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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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* CUDA multi GPU + scratch
ggml_cuda_compute_forward
Tensor parallelism
ggml_cuda_add
ggml_cuda_rms_norm
ggml_cuda_silu
CUDA scratch buffer
--main-gpu CLI option
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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
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* mtl : export the LLaMA computation graph
* ci : disable temporary
* mtl : adapt the MNIST example as starter
* mtl : no need for mtl-export tool, add cli arg for main instead
* mtl : export just a small part of the graph for now to make it easier
* mtl : move MSL code into separate file for easy editing
* mtl : initial get_rows_q4_0 kernel
* mtl : confirmed get_rows_q4_0 is working correctly
* mtl : add rms_norm kernel + confirm working
* mtl : add mul kernel + confirm working
* mtl : initial mul_mat Q4 kernel (wrong results)
* mtl : mul_mat fixes (still wrong)
* mtl : another mul_mat Q4 (still does not work)
* mtl : working mul_mat q4
* ggml : fix handling of "view" ops in ggml_graph_import()
* mtl : add rope kernel
* mtl : add reshape and transpose handling
* ggml : store offset as opt arg for ggml_view_xd() operators
* mtl : add cpy kernel + handle view ops
* mtl : confirm f16 x f32 attention mul mat
* mtl : add scale kernel
* mtl : add diag_mask_inf kernel
* mtl : fix soft_max kernel
* ggml : update ggml_nbytes() to handle non-contiguous tensors
* mtl : verify V tensor contents
* mtl : add f32 -> f32 cpy kernel
* mtl : add silu kernel
* mtl : add non-broadcast mul kernel
* mtl : full GPU inference of the computation graph
* mtl : optimize rms_norm and soft_max kernels
* mtl : add f16 mat x f32 vec multiplication kernel
* mtl : fix bug in f16 x f32 mul mat + speed-up computation
* mtl : faster mul_mat_q4_0_f32 kernel
* mtl : fix kernel signature + roll inner loop
* mtl : more threads for rms_norm + better timing
* mtl : remove printfs from inner loop
* mtl : simplify implementation
* mtl : add save/load vocab to ggml file
* mtl : plug Metal inference into llama.cpp (very quick-n-dirty)
* mtl : make it work with main example
Lots of hacks but at least now it generates text
* mtl : preparing for merge
* mtl : clean-up ggml mtl interface + suport scratch / inplace
* mtl : remove temp / debug code
* metal : final refactoring and simplification
* Revert "ci : disable temporary"
This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63.
* metal : add comments
* metal : clean-up stuff, fix typos
* readme : add Metal instructions
* readme : add example for main
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1. Add a `LLAMA_SUPPORTS_GPU_OFFLOAD` define to `llama.h` (defined when compiled with CLBlast or cuBLAS)
2. Update the argument handling in the common example code to only show the `-ngl`, `--n-gpu-layers` option when GPU offload is possible.
3. Add an entry for the `-ngl`, `--n-gpu-layers` option to the `main` and `server` examples documentation
4. Update `main` and `server` examples documentation to use the new style dash separator argument format
5. Update the `server` example to use dash separators for its arguments and adds `-ngl` to `--help` (only shown when compiled with appropriate support). It will still support `--memory_f32` and `--ctx_size` for compatibility.
6. Add a warning discouraging use of `--memory-f32` for the `main` and `server` examples `--help` text as well as documentation. Rationale: https://github.com/ggerganov/llama.cpp/discussions/1593#discussioncomment-6004356
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The underlying representation of multibyte character literals is
implementation-defined. This could, at least in principle, cause
cross-build data export/import issues independent of endianness.
Define magic numbers as integer literals to be on the safe side.
Signed-off-by: Juuso Alasuutari <juuso.alasuutari@gmail.com>
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* ggml : use F16 instead of F32 in Q4_0, Q4_1 and Q8_0
* llama : bump LLAMA_FILE_VERSION to 3
* cuda : update Q4 and Q8 dequantize kernels
* ggml : fix AVX dot products
* readme : update performance table + hot topics
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* CUDA kernel for q4_0 dequant. + mat. vec. mult.
* Added q4_1 via template
* Added missing __syncthreads();
* --gpu_layers -> --gpu-layers
* Shorter dequantize_mul_mat_vec line
* q5_0 dequantize_mul_mat kernel
* More readable dequantize_mul_mat_vec logic
* dequantize_mul_mat_vec kernels for q5_1, q8_0, f16
* llama : offload "output" tensor to GPU too + coding style fixes
---------
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
---------
Co-authored-by: Stephan Walter <stephan@walter.name>
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* llama : only copy used KV cache in get / set state
* switch to ggml for copying k, v
* avoid designated initializers
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* Sample interface, new samplers.
New samplers:
- locally typical sampling
- tail free sampling
- frequency and presence penalty
- mirostat
Ignore EOS fix: -inf should be used.
* mirostat
* Added --logit-bias and --no-penalize-nl, removed std::span
* Use C++11, clarify llama API documentation, rename Mirostat parameters to --mirostat_lr and --mirostat_ent, add temperature sampling for Mirostat, simplify Mirostat sampling API parameters (removed N and *k)
Use C++11, clarify llama API documentation, rename Mirostat parameters to --mirostat_lr and --mirostat_ent, add temperature sampling for Mirostat, simplify Mirostat sampling API parameters (removed N and *k)
* Save and load example adjust
* Tests
* Windows build fix
* Windows test fix
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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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The llama_set_state_data function restores the rng state to what it
was at the time llama_copy_state_data was called. But users may want
to restore the state and proceed with a different seed.
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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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embedding and kv_cache (#1105)
* reserve correct size for logits
* add functions to get and set the whole llama state:
including rng, logits, embedding and kv_cache
* remove unused variables
* remove trailing whitespace
* fix comment
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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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Hide it behind an #ifdef
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- Support all three formats (ggml, ggmf, ggjt). (However, I didn't
include the hack needed to support GPT4All files without conversion.
Those can still be used after converting them with convert.py from my
other PR.)
- Support both mmap and read (mmap is used by default, but can be
disabled with `--no-mmap`, and is automatically disabled for pre-ggjt
files or on platforms where mmap is not supported).
- Support multi-file models like before, but automatically determine the
number of parts rather than requiring `--n_parts`.
- Improve validation and error checking.
- Stop using the per-file type field (f16) entirely in favor of just
relying on the per-tensor type/size fields. This has no immediate
benefit, but makes it easier to experiment with different formats, and
should make it easier to support the new GPTQ-for-LLaMa models in the
future (I have some work in progress on that front).
- Support VirtualLock on Windows (using the same `--mlock` option as on
Unix).
- Indicate loading progress when using mmap + mlock. (Which led me
to the interesting observation that on my Linux machine, with a
warm file cache, mlock actually takes some time, whereas mmap
without mlock starts almost instantly...)
- To help implement this, move mlock support from ggml to the
loading code.
- madvise/PrefetchVirtualMemory support (based on #740)
- Switch from ifstream to the `fopen` family of functions to avoid
unnecessary copying and, when mmap is enabled, allow reusing the same
file descriptor for both metadata reads and mmap (whereas the existing
implementation opens the file a second time to mmap).
- Quantization now produces a single-file output even with multi-file
inputs (not really a feature as much as 'it was easier this way').
Implementation notes:
I tried to factor the code into more discrete pieces than before.
Regarding code style: I tried to follow the code style, but I'm naughty
and used a few advanced C++ features repeatedly:
- Destructors to make it easier to ensure everything gets cleaned up.
- Exceptions. I don't even usually use exceptions when writing C++, and
I can remove them if desired... but here they make the loading code
much more succinct while still properly handling a variety of errors,
ranging from API calls failing to integer overflow and allocation
failure. The exceptions are converted to error codes at the
API boundary.)
Co-authored-by: Pavol Rusnak <pavol@rusnak.io> (for the bit I copied from #740)
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Command that calculates some statistics over the errors introduced by
quantization, like mean square error, max error and some percentile errors for layer
weights. Should be useful for testing quantization improvements.
Exposes some internal state from ggml and llama for testing
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The api provides access methods for retrieving the current memory buffer for the kv_cache and its token number.
It also contains a method for setting the kv_cache from a memory buffer.
This makes it possible to load/save history - maybe support --cache-prompt paramater as well?
Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
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This is a breaking change that's going to give you three benefits:
1. Your inference commands should load 100x faster
2. You may be able to safely load models 2x larger
3. You can run many concurrent inference processes
This was accomplished by changing the file format so we can mmap()
weights directly into memory without having to read() or copy them
thereby ensuring the kernel can make its file cache pages directly
accessible to our inference processes; and secondly, that the file
cache pages are much less likely to get evicted (which would force
loads to hit disk) because they're no longer competing with memory
pages that were needlessly created by gigabytes of standard i/o.
The new file format supports single-file models like LLaMA 7b, and
it also supports multi-file models like LLaMA 13B. Our Python tool
now merges the foo.1, foo.2, etc. files back into a single file so
that the C++ code which maps it doesn't need to reshape data every
time. That's made llama.cpp so much simpler. Much of its load code
has now been deleted.
Furthermore, this change ensures that tensors are aligned properly
on a 32-byte boundary. That opens the door to seeing if we can get
additional performance gains on some microprocessors, by using ops
that require memory alignment.
Lastly note that both POSIX and the Windows platform are supported
Fixes #91
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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>
---------
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
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* File load progress reporting
* Move llama_progress_handler into llama_context_params
* Renames
* Use seekg to find file size instead
* More correct load progress
* Call progress callback more frequently
* Fix typo
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`llama_sample_top_p_top_k` was missing the struct annotation on line 126.
This causes a compiler issue when being parsed by the Kotlin C interop generator.
This commit fixes the above issue by adding the struct annotation.
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* Support calling mlock() on loaded model data on Linux and macOS
This is enabled by a new --mlock command line option.
Using mlock() disables swapping and memory compression for the model
data. Doing so can be useful on systems where the model takes up a
large fraction of system RAM. In my experience, macOS is quite eager to
start compressing llama.cpp's memory, which then makes it halt for a few
seconds while it decompresses, even with a model that uses "only" 25GB
out of 32GB.
Of course, this comes at the cost of forcing the system to swap or
compress other processes' memory instead, so it needs to be used with
care and shouldn't be enabled by default.
In theory it should be possible to support this on Windows as well using
VirtualLock(), but I'm not much of a Windows user.
* Update llama.cpp
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* working but ugly
* add arg flag, not working on embedding mode
* typo
* Working! Thanks to @nullhook
* make params argument instead of hardcoded boolean. remove useless time check
* start doing the instructions but not finished. This probably doesnt compile
* Embeddings extraction support
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* Major refactoring - introduce C-style API
* Clean up
* Add <cassert>
* Add <iterator>
* Add <algorithm> ....
* Fix timing reporting and accumulation
* Measure eval time only for single-token calls
* Change llama_tokenize return meaning
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