* cuda sqrt support
* enable cuda in pca
* fix comments in pca
* add test
* add sqrt to ggml_backend_cuda_supports_op
* fix test
* new line
* Use F32 sqrtf instead of F64 sqrt
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : fix bounds check for src0 rows in MMVQ kernel
* Update ggml-cuda/mmvq.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* update HIP_UMA #7399
add use of hipMemAdviseSetCoarseGrain when LLAMA_HIP_UMA is enable.
- get x2 on prompte eval and x1.5 on token gen with rocm6.0 on ryzen 7940HX iGPU (780M/gfx1103)
* simplify code, more consistent style
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Co-authored-by: slaren <slarengh@gmail.com>
* cuda : fix rope pos data
ggml-ci
* ggml : drop mode & 1 == 1 support for ggml_rope
ggml-ci
* ggml : support freq_factors for f16 rope (CPU)
ggml-ci
* tests : add rope tests using frequency factors
ggml-ci
* add phi3 128k support in convert-hf-to-gguf
* add phi3 128k support in cuda
* address build warnings on llama.cpp
* adjust index value in cuda long rope freq factors
* add long rope support in ggml cpu backend
* make freq factors only depend on ctx size
* remove unused rope scaling type 'su' frin gguf converter
* fix flint warnings on convert-hf-to-gguf.py
* set to the short freq factor when context size is small than trained context size
* add one line of comments
* metal : support rope freq_factors
* ggml : update ggml_rope_ext API to support freq. factors
* backends : add dev messages to support rope freq. factors
* minor : style
* tests : update to use new rope API
* backends : fix pragma semicolons
* minor : cleanup
* llama : move rope factors from KV header to tensors
* llama : remove tmp assert
* cuda : fix compile warning
* convert : read/write n_head_kv
* llama : fix uninitialized tensors
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* initial commit with CPU implementation of upscale to shape and test, cuda implementation next
* experimental commit to see if dst shape is correct
* test version
* test
* removed unnecessary params
* refactor
* fixed tests
* ggml : metal impl + cleanup + sycl dev warnings
* patched ggml_upscale cuda op to handle non-contiguous tensors, added test for non-contiguous behavior
* metal : fix upsacle op to support nb00 + style
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* DRAFT: Introduction of CUDA Graphs to LLama.cpp
* FIx issues raised in comments
* Tidied to now only use CUDA runtime (not mixed with driver calls)
* disable for multi-gpu and batch size > 1
* Disable CUDA graphs for old GPU arch and with env var
* added missing CUDA_CHECKs
* Addressed comments
* further addressed comments
* limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake
* Added more comprehensive graph node checking
* With mechanism to fall back if graph capture fails
* Revert "With mechanism to fall back if graph capture fails"
This reverts commit eb9f15fb6fcb81384f732c4601a5b25c016a5143.
* Fall back if graph capture fails and address other comments
* - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS
- rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS
- updated Makefile build to enable CUDA graphs
- removed graph capture failure checking in ggml_cuda_error
using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string
if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context
- fixed several resource leaks
- fixed issue with zero node graphs
- changed fixed size arrays to vectors
- removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed
- removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row
- changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX
- code style fixes
- things to look into
- VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional
- possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes
* fix build without cuda graphs
* remove outdated comment
* replace minimum cc value with a constant
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Co-authored-by: slaren <slarengh@gmail.com>
