Commit Graph

1382 Commits

Author SHA1 Message Date
Stefan Sydow
7780e4f479
make : .PHONY clean (#1553) 2023-05-21 17:03:44 +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
Zenix
b8ee340abe
feature : support blis and other blas implementation (#1536)
* 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>

* Fix: blas changes on ci

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 17:58:31 +03:00
Henri Vasserman
9ecb30f959
OpenCL: Fixes for older devices. (#1435)
* Remove `constant`

* Rewrite platform and device selection

* Fix Q8_0
2023-05-20 17:57:39 +03:00
Juuso Alasuutari
29cf5596fe
llama : define magic numbers as integer constants (#1518) (#1520)
The underlying representation of multibyte character literals is
implementation-defined. This could, at least in principle, cause
cross-build data export/import issues independent of endianness.

Define magic numbers as integer literals to be on the safe side.

Signed-off-by: Juuso Alasuutari <juuso.alasuutari@gmail.com>
2023-05-20 15:58:15 +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
Georgi Gerganov
ea600071cb
Revert "feature : add blis and other BLAS implementation support (#1502)"
This reverts commit 07e9ace0f9.
2023-05-20 12:03:48 +03:00
Zenix
07e9ace0f9
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>
2023-05-20 12:02:48 +03:00
Georgi Gerganov
ec2e10c444
llama : add llama_init_backend() API (close #1527) 2023-05-20 11:06:37 +03:00
DannyDaemonic
d2c59b8ba4
Fix for mingw (#1462) 2023-05-20 00:40:02 -07:00
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
8a203f9fa1 llama : fix compile warnings in llama_set_state_data() 2023-05-20 10:14:43 +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
Georgi Gerganov
6986c7835a
tests : add missing header 2023-05-19 21:17:28 +03:00
Evan Jones
943e6081cc
examples : add persistent chat (#1495)
* examples : add persistent chat

* examples : fix whitespace

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-19 20:39:51 +03:00
Jason McCartney
7694b52b9a
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
2023-05-19 20:24:59 +03:00
David Kennedy
79e3efb0e9
readme : adds WizardLM to the list of supported models (#1485) 2023-05-19 20:16:30 +03:00
Georgi Gerganov
4b7e245adf
minor : fix compile warnings 2023-05-19 20:14:51 +03:00
Erik Scholz
5ea4339273
make kv_f16 the default for api users (#1517) 2023-05-18 19:31:01 +02:00
DannyDaemonic
ee9654138a
Fixes #1511 lambda issue for w64devkit (mingw) (#1513)
* Fix for w64devkit and mingw
2023-05-18 19:30:40 +02:00
Stephan Walter
dc271c52ed
Remove unused n_parts parameter (#1509) 2023-05-17 22:12:01 +00:00
rankaiyx
c238b5873a
benchmark-matmul: Print the average of the test results (#1490) 2023-05-17 16:47:58 +02:00
Tom Jobbins
2b2646931b
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
2023-05-17 00:04:35 +02:00
Ilya Kurdyukov
42627421ec
~7% faster Q5_1 AVX2 code (#1477) 2023-05-16 18:36:47 +00:00
András Salamon
9560655409
define default model path once, sync path with readme (#1366) 2023-05-16 17:46:34 +02:00
sandyiscool
2a5ee023ad
Add alternate include path for openblas (#1476)
In some linux distributions (fedora, for example), the include path for openblas is located at '/usr/local/include'
2023-05-16 10:30:15 +02:00
zrm
63d20469b8
fix get_num_physical_cores() (#1436)
* fix get_num_physical_cores()
had been broken on complex topologies because "cpu cores" in /proc/cpuinfo is per-"physical id"

* Add spaces to maintain consistent formatting

---------

Co-authored-by: slaren <ddevesa@gmail.com>
2023-05-15 04:25:42 +02:00
slaren
b5c9295eef
benchmark-matmul: fix clang-tidy issues, report results in GFLOPS (#1458)
* benchmark-matmul: fix command line parsing, replace macros with functions, report results in GFLOPS
2023-05-14 22:46:00 +02:00
Johannes Gäßler
eb363627fd
cuda : deduplicated dequantization code (#1453) 2023-05-14 21:53:23 +03:00
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
601a033475
ggml : add GGML_QNT_VERSION to track quantization format changes
https://github.com/ggerganov/ggml/issues/150#issuecomment-1546625668
2023-05-14 10:20:19 +03:00
Georgi Gerganov
08737ef720 cuda : fix convert function (#1412) 2023-05-13 17:40:58 +03:00
Georgi Gerganov
bda4d7c215 make : fix PERF build with cuBLAS 2023-05-13 17:25:09 +03:00
Georgi Gerganov
5a5aeb1e91
llama : fix unused warning 2023-05-13 16:55:14 +03:00
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
0cd22e190a
llama : fix various warnings 2023-05-13 11:23:15 +03:00
Rinne
6456a4eb9f
embedding : remove unused code (#1426) 2023-05-13 10:24:20 +03:00
Georgi Gerganov
cdd5350892
readme : update Q4_0 perplexities
I think these were affected by the removal of the `round` during quantization
2023-05-13 09:12:44 +03:00
Georgi Gerganov
738ace394a
llama : free ggml context in set / copy state data (close #1425) 2023-05-13 09:08:52 +03:00
Henri Vasserman
699b1ad7fe
opencl : fix kernels for the new formats (#1422)
* Fix OpenCL kernels for the new formats

* Fix Q5_0 alignment issues.
2023-05-13 09:01:15 +03:00
Georgi Gerganov
fb62f92433
llama : fix --mtest option (close #1414) 2023-05-12 21:44:20 +03:00