* model: add support for extra bufs for all devices
* hexagon: add experimental ggml-hexagon backend for the Hexagon NPU
This commit introduces a new experimental backend `ggml-hexagon` with support for the Hexagon NPU.
Highlights:
- Supports Hexagon versions: v73, v75, v79, and v81
- Targets Android devices based on Snapdragon SoCs: Gen3, 8-Elite, and 8-Elite Gen5
- Supports Q4_0, Q8_0, MXFP4, and FP32 data types
- Implements core LLM ops: MUL_MAT/MUL_MAT_ID, ADD/SUB/MUL/ADD_ID, RMS_NORM, ROPE, GLU/SWIGLU, SOFTMAX
**Note:** This backend is experimental and may exhibit instability or limited performance across supported devices.
It is intended for early testing and feedback from llama.cpp/ggml developer and user community.
Co-Authored-By: Rajdeep Ganguly <rganguly@qti.qualcomm.com>
Co-Authored-By: Todor Boinovski <todorb@qti.qualcomm.com>
* hexagon: fix format checker errors
* hexagon: update readme and cmake presets
* ci: add android-ndk-build jobs that build plain ARM64 and Snapdragon versions
* hexagon: add simple graph optimizer for stacking MUL_MAT ops with the same input
* hexagon: move ADB helper scripts into scripts/snapdragon/adb
* hexagon: replace all f/printfs with GGML_LOG_...
* readme: add hexagon to the list supported backends
* hexagon: stack malmuts with quantized inputs only
* hexagon: add TODO for fixing issues in hexagon_graph_optimize
* hexagon: update to hex-sdk 6.4.0 and add scripts for running on QDC
* scripts: fix lint errors
* scripts: update qdc pytest script to make linter happy
* hexagon: add reduce sum in fp32
* hexagon: reduce number of vector stores in matmul output
* hexagon: remove the need for vdelta in reduce-multiply-x8
* hexagon: consistent use of reduce_sum_fp32 for row_sums
* hexagon: some more matmul optimizations and comments
Optimize cases where tensor dims are not multiple of 1024 (e.g in Qwen models).
We've handled those cases already but at a higher overhead.
* hexagon: update cmake presets
* hexagon: add OPMASK support for run-bench.sh wrapper
* hexagon: update to use GGML_BACKEND_API
* hexagon: remove unused logic for setting tensor flags for the views
* hexagon: add asserts to set/get_tensor to make sure we handle complete tensors
Same asserts as the CPU backend.
* hexagon: use cpy_tensor slow path for non-host buffers
* hexagon: error checks in the buffer allocator
* cmake: move include(extProj) under ggml-hexagon
* hexagon: don't forget to delete the backend on free
* hexagon: set/get_tensor size assert apply only to quantized tensors
* hexagon: reintroduce HEX_VERBOSE wrapper for GGML_LOG_DEBUG for now
GGML_LOG_DEBUG is always enabled for test-backend-ops and the output gets in the way.
Ideally we need a bit more finer log levels.
* docs: typos in hexagon developer docs (libggm-...)
* hexagon: overhaul error handling in the session/device allocation
this should handle all failure paths in the session allocation.
* hexagon: update cmake presets to enable fp16 vectors
* hexagon: remove unused time_usec function
* hexagon: don't forget to release buffer contexts
* hexagon: fixed indents in hvx-utils (missed clang-format auto-format failure)
* hexagon: remove custom can_repeat function and use ggml_can_repeat
---------
Co-authored-by: Rajdeep Ganguly <rganguly@qti.qualcomm.com>
Co-authored-by: Todor Boinovski <todorb@qti.qualcomm.com>
* add BailingMoeV2 support
* update llm types
* undo
* undo
* update llm types
* add model collection link
* update
* almost working
* correct group selection and rename n_group_exp
* avoid large top_k and use argmax instead for now
if we had something like argmax2 that would be equivalent, but this works fine until then
* poke
* skip group selection when there are no tokens
* fix 1T conversion
* hopefully fixed expert group selection
third time's the charm?
* make expert group selection generally available
The new LLaDA2Moe model uses this method too, make it generally available regardless of architecture.
* allow n_expert_groups to be 1 (Kimi K2)
* address review suggestions
## Why it failed
When compiling with strict compiler flags (-Wmissing-braces -Werror=missing-braces),
the build fails with the following error:
```
cmake \
-S . \
-B ../llama.cpp.build \
--preset=x64-linux-gcc-debug \
-DCMAKE_INSTALL_PREFIX=/tmp/local \
-DCMAKE_CXX_FLAGS="-Wmissing-braces -Werror=missing-braces" && \
cmake --build ../llama.cpp.build/
...
In file included from /home/otegami/work/cpp/llama.cpp/src/llama-graph.h:4,
from /home/otegami/work/cpp/llama.cpp/src/llama-model.h:5,
from /home/otegami/work/cpp/llama.cpp/src/llama.cpp:8:
/home/otegami/work/cpp/llama.cpp/src/llama-batch.h:126:48: error: missing braces around initializer for 'std::__array_traits<int, 1>::_Type' {aka 'int [1]'} [-Werror=missing-braces]
126 | std::array<llama_seq_id, 1> seq_id_0 = { 0 }; // default sequence id
| ^
cc1plus: some warnings being treated as errors
```
The issue is that std::array initialization requires double braces.
## How to fix
This PR changes `{ 0 }` to `{{ 0 }}` for std::array initialization.
This is part of a series of commits to fix missing braces warnings across the codebase.
- src/llama-batch.h <- This PR is here.
- src/llama-context.cpp
- tests/test-backend-ops.cpp
- tests/test-gguf.cpp
- tools/mtmd/clip.cpp
Benefits:
- std::array is a struct containing a C-style array, requiring nested braces
- Enables stricter compiler warnings to catch potential issues
The unexpeced pooling_type warning was incorrectly shown when users did not
specify the --pooling-type parameter. In this case, the parameter
defaults to `LLAMA_POOLING_TYPE_UNSPECIFIED (-1)`, and the code
automatically applies the model's default pooling type.
Example of spurious warning:
```
$ llama-embedding -hf ggml-org/bge-m3-Q8_0-GGUF -p "hello"
...
llama_init_from_model: model default pooling_type is [2], but [-1] was specified
...
```
This fix ensures the warning only appears when users explicitly specify
a pooling type that differs from the model's default (e.g., using
--pooling-type mean on a model that expects CLS pooling).
* llama-quant: add support for mmproj
* Update src/llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* check prefix instead
* small fix
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* hparams : add check for layer index in is_recurrent
This commit adds a check in the is_recurrent method to ensure that the
provided layer index is within the valid range.
The motivation for this change is to prevent potential out-of-bounds
and also be consistent with other methods in the class that perform
similar checks, like is_swa.
* minor : code style
* server : fix prompt similarity calculation
* server : initial host-memory prompt caching
* cont
* server : refactor
* cont
* cont : make the server task of the slot const
* cont : minor [no ci]
* server : cache prompts and checkpoints only for completion tasks
* server : improve prompt caching logic
* cont : fix check for number of cached prompts [no ci]
* server : improve caching logic, add -cram CLI arg
* server : print prompt mismatch info
* cont : better naming [no ci]
* server : improve prompt cache loading logic
* server : add option to debug the slot contents (#16482)
* server : add option to debug the slot contents
* Update tools/server/server.cpp
---------
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* server : add option to disable prompt cache
---------
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* model: EmbeddingGemma sentence-transformers dense linear projections support
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
Adding support for the Dense modules used in EmbeddingGemma models.
EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone.
See: https://developers.googleblog.com/en/gemma-explained-embeddinggemma-architecture-and-recipe/
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
- converting model with dense-layers is optional
- introduced dense config params
* Update convert_hf_to_gguf.py
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* fixed formatting issues
* Update src/llama-graph.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* - removed pooling_type_opt, always allow overriding pooling_type
- asserts checking dense features dims
* fix python lint
* fix ubuntu gcc build warning
* - fixed thread-safety test
- moved asserts to load_hparams
* - tidying up code
- simplifying graph-context expecting both dense weights
* minor : add TODO
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* implement --no-host to disable host buffer
* fix equal_mparams
* move no-host enumeration order together with other model params
---------
Co-authored-by: slaren <slarengh@gmail.com>
* fix: Fix duplicate fake image before token on first slice
Branch: GraniteDoclingStopping
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use double-newline before overview image
Branch: GraniteDoclingStopping
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove incorrect newline at the end of granite chat template gen prompt
There should not be one, even for the language models.
Branch: GraniteDoclingStopping
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* tests: Remove bad newline from granite chat template test (legacy)
Branch: GraniteDoclingStopping
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add granite-docling conversion using trillion pretokenizer
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add granite-docling vocab pre enum
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use granite-docling pre
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add clip_is_idefics3
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Allow multi-token boundary sequences for image templating
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add tiling support for idefices3 in clip.cpp
This should likely be moved into llava_uhd::get_slice_instructions, but for
now this avoids disrupting the logic there.
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Partial support for full templating for idefics3 in mtmd
There are still errors encoding some of the image chunks, but the token
sequence now matches transformers _almost_ perfectly, except for the double
newline before the global image which shows up as two consecutive newline
tokens instead of a single double-newline token. I think this is happening
because the blocks are tokenized separately then concatenated.
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Fully working image preprocessing for idefics3 w/ resize and slicing
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Parse the preprocessor config's longest side and add it to the mmproj hparams
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use the longest side instead of size * scale_factor
For Granite Docling, these come out to the same value, but that was just a
conicidence.
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Allow batch encoding and remove clip_is_idefics3
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Remove unnecessary conditionals for empty token vectors
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Use image_manipulation util
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* add test model
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* initial commit for branch 3
* generalize `swa_checkpoint` to `ctx_checkpoint`
this extends `llama-server`'s SWA checkpointing logic to include
hybrid/recurrent models such as Jamba, Granite
* oops
* disable debug prints
* keep backwards compat with `--swa-checkpoints`
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* update prompt re-processing message
* fix off-by-one error per GG
* keep `seq_rm` log per GG
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : fix checkpoint logic to support recurrent caches
* server : cleanup and fixes
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* First attempt
* No permute during convert (fixes qk tensors), proper norm application.
* RoPE = NeoX
* Coherence!
* Migrate xielu params from tensors to hyperparameters
* Simple CUDA kernel
* Revert stupid LLM refactorings
* Chat template support
* configchecker / flake8 errors
* Reorder unary.cu
* I do conclude that LLMs are, in fact, stupid.
* Fix after merge
* Final newline
* Make xIELU an UNARY_OP
* Final newline
* Correctly account for parameter shift
* Argh.
* Update ggml/src/ggml-cpu/unary-ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Refactor: remove unused methods, inline and factorize softplus, add const modifiers
* Revert CUDA changes, implement xIELU as a separate OP
* Pesky newline
* Add float2half / half2float for F16 inputs/outputs
* CUDA variants, attempt 2
* Actually, attempt 3
* Update ggml/src/ggml-cuda/unary.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Missing convert header
* Proper formula and reference for xIELU in the comments.
* Modify unary-ops.cpp to add the functor-based logic besides the template system to retain optimizations
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tensor mappings for Apertus to global list instead
* Fix lazy on scalars
* Update ggml/src/ggml-cuda/unary.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Add comment about the constraints on positive/negative alpha
* Change `softplus` to `ggml_softplus`
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Fix to use hidden_size_per_head
* Fix num heads
* Fix array
* Fix loading weights
* Support old GGUF converted by the previous version of llama.cpp
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Move shared parameter definitions to the outside of loop
* Not calculating n_embd_head_k,v by n_embd / n_head
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Make a few GLM tensors not required
layer.nextn.shared_head_head and layer.nextn.embed_tokens are both excluded from GLM 4.6 resulting in the model not loading after conversion/quantization, this marks those tensors as not required which makes it work
* Update llama-model.cpp
layer.nextn.shared_head_norm also not required in case of future models
* minicpm: make GGUF scaling keys optional with legacy defaults
Older MiniCPM GGUFs do not include the scaling metadata keys (minicpm.embedding_scale, minicpm.residual_scale, minicpm.logit_scale). The loader currently treats these as required, so quantization fails with:
key not found in model: minicpm.embedding_scale
This change restores backward compatibility by treating these keys as optional in the loader and using the older MiniCPM scaling values:
embedding_scale = 12.0f
residual_scale = 1.4f / sqrt(n_layer)
logit_scale = 256.0f / n_embd
When the GGUF provides the keys, their values override the defaults; otherwise the legacy defaults are used. Newer GGUFs that already include these keys are unaffected.
Fixes: #16192
Signed-off-by: Vinkal Chudgar <vinkal.chudgar@gmail.com>
* Update src/llama-model.cpp
Committed as suggested. Thanks!
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Signed-off-by: Vinkal Chudgar <vinkal.chudgar@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* devops: move s390x and ppc64le ci build
we have access to ubuntu-24.04-s390x and ppc64le images now
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: disable ppc64le for now since they have compiler errors
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: stop warnings as errors
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: switch to non-macro flag
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: going the llama macro route
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: add big-endian gguf test models
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: disable ppc64le to test s390x, check test build
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: dup .gguf.inp files for big-endian tests
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: dup .gguf.out files for big-endian too
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: add python setup and endian byteswap
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: pooring thing does not have s390x python3
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: add missing rust compiler for s390x
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: try rust actions runner
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Revert "devops: try rust actions runner"
This reverts commit 3f8db04356033d6c1d7eccc75ca396bc5298250c.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: try a different path for rust
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: dump home directory and user info
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: install gguf-py only
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: missed relative path
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: remove big-endian files since local swapping is working
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: revert test-tokenizer-0 cmakelists
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Fix unicode flags conversion from and to uint16_t
Bitfields are allocated in different order on s390x
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Simplify byteswap command
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Add byteswapping and git-lfs for test-tokenizers-ggml-vocabs
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Fix endianness detection in vocab loader
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Disable test-thread-safety on s390x
In this test a model is downloaded,
then immediately loaded to check if more downloads are needed,
and then used for test.
There is no clean way to separate all those steps
to add byteswapping between them, so just skip this test.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Fix q8_0 test in test-quantize-fns
vec_signed uses unexpected rounding mode.
Explicitly use different rounding function.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: add big-endian stories260K
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: add s390x test-eval-callback
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: fix test does not exist
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: fix model not found llama-eval-callback
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Fix q3_K dot product error in test-quantize-fns on s390x
Array q8bytes had only 4 elements allocated, but 8 elements accessed.
This lead to write out of bounds and later read of overwritten values out of bounds
and incorrect result.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: re-enable ppc64le for testing
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: activate test-thread-safety for s390x
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: disable ppc64le tests
for some reason it keeps failing test-thread-safety tests and I do not
have a machine that is able to replicate the tests.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* devops: LLAMA_FATAL_WARNINGS=ON
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Correct repository URL for s390x for test-thread-safety model
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Fix fs_get_cache_directory
Ensure it works even if both XDG_CACHE_HOME and HOME are unset.
This might happen in containers.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Re-enable CI for ppc64le
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Fortify ggml_rope_impl
Only memcpy data from sections argument if it's non-NULL.
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* Add TODO in struct unicode_cpt_flags to reimplement it in endian-independent way
* Update URL for big-endian model
* Update .github/workflows/build.yml
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update remaining mentions of BE models to ggml-org/models repo
---------
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
Co-authored-by: Aleksei Nikiforov <aleksei.nikiforov@linux.ibm.com>
Co-authored-by: Aleksei Nikiforov <103434461+AlekseiNikiforovIBM@users.noreply.github.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* CUDA: add a fused top-K MoE kernel
This kernel does the following:
1. softmax over the logits per token [n_experts, n_tokens]
2. argmax reduce over the top-k (n_experts_used) logits
3. write weights + ids to global memory
It is intended as fusion of softmax->top-k->get_rows pipeline for MoE models
* Refactor into ggml_cuda_should_use_topk_moe
* Review: Use better coalescing pattern, use WARP_SIZE, store logits into registers before
* Review: format + micro-optimizations
* Fix bug: fix tie breakers
* Add optional norm + clean-up code
* Use smem for final write
* Add bounds check
* Use better memory pattern for writeback
* add grok-2 support
* type fix
* type fix
* type fix
* "fix" vocab for invalid sequences
* fix expert tensor mapping and spaces in vocab
* add chat template
* fix norm tensor mapping
* rename layer_out_norm to ffn_post_norm
* ensure ffn_post_norm is mapped
* fix experts merging
* remove erroneous FFN_GATE entry
* concatenate split tensors and add more metadata
* process all expert layers and try cat instead of hstack
* add support for community BPE vocab
* fix expert feed forward length and ffn_down concat
* commit this too
* add ffn_up/gate/down, unsure if sequence is right
* add ffn_gate/down/up to tensor names
* correct residual moe (still not working)
* mess--
* fix embedding scale being applied twice
* add built in chat template
* change beta fast for grok if default value
* remove spm vocab in favor of community bpe vocab
* change attention temp length metadata type to integer
* update attention temp length metadata
* remove comment
* replace M_SQRT2 with std::sqrt(2)
* add yarn metadata, move defaults to hparams
* ggml-backend : add GGML_BACKEND_DEVICE_TYPE_IGPU device type
ggml-backend : add device id to device props
llama : only use iGPU devices if there are no GPU devices
llama : do not use multiple devices from different backends with the same device id
* support non-contiguous Q in build_attn_mha
* Update src/llama-graph.cpp
ggml-ci
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit adds check for two function pointers returned from
ggml_backend_reg_get_proc_address.
The motivation for this is that the function pointer could be nullptr if
the get proc address function changes in the future. This is also
consistent with all the other calls to ggml_backend_reg_get_proc_address
in the code base.
* feat: Add python-side constants and conversion for adapter.lora.invocation_string
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add c++ side constants for adapter.lora.invocation_string
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Parse invocation string for adapters from GGUF
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(python): Update conversion to alora_invocation_tokens
This is the preferred method in PEFT which is the source of ground truth
https://github.com/huggingface/peft/pull/2609/files#diff-13380145401d203d5935c5189dd09879f990b81aa63e8e3aaff8ce9110333f0e
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix(cpp): Update to alora_invocation_tokens on c++ side
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add C APIs to get alora invocation token array from lora
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Initial implementation of alora cache logic in server
This does not yet do the part to identify the invocation tokens and only
apply the lora adapter afterwards, but it does seem to produce correct
results if the invocation tokens are the beginning of the uncached input.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Identify alora invocation sequences
This currently limits to a single enabled alora per slot. Multiple aloras
with different invocation sequences would be possible, but it would require
a more complex integration of the adapter toggling and is not really a well
studied case for alora since it's unclear if one alora can reuse cache from
previous prefill computed with a different alora.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Only reuse cache for tokens before the alora invocation start
This is a bit of an edge case, but theoretically a user could try the same
query with the alora disabled (just using the base model), then retry with
the alora. The cached tokens from the first pass should be invalid.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Handle un-cached tokens that come before the alora activation
The solution is to only fill up to the token before the invocation start in
the batch if there are any tokens to be prefilled between those pulled from
cache and the invocation start. When this is detected, the alora is
temporarily disabled with a scale of 0.0, then immediately re-enabled after
it has been initialized for the internal graph. Since the batch does not
complete the prompt tokens, the remaining prompt tokens are handled in the
next task, pulling all of the non-alora tokens from cache and proceeding
with prefill for the alora tokens.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Use || instead of 'or'
Too much python 🤦
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix off-by-one for limiting cached tokens to before alora start
This was the cause of the inconsistent results from the dummy test script
with and without the turn that runs the prompt without the adapter before
running it with the adapter.
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Support backwards-compatibility for "invocation_string" in adapter_config.json
While this has been replaced in the PEFT PR in favor of
alora_invocation_tokens, the existing adapters in the ibm-granite org on HF
use "invocation_string," so this will enable backwards compatibility and
enable testing now (before PEFT PR changes have percolated everywhere).
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove duplicate logging
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* feat: Report alora_invocation_string and alora_invocation_tokens from /lora-adapters
Branch: gabe-l-hart/alora-support
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
