* iq2_xxs: basics
* iq2_xxs: scalar and AVX2 dot products
Needed to change Q8_K to have quants in the -127...127 range,
else the IQ2_XXS AVX implementation becomes very awkward.
The alternative would have been to use Q8_0 instead. Perhaps
I'll change later, for now this is what we have.
* iq2_xxs: ARM_NEON dot product
Somehow strangely slow (112 ms/token).
* iq2_xxs: WIP Metal
Dequantize works, something is still wrong with the
dot product.
* iq2_xxs: Metal dot product now works
We have
PP-512 = 475 t/s
TG-128 = 47.3 t/s
Not the greatest performance, but not complete garbage either.
* iq2_xxs: slighty faster dot product
TG-128 is now 48.4 t/s
* iq2_xxs: slighty faster dot product
TG-128 is now 50.9 t/s
* iq2_xxs: even faster Metal dot product
TG-128 is now 54.1 t/s.
Strangely enough, putting the signs lookup table
into shared memory has a bigger impact than the
grid values being in shared memory.
* iq2_xxs: dequantize CUDA kernel - fix conflict with master
* iq2_xxs: quantized CUDA dot product (MMVQ)
We get TG-128 = 153.1 t/s
* iq2_xxs: slightly faster CUDA dot product
TG-128 is now at 155.1 t/s.
* iq2_xxs: add to llama ftype enum
* iq2_xxs: fix MoE on Metal
* Fix missing MMQ ops when on hipBLAS
I had put the ggml_supports_mmq call at the wrong place.
* Fix bug in qequantize_row_iq2_xxs
The 0.25f factor was missing.
Great detective work by @ggerganov!
* Fixing tests
* PR suggestion
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* fixed mul-mat error for old GPUs
* style fixes
* add mul mat src1 f16 test cases, fix more cases
ggml-ci
---------
Co-authored-by: bssrdf <bssrdf@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
* cuda : fix vmm pool with multi GPU
* hip
* use recommended granularity instead of minimum
* better error checking
* fix mixtral
* use cudaMemcpy3DPeerAsync
* use cuda_pool_alloc in ggml_cuda_op_mul_mat
* consolidate error checking in ggml_cuda_set_device
* remove unnecessary inlines
ggml-ci
* style fixes
* only use vmm for the main device
* fix scratch buffer size, re-enable vmm pool for all devices
* remove unnecessary check id != g_main_device
* cuda : improve cuda pool efficiency using virtual memory
* fix mixtral
* fix cmake build
* check for vmm support, disable for hip
ggml-ci
* fix hip build
* clarify granularity
* move all caps to g_device_caps
* refactor error checking
* add cuda_pool_alloc, refactor most pool allocations
ggml-ci
* fix hip build
* CUBLAS_TF32_TENSOR_OP_MATH is not a macro
* more hip crap
* llama : fix msvc warnings
* ggml : fix msvc warnings
* minor
* minor
* cuda : fallback to CPU on host buffer alloc fail
* Update ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* Update ggml-cuda.cu
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ensure allocations are always aligned
* act_size -> actual_size
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* fix old jetson compile error
* Update Makefile
* update jetson detect and cuda version detect
* update cuda marco define
* update makefile and cuda,fix some issue
* Update README.md
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update Makefile
* Update README.md
---------
Co-authored-by: Georgi Gerganov <ggerganov@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>
* 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
regression of #4490
Adds defines for two new datatypes
cublasComputeType_t, cudaDataType_t.
Currently using deprecated hipblasDatatype_t since newer ones very recent.
