* support splits in convert.py
* Support split by size and dry run to write estimated shards/filesizes
* Move split functionality to new GGUFManager class
* fix improper function signature
* tentative push of convert-hf-to-gguf support
* resolve merge + SplitArguments for easier parsing
* Fix eager tensor memory leak and remove convert.py changes
Removed a memory leak caused by unexpected reference retention to eager tensors.
Also removed GGUFManager functionality in convert.py in favor of specializing for convert-hf-to-gguf.py.
* refactor SplitStrategy to be a deque
Instead of having SplitStrategy have a `data` field that is a deque, just have SplitStrategy be a subclass of deque itself.
* fix Q8 quantization
* remove unnecessary imports in gguf_manager
* fix final? merge issue
* fix gguf_writer placement and remove comments
* oops, actually fix gguf_writer placement
* reduce duplicated code from gguf_writer
* further simplify GGUFManager
* simplify even further and standardize with GGUFWriter
* reduce diffs with master
* form shards while adding tensors, SHA256 sums agree with master
* re-add type hint
Co-authored-by: compilade <git@compilade.net>
* GGUFWriter compatibility fix
Co-authored-by: compilade <git@compilade.net>
* Shard dataclass and un-negative dont_add_architecture
* type consistency in format_n_bytes_to_str
* move kv keys to constants.py
* make pathlib explicit
* base-1024 bytes to base-1000
* rename GGUFManager to GGUFWriterSplit
* Update gguf-py/gguf/constants.py
Co-authored-by: compilade <git@compilade.net>
* fix convert-hf-to-gguf.py permissions
* fix line endings
* Update gguf-py/gguf/gguf_writer_split.py
Co-authored-by: compilade <git@compilade.net>
* convert-hf : restore executable file permission
* examples/convert-legacy-llama.py: restore executable file permission
* reinstate original gguf package import and fix type annotation
* attempt to appease the linter
* attempt 2 to appease the linter
* attempt 3 to appease the linter
* comma consistency
* Update convert-hf-to-gguf.py
Co-authored-by: compilade <git@compilade.net>
* edit cmd line args
* use simplification from #7827
* kv/ti data are still wrong
* try to refactor kv data (still fails)
* fix ti data messiness
* tidy up
* fix linting
* actually make the linter happy
* cleanup round 1
* remove SplitStrategy, SplitArguments
* appease linter
* fix typing and clean up
* fix linting
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* progress bar, fix split logic
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* catch oversights
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* swap bar orders
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* compatibility fix
* Update gguf-py/gguf/gguf_writer.py
Co-authored-by: compilade <git@compilade.net>
* Update convert-hf-to-gguf.py
Co-authored-by: compilade <git@compilade.net>
---------
Co-authored-by: Brian <mofosyne@gmail.com>
Co-authored-by: compilade <git@compilade.net>
* 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>
* gguf-py : add T5 model architecture
* gguf-py : add separate tensors for encoder and decoder
* gguf-py : add new model header parameters: decoder_start_token_id, attention.relative_buckets_count, tokenizer.ggml.remove_extra_whitespaces, tokenizer.ggml.precompiled_charsmap
* convert-hf : add model conversion support for T5ForConditionalGeneration and T5WithLMHeadModel
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* ggml : remove ggml_task_type and GGML_PERF
* check abort_callback on main thread only
* vulkan : remove usage of ggml_compute_params
* remove LLAMA_PERF
* Refactor Vulkan backend to allow multiple contexts
* Fix too many shader groups called validation error in llama3 on AMD and Intel GPUs
* Fix Vulkan debug build error
* Adding simple bare-bones test for end-to-end integration test for json validation against auto-generated JSON-schema grammars.
* Adding additional examples as documented in #7789 . Also adding the ability to automatically output improperly failing grammars to debug output files so they can more easily be examined in the gbnf-validator program.
* Uncommenting formerly commented tests so that they fail for others who are attempting to reproduce the bugs.
* Merging improved schema test methods added by @ochafik in #7797
* Adding #define to temporarily remove failing tests so that this PR can pass CI, but still be useful for other PRs that want to leverage the framework.
* Fixing nits from ochafik. Removing escape slashes, adding additional failing cases, fixing some other strings.
* Fixing grammar indentation to be consistent throughout file.
* 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
Currently the Metal backend does not support BF16. `ggml_metal_supports_op` was returning true in these cases, leading to a crash with models converted with `--leave-output-tensor`. This commit checks if the first few sources types are BF16 and returns false if that's the case.
* un-ignore `build-info.cmake` and `build-info.sh`
I am assuming that ignoring them was unintentional. If they are ignored, some tools, like cargo, will consider the files inexistent, even if they're comitted, for the purpose of publishing. This leads to the build failing in such cases.
* un-ignore `build-info.cpp.in`
For the same reason as the previous two files.
* Reorganize `.gitignore`
* Add exceptions for files mentioned by @slaren
I did leave .clang-tidy since it was explicitly ignored before.
* Add comments for organization
* Sort some lines for pretty
* Test with `make` and `cmake` builds to ensure no build artifacts might be comitted
* Remove `.clang-tidy` from `.gitignore`
Per comment by @ggerganov
* Remove `IDEWorkspaceChecks.plist` from root-level `.gitignore`
On hosts which are not prepared/dedicated to execute code using CUDA
it is still possible to compile llama.cpp with CUDA support by just
installing the development packages. Missing are the runtime
libraries like /usr/lib64/libcuda.so* and currently the link step
will fail.
The development environment is prepared for such situations. There
are stub libraries for all the CUDA libraries available in the
$(CUDA_PATH)/lib64/stubs directory. Adding this directory to the end
of the search path will not change anything for environments which
currently work fine but will enable compiling llama.cpp also in case
the runtime code is not available.
* update: convert-hf-to-gguf.py to support Qwen2-57B-A14B
* fix: QWEN2MOE support for expert_feed_forward_length
previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH
n_ff_exp and n_ff_shared_exp are now properly calculated
* update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM
* fix: QWEN2MOE support for expert_feed_forward_length
previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH
n_ff_exp and n_ff_shexp are now properly calculated
* Implement non-mapped async IO for CUDA on Windows. On a fast Gen5 NVMe drive this change improves model load time by >3x while it should be the same (or slightly faster) on any other drive.
* Free resources except for backend.
* Change assertions to exceptions in llama_file, find correct cuda backend to create CUDA resources and respect the use_mmap flag again for CUDA.
* Apply suggestions from code review
Co-authored-by: slaren <slarengh@gmail.com>
* Fix editorconfig and unused variable
* Fix issues with Windows build
---------
Co-authored-by: slaren <slarengh@gmail.com>
