAMA_API_INTERNAL`
- Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`
* ggml-quants : 1.625 bpw ternary packing for BitNet 1.58b
* ggml-quants : faster 1.625 bpw AVX2 vec_dot
Not using a lookup table anymore makes it match q4_0 speed.
* gguf-py : fix formatting
* llama : remove spaces on empty line
* ggml-quants : subtract 1 when back in epi8
This makes the 1.625 bpw type go faster than q4_0. Still not the fastest.
* ggml-quants : Q2_2 now faster than Q4_K on with AVX2
* ggml-quants : cleanup Q1_3 code formatting
* ggml-quants : ARM NEON vec_dot for q2_2 and q1_3
* ggml-quants : use ceiling division when quantizing q1_3
* convert-hf : simplify BitNet pre-quantization
This still results in the exact same tensor weights and scales,
but it reveals some weirdness in the current algorithm.
* convert-hf : allow converting the weird BitNet 1.3B
Its FFN size is 5460 which is not convenient.
The offending tensors are kept in F16,
which makes the final model 5.01 bpw.
* bitnet : replace 1.58b with b1.58, as in the paper
* ggml-quants : fix build failure on Windows
* ggml-quants : attempt to fix Arm 32-bit support
* ggml : add some informative comments in q1_3 vec_dot
* ggml : add TQ1_0 and TQ2_0 ternary quantization types
* ggml : even faster TQ2_0
* ggml : also faster TQ1_0
Same optimization as for TQ2_0 by offsetting the sum instead of the weights.
This makes TQ1_0 almost as fast as Q8_0 on AVX2.
* ggml : fix build issues in certain environments
* ggml : add NEON vec_dot implementation for TQ1_0 and TQ2_0
* ggml : avoid directly using vmlal_high_s8, for 32-bit ARM compat
The compiler seems smart enough to use the same instruction
even when using vget_high_s8 instead.
* ggml : remove q1_3 and q2_2
No more 1.625 bpw and 2.000 bpw,
now instead using 1.6875 bpw and 2.0625 bpw
with TQ1_0 and TQ2_0, respectively.
* llama : remove the separate scale tensors of BitNet b1.58
They won't be needed, since the remaining ternary quant types have
built-in scales.
* ggml-quants : rename fields of TQ1_0 and TQ2_0 structs for consistency
* ggml-quants : allow using vdotq_s32 in TQ2_0 vec_dot
Not yet tested on hardware which supports it,
might not work or might not even compile. But also it might.
It should make the performance better on recent ARM CPUs.
* ggml-quants : remove comment about possible format change of TQ2_0
Making it slightly more convenient for AVX512
but less convenient for everything else is not worth the trouble.
* gguf-py : Numpy (de)quantization for TQ1_0 and TQ2_0
* ggml-quants : use roundf instead of nearest_int for TQ1_0 and TQ2_0
This does not change anything for ternary models,
since their values should never end up being in halfway cases anyway.
* convert : allow direct conversion to TQ1_0 and TQ2_0
The token embeddings and output tensors are kept in F16
to allow quantizing them to Q4_K and Q6_K with llama-quantize.
* llama : handle fallback for TQ1_0 and TQ2_0 with Q4_0
Q4_0 is not completely symmetric (so not lossless for ternary models),
but it should be good enough.
* ggml-quants : allow using ARM dot product instructions for TQ1_0
* ggml-quants : deduplicate TQ1_0 and TQ2_0 __ARM_FEATURE_DOTPROD support
* ggml : remove unused ggml_mul special case
It would otherwise conflict with the more general
optimization coming with Mamba-2.
* ggml : handle TQ1_0 and TQ2_0 in dequantization-based operators
* test-backend-ops : add TQ1_0 and TQ2_0 comments for later
Not yet adding uncommented, because some backends like SYCL and Metal
do not properly handle unknown types in supports_op for GGML_OP_MUL_MAT.
(and Metal also doesn't handle it with GGML_OP_GET_ROWS)
Support for TQ1_0 and TQ2_0 for other backends than CPU
will be added in follow-up pull requests.
* server : remove multitask from server_task
* refactor completions handler
* fix embeddings
* use res_ok everywhere
* small change for handle_slots_action
* use unordered_set everywhere
* (try) fix test
* no more "mutable" lambda
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* use deque
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Introduce ggml_compute_threadpool
- OpenMP functional: check
- Vanilla ggml functional: Check
- ggml w/threadpool functional: Check
- OpenMP no regression: No glaring problems
- Vanilla ggml no regression: No glaring problems
- ggml w/threadpool no regression: No glaring problems
* Minor fixes
* fixed use after release bug
* fixed a harmless race condition
* Fix Android bulid issue
* fix more race conditions
* fix deadlock for cases where cgraph.n_nodes == 1
and fix --poll case
* threadpool: use cpu_get_num_math to set the default number of threadpool threads
This way we avoid using E-Cores and Hyperthreaded siblings.
* bench: create fresh threadpool for each test
For benchmarking it's better to start a fresh pool for each test with the exact number of threads
needed for that test. Having larger pools is suboptimal (causes more load, etc).
* atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier
This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior.
* threadpool: make polling the default to match openmp behavior
All command line args now allow for setting poll to 0 (false).
* threadpool: do not wakeup threads in already paused threadpool
* fix potential race condition in check_for_work
* threadpool: do not create two threadpools if their params are identical
* threadpool: reduce pause/resume/wakeup overhead in common cases
We now start threadpool in paused state only if we have two.
The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead.
* threadpool: add support for hybrid polling
poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var.
poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ...
The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms.
We can tune this further as things evolve.
* threadpool: reduce the number of barrier required
New work is now indicated with an atomic counter that is incremented for
each new graph that needs to be computed.
This removes the need for extra barrier for clearing the "new_work" and
removes the special case for trivial graphs.
* threadpool: remove special-casing for disposable threadpools
With the efficient hybrid polling there is no need to make disposable pools any different.
This simplifies the overall logic and reduces branching.
Include n_threads in debug print for disposable threadpool.
Declare pause and stop flags as atomic_bool
This doesn't actually generate any memory barriers and simply informs
the thread sanitizer that these flags can be written & read by different
threads without locking.
* threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs)
This fixes the race condition with very small graphs where the main thread happens to
start a new graph while the workers are just about to exit from barriers.
* threadpool: use relaxed order for chunk sync
Full memory barrier is an overkill for this since each thread works on different chunk
* threadpool: remove abort_callback from threadpool state
* threadpool: better naming for thread/cpumask releated functions
* threadpool: consistent use of int type for n_threads params
* threadpool: add support for ggml_threadpool_params_default/init
Also removes the need for explicit mask_specified param.
all-zero cpumask means use default (usually inherited) cpu affinity mask.
* threadpool: move typedef into ggml.h
* threadpool: fix apply_priority() function name
* threadpool: fix swift wrapper errors due to n_threads int type cleanup
* threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled
* threadpool: replace checks for compute_thread ret code with proper status check
* threadpool: simplify threadpool init logic and fix main thread affinity application
Most of the init code is now exactly the same between threadpool and openmp.
* threadpool: update threadpool resume/pause function names
* threadpool: enable openmp by default for now
* threadpool: don't forget to free workers state when omp is enabled
* threadpool: avoid updating process priority on the platforms that do not require it
On Windows we need to change overall process priority class in order to set thread priorities,
but on Linux, Mac, etc we do not need to touch the overall process settings.
* threadpool: update calling thread prio and affinity only at start/resume
This avoids extra syscalls for each graph_compute()
* llama-bench: turn threadpool params into vectors, add output headers, etc
* llama-bench: add support for cool off between tests --delay
This helps for long running tests on platforms that are thermally limited (phones, laptops, etc).
--delay (disabled by default) introduces the sleep for N seconds before starting each test.
* threadpool: move process priority setting into the apps (bench and cli)
This avoids changing the overall process priority on Windows for the apps
that use ggml/llama.cpp directy.
* threadpool: move all pause/resume logic into ggml
* threadpool: futher api cleanup and prep for future refactoring
All threadpool related functions and structs use ggml_threadpool prefix.
* threadpool: minor indent fixes
* threadpool: improve setprioty error message
* Update examples/llama-bench/llama-bench.cpp
Co-authored-by: slaren <slarengh@gmail.com>
* threadpool: fix indent in set_threadpool call
* use int32_t for n_thread type in public llama.cpp API
* threadpool: use _new and _free instead of _create and _release
* fix two more public APIs to use int32_t for n_threads
* build: set _GNU_SOURCE for Adroid
---------
Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>
Co-authored-by: fmz <quic_fzaghlou@quic.com>
Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
This change fixes a bug where replacing text in a very long string could
cause llama.cpp to hang indefinitely. This is because the algorithm used
was quadratic, due to memmove() when s.replace() is called in a loop. It
seems most search results and LLM responses actually provide the O(n**2)
algorithm, which is a great tragedy. Using a builder string fixes things
* llava: Add ACC OP for GPU acceleration to the Vulkan backend in the LLAVA CLIP model.
- The CLIP model now prioritizes the Vulkan backend over the CPU when vulkan available.
- A GGML_OP_ACC shader has been added.
- The encoding performance of the CLIP model improved from 4.2s on the CPU to 0.9s on the GPU.
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
* fix-up coding style.
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
* Fix-up the missing initial parameter to resolve the compilation warning.
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
* [fix] Add missing parameters.
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
* [fix] Use nb1 and nb2 for dst.
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
* Fix check results ggml_acc call
---------
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
Co-authored-by: 0cc4m <picard12@live.de>
* server : refactor middleware and /health endpoint
* move "fail_on_no_slot" to /slots
* Update examples/server/server.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* fix server tests
* fix CI
* update server docs
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* gguf-py : add T5ENCODER model architecture
* common : call llama_decode() during warmup only if the model has decoder
* convert-hf : add T5EncoderModel
* llama : add llama_model_has_decoder() API function
* llama : split build_t5() into build_t5_encoder() and build_t5_decoder()
* llama : add support for LLM_ARCH_T5ENCODER
* llama-embedding : add support for LLAMA_POOLING_TYPE_NONE
* llama-embedding : add support for encoder-only models
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
This commit adds the `--pooling` option to the README.md file in the
`examples/embedding` directory.
The motivation for adding this options is that currently if the model
used does not specify a pooling type the embedding example will fail
with the following error message:
```console
main: error: pooling type NONE not supported
```
This commit also updates the name of the executable in the examples
section.
* common : Changed tuple to struct (TODO fix)
Use struct `llama_init_result` to replace the previous
std::tuple<struct llama_model *, struct llama_context *>
* delete llama_init_default_params()
* delete the extra whitespace
* [example] batched-bench "segmentation fault"
When `llama-batched-bench` is invoked _without_ setting `-npl`, "number
of parallel prompts", it segfaults.
The segfault is caused by invoking `max_element()` on a zero-length
vector, `n_pl`
This commit addresses that by first checking to see if the number of
parallel prompts is zero, and if so sets the maximum sequence size to 1;
otherwise, sets it to the original, the result of `max_element()`.
Fixes, when running `lldb build/bin/llama-batched-bench -- -m models/Meta-Llama-3-8B.gguf`
```
* thread #1, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=1, address=0x0)
frame #0: 0x000000010000366c llama-batched-bench`main(argc=3, argv=0x000000016fdff268) at batched-bench.cpp:72:28
69 llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
70
71 // ensure enough sequences are available
-> 72 ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
```
* Update examples/batched-bench/batched-bench.cpp
Co-authored-by: compilade <git@compilade.net>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: compilade <git@compilade.net>
* llama : refactor session file management
* llama : saving and restoring state checks for overflow
The size of the buffers should now be given to the functions working
with them, otherwise a truncated file could cause out of bound reads.
* llama : stream from session file instead of copying into a big buffer
Loading session files should no longer cause a memory usage spike.
* llama : llama_state_get_size returns the actual size instead of max
This is a breaking change, but makes that function *much* easier
to keep up to date, and it also makes it reflect the behavior
of llama_state_seq_get_size.
* llama : share code between whole and seq_id-specific state saving
Both session file types now use a more similar format.
* llama : no longer store all hparams in session files
Instead, the model arch name is stored.
The layer count and the embedding dimensions of the KV cache
are still verified when loading.
Storing all the hparams is not necessary.
* llama : fix uint64_t format type
* llama : various integer type cast and format string fixes
Some platforms use "%lu" and others "%llu" for uint64_t.
Not sure how to handle that, so casting to size_t when displaying errors.
* llama : remove _context suffix for llama_data_context
* llama : fix session file loading
llama_state_get_size cannot be used to get the max size anymore.
* llama : more graceful error handling of invalid session files
* llama : remove LLAMA_MAX_RNG_STATE
It's no longer necessary to limit the size of the RNG state,
because the max size of session files is not estimated anymore.
* llama : cast seq_id in comparison with unsigned n_seq_max
Changes:
- Move each example into its own function. This makes the code much
easier to read and understand.
- Make the program easy to only run one test by commenting out function
calls in main().
- Make the output easy to parse by indenting the output for each example.
- Add shebang and +x bit to make it clear it's an executable.
- Make the host configurable via --host with a default 127.0.0.1:8080.
- Make the code look in the tools list to call the registered tool,
instead of hardcoding the returned values. This makes the code more
copy-pastable.
- Add error checking, so that the program exits 1 if the LLM didn't
returned expected values. It's super useful to check for correctness.
Testing:
- Tested with Mistral-7B-Instruct-v0.3 in F16 and Q5_K_M and
Meta-Llama-3-8B-Instruct in F16 and Q5_K_M.
- I did not observe a failure even once in Mistral-7B-Instruct-v0.3.
- Llama-3 failed about a third of the time in example_concurrent: it
only returned one call instead of 3. Even for F16.
Potential follow ups:
- Do not fix the prompt encoding yet. Surprisingly it mostly works even
if the prompt encoding is not model optimized.
- Add chained answer and response.
Test only change.
* fix continuing generating blank lines after getting EOT token or EOS token from LLM
* change variable name to is_done (variable name suggested by ggerganov)
* minor : fix trailing whitespace
* minor : add space
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Main thing is that the default output filename will take this form
{name}{parameters}{finetune}{version}{encoding}{kind}
In addition this add and remove some entries in the KV store and adds a metadata class with automatic heuristics capability to derive some values based on model card content
* No Change:
- Internal GGUF Spec
- `general.architecture`
- `general.quantization_version`
- `general.alignment`
- `general.file_type`
- General Model Details
- `general.name`
- `general.author`
- `general.version`
- `general.description`
- Licensing details
- `general.license`
- Typically represents the converted GGUF repo (Unless made from scratch)
- `general.url`
- Model Source during conversion
- `general.source.url`
* Removed:
- Model Source during conversion
- `general.source.huggingface.repository`
* Added:
- General Model Details
- `general.organization`
- `general.finetune`
- `general.basename`
- `general.quantized_by`
- `general.size_label`
- Licensing details
- `general.license.name`
- `general.license.link`
- Typically represents the converted GGUF repo (Unless made from scratch)
- `general.doi`
- `general.uuid`
- `general.repo_url`
- Model Source during conversion
- `general.source.doi`
- `general.source.uuid`
- `general.source.repo_url`
- Base Model Source
- `general.base_model.count`
- `general.base_model.{id}.name`
- `general.base_model.{id}.author`
- `general.base_model.{id}.version`
- `general.base_model.{id}.organization`
- `general.base_model.{id}.url` (Model Website/Paper)
- `general.base_model.{id}.doi`
- `general.base_model.{id}.uuid`
- `general.base_model.{id}.repo_url` (Model Source Repository (git/svn/etc...))
- Array based KV stores
- `general.tags`
- `general.languages`
- `general.datasets`
---------
Co-authored-by: compilade <git@compilade.net>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* [CANN] Add Ascend NPU backend
Ascend is a full-stack AI computing infrastructure for industry
applications and services based on Huawei Ascend processors and
software.
CANN (Compute Architecture of Neural Networks), developped by
Huawei, is a heterogeneous computing architecture for AI.
Co-authored-by: wangshuai09 <391746016@qq.com>
* delete trailing whitespaces
* Modify the code based on review comment
* Rename LLAMA_CANN to GGML_CANN
* Make ggml-common.h private
* add ggml_cann prefix for acl funcs
* Add logging for CANN backend
* Delete Trailing whitespace
---------
Co-authored-by: wangshuai09 <391746016@qq.com>
* Update clib.json to point to Cyan4973 original xxhash
Convinced Cyan4973 to add clib.json directly to his repo, so can now point the clib package directly to him now. Previously pointed to my fork with the clib.json package metadata
https://github.com/Cyan4973/xxHash/pull/954
* gguf-hash: readme update to point to Cyan4973 xxHash repo [no ci]
The --help option on export-lora isn't accepted as valid. The help still gets displayed by default, but the script exits with an error message and nonzero status.
The README.md had a stale information. In particular, the --ctx-size
"defaults to 512" confused me and I had to check the code to confirm
this was false. This the server is evolving rapidly, it's probably
better to keep the source of truth at a single place (in the source) and
generate the README.md based on that.
Did:
make llama-server
./llama-server --help > t.txt
vimdiff t.txt examples/server/README.md
I copied the content inside a backquote block. I would have preferred
proper text but it would require a fair amount of surgery to make the
current output compatible with markdown. A follow up could be to
automate this process with a script.
No functional change.
* server : handle content array in chat API
* Update examples/server/utils.hpp
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
---------
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
* Arm AArch64: optimized GEMV and GEMM kernels for q4_0_q8_0, and q8_0_q8_0 quantization
* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions
* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions
* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions
* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions
* Arm AArch64: add copyright claim only to ggml-aarch64.cpp and ggml-aarch64.h files
* Arm AArch64: minor code refactoring for rebase
* Arm AArch64: minor code refactoring for resolving a build issue with cmake
* Arm AArch64: minor code refactoring to split the Q4_0_AARC64 type into three separate types: Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8
* Arm AArch64: minor code change for resolving a build issue with server-windows
* retrigger checks
* Arm AArch64: minor code changes for rebase
* Arm AArch64: minor changes to skip the pr#7433 vec_dot code for arm cpus with SVE VL not equal to 256 bits
* Arm AArch64: remove stale LLAMA_QKK_64 from CMakeLists.txt and delete build.zig
* Arm AArch64: add reference scalar gemm and gemv, and avoid dynamic memory allocations during quantization for Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8
* Arm AArch64: add multithreaded quantization support for the new types: Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8
* Arm AArch64: minor code refactoring
* Arm AArch64: simplify logic for calling gemm and gemv functions in ggml_compute_forward_mul_mat
* Arm AArch64: minimize changes in ggml_compute_forward_mul_mat
* Arm AArch64: minor code refactoring, and add reference scalar code to quantize routines for new quant types
* Arm AArch64: minor code refactoring
* Arm AArch64: minor code refactoring
* Arm AArch64: minor code refactoring
* rebase on the latest master commit 3fd62a6 and adapt to the new directory structure
* Arm AArch64: remove a redundant comment
* Arm AArch64: add pragma in ggml-aarch64.c to turn -Woverlength-strings warning off
* Arm AArch64: use __aarch64__ check to guard 64-bit neon kernels
* Arm AArch64: update docs/build.md README to include compile time flags for buiilding the Q4_0_4_4 quant type
* Adding a simple program to provide a deprecation warning that can exist to help people notice the binary name change from #7809 and migrate to the new filenames.
* Build legacy replacement binaries only if they already exist. Check for their existence every time so that they are not ignored.
* py : type-check all Python scripts with Pyright
* server-tests : use trailing slash in openai base_url
* server-tests : add more type annotations
* server-tests : strip "chat" from base_url in oai_chat_completions
* server-tests : model metadata is a dict
* ci : disable pip cache in type-check workflow
The cache is not shared between branches, and it's 250MB in size,
so it would become quite a big part of the 10GB cache limit of the repo.
* py : fix new type errors from master branch
* tests : fix test-tokenizer-random.py
Apparently, gcc applies optimisations even when pre-processing,
which confuses pycparser.
* ci : only show warnings and errors in python type-check
The "information" level otherwise has entries
from 'examples/pydantic_models_to_grammar.py',
which could be confusing for someone trying to figure out what failed,
considering that these messages can safely be ignored
even though they look like errors.
CLI to hash GGUF files to detect difference on a per model and per tensor level
The hash type we support is:
- `--xxh64`: use xhash 64bit hash mode (default)
- `--sha1`: use sha1
- `--uuid`: use uuid
- `--sha256`: use sha256
While most POSIX systems already have hash checking programs like sha256sum, it
is designed to check entire files. This is not ideal for our purpose if we want
to check for consistency of the tensor data even if the metadata content of the
gguf KV store has been updated.
This program is designed to hash a gguf tensor payload on a 'per tensor layer'
in addition to a 'entire tensor model' hash. The intent is that the entire
tensor layer can be checked first but if there is any detected inconsistencies,
then the per tensor hash can be used to narrow down the specific tensor layer
that has inconsistencies.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This patch replaces an old commad "main" with "llama-cli"
in finetune.sh.
The part that I fixed is comment, so it doesn't change
the script.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
* server: Retrieve prompt template in /props
This PR adds the following:
- Expose the model's Jinja2 prompt template from the model in the /props endpoint.
- Change log-level from Error to Warning for warning about template mismatch.
The front-end stands a better chance of actually executing the Jinja template format correctly. Server is currently just guessing it.
Ideally this should have been inside a JSON block that expose the same key/value pairs as listed during startup in "llm_load_print_meta" function.
* Make string buffer dynamic
* Add doc and better string handling
* Using chat_template naming convention
* Use intermediate vector for string assignment
* Add llama_detokenize():
- Update header files location
- UNKNOWN and CONTROL are 'special pieces'
- Remove space after UNKNOWN and CONTROL
- Refactor llama_token_to_piece()
- Add flag: clean_up_tokenization_spaces
- Symmetric params for llama_tokenize() and llama_detokenize()
* Update and fix tokenizer tests:
- Using llama_detokenize()
- Unexpected vocab type as test fail instead of error
- Useful when automating tests:
- If you don't know in advance the vocab type
- Differenciate other loading errors
- Skip unicode surrogaes and undefined
- Gracefully exit threads
- Using exit() is throwing random exceptions
- Clean old known problematic codepoints
- Minor: confusing hexadecimal codepoint
* Update bruteforce random tests
- Add detokenizer checks
- New generator: ascii_lr_strip
- New generator: apostrophe
- Add more vocabs files
- Detokenize special tokens.
- Replace errors with '\uFFFD' when detokenizing to 'utf-8'
- More edge cases
- Better detokenization results check
* Fix add_space_prefix, set false by default
* Better leading space removal
* Do not remove space when decoding special tokens
* Bugfix: custom regexs splits undefined unicode codepoints
* 'viking' detokenizer clean spaces
* passkey : add short intro to README.md [no-ci]
This commit adds a short introduction to the README.md file in the
examples/passkey directory.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* Update examples/passkey/README.md
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit adds a new option to the tokenize example, --show-count.
When this is set the total number of tokens are printed to stdout.
This was added as an option as I was concerned that there might be
scripts that use the output from this program and it might be better to
not print this information by default.
The motivation for this is that can be useful to find out how many
tokens a file contains, for example when trying to determine prompt
input file sizes for testing.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* llama : add inference support and model types for T5 and FLAN-T5 model families
* llama : add new API functions to support encoder-decoder models: llama_encode(), llama_model_has_encoder(), llama_model_decoder_start_token()
* common, llama-cli, llama-batched : add support for encoder-decoder models
* convert-hf : handle shared token embeddings tensors in T5Model
* convert-hf : add support for SentencePiece BPE tokenizer in T5Model (for Pile-T5 models)
* convert-hf : add MT5ForConditionalGeneration and UMT5ForConditionalGeneration to architectures supported by T5Model
* convert : add t5 tokenizer tests, use "slow" HF tokenizer for t5
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Delete examples/llama.android/llama/CMakeLists.txt
https://github.com/ggerganov/llama.cpp/pull/8145#issuecomment-2194534244
This file is not being used for building on Android. `llama.cpp/examples/llama.android/llama/src/main/cpp/CMakeLists.txt` is being used instead.
* Update CMakeLists.txt
Pick local llama.cpp files instead of fetching content from git
- Path seems to be wrong for the common.h header file in llama-android.cpp file. Fixing the path so the Android Build doesn't fail with the error "There is no file common/common.h"
* clip : suppress unused variable warnings
This commit suppresses unused variable warnings for the variables e in
the catch blocks.
The motivation for this change is to suppress the warnings that are
generated on Windows when using the MSVC compiler. The warnings are
not displayed when using GCC because GCC will mark all catch parameters
as used.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* squash! clip : suppress unused variable warnings
Remove e (/*e*/) instead instead of using GGML_UNUSED.
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* json: default additionalProperty to true
* json: don't force additional props after normal properties!
* json: allow space after enum/const
* json: update pydantic example to set additionalProperties: false
* json: prevent additional props to redefine a typed prop
* port not_strings to python, add trailing space
* fix not_strings & port to js+py
* Update json-schema-to-grammar.cpp
* fix _not_strings for substring overlaps
* json: fix additionalProperties default, uncomment tests
* json: add integ. test case for additionalProperties
* json: nit: simplify condition
* reformat grammar integ tests w/ R"""()""" strings where there's escapes
* update # tokens in server test: consts can now have trailing space
* llama : return nullptr from llama_grammar_init
This commit updates llama_grammar_init to return nullptr instead of
throwing an exception.
The motivation for this is that this function is declared inside an
extern "C" block and is intended/may be used from C code which will not
be able to handle exceptions thrown, and results in undefined behavior.
On Windows and using MSVC the following warning is currently generated:
```console
C:\llama.cpp\llama.cpp(13998,1): warning C4297: 'llama_grammar_init':
function assumed not to throw an exception but does
C:\llama.cpp\llama.cpp(13998,1): message :
__declspec(nothrow), throw(), noexcept(true), or noexcept was specified
on the function
```
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* squash! llama : return nullptr from llama_grammar_init
Add checks for nullptr when calling llama_grammar_init.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Clint Herron <hanclinto@gmail.com>
* SimpleChat: Allow for chat req bool options to be user controlled
* SimpleChat: Allow user to control cache_prompt flag in request
* SimpleChat: Add sample GUI images to readme file
Show the chat screen and the settings screen
* SimpleChat:Readme: Add quickstart block, title to image, cleanup
* SimpleChat: RePosition contents of the Info and Settings UI
Make it more logically structured and flow through.
* SimpleChat: Rename to apiRequestOptions from chatRequestOptions
So that it is not wrongly assumed that these request options are
used only for chat/completions endpoint. Rather these are used
for both the end points, so rename to match semantic better.
* SimpleChat: Update image included with readme wrt settings ui
* SimpleChat:ReadMe: Switch to webp screen image to reduce size
* add parameters for embeddings
--embd-normalize
--embd-output-format
--embd-separator
description in the README.md
* Update README.md
fix tipo
* Trailing whitespace
* fix json generation, use " not '
* fix merge master
* fix code formating
group of parameters // embedding
print usage for embedding parameters
---------
Co-authored-by: Brian <mofosyne@gmail.com>
* create append_pooling operation; allow to specify attention_type; add last token pooling; update examples
* find result_norm/result_embd tensors properly; update output allocation logic
* only use embd output for pooling_type NONE
* get rid of old causal_attn accessor
* take out attention_type; add in llama_set_embeddings
* bypass logits when doing non-NONE pooling
* cuda sqrt support
* enable cuda in pca
* fix comments in pca
* add test
* add sqrt to ggml_backend_cuda_supports_op
* fix test
* new line
* Use F32 sqrtf instead of F64 sqrt
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* add control-vector-generator
* calc diff
* add comments
* proof-of-concept stdlib implementation
Implements PCA and file writing using mostly standard libraries. The output is recognized as a functional control vector, but outputs gibberish.
* param parsing, refactor, comments
Added basic command-line parameters for outfile and one each positive/negative prompt.
Refactored some messy code in PCA computation and GGUF exporting.
Left a bunch of comments regarding further work needed.
* example template completions
Implements an example template set built from the positive/negative prompts like the control vector Python implementation.
* add multi prompts, multi-thread for PCA
* fix mem error
* add debugs
* fix matrix transpose multiplication
you have got to be kidding me
* preliminary template/multiprompt support
model is running out of context and that ought to be fixed (segfaulting) but other than that it looks goodish
* fix zero output & param parsing, functional templating
fixed a bug where the output file had no tensor data/was all zero
fixed a bug where single hyphen flags were not being correctly parsed
implements creation of templated prompts from input (still need to adapt based on model)
* fix square_diff matmul index range and CRLF->LF line endings
fixed a logic error where square_diff would not multiply all rows
fixed a formatting error where the provided completions.txt had CRLF line endings
* add command-line args for num threads, num completions file lines, always reload model
refactored a few things and did what the commit message says on the tin
* code aestheticization
* fix compiler warnings
* in-series multithreading for prompt embedding?
added commented-out code to attempt to start implementing mutlithreading for embedding in main
* remove unnecessary multithreading
* interim fix memory leak
* translated everything but PCA (I think)
* tentatively translate the rest
* fix ggml errors and make new ones
at least it compiles and runs
* fix cb_eval
* temporary commit while I move dev environments
it finally outputs a functioning control vector - "functioning" in the sense that it can be loaded and it clearly has the right idea, but makes the model incoherent
* update debug statements
* pre-tokenize so we can allocate correct memory to ctx_diffs_wrapped
* update comments
* (wip) refactor
* clean up PCA ggml implementation
* fix shape of v_diff_original
* add n_batch for pca
* working version
* remember to copy back the last_eigenvector
* fix n_completions
* bring back n_completions
* default n_pca_batch to 20
* fix macos build
* add to makefile all targets
* use ggml_format_name
* add readme
* fix .editorconfig
* use ggml_backend_tensor_copy
* attemp to fix compile problem on mac
* fix compile warn
* reuse allocr
* move param parser to common
* better error handling
* clean up a bit
* add print_usage
* shorten help msg
* beautify help msg
* escape prompt by default
* change compile target to llama-cvector-generator
* typo
* disable GPU for PCA
* code style
---------
Co-authored-by: Christian Zhou-Zheng <christianzhouzheng@gmail.com>
* move BLAS to a separate backend
* rename GGML_USE_OPENBLAS to GGML_USE_BLAS
* alloc : reuse same buffer when the same buffer type if used multiple times
* set number of threads automatically for openblas and blis
* sched : print assignments when GGML_SCHED_DEBUG env variable is set
* sched : allow ops with weights on an incompatible buffer type
This will cause the weight to be copied to a backend that supports the
op, which is very costly. The weight should have been stored in a buffer
of a backend that can run the op, but llama.cpp cannot do this
automatically at the moment.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : Smart selection of available slot using Longest Common Substring
* add usage
* remove trailing whitespaces
* Use Longest Common Prefix (LCP) instead of LCS
* Rename argument
* avoid to get prompt in infill mode and embedding mode
* remove embedding mode
* refactor format
---------
Co-authored-by: wudexiang <wudexiang@bytedance.com>
* common : gpt_params_parse do not print usage
* common : rework usage print (wip)
* common : valign
* common : rework print_usage
* infill : remove cfg support
* common : reorder args
* server : deduplicate parameters
ggml-ci
* common : add missing header
ggml-ci
* common : remote --random-prompt usages
ggml-ci
* examples : migrate to gpt_params
ggml-ci
* batched-bench : migrate to gpt_params
* retrieval : migrate to gpt_params
* common : change defaults for escape and n_ctx
* common : remove chatml and instruct params
ggml-ci
* common : passkey use gpt_params
* ic
* migrate my eary work
* add the belonging stuff: css,favicon etc
* de prompts
* chore: Update HTML meta tags in index.html file
* add api-key css classes
* some necessary fixes
* Add API key CSS classes and update styling in style.css
* clean the code
* move API to the top, rearrange param sliders. update css
* add tooltips to the parameters with comprehensible explanations
* fix FloatField and BoolField tooltips
* fix grammar field width
* use template literales for promptFormats.js
* update const ModelGenerationInfo
* remove ms per token, since not relevant for most webui users and use cases
* add phi-3 prompt template
* add phi3 to dropdown
* add css class
* update forgotten css theme
* add user message suffix
* fix chatml & add llama3 format
* fix llama3 prompt template
* more prompt format fixes
* add more comon stop tokens
* add missing char
* do not separate with new line or comma
* move prompt style
* add hacky llama2 prompt solution, reduce redundancy in promptFormats.js
* fix toggle state localstorage
* add cmd-r prompt et reduce redundancy
* set default prompt to empty
* move files, clean code
* fix css path
* add a button to the new ui
* move new ui to "/public" due to otherwise problematic CORS behaviour
* include new ui in cpp
* fix wrong link to old ui
* renaming to ensure consistency
* fix typos "prompt-format" -> "prompt-formats"
* use correct indent
* add new ui files to makefile
* fix typo
* SimpleChat:DU:BringIn local helper js modules using importmap
Use it to bring in a simple trim garbage at end logic, which is
used to trim received response.
Also given that importmap assumes esm / standard js modules, so
also global variables arent implicitly available outside the
modules. So add it has a member of document for now
* SimpleChat:DU: Add trim garbage at end in loop helper
* SimpleChat:DU:TrimGarbage if unable try skip char and retry
* SimpleChat:DU: Try trim using histogram based info
TODO: May have to add max number of uniq chars in histogram at
end of learning phase.
* SimpleChat:DU: Switch trim garbage hist based to maxUniq simple
Instead of blindly building histogram for specified substring
length, and then checking if any new char within specified min
garbage length limit, NOW exit learn state when specified maxUniq
chars are found. Inturn there should be no new chars with in
the specified min garbage length required limit.
TODO: Need to track char classes like alphabets, numerals and
special/other chars.
* SimpleChat:DU: Bring in maxType to the mix along with maxUniq
Allow for more uniq chars, but then ensure that a given type of
char ie numerals or alphabets or other types dont cross the
specified maxType limit. This allows intermixed text garbage
to be identified and trimmed.
* SimpleChat:DU: Cleanup debug log messages
* SimpleChat:UI: Move html ui base helpers into its own module
* SimpleChat:DU:Avoid setting frequence/Presence penalty
Some models like llama3 found to try to be over intelligent by
repeating garbage still, but by tweaking the garbage a bit so that
it is not exactly same. So avoid setting these penalties and let
the model's default behaviour work out, as is.
Also the simple minded histogram based garbage trimming from end,
works to an extent, when the garbage is more predictable and
repeatative.
* SimpleChat:UI: Add and use a para-create-append helper
Also update the config params dump to indicate that now one needs
to use document to get hold of gMe global object, this is bcas of
moving to module type js.
Also add ui.mjs to importmap
* SimpleChat:UI: Helper to create bool button and use it wrt settings
* SimpleChat:UI: Add Select helper and use it wrt ChatHistoryInCtxt
* SimpleChat:UI:Select: dict-name-value, value wrt default, change
Take a dict/object of name-value pairs instead of just names.
Inturn specify the actual value wrt default, rather than the
string representing that value.
Trap the needed change event rather than click wrt select.
* SimpleChat:UI: Add Div wrapped label+element helpers
Move settings related elements to use the new div wrapped ones.
* SimpleChat:UI:Add settings button and bring in settings ui
* SimpleChat:UI:Settings make boolean button text show meaning
* SimpleChat: Update a bit wrt readme and notes in du
* SimpleChat: GarbageTrim enable/disable, show trimmed part ifany
* SimpleChat: highlight trim, garbage trimming bitmore aggressive
Make it easy for end user to identified the trimmed text.
Make garbage trimming logic, consider a longer repeat garbage
substring.
* SimpleChat: Cleanup a bit wrt Api end point related flow
Consolidate many of the Api end point related basic meta data into
ApiEP class.
Remove the hardcoded ApiEP/Mode settings from html+js, instead use
the generic select helper logic, inturn in the settings block.
Move helper to generate the appropriate request json string based
on ApiEP into SimpleChat class itself.
* SimpleChat:Move extracting assistant response to SimpleChat class
so also the trimming of garbage.
* SimpleChat:DU: Bring in both trim garbage logics to try trim
* SimpleChat: Cleanup readme a bit, add one more chathistory length
* SimpleChat:Stream:Initial handshake skeleton
Parse the got stream responses and try extract the data from it.
It allows for a part read to get a single data line or multiple
data line. Inturn extract the json body and inturn the delta
content/message in it.
* SimpleChat: Move handling oneshot mode server response
Move handling of the oneshot mode server response into SimpleChat.
Also add plumbing for moving multipart server response into same.
* SimpleChat: Move multi part server response handling in
* SimpleChat: Add MultiPart Response handling, common trimming
Add logic to call into multipart/stream server response handling.
Move trimming of garbage at the end into the common handle_response
helper.
Add new global flag to control between oneshot and multipart/stream
mode of fetching response. Allow same to be controlled by user.
If in multipart/stream mode, send the stream flag to the server.
* SimpleChat: show streamed generative text as it becomes available
Now that the extracting of streamed generated text is implemented,
add logic to show the same on the screen.
* SimpleChat:DU: Add NewLines helper class
To work with an array of new lines. Allow adding, appending,
shifting, ...
* SimpleChat:DU: Make NewLines shift more robust and flexible
* SimpleChat:HandleResponseMultiPart using NewLines helper
Make handle_response_multipart logic better and cleaner. Now it
allows for working with the situation, where the delta data line
got from server in stream mode, could be split up when recving,
but still the logic will handle it appropriately.
ALERT: Rather except (for now) for last data line wrt a request's
response.
* SimpleChat: Disable console debug by default by making it dummy
Parallely save a reference to the original func.
* SimpleChat:MultiPart/Stream flow cleanup
Dont try utf8-decode and newlines-add_append if no data to work on.
If there is no more data to get (ie done is set), then let NewLines
instance return line without newline at end, So that we dont miss
out on any last-data-line without newline kind of scenario.
Pass stream flag wrt utf-8 decode, so that if any multi-byte char
is only partly present in the passed buffer, it can be accounted
for along with subsequent buffer. At sametime, bcas of utf-8's
characteristics there shouldnt be any unaccounted bytes at end,
for valid block of utf8 data split across chunks, so not bothering
calling with stream set to false at end. LATER: Look at TextDecoder's
implementation, for any over intelligence, it may be doing..
If needed, one can use done flag to account wrt both cases.
* SimpleChat: Move baseUrl to Me and inturn gMe
This should allow easy updating of the base url at runtime by the
end user.
* SimpleChat:UI: Add input element helper
* SimpleChat: Add support for changing the base url
This ensures that if the user is running the server with a
different port or wants to try connect to server on a different
machine, then this can be used.
* SimpleChat: Move request headers into Me and gMe
Inturn allow Authorization to be sent, if not empty.
* SimpleChat: Rather need to use append to insert headers
* SimpleChat: Allow Authorization header to be set by end user
* SimpleChat:UI+: Return div and element wrt creatediv helpers
use it to set placeholder wrt Authorization header.
Also fix copy-paste oversight.
* SimpleChat: readme wrt authorization, maybe minimal openai testing
* SimpleChat: model request field for openai/equivalent compat
May help testing with openai/equivalent web services, if they
require this field.
* SimpleChat: readme stream-utf-8 trim-english deps, exception2error
* Readme: Add a entry for simplechat in the http server section
* SimpleChat:WIP:Collate internally, Stream mode Trap exceptions
This can help ensure that data fetched till that point, can be
made use of, rather than losing it.
On some platforms, the time taken wrt generating a long response,
may lead to the network connection being broken when it enters
some user-no-interaction related power saving mode.
* SimpleChat:theResp-origMsg: Undo a prev change to fix non trim
When the response handling was moved into SimpleChat, I had changed
a flow bit unnecessarily and carelessly, which resulted in the non
trim flow, missing out on retaining the ai assistant response.
This has been fixed now.
* SimpleChat: Save message internally in handle_response itself
This ensures that throwing the caught exception again for higher
up logic, doesnt lose the response collated till that time.
Go through theResp.assistant in catch block, just to keep simple
consistency wrt backtracing just in case.
Update the readme file.
* SimpleChat:Cleanup: Add spacing wrt shown req-options
* SimpleChat:UI: CreateDiv Divs map to GridX2 class
This allows the settings ui to be cleaner structured.
* SimpleChat: Show Non SettingsUI config field by default
* SimpleChat: Allow for multiline system prompt
Convert SystemPrompt into a textarea with 2 rows. Reduce
user-input-textarea to 2 rows from 3, so that overall
vertical space usage remains same.
Shorten usage messages a bit, cleanup to sync with settings ui.
* SimpleChat: Add basic skeleton for saving and loading chat
Inturn when ever a chat message (system/user/model) is added,
the chat will be saved into browser's localStorage.
* SimpleChat:ODS: Add a prefix to chatid wrt ondiskstorage key
* SimpleChat:ODS:WIP:TMP: Add UI to load previously saved chat
This is a temporary flow
* SimpleChat:ODS:Move restore/load saved chat btn setup to Me
This also allows being able to set the common system prompt
ui element to loaded chat's system prompt.
* SimpleChat:Readme updated wrt save and restore chat session info
* SimpleChat:Show chat session restore button, only if saved session
* SimpleChat: AutoCreate ChatRequestOptions settings to an extent
* SimpleChat: Update main README wrt usage with server
