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* Webui: add Rename/Upload conversation in header and sidebar
webui: don't change modified date when renaming conversation
* webui: add a preset feature to the settings #14649
* webui: Add editing assistant messages #13522
Webui: keep the following message while editing assistance response.
webui: change icon to edit message
* webui: DB import and export #14347
* webui: Wrap long numbers instead of infinite horizontal scroll (#14062)
fix sidebar being covered by main content #14082
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Co-authored-by: firecoperana <firecoperana>
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* iq1_kt: basics
* iq1_kt: CUDA dequantize
Testing with LlaMA-3.1-8B-Instruct, we get almost the same PPL
as iq2_xxs, so about 0.2 bpw fewer bits for the same quality.
* iq1_kt: CUDA MMQ
* iq1_kt: CUDA MMVQ
* iq1_kt: AVX2 GEMM/GEMV
* iq1_kt: convert/repack to q8_0_r8 (AVX2)
* iq1_kt: slightly faster GEMV
18.6 t/s -> 19.4 t/s
* iq1_kt: NEON GEMM/GEMV
Pathetic as usual
* iq1_kt: slightly faster NEON - still pathetic
* iq1_kt: tiny bit better GEMV on NEON
* iq1_kt: convert/repack to q8_0_r8 (NEON)
* iq1_kt: very slightly faster convert/repack to q8_0_r8 on NEON
* Adding frgotten file
* iq1_kt: add to constants.py
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Experiments for 2.6875 bpw quants
At least according to rmse, this is significantly better than
q2_K, while using only 1/16 more bits per weight.
* iq2_kl: basics
* iq2_kl: CUDA dequantize
* iq2_kl: small improvement in PPL
Also check the two neighbouring values for the block scale
and use the one that minimizes RMSE.
* iq2_kl: MMQ
Quite good: PP-512(L3-8B) = 8472 t/s.
* iq2_kl: MMVQ
We get PP-128(L3-8B) = 162 t/s.
Which means that this is not quite as good as it should be as
(almost) same bpq q2_K is at 170 t/s.
* iq2_kl: Zen4 GEMM/GEMV
Not particularly fast. I may need to think about rearranging the bits.
* iq2_kl: better Zen4
* iq2_kl: convert/repack to q8_k_r8 (AVX2)
* iq2_kl: AVX2 GEMM/GEMV
* iq2_kl: WIP NEON
The compiler started crashing!!!
* iq2_kl: NEON
Had to work around a compiler crash when using vzip2q_u8 using
vqtbl2q_u8.
* iq2_kl: convert/repack to q8_k_r8 (NEON)
* iq2_kl: Metal dequantize
* iq2_kl: Metal GEMV - pretty slow
* iq2_kl: Metal GEMV - slightly better (40 t/s -> 44.5 t/s)
* iq2_kl: Metal GEMV - slightly better (44.5 t/s -> 46.5 t/s)
* iq2_kl: Metal GEMV - slightly better (46.5 t/s -> 47.2 t/s)
* iq2_kl: slightly better Metal dequantize
PP-512 goes to 476 t/s up from 466 t/s.
* iq2_kl: slightly better Metal dequantize
PP-512 goes to 492 t/s up from 476 t/s.
* Add iq2_kl to constants.py
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* iq3_ks: basics
* iq3_ks: CUDA dequantize
* iq3_ks: CUDA mmvq
* iq3_ks: mmq
* iq3_ks: faster mmq
* iq3_ks: Zen4
* iq3_ks: AVX2 convert to q8_k_r8
This gives usPP-512 = 360 t/s.
* iq3_ks: AVX2 GEMM/GEMV
* iq3_ks: NEON GEMM/GEMV
* iq3_ks: NEON convert to q8_k_r8
This gives us PP-512 = 164 t/s.
* iq3_ks: Metal dequantize
* iq3_ks: Metal gemv - pathetic performance
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Merge vulkan code from mainline up to commit of 6/28/2025
* Vulkan Optimizations and Fixes (#8959)
* Optimize Vulkan REPEAT performance
* Use Vulkan GLSL fused multiply-add instruction where possible
* Add GGML_VULKAN_PERF option to output performance data per operator
* Rework and fix Vulkan descriptor set and descriptor pool handling
* Fix float32 concat f16 shader validation error
* Add Vulkan GROUP_NORM eps parameter
* Fix validation error with transfer queue memory barrier flags
* Remove trailing whitespaces
vulkan : do not use tensor->extra (#9407)
* vulkan : do not use tensor->extra
This patch allows using the Vulkan backend with the RPC backend as
tensor->extra is no longer used.
Ref: #8536
* Adapt GGML_VULKAN_CHECK_RESULTS to extra removal (#2)
---------
Co-authored-by: 0cc4m <picard12@live.de>
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan : fix build (#0)
ggml-ci
Improve Vulkan shader build system (#9239)
* Improve Vulkan shader builds system
- Add dependency to vulkan-shaders-gen to rebuild shaders when changing the shader compilation utility.
- Add option to generate debug info for Vulkan shaders to provide shader source to Vulkan shader profiling tools
* remove not required self dependency
ggml : fix build break for the vulkan-debug (#9265)
- windows build : Ok.
- linux build : Ok.
Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>
vulkan: correctly report support for OP_CONT (ggml/946)
test-backend-ops fails because ggml_cont aborts
when invoked passing an unsupported type.
This commit makes ggml_cont tests pass
Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>
vulkan: add dryrun support to sin and cos ops (ggml/947)
sin and cos failed test-backend-ops because they
tried to dereference a context pointer that is null
on dry runs.
This commit prevents that segfault.
Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>
# Conflicts:
# ggml/src/ggml-vulkan.cpp
Overlap cmdbuffer creation and cmdbuffer execution in Vulkan backend by submitting smaller cmdbuffers early. (#9118)
* Overlap cmdbuffer creation and cmdbuffer execution in Vulkan backend by submitting smaller cmdbuffers early.
* fix compile issues
* Fix issues where the last submit wasn't executed or handled properly.
* remove trailing whitespace
* Repair GGML_VULKAN_CHECK_RESULTS
* Increase submit counter only if actual work has been submitted and increase submit count to 100.
* Fix some nodes are not checked with GGML_VULKAN_CHECK_RESULTS enabled.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
Enable use to the rebar feature to upload buffers to the device. (#9251)
vulkan : argsort barriers must be under uniform control flow (ggml/951)
a return before a barrier (that happens only in some threads in
a workgroup) leads to UB.
While the old code actually works on some devices,
it fails on some others (i.e. "smaller" GPUs).
BTW, I think it would be better to set specialization constants
when the graph is built, in that way the local workgroup
could be sized appropriately.
But it would take a lot of work.
Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>
vulkan : fix build for GGML_VULKAN_RUN_TESTS, add TFLOPS to log (ggml/961)
vulkan : multithread pipeline creation (ggml/963)
vulkan : mul_mat: fix UB with small warps (ggml/952)
When the device's warp size is less than 16,
it is possible for loadstride_a (mul_mm.comp:114)
and loadstride_b (mul_mm.comp:115) to be set to 0.
Because they are calculated as: the workgroup size,
multiplied by LOAD_VEC_* (which can be 1) and divided by 16.
And the workgroup size is set to be the same as the
warp/subgroup size.
The loadstride_* variables are used as increments in the
loops that populate the buffers used for the multiplication.
When they are 0 they cause an infinite loop.
But infinite loops without side-effects are UB and the
values of loadstride_* are known at compile time.
So, the compiler quietly optimizes all the loops away.
As a consequence, the buffers are not populated and
the multiplication result is just a matrix with all elements
set to 0.
We prevent the UB by making sure that the workgroup size
will never be less than 16, even if our device has a
smaller warp size (e.g. 8).
Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>
vulkan : retry allocation with fallback flags (whisper/2451)
Co-authored-by: Samuel Morris <samuel.morris@artlist.io>
vulkan : improve ggml_vk_create_buffer error handling (#9898)
vulkan: Fix newly added tests for permuted mul_mat and 1D im2col (#10226)
vulkan: Throttle the number of shader compiles during the build step. (#10222)
Fixes #9582
Spawning too many concurrent copies of glslc leads to "Failed to create pipes"
errors on Linux. This change applies the same throttling we use for
multithreaded pipeline creation.
# Conflicts:
# ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
vulkan: Optimize contiguous copies (#10254)
* tests: Fix memory bandwidth calculation for perf tests
Add a flops calculation for flash attention.
Add one GGML_OP_CPY perf test.
* vulkan: Optimize contiguous copies
Add a variant of the copy shader for when the tensors are contiguous. Avoid
the complex addressing calculations, and do four elements per invocation
to hide some other overhead.
Apply similar changes to the scale shader, since scale is always contiguous.
Add a "progress bar" for shader compiles.
# Conflicts:
# tests/test-backend-ops.cpp
vulkan: Use macros to make the mat mul pipeline creation more concise (#10259)
Also add vk_matmul_pipeline2 to hold f16/f32 accumulator versions of a
pipeline. This isn't really used yet.
vulkan: Optimize binary ops (#10270)
Reuse the index calculations across all of src0/src1/dst. Add a shader
variant for when src0/src1 are the same dimensions and additional modulus
for src1 aren't needed. Div/mod are slow, so add "fast" div/mod that
have a fast path when the calculation isn't needed or can be done more
cheaply.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/acc.comp
ggml : vulkan logs (whisper/2547)
vulkan: Optimize some mat-vec mul quant shaders (#10296)
Compute two result elements per workgroup (for Q{4,5}_{0,1}). This reuses
the B loads across the rows and also reuses some addressing calculations.
This required manually partially unrolling the loop, since the compiler
is less willing to unroll outer loops.
Add bounds-checking on the last iteration of the loop. I think this was at
least partly broken before.
Optimize the Q4_K shader to vectorize most loads and reduce the number of
bit twiddling instructions.
Vulkan: Fix device info output format specifiers (#10366)
* Vulkan: Fix device info output format specifiers
* Vulkan: Use zu printf specifier for size_t instead of ld
vulkan: remove use of null initializer (#10372)
Seems like this isn't working for vulkan-over-metal when the array is sized
by a spec constant. Maybe a spirv-cross limitation?
vulkan: Optimize soft_max (#10301)
* vulkan: Optimize soft_max
Large soft_max could already saturate memory, but small/medium sizes were
pretty slow. The bulk of the gains for them comes from using a smaller
workgroup size, and making the workgroup size match the subgroup size also
makes the barriers much cheaper.
Cache some values in locals to avoid refetching/recomputing. And stamp
out a few "template instantiations" so smaller cases will fully unroll.
Add a missing early return for OOB rows. This happens when there are more
than 512 rows and the dispatch is 512 x H.
* vulkan: Further soft_max optimizations
Restore the workgroup size of 512 case, use it for >1024.
Use unrollable loops for more iteration counts.
vulkan: further optimize mul_mat_vec using larger loads (#10387)
* vulkan: Use pipeline_robustness to disable robustness in mul_mat_vec.
Add some early returns for nonexistent rows in mul_mat_vec shaders. These
can only be hit when dispatching a 2D grid of workgroups. Fix the logic
for the 2D grid of workgroups to round up.
Enable the pipeline robustness extension if it's available, and use it to
disable robustness for these pipelines. The instructions to do the bounds
checking contend for the same ALU resources as the bit twiddling dequant
instructions.
* vulkan: Add GLSL structure aliases for quant types to allow larger loads
In Vulkan it's not possible to cast pointer types, so instead you have to
declare an aliased binding for the memory with a different type. This
commit adds aliases for the quant formats using 16b ints, and in a few
places where the struct size is a multiple of 4 also using 32b ints.
Currently only q4_k's aliases are used, but others will be used in
subsequent commits.
* vulkan: use larger loads in q5_k and q6_k shaders.
Similar to the optimization I did in q4_k recently, this vectorizes some loads
and reduces the number of bit twiddling instructions.
* vulkan: use larger K step per iteration in mul_mat_vec.
Add vec4 dequantization functions, and use them to do K=8 per iteration in
mul_mat_vec. This uses 16b loads for the quant values and 128b loads for B
which helps reduce the load on the memory system.
The K_PER_ITER==2 logic is still there, just for F16/F32, and really only
because they support unaligned sizes.
Tweak the num_iters/unrolling logic to be simpler and catch a couple missed
unrolling opportunities.
vulkan: copy iq4_nl LUT into shared memory (#10409)
vulkan: predicate max operation in soft_max shaders/soft_max (#10437)
Fixes #10434
vulkan: Fix a vulkan-shaders-gen arugment parsing error (#10484)
The vulkan-shaders-gen was not parsing the --no-clean argument correctly.
Because the previous code was parsing the arguments which have a value only
and the --no-clean argument does not have a value, it was not being parsed
correctly. This commit can now correctly parse arguments that don't have values.
vulkan: fix group_norm (#10496)
Fix bad calculation of the end of the range. Add a backend test that
covers the bad case (taken from stable diffusion).
Fixes https://github.com/leejet/stable-diffusion.cpp/issues/439.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: optimize Q2_K and Q3_K mul_mat_vec (#10459)
vulkan: skip integer div/mod in get_offsets for batch_idx==0 (#10506)
vulkan: further optimize q5_k mul_mat_vec (#10479)
vulkan: Handle GPUs with less shared memory (#10468)
There have been reports of failure to compile on systems with <= 32KB
of shared memory (e.g. #10037). This change makes the large tile size
fall back to a smaller size if necessary, and makes mul_mat_id fall
back to CPU if there's only 16KB of shared memory.
vulkan: define all quant data structures in types.comp (#10440)
vulkan: get the first command buffer submitted sooner (#10499)
This is an incremental improvement over #9118 to get work to the GPU a bit
sooner. The first part is to start with a smaller number of nodes before
the first submit, and ramp it up to the current 100 nodes/submit. The
second part is to reduce the dryrun overhead for all the nodes that just
need to request descriptor space.
With these changes I get around 1-2% speedup on RTX 4070 combined with my
old Haswell-era CPU.
vulkan: Dynamic subgroup size support for Q6_K mat_vec (#10536)
* subgroup 64 version with subgroup add. 15% faster
scalable version
tested for subgroup sizes 16-128
* check for subgroup multiple of 16 and greater than 16
* subgroup sizes are always a power of 2 (https://github.com/KhronosGroup/GLSL/issues/45)
* force 16 sequential threads per block
* make 16 subgroup size a constant
vulkan: optimize and reenable split_k (#10637)
Use vector loads when possible in mul_mat_split_k_reduce. Use split_k
when there aren't enough workgroups to fill the shaders.
vulkan: Implement "fast divide" (mul+shift) for unary ops like copy (#10642)
vulkan: Add VK_NV_cooperative_matrix2 support for mul_mat and flash attention (#10206)
# Conflicts:
# ggml/src/vulkan-shaders/dequant_funcs_cm2.comp
# ggml/src/vulkan-shaders/flash_attn_cm2.comp
# ggml/src/vulkan-shaders/mul_mm_cm2.comp
Vulkan: VK_KHR_cooperative_matrix support to speed up prompt processing (#10597)
* Vulkan: Implement VK_KHR_cooperative_matrix support in the matrix matrix multiplication shader
* Improve performance with better q4_k and q5_k dequant and store unrolling
* Add Vulkan MUL_MAT and MUL_MAT_ID accumulator precision selection
* Rework mulmat shader selection and compilation logic, avoid compiling shaders that won't get used by device
* Vulkan: Implement accumulator switch for specific mul mat mat shaders
* Vulkan: Unroll more loops for more mul mat mat performance
* Vulkan: Add VK_AMD_shader_core_properties2 support to read Compute Unit count for split_k logic
* Disable coopmat support on AMD proprietary driver
* Remove redundant checks
* Add environment variable GGML_VK_DISABLE_COOPMAT to disable VK_KHR_cooperative_matrix support
* Fix rebase typo
* Fix coopmat2 MUL_MAT_ID pipeline selection
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: compile a test shader in cmake to check for coopmat2 support (#10713)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/ggml-vulkan/CMakeLists.txt
# ggml/src/vulkan-shaders/test_coopmat2_support.comp
Vulkan: fix NaN in tanh.comp with AMD proprietary driver on Windows (#10723)
* Vulkan: fix NaN in tanh.comp
* Faster NaN-free tanh
vulkan: fix compile warnings (#10731)
vulkan: disable spirv-opt for coopmat shaders (#10763)
There are some bugs in the 1.3.296 SDK, so disable this. It isn't strictly
necessary anyway.
Add missing dependency on vulkan-shaders-gen, so shaders get recompiled when it
changes.
Fix coopmat support reporting when glslc doesn't support NV_coopmat2.
vulkan: dynamic subgroup size for the remaining k quants (#10745)
* q5_k
q4_k
q3_k
q2_k
q6_k multi row example
* revert as multi row isnt faster for k quants
vulkan: request round-to-even for fp16 in im2col/rope_head (#10767)
Vulkan doesn't mandate a specific rounding mode, but the shader_float_controls
feature allows rounding mode to be requested if the implementation supports it.
Vulkan: Add VK_EXT_subgroup_size_control support to ensure full subgroups for coopmats (#10721)
* Vulkan: Add VK_EXT_subgroup_size_control support to ensure full subgroups for coopmats
* Fix subgroup size control extension support check
Add accf32 and accf16 checks for coopmats
* Also disable coopmats on amdvlk
Vulkan: Use improved q4_k and q5_k dequant code in dequant shaders (#10798)
vulkan: small mul_mat_vec optimizations (#10665)
* double the number of rows per workgroup
* Update ggml-vulkan.cpp
* Vulkan: Add VK_EXT_subgroup_size_control support to ensure full subgroups for coopmats
* only increase the number of rows for amd and subgroup size 64
* fix missing NUM_ROWS for mul_mat_vec_iq4_nl_f16_f32, untested
* use subgroup min and max to check for gcn (requires https://github.com/ggerganov/llama.cpp/pull/10721)
* manual merge ggml-vulkan.cpp
* set min and max subgroup size in any case
* Also double the number of rows for Intel GPUs
Change Debug print name
add GGML_ROPE_TYPE_MROPE
rwkv6: add wkv6 support for Vulkan backend (#10829)
* rwkv_wkv6 vulkan shader
* RWKV_WKV6 Vulkan op tests passed
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
* Apply code format changes
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
* add [[unroll]] and remove unnecessary conditions
* add uma support
* fix erros in EditorConfig Checker
---------
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: Molly Sophia <mollysophia379@gmail.com>
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/wkv6.comp
vulkan: bugfixes for small subgroup size systems + llvmpipe test (#10809)
* ensure mul mat shaders work on systems with subgroup size less than 32
more fixes
add test
* only s_warptile_mmq needs to be run with 32 threads or more
# Conflicts:
# .github/workflows/build.yml
vulkan : fix soft_max.comp division by zero (whisper/2633)
This change prevents a division by zero error when p.KY is 0.
vulkan: optimize coopmat2 dequant functions (#10855)
Change the code to do 16b loads when possible and extract the appropriate
component late, so the code is effectively decoding a pair of elements and
then selecting one. This can allow more commoning to happen in the compiler
when neighboring elements are loaded.
vulkan: build fixes for 32b (#10927)
* vulkan: build fixes for 32b
Should fix #10923
* vulkan: initialize some buffer/offset variables
examples, ggml : fix GCC compiler warnings (#10983)
Warning types fixed (observed under MSYS2 GCC 14.2.0):
* format '%ld' expects argument of type 'long int', but argument has type 'size_t'
* llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp:81:46: warning: missing initializer for member '_STARTUPINFOA::lpDesktop' [-Wmissing-field-initializers] (emitted for all struct field except first)
# Conflicts:
# examples/export-lora/export-lora.cpp
vulkan: multi-row k quants (#10846)
* multi row k quant shaders!
* better row selection
* more row choices
* readjust row selection
* rm_kq=2 by default
vulkan: Use push constant offset to handle misaligned descriptors (#10987)
vulkan: im2col and matmul optimizations for stable diffusion (#10942)
* tests: Add im2col perf tests
* vulkan: optimize im2col, more elements per thread
* vulkan: increase small tile size for NV_coopmat2
* vulkan: change im2col to 512 elements per workgroup
vulkan: optimize mul_mat for small values of N (#10991)
Make the mul_mat_vec shaders support N>1 (as a spec constant, NUM_COLS) where
the batch_strides are overloaded to hold the row strides. Put the loads from the
B matrix in the innermost loop because it should cache better.
Share some code for reducing the result values to memory in mul_mat_vec_base.
# Conflicts:
# tests/test-backend-ops.cpp
fix: Vulkan shader gen binary path (#11037)
Vulkan: Add device-specific blacklist for coopmat for the AMD proprietary driver (#11074)
* Vulkan: Add device-specific blacklist for coopmat for the AMD proprietary driver
* Add (TM) to AMD name check
fix lora print
Disable GL_KHR_cooperative_matrix Vulkan extension if not available. (#11117)
* Disable GL_KHR_cooperative_matrix Vulkan extension if not available.
* Perform Vulkan extensions checks in a more sensible order
* Remove unnecessary #ifdef directive
# Conflicts:
# ggml/src/vulkan-shaders/test_coopmat_support.comp
llama: add support for QRWKV6 model architecture (#11001)
Vulkan: Fix float16 use on devices without float16 support + fix subgroup_size_control validation error (#11161)
* Vulkan: Remove float16 use in shaders
* Fix validation error about subgroup_size_control extension
fix: ggml: fix vulkan-shaders-gen build (#10448)
* fix: ggml: fix vulkan-shaders-gen build
The vulkan-shaders-gen target was not being built correctly
in case of cross-compilation.
Other outputs need to be built for the cross compile target,
but vulkan-shaders-gen needs to be built for the host.
* refactor: ggml: Improve vulkan-shaders-gen toolchain setup
- Add GGML_SHADERS_GEN_TOOLCHAIN CMake option.
- Auto-detect host toolchain if not set.
* refactor: ggml: Improve vulkan-shaders-gen toolchain setup
Use configure_file to generate host_toolchain.cmake from template
* fix: ggml: Fix compile error
Fix compile error not finding vulkan-shaders-gen
* fix: vulkan-shaders-gen build and path handling
Fix build issues with vulkan-shaders-gen:
- Add target dependency for correct build order
- Use CMAKE_HOST_SYSTEM_NAME for executable suffix
- Fix MSVC output directory in host toolchain
- Normalize path handling for cross-compilation
* fix: improve host compiler detection in vulkan shader build
Improve host compiler detection for vulkan shader generation:
- Add NO_CMAKE_FIND_ROOT_PATH to all compiler searches
- Consolidate compiler detection logic
- Fix Windows-specific MSVC detection
- Ensure correct compiler search in cross-compilation
* refactor: Simplify CMake function for detecting host compiler
Simplified the CMake function to improve the process of detecting the host compiler.
* fix: Remove unnecessary Vulkan library linkage in CMakeLists.txt
Since `vulkan-shader-gen.cpp` only requires the `glslc` executable
and not the Vulkan headers or libraries, CMakeLists.txt needs to
be corrected.
(See: ecc93d0558fc3ecb8a5af69d2ece02fae4710ade)
* refactor: Rename host_toolchain.cmake.in
- Rename host_toolchain.cmake.in to cmake/host-toolchain.cmake.in
* refactor: GGML_VULKAN_SHADERS_GEN_TOOLCHAIN
Rename the macro GGML_SHADERS_GEN_TOOLCHAIN to GGML_VULKAN_SHADERS_GEN_TOOLCHAIN
# Conflicts:
# ggml/src/ggml-vulkan/CMakeLists.txt
vulkan: scale caching for k quants + misc fixes (#11081)
* q6_k scale caching
* 16 bit unpack
* q4_k test (slow)
* revert it
* q3_k
* q2_k
* little stuff
* try precalculating products of a and q2_k scales
* Revert "try precalculating products of a and q2_k scales"
This reverts commit 65110b81f23f66331a50c6e889a7c1ab9470a86b.
* unpack should be u16, add vim swap to gitignore (about time)
* better q4_k scales
* q5_k
* better q6_k with separate paths for all threads and partial threads in use, plus some more optimizations
* q2_k better dequant
* q3_k optimizations
* q3_k use hmask simd from cpu avx version
* make the caches happy
* q3_k separate out calculation
* q2_k separate out
* little stuff
* use calc_superblock everywhere
* q2_k optimize scale calculation
* more barriers
vulkan: optimize coopmat2 q2_k dequant function (#11130)
vulkan: optimize coopmat2 q4_k/q5_k dequant functions. (#11206)
Do masking on whole dwords, fetch all scales at once.
vulkan: support copy from f32 to q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl (#11166)
* vulkan: support copy from f32 to q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl
Shaders are based on cpy.cu.
* vulkan: support copy from q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl to f32
* ggml: copy q->f32 assumes some contiguity in the destination
# Conflicts:
# ggml/src/ggml-cpu/ggml-cpu.c
# ggml/src/vulkan-shaders/copy_from_quant.comp
# ggml/src/vulkan-shaders/copy_to_quant.comp
vulkan: fix coopmat2 flash attention for non-contiguous inputs (#11281)
Add code similar to mul_mm_cm2 to force alignment of strides, to avoid
a performance regression.
Add noncontiguous FA tests in test-backend-ops.
Fixes #11268.
# Conflicts:
# tests/test-backend-ops.cpp
vulkan: fix coopmat2 validation failures (#11284)
mul mat and flash attention shaders were loading f32 types directly into
A/B matrices, which happens to work but is technically invalid usage.
For FA, we can load it as an Accumulator matrix and convert and this
is not in the inner loop and is cheap enough. For mul mat, it's more
efficient to do this conversion in a separate pass and have the input(s)
be f16.
coopmat2 requires SPIR-V 1.6 (related using to LocalSizeId). LocalSizeId
requires maintenance4 be enabled, and SPIR-V 1.6 requires Vulkan 1.3.
vulkan: fix diag_mask_inf (#11323)
With robustbufferaccess disabled, this shader was showing OOB stores. There
is a bounds check in the code, but the workgrouop dimensions were reversed vs
CUDA and it was running the wrong number of threads. So fix the workgroup
dimensions and disable robustness for this pipeline.
vulkan: sort shaders for more deterministic binary (#11315)
Fixes #11306.
Vulkan-run-test: fix mmq_wg_denoms (#11343)
There should be a copy-and-paste error here.
*mmq_wg_denoms should be used together with *warptile_mmq, instead of
wg_denoms.
vulkan: compile shaders on-demand (#11406)
Reduce first-run startup time and memory consumption.
Should fix #11339.
vulkan: Catch pipeline creation failure and print an error message (#11436)
* vulkan: Catch pipeline creation failure and print an error message
Also, fix some warnings from my on-demand compile change.
* vulkan: fix pipeline creation logging
vulkan: implement initial support for IQ2 and IQ3 quantizations (#11360)
* vulkan: initial support for IQ3_S
* vulkan: initial support for IQ3_XXS
* vulkan: initial support for IQ2_XXS
* vulkan: initial support for IQ2_XS
* vulkan: optimize Q3_K by removing branches
* vulkan: implement dequantize variants for coopmat2
* vulkan: initial support for IQ2_S
* vulkan: vertically realign code
* port failing dequant callbacks from mul_mm
* Fix array length mismatches
* vulkan: avoid using workgroup size before it is referenced
* tests: increase timeout for Vulkan llvmpipe backend
---------
Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
# Conflicts:
# ggml/src/vulkan-shaders/dequant_iq2_s.comp
# ggml/src/vulkan-shaders/dequant_iq2_xs.comp
# ggml/src/vulkan-shaders/dequant_iq2_xxs.comp
# ggml/src/vulkan-shaders/dequant_iq3_s.comp
# ggml/src/vulkan-shaders/dequant_iq3_xxs.comp
CUDA: non-contiguous (RMS) norm support (#11659)
vulkan: use smaller combined allocations to avoid fragmentation (#11551)
# Conflicts:
# ggml/src/ggml-alloc.c
vulkan: initial support for IQ4_XS quantization (#11501)
# Conflicts:
# ggml/src/vulkan-shaders/dequant_iq4_xs.comp
vulkan: optimize coopmat2 iq2/iq3 callbacks (#11521)
* vulkan: optimize coopmat2 iq2/iq3 callbacks
* build: trigger CI on GLSL compute shader changes
vulkan: print shared memory size (#11719)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: account for lookup tables when checking shared memory size (#11502)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: add environment variable GGML_VK_PREFER_HOST_MEMORY to avoid VRAM allocation (#11592)
vulkan: linux builds + small subgroup size fixes (#11767)
* mm subgroup size
* upload vulkan x86 builds
vulkan: initial support for IQ1_S and IQ1_M quantizations (#11528)
* vulkan: initial support for IQ1_S and IQ1_M quantizations
* vulkan: define MMV kernels for IQ1 quantizations
* devops: increase timeout of Vulkan tests again
* vulkan: simplify ifdef for init_iq_shmem
# Conflicts:
# ggml/src/vulkan-shaders/dequant_iq1_m.comp
# ggml/src/vulkan-shaders/dequant_iq1_s.comp
# ggml/src/vulkan-shaders/mul_mat_vec_iq1_m.comp
# ggml/src/vulkan-shaders/mul_mat_vec_iq1_s.comp
vulkan: support multi/vision rope, and noncontiguous rope (#11902)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/rope_multi.comp
# ggml/src/vulkan-shaders/rope_vision.comp
vulkan: implement several ops relevant for ggml_opt (#11769)
* vulkan: support memset_tensor
* vulkan: support GGML_OP_SUM
* vulkan: implement GGML_OP_ARGMAX
* vulkan: implement GGML_OP_SUB
* vulkan: implement GGML_OP_COUNT_EQUAL
* vulkan: implement GGML_OP_OPT_STEP_ADAMW
* vulkan: fix check_results RWKV_WKV6 crash and memory leaks
* vulkan: implement GGML_OP_REPEAT_BACK
* tests: remove invalid test-backend-ops REPEAT_BACK tests
* vulkan: fix COUNT_EQUAL memset using a fillBuffer command
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/argmax.comp
# ggml/src/vulkan-shaders/count_equal.comp
# ggml/src/vulkan-shaders/opt_step_adamw.comp
# ggml/src/vulkan-shaders/repeat_back.comp
# ggml/src/vulkan-shaders/sub.comp
# tests/test-backend-ops.cpp
vulkan: implement more backpropagation operators (#11914)
* vulkan: implement GGML_OP_ROPE_BACK
* vulkan: implement GGML_OP_RMS_NORM_BACK
* vulkan: implement GGML_OP_SILU_BACK
* vulkan: implement GGML_OP_SOFTMAX_BACK
# Conflicts:
# ggml/src/vulkan-shaders/rms_norm_back.comp
# ggml/src/vulkan-shaders/silu_back.comp
# ggml/src/vulkan-shaders/soft_max_back.comp
Add memset tensor in all backend interface
SYCL: implement memset ggml backend buffer interface (#12580)
* SYCL: implement memset ggml backend buffer interface
* use GGML_ABORT macro
* Do not wait for all queues to finish for memset operation
# Conflicts:
# ggml/src/ggml-sycl.cpp
add OP sigmoid (#12056)
Co-authored-by: Judd <foldl@boxvest.com>
# Conflicts:
# ggml/src/vulkan-shaders/sigmoid.comp
vulkan: fix assertion when qy_needs_dequant (#12068)
Looks like a copy/paste bug from qx_needs_dequant.
vulkan: improve im2col (#11826)
* vulkan: improve im2col performance
vulkan: matmul dequantization improvements (#12015)
* faster dequant for old quants
* dont use unpack for iq4_nl
* vec2 unpack for q8
vulkan: add specific MMV kernels for IQ2 and IQ3 quants + optimizations (#11595)
* vulkan: implement specialized MMV kernels for IQ2 quantizations
* vulkan: add MMV kernels for IQ3 quants
* vulkan: Increase MMV batch size and unroll IQ LUT setup
* vulkan: fix init_iq_shmem for WG sizes larger than tables
* vulkan: common batch size for all I-quants
# Conflicts:
# ggml/src/vulkan-shaders/mul_mat_vec_iq2_s.comp
# ggml/src/vulkan-shaders/mul_mat_vec_iq2_xs.comp
# ggml/src/vulkan-shaders/mul_mat_vec_iq2_xxs.comp
# ggml/src/vulkan-shaders/mul_mat_vec_iq3_s.comp
# ggml/src/vulkan-shaders/mul_mat_vec_iq3_xxs.comp
cuda/vulkan: specify fp32-only support for some operations in supports_op (ggml/1129)
ggml-ci
# Conflicts:
# ggml/src/ggml-cuda.cu
# tests/test-backend-ops.cpp
mat vec double buffer (#12188)
vulkan: fix bug in coopmat1 mul_mat_id (#12316)
* tests: run mul_mat_id with a larger N
* vulkan: fix bug in coopmat1 mul_mat_id
Update build.yml for Windows Vulkan builder to use Vulkan 1.4.304 SDK for VK_NV_cooperative_matrix2 support (#12301)
vulkan: Adjust coopmat2 tile sizes and selection heuristic (#12258)
vulkan: Pad N dimension of B matrix for coopmat2 perf, to avoid bounds checking (#12273)
* vulkan: Pad N dimension of B matrix for coopmat2 perf, to avoid bounds checking
vulkan: use fp32 in coopmat2 q4_k dequant function (#12309)
vulkan: subgroup size tuning (#12087)
* vulkan: subgroup size test
* Vulkan: Add device architecture enum and logic to recognize AMD generations
* vulkan: use new architecture logic to specify subgroup size
* Initial vulkan subgroup size tuning for RDNA3
* vulkan: commonize RDNA subgroup tuning
* vulkan: override subgroup size if required_subgroup_size = 0
* vulkan: disable warp 32 for RDNA3
* vulkan: fine tuned RDNA1 subgroup sizes
* vulkan: adjusted subgroup size map
* vulkan: fixed RDNA2 subgroup map
---------
Co-authored-by: 0cc4m <picard12@live.de>
vulkan: Add N/2 and N/4 optimized paths in coopmat2 shader (#12312)
ggml-vulkan: remove unused find_program(glslc) (#12416)
It's already found by FindVulkan.cmake in the parent CMakeLists
Vulkan: Default to 1GB allocations instead of 4GB to avoid fragmentation and driver issues (#12434)
vulkan: Submit once enough matmul work has been recorded (#12406)
I've been seeing significantly worse performance for tg with flash attention
enabled vs disabled, and it seems to be related to the submit heuristic.
Change the heuristic to check how many bytes worth of weight matrix are
used and flush every 100MB, and ramp up after the first few submits.
This seems to resolve the issue, and also increases perf for non-FA a bit.
vulkan: optimize iq1 coopmat2 dequant functions (#12427)
vulkan: workaround for AMD Windows driver 16 bit unpack8 bug (#12472)
Vulkan: RTE rounding for cpy to quant (#12480)
* Vulkan: RTE rounding for cpy to quant
Co-Authored-By: Jeff Bolz <jbolz@nvidia.com>
* remove trailing whitespace
* avoid duplicating pipeline_cpy_f32_quant
* fix copypasting issue
* remove duplicated code
---------
Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
vulkan: Optimize mul_mat_vec p021 and nc shaders (#12505)
* tests: add mul_mat perf/functional tests for p021/nc vulkan shaders
* vulkan: Optimize mul_mat_vec p021 and nc shaders.
These shaders are used in attention calculations, and when the KV cache grows
large they start to dominate the run time. For the nc shader (which is called
with large 'k' dimension), use unrolling and vector loads. For the p021 shader
(which is called with large 'm' and small 'k' dimensions), take advantage of
grouped query attention to reuse loads from the A matrix for the whole group,
and reduce the number of workgroups (too much overhead from tiny dispatches).
Using subgroupAdd in the p021 shader also helps, use that conditionally.
# Conflicts:
# tests/test-backend-ops.cpp
vulkan: fix mul_mat_vec failure in backend tests (#12529)
The OOB calculation could be wrong if the last iteration was during one of
the unrolled loops. Adjust the unrolling counts to avoid this. Add a couple
new backend tests that hit this failure on NVIDIA GPUs.
vulkan: fix coopmat shader generation when cross-compiling (#12272)
* vulkan: fix coopmat shader generation when cross-compiling
Previously the status of coopmat{,2} support isn't passed to the
vulkan-shaders-gen project building on the host, which leads to build
failure because of the cross-compiling code expecting coopmat{,2}
shaders that didn't get generated.
Fix this by passing the coopmat{,2} support status to vulkan-shaders
subproject.
Signed-off-by: Icenowy Zheng <uwu@icenowy.me>
* Only call coop-mat shaders once
* Fix whitespace
---------
Signed-off-by: Icenowy Zheng <uwu@icenowy.me>
Co-authored-by: bandoti <141645996+bandoti@users.noreply.github.com>
cmake: improve Vulkan cooperative matrix support checks (whisper/2966)
Co-authored-by: Sandro Hanea <me@sandro.rocks>
cmake : fix whitespace (#0)
Vulkan: Add DP4A MMQ and Q8_1 quantization shader (#12135)
* Vulkan: Add DP4A MMQ and Q8_1 quantization shader
* Add q4_0 x q8_1 matrix matrix multiplication support
* Vulkan: Add int8 coopmat MMQ support
* Vulkan: Add q4_1, q5_0 and q5_1 quants, improve integer dot code
* Add GL_EXT_integer_dot_product check
* Remove ggml changes, fix mmq pipeline picker
* Remove ggml changes, restore Intel coopmat behaviour
* Fix glsl compile attempt when integer vec dot is not supported
* Remove redundant code, use non-saturating integer dot, enable all matmul sizes for mmq
* Remove redundant comment
* Fix integer dot check
* Fix compile issue with unsupported int dot glslc
* Update Windows build Vulkan SDK version
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/mul_mmq.comp
# ggml/src/vulkan-shaders/mul_mmq_funcs.comp
# ggml/src/vulkan-shaders/quantize_q8_1.comp
# ggml/src/vulkan-shaders/test_integer_dot_support.comp
vulkan: fix build when glslc doesn't support coopmat (#12683)
Vulkan: Fix mmq int dot float cache size (#12722)
vulkan: Implement grouped query attention in the coopmat2 FA shader (#12559)
When adjacent batches of Q share the same batches of K/V, batch them into
the same workgroup. For example, when:
dst(128,32,1,1) = FA(q(128,1,32,1), k(128,16640,8,1), v(128,16640,8,1))
previously we would run 32 workgroups computing 1 result each, now we will
run 8 workgroups computing 4 results each.
This doesn't directly translate to better performance (at least when you have
>=32 SMs), but in a subsequent change I'll enable split_k which will scale much
better with 4x fewer workgroups.
cmake: remove caching from vulkan coopmat checks (#12719)
vulkan: Implement split_k for coopmat2 flash attention. (#12627)
When using group query attention, we have one workgroup per KV batch and this
can be very few workgroups (e.g. just 8 in some models). Enable split_k to
spread the work across SMs. This helps a lot when the KV cache is large.
# Conflicts:
# ggml/src/vulkan-shaders/flash_attn_split_k_reduce.comp
vulkan: Fix missing cmake logic for dot product extension (#12721)
vulkan: set cmake minimum and project name in vulkan-shaders (#12744)
vulkan: Hybrid waitForFences/getFenceStatus to reduce fence latency (#12630)
There seems to be a bubble waking up from waitForFences, which costs a few
percent performance and also increased variance in performance. This change
inserts an "almost_ready" fence when the graph is about 80% complete and we
waitForFences for the almost_ready fence and then spin (with _mm_pauses) waiting
for the final fence to be signaled.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
cmake: fix ggml-shaders-gen compiler paths containing spaces (#12747)
fixes error for compiler paths with spaces
Vulkan: Tune Vulkan mmq int dot shader for performance (#12767)
vulkan: Use unclamped loads for flash attention mask (#12720)
nem1 must be a multiple of GGML_KQ_MASK_PAD, and GGML_KQ_MASK_PAD is a multiple
of the number of rows in the matrix. The KV dim is a multiple of the number of
columns for the aligned shader.
vulkan: fix NaN issue in flash attention shader (#12776)
Use -FLT_MAX/2 rather than -inf as the initial value for computing the maximum.
vulkan: Use fp16 for the flash attention P*V multiplication (#12783)
This is consistent with the ggml-cuda behavior and the mul_mat fallback.
vulkan: In coopmat2 mmq, load q4_k/q5_k scales through shared memory (#12833)
q4_k and q5_k had a lot of redundant global loads where the same 16B of
scale information is repeatedly loaded and decoded during each loop iteration.
This change restructures the loops to more explicitly iterate over whole
blocks in the outer loop (with unrolled inner loop) and to copy/decode the
scale data into shared memory once at the start of each outer loop. The copy
is pipelined so the scale load from global memory is relatively cheap.
This improves q4_k/q5_k model prompt processing performance by around 5-7%.
I briefly tried applying this to q6_k and q4_0, and it didn't help for q6_k
and hurt for q4_0.
The big "else" path in mul_mm_cm2.comp that had all the clamped/unclamped
variants isn't used as often as it originally was (e.g. due to the padded_N
change), so I trimmed it down to offset some of the new complexity of the
semi-manual loop unrolling.
vulkan: use aligned loads for flash attention mask (#12853)
Rewrite the stride logic for the mask tensor in the FA shader to force the
stride to be aligned, to allow using more efficient loads.
vulkan: enable coopmat2 FA gqa and split_k optimizations more often (#12931)
The grouped query attention optmization doesn't require a power of two ratio,
the only thing relying on it was the modulo operation written as bitwise &.
split_k need not depend on gqa_ratio - enable it any time there's only one
workgroup in the X dimension. The shader gets the split index from the x coord,
and multiple workgroups in the X dimension (pre-split) indicates a larger
FA operation that wouldn't need splitting.
vulkan: support noncontiguous rms_norm (#13031)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: matmul gcn tuning (#13016)
* tune matmul for gcn
* this one is more power efficient
* Update ggml/src/ggml-vulkan/ggml-vulkan.cpp
Co-authored-by: 0cc4m <picard12@live.de>
* disable this tune for the proprietary driver
---------
Co-authored-by: 0cc4m <picard12@live.de>
vulkan: use uint array index to avoid glslang bug (#13193)
vulkan: Handle src1 batch dimension in non-contiguous mat-vec-mul shader (#13191)
* vulkan: Handle src1 batch dimension in non-contiguous mat-vec-mul shader
vulkan: Add bfloat16 support (#12554)
* vulkan: Add bfloat16 support
This adds bfloat16 matrix multiply support based on VK_KHR_shader_bfloat16.
The extension is required for coopmat multiply support, but matrix-vector
multiply trivially promotes bf16 to fp32 and doesn't require the extension.
The copy/get_rows shaders also don't require the extension.
It's probably possible to fall back to non-coopmat and promote to fp32 when
the extension isn't supported, but this change doesn't do that.
The coopmat support also requires a glslc that supports the extension, which
currently requires a custom build.
* vulkan: Support bf16 tensors without the bf16 extension or coopmat support
Compile a variant of the scalar mul_mm shader that will promote the bf16
values to float, and use that when either the bf16 extension or the coopmat
extensions aren't available.
* vulkan: bfloat16 fixes (really works without bfloat16 support now)
* vulkan: fix spirv-val failure and reenable -O
# Conflicts:
# ggml/src/vulkan-shaders/test_bfloat16_support.comp
vulkan: Additional type support for unary, binary, and copy (#13266)
Support f16->f32 copy.
Support f16->f16 and f32->f32 unary ops.
Support all combinations of f16/f32 for src0/src1/dst for add/sub/mul/div.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: Allow up to 4096 elements for mul_mat_id row_ids (#13326)
This assert fired running Qwen_Qwen3-30B-A3B-Q2_K.gguf:
GGML_ASSERT(nei0 * nei1 <= 3072);
The tensor is 8 x 512. Increase this array size to accommodate.
vulkan: scalar flash attention implementation (#13324)
* vulkan: scalar flash attention implementation
* vulkan: always use fp32 for scalar flash attention
* vulkan: use vector loads in scalar flash attention shader
* vulkan: remove PV matrix, helps with register usage
* vulkan: reduce register usage in scalar FA, but perf may be slightly worse
* vulkan: load each Q value once. optimize O reduction. more tuning
* vulkan: support q4_0/q8_0 KV in scalar FA
* CI: increase timeout to accommodate newly-supported tests
* vulkan: for scalar FA, select between 1 and 8 rows
* vulkan: avoid using Float16 capability in scalar FA
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/flash_attn.comp
vulkan: workaround FA compile failures on macos (#13517)
vulkan: KHR_coopmat flash attention (#13506)
This shader uses coopmat1 to do the Q*K^T multiply. The P*V multiply is more
difficult for various reasons so I haven't done it. Performance for this
shader is around 2.5x better than for the scalar shader when doing prompt
processing. Some of the benefit may be from other optimizations like staging
through shared memory, or splitting by rows.
# Conflicts:
# ggml/src/vulkan-shaders/flash_attn_cm1.comp
cmake: simplify vulkan shader test logic (#13263)
vulkan: use scalar FA rather than coopmat2 when N==1 (#13554)
Add pipeline_acc_f32
vulkan: move common FA code to flash_attn_base.comp (#13556)
* vulkan: move common FA code to flash_attn_base.comp
* vulkan: move common FA index/stride setup code to flash_attn_base.comp
* build fix
# Conflicts:
# ggml/src/vulkan-shaders/flash_attn_base.comp
cmake: use the current build config for vulkan-shaders-gen (#13595)
* fix: use the current build config for `vulkan-shaders-gen`
* fix: only pass a valid build type to `--config`
Vulkan: Add f32 accumulator support to quantized mul mat to fix GLM4 32B incoherence (#13607)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: fix warnings (#13626)
* small fixes
* remove ifdef
use LOG_WARN to replace `std::cerr` (#13657)
vulkan: Disable coopmat/coopmat2/bfloat extensions if glslc doesn't support it (#13696)
vulkan: support CPY from any type to itself (#13695)
Reuse the f16/f32 copy shaders, and just scale the number of elements
according to the type size.
add GGML_LOG_WARN
vulkan: mark IM2COL as supporting non-contig (#13783)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: use timestamp queries for GGML_VULKAN_PERF (#13817)
Also change it to be controlled by an env var rather than cmake flag
vulkan : Remove unexpected ; (ggml/1253)
vulkan: fix warnings in perf logger querypool code (#13937)
ggml-vulkan: adds support for op CONV_TRANSPOSE_1D (#13813)
* * ggml-vulkan: adds op CONV_TRANSPOSE_1D
* test-backend-ops: adds more spohisticated tests for CONV_TRANSPOSE_1D
* Missing barrier added to shader.
Number of additional tests reduced to 108.
* * Fixes typo in variable name.
* Removes extra whitespaces.
* Adds int64->int32 casts to prevent possible warnings.
* Problem size reduced in tests to pass tests with llvmpipe.
* supports_op condition moved from unintended position
# Conflicts:
# ggml/src/ggml-vulkan.cpp
# ggml/src/vulkan-shaders/conv_transpose_1d.comp
vulkan: Enable VK_KHR_cooperative_matrix extension for Intel Xe2 GPUs (#14001)
* allowing B580 and U9-288V
* experimenting code to detect Xe2
* allowing coopmat only for Xe2 GPUs
* fixed comment wording
* fixed comment wording
* removed unnecessary driver check
Vulkan: Don't default to CPU device (like llvmpipe), even if no other device is available, to allow fallback to CPU backend (#14099)
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: force device 0 in CI (#14106)
Add GGML_LOG_INFO
vulkan: Track descriptor pools/sets per-context (#14109)
Use the same descriptor set layout for all pipelines (MAX_PARAMETER_COUNT == 8)
and move it to the vk_device. Move all the descriptor pool and set tracking to
the context - none of it is specific to pipelines anymore. It has a single vector
of pools and vector of sets, and a single counter to track requests and a single
counter to track use.
vulkan: Better thread-safety for command pools/buffers (#14116)
This change moves the command pool/buffer tracking into a vk_command_pool
structure. There are two instances per context (for compute+transfer) and
two instances per device for operations that don't go through a context.
This should prevent separate contexts from stomping on each other.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: mutex around vkQueueSubmit (#14127)
This fixes the remaining crash in test-thread-safety on my system.
cmake: clean up external project logic for vulkan-shaders-gen (#14179)
* Remove install step for vulkan-shaders-gen
* Add install step to normalize msvc with make
* Regenerate modified shaders at build-time
# Conflicts:
# .github/workflows/build.yml
cmake: remove shader-gen step-targets from ggml-vulkan (#14226)
* Remove step-targets from vulkan-shaders-gen
* Unset DESTDIR when building vulkan-shaders-gen
Vulkan: Set device max size for host memory to avoid OOM warning and fallback to CPU buffer (#14249)
Add support for VK_EXT_debug_utils to add labels to Vulkan objects. (#13792)
* Add support for VK_EXT_debug_utils to add labels to Vulkan objects. In step 1 compute pipelines are getting labeled.
* remove #ifdef for debug utils and add queue marker.
# Conflicts:
# ggml/src/ggml-vulkan.cpp
vulkan: update windows SDK in CI (#14334)
vulkan: update windows SDK in release.yml (#14344)
# Conflicts:
# .github/workflows/release.yml
cmake: regen vulkan shaders when shaders-gen sources change (#14398)
* Add shaders-gen sources as target deps
vulkan: Fix GGML_VULKAN_SHADER_DEBUG_INFO (#14427)
This setting needs to be passed through to vulkan-shaders-gen
vulkan: lock accesses of pinned_memory vector (#14333)
vulkan: handle noncontig in the final case of ggml_vk_get_cpy_pipeline (#14378)
Fix cuda build error
test
* remove new cpu backend and yml files
* remove new op and GGML_ROPE_TYPE_NEOX
* fix build error
* change cmake file to add matrix operation
* remove coopmat2 check in flash attention
* print gpu info for vulkan
* disable fuse to recover vulkan performance
---------
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: firecoperana <firecoperana>
|
|
* add dry sampler
* use vocab instead of model in dry_init function
* fix compile error for build test
---------
Co-authored-by: firecoperana <firecoperana>
|
|
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
Co-authored-by: firecoperana <firecoperana>
|
|
* use npm as deps manager and vite as bundler
* update XTC docs
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
|
|
* Add RPC backend in device list to override tensors.
* rpc : prevent crashes on invalid input (#9040)
Add more checks which prevent RPC server from crashing if invalid input
is received from client
# Conflicts:
# ggml/src/ggml-rpc.cpp
* rpc : print error message when failed to connect endpoint (#9042)
* Fix RPC error
* Add vulkan, sycl to rpc backend
* add thread in rpc cpu backend
* add cache folder and other improvement in rpc
* add header file
* support for models with non-512 aligned tensors
* rpc : do not wait for response when sending RPC_CMD_SET_TENSOR (#12943)
RPC_CMD_SET_TENSOR always returns an empty response and we send this 4
times per token. We can improve TG speed if we don't wait for this empty
response.
The performance impact of this change depends on the network latency.
# Conflicts:
# ggml/src/ggml-rpc.cpp
* fix(rpc): Improve input validation and error handling (#13069)
* fix(rpc): Improve input validation and error handling
The `rpc-server` was vulnerable to Denial of Service attacks via
several RPC commands (`SET_TENSOR`, `GRAPH_COMPUTE`, etc.). Malformed
messages could trigger failed assertions (e.g., invalid `ggml_type`)
or out-of-bounds reads/writes leading to `GGML_ABORT` calls,
crashing the server process.
This PR introduces robust input validation and replaces `abort()`
calls with graceful error handling:
- **Type Validation:** `deserialize_tensor` now checks if the
`tensor->type` is within the valid `GGML_TYPE_COUNT` range
*before* calling `ggml_new_tensor_4d`. Returns `nullptr` on
invalid type.
- **Bounds Checks:** Replaced `GGML_ABORT` in `set_tensor`,
`set_tensor_hash`, and `get_tensor` handlers with error
logging and returning `false` when data/offset parameters
are out of buffer bounds.
- **Size Checks:** Added safe arithmetic checks (for overflow) in
`graph_compute` when calculating required message sizes based
on client-provided `n_nodes` and `n_tensors`. Returns early
if the reported sizes conflict with the actual message size or
would lead to overflow.
- **Error Propagation:**
- `create_node` now checks for `nullptr` return values from
`deserialize_tensor` and its recursive calls, propagating
`nullptr` upwards on failure. Uses `find` instead of `at`
for safer map access.
- `copy_tensor` now checks for `nullptr` from `deserialize_tensor`
and sets the response status to failure if deserialization
or bounds checks fail.
- `graph_compute` now checks for `nullptr` return from
`create_node` and returns failure status correctly. The final
return value now reflects the actual computation status.
These changes improve the RPC server's resilience
against malformed client requests, preventing crashes and ensuring
errors are handled more gracefully.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): address pr comments
removed comments and unnecessary returns
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): ambiguous nullptr from create_node
rpc_server::create_node could previously return nullptr if the input ID
was 0 (valid) or if an internal error (deserialization, recursion
failure) occurred (invalid). This ambiguity made error handling
difficult for the caller (`graph_compute`).
This commit clarifies the meaning of nullptr:
- `graph_compute` now checks if the input 'id' was non-zero when
`create_node` returns nullptr, correctly identifying failures
versus intentional null links.
- `create_node` avoids recursive calls for zero IDs and propagates
nullptr unambiguously on failure during recursion.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): initial zero check in create_node
The caller (`graph_compute`) already checks `id != 0` when handling
a `nullptr` return from `create_node`, correctly distinguishing
intentional null links from actual errors. This makes the initial
`if (id == 0)` check redundant.
Also removes the log message when a tensor ID is not found in the
provided map which was added in this branch.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* fix(rpc): Handle get_alloc_size failure in server
Check the return value of `server.get_alloc_size` in the RPC server
loop. If the call fails, return early to close the connection.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): input size validation in graph_compute
Removes detailed, step-by-step size calculations and overflow
checks in favor of simpler direct comparisons, assuming 64-bit
overflow is unlikely.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): remove extra status code setting
Removes the explicit setting of `response.result = GGML_STATUS_FAILED`
when `create_node` returns `nullptr` within `graph_compute`.
Primary signal is the `false` return value in case of failure.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): remove redundant check for tensor->type
Breaks CI on ubuntu-cpu-make. Tensor type is uint32_t, thus
the check is not needed.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
---------
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
# Conflicts:
# ggml/src/ggml-rpc.cpp
* rpc : fix cache directory initialization (#13188)
Signed-off-by: xiaofei <hbuxiaofei@gmail.com>
# Conflicts:
# examples/rpc/rpc-server.cpp
* rpc : avoid uninitialized memory in serialize_tensor (#13210)
Zero out the name and padding buffers.
* fix merge error
* Add hello command in RPC
* bug fix
* add rpc header
* fix bug for missing rpc names
* add tpc no delay for rpc
* add back webui
* fix rpc function not found error
---------
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
Signed-off-by: xiaofei <hbuxiaofei@gmail.com>
Co-authored-by: firecoperana <firecoperana>
Co-authored-by: Radoslav Gerganov <rgerganov@gmail.com>
Co-authored-by: matt23456 <matt23456>
Co-authored-by: Ville Vesilehto <ville@vesilehto.fi>
Co-authored-by: xiaofei <hbuxiaofei@gmail.com>
Co-authored-by: Justin Santa Barbara <justinsb@google.com>
|
|
This reverts commit 8a5f8573aefc23282200041abbfa12886083334a.
|
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* Add RPC backend in device list to override tensors.
* rpc : prevent crashes on invalid input (#9040)
Add more checks which prevent RPC server from crashing if invalid input
is received from client
# Conflicts:
# ggml/src/ggml-rpc.cpp
* rpc : print error message when failed to connect endpoint (#9042)
* Fix RPC error
* Add vulkan, sycl to rpc backend
* add thread in rpc cpu backend
* add cache folder and other improvement in rpc
* add header file
* support for models with non-512 aligned tensors
* rpc : do not wait for response when sending RPC_CMD_SET_TENSOR (#12943)
RPC_CMD_SET_TENSOR always returns an empty response and we send this 4
times per token. We can improve TG speed if we don't wait for this empty
response.
The performance impact of this change depends on the network latency.
# Conflicts:
# ggml/src/ggml-rpc.cpp
* fix(rpc): Improve input validation and error handling (#13069)
* fix(rpc): Improve input validation and error handling
The `rpc-server` was vulnerable to Denial of Service attacks via
several RPC commands (`SET_TENSOR`, `GRAPH_COMPUTE`, etc.). Malformed
messages could trigger failed assertions (e.g., invalid `ggml_type`)
or out-of-bounds reads/writes leading to `GGML_ABORT` calls,
crashing the server process.
This PR introduces robust input validation and replaces `abort()`
calls with graceful error handling:
- **Type Validation:** `deserialize_tensor` now checks if the
`tensor->type` is within the valid `GGML_TYPE_COUNT` range
*before* calling `ggml_new_tensor_4d`. Returns `nullptr` on
invalid type.
- **Bounds Checks:** Replaced `GGML_ABORT` in `set_tensor`,
`set_tensor_hash`, and `get_tensor` handlers with error
logging and returning `false` when data/offset parameters
are out of buffer bounds.
- **Size Checks:** Added safe arithmetic checks (for overflow) in
`graph_compute` when calculating required message sizes based
on client-provided `n_nodes` and `n_tensors`. Returns early
if the reported sizes conflict with the actual message size or
would lead to overflow.
- **Error Propagation:**
- `create_node` now checks for `nullptr` return values from
`deserialize_tensor` and its recursive calls, propagating
`nullptr` upwards on failure. Uses `find` instead of `at`
for safer map access.
- `copy_tensor` now checks for `nullptr` from `deserialize_tensor`
and sets the response status to failure if deserialization
or bounds checks fail.
- `graph_compute` now checks for `nullptr` return from
`create_node` and returns failure status correctly. The final
return value now reflects the actual computation status.
These changes improve the RPC server's resilience
against malformed client requests, preventing crashes and ensuring
errors are handled more gracefully.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): address pr comments
removed comments and unnecessary returns
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): ambiguous nullptr from create_node
rpc_server::create_node could previously return nullptr if the input ID
was 0 (valid) or if an internal error (deserialization, recursion
failure) occurred (invalid). This ambiguity made error handling
difficult for the caller (`graph_compute`).
This commit clarifies the meaning of nullptr:
- `graph_compute` now checks if the input 'id' was non-zero when
`create_node` returns nullptr, correctly identifying failures
versus intentional null links.
- `create_node` avoids recursive calls for zero IDs and propagates
nullptr unambiguously on failure during recursion.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): initial zero check in create_node
The caller (`graph_compute`) already checks `id != 0` when handling
a `nullptr` return from `create_node`, correctly distinguishing
intentional null links from actual errors. This makes the initial
`if (id == 0)` check redundant.
Also removes the log message when a tensor ID is not found in the
provided map which was added in this branch.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* fix(rpc): Handle get_alloc_size failure in server
Check the return value of `server.get_alloc_size` in the RPC server
loop. If the call fails, return early to close the connection.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): input size validation in graph_compute
Removes detailed, step-by-step size calculations and overflow
checks in favor of simpler direct comparisons, assuming 64-bit
overflow is unlikely.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): remove extra status code setting
Removes the explicit setting of `response.result = GGML_STATUS_FAILED`
when `create_node` returns `nullptr` within `graph_compute`.
Primary signal is the `false` return value in case of failure.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
* refactor(rpc): remove redundant check for tensor->type
Breaks CI on ubuntu-cpu-make. Tensor type is uint32_t, thus
the check is not needed.
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
---------
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
# Conflicts:
# ggml/src/ggml-rpc.cpp
* rpc : fix cache directory initialization (#13188)
Signed-off-by: xiaofei <hbuxiaofei@gmail.com>
# Conflicts:
# examples/rpc/rpc-server.cpp
* rpc : avoid uninitialized memory in serialize_tensor (#13210)
Zero out the name and padding buffers.
* fix merge error
* Add hello command in RPC
* bug fix
* add rpc header
* fix bug for missing rpc names
* add tpc no delay for rpc
* add back webui
---------
Signed-off-by: Ville Vesilehto <ville@vesilehto.fi>
Signed-off-by: xiaofei <hbuxiaofei@gmail.com>
Co-authored-by: firecoperana <firecoperana>
Co-authored-by: Radoslav Gerganov <rgerganov@gmail.com>
Co-authored-by: matt23456 <matt23456>
Co-authored-by: Ville Vesilehto <ville@vesilehto.fi>
Co-authored-by: xiaofei <hbuxiaofei@gmail.com>
Co-authored-by: Justin Santa Barbara <justinsb@google.com>
|
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* update webui
* add token/s in webui
* add webui files
* fix webui first message disappear in some browser
* add missing html files
---------
Co-authored-by: firecoperana <firecoperana>
|
|
|
|
* Adding top-n-sigma sampler
* Fix typos in XTC PR
* Update README.md for main and server
* More README
* More README
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
|
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* Fix MSVC compilation
* MSVC cannot capture constexpr in lambdas
* Arghhh
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* WIP
* WIP
* WIP
* Testing Trellis quantization
Using 12 bits per 8 weights I get a better rmse than
iq2_xxs. I still need to see how quantizing the group-of-8
scales will affect accuracy. By AVX2 SIMDifying the search
for the best code, LLaMA-3.1-8B gets quantized in 130 seconds
on the Ryzen-7950X CPU - sluggish but still acceptable.
* Testing Trellis quantization: 4-bit quantized block scales
rmse increases by just 3%, so this is beating iq2_xss in terms
of rmse at the same 2.0625 bpw.
* Testing Trellis quantization: playing with scales and generators
* iq2_kt: quantize / dequantize
I now see that I was comparing apples to oranges:
iq2_xxs was using a weight of sigma^2/4 + x^2, while
the Trellis approach wasn't (weight = 1). Once I use the same weight,
iq2_kt is actually slightly worse than iq2_xxs in terms
of rmse, so does not look promising at this point.
Also, once each group of 8 Trellis values no longer has a
constant sum(q^2) that we can precompute, quantization
becomes significantly slower (476 seconds for LLaMA-3.1-8B).
* iq2_kt: CUDA dequantize
so we can run perplexity calcs.
As already indicated by rmse, the 2-bit trellis approach is
quite a bit worse than iq2_xxs.
* WIP
* WIP
* WIP - try larger blocks
With blocks of 32 and 16 bits per groups of 8 the brute force
seach becomes prohibitive in terms of CPU time (30+ minutes
for 8B LLaMA after SIMDifying with AVX2). The trick is to
group the points in clusters, find the nearest cluster,
and only search within the cluster.
* iq2_kt - this is better
Using blocks of 32 and 16 bits per group of 8 weights
it beats iq2_xxs in terms of PPL by a significant margin.
It is 0.0625 bpw larger, but even if we go to 15 bits per
group od 8 (so 0.0625 bpw less than iq2_xxs), PPL is still
lower.
* iq2_kt - even better
Re-quantize after determining block scales
(at the epxense of much longer quantization time).
* iq2_kt: CUDA dot product
Implemented as DMMV.
Very slow - just 81 t/s for LLaMA-3.1-8B.
Then again, Q2_K_S with forced to use DMMV only
gets 112 t/s vs 145 t/s via MMVQ. My memory is that
when the DMMV kernels were properly maintained/used,
DMMV was about on par with MMVQ for k-quants on my GPU.
* iq2_kt: very slightly faster CUDA dot product
* iq2_kt: f16 CUDA dot product
We arrive at 112 t/s.
* iq2_kt: faster f16 CUDA dot product
We arrive at 139 t/s (no FA), and 149 t/s (FA).
My RTX-4080 is ~20% slower than the RTX-6000 quoted in the
QTIP repository, so with FA (which I'm sure they also used)
we are at around ~180 t/s on their GPU, so almost matching
their performance.
* iq2_kt: faster f16 CUDA dot product
We arrive at 146 t/s (no FA), and 158 t/s (FA).
This is measured for LLaMA-3.1-8B with output.weight
left as f16.
* Minor
* Adding iq3_kt
3.125 bpw. So far does not look good on the PPL vs bpw plot.
* Forgotten change
* WIP
* WIP
* iq3_kt WIP: slowly improving
PPL(LLaMA-3.1-8B-Instruct, 8192) is now 6.8322, which is
starting to be competitive/slightly better than other quants.
* WIP
* iq3_kt WIP: slowly improving
PPL(LLaMA-3.1-8B-Instruct, 8192) is now 6.7892
* iq3_kt WIP: slowly improving
PPL(LLaMA-3.1-8B-Instruct, 8192) is now 6.7689 after shrinking
by 0.015 bpw by using iq4_k instead of q5_k for attn_v.
* iq3_kt WIP: speed up quantization
Nearly 60% improvement of quantization speed by having the
points nelonging to a cluster copied to contiguous memory
during initialization, and then accessed sequantially while
searching for the closest point. LLaMA-3.1-8B now gets
quantized in ~150 seconds on the Ryzen-5975WX.
* iq3_kt speed up quantization
Same trick as last commit applied to iq2_kt. Here we get
an even larger speedup: quantization time on the Ryzen-5975WX
for LLaMA-3.1-8B drops to 195 seconds from 375 seconds!
* iq3_kt: CUDA dot product
* iq2_kt: SOTA
We arrive at
PPL(LLaMA-3.1-8B-Instruct, 8192) = 9.2406
PPL(LLaMA-2-7B, 4096) = 6.4179
* iq2_kt: SOTA
We arrive at
PPL(LLaMA-3.1-8B-Instruct, 8192) = 9.1642
PPL(LLaMA-2-7B, 4096) = 6.3920
* Adding iq4_kt - not competitive at this point
* WIP
* WIP
* iq4_kt: CUDA dot product
* iq4_kt: minor tweaks
* iq2_kt: SOTA
We arrive at
PPL(LLaMA-3.1-8B-Instruct, 8192) = 9.1642
PPL(LLaMA-2-7B, 4096) = 6.3920
* iq2_kt: SOTA
We arrive at
PPL(LLaMA-3.1-8B-Instruct, 8192) = 9.0297
PPL(LLaMA-2-7B, 4096) = 6.3913
Ah, quantization is faster too. About 20% faster.
* iq3_kt: small improvements and faster quantization
* iq2_kt: SOTA
We arrive at
PPL(LLaMA-3.1-8B-Instruct, 8192) = 8.9627
PPL(LLaMA-2-7B, 4096) = 6.3825
Quantization is faster too: ~200 seconds for LLaMA-3.1-8B
on Ryzen-5975WX.
* iq3_kt: small progress
* WIP
* iq4_kt: go to 4.0 bpw
15 bits per group of 4, plus 8 bit scales ifor blocks of 32.
This gives a slightly better PPL than iq4_kss.
* iq4_kt: very slightly better
at the expense of much longer quantization time.
* iq4_kt: failed attemt to adjust CUDA dot product
It was working for 4.125 bpw. But after changing to 4.0 bpw
there is something wrong and I don't see the bug.
* DRY
* DRY
* iq4_kt: CUDA dot product works
* DRY
* Report actual bpw
* Minor tweaks
* Checkpoint
Go to groups of 8 for iq3_kt. 2 x 8 = 16 bits for the magnitude
plus 1 bpw for the sign. It goves a visible improvement in the
PPL vs bpw plot, but that comes at the expense of much longer
quantization time (7.5 minutes for LLaMA-3.1-8B on the Ryzen-5975WX).
I also notices that the 3INST generator is not actually generating a
Gaussian distribution. But going to a better generator means
readjusting all the hyper-parameters, so leaving it for later.
* WIP for IQ2_KT
* WIP - working basic iq2_kt
* still super slow (0.17t/s eval)
* flatten 3inst iters + avx2 (0.3t/s eval)
* iq3_kt (0.3t/s eval) and renames
* wip buggy iq4_KT
* fix (0.22t/s eval)
* naming and remove unused fn
* cleanup
* more cleanup
* delete unused and noncompiling mmvq functions
* Some performance tweaks
* Slighty faster iq2_kt
* port Trellis struct to iq3_kt, iq4_kt
* oops untracked files
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
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gguf-split : improve --split and --merge logic (#9619)
* make sure params --split and --merge are not specified at same time
* update gguf-split params parse logic
* Update examples/gguf-split/gguf-split.cpp
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
---------
gguf-split : add basic checks (#9499)
* gguf-split : do not overwrite existing files when merging
* gguf-split : error when too many arguments are passed
Authored-by: slaren <slarengh@gmail.com>
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* iq5_ks_r4: basics
* iq5_ks_r4: Zen4 works
* iq5_ks_r4: AVX2 works
* iq5_ks_r4: NEON
* Fix iq5_ks on NEON
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* iq5_ks: basics
* iq5_ks: quantize
* iq5_ks: CUDA dequantize works
* iq5_ks: dot product works on CUDA
* iq5_ks: MMQ works
* iq5_ks: Zen4
* iq5_ks: AVX2
But is is not quite right, just like iq4_k, iq5_k, iq6_k, iq4_ks.
All these need fixing on AVX2.
* iq5_ks: NEON
* iq5_ks: Metal dequantize
* iq5_ks: Metal dot product
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
* imatrix: collect layer influence statistics
* imatrix: collect layer influence statiscs also for the last layer
For the last layer we need to use the input for the output.weight
tensor. Last layer(s) tend(s) to be important, so it is useful to also
have its influence metric.
* imatrix: separate metric for attention and ffn importance
* Use stripped tensor name, not src0->name
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
|
* llama-bench: enable having different number of threads for tg and pp
* Add -tgb to usage
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
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* Update sweep bench (depracating .jsonl support)
* Fix README.md
|
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* Adding ability to use THP on Linux
* Use the actual page size4 used for mmap also in munmap
* Add -thp to llama-bench
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
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* Repack a model with the quantize tool
* WIP
* Fixed various issues
As we don't have a way to tell if a repacked quant has been modified,
I had to remove the modification at the expense of a slight decrease
in performance. This affects q8_0_r8, q8_KV_r8, q8_k_r8 on Zen4, and
q4_0_r8 on ARM.
* Create wk_b and wv_b as Q8_0_R8 if the wkv_b type is interleaved
* Fix GCC 13.3 compilation error
* Another one
* Add missing include
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
|
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* FlashMLA-2: eliminate intermediate f32 tensors
This works on the CPU. PP performance is ~13% better for 16k tokens
and compute buffer is quite a bit smaller.
* FlashMLA-2: enable fast path only on the CPU for now
I did implement the necessary ops on CUDA, but something is
still wrong there, so for now we only use it when running
CPU-only.
* FlashMLA-2: slightly smaller computer buffer size
* Prepare wk_b when loading DeepSeek models (if wk_b is missing)
* Add some comments
* Fix case where wkv_b is quantized with k- or i-quants.
* Fix CUDA
There is an issue with quantized GEMV on CUDA when the left operand
(the matrix) is not contiguous. So, for now, we also create wv_b
during model loading and use that instead of the 3D view of wkv_b.
* FlashMLA-2: avoid conversions to f32 also on CUDA
* Be able to compute for more than 65535 tokens
On CUDA just a quick hack that allows us to cancatenate tensors
with more than 65535 rows along zroth dimension as needed by
FlashMLA-2. Also needed some care in the perplexity tool to
avoid int overflows when evaluating the computed logits.
* Reduce memory usage for FlashMLA-2
Oh, also fix int overflow in the CUDA concat implementation.
It is funny how the llama.cpp 64-bit police has gone (almost) everywhere
and replaced 32-bit ints with 64-bit ints, needed or not,
but hasn't done it where it is actually needed.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* This gives us ~20% TG speedup for DeepSeek on CUDA
* Slightly better
* Also do it for plain (not fused) mul_mat_id
* Guard against numerical precision issues for MLA on CUDA
* imatrix: wv_b <-> wkv_b
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Custom quantization rules with regular expressions
* Add the --custom-q option to the help
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* A better way to measure the cost of ggml_barrier
* Smart expert selection
* Add ser option to llama-bench
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* This reduces compute buffer size for MLA
* This should accomplish it for standard attention
* Much better
* Better concat for contiguous tensors
If all the op does is to concatenate the second tensor
to the first, why would we want to have a loop?
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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The `-mla` command line option turns into an int from a bool.
mla = 0: use standard attention
mla = 1: use MLA with transposed cache
mla > 1: use MLA without transposed cache
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Give the user the option to override where model weights are stored
* Fix ggml_nbytes() problem and cleanup
For a tensor with zero elements ggml_nbytes() was returning
uint64_t::max, and this was causing graph allocation failure.
* Add timing info to CUDA graph evaluation
* Add more timing info
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Fusing MoE up * unary(gate)
* Fusing MoE up * unary(gate): CUDA
We get ~13% speedup for PP-512 and ~2% for TG-128
for DeepSeek-Lite
* On CUDA also fuse MoE down * (up * unary(gate))
in case the MUL_MAT_ID op for the down experts is the next
op in the graph.
* Command line option to enable fused MoE up*unary(gate)
* Add fmoe option to llama-bench
* Adding forgotten gelu, relu, silu on ARM
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* examples : add new sweep-bench benchmark
* Change documentation to reference ik_llama.cpp
* Made it compile with ik_llama
* Fix JSONL output
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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* Adding q8_KV - Basics + AVX2 gemm/gemv
* q8_KV: Better AVX2 gemm
* q8_KV: Better Zen4 gemm
We get 225.7 t/s for L3-8B. In comparison q8_0 without
run-tinme-repacking is at 169 t/s.
* q8_KV: AVX2 gemm/gemv
We get 254 t/s for L3-8B vs 194 t/s for q8_0 without rtr.
* q8_KV: be able to use it for K cache
This required quite a few fixes in ggml and llama.cpp:
* ggml: do not calculate row size as n/block_size*type_size. I had
removed most of it when implementing the quants with per row scale,
bit it was stull lurking in ggml_copy. Not sure if these were the last
remnants of ggmil-style row sizes, or if there are still places left
* llama.cpp: get rid of the the 1d K cache assumption. Create and manage
the K-cache as a 2D tensor so we can have per row meta data as needed
by q8_KV.
Using q8_KV for K-cache results in non-negligible performance gains.
More details to follow, but for DeepSeek-Lite with MLA, we get
18% speedup for PP-8192 compared to q8_0 K-cache.
* q8_KV: be able to use it for K cache in FA
* q8_KV: repack it for K*Q in FA
* q8_KV: slightly faster gemv on Zen4
* q8_KV: slightly faster gemv on Zen4
* q8_KV: ARM_NEON
We get PP-512 = 167 t/s for L3-8B without interleaving!
We do the interleaving on the fly, so I wonder if this
could be done for other quants as well.
* q8_KV: use it in FA on NEON
* q8_KV_r8 - repacked q8_KV
On Zen4 it is slower than q8_k_r8 (292 vs 370 t/s)
This makes no sense whatsoever as the q8_KV_r8 GEMM is
basically the q8_k_r8 GEMM with the unnecessary block stuff
removed (so, one would think that it would be faster).
* q8_KV_r8: don't use nrc_y = 16 on Zen4
This is faster - 350 t/s. Why?
Much better than the 290 t/s we had before, but still slower
than the 370 t/s for q8_k_r8.
* q8_KV: nrc_y = 16 also doesn't pay off in FA
* Minor
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Load all MoE experts during warmup
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Unify warmup to one token
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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* Deepseek MLA Optimizations
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Make MLA optional
* Remove some unnecessary copies in the MLA attention
* Deepseek MLA Optimizations V2 (#195)
* Avoid allocating MHA KV cache when MLA is turned on
* Added missing gguf-py file
* Added final optimizations
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* Make sure we do have wk_b and wv_b before enabling MLA
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Use type_k and type_v to set the types of the MLA caches
They were hard-coded at f16.
On my Ryzen-7950X with native bf16 support I get a fairly
significant PP performance boost with bf16 KV-cache:
PP-4096 = 320 t/s up from 292 t/s with fp16 KV-cache.
* Better gemm strategy when nth > nhead
It gives a ~10% PP performance boost for DeepSeek-Lite with 32 threads
(with or without MLA).
Before this commit, when nth > nhead heads were processed
sequentially with all nth threads participating in each
matrix multiplication. Now we ind the gcd of nhead and
nth and split threads into nth/gcd groups, each group
processing nhead/gcd heads.
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Co-authored-by: Saood Karim <saood05@gmail.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Rename q4_0_r4 to q4_0_r8 to reflect actual row interleaving
* Rename q8_0_r4 to q8_0_r8 to reflect actual row interleaving
* Rename iq4_xs_r4 to iq4_xs_r8 to reflect actual row interleaving
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* iq1_m_r4: basics (quantize/dequantize)
* iq1_m_r4: Zen4 gemm
* iq1_m_r4: neon gemm
* iq1_m_r4: switch to q8_0_x4 also on AVX2/Zen4
With the deltas being per group of 8, we cannot make use
of the q8 sums stored in q8_1, so we get a tiny gain by
using q8_0_x4.
* iq1_m_r4: rename mul_mat_iq1_m_r4_q8_1 to mul_mat_iq1_m_r4_q8_0
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* iq1_s_r4: basics - quantize/dequantize
* iq1_s_r4: gemm/gemv works on AVX2/Zen4
* Don't forget to make sure we have a multiple of 4 rows per thread
* iq1_s_r4: this is better
* iq1_s_r4: fix Zen4 after AVX2 changes
* iq1_s_r4: NEON gemm/gemv
* iq1_s_r4: more bits for shared experts
With this mix we arrive at PPL(512) = 9.4140
for Deepseek-Lite using 1.766 bpw for the repeating layers.
On the Ryzen-7950X we get PP-512 = 494 t/s and
TG-128 = 52 t/s @ 16 threads.
* Forgotten counter increment
* iq1_s_r4: slightly faster AVX2/Zen4 gemm/gemv
* Compiler warnings
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Slightly faster AVX2 implementation for q4_k_r4
* Even better AVX2 implementation for q4_k_r4
We now arrive at PP-512 = 328 t/s for LLaMA-3.1-8B on a
Ryzen-5975WX CPU, up from 291 t/s when I last measured
on 3c5f8722.
With FA and Q8_0 K-cache we get to 339.5 t/s.
* Fix llama-bench labels that I broke with #181
* Faster AVX2 implementation for q5_k_q4
We arrive at 302 t/s for LLaMA-3.1-8B on a Ryzen-5975WX CPU,
up from 273 t/s.
* Use AVX2 implementation of q4_k_r4 and q5_k_r4 also on Zen4
After the changes I made to AVX2, it ends up being slightly faster
compared to what I had for Zen4.
* Minor tweak
* Cleanup
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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