* Using cuda memory pools for async alloc/dealloc.
* If cuda device doesnt support memory pool than use old implementation.
* Removed redundant cublasSetStream
---------
Co-authored-by: Oleksii Maryshchenko <omaryshchenko@dtis.com>
* 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>
* 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
* cmake : add helper for faster CUDA builds
* batched : add NGL arg
* ggml : skip nops in compute_forward
* cuda : minor indentation
* cuda : batched cuBLAS GEMMs for src0 F16 and src1 F32 (attention ops)
* Apply suggestions from code review
These changes plus:
```c++
#define cublasGemmBatchedEx hipblasGemmBatchedEx
```
are needed to compile with ROCM. I haven't done performance testing, but it seems to work.
I couldn't figure out how to propose a change for lines outside what the pull changed, also this is the first time trying to create a multi-part review so please forgive me if I mess something up.
* cuda : add ROCm / hipBLAS cublasGemmBatchedEx define
* cuda : add cublasGemmStridedBatchedEx for non-broadcasted cases
* cuda : reduce mallocs in cublasGemmBatchedEx branch
* cuda : add TODO for calling cublas from kernel + using mem pool
---------
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.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>
* 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
