Commit Graph

2738 Commits

Author SHA1 Message Date
SuperUserNameMan
b41b4cad6f
examples : add "simple" (#1840)
* Create `simple.cpp`

* minimalist example `CMakeLists.txt`

* Update Makefile for minimalist example

* remove 273: Trailing whitespace

* removed trailing white spaces simple.cpp

* typo and comments simple.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-16 21:58:09 +03:00
Zenix
13fe9d2d84
cmake : add auto detection of BLAS_INCLUDE_DIRS (#1886) 2023-06-16 21:53:04 +03:00
Johannes Gäßler
ac3b886953
llama : fix embd when offloading non-repeating layers (#1891) 2023-06-16 21:25:51 +03:00
FrankHB
5b9ccaf104
Fixed possible macro redefinition (#1892)
MinGW libstdc++ may define `NOMINMAX` unconditionally. This fixes the case when it is already defined.
2023-06-16 21:25:01 +03:00
Borislav Stanimirov
9cbf50c041
build : fix and ignore MSVC warnings (#1889) 2023-06-16 21:23:53 +03:00
Kawrakow
3d01122610
CUDA : faster k-quant dot kernels (#1862)
* cuda : faster k-quant dot kernels

* Imrove Q2_K dot kernel on older GPUs

We now have a K_QUANTS_PER_ITERATION macro, which should be
set to 1 on older and to 2 on newer GPUs.
With this, we preserve the performance of the original
PR on RTX-4080, and are faster compared to master on
GTX-1660.

* Imrove Q6_K dot kernel on older GPUs

Using the same K_QUANTS_PER_ITERATION macro as last commit,
we preserve performance on RTX-4080 and speed up
Q6_K on a GTX-1660.

* Add LLAMA_CUDA_KQUANTS_ITER to CMakeLists.txt and Makefile

Allowed values are 1 or 2. 2 gives the best performance on
modern GPUs and is set as default. On older GPUs 1 may work
better.

* PR comments

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-16 20:08:44 +03:00
Borislav Stanimirov
602c748863
gitignore : add several entries specific to Visual Studio (#1888) 2023-06-16 09:58:11 +03:00
Johannes Gäßler
a09f9195be
Fixed CUDA runtime version check (#1879) 2023-06-15 21:49:08 +02:00
Georgi Gerganov
bed9275617
cmake : remove whitespaces 2023-06-15 21:56:50 +03:00
yangli2
c36e81da62
examples : add chat-vicuna.sh (#1854)
Co-authored-by: Yang Li <yangliyl@google.com>
2023-06-15 21:05:53 +03:00
Igor Okulist
3559433fec
cmake : set include path for OpenBlas (#1830) 2023-06-15 20:51:26 +03:00
Frederik Vogel
69b34a0e80
swift : Package compile breaks due to ggml-metal.metal (#1831)
* Ignore metal file in spm

* Add ggml.h to spm public Headers

---------

Co-authored-by: Vogel Frederik <vogel.frederik@linecorp.com>
2023-06-15 20:47:04 +03:00
daboe01
cf267d1c71
make : add train-text-from-scratch (#1850)
* make finetuning example accessible

* fixed: targed was in wrong line

* fixed: name of executable was wrong

* fixed: naming of binary

* fixed: model path was wrong

* fixed clean target

* Update examples/train-text-from-scratch/README.md

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-15 20:42:48 +03:00
Srinivas Billa
9dda13e5e1
readme : server compile flag (#1874)
Explicitly include the server make instructions for C++ noobsl like me ;)
2023-06-15 20:36:38 +03:00
sandyiscool
37e257c48e
make : clean *.so files (#1857) 2023-06-15 20:36:06 +03:00
Howard Su
64cc19b4fe
Fix the validation of main device (#1872) 2023-06-15 19:29:59 +02:00
Georgi Gerganov
4bfcc855ab
metal : parallel command buffer encoding (#1860)
* metal : parallel command buffer encoding

* metal : determine number of command buffers based on gf->n_threads
2023-06-15 20:29:48 +03:00
Johannes Gäßler
6b8312e797
Better error when using both LoRA + GPU layers (#1861) 2023-06-15 19:06:46 +02:00
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
0xspringtime
9254920265
baby-llama : fix operator!= (#1821)
* Update baby-llama.cpp

Seems to be an error in the implementation of the operator!= function. It attempts to compare the this pointer (a llama_hparams_lora object) with the other pointer (a llama_hparams object) using memcmp. This can lead to incorrect results because the sizes of the objects being compared (sizeof(llama_hparams) and sizeof(llama_hparams_lora)) are different, should now be able to compare two llama_hparams_lora objects for inequality.

* Update baby-llama.cpp

* Update baby-llama.cpp
2023-06-13 22:37:54 +03:00
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
Georgi Gerganov
2347e45e7b
llama : do a warm-up eval at start for better timings (#1824) 2023-06-13 20:20:07 +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
Kawrakow
74a6d922f1
Metal implementation for all k_quants (#1807)
* metal : improve q4_K

28.3 -> 26.0 ms/token by avoiding a branch in the
calculation of the scales.

* metal : small improvement for Q4_K

* metal : still optimizing Q4_K

This commit pushes it down to 25.3 ms / token.

The crazy idea of using 6 bits for the scales is really costly on
Metal: if I remove the bit fiddling necessary to make the block
scales, time goes almost to the Q4_0 23 ms/token.

Before pushing the k-quants upstream I had a Q4_K variant that
had used 8-bit scales. It wasn't more accurate, used 0.125 bits more per weight,
was running slightly slower on the CPU (due to the larger model size
and being memory bound there), and the difference was entirely
negligible under CUDA. So, I decided to publish the version with 6-bit
scales. Perhaps I should re-consider and change to 8-bit scales?

* metal : some more optimizations

Q2_K: 25.4 ms/token
Q6_K: 27.3 ms/token
Q4_0: 22.8 ms/token
Q4_1: 23.1 ms/token

* metal : Q3_K support

Something is not quite right yet.

* metal : Q5_K support

Initial version achieves 31.2 ms/token, 210 GB/s

* metal : still not able to figure out why q3_K does not work

* Minor

* metal : yet another failed attempt to make q3_K work

* metal : optimize Q5_K

31.2 ms -> 27.8 ms.
250 GB/s.

* metal : q3_K still not working

Adding a heavily commented q3_K metal kernel to explain
my obviously faulty logic. Perhaps someone could spot the issue?

* metal : q3_K finally working

Not optimized at all.

What was the issue? The scales are not 4-bytes aligned,
and I was accessing them with a uint32_t pointer.
When I tried that on CUDA, I got an error (illegal memory access)
and added a memcpy to a local array of 3 uint32_t's.
But on Metal it told me there is no memcpy, so I tried
accessing directly. There is no error, just garbage results.
At some point I did try accessing the scales with an uint16_t
pointer (the scales are for sure 2-byte aligned), but was
still getting garbage. I guess, there must have been another bug.

No access to scales is via a uint16_t pointer and, after starting
from scratch from the C dequantize function, it finally works.

* metal : Q3_K 1st optimization pass

* metal : Q3_K second optimization pass - 29.6 ms/token

* metal : Q3_K cleanup

* metal : fixed accidentally broken Q2_K

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-12 22:39:21 +03:00
slaren
e4caa8da59
ci : run when changing only the CUDA sources (#1800) 2023-06-12 20:12:47 +03:00
Howard Su
58970a4c39
Leverage mmap for offloading tensors to GPU (#1597)
* Rebase to latest

* Show progress

* Add assert to make sure we only allocate temp buffer for non-CPU backend tensor

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2023-06-12 14:44:16 +02:00
Kawrakow
8c0a10e64d
metal : fix failure to load model (#1817)
The number of buffers in the ggml context was left unitialized.
This leads to sporadic failures to load the model on
startup. It is actually strange that the failure occurred so
infrequantly.

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-12 14:31:36 +03:00
Kerfuffle
fa84c4b3e8
Fix issue where interactive mode crashes when input exceeds ctx size (#1789)
* Fix issue where interactive mode in the main example crashes when input exceeds ctx size

* Ensure the context size is at least 8 tokens in the main example.

Closes #1768
2023-06-11 08:19:17 -06:00
Kyle Liang
12b063f0ec
Fixed WSL cuda's OOM error (#1594)
* In the function , add the cuda error bypass.

* remove excessive codes and prints

---------

Co-authored-by: liang <liangmanlai@126.com>
2023-06-11 15:20:52 +02:00
Ryan Landay
31d2b5f4a4
Update SHA256SUMS with current hashes for models quantized using q4_0 (#1798) 2023-06-11 12:38:53 +03:00
Georgi Gerganov
4de0334f5c
cmake : fix Metal build (close #1791) 2023-06-10 22:56:53 +03:00
Artyom Lebedev
3f1223155a
k-quants : GCC12 compilation fix (#1792) 2023-06-10 22:51:36 +03:00
Andrei
303f5809f1
metal : fix issue with ggml-metal.metal path. Closes #1769 (#1782)
* Fix issue with ggml-metal.metal path

* Add ggml-metal.metal as a resource for llama target

* Update flake.nix metal kernel substitution
2023-06-10 17:47:34 +03:00
Aisuko
059e99066d
doc : fix wrong address of BLIS.md (#1772)
Signed-off-by: Aisuko <urakiny@gmail.com>
2023-06-10 17:08:11 +03:00
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
Kawrakow
e9b66ee982
metal : add Q4_1 implementation (#1785)
23.3 ms / token, so just ~1% slower than q4_0.
Achieves 290 GB/s memory throughput.

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-10 11:28:11 +03:00
Kerfuffle
4f0154b0ba
llama : support requantizing models instead of only allowing quantization 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.
2023-06-10 10:59:17 +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
rankaiyx
555275a693
make : add SSSE3 compilation use case (#1659) 2023-06-10 09:41:59 +03:00
Robert Sung-wook Shin
98ed165574
OpenCL: Add release memory (#1741)
* Add opencl release memory

* Rename function name
2023-06-09 18:24:40 +02:00
Johannes Gäßler
ae9663f188
Windows nvcc workaround (#1753)
Fix gibberish output on Windows when using CUDA
2023-06-09 13:58:15 +02:00
Georgi Gerganov
b33dee282f
metal : fix build "tanhf" -> "tanh" 2023-06-09 11:11:04 +03:00
AT
92f44ff7f7
metal : add GELU implementation (#1770)
Co-authored-by: Adam Treat <adam@nomic.ai>
2023-06-09 11:00:51 +03:00
Kawrakow
245fc3c37d
metal : faster q4_0 (#1775)
* metal : 8% faster q4_0

Avoid copying into local uchar4 anf float4.

* metal : 17% faster Q4_0

Use 64 threads in a thread group.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-09 10:39:59 +03:00
Kawrakow
72ff5282bf
metal : add Q2_K implementation (#1762)
* metal : add Q2_K implementation

27.1 ms / token on M2 Max 30-core GPU, so about the
same speed as Q4_0. Memory throughput is ~156 GB/s.

The access pattern used in the Q2_K
CUDA implementation resulted in significantly lower
performance (~31 ms/token).

* Fixing merge conflicts

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-08 22:28:21 +03:00
Georgi Gerganov
0bf7cf1b29
Revert "ggml : load data into int8x16x4_t using vld4q_s8 on arm64 (#1738)"
This reverts commit 8432d4d9f7.
2023-06-08 20:48:14 +03:00
le.chang
8432d4d9f7
ggml : load data into int8x16x4_t using vld4q_s8 on arm64 (#1738) 2023-06-08 19:47:56 +03:00
Kawrakow
0f291e1f65
metal : Q6_K implementation (#1752)
* Metal implementation for Q4_K

Very slow for now:
42 ms / token, Q4_0 runs in 28 ms/token on my
30-core M2 Max GPU.

* Optimizing Q4_K on metal

The first token always takes longer, I guess because
the metal kernel is being jit-compiled.
So, using n = 128 to measure time.

At this point Q4_K takes 29.5 ms / token
compared to 27.2 ms / token for Q4_0.
Quite a bit better than the initial attempt,
but still not good enough.

* Optimizing q4_K metal dot some more

For n = 256 it is now 28.1 ms/token compared to
27 ms/token for q4_0.

* Fix after merge with master

* Metal implementation for Q6_K

Similar to the CUDA implementation.
No idea if this is the optimum for Metal, but the few
alternative variants I tried all had a lower performance.

We get 36.5 ms / token on M2 Max with 30 GPU cores.
This corresponds to ~200 GB/second throughput.

* clang-tidy : add config back

* Much better Q6_K implementation for metal

28.3 ms / token for 7B. Subtracting ~9 ms that is spent in
other compute graph operations, we are left with ~19 ms
for the matrix multiplications. The model is ~5.5 GB,
so we are getting 1000 / 19 * 5.5 = 290 GB/s!

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-08 19:46:22 +03:00
qingfengfenga
8fc8179919
Add llama.cpp docker support for non-latin languages (#1673)
* Modify Dockerfile default character set to improve compatibility (#1673)
2023-06-08 00:58:53 -07:00
Steven Roussey
b50b570ed9
ggml : fix fprintf warnings (#1720) 2023-06-08 10:12:28 +03:00