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* detect NUMA systems and pin work threads to nodes (linux)
* disable mmap prefetch/readahead for NUMA systems
* avoid sending finalize op to thread pool if it does nothing
* silence robot
* fix args
* make --numa a param
* recommendation that n_nodes evenly divide n_threads did not warrant such aggressive enforcement
* lower synchronization overhead
* statically allocate
* move numa state to g_state
* add description for --numa
* ggml : minor style changes
* ggml : minor style + try fix sanitizer build
* llama : allow to initialize backend with NUMA support
* llama : avoid ggml include in llama-util.h
* ggml : style / formatting
* ggml : fix handling of ops with n_threads > n_tasks > 1
* server : utilize numa parameter
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Allow "quantizing" to f16 and f32
Fix an issue where quantizing didn't respect LLAMA_NO_K_QUANTS
Add brief help to the list of quantization types in the quantize tool
Ignore case for quantization type arguments in the quantize tool
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from 16/32bit (#1691)
* Add support for quantizing already quantized models
* Threaded dequantizing and f16 to f32 conversion
* Clean up thread blocks with spares calculation a bit
* Use std::runtime_error exceptions.
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* Starting to add k-quantization to ggml
I think it is better to have quantization separate from
ggml. For now just adding the k-quants there, but it would be
better to also factor out the existing ggml quantizations.
* Adding Q3_K and Q8_K (de)-quantization
* Q3_K now working on CUDA and AVX2/scalar
CUDA is not ideal - ~50% slower than Q4_0 for
single token prediction, about the same in batch
mode (perplexity). CPU single token is ~55 ms
(on Ryzen 7950X).
* Some improvement for Q3_K on CUDA
It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0.
* Some more CUDA optimizations for Q3_K
Single token is now 20.5 ms/token (~20% slower than Q4_0).
Perplexity is on par with Q4_0.
* Adding Q4_K - scalar, AVX2, CUDA
Performance is the same or perhaps very slightly better than Q4_0 on the CPU.
On the GPU, single token prediction is ~10% better than Q4_0,
batch mode (perplexity is about the same).
* Adding Q6_K - scalar, AVX2, CUDA
Performance is ~40% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 6-bit model is ~44% larger than the 4-bit.
On the GPU, single token prediction is ~6% lower than Q4_0,
batch mode (perplexity) is even closer (but still slower).
* Adding Q5_K - scalar, AVX2, CUDA
Performance is ~20% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 5-bit model is ~22% larger than the 4-bit.
On the GPU, single token prediction is about the same as Q4_0
for both, single token and batch prediction.
* Per convention, all QX_K quantizations use Q5_K for output.weight
* Adding quantization mixes
* Quantization mixes: didn't quite get what I wanted in the last commit
* Q4_K dot product for ARM_NEON
* Q6_K dot product for ARM_NEON
* Q5_K dot product for ARM_NEON
* Adding Q3_K dot for ARM_NEON
It is 22% slower than Q4_K, despite the smaller model size.
On x86_64, where we are memory bound, the Q3_K model is
quite a bit faster than Q4_K.
* A very slightly faster ARM_NEON Q3_K dot
* Adding Q2_K - just CUDA for now
Token prediction is pretty good - about 15.5 ms on a RTX 4080.
Perplexity is about the same as Q4_K.
* Adding scalar and AVX2 Q2_K dot
* Adding ARM_NEON Q2_K dot
About the same performance as Q4_K.
* A slightly faster ARM_NEON Q2_K dot
Single token prediction is now ~36 ms on M2 Max.
The code is much simpler too.
* Fixed bug in Q2_K CUDA dot product kernel
Stranegly enough, for the few prompts I tried with the 7B model
the responses looked perfectly reasonable. Only realized something
is not quite right when I tried the larger models and started getting
nonse back.
In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X
box iusing CUDA and model fully loaded on the GPU are
~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B.
The max number of layers that fit in VRAM for The 65B is 32.
With that, we get ~330 ms per token, which is not that much faster
than just running on the CPU (~470 ms per token).
* Don't print zeros/NaNs when no count histogram has been collected
* A 10% faster CUDA vector dot kernel for Q3_K
Q3_K is now running at ~18.5 ms / token on CUDA,
so the gap to Q4_0 is only 10%.
It seems memory acccess pattern is more important for
performance than the amount of computation the kernel
does.
* A slightly daster Q4_K AVX2 dot product
For perplexity, where we are less memory bound, time per
pass drops by ~5%. Barely measurable difference for single
token prediction.
* A slightly faster ARM_NEON A4_K dot product
* Minor
* Fix quantization error test
We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit
quantization variants.
* Fix docker build
I have been sloppy with vector reinterpret casts on ARM_NEON.
It seems clang is very forgiving in that regard.
* Added forgotten ggml.o dependence on k_quants.h to the Makefile
* Had unintentionally committed the Makefile with -Ofast enabled
* ggml : rename k_quants -> ggml-quants-k, use lowercase in code
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* ggml : remove Q4_0 bit shufling (ARM NEON)
* ggml : remove Q4_1 bit shuffling (ARM NEON + reference)
* ggml : nibbles_from_floats() + bytes_from_nibbles() (ARM NEON)
* ggml : remove Q4_2 bit shuffling (WIP, BROKEN)
* ggml : remove Q5_0 bit shuffling (ARM NEON)
* ggml : 2x faster scalar implementations
* ggml : remove Q5_1 bit shuffling (ARM NEON + scalar)
* ggml : simplify scalar dot
* ggml : remove WASM SIMD bit shuffling + remove vzip for ARM 32-bit
* ggml : fix Q4_1 quantization
* ggml : update cuBLAS + normalize variable names
* ggml : remove Q4_2 mode
* ggml : minor formatting
* ggml : fix Q5_0 quantization
* scripts : add script for measuring the time per token
* AVX implementations (#1370)
* ggml : uniform 5th bit extraction
* llama : produce error upon loading old model files
* llama : fix model magic/version write
* ggml : speed-up Q5_0 + Q5_1 at 4 threads
* ggml : preserve old Q4 and Q5 formats
* ggml : simplify Q8_1 - no need for low / high sums anymore
* ggml : fix Q8_0 and Q8_1 rounding
* Revert "AVX implementations (#1370)"
This reverts commit 948d124837f9d287d8490f41338e0e4cceb0814f.
* ggml : fix AVX2 implementation
* sha : update hashes for 7B and 13B
* readme : update timings + remove warning banner
* llama : update v2 PR number to 1405
* ggml : fix WASM comments
* ggml : back to original bit order
* readme : add note that Q4 and Q5 have been changed
* llama : fix return for unknown version
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Co-authored-by: Stephan Walter <stephan@walter.name>
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(#1301)
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* Add git-based build information for better issue tracking
* macOS fix
* "build (hash)" and "CMAKE_SOURCE_DIR" changes
* Redo "CMAKE_CURRENT_SOURCE_DIR" and clearer build messages
* Fix conditional dependency on missing target
* Broke out build-info.cmake, added find_package fallback, and added build into to all examples, added dependencies to Makefile
* 4 space indenting for cmake, attempt to clean up my mess in Makefile
* Short hash, less fancy Makefile, and don't modify build-info.h if it wouldn't change it
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* ggml : add Q5_0 quantization (cuBLAS only)
* ggml : fix Q5_0 qh -> uint32_t
* ggml : fix q5_0 histogram stats
* ggml : q5_0 scalar dot product
* ggml : q5_0 ARM NEON dot
* ggml : q5_0 more efficient ARM NEON using uint64_t masks
* ggml : rename Q5_0 -> Q5_1
* ggml : adding Q5_0 mode
* quantize : add Q5_0 and Q5_1 to map
* ggml : AVX2 optimizations for Q5_0, Q5_1 (#1195)
---------
Co-authored-by: Stephan Walter <stephan@walter.name>
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instead of `int` (while `int` option still being supported)
This allows the following usage:
`./quantize ggml-model-f16.bin ggml-model-q4_0.bin q4_0`
instead of:
`./quantize ggml-model-f16.bin ggml-model-q4_0.bin 2`
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(#1179)
* ggml : add Q8_0 quantization format (rename the old one to Q8_1)
* tests : fix test-quantize-fns
* ggml : finalize Q8_0 implementation
* ggml : use q4_0_q8_0 and q4_2_q8_0
* ggml : fix Q8_0 dot product bug (ARM)
* ggml : Q8_0 unroll x2
* ggml : fix bug - using wrong block type
* ggml : extend quantize_fns_t with "vec_dot_type"
* ggml : fix Q8_0 to use 255 values out of 256
* ggml : fix assert using wrong QK4_2 instead of QK4_3
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* Multi-threading quantization.
Not much gain for simple quantizations, bit it will be important
for quantizations that require more CPU cycles.
* Multi-threading for quantize-stats
It now does the job in ~14 seconds on my Mac for
Q4_0, Q4_1 and Q4_2. Single-threaded it was taking
more than 2 minutes after adding the more elaborate
version of Q4_2.
* Reviewer comments
* Avoiding compiler confusion
After changing chunk_size to const int as suggested by
@ggerganov, clang and GCC starting to warn me that I don't
need to capture it in the lambda. So, I removed it from the
capture list. But that makes the MSVC build fail. So,
making it a constexpr to make every compiler happy.
* Still fighting with lambda captures in MSVC
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* ggml : Q4_2 ARM
* ggml : add ggml_is_quantized()
* llama : update llama_type_name() with Q4_2 entry
* ggml : speed-up q4_2
- 4 threads: ~100ms -> ~90ms
- 8 threads: ~55ms -> ~50ms
* ggml : optimize q4_2 using vmlaq_n_f32 + vmulq_n_f32
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* Revert 7e53955 (#542)
Still needs to be fixed properly
* Fix linking on mingw32
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* Be more strict about converting float to double
* Test equivalence of round, SILU implementations
Test module is commented out in CMakeLists.txt because the tests may
take a long time, depending on how much the compiler optimizes.
* Fix softmax in perplexity.cpp
* all : prefer float over double where appropriate
* perplexity : add <cmath>
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Introduce structs for the q4 data blocks
* ggml : rename quant struct variables + fix ARM_NEON
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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- main -> examples
- utils -> examples (renamed to "common")
- quantize -> examples
- separate tools for "perplexity" and "embedding"
Hope I didn't break something !
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