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* ggml : add IQ2 to test-backend-ops + refactoring
ggml-ci
* cuda : update supports_op for IQ2
ggml-ci
* ci : enable LLAMA_CUBLAS=1 for CUDA nodes
ggml-ci
* cuda : fix out-of-bounds-access in `mul_mat_vec_q`
ggml-ci
* tests : avoid creating RNGs for each Q tensor
ggml-ci
* tests : avoid creating RNGs for each tensor
ggml-ci
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* imatrix: adding support for legacy quants
* imatrix: guard Q4_0/Q5_0 against ffn_down craziness
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* imatrix: load
* imatrix: WIP
* imatrix: Add Q2_K quantization
* imatrix: also guard against Q2_K_S quantization without importance matrix
* imatrix: guard even more against low-bit quantization misuse
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* ggml : fix 32-bit ARM compat
* ggml : fix fix
* ggml : fix fix fix
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* iq2_xs: basics
* iq2_xs: this should have been in the basics
* iq2_xs: CUDA and scalar CPU works
* iq2_xs: WIP Metal
* iq2_xs: Metal now works
* iq2_xs: working, but dog slow, ARM_NEON dot product
* iq2_xs: better ARM_NEON dot product
We are now at 19.5 t/s for TG-128 and 61 t/s for PP-512 when
running on the CPU.
* iq2_xs: AVX2 dot product - 19.5 t/s
* iq2_xs: faster AVX2 dit product
21.4 t/s for TG-128, 59.2 t/s for PP-512.
The latter is 2x compared to the previous version.
* iq2_xs: had forgotten to delete iq2-data.h
* Add llama enum for IQ2_XS
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* ggml : fix vld1q_s8_x4 32-bit compat
ggml-ci
* ggml : fix 32-bit ARM compat (cont)
ggml-ci
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* 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
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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ggml-ci
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ggml-ci
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* 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
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* build: support ppc64le build for make and CMake
* build: keep __POWER9_VECTOR__ ifdef and extend with __powerpc64__
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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ggml-ci
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* 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
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
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* ggml : move FP16 <-> FP32 stuff to ggml-impl.h
ggml-ci
* tests : fix ARM build
* ggml : explicitly initialize deprecated type traits
* ggml : add math.h to ggml-impl.h
* ggml : remove duplicate static assert macros
* ggml : prefix lookup tables with ggml_
ggml-ci
* ggml-impl : move extern "C" to start of file
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* ggml : factor all quantization code in ggml-quants
ggml-ci
* ggml-quants : fix Zig and Swift builds + quantize tool
ggml-ci
* quantize : --pure option for disabling k-quant mixtures
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Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
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