* 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>
* AMD ROCm: handle UMA memory VRAM expansions
This resolves#2797 by allowing ROCm AMD GPU users with a UMA to
dynamically expand the VRAM allocated to the GPU.
Without this, AMD ROCm users with shared CPU/GPU memory usually are
stuck with the BIOS-set (or fixed) framebuffer VRAM, making it
impossible to load more than 1-2 layers.
Note that the model is duplicated in RAM because it's loaded once for
the CPU and then copied into a second set of allocations that are
managed by the HIP UMA system. We can fix this later.
* clarify build process for ROCm on linux with cmake
* avoid using deprecated ROCm hipMallocHost
* keep simplifying the change required for UMA
* cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
Otherwise, on Windows converting bling-phi-2-v0 (<https://huggingface.co/llmware/bling-phi-2-v0>) via convert-hf-to-gguf.py will fail with the following error:
```
Traceback (most recent call last):
File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 1061, in <module>
model_instance.set_vocab()
File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 52, in set_vocab
self._set_vocab_gpt2()
File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 264, in _set_vocab_gpt2
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
File "C:\Users\User\git\gguf\gguf\vocab.py", line 33, in __init__
self._load(Path(path))
File "C:\Users\User\git\gguf\gguf\vocab.py", line 81, in _load
self._try_load_merges_txt(path)
File "C:\Users\User\git\gguf\gguf\vocab.py", line 95, in _try_load_merges_txt
for line in fp:
File "C:\Users\User\miniconda3\envs\gguf\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 1415: character maps to <undefined>
```
regression of #4490
Adds defines for two new datatypes
cublasComputeType_t, cudaDataType_t.
Currently using deprecated hipblasDatatype_t since newer ones very recent.
* build : Check the ROCm installation location
* more generic approach
* fixup! It was returning the path instead of the command output
* fixup! Trailing whitespace
* Add API key authentication for enhanced server-client security
* server : to snake_case
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : group mul_mat_id rows by matrix (cpu only)
* remove mmid parameters from mm forward
* store row groups in wdata and calculate only once in GGML_TASK_INIT
ggml-ci
