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* Add SVE support for q4_0_q8_0 q8_0_q8_0
* remove ifdef
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* add loongarch lsx and lasx optimize code
* Add loongarch compilation support to makefile
* revert stb_image.h
* opt bytes_from_nibbles_32 and sum_i16_pairs_float
* fix undeclared
* format code
* update
* update 2
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Co-authored-by: Jinyang He <hejinyang@loongson.cn>
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for enabling AVX512_BF16 (#7258)
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* Introduce bfloat16 support
Many models on Hugging Face (e.g. Mistral, TinyLLaMA) use bfloat16 as
their canonical floating point format.
┌sign
│
│ ┌exponent
│ │
│ │ ┌mantissa
│ │ │
│┌──┴───┐┌─┴───┐
0b0000000000000000 brain16
This encoding has the same number of exponent bits as float32. That
makes conversion relatively straightforward, even in the absence of
hardware support. For example, converting brain16 to binary32 means
simply shifting 16 bits to the left.
┌sign
│
│ ┌exponent
│ │
│ │ ┌mantissa
│ │ │
│┌──┴───┐┌─┴───────────────────┐
0b00000000000000000000000000000000 IEEE binary32
The issue is that converting bf16 to fp16 can result in information
loss. Only 13% of bf16 numbers can be precisely represented in fp16
which in practice ends up being 99.71% of Mistral 7b v0.2's weights
however there is currently no way other than fp32 to get the others
┌sign
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│ │ ┌mantissa
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│┌─┴─┐┌─┴──────┐
0b0000000000000000 IEEE binary16
This change fixes that, by adding a bf16 data type to GGML. Support
for CPU inference has been implemented along with optimizations for
the AVX2, AVX512, and AVX512BF16 ISAs. Perplexity on Mistral 7b 0.2
improves somewhere around -0.0024 to -0.0046 compared to using fp16
* Remove GGML code that's not needed
* Minimize the GGML API surface area for BF16
* Remove bf16 luts
* Make the GGML header look nicer
* Fix documentation
* Apply ggerganov's fixes for test-backend-ops
* Add BF16 code for new ggml_validate_row_data() function
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ggml-ci
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ggml-ci
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ggml-ci
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This change upstreams llamafile's cpu matrix multiplication kernels
which improve image and prompt evaluation speed. For starters, Q4_0
and Q8_0 weights should go ~40% faster on CPU. The biggest benefits
are with data types like f16 / f32, which process prompts 2x faster
thus making them faster than quantized data types for prompt evals.
This change also introduces bona fide AVX512 support since tinyBLAS
is able to exploit the larger register file. For example, on my CPU
llama.cpp llava-cli processes an image prompt at 305 tokens/second,
using the Q4_K and Q4_0 types, which has always been faster than if
we used f16 LLaVA weights, which at HEAD go 188 tokens/second. With
this change, f16 LLaVA performance leap frogs to 464 tokens/second.
On Intel Core i9-14900K this change improves F16 prompt perf by 5x.
For example, using llama.cpp at HEAD with Mistral 7b f16 to process
a 215 token prompt will go 13 tok/sec. This change has fixes making
it go 52 tok/sec. It's mostly thanks to my vectorized outer product
kernels but also because I added support for correctly counting the
number of cores on Alderlake, so the default thread count discounts
Intel's new efficiency cores. Only Linux right now can count cores.
This work was sponsored by Mozilla who's given permission to change
the license of this code from Apache 2.0 to MIT. To read more about
what's improved, and how it works, see: https://justine.lol/matmul/
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* windows arm ci
* fix `error C2078: too many initializers` with ggml_vld1q_u32 macro for MSVC ARM64
* fix `warning C4146: unary minus operator applied to unsigned type, result still unsigned`
* fix `error C2065: '__fp16': undeclared identifier`
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* ggml : always define ggml_fp16_t as uint16_t
ggml-ci
* ggml : cont
ggml-ci
* ggml : cont
* ggml : cont
ggml-ci
* ggml : cont
ggml-ci
* cuda : no longer ggml headers last
ggml-ci
* ggml : fix q6_K FP16 -> FP32 conversion
ggml-ci
* ggml : more FP16 -> FP32 conversion fixes
ggml-ci
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* Make use of ggml-quants.h possible in C++ code
* One cannot possibly be defining static_assert in a C++ compilation
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* llama : ggml-backend integration
* ggml-backend : add names to buffers
* fix unmap after loading
* batched-bench : add tensor_split param
* llama : check for null tensor_split
* ggml-backend : increase GGML_MAX_BACKENDS
* improve graph splitting, partial fix for --no-kv-offload
* cuda : add ggml-backend split buffer support
* cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available)
* ggml : fix null backend dereference (#4807)
* ggml : fix null backend dereference
* ggml : also check ggml_backend_is_cpu
* test-backend-ops : check buffer allocation failures
* llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row)
* ggml : fix mul_mat_id work size
* llama : rewrite session kv load/set without graphs
* minor
* llama : only initialize used backends, free backends on context free
* llama : abort ctx if cuda backend init fails
* llama : rewrite lora with ggml-backend and compute on CPU
ggml-ci
* llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer
* opencl : add ggml-backend buffer type
* cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf)
* llama : on Metal, by default offload the full model
ggml-ci
* metal : page align the data ptr (#4854)
* Apply suggestions from code review
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : fix split buffer free
* address review comments
* llama-bench : add split-mode parameter
* fix whitespace
* opencl : fix double initialization
* server : add --split-mode parameter
* use async copy and compute to improve multi-gpu performance
ggml-ci
* use async memcpys to copy the graph outputs to the CPU
* fix opencl
* use a host buffer for the cpu compute buffer for faster copies to the gpu
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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* sync : ggml (part 1)
* sync : ggml (part 2, CUDA)
* sync : ggml (part 3, Metal)
* ggml : build fixes
ggml-ci
* cuda : restore lost changes
* cuda : restore lost changes (StableLM rope)
* cmake : enable separable compilation for CUDA
ggml-ci
* ggml-cuda : remove device side dequantize
* Revert "cmake : enable separable compilation for CUDA"
This reverts commit 09e35d04b1c4ca67f9685690160b35bc885a89ac.
* cuda : remove assert for rope
* tests : add test-backend-ops
* ggml : fix bug in ggml_concat
* ggml : restore `ggml_get_n_tasks()` logic in `ggml_graph_plan()`
* ci : try to fix macOS
* ggml-backend : remove backend self-registration
* ci : disable Metal for macOS cmake build
ggml-ci
* metal : fix "supports family" call
* metal : fix assert
* metal : print resource path
ggml-ci
---------
Co-authored-by: slaren <slarengh@gmail.com>
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ggml-ci
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* sync : ggml (backend v2) (wip)
* sync : migrate examples and llama.cpp to dynamic graphs (wip)
* sync : update tests + fix max op params to 64
ggml-ci
* sync : ggml-cuda
ggml-ci
* llama : fix save/load state context size
ggml-ci
* sync : try to fix build on tvOS
* sync : pass custom graph sizes in training examples
* sync : update graph copies to new ggml API
* sync : update sync-ggml.sh with new files
* scripts : fix header in sync script
* train : fix context size calculations
* llama : increase inference graph size up to 4096 nodes
* train : allocate grads for backward graphs
* train : allocate grads for gb_tmp
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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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