* Fix ffn_down quantization mix for MoE models
In #4872 I did not consider the part where every third
tensor is quantized with more bits. Fir MoE this leads to tensors
of the same layer being quantized with different number of bits,
which is not considered as a possibility in the inference implementation
(it is assumed all experts use the same quantization).
* Fix the fix
* Review suggestion
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* examples : save-load-state: save only required state
* llama : only reserve n_vocab * n_batch at most for logits
llama_decode asserts that only n_batch tokens are passed each call, and
n_ctx is expected to be bigger than n_batch.
* llama : always reserve n_vocab * n_batch for logits
llama_context de-serialization breaks if the contexts have differing
capacity for logits and llama_decode will at maximum resize to
n_vocab * n_batch.
* llama : only save and restore used logits
for batch sizes of 512 this reduces save state in the best case by
around 62 MB, which can be a lot if planning to save on each message
to allow regenerating messages.
* llama : use ostringstream and istringstream for save and load
* llama : serialize rng into minimum amount of space required
* llama : break session version due to serialization changes
* llama : ggml-backend integration
* ggml-backend : add names to buffers
* fix unmap after loading
* batched-bench : add tensor_split param
* llama : check for null tensor_split
* ggml-backend : increase GGML_MAX_BACKENDS
* improve graph splitting, partial fix for --no-kv-offload
* cuda : add ggml-backend split buffer support
* cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available)
* ggml : fix null backend dereference (#4807)
* ggml : fix null backend dereference
* ggml : also check ggml_backend_is_cpu
* test-backend-ops : check buffer allocation failures
* llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row)
* ggml : fix mul_mat_id work size
* llama : rewrite session kv load/set without graphs
* minor
* llama : only initialize used backends, free backends on context free
* llama : abort ctx if cuda backend init fails
* llama : rewrite lora with ggml-backend and compute on CPU
ggml-ci
* llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer
* opencl : add ggml-backend buffer type
* cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf)
* llama : on Metal, by default offload the full model
ggml-ci
* metal : page align the data ptr (#4854)
* Apply suggestions from code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : fix split buffer free
* address review comments
* llama-bench : add split-mode parameter
* fix whitespace
* opencl : fix double initialization
* server : add --split-mode parameter
* use async copy and compute to improve multi-gpu performance
ggml-ci
* use async memcpys to copy the graph outputs to the CPU
* fix opencl
* use a host buffer for the cpu compute buffer for faster copies to the gpu
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Restore intended k-quants quantization mixes for MoE models
* Update Q2_K_S values in the quantize tool
Still using LLaMA-v1 PPL values in the quant description
today does not make much sense. But let's leave this update
for another PR.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* iq2_xs: basics
* iq2_xs: this should have been in the basics
* iq2_xs: CUDA and scalar CPU works
* iq2_xs: WIP Metal
* iq2_xs: Metal now works
* iq2_xs: working, but dog slow, ARM_NEON dot product
* iq2_xs: better ARM_NEON dot product
We are now at 19.5 t/s for TG-128 and 61 t/s for PP-512 when
running on the CPU.
* iq2_xs: AVX2 dot product - 19.5 t/s
* iq2_xs: faster AVX2 dit product
21.4 t/s for TG-128, 59.2 t/s for PP-512.
The latter is 2x compared to the previous version.
* iq2_xs: had forgotten to delete iq2-data.h
* Add llama enum for IQ2_XS
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This update categorizes models with 24 layers as MODEL_1B, ensuring compatibility with different Phi model variants without impacting existing Phi-2 model functionality.
* iq2_xxs: basics
* iq2_xxs: scalar and AVX2 dot products
Needed to change Q8_K to have quants in the -127...127 range,
else the IQ2_XXS AVX implementation becomes very awkward.
The alternative would have been to use Q8_0 instead. Perhaps
I'll change later, for now this is what we have.
* iq2_xxs: ARM_NEON dot product
Somehow strangely slow (112 ms/token).
* iq2_xxs: WIP Metal
Dequantize works, something is still wrong with the
dot product.
* iq2_xxs: Metal dot product now works
We have
PP-512 = 475 t/s
TG-128 = 47.3 t/s
Not the greatest performance, but not complete garbage either.
* iq2_xxs: slighty faster dot product
TG-128 is now 48.4 t/s
* iq2_xxs: slighty faster dot product
TG-128 is now 50.9 t/s
* iq2_xxs: even faster Metal dot product
TG-128 is now 54.1 t/s.
Strangely enough, putting the signs lookup table
into shared memory has a bigger impact than the
grid values being in shared memory.
* iq2_xxs: dequantize CUDA kernel - fix conflict with master
* iq2_xxs: quantized CUDA dot product (MMVQ)
We get TG-128 = 153.1 t/s
* iq2_xxs: slightly faster CUDA dot product
TG-128 is now at 155.1 t/s.
* iq2_xxs: add to llama ftype enum
* iq2_xxs: fix MoE on Metal
* Fix missing MMQ ops when on hipBLAS
I had put the ggml_supports_mmq call at the wrong place.
* Fix bug in qequantize_row_iq2_xxs
The 0.25f factor was missing.
Great detective work by @ggerganov!
* Fixing tests
* PR suggestion
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* move Dynatemp changes to new branch
* fix float header
* Properly reintroduce variable expert count
Controllable through experts.txt
* first pass at DynaTemp UI
Checkbox partial implemented, Min and Max Temp implemented
* DynaTemp UI Checkbox
Trigger DynaTemp on checkbox
* DynaTemp UI checkbox edition
Hell Yeah! DynaTemp!
* Remove greedy dynatemp
* Fix race condition caused by debug print
* Fixed broken presets and miro
Fixes broken presets and mirostat
* Remove debug function + HHI temp
Also removed unnecessary softmax double precision
* Fix whitespace (?) for generate function
* epic upstream renaming scheme fix
* fix stupid indents
* Other cleanup
Reintroduce unused rep pen function, move temp functions first before entropy dynamic temp
* Slight indent fix
* revert batch pyinstaller maker to mainline
and also delete experts.txt since adjustable routing is also being removed for the PR
* compact dynatemp into a single value dynatemp_range. This is a float which represents the allowed deviation from the min and max temperature when using dynatemp. Thus, if we want a value of dynatemp_min=0.3, dynatemp_max=0.5, then we would simply set temperature=0.4 and dynatemp_range=0.1. Functionally dynatemp would operate the same, but it would simplify usage and make it a single easy to adjust value.
---------
Co-authored-by: Alexander Abushady <aabushady214@gmail.com>
Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
* Add n_key_dim and n_value_dim
Some models use values that are not derived from `n_embd`.
Also remove `n_embd_head` and `n_embd_gqa` because it is not clear
which "head" is referred to (key or value).
Fix issue #4648.
* Fix `llm_build_kqv` to use `n_value_gqa`
* Rebase
* Rename variables
* Fix llm_build_kqv to be more generic wrt n_embd_head_k
* Update default values for n_embd_head_k and n_embd_head_v
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fix llm_load_tensors: the asserts were not backcompat
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: add avx_vnni based on intel documents
* ggml: add avx vnni based on intel document
* llama: add avx vnni information display
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* docs: add more details about using oneMKL and oneAPI for intel processors
* Update ggml.c
Fix indentation upgate
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cuda : fix vmm pool with multi GPU
* hip
* use recommended granularity instead of minimum
* better error checking
* fix mixtral
* use cudaMemcpy3DPeerAsync
* use cuda_pool_alloc in ggml_cuda_op_mul_mat
* consolidate error checking in ggml_cuda_set_device
* remove unnecessary inlines
ggml-ci
* style fixes
* only use vmm for the main device
* fix scratch buffer size, re-enable vmm pool for all devices
* remove unnecessary check id != g_main_device
* cuda : improve cuda pool efficiency using virtual memory
* fix mixtral
* fix cmake build
* check for vmm support, disable for hip
ggml-ci
* fix hip build
* clarify granularity
* move all caps to g_device_caps
* refactor error checking
* add cuda_pool_alloc, refactor most pool allocations
ggml-ci
* fix hip build
* CUBLAS_TF32_TENSOR_OP_MATH is not a macro
* more hip crap
* llama : fix msvc warnings
* ggml : fix msvc warnings
* minor
* minor
* cuda : fallback to CPU on host buffer alloc fail
* Update ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ensure allocations are always aligned
* act_size -> actual_size
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* llama : Add ability to cancel model load
Updated llama_progress_callback so that if it returns false, the model
loading is aborted.
* llama : Add test for model load cancellation
* Fix bool return in llama_model_load, remove std::ignore use
* Update llama.cpp
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
* Fail test if model file is missing
* Revert "Fail test if model file is missing"
This reverts commit 32ebd525bf7e5a87ee8a3dbaab3d92ce79fbf23d.
* Add test-model-load-cancel to Makefile
* Revert "Revert "Fail test if model file is missing""
This reverts commit 2796953257ee5383fa7c8fe8fa8fc888c048fb0b.
* Simplify .gitignore for tests, clang-tidy fixes
* Label all ctest tests
* ci : ctest uses -L main
* Attempt at writing ctest_with_model
* ci : get ci/run.sh working with test-model-load-cancel
* ci : restrict .github/workflows/build.yml ctest to -L main
* update requirements.txt
* Disable test-model-load-cancel in make
* Remove venv before creation
* Restructure requirements.txt
Top-level now imports the specific additional requirements for each
python file. Using `pip install -r requirements.txt` will fail if
versions become mismatched in the per-file requirements.
* Make per-python-script requirements work alone
This doesn't break the main requirements.txt.
* Add comment
* Add convert-persimmon-to-gguf.py to new requirements.txt scheme
* Add check-requirements.sh script and GitHub workflow
* Remove shellcheck installation step from workflow
* Add nocleanup special arg
* Fix merge
see: https://github.com/ggerganov/llama.cpp/pull/4462#discussion_r1434593573
* reset to upstream/master
* Redo changes for cancelling model load
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
