* 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 32ebd525bf.
* Add test-model-load-cancel to Makefile
* Revert "Revert "Fail test if model file is missing""
This reverts commit 2796953257.
* 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>
* llama : initial ggml-backend integration
* add ggml-metal
* cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST
access all tensor data with ggml_backend_tensor_get/set
* add ggml_backend_buffer_clear
zero-init KV cache buffer
* add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data
* disable gpu backends with ngl 0
* more accurate mlock
* unmap offloaded part of the model
* use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap
* update quantize and lora
* update session copy/set to use ggml-backend
ggml-ci
* use posix_fadvise instead of posix_fadvise64
* ggml_backend_alloc_ctx_tensors_from_buft : remove old print
* llama_mmap::align_offset : use pointers instead of references for out parameters
* restore progress_callback behavior
* move final progress_callback call to load_all_data
* cuda : fix fprintf format string (minor)
* do not offload scales
* llama_mmap : avoid unmapping the same fragments again in the destructor
* remove unnecessary unmap
* metal : add default log function that prints to stderr, cleanup code
ggml-ci
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* allowed getting n_batch from llama_context in c api
* changed to use `uint32_t` instead of `int`
* changed to use `uint32_t` instead of `int` in `llama_n_ctx`
* Update llama.h
---------
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>
* convert : support Mixtral as LLAMA arch
* convert : fix n_ff typo
* llama : model loading
* ggml : sync latest ggml_mul_mat_id
* llama : update graph to support MoE
* llama : fix cur -> cur_expert
* llama : first working version
* llama : fix expert weighting in the FFN
* ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only)
* ggml : add n_as argument to ggml_mul_mat_id
* ggml : fix ggml_get_rows to take into account ne02 / ne11
* metal : add more general support for ggml_get_rows + tests
* llama : add basic support for offloading moe with CUDA
* metal : add/mul/div use general kernel when src1 not cont
* metal : reduce the kernel launches for ggml_mul_mat_id
* ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D
* ggml : update get_rows f16 and q
* cuda : support non-contiguous src1 in get_rows
* llama : offload missing ffn_moe_silu
* metal : fix ggml_get_rows to work with non-cont src1
* metal : add indirect mat-vec kernels for all quantization types
* llama : do not quantize expert gating tensors
* llama : add n_expert and n_expert_used to hparams + change quants
* test-backend-ops : add moe test
* cuda : fix get_rows when ncols is odd
* convert : determine n_ctx correctly
* metal : fix ggml_mul_mat_id for F32
* test-backend-ops : make experts more evenly probable (test_moe)
* test-backend-ops : cleanup, add moe test for batches
* test-backend-ops : add cpy from f32 -> all types test
* test-backend-ops : fix dequantize block offset
* llama : fix hard-coded number of experts
* test-backend-ops : simplify and disable slow tests to avoid CI timeout
* test-backend-ops : disable MOE test with thread sanitizer
* cuda : fix mul_mat_id with multi gpu
* convert : use 1e6 rope_freq_base for mixtral
* convert : fix style
* convert : support safetensors format
* gguf-py : bump version
* metal : add cpy f16 -> f32 kernel
* metal : fix binary ops for ne10 % 4 != 0
* test-backend-ops : add one more sum_rows test
* ggml : do not use BLAS with ggml_mul_mat_id
* convert-hf : support for mixtral-instruct (#4428)
* convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct
* convert : use sentencepiece tokenizer for Mixtral-instruct
* convert : make flake8 happy
* metal : fix soft_max kernels
ref: 1914017863
* metal : limit kernels to not use more than the allowed threads
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Radek Pilar <github@mrkva.eu>
* reserve space for codepoints
* improvement for the appended 0
* used precomputed token text for grammar sample
* reserve canidates_decoded
* reserve canidates_grammar
* remove candidates_decoded
* Revert "remove candidates_decoded"
This reverts commit 3773328080.
* changed decode_utf8 to take src by ref
* feat: Allow overriding GGUF metadata when loading model
* Fix the one time GCC is stricter than clang about something
* Step1
* Refactor... basically everything!
* Nuke obsolete GetArrayLen struct
* simplify std::string specialization
* Various cleanups
Add informational output when overrides are applied
Warn user when an override with the wrong type is specified
* Fix broken logic for parsing bool KV overrides
Fix issue where overrides didn't apply when key missing in GGUF metadata
Resolve merge changes
* llama : rearrange model params
* Update new GET_KEY call
Add note that metadata KV overrides aren't reflected in initial metadata KV info dump
---------
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@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
* cmake : fix joining of REAL_GIT_DIR
* fix includes with help from include-what-you-use
* make : remove unneeded deps and add test-rope target
* fix C includes in C++ source files
* Revert "fix includes with help from include-what-you-use"
This reverts commit 635e9fadfd.
* llama: fix alignment of general.name in print meta
This commit fixes the alignment of the general.name field in the
llm_load_print_meta function.
Currently the output looks like this:
```console
llm_load_print_meta: model ftype = mostly Q4_0
llm_load_print_meta: model params = 13.02 B
llm_load_print_meta: model size = 6.86 GiB (4.53 BPW)
llm_load_print_meta: general.name = LLaMA v2
```
And with this commit it looks like this:
```console
llm_load_print_meta: model ftype = mostly Q4_0
llm_load_print_meta: model params = 13.02 B
llm_load_print_meta: model size = 6.86 GiB (4.53 BPW)
llm_load_print_meta: general.name = LLaMA v2
```
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* llama: fix alignment of special tokens
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Typical sampling was broken because after copying new_candidates into canditates, the "sorted" bool is left at "true", but the new data is no longer sorted according to probability. Patch to set "sorted" to false.
Test: Generating with temp=0.0001 (approx. argmax) should generate the same sequence at typical>=1.0 and typical=0.9999 (approx. disabled, but enters the typical sampling codepath).
* ggml : use blas even if src0 is not F32
* llama : use n_threads_batch only when n_tokens >= 32
ggml-ci
* llama : revert n_threads_batch logic
ggml-ci
* 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>
* 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
* 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
* Allow quantizing k-quants to fall back when tensor size incompatible
* quantizing: Add warning when tensors were incompatible with k-quants
Clean up k-quants state passing a bit
* cuda : prints wip
* cuda : new cublas gemm branch for multi-batch quantized src0
* cuda : add F32 sgemm branch
* cuda : fine-tune >= VOLTA params + use MMQ only for small batches
* cuda : remove duplicated cuBLAS GEMM code
* cuda : add CUDA_USE_TENSOR_CORES and GGML_CUDA_FORCE_MMQ macros
* build : add compile option to force use of MMQ kernels
* added `llama_model_token_*` variants to all the `llama_token_*` functions.
* added `LLAMA_API`
* formatting
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* removed old `llama_token` functions
* changed 3 more functions to take in model
- `llama_token_get_text`
- `llama_token_get_score`
- `llama_token_get_type`
* added back docs
* fixed main.cpp
* changed token functions to use new model variants
* changed token functions to use new model variants
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add validation for special token ids to llama.cpp
Small optimization for llama_byte_to_token SPM mode
* Fix BPE newline check, only I could break something so simple
* Killll meeeeee
* Account for GGUF_KEY_KEY only setting when the key exists
* Minor code cleanups.
* Fix convert.py error msg when added tokens are out of range
* Make gguf SpecialVocab vocab size-aware
Update conversion scripts accordingly
* Avoid a string copy
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Rewrite special token handling from #1931
* shorten param name, add st verification by type
* use offsets instead of copy by substr
* formatting, remove copying iterator on delete
* llama : normalize code-style
* swift fix
* print pfx/sfx if verb, main: split pfx input sfx
* dont add space when using special tokens
* minor : comment + spacing
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit removes `n_threads` from the `llama_decode_internal`
functions doc comment as it does not exist anymore.
It looks like this parameter was removed in
Commit 16bc66d947 ("llama.cpp : split
llama_context_params into model and context params").
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* CUDA: added support for ggml_clamp (see also: https://github.com/ggerganov/ggml/issues/545)
* mpt : added an implementation based (mostly) on falcon integration, modified with deltas from ggml/examples/mpt
* mpt : protect against "clip_qkv": null in mpt-7b
* mpt : quick fix to avoid "Strange model" warning when quantizing MPT models
* mpt : addendum to changeset:84e30e8 - leave parameter clamp_kqv out from metadata rather than use 0.0 to indicate "no clamping" (more compliant with the current GGUF spec?)
* mpt : standardized all tensor names to follow GGUF spec
* mpt : addendum to changeset:1be89c40 - use "req" parameter of GGUF_GET_KEY macro instead of duplicate code
* mpt : fixed comment s/gptneox/mpt/
* mpt : remove tabs, trailing whitespace
* mpt : removed ne01 + n_past == ne00 assertion from alibi (cuda/f32) and rope_shift from build_mpt
* mpt : updated convert-mpt-hf-to-gguf.py to reflect changes made to convert-gptneox-hf-to-gguf.py in pr:3252
* comment out n_past instead of marking it unused
* mpt : removed hardcoded +178 from convert script in favor of utilizing hparams["vocab_size"]
* mpt : remove unused tokenizer_json in convert script
* ggml : remove obsolete n_past assert in ggml_alibi
* llama : print clam_kqv and max_alibi_bias hparams
---------
Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* kv cache slot search improvements
* Use n_ctx in kv find slot for consistency
* Ensure kv cache head points to a valid slot in llama_decode internal
* Add some comments to prevent dumb people (like me) from getting confused.
