Commit Graph

123 Commits

Author SHA1 Message Date
Georgi Gerganov
035c4f01e6
wip 2024-01-24 00:01:54 +02:00
Georgi Gerganov
06c2d0d117
wip 2024-01-23 22:42:43 +02:00
Georgi Gerganov
17720fad66
metal : parallel reduce across heads 2024-01-21 23:01:46 +02:00
Georgi Gerganov
77d08f3272
metal : parallelize across KV size 2024-01-21 22:26:45 +02:00
Georgi Gerganov
a4b6341c7b
wip : template for rows per warp 2024-01-21 19:06:30 +02:00
Georgi Gerganov
f31955f5d1
wip : 4 rows per simd group 2024-01-21 18:01:28 +02:00
Georgi Gerganov
8cde449b8b
wip : 8 rows per simd group 2024-01-21 17:37:24 +02:00
Georgi Gerganov
b97325800a
metal : specialize for head size 2024-01-21 12:01:55 +02:00
Georgi Gerganov
528da7515e
metal : f16 precision 2024-01-21 11:13:24 +02:00
Georgi Gerganov
1173f49c3b
metal : initial implementation 2024-01-21 10:15:02 +02:00
Georgi Gerganov
a9681febd6
ggml : online attention (CPU) 2024-01-20 16:45:41 +02:00
Georgi Gerganov
a1c004ef2e
ggml : add ggml_flash_attn_ext API 2024-01-18 18:55:48 +02:00
Paul Tsochantaris
1e605f4102
metal : fix memory leak, dangling pointer and unused autorel (#5007)
* Metal memory: Small memory leak on init, dangling pointer, and unused autorelease pool in graph compute

* SPM header potential fix

* Reverting symlinks
2024-01-18 10:47:24 +02:00
Georgi Gerganov
c918fe8dca
metal : create autorelease pool during library build (#4970)
* metal : create autorelease pool during library build

ggml-ci

* test : simplify

ggml-ci
2024-01-17 18:38:39 +02:00
Paul Tsochantaris
7563293665
metal : remove unnecessary nil check (#4986) 2024-01-17 10:07:24 +02:00
Paul Tsochantaris
158f8c9e21
metal : localized logic in ggml_metal_graph_compute (#4924)
* Metal: Localized logic in `ggml_metal_graph_compute`, minor performance improvement

* Whitespace

* Collecting command buffer completions on single thread

* Whitespace

* Reduce diff noise
2024-01-16 19:05:19 +02:00
Alex Azarov
3a48d558a6
metal : replace loop of dispatch_async with dispatch_apply (#4934)
* Replace loop of dispatch_async with dispatch_apply

* Update ggml-metal.m

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-16 15:41:27 +02:00
Alex Azarov
7c8d3abd1a
metal : log recommendedMaxWorkingSetSize on iOS 16+ (#4936)
* metal: Log `recommendedMaxWorkingSetSize` on iOS 16+

* Only log on iOS and macOS, ignoring tvOS and other platforms

* Check for Xcode version before using recommendedMaxWorkingSetSize

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-16 15:33:02 +02:00
Justine Tunney
a0b3ac8c48
ggml : introduce GGML_CALL function annotation (#4850)
This change makes it possible to build ggml-cuda.cu and ggml-metal.m as
independent dynamic shared objects, that may be conditionally linked at
runtime in a multiplatform binary. It introduces a GGML_CALL annotation
that documents which functions have a cyclic call relationship, between
the application code and GPU modules.

This change does nothing, unless the build defines -DGGML_MULTIPLATFORM
which causes back-references and function pointers to conform to MS ABI
which is supported by NVCC, ROCm, XCode, GCC and Clang across platforms
2024-01-16 13:16:33 +02:00
Alex Azarov
5f5fe1bd60
metal : correctly set SIMD support flags on iOS (#4923)
* Correctly set support_simdgroup_reduction and support_simdgroup_mm on iPhone/iPad

* log a little bit more info on iOS
2024-01-14 10:44:39 +02:00
Georgi Gerganov
4be5ef556d
metal : remove old API (#4919)
ggml-ci
2024-01-13 20:45:45 +02:00
Georgi Gerganov
2d57de5255
metal : disable log for loaded kernels (#4794) 2024-01-13 18:46:37 +02:00
Georgi Gerganov
b38b5e93ae
metal : refactor kernel loading code (#4794)
* metal : detect more GPU families

* metal : refactor kernel loading

* metal : set kernel family requirements

* metal : fix kernel init + fix compile options

* metal : take into account simdgroup reduction support

* metal : print only skipped kernels

* metal : fix check for simdgroup reduction support

* metal : check for Metal 3

* metal : free allocations

* metal : normalize encoder:setComputePipelineStatus calls

ggml-ci

* metal : fix Metal3 family check

ggml-ci

* metal : check for simdgroup matrix mul. feature

ggml-ci
2024-01-13 18:03:45 +02:00
slaren
e7e4df031b
llama : ggml-backend integration (#4766)
* llama : ggml-backend integration

* ggml-backend : add names to buffers

* fix unmap after loading

* batched-bench : add tensor_split param

* llama : check for null tensor_split

* ggml-backend : increase GGML_MAX_BACKENDS

* improve graph splitting, partial fix for --no-kv-offload

* cuda : add ggml-backend split buffer support

* cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available)

* ggml : fix null backend dereference (#4807)

* ggml : fix null backend dereference

* ggml : also check ggml_backend_is_cpu

* test-backend-ops : check buffer allocation failures

* llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row)

* ggml : fix mul_mat_id work size

* llama : rewrite session kv load/set without graphs

* minor

* llama : only initialize used backends, free backends on context free

* llama : abort ctx if cuda backend init fails

* llama : rewrite lora with ggml-backend and compute on CPU

ggml-ci

* llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer

* opencl : add ggml-backend buffer type

* cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf)

* llama : on Metal, by default offload the full model

ggml-ci

* metal : page align the data ptr (#4854)

* Apply suggestions from code review

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

* cuda : fix split buffer free

* address review comments

* llama-bench : add split-mode parameter

* fix whitespace

* opencl : fix double initialization

* server : add --split-mode parameter

* use async copy and compute to improve multi-gpu performance

ggml-ci

* use async memcpys to copy the graph outputs to the CPU

* fix opencl

* use a host buffer for the cpu compute buffer for faster copies to the gpu

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-01-12 20:07:38 +01:00
Kawrakow
49662cbed3
ggml : SOTA 2-bit quants (add IQ2_XS) (#4856)
* iq2_xs: basics

* iq2_xs: this should have been in the basics

* iq2_xs: CUDA and scalar CPU works

* iq2_xs: WIP Metal

* iq2_xs: Metal now works

* iq2_xs: working, but dog slow, ARM_NEON dot product

* iq2_xs: better ARM_NEON dot product

We are now at 19.5 t/s for TG-128 and 61 t/s for PP-512 when
running on the CPU.

* iq2_xs: AVX2 dot product - 19.5 t/s

* iq2_xs: faster AVX2 dit product

21.4 t/s for TG-128, 59.2 t/s for PP-512.
The latter is 2x compared to the previous version.

* iq2_xs: had forgotten to delete iq2-data.h

* Add llama enum for IQ2_XS

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-11 21:39:39 +02:00
Paul Tsochantaris
2a7c94db5f
metal : put encoder debug group behind a define (#4873) 2024-01-11 16:31:52 +02:00
Georgi Gerganov
3267c2abc7
metal : fix deprecation warning (ggml/690) 2024-01-11 09:39:05 +02:00
Jack Mousseau
5362e43962
metal : wrap each operation in debug group (ggml/690) 2024-01-11 09:39:05 +02:00
Kawrakow
dd5ae06405
SOTA 2-bit quants (#4773)
* iq2_xxs: basics

* iq2_xxs: scalar and AVX2 dot products

Needed to change Q8_K to have quants in the -127...127 range,
else the IQ2_XXS AVX implementation becomes very awkward.
The alternative would have been to use Q8_0 instead. Perhaps
I'll change later, for now this is what we have.

* iq2_xxs: ARM_NEON dot product

Somehow strangely slow (112 ms/token).

* iq2_xxs: WIP Metal

Dequantize works, something is still wrong with the
dot product.

* iq2_xxs: Metal dot product now works

We have
PP-512 = 475 t/s
TG-128 = 47.3 t/s

Not the greatest performance, but not complete garbage either.

* iq2_xxs: slighty faster dot product

TG-128 is now 48.4 t/s

* iq2_xxs: slighty faster dot product

TG-128 is now 50.9 t/s

* iq2_xxs: even faster Metal dot product

TG-128 is now 54.1 t/s.

Strangely enough, putting the signs lookup table
into shared memory has a bigger impact than the
grid values being in shared memory.

* iq2_xxs: dequantize CUDA kernel - fix conflict with master

* iq2_xxs: quantized CUDA dot product (MMVQ)

We get TG-128 = 153.1 t/s

* iq2_xxs: slightly faster CUDA dot product

TG-128 is now at 155.1 t/s.

* iq2_xxs: add to llama ftype enum

* iq2_xxs: fix MoE on Metal

* Fix missing MMQ ops when on hipBLAS

I had put the ggml_supports_mmq call at the wrong place.

* Fix bug in qequantize_row_iq2_xxs

The 0.25f factor was missing.
Great detective work by @ggerganov!

* Fixing tests

* PR suggestion

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-08 16:02:32 +01:00
Georgi Gerganov
91d38876df metal : switch back to default.metallib (ggml/681)
ggml-ci
2024-01-05 18:02:06 +02:00
Finn Voorhees
1bf681f90e ggml : add error handling to graph_compute (whisper/1714) 2024-01-05 18:02:06 +02:00
Georgi Gerganov
289313716f metal : add kernel_get_rows_i32
ggml-ci
2024-01-03 14:38:38 +02:00
Georgi Gerganov
f3f62f0d83
metal : optimize ggml_mul_mat_id (faster Mixtral PP) (#4725)
* ggml : disable fast-math for Metal (cmake build only)

ggml-ci

* metal : fix Metal API debug warnings

* cmake : add -fno-inline for Metal build (#4545)

* metal : fix API debug warnings

* metal : fix compile warnings

* metal : use uint64_t for strides

* cmake : rename option to LLAMA_METAL_SHADER_DEBUG

* metal : fix mat-vec Q8_0 kernel for BS > 1

* metal : normalize mat-vec kernel signatures

* cmake : respect LLAMA_QKK_64 option

* metal : fix mat-vec Q4_K kernel for QK_K == 64

* metal : optimizing ggml_mul_mat_id (wip)

* metal : minor fix

* metal : opt mul_mm_id
2024-01-02 21:07:47 +02:00
Georgi Gerganov
58ba655af0
metal : enable shader debugging (cmake option) (#4705)
* ggml : disable fast-math for Metal (cmake build only)

ggml-ci

* metal : fix Metal API debug warnings

* cmake : add -fno-inline for Metal build (#4545)

* metal : fix API debug warnings

* metal : fix compile warnings

* metal : use uint64_t for strides

* cmake : rename option to LLAMA_METAL_SHADER_DEBUG

* metal : fix mat-vec Q8_0 kernel for BS > 1

* metal : normalize mat-vec kernel signatures

* cmake : respect LLAMA_QKK_64 option

* metal : fix mat-vec Q4_K kernel for QK_K == 64

ggml-ci
2024-01-02 10:57:44 +02:00
Georgi Gerganov
afefa319f1
ggml : change ggml_scale to take a float instead of tensor (#4573)
* ggml : change ggml_scale to take a float instead of tensor

* ggml : fix CPU implementation

* tests : fix test-grad0

ggml-ci
2023-12-21 23:20:49 +02:00
slaren
d232aca5a7
llama : initial ggml-backend integration (#4520)
* llama : initial ggml-backend integration

* add ggml-metal

* cuda backend can be used though ggml-backend with LLAMA_GGML_BACKEND_CUDA_TEST
access all tensor data with ggml_backend_tensor_get/set

* add ggml_backend_buffer_clear
zero-init KV cache buffer

* add ggml_backend_buffer_is_hos, used to avoid copies if possible when accesing tensor data

* disable gpu backends with ngl 0

* more accurate mlock

* unmap offloaded part of the model

* use posix_fadvise64(.., POSIX_FADV_SEQUENTIAL) to improve performance with mmap

* update quantize and lora

* update session copy/set to use ggml-backend

ggml-ci

* use posix_fadvise instead of posix_fadvise64

* ggml_backend_alloc_ctx_tensors_from_buft : remove old print

* llama_mmap::align_offset : use pointers instead of references for out parameters

* restore progress_callback behavior

* move final progress_callback call to load_all_data

* cuda : fix fprintf format string (minor)

* do not offload scales

* llama_mmap : avoid unmapping the same fragments again in the destructor

* remove unnecessary unmap

* metal : add default log function that prints to stderr, cleanup code

ggml-ci

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-21 21:07:46 +01:00
Georgi Gerganov
4d98d9a656
sync : ggml (SD ops, tests, kernels) (#4444)
* sync : ggml (SD ops, tests, kernels)

ggml-ci

* cuda : restore im2col

ggml-ci

* metal : fix accuracy of dequantization kernels

ggml-ci

* cuda : restore correct im2col

ggml-ci

* metal : try to fix moe test by reducing expert size

ggml-ci

* cuda : fix bin bcast when src1 and dst have different types

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-13 21:54:54 +02:00
slaren
799a1cb13b
llama : add Mixtral support (#4406)
* convert : support Mixtral as LLAMA arch

* convert : fix n_ff typo

* llama : model loading

* ggml : sync latest ggml_mul_mat_id

* llama : update graph to support MoE

* llama : fix cur -> cur_expert

* llama : first working version

* llama : fix expert weighting in the FFN

* ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only)

* ggml : add n_as argument to ggml_mul_mat_id

* ggml : fix ggml_get_rows to take into account ne02 / ne11

* metal : add more general support for ggml_get_rows + tests

* llama : add basic support for offloading moe with CUDA

* metal : add/mul/div use general kernel when src1 not cont

* metal : reduce the kernel launches for ggml_mul_mat_id

* ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D

* ggml : update get_rows f16 and q

* cuda : support non-contiguous src1 in get_rows

* llama : offload missing ffn_moe_silu

* metal : fix ggml_get_rows to work with non-cont src1

* metal : add indirect mat-vec kernels for all quantization types

* llama : do not quantize expert gating tensors

* llama : add n_expert and n_expert_used to hparams + change quants

* test-backend-ops : add moe test

* cuda : fix get_rows when ncols is odd

* convert : determine n_ctx correctly

* metal : fix ggml_mul_mat_id for F32

* test-backend-ops : make experts more evenly probable (test_moe)

* test-backend-ops : cleanup, add moe test for batches

* test-backend-ops : add cpy from f32 -> all types test

* test-backend-ops : fix dequantize block offset

* llama : fix hard-coded number of experts

* test-backend-ops : simplify and disable slow tests to avoid CI timeout

* test-backend-ops : disable MOE test with thread sanitizer

* cuda : fix mul_mat_id with multi gpu

* convert : use 1e6 rope_freq_base for mixtral

* convert : fix style

* convert : support safetensors format

* gguf-py : bump version

* metal : add cpy f16 -> f32 kernel

* metal : fix binary ops for ne10 % 4 != 0

* test-backend-ops : add one more sum_rows test

* ggml : do not use BLAS with ggml_mul_mat_id

* convert-hf : support for mixtral-instruct (#4428)

* convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct

* convert : use sentencepiece tokenizer for Mixtral-instruct

* convert : make flake8 happy

* metal : fix soft_max kernels

ref: 1914017863

* metal : limit kernels to not use more than the allowed threads

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 14:04:25 +02:00
Georgi Gerganov
fe680e3d10
sync : ggml (new ops, tests, backend, etc.) (#4359)
* sync : ggml (part 1)

* sync : ggml (part 2, CUDA)

* sync : ggml (part 3, Metal)

* ggml : build fixes

ggml-ci

* cuda : restore lost changes

* cuda : restore lost changes (StableLM rope)

* cmake : enable separable compilation for CUDA

ggml-ci

* ggml-cuda : remove device side dequantize

* Revert "cmake : enable separable compilation for CUDA"

This reverts commit 09e35d04b1.

* cuda : remove assert for rope

* tests : add test-backend-ops

* ggml : fix bug in ggml_concat

* ggml : restore `ggml_get_n_tasks()` logic in `ggml_graph_plan()`

* ci : try to fix macOS

* ggml-backend : remove backend self-registration

* ci : disable Metal for macOS cmake build

ggml-ci

* metal : fix "supports family" call

* metal : fix assert

* metal : print resource path

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-07 22:26:54 +02:00
Georgi Gerganov
bcc0eb4591
llama : per-layer KV cache + quantum K cache (#4309)
* per-layer KV

* remove unnecessary copies

* less code duplication, offload k and v separately

* llama : offload KV cache per-layer

* llama : offload K shift tensors

* llama : offload for rest of the model arches

* llama : enable offload debug temporarily

* llama : keep the KV related layers on the device

* llama : remove mirrors, perform Device -> Host when partial offload

* common : add command-line arg to disable KV cache offloading

* llama : update session save/load

* llama : support quantum K cache (#4312)

* llama : support quantum K cache (wip)

* metal : add F32 -> Q8_0 copy kernel

* cuda : add F32 -> Q8_0 copy kernel

ggml-ci

* cuda : use mmv kernel for quantum cache ops

* llama : pass KV cache type through API

* llama : fix build

ggml-ci

* metal : add F32 -> Q4_0 copy kernel

* metal : add F32 -> Q4_1 copy kernel

* cuda : wip

* cuda : add F32 -> Q4_0 and F32 -> Q4_1 copy kernels

* llama-bench : support type_k/type_v

* metal : use mm kernel only for quantum KV cache

* cuda : add comment

* llama : remove memory_f16 and kv_f16 flags

---------

Co-authored-by: slaren <slarengh@gmail.com>

* readme : add API change notice

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-07 13:03:17 +02:00
Georgi Gerganov
d7b800b8bc
llama : pad KV cache size (#4280)
* llama : pad KV cache size to 32

* metal : try to improve batched decoding
2023-12-03 10:58:16 +02:00
Georgi Gerganov
ef47ec18da
ggml : add ggml_soft_max_ext (#4256)
* metal : implement soft_max_ext

* cuda : implement soft_max_ext

* ggml : implement soft_max_ext (CPU)

* batched-bench : print threads

ggml-ci

* metal : simplify soft_max encoding

ggml-ci

* cuda : use 512 threads for soft_max instead of 32

* ggml : update soft max cpu

* cuda : do warp-based block reduce

* cuda : increase max block size to 1024

* cuda : fix warp reduction initialization of shared mem

* metal : warp-based reduction for soft max kernel

* metal : warp-based reduce for rms_norm

* metal : simplify soft max kernel

ggml-ci

* alloc : fix build with debug
2023-12-01 10:51:24 +02:00
Xiao-Yong Jin
22da05536f
metal : fix yarn (#4220)
get the correct n_orig_ctx in metal
2023-11-26 10:30:02 +02:00
Georgi Gerganov
4f447a4833
llama : fix data units (#4101)
* llama : fix data units

ggml-ci

* Revert "llama : fix data units"

This reverts commit f5feac831f.

* llama : disambiguate data units

ggml-ci
2023-11-17 10:00:15 +02:00
Georgi Gerganov
3d68f364f1
ggml : sync (im2col, GPU conv, 32-bit arm compat) (#4060)
ggml-ci
2023-11-13 16:55:52 +02:00
Georgi Gerganov
4760e7cc0b
sync : ggml (backend v2) (#3912)
* sync : ggml (backend v2) (wip)

* sync : migrate examples and llama.cpp to dynamic graphs (wip)

* sync : update tests + fix max op params to 64

ggml-ci

* sync : ggml-cuda

ggml-ci

* llama : fix save/load state context size

ggml-ci

* sync : try to fix build on tvOS

* sync : pass custom graph sizes in training examples

* sync : update graph copies to new ggml API

* sync : update sync-ggml.sh with new files

* scripts : fix header in sync script

* train : fix context size calculations

* llama : increase inference graph size up to 4096 nodes

* train : allocate grads for backward graphs

* train : allocate grads for gb_tmp
2023-11-13 14:16:23 +02:00
Peter Sugihara
d9b33fe95b
metal : round up to 16 to fix MTLDebugComputeCommandEncoder assertion (#3938) 2023-11-03 21:18:18 +02:00
Xiao-Yong Jin
5ba3746171
ggml-metal: fix yarn rope (#3937) 2023-11-03 14:00:31 -04:00
Georgi Gerganov
183b3fac6c
metal : fix build errors and kernel sig after #2268 (#3898) 2023-11-02 08:33:37 +02:00
cebtenzzre
898aeca90a
llama : implement YaRN RoPE scaling (#2268)
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
Co-authored-by: Jeffrey Quesnelle <jquesnelle@gmail.com>
2023-11-01 18:04:33 -04:00