Fix uploading tensor data to device, including 3D, 4D, and non-contiguous tensors.
Use correct offsets into data that is already in VRAM.
Correct handling of OpenCL events when multiple commands are queued.
* sync : ggml (conv 1d + 2d updates)
ggml-ci
* ggml : fix UB in q5_0 and q5_1 quantize code
ggml.c:1033:39: runtime error: left shift of 1 by 31 places cannot be represented in type 'int'
SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior
ggml.c:1081:39: runtime error: left shift of 1 by 31 places cannot be represented in type 'int'
SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior
ggml-ci
* tests : fix UB in test-quantize-perf
* Added RVV intrinsics support for Q8 quantize row and also improved the existing dot product function for risc-v.
The RVV intrinsics is added for the following quantize row functions
quantize_row_q8_0
quantize_row_q8_1
The following dot product functions have also been optimized by using LMUL = 1/2 instead of LMUL = 1
ggml_vec_dot_q4_0_q8_0
ggml_vec_dot_q4_1_q8_1
ggml_vec_dot_q5_0_q8_0
ggml_vec_dot_q5_1_q8_1
And vector initialization in Q5 by temporary array is also replaced by the vid intrinsics
Signed-off-by: Ahmad Tameem <ahmad.tameem@10xengineers.ai>
* Added RVV intrinsics support for k_quants
This adds RISC-V Vector intrinsics support for the following K_quants functions for both QKK = 256 and QKK = 64
ggml_vec_dot_q2_K_q8_K
ggml_vec_dot_q3_K_q8_K
ggml_vec_dot_q4_K_q8_K
ggml_vec_dot_q5_K_q8_K
ggml_vec_dot_q6_K_q8_K
Signed-off-by: Ahmad Tameem <ahmad.tameem@10xengineers.ai>
---------
Signed-off-by: Ahmad Tameem <ahmad.tameem@10xengineers.ai>
* fix LLAMA_NATIVE
* syntax
* alternate implementation
* my eyes must be getting bad...
* set cmake LLAMA_NATIVE=ON by default
* march=native doesn't work for ios/tvos, so disable for those targets. also see what happens if we use it on msvc
* revert 8283237 and only allow LLAMA_NATIVE on x86 like the Makefile
* remove -DLLAMA_MPI=ON
---------
Co-authored-by: netrunnereve <netrunnereve@users.noreply.github.com>
* Work on the BPE tokenizer
Tokenizer tests work for Falcon-7B
* Try to fix build problem
* Fix debug assertion failure
* Fix MSVC Unicode BOM problem
* Cleanup and an improvement
* Fix compiler warning
* Cleanup
* Test doesn't work over the full range of Unicodes
* Update .gitignore and Makefile
* Another Makefile rule
* Testing Aquila
* Moving byte decoding back to `token_to_piece` ...
... because everyone is using it.
* Guarding some unusable code pathes
* Streamlining code and adding some more assertions
Important change: I'm classifying added tokens as control tokens now for BPE.
* Adding a comment
* Adding another assertion
* Fixed vocabulary guarding assertions
* Fix PR for recent change
* Fix PR for recent change
* Fix for compiler warning
* Fix PR for recent change
* Fix PR for recent change
* Fix PR for recent change
* Fix for compiler warning
* Fixes for more compiler warnings
* Remove unused code
* Fix initialization of static maps
* Add scores and token types back, adapt gptneox
* Update llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update unicode.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update unicode.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Ported Starcoder and added some assertions
* Fix coding style
* Apply @jploski 's fix for missing tokens
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* vvhg-code-infill (#1)
* infill in separate example (#2)
* reverted changes to main and added infill example
* cleanup
* naming improvement
* make : add missing blank line
* fix missing semicolon
* brought infill up to current main code
* cleanup
---------
Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com>
* ggml-cuda : perform cublas matrix multiplication of quantized types as fp16
* rename CC_TURING to CC_VOLTA
* disable fp16 mat mul completely with multi GPU
* llama.cpp : split llama_context_params into model and context params
ggml-ci
* fix metal build
* fix freq_base/scale default to model value
* llama-bench : keep the same model between tests when possible
* move n_threads to llama_context_params, add n_threads_batch
* fix mpi build
* remove kv_size(), cuda scratch fixes
* remove low-vram option
* add n_threads_batch to system info, refactor to get_system_info()
* add documentation about --threads-batch to the READMEs
* llama-bench fix
* main : fix rope freq/scale warning
* llama.cpp : add llama_get_model
common : add llama_tokenize from model
* remove duplicated ctx/model functions
ggml-ci
* cuda : print total VRAM used
