Commit Graph

385 Commits

Author SHA1 Message Date
Georgi Gerganov
18b35625c3
ggml : fix bug in LBFGS optimizer (found by ggml tests) 2023-06-19 20:43:30 +03:00
Georgi Gerganov
b97ca431db
ggml : sync latest ggml repo (#1924)
* ggml : sync latest ggml repo

* ggml : remove unused comments

* ggml : asserts
2023-06-19 18:12:33 +03:00
l3utterfly
8596af4277
ggml : fix bug in ggml_compute_forward_add_q_f32 (#1918) 2023-06-18 14:19:16 +03:00
Georgi Gerganov
ce2c7d72e2
metal : handle buffers larger than device's maxBufferLength (#1826)
* metal : handle buffers larger than device's maxBufferLength

* metal : print more verbose device info + handle errors

* metal : fix prints for overlapping views

* metal : minimize view overlap to try to utilize device memory better
2023-06-18 09:09:47 +03:00
Borislav Stanimirov
9cbf50c041
build : fix and ignore MSVC warnings (#1889) 2023-06-16 21:23:53 +03:00
Johannes Gäßler
254a7a7a5f
CUDA full GPU acceleration, KV cache in VRAM (#1827)
* Fixed CUDA RoPE

* ggml_cuda_mul_mat_vec_p021

* ggml_cuda_scale

* ggml_cuda_diag_mask_inf

* ggml_is_permuted

* ggml_cuda_cpy

* flatten rows for ggml_cuda_op

* Added a --low-vram option

* Fixed Windows performance

* Fixed LLAMA_CUDA_DMMV_Y > 1 for WizardLM
2023-06-14 19:47:19 +02:00
xaedes
e32089b2c2
train : improved training-from-scratch example (#1652)
* add python wrapper

https://gist.github.com/abetlen/2b90e5f153f6efd00931d098de5c73ce

* fix decoding error. adds errors=ignore parameter

* add python bindings for functions to get and set the whole llama state
(rng, logits, embedding and kv_cache)

* update python bindings

* add text generating baby-llama from scratch example

* fix race condition bug in ggml_compute_forward_diag_mask_f32

* implement ggml_soft_max_back for more performant backward pass of soft_max

avoids creating big intermediate matrices of size n_embd x n_embd for llama layers and n_vocab x n_vocab for cross entropy loss

* improve softmax backward pass

go from quadratic runtime to linear runtime by simplifying the formulas

* fix race condition bug in non-inplace ggml_compute_forward_diag_mask_f32

memcpy needs to be synchronized across threads to avoid race conditions.
=> do it in INIT phase

* fix bug in ggml_compute_forward_soft_max_back_f32 on DEBUG build

* improve performance of mul_mat backward pass

avoid transpose by using mul_mat with swapped arguments

* avoid printing too much newlines in baby-llama-text

* activate threading in baby-llama-text

* add ggml_out_prod and use it for mul_mat backward pass for improved performance

performance stats report improvement from 37 seconds to 16 seconds runtime during my training tests

* better weight initialization improves training convergence at start

* better weight initialization improves training convergence at start

* improve ggml_out_prod performance

- change iteration order (>15s -> 10s runtime)
- parallelize over one more dimension: over dst matrix rows (10s -> <5s runtime)

* add llama sampler, shuffle samples and constrain sampling to tokens occurring in train data

* fix get_samples call, add model tensor names, increase model size, start training samples after newline

* save train trained model to checkpoint and load model to be trained from checkpoint

* use inplace functions where possible

* initialize rng with srand

* use different arguments for input and output checkpoint

* ggml fixes to support backward pass on inplace operations

* remove duplicate include

* fix cross entropy loss

- add target probabilities for each sample which is then used in cross entropy loss

* print used memory before and after optimization

* sample with non-greedy sampling parameters at the end of training

* add cmake target for baby-llama-text

* add ggml_add1_inplace to header

* enable gradient propagation for inplace add1 and scale operations

those functions backward passes don't need the original src0, so they also work when forward is inplace

* implement AdamW in ggml_opt_adam by adding weight decay parameter (default 0.001f)

also add a schedule parameter (default 1.0f) that can be used to scale alpha and decay according to learning schedule.
setting the decay parameter to zero disables AdamW resulting in normal Adam optimizer.

since the difference between Adam and AdamW is minimal it is not implemented as another optimizer, but integrated into the existing Adam optimizer.

* use inplace operations in cross_entropy_loss

* fix random weight initialization scale

* add missing default parameters for adam optimizer

* add ggml_opt_context, so that we can properly resume training

otherwise the optimizer states, tracking statistics about the error function and its derivates,
will reset to zero each time ggml_opt is called, hindering convergence on resumed training.

now the optimizer context and all its memory is stored in a separate struct.

* fix bug in llama_sample_token_mirostat_v2

when all candidates are filtered out through mu threshold, the following soft_max operation will fail.
so keep at least one.

* add forward function without using cache, for more performant training

during training on whole samples no cache is required.
removing the cache and simplifying the remaining code results in performance and memory usage improvement.

* print suppressed newline tokens as string "\n"

printing too much actual newlines is suppressed to avoid flooding the console.

* store optimizer state in training checkpoint and add learning schedule

persistent optimizer state allows to resume training without resetting the optimizer
learning schedule consists of linear warmup ramp followed by cosine decay with restarts

* remove unused functions

* fix bug in get_samples which corrupted training targets

* save checkpoint only when it was trained

* simplify code

* remove trailing whitespace

* simplify backward pass for SQRT

* replace inefficient repeat backward pass with dedicated repeat_back operation

* add ggml_cross_entropy_loss with backward pass for faster training

cross entropy loss can also be implemented using softmax and log, but as dedicated operation it is faster and especially avoids unnecessary memory overhead.

* add tests for cross_entropy_loss backward pass

finite differences regularly results in estimated gradient of zero, despite the backward pass giving non zero gradient.
_probably_ the finite differences fails due to numerical issues

* use ggml_cross_entropy_loss in text training example

* remove trailing whitespace

* slightly improve how cross entropy loss is compute

btw: directly implemented cross entropy loss seems to have way lower magnitudes than when implemented with softmax and log.
probably the input to log gets closer to zero due to float numerics.
maybe the multiplication by (1.0-eps)/sum is more accurate..

* add llama_get_vocab to get the vocabulary as output parameters

* set default model.type for unknown models with few layers

* add export of training checkpoint to llama compatible model file

* get vocabulary for exporting training checkpoint to llama compatible model file

* implement backward pass of flash attention

* bugfixes for backward pass of flash attention

* test flash attention backward pass

need to set loose error bounds to pass.
the finitie differences are close to numeric limits and often return quite different values than the backward pass.
reducing eps further lets the gradients vanish completely.
likewise setting eps to big results in wronger values.
the softmax in the middle of the function is probably the most responsible for the numeric issues using finite differences.

* add option to train with flash attention and move options to the top of the main function

training from scratch also works with flash attention
training convergence and generation results after fix number of iterations are worse than when not using flash attention.
maybe there still lingers a bug in the flash attention backward pass?
but training works, just with slower convergence.

flash attention is still worth to use, because it requires way less memory and is faster with high n_ctx

* add train_params and command line option parser

* remove unnecessary comments

* add train params to specify memory size

* remove python bindings

* rename baby-llama-text to train-text-from-scratch

* replace auto parameters in lambda function

* add #include <climits>

* add explicit cast to fix compile error

"error: non-constant-expression cannot be narrowed from type 'int64_t' (aka 'long long') to 'uint32_t' (aka 'unsigned int') in initializer list [-Wc++11-narrowing]"

* remove trailing whitespace

* add ggml_opt_resume_g which accepts forward and backward cgraphs

* fix formulas in comments

* bug fix for ggml_compute_forward_get_rows_back_f32

the result should be set to zero, not to whatever data is in opt0

* improve training memory usage with scratch buffers

instead of relying on the automatic backward pass, we manually create the graph for the backward pass.
it turns out that all backward pass operations need only temporary memory which can be reused after each layer.

will compute backward pass for ALL model parameters

* add option to use scratch buffers in training or not

make it configurable because currently training with scratch buffers implies flash attention and optimization over all parameters.

* ci : disable temporary

* store view offset and permute axes in opt[0] instead of storing it in padding

use memcpy to store offset, because offset is of type size_t.
when storing it as int32_t offset would have to be smaller than 2^31 which is not necessarily true.

* minor : fix compile warnings + minor style changes

* fix bug in threaded indices calculation of ggml_compute_forward_flash_attn_back_f32

* store view offset like in master branch

* bug fix in forward_batch_wo_cache_flash_attn_train

* scratch buffer bug fixes in forward_batch_wo_cache_flash_attn_train

data of permute and reshape is the same as their input.
if we want to preserve the output of permute/reshape, we also need to preserve their inputs.

replace reshape(src0, src1) with reshape_nd calls so that we don't need src1.

replace (temporary) t03 with ggml_repeat(ctx0, layer.attention_norm, t02).
in the future we could also use the new broadcasting ggml_mul to avoid these repeat calls.
for this we need backward pass of broadcasting ggml_mul.

* remove unnecessary scratch buffer 0

buf 0 is persistent memory, so we can just disable scratch for this by using buf -1

* avoid creating unnecessary grad tensors

previously we need to create grads for model parameters, so that expand(..) correctly populates cgraph->leafs & cgraph->grads
this wasted memory, because unnecessary grad for each op were automatically created:
the automatically generated grad was unnecessary because we later manually set the grad (e.g. t35->grad = expand(gb, ...) ).
this discarded the automatically generated grad resulting in wasted memory.

improved this by changing expand(..) to not use ggml_build_forward_expand.
expand set cgraph->nodes but not the leafs.
cgraph->leafs & cgraph->grads are set in another pass after the last expand call.

* print used training seed

* zero initialize gfbuf and gbbuf

* ci : re-enable workflows + add README for training

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-13 22:04:40 +03:00
Kerfuffle
74d4cfa343
Allow "quantizing" to f16 and f32 (#1787)
* 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
2023-06-13 04:23:23 -06:00
Georgi Gerganov
17c10acfb4
ggml : force no_alloc == false when creating opt tensors (close #1699)
This is needed to make operators like ggml_view() be able to store their
parameters in the ggml context's memory and not get discarded when
no_alloc is true
2023-06-10 12:08:15 +03:00
Xingchen Song(宋星辰)
ef3171d162
ggml : workaround for missing _mm256_setr_m128i in GCC < 8 (#1638) 2023-06-10 10:49:40 +03:00
Steven Roussey
b50b570ed9
ggml : fix fprintf warnings (#1720) 2023-06-08 10:12:28 +03:00
Georgi Gerganov
5c64a0952e
k-quants : allow to optionally disable at compile time (#1734)
* k-quants : put behind optional compile flag LLAMA_K_QUANTS

* build : enable k-quants by default
2023-06-07 10:59:52 +03:00
Georgi Gerganov
2a4e41a086
llama : fix compile warnings 2023-06-06 22:41:53 +03:00
Johannes Gäßler
17366df842
Multi GPU support, CUDA refactor, CUDA scratch buffer (#1703)
* 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
2023-06-06 21:33:23 +02:00
Georgi Gerganov
2d43387daf
ggml : fix builds, add ggml-quants-k.o (close #1712, close #1710) 2023-06-06 10:18:03 +03:00
kiltyj
9d0693bce3
metal : use shared buffers between CPU and GPU (#1696)
* Use MTLDevice.newBufferWithBytesNoCopy to share buffers between CPU and GPU

* Page-align buffers used by Metal

* Remove trailing whitespace

* Only import unistd.h for Metal builds

* metal : remove unnecessary copies

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 23:24:04 +03:00
grahameth
efe0507632
ggml : fix internal overflow in ggml_time_us on Windows (#1702)
Co-authored-by: grahameth <->
2023-06-05 23:11:49 +03:00
Kawrakow
99009e72f8
ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684)
* 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>
2023-06-05 22:56:18 +03:00
Georgi Gerganov
ecb217db4f
llama : Metal inference (#1642)
* 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 98c267fc77.

* metal : add comments

* metal : clean-up stuff, fix typos

* readme : add Metal instructions

* readme : add example for main
2023-06-04 23:34:30 +03:00
0cc4m
dcb2ed4826
OpenCL: Fix duplication of layers in VRAM and RAM, add GPU mul kernel (#1653)
* Use events instead of clFinish, where possible

* OpenCL: Don't load gpu layers into RAM, add mul_f32 kernel

* Reduce queueing overhead for contiguous tensors by using single mul kernel call

* Adapt to #1612 cl_mem malloc changes

* Reduce code duplication between cuda and opencl branches

* Improve implementation
2023-06-04 08:12:05 +02:00
Georgi Gerganov
7552ac5863
ggml : sync cgraph import / export API 2023-05-29 19:31:44 +03:00
Georgi Gerganov
5d1830b99d
ggml : fix bug in ggml_alibi 2023-05-29 19:30:49 +03:00
apcameron
a6704643b6
ggml : add support for the RISCV architecture (#1616) 2023-05-27 23:03:25 +03:00
Georgi Gerganov
93618031c7
ggml : add ggml_tensor_overhead() 2023-05-27 16:19:56 +03:00
Georgi Gerganov
bdbda1b17a
ggml : sync ggml core (minor additions, e.g. ggml_get_tensor_by_name()) 2023-05-27 12:23:16 +03:00
0cc4m
2e6cd4b025
OpenCL Token Generation Acceleration (#1459)
* Move back to C++ for OpenCL

* Refactor OpenCL code to work more like the CUDA code, add missing functions

* Deduplicate dequant kernels

* Add OpenCL compile options

* Use compile args for preprocessing constants

* Restore default platform + device selection by id behavior

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Henri Vasserman <henv@hot.ee>
2023-05-23 00:33:24 +03:00
Georgi Gerganov
265db9834e
ggml : output 3d sizes in ggml_graph_dump_dot() 2023-05-21 11:56:23 +03:00
Georgi Gerganov
fab49c685e
ggml : update WASM SIMD 2023-05-20 20:00:41 +03:00
Georgi Gerganov
3de84b2606
ggml : add ggml_clamp() (#1539)
* ggml : add ggml_clamp()

* ggml : indentation
2023-05-20 15:34:45 +03:00
Johannes Gäßler
affc76edfd
cuda : loading models directly into VRAM, norm calculation on GPU, broadcasting for ggml_mul (#1483)
* Broadcasting for ggml_mul

* CUDA kernel for ggml_mul, norms in VRAM

* GPU weights not in RAM, direct loading with cuFile

* fixup! GPU weights not in RAM, direct loading with cuFile

* fixup! GPU weights not in RAM, direct loading with cuFile

* define default model path once, sync path with readme (#1366)

* ~7% faster Q5_1 AVX2 code (#1477)

* convert.py: Support models which are stored in a single pytorch_model.bin (#1469)

* Support models in a single pytorch_model.bin

* Remove spurious line with typo

* benchmark-matmul: Print the average of the test results (#1490)

* Remove unused n_parts parameter (#1509)

* Fixes #1511 lambda issue for w64devkit (mingw) (#1513)

* Fix for w64devkit and mingw

* make kv_f16 the default for api users (#1517)

* minor : fix compile warnings

* readme : adds WizardLM to the list of supported models (#1485)

* main : make reverse prompt option act as a stop token in non-interactive mode (#1032)

* Make reverse prompt option act as a stop token in non-interactive scenarios

* Making requested review changes

* Update gpt_params_parse and fix a merge error

* Revert "Update gpt_params_parse and fix a merge error"

This reverts commit 2bb2ff1748.

* Update gpt_params_parse and fix a merge error take 2

* examples : add persistent chat (#1495)

* examples : add persistent chat

* examples : fix whitespace

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* tests : add missing header

* ggml : use F16 instead of F32 in Q4_0, Q4_1, Q8_0 (#1508)

* 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

* ggml : fix scalar implementation of Q4_1 dot

* llama : fix compile warnings in llama_set_state_data()

* llama : fix name shadowing and C4146 (#1526)

* Fix name shadowing and C4146

* Fix if macros not using defined when required

* Update llama-util.h

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Update llama-util.h

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Code style

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Fix for mingw (#1462)

* llama : add llama_init_backend() API (close #1527)

* feature : add blis and other BLAS implementation support (#1502)

* feature: add blis support

* feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927

* fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake

* Fix typo in INTEGER

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Revert "feature : add blis and other BLAS implementation support (#1502)"

This reverts commit 07e9ace0f9.

* GPU weights not in RAM, direct loading with cuFile

* llama : code style fixes + progress print fix

* ggml : ggml_mul better broadcast support

* cmake : workarounds for cufile when CMake version < 3.25

* gg rebase fixup

* Loop in llama.cpp, fixed progress callback

* Attempt clang-tidy fix

* llama : fix vram size computation

* Add forgotten fclose()

---------

Co-authored-by: András Salamon <ott2@users.noreply.github.com>
Co-authored-by: Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com>
Co-authored-by: Tom Jobbins <784313+TheBloke@users.noreply.github.com>
Co-authored-by: rankaiyx <rankaiyx@rankaiyx.com>
Co-authored-by: Stephan Walter <stephan@walter.name>
Co-authored-by: DannyDaemonic <DannyDaemonic@gmail.com>
Co-authored-by: Erik Scholz <Green-Sky@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: David Kennedy <dakennedyd@gmail.com>
Co-authored-by: Jason McCartney <jmac@theroot.org>
Co-authored-by: Evan Jones <evan.q.jones@gmail.com>
Co-authored-by: Maxime <672982+maximegmd@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Zenix <zenixls2@gmail.com>
2023-05-20 15:19:28 +03:00
Maxime
503db28849
llama : fix name shadowing and C4146 (#1526)
* Fix name shadowing and C4146

* Fix if macros not using defined when required

* Update llama-util.h

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Update llama-util.h

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Code style

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 10:22:37 +03:00
Georgi Gerganov
4fd3e29297 ggml : fix scalar implementation of Q4_1 dot 2023-05-20 10:13:19 +03:00
Georgi Gerganov
2d5db48371
ggml : use F16 instead of F32 in Q4_0, Q4_1, Q8_0 (#1508)
* 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
2023-05-19 22:17:18 +03:00
Ilya Kurdyukov
42627421ec
~7% faster Q5_1 AVX2 code (#1477) 2023-05-16 18:36:47 +00:00
xaedes
79b2d5b69d
ggml : alternative fix for race condition bug in non-inplace ggml_compute_forward_diag_mask_f32 (#1454)
* fix race condition bug in non-inplace ggml_compute_forward_diag_mask_f32

memcpy needs to be synchronized across threads to avoid race conditions.
=> do it in INIT phase

* remove trailing whitespace

* Update ggml.c

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-14 18:55:02 +03:00
Georgi Gerganov
13c351ad72
ggml : various fixes (#1450)
- `ggml_rope()`
- `ggml_diag_mask_inf()` multi-threaded
- compatibility with scratch buffers
2023-05-14 18:22:50 +03:00
katsu560
60f8c361ca
ggml : add AVX support based on AVX2 code (#1430) 2023-05-14 10:03:51 +00:00
Georgi Gerganov
66841fdb0e
ggml : multi-thread mul and diag_mask ops (#1428) 2023-05-13 16:48:03 +03:00
Johannes Gäßler
905d87b70a
ggml : GPU-accelerated token generation (#1412)
* 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>
2023-05-13 16:38:36 +03:00
xaedes
f954edda93
ggml : implement backward pass for llama + small training-llama-from-scratch example (#1360)
* implement 8 of 14 missing backward pass operations used by llama

- GGML_OP_ADD_AT
- GGML_OP_CPY
- GGML_OP_MUL_MAT (src0.grad)
- GGML_OP_PERMUTE
- GGML_OP_RESHAPE
- GGML_OP_SCALE
- GGML_OP_TRANSPOSE
- GGML_OP_VIEW

implement additional ggml operation GGML_OP_ADD_AT, which is necessary for backward pass of GGML_OP_VIEW.

this operation adds src1 to src0 with data offset, i.e. to view(src0, ..., offset).
the values are return in a tensor size of src0. values outside of [data+offset:data+offset+nbytes(src1)] are just the original values from src0.

still missing backward passes for llama:

- GGML_OP_DIAG_MASK_INF
- GGML_OP_GET_ROWS
- GGML_OP_RMS_NORM
- GGML_OP_ROPE
- GGML_OP_SILU
- GGML_OP_SOFT_MAX

* implement 5 of 6 missing backward pass operations used by llama

- GGML_OP_DIAG_MASK_INF
- GGML_OP_GET_ROWS
- GGML_OP_RMS_NORM
- GGML_OP_SILU
- GGML_OP_SOFT_MAX

add necessary ggml operations GGML_OP_ADD1, GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK, GGML_OP_DIAG_MASK_ZERO, and GGML_OP_ROPE_BACK

GGML_OP_ADD1 is necessary to add a scalar value in the backward pass of GGML_OP_SOFT_MAX
GGML_OP_ADD1 could also be replaced by using GGML_OP_ADD and GGML_OP_REPEAT, but the performance would be worse. additionally GGML_OP_REPEAT will return unexpected value when the the input to GGML_OP_SOFT_MAX contains only a single scalar. in this case GGML_OP_REPEAT will not return the value that should be repeated (src1) but the value which shape the result should take (src0). So in this case it can not replace GGML_OP_ADD1.

GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK and GGML_OP_ROPE_BACK are necessary for backward pass of GGML_OP_SILU, GGML_OP_RMS_NORM and GGML_OP_ROPE. The backward pass for these functions cannot be easily composed of existing operations. Since the backward pass builds a computation graph we need operations forward pass implementations of the the required backward passes. Sounds a bit confusing at first, I know...

GGML_OP_DIAG_MASK_ZERO is necessary for backward pass of GGML_OP_DIAG_MASK_INF.

Some operations where previously inplace-only. for backward pass there needs to be non-inplace variants.
staying consistent with other operations that have non-inplace and inplace variants, the operations are changed to non-inplace and
functions with "_inplace" are added which are inplace.
in llama we need to call the inplace variants so that it is implemented as before.
for llama backward pass we need to use the non-inplace variants.

still not completely implemented backward passes for llama:

- GGML_OP_ROPE: needs forward pass for GGML_OP_ROPE_BACK
- GGML_OP_GET_ROWS: only necessary for tokenizer

* norm & rms_norm can not be threaded:

after investigation rms norm for quite some time I come to the conclusion that neither norm, nor rms_norm can be threaded, because we need mean over all items, not just of the slices each thread sees.

* remove already resolved TODO

* implement backward pass of ggml_rope and ggml_rope_back

* implement backward pass for ggml_get_rows and for new operation ggml_get_rows_back

* add test-grad0.c

* use GGML_PRINT_DEBUG for debug messages which will otherwise flood the console

* test both gradients of mul_mat

* disable graph dot export as it floods console

* bug fixes for silu_back

* successfully test silu backward

* bug fix for scale backward pass

use sum instead of mean for gradient of scalar scale parameter

* successfully test scale backward

* improve performance of sum backward pass

use add1(x,y) instead of add(x,repeat(y,x))

* improve performance of sqr backward pass

use scale(x,y) instead of mul(x,repeat(y,x))

* successfully test rope backward

* bug fix for cpy backward pass

* successfully test cpy backward

* bug fix for reshape backward pass

* successfully test reshape backward

* add test-opt.c

this uses ggml_opt to train a,b for minimal e=sum(sqr(c - a*b)) for random initial a,b,c

* correctly implement softmax backward pass using new operation ggml_diag

ggml_diag constructs diagonal matrices with entries.
ggml_diag(shape[a,1,c,d]) -> shape[a,a,c,d]

* successfully test soft_max backward

* align shape annotations

* add shape annotations for llama

* de-duplicate ggml_forward_dup code taking care of contiguous tensors of same type.

with this we can duplicate tensor of any typ as long as they are contiguous.

* fix ggml_compute_forward_dup_same_cont for when nelements < nthreads

when more threads are used than elements exist ie1 was less than ie0, resulting in invalid negative byte count argument in memcpy

* bug fix for add_at forward

required for view backward pass

src0 values must be copied to dst, because during addition we don't touch all dst elements in contrast to the normal add function.

* successfully test view backward

* minor code format improvement

* fix ggml_forward_add functions to work correctly with transposed tensors

uses the same logic as in ggml_compute_forward_add_q_f32, but make it consistent across all ggml_compute_forward_add_... functions.
this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add_q_f32.

* fix ggml_forward_add1 functions to work correctly with transposed tensors

uses the same logic as in ggml_compute_forward_add1_q_f32, but make it consistent across all ggml_compute_forward_add1_... functions.
this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add1_q_f32.

* test-grad0.c : add print_elements to help with debugging

* successfully test permute backward

* some minor test-grad0 fixes

* fix sub, mul and div functions to work correctly with transposed tensors

uses the same logic as in add

* implement ggml_cont backward pass

* successfully test transpose backward and permute for all permutations

also test sub, mul and div up to max n_dims

* test-grad0.c add TODO for view_2d and view_3d

add_at (required for view backward pass) is a bit tricky for n_dims > 1.

* fix comments

* successfully test diag_mask_inf and diag_mask_zero backward

* test-grad0 : fix test for div

nargs and ndims was swapped, corrupting the stack

* fix diag_mask to work with non-inplace input

* move dup call into the actual add_at functions

* fix get rows backward pass

* successfully test get_rows backward

* fix view backward pass

add nb parameters to add_at like in view.
together with offset they define how to view dst and src0 during the add_at operation.

* successfully test backward pass of view_1d, view_2d and view_3d

* fix backward pass for rms_norm

I would have used formulas from other frameworks, but they differed so I could not decide which is correct.
Instead it was derived here in comment using manual forward-backward automatic differention of rms_norm and simplification.

* successfully test backward pass of rms_norm

some tests may fail when gradients are large.
could not find a satisfying configuration to check for abs error and relative error that passes all tests while still actually testing the results with tight enough error bounds.
when looking at the values the "failed" tests look actually ok. for example:

rms_norm: ndims=2, i=0, k=2, x0=0.000153, xm=0.000053, xp=0.000253, f0=0.278594, f1=0.086213, g0=961.905457, g1=966.064941, eps=0.000100, error_abs=4.159485, error_rel=0.004324

it is due to the test logic in check_gradients that they fail.

* add todos for llama backward pass

- implementation for ADD1 backward pass should probably use sum instead of mean (but this backward pass is not required)
- repeat is not yet tested and looks like it only works for single element src0 inputs.

* add operation ggml_sum_rows

ggml_sum_rows(shape[a,b,c,d]) -> shape[1,b,c,d]

* add missing GGML_OP_SUM_ROWS

* fix backward pass for repeat

requires ggml_sum_rows

* successfully test backward pass of repeat

* update quantization types in switch-case of add_at and add1

* add baby-llama example training a very small llama model from scratch to output a sinusoidal wave.

had to increase maximum number of optimization parameters to train from scratch.

* fix softmax in baby-llama example

* switching from training with adam to lbfgs produces much better results in the baby-llama example

* train with two examples, creating new tensors each time..

* fix bug when using ggml_opt to optimize params in one context and use a renewable context for eval and opt

when not keeping gradients of model parameters they are overwritten by tensors created by opt, which may be invalid after opt context is renewed.
so we need to keep the original gradients and make dups for opt

* train on multiple examples, generate & print tokens with trained model afterwards

ctx0 for evaluation and optimization is renewed for each sample

* add ggml_reshape_1d, ggml_reshape_4d and ggml_view_4d

* fix soft_max backward pass for input->ne[1] != 1

* add ggml_log operation necessary for cross entropy loss

* add test for ggml_log gradients

* implement backward pass for ggml_sum_rows, necessary for cross entropy loss

* implement ggml_repeat support for rank > 2 tensors

* add test for ggml_sum_rows gradients

* fix training get_example_targets

predict the next token, not the current token!

* add square_error_loss and cross_entropy_loss functions

* optimize loss over multiple samples

this increases computation graph, need parallel batched forward for more efficiency.

* fix backward pass for add_at and change arguments to have same order as in view

* add ggml_set(ctx, a, b) to set b in view of a and return modified a

necessary to set values into kv_self cache and properly propagate the gradients

* fix kv_self gradients for training

use ggml_set instead of ggml_cpy to set kv_self cache with properly propagating gradients

* replace inplace operations for training with copying operations to allow gradient propagation

* add GGML_ASSERT to catch ggml_rope and back value errors

* add trainable lora-only model with all big matrices C split into A,B with A*B=C

this is not a lora-finetune, but the whole model changed to have only low-rank "lora" matrices.

training this instead of the normal model resulted in much worse results though...

* vastly improve training results

instead of logit targets 0 and 1 use -1 and +1.

* shorten code using a variable

* change name of GGML_OP_ADD_AT to GGML_OP_ACC

* smaller default values for baby llama model parameters

* update static assert of GGML_OP_COUNT

* remove shape annotations in llama_eval_internal

* revert disabling of threading for rms_norm and norm

* rename print functions in baby-llama example

* fix call to ggml_set_name

* add missing include for strcmp, etc

* remove trailing whitespace

* reduce number of test-grad0 iterations

avoid exceeding timeout of automated tests

* remove busy loop that was used as sleep for slower sinus wave generation

* disable slow tests grad0 and opt to avoid exceeding timeouts

* c++ in baby-llama example

use c++ includes instead of c includes
use std::min, std::max instead of MIN, MAX macros

* c++ in baby-llama example

use c++ includes instead of c includes
use std::min, std::max instead of MIN, MAX macros

* ggml : fix compiler warnings + cosmetic changes

* ggml : fix nullptr derefs in GGML_OP_CONT and GGML_OP_RESHAPE back

* swap arguments to vDSP_vdiv call

documentation for vDSP_vdiv states: "Note that B comes before A!"

* swap arguments to vDSP_vdiv call

documentation for vDSP_vdiv states: "Note that B comes before A!"

* ggml : swap vDSP_vsub args as per documentation

* add parallel batched forward function for baby-llama training

* cleanup code for batched training

* remove trailing whitespace

* minor : fix compiler warnings + indentation style

* ggml : fix null ptr deref in backward pass

* ggml : remove Q4_2 remnants

* ggml : fix clang-tidy warnings

* baby-llama : couple of clang-tidy warnings

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-13 15:56:40 +03:00
Georgi Gerganov
f048af0230
ggml : sync alibi fix from ggml repo 2023-05-13 11:54:33 +03:00
3ooabkhxtn
ac0cd259d5
Adding SSE instructions to ggml_vec_dot_q4_0_q8_0 (#1413) 2023-05-13 08:43:33 +00:00
Georgi Gerganov
b9fd7eee57
ggml : remove bit shuffling (#1405)
* 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 948d124837.

* 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>
2023-05-12 00:23:08 +03:00
Sami Farin
9f8dbc4787
use pause asm insn in busyloop to run the CPU (13600K) 10 °C cooler (#1314)
* use pause asm insn in busyloop to run the CPU (13600K) 10 °C cooler

Tested with a 13B model.

* use _mm_pause() in busyloop

* use _mm_pause() in busyloop on x86_64 to reduce power consumption
2023-05-09 14:29:20 +02:00
swittk
1b0fd45465
ggml : Allow usage of CLBlast alongside Accelerate.framework (#1336)
Minor edit in ggml.c which originally would prevent OpenCL from loading completely if GGML_USE_ACCELERATE was defined.
Minor speedup in prompt eval time.
2023-05-06 23:03:23 -04:00
Ron Jailall
20fbf2a2a0
ggml : change immintrin.h to intrin.h for compatibility (#1307)
* change immintrin.h to intrin.h for compatibility

Building on windows11 arm throws an error on this line. Seems like using intrin.h covers x86 and and arm

* conditional def of intrin.h

* fix typo in ggml.c
2023-05-04 18:05:59 +03:00
Georgi Gerganov
799fdc1b5d
ggml : vectorize Q8_0 quantization
https://github.com/ggerganov/ggml/pull/127#issuecomment-1533648531
2023-05-03 23:24:20 +03:00
Georgi Gerganov
5d5817ca60
ggml : fix 32-bit ARM 2023-05-02 22:14:50 +03:00
Marvin Gießing
cc0bb7235c
ggml : fix ppc64le build error and make cmake detect Power processors (#1284)
* Fix ppc64le build issue

* Added support to detect ppc64* processors
2023-05-02 19:42:16 +03:00
slaren
2d099e5193
ggml: add names to tensors (#1268)
* ggml: add names to tensors

* minor improvements to dot file formatting
2023-05-02 16:03:00 +02:00
slaren
58b367c2d7
cuBLAS: refactor and optimize f16 mat mul performance (#1259)
* cuBLAS: refactor, convert fp16 to fp32 on device

* cuBLAS: use multiple streams, choose smartly between mul_mat_q and mul_mat_f16

* fix build

* cuBLAS: update block_q5_1
2023-05-01 18:11:07 +02:00
Kerfuffle
2bdc09646d
ggml : fix ggml_used_mem() (#1264) 2023-05-01 14:56:07 +03:00
Georgi Gerganov
7ff0dcd320
ggml : fix UB (int << 31) 2023-04-30 22:28:51 +03:00
Georgi Gerganov
6bc4400e67
ggml : add Q5 WASM SIMD + GGML_FTYPE 2023-04-30 19:07:43 +03:00
Georgi Gerganov
3e5aa8a1c4
ggml : fix labels for GGML_OP_ALIBI 2023-04-30 10:25:46 +03:00
Georgi Gerganov
c3ca7a5f05
ggml : fix 32-bit ARM NEON 2023-04-29 21:34:23 +03:00
Georgi Gerganov
e8c051611a
ggml : use vzip instead of vuzp for consistency 2023-04-29 21:12:56 +03:00
Georgi Gerganov
0b5a935099
ggml : fix visibility and unused warnings 2023-04-29 19:28:36 +03:00
Georgi Gerganov
ec728e44d7
ggml : fix #if for f32_f32 mul_mat (CLBlast) (#1229) 2023-04-29 18:43:42 +03:00
Georgi Gerganov
214b6a3570
ggml : adjust mul_mat_f16 work memory (#1226)
* llama : minor - remove explicity int64_t cast

* ggml : reduce memory buffer for F16 mul_mat when not using cuBLAS

* ggml : add asserts to guard for incorrect wsize
2023-04-29 18:43:28 +03:00
slaren
7fc50c051a
cuBLAS: use host pinned memory and dequantize while copying (#1207)
* cuBLAS: dequantize simultaneously while copying memory

* cuBLAS: use host pinned memory

* cuBLAS: improve ggml_compute_forward_mul_mat_f16_f32 with pinned memory

* cuBLAS: also pin kv cache

* fix rebase
2023-04-29 02:04:18 +02:00
Henri Vasserman
b1ee8f59b4
cuBLAS: non-contiguous tensor support (#1215)
* Cuda: non-contiguous tensor support

* remove extra stuff

* rename

* fix error

* more fixes, now OpenBLAS and CLBlast build too

* now then?
2023-04-29 01:31:56 +02:00
Stephan Walter
36d19a603b
Remove Q4_3 which is no better than Q5 (#1218) 2023-04-28 23:10:43 +00:00
Georgi Gerganov
55390bcaf2
ggml : sync ggml (ggml_alibi) 2023-04-28 20:51:05 +03:00
Georgi Gerganov
11d902364b
ggml : add helper debug printf in soft_max 2023-04-28 17:59:08 +03:00
0cc4m
7296c961d9
ggml : add CLBlast support (#1164)
* Allow use of OpenCL GPU-based BLAS using ClBlast instead of OpenBLAS for context processing

* Improve ClBlast implementation, avoid recreating buffers, remove redundant transfers

* Finish merge of ClBlast support

* Move CLBlast implementation to separate file

Add buffer reuse code (adapted from slaren's cuda implementation)

* Add q4_2 and q4_3 CLBlast support, improve code

* Double CLBlast speed by disabling OpenBLAS thread workaround

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>

* Fix device selection env variable names

* Fix cast in opencl kernels

* Add CLBlast to CMakeLists.txt

* Replace buffer pool with static buffers a, b, qb, c

Fix compile warnings

* Fix typos, use GGML_TYPE defines, improve code

* Improve btype dequant kernel selection code, add error if type is unsupported

* Improve code quality

* Move internal stuff out of header
* Use internal enums instead of CLBlast enums
* Remove leftover C++ includes and defines
* Make event use easier to read

Co-authored-by: Henri Vasserman <henv@hot.ee>

* Use c compiler for opencl files

* Simplify code, fix include

* First check error, then release event

* Make globals static, fix indentation

* Rename dequant kernels file to conform with other file names

* Fix import cl file name

---------

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-28 17:57:16 +03:00
Yann Follet
04aaae1d79
add avx2 for dot_q8_0_q8_0, 2x faster than scalar (#1211) 2023-04-28 11:59:48 +00:00
Stephan Walter
0b2da20538
ggml : slightly faster AVX2 implementation for Q5 (#1197) 2023-04-26 23:26:42 +03:00
Georgi Gerganov
574406dc7e
ggml : add Q5_0 and Q5_1 quantization (#1187)
* 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>
2023-04-26 23:14:13 +03:00
Georgi Gerganov
7a32fcb3b2
ggml : add Q8_0 quantization format (rename the old one to Q8_1) (ARM NEON) (#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
2023-04-25 23:40:51 +03:00
unbounded
dd0eabc049
ggml : use full range for Q4_0 and Q4_2 quantization (#729)
* Use full range for q4_0 quantization

By keeping the sign of the highest magnitude, we can make sure the
highest value maps to -8, which is currently unused.
This is a bit of a freebie since it is fully backwards compatible with
the current format.

* Update quantize_row_q4_0 for AVX/AVX2

* Update quantize_row_q4_0 for WASM

Untested

* Update quantize_row_q4_0 for Arm NEON

* Update quantize_row_q4_0 for PowerPC

Untested

* Use full range for q4_2 quantization
2023-04-25 20:20:46 +03:00
xaedes
54bb60e268
ggml : fix bug in ggml_compute_forward_sum_f32 (#1162)
The sum over all rows is now computed instead of just the last row
2023-04-24 23:02:02 +02:00
Stephan Walter
2ec83428de
Fix build for gcc 8 and test in CI (#1154) 2023-04-24 15:38:26 +00:00
Georgi Gerganov
ec9cdb6752
ggml : do not print perf ops that have not been used at all 2023-04-23 18:32:52 +03:00
Georgi Gerganov
e4422e299c
ggml : better PERF prints + support "LLAMA_PERF=1 make" 2023-04-23 18:15:39 +03:00
Stephan Walter
53c8434398
Improve AVX2 for vec_dot_q4_3_q8_0 (#1138) 2023-04-23 11:01:03 +00:00
Yishuo Wang
c9e2c26f41
A better packNibbles and mul_sum_i8_pairs_float implementation using AVX512 (#1119) 2023-04-23 07:57:05 +00:00
Georgi Gerganov
0e018fe008
ggml : fix Q4_3 cuBLAS 2023-04-22 16:32:07 +03:00
Stephan Walter
c50b628810
Fix CI: ARM NEON, quantization unit tests, editorconfig (#1122) 2023-04-22 10:54:13 +00:00
Georgi Gerganov
872c365a91 ggml : fix AVX build + update to new Q8_0 format 2023-04-22 11:08:12 +03:00
Georgi Gerganov
955ef9a5d5
ggml : alternative Q4_3 implementation using modified Q8_0 (#1109)
* ggml : prefer vzip to vuzp

This way we always use the same type of instruction across all quantizations

* ggml : alternative Q4_3 implementation using modified Q8_0

* ggml : fix Q4_3 scalar imlpementation

* ggml : slight improvement of Q4_3 - no need for loop unrolling

* ggml : fix AVX paths for Q8_0 quantization
2023-04-22 10:55:35 +03:00
Stephan Walter
c5aa5e5777
ggml : AVX2 optimization for vec_dot_q4_3_q8_0 and refactoring (#1099)
* AVX2 optimization for vec_dot_q4_3_q8_0 and refactoring

* finish AVX vectorization of quantize_row_q8_0

* Rename hsum_int_8 to hsum_i32_8
2023-04-22 10:37:05 +03:00
slaren
50cb666b8a
Improve cuBLAS performance by using a memory pool (#1094)
* Improve cuBLAS performance by using a memory pool

* Move cuda specific definitions to ggml-cuda.h/cu

* Add CXX flags to nvcc

* Change memory pool synchronization mechanism to a spin lock
General code cleanup
2023-04-21 21:59:17 +02:00
Kawrakow
1bfc153e2f
ggml : a faster version for Q4_1 x Q8_0 dot products (#1083)
* A faster version for Q4_1 x Q8_0 dot products

The idea nehind being that Q8_0 quantized
values get used many times in the matrix multiplications
where they are involved. In the current implementations,
when we are evaluating the dot products, we need to compute
the sum of the quants in the Q8_0 vector, so the same
operation is repeated many times. Here we pre-compute
the sum during Q8_0 quantization, store it in the
now modified block_q8_0 struct, and then reuse this
result in the subsequent dot products.

In a synthetic benchmark (just compute a bunch of dot
products), this change speeds up the Q4_1 * Q8_0 dot
product by 80%, making the performance identical to
Q4_0 * Q8_0.

In practical application, I see a ~15% gain in speed for
token prediction on M2, and ~5% gain on Ryzen 7950X.
The speed gain in the prompt evaluation is much bigger
(around 50%).

I have only done the change for the scalar version,
ARM_NEON, and AVX2, so we still need an AVX implementation.

* Cleaning up

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-04-21 18:18:26 +03:00
Georgi Gerganov
12b5900dbc
ggml : sync ggml (add GPT-NeoX RoPE implementation) 2023-04-20 23:32:59 +03:00
Georgi Gerganov
9ff334f3c9
ggml : fix bug in ggml_compute_forward_dup_f32() 2023-04-20 21:58:38 +03:00
Georgi Gerganov
8a1756abdf
ggml : do not break cuBLAS build (Q4_3 is not yet implemented) 2023-04-20 21:43:50 +03:00
Georgi Gerganov
66aab46079
ggml : fix Q4_3 quantization
Broke it during conflict resolution in last PR
2023-04-20 20:44:05 +03:00
Kawrakow
38de86a711
llama : multi-threaded quantization (#1075)
* 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>
2023-04-20 20:42:27 +03:00
Georgi Gerganov
e0305ead3a
ggml : add Q4_3 quantization (#1082) 2023-04-20 20:35:53 +03:00
Stephan Walter
c8c2c52482
AVX2 optimization for vec_dot_q4_2_q8_0 (#1068) 2023-04-20 08:45:41 +02:00
slaren
02d6988121
Improve cuBLAS performance by dequantizing on the GPU (#1065) 2023-04-20 03:14:14 +02:00
Kawrakow
f7d05095b4
Q4_2 quantization with rmse-optimized scale and quants (#1062)
* Q4_2 quantization with rmse-optimized scale and quants

For quantize-stats we get
q4_2: rmse 0.00159301, maxerr 0.17480469, 95pct<0.0030, median<0.0012

For 7B perplexity with BLAS enabled we get 6.2038 after 655 chunks.

Quantization is slow (~90 seconds on my Mac for 7B) as not
multi-threaded as in PR #896.

* ggml : satisfy the sanitizer builds

Not sure why this makes them fail

* Better follow ggml conventions for function names

* Fixed type as per reviewer comment

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-19 20:20:14 +02:00
Georgi Gerganov
884e7d7a2b
ggml : use 8-bit precision for Q4_1 intermediate results (#1047)
* ggml : use 8-bit precision for Q4_1 intermediate results (ARM)

* ggml : optimize ggml_vec_dot_q4_1_q8_0() via vmalq_n_f32

56 ms/token with Q4_1 !

* ggml : AVX2 implementation of ggml_vec_dot_q4_1_q8_0 (#1051)

* gitignore : ignore ppl-*.txt files

---------

Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
2023-04-19 20:10:08 +03:00
Stephan Walter
f3d4edf504
ggml : Q4 cleanup - remove 4-bit dot product code (#1061)
* Q4 cleanup

* Remove unused AVX512 Q4_0 code
2023-04-19 19:06:37 +03:00
slaren
8944a13296
Add NVIDIA cuBLAS support (#1044) 2023-04-19 11:22:45 +02:00
slaren
6667401238
Multi-threaded ggml_cpy (#1035)
* Multi-threaded ggml_cpy

* Update ggml.c

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Also fix wdata offset in ggml_compute_forward_add_q_f32

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-19 00:53:24 +02:00
Georgi Gerganov
77a73403ca
ggml : add new Q4_2 quantization (ARM only) (#1046)
* 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
2023-04-18 23:54:57 +03:00
Georgi Gerganov
50a8a2af97
ggml : scratch that - vmlaq_n_f32 is always better
Had a background process that was messing with the timings
2023-04-18 23:11:23 +03:00
Georgi Gerganov
dcdd65e296
ggml : optimize ggml_vec_dot_q4_0_q8_0() using vectorized accumulators 2023-04-18 22:59:17 +03:00
slaren
315a95a4d3
Add LoRA support (#820) 2023-04-17 17:28:55 +02:00
Georgi Gerganov
69b740289f
ggml : avoid using ggml_fp16_to_fp32() and ggml_fp32_to_fp16() in ggml.c 2023-04-17 16:16:23 +03:00
Ivan Komarov
f266259ad9
Speedup the AVX-512 implementation of ggml_vec_dot_q4_0() (#933) 2023-04-17 15:10:57 +02:00
Stephan Walter
2f7c8e014e
Fix potential int8 overflow in non-SIMD vec_dot (#986) 2023-04-15 18:28:56 +00:00
Stephan Walter
0ad964631f
Refactor ggml.c for future tensor types (#1001) 2023-04-15 16:25:38 +00:00
Georgi Gerganov
e95b6554b4
ggml : add Q8_0 quantization for intermediate results (#951)
* ggml : add Q8_0 quantization for intermediate results

* quantize-stats : fix test + add it to Makefile default

* Q8: use int8_t, AVX/AVX2 optimizations

* ggml : fix quantize_row_q8_0() ARM_NEON rounding

* minor : updates after rebase to latest master

* quantize-stats : delete obsolete strings

* ggml : fix q4_1 dot func

---------

Co-authored-by: Stephan Walter <stephan@walter.name>
2023-04-15 17:53:22 +03:00
Georgi Gerganov
aa485cee33
ggml : use posix_memalign on non-Windows env 2023-04-15 14:25:45 +03:00
Pavol Rusnak
c56b715269
Expose type name from ggml (#970)
Avoid duplication of type names in utils

Co-authored-by: Håkon H. Hitland <haakon@likedan.net>
2023-04-14 20:05:37 +02:00
Kerfuffle
c9a59b70a5
ggml : add unary and binary map operations (#874)
* GGML map ops proof of concept.

* Various cleanups.

Add handling for task setting.

Add handling for ggml_compute_backward.

Rename functions to ggml_map_unary_f32 and ggml_map_binary_f32

Fix compiler warnings related to casting function pointers and `void *`

Reorder functions and definitions based on the GGML op number.

Use typedefs for map op function pointer types.

* Fix position of map ops cases in ggml_compute_forward
2023-04-14 17:43:55 +03:00
Georgi Gerganov
1623a6e9b4
ggml : minor 2023-04-14 13:31:29 +03:00
Georgi Gerganov
c14e0d2f23
ggml : always allocate buffers with size multiple of GGML_MEM_ALIGN 2023-04-14 13:31:15 +03:00
Georgi Gerganov
0f07cacb05
ggml : fix q4_1 dot product types 2023-04-14 09:45:42 +03:00
Howard Su
c5d70f5c9e
ggml : optimize rope function to avoid call powf in the tight loop (#807) 2023-04-14 09:24:52 +03:00
Georgi Gerganov
a3a2a0eda8
ggml : add GGML_DEFAULT_N_THREADS 2023-04-13 18:36:48 +03:00
Georgi Gerganov
d990e3fffc
ggml : speed-up ggml_vec_dot_q4_1() ARM_NEON + 32-bit ARM support (#900)
* ggml : speed-up q4_1 ARM_NEON by ~5%

* ggml : implement vaddvq when missing

* ggml : implement vminvq and vmaxvq when missing

* ggml : implement vzip when missing

* ggml : fix comment

* ggml : try to use correct ifdef
2023-04-13 18:32:36 +03:00
Stephan Walter
6232f2d7fd
ggml : optimize non-SIMD Q4_0 vector dot product (#703) 2023-04-13 17:59:50 +03:00
Pavol Rusnak
6c248707f5
ggml : introduce GGML_ALIGNED_MALLOC/GGML_ALIGNED_FREE macros (#884)
which allows us to use aligned_alloc or _aligned_malloc functions
2023-04-13 17:08:32 +03:00
Vladimir
8c3ffc2f04
ggml : update cblas_sgemm columns var to be more reasonable (#838) 2023-04-13 16:24:30 +03:00
Pavol Rusnak
8b679987cd
Fix whitespace, add .editorconfig, add GitHub workflow (#883) 2023-04-11 19:45:44 +00:00
Stephan Walter
3e6e70d8e8
Add enum llama_ftype, sync ggml_type to model files (#709) 2023-04-11 15:03:51 +00:00
comex
2663d2c678
Windows fixes (#890)
Mostly for msys2 and mingw64 builds, which are different from each other
and different from standard Visual Studio builds.  Isn't Windows fun?

- Define _GNU_SOURCE in more files (it's already used in ggml.c for
  Linux's sake).

- Don't use PrefetchVirtualMemory if not building for Windows 8 or later
  (mingw64 doesn't by default).  But warn the user about this situation
  since it's probably not intended.

- Check for NOMINMAX already being defined, which it is on mingw64.

- Actually use the `increment` variable (bug in my `pizza` PR).

- Suppress unused variable warnings in the fake pthread_create and
  pthread_join implementations for Windows.

- (not Windows-related) Remove mention of `asprintf` from comment;
  `asprintf` is no longer used.

Fixes #871.
2023-04-11 15:19:54 +02:00
Georgi Gerganov
461ba9e66e
ggml : fix WASM build 2023-04-10 23:20:01 +03:00
Georgi Gerganov
c3ac702e5e
ggml : add ggml_cont() + optimize ggml_cpy() for contiguous dst 2023-04-10 22:42:28 +03:00
Georgi Gerganov
9d634ef452
ggml : remove trailing whitespaces 2023-04-10 22:42:28 +03:00
Marco Matthies
d9a239c410
Simplify to include lower-case windows.h always, fix compile on mingw32 (#747) 2023-04-10 19:57:59 +02:00
Georgi Gerganov
684da25926
ggml : fix quantize_row_q4_1() ARM_NEON (close #876) 2023-04-10 19:29:48 +03:00
comex
f963b63afa Rewrite loading code to try to satisfy everyone:
- 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)
2023-04-10 01:10:46 +02:00
unbounded
62cfc54f77
Add quantize-stats command for testing quantization (#728)
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
2023-04-08 00:09:18 +02:00
Georgi Gerganov
eeaa7b0492
ggml : multi-thread ggml_rope() (~3-4 times faster on M1) (#781) 2023-04-05 22:11:03 +03:00
Georgi Gerganov
986b6ce9f9
ggml, llama : avoid heavy V transpose + improvements (#775)
ggml :

- added ggml_view_3d()
- ggml_view_tensor() now inherits the stride too
- reimplement ggml_cpy() to account for dst stride
- no longer require tensor->data to be memory aligned

llama :

- compute RoPE on 32-bit tensors (should be more accurate)
- store RoPE-ed K in the KV cache
- store transposed V in the KV cache (significant speed-up)
- avoid unnecessary Q copy
2023-04-05 22:07:33 +03:00
SebastianApel
437e77855a
10+% performance improvement of ggml_vec_dot_q4_0 on AVX2 (#654)
* Performance improvement of AVX2 code
* Fixed problem with MSVC compiler
* Reviewer comments: removed double semicolon, deleted empty line 1962
2023-04-03 09:52:28 +02:00
Marian Cepok
c0bb1d3ce2
ggml : change ne to int64_t (#626) 2023-04-02 13:21:31 +03:00
Stephan Walter
3525899277
Enable -std= for cmake builds, fix warnings (#598) 2023-03-31 19:19:16 +00:00
slaren
1d08882afa
Optimize AVX2 ggml_vec_dot_q4_0 (#642) 2023-03-31 15:55:52 +00:00
perserk
02c5b27e91
Add AVX acceleration (#617)
* ggml : add AVX quantize_row_q4_0()

* ggml : add AVX ggml_vec_dot_q4_0()

* ggml : refactor AVX part of ggml_vec_dot_q4_0()

https://github.com/ggerganov/llama.cpp/pull/617#issuecomment-1489985645
2023-03-31 13:55:44 +02:00
Justine Tunney
6f23ba5ee2 Ensure --mlock works properly with mmap() support 2023-03-30 12:28:25 -07:00
Slaren
c03ae8dca1 Add mmap support for model files 2023-03-30 12:28:25 -07:00
Casey Primozic
a4755cf288
Remove unused variable (#607)
* It seems some new warning were added recently that exposed this.  I wrote the code that included this unused variable originally and it is indeed not needed.
2023-03-30 17:53:35 +00:00
Georgi Gerganov
77efdf5a50
ggml : fix NEON signs (close #620, #622) 2023-03-30 20:27:32 +03:00
slaren
ed3c680bcd
Fix GGML_F32Cx8_STORE in AVX without F16C path (#619) 2023-03-30 11:16:30 +02:00
Georgi Gerganov
b51c717d5c
ggml : init time on first ggml_init() call 2023-03-29 22:15:34 +03:00
Georgi Gerganov
cea1c85948
ggml : add ARM_NEON dequantize_row_q4_1() 2023-03-29 22:10:01 +03:00
Georgi Gerganov
f202ada131
ggml : add ARM_NEON quantize_row_q4_1() 2023-03-29 22:03:07 +03:00
Georgi Gerganov
3b44d30d9b
ggml : add ARM_NEON ggml_vec_dot_q4_1() 2023-03-29 22:03:07 +03:00
anzz1
83df5639eb
Fix GCC warning about binary literal (#595)
0b10101010 -> 0xAA /* 0b10101010 */
2023-03-29 13:20:07 +00:00
anzz1
5a5f8b1501
Enable Fused-Multiply-Add (FMA) and F16C/CVT16 vector extensions on MSVC (#375)
* Enable Fused-Multiply-Add (FMA) instructions on MSVC

__FMA__ macro does not exist in MSVC

* Enable F16C/CVT16 vector extensions on MSVC

__F16C__ macro does not exist in MSVC, but is implied with AVX2/AVX512

* MSVC cvt intrinsics

* Add __SSE3__ macro for MSVC too because why not

even though it's not currently used for anything when AVX is defined
2023-03-28 22:44:29 +03:00
slaren
2a98bc18ea
ggml : add AVX2 implementation of quantize_row_q4_1 (#515)
* Add AVX2 implementation of quantize_row_q4_1

* Actually use AVX2

* Make quantize_row_q4_1 static

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-28 21:06:03 +03:00
Stephan Walter
99c5b27654
ggml : refactor quantized processing functions (#509)
* Refactor quantized processing functions

* ggml : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-28 20:13:01 +03:00
Stephan Walter
436e561931
all : be more strict about converting float to double (#458)
* 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>
2023-03-28 19:48:20 +03:00
Stephan Walter
c1f885067c
ggml : introduce structs for the q4 data blocks (#356)
* Introduce structs for the q4 data blocks

* ggml : rename quant struct variables + fix ARM_NEON

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-28 18:56:03 +03:00
slaren
a6bdc47cba
Fix usage of F16C intrinsics in AVX code (#563)
* Fix usage of F16C intrinsics in AVX code when F16C is not defined
2023-03-28 17:26:55 +03:00
Stephan Walter
939ad2d3a5
Fix undefined variables in debug build, remove unused variables (#531) 2023-03-26 15:34:02 +00:00
slaren
459e93cce0
Add AVX2 implementation of dequantize_row_q4_1 (#505) 2023-03-25 20:31:48 +02:00
Georgi Gerganov
a316a425d0
Overhaul the examples structure
- main -> examples
- utils -> examples (renamed to "common")
- quantize -> examples
- separate tools for "perplexity" and "embedding"

Hope I didn't break something !
2023-03-25 20:26:40 +02:00
Georgi Gerganov
ecbe466a36
Retire the ggml_mul_mat() branch for transposed src0 (#500)
* Retire the ggml_mul_mat() for transposed src0

- It can always be made contiguous with ggml_cpy()
- The code is now simplified
- The results are deterministic in respect to num threads

* SIMD-ify dequantize_row_q4_0() for ARM_NEON (#502)

* Attempt to SIMD-ify dequantize_row_q4_0() for ARM_NEON

* Fix dequantization - forgot to interleave the quants
2023-03-25 19:47:21 +02:00
slaren
09aecbf628
Add AVX2 implementation of dequantize_row_q4_0 (#467) 2023-03-25 17:06:49 +02:00
Georgi Gerganov
6b6dbc8910
Remove obsolete assert and fix compiler warning 2023-03-25 16:22:05 +02:00
Georgi Gerganov
2a2e63ce05
Fix nasty bug in ggml_compute_forward_mul_mat_f32() and reenable BLAS 2023-03-25 16:10:14 +02:00
Georgi Gerganov
8520fc310e
Disable BLAS altogether - the bug is not just for qunatized mat mul 2023-03-24 23:47:06 +02:00
Georgi Gerganov
b3f460e941
Disable BLAS branch in mul_mat - seems there is a bug 2023-03-24 23:39:17 +02:00
Georgi Gerganov
7a9b6c3a8b
Reduce memory usage and allocate enough memory for largest context (#473)
* Reduce memory usage and allocate enough memory for large contexts

* Simpler scratch buffer usage

* Reenable BLAS for quantized mul_mat

* Fix number of layers in 30B and 65B

* Fix KV cache size for F32
2023-03-24 23:17:37 +02:00
Cameron Kaiser
481044d50c
additional optimizations for POWER9 (#454) 2023-03-24 17:19:26 +02:00
comex
563cdc391d
Support calling mlock() on loaded model data on Linux and macOS (#453)
* 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>
2023-03-24 17:19:05 +02:00
Stephan Walter
69c92298a9
Deduplicate q4 quantization functions (#383)
* Deduplicate q4 quantization functions

* Use const; add basic test

* Re-enable quantization test

* Disable AVX2 flags in CI

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-22 19:29:06 +02:00
Valentyn Bezshapkin
97940520e8
fix: add POSIX functionality for Linux compilation (#51)
* fix: add POSIX functionality for Linux compilation

* fix: older standard for compatibility
2023-03-22 19:20:25 +02:00
Georgi Gerganov
f5a77a629b
Introduce C-style API (#370)
* 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
2023-03-22 07:32:36 +02:00
Kevin Lo
715d292ee0
Add OpenBSD support (#314) 2023-03-21 17:50:09 +02:00
Casey Primozic
2e664f1ff4
Add initial AVX512 support for dot product on Linux (#320)
* Update Makefile to detect AVX512 support and add compiler flags if it's available
 * Based on existing AVX2 implementation, dot product on one 32-value block of 4-bit quantized ints at a time
 * Perform 8 bit -> 16 bit sign extension and multiply+add on 32 values at time instead of 16
 * Use built-in AVX512 horizontal reduce add to get sum at the end
 * Manual unrolling on inner dot product loop to reduce loop counter overhead
2023-03-21 15:35:42 +01:00
Georgi Gerganov
22213a17b5
Change RMSNorm eps to 1e-6 (#173)
I think this is what is used in the Python code
2023-03-19 17:30:00 +02:00
Stephan Walter
367946c668
Don't tell users to use a bad number of threads (#243)
The readme tells people to use the command line option "-t 8", causing 8
threads to be started. On systems with fewer than 8 cores, this causes a
significant slowdown. Remove the option from the example command lines
and use /proc/cpuinfo on Linux to determine a sensible default.
2023-03-17 19:47:35 +02:00
Matvey Soloviev
904d2a8d6a
Q4_1 quantization (#193)
* Add AVX2 version of ggml_vec_dot_q4_1

* Small optimisations to q4_1 dot product (@Const-me)

* Rearrange Q4_1 quantization to work for multipart models. (Fix #152)

* Fix ggml_vec_mad_q4_1 too

* Fix non-vectorised q4_1 vec mul
2023-03-17 06:48:39 +02:00
Nebula
9b4a15b17d
Fix RMS norm in GGML (#191) 2023-03-15 19:29:25 -04:00
hoangmit
6eac39ba95
Add RMS norm and use it (#187)
* add ggml_rms_norm

* update op num
2023-03-16 00:41:38 +02:00
hoangmit
113e685d18
inline -> static inline for "bytesFromNibbles" (#161)
Without "static" prefix, it fails to compile in clang
2023-03-15 21:05:14 +02:00
Ronsor
47857e564c
Don't use vdotq_s32 if it's not available (#139)
* Don't use vdotq_s32 if it's not available

`dotprod` extensions aren't available on some ARM CPUs (e.g. Raspberry Pi 4), so check for them and only use them if they're available.

Reintroduces the code removed in 84d9015 if `__ARM_FEATURE_DOTPROD` isn't defined.

* Update ggml.c

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-14 21:34:37 +02:00
Thomas Klausner
41be0a3b3d
Add NetBSD support. (#90) 2023-03-13 18:40:54 +02:00
Georgi Gerganov
84d9015c4a
Use vdotq_s32 to improve performance (#67)
* 10% performance boost on ARM

* Back to original change
2023-03-13 18:36:44 +02:00
Georgi Gerganov
c80e2a8f2a
Revert "10% performance boost on ARM"
This reverts commit 113a9e83eb.

There are some reports for illegal instruction.
Moved this stuff to vdotq_s32 branch until resolve
2023-03-13 01:28:08 +02:00
Georgi Gerganov
54a0e66ea0
Check for vdotq_s32 availability 2023-03-13 01:21:03 +02:00
Georgi Gerganov
543c57e991
Ammend to previous commit - forgot to update non-QRDMX branch 2023-03-13 01:05:24 +02:00
Georgi Gerganov
113a9e83eb
10% performance boost on ARM 2023-03-13 00:56:10 +02:00
Sebastián A
eb062bb012
Windows fixes (#31)
* Apply fixes suggested to build on windows

Issue: https://github.com/ggerganov/llama.cpp/issues/22

* Remove unsupported VLAs

* MSVC: Remove features that are only available on MSVC C++20.

* Fix zero initialization of the other fields.

* Change the use of vector for stack allocations.
2023-03-12 22:15:00 +02:00
Georgi Gerganov
f1eaff4721 Add AVX2 support for x86 architectures thanks to @Const-me ! 2023-03-11 18:04:25 +02:00
Georgi Gerganov
007a8f6f45
Support all LLaMA models + change Q4_0 quantization storage 2023-03-11 11:28:30 +02:00
Georgi Gerganov
26c0846629
Initial release 2023-03-10 20:56:40 +02:00