happyz
/
llama.cpp
mirror of https://github.com/ggerganov/llama.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
7,468 Commits 519 Branches 5,149 Tags 1 GiB
Commit Graph

1788 Commits

Author SHA1 Message Date
Georgi Gerganov 81cb5783c8
Merge branch 'master' into HEAD 2025-12-10 13:41:32 +02:00
Neo Zhang Jianyu 2e9eab80c2
fix softmax for iGPU (#17838) 2025-12-10 16:59:57 +08:00
Gabe Goodhart 086a63e3a5
metal: SSM kernel improvements (#17876) 
* feat: Add a batched version of ssm_conv

This was done using Claude Code. It found a number of optimizations around
how the threads were organized, resulting in a huge performance boost!

Branch: Mamba2SSD

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Optimized SSM_SCAN kernel for metal

This used Claude Code and resulted in a modest performance improvement
while maintaining correctness.

Branch: Mamba2SSD

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* test: Add test-backend-ops perf tests for SSM_CONV

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* test: Real representitive tests for SSM_CONV

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* refactor: Use function constant for ssm_conv batch size

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* test: backend op tests for ssm_scan from granite4 1b-h

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* style: remove commented out templates

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: float4 version of ssm_conv_batched

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Add missing ggml_metal_cv_free

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

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

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
 2025-12-09 21:30:02 +02:00
Piotr Wilkin (ilintar) b63509262a
Add DIAG for CUDA (#17873) 
* Add DIAG for CUDA

* Refactor parameters
 2025-12-09 20:28:57 +01:00
Oliver Simons 6dc6614bf0 Disable cooperative groups for musa 
Didn't find any doc online, so I don't even know if they support this
 2025-12-09 19:09:52 +01:00
Oliver Simons a25fda5290 Fix launch logic when supports_cooperative_launch=false 2025-12-09 19:03:47 +01:00
Oliver Simons 3f0594ad0b Try fixing HIP build errors by adding corresponding #defines 
Will likely have to disable for MUSA as I didn't find any docs online
 2025-12-09 18:51:28 +01:00
Gabe Goodhart 02e409a5be
ggml : Provide macos-specific backtrace printing to avoid terminal death (#17869) 
* fix: Provide macos-specific backtrace printing to avoid terminal death

Branch: MacOSSafeBacktrace

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Add GGML_BACKTRACE_LLDB env var to enable using lldb for backtrace

Branch: MacOSSafeBacktrace

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
 2025-12-09 18:29:07 +02:00
Georgi Gerganov 6b82eb7883
metal : print node names for debugging (#17882) 2025-12-09 15:25:49 +02:00
Oliver Simons 07003f1ffb Fix compiler warnings by casting `const` away 2025-12-09 13:05:43 +01:00
Oliver Simons 886c3668b5 Add TODOs to and adjust heuristics of row-wise soft_max in CUDA 
Heuristics were selected based on the following numbers:

```
-- Before
Backend 1/2: CUDA0
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96691 MB free)

  SOFT_MAX(type=f32,ne=[4096,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                2236 runs -   450.34 us/run -   655360 kB/run - 1401.20 GB/s
  SOFT_MAX(type=f32,ne=[12888,256,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               17748 runs -    56.80 us/run -   128880 kB/run - 2168.19 GB/s
  SOFT_MAX(type=f32,ne=[77,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 57204 runs -    18.35 us/run -    12320 kB/run -  640.57 GB/s
  SOFT_MAX(type=f32,ne=[1024,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               9840 runs -   102.46 us/run -    81920 kB/run -  763.45 GB/s
  SOFT_MAX(type=f32,ne=[77,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98064 runs -    10.25 us/run -     6160 kB/run -  573.43 GB/s
  SOFT_MAX(type=f32,ne=[256,256,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98310 runs -    10.25 us/run -    10240 kB/run -  953.20 GB/s
  SOFT_MAX(type=f32,ne=[64,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     5.99 us/run -      640 kB/run -  101.84 GB/s
  SOFT_MAX(type=f32,ne=[77,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     5.97 us/run -      770 kB/run -  123.02 GB/s
  SOFT_MAX(type=f32,ne=[8192,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     6.00 us/run -       64 kB/run -   10.16 GB/s
  SOFT_MAX(type=f32,ne=[8192,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 163820 runs -     6.12 us/run -      256 kB/run -   39.91 GB/s
  SOFT_MAX(type=f32,ne=[8192,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                147438 runs -     6.88 us/run -     1024 kB/run -  141.92 GB/s
  SOFT_MAX(type=f32,ne=[16384,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                122865 runs -     8.20 us/run -      128 kB/run -   14.89 GB/s
  SOFT_MAX(type=f32,ne=[16384,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                114674 runs -     8.87 us/run -      512 kB/run -   55.06 GB/s
  SOFT_MAX(type=f32,ne=[16384,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98292 runs -    10.24 us/run -     2048 kB/run -  190.82 GB/s
  SOFT_MAX(type=f32,ne=[32768,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 49146 runs -    21.37 us/run -      256 kB/run -   11.43 GB/s
  SOFT_MAX(type=f32,ne=[32768,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 49146 runs -    22.54 us/run -     1024 kB/run -   43.33 GB/s
  SOFT_MAX(type=f32,ne=[32768,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                49146 runs -    23.92 us/run -     4096 kB/run -  163.32 GB/s
  SOFT_MAX(type=f32,ne=[65536,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 32764 runs -    38.94 us/run -      512 kB/run -   12.54 GB/s
  SOFT_MAX(type=f32,ne=[65536,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 24573 runs -    41.94 us/run -     2048 kB/run -   46.57 GB/s
  SOFT_MAX(type=f32,ne=[65536,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                24582 runs -    43.09 us/run -     8192 kB/run -  181.32 GB/s
  SOFT_MAX(type=f32,ne=[131072,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                16382 runs -    74.56 us/run -     1024 kB/run -   13.10 GB/s
  SOFT_MAX(type=f32,ne=[131072,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                16382 runs -    79.85 us/run -     4096 kB/run -   48.92 GB/s
  SOFT_MAX(type=f32,ne=[131072,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               12294 runs -    82.41 us/run -    16384 kB/run -  189.64 GB/s
  SOFT_MAX(type=f32,ne=[262144,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 8191 runs -   145.16 us/run -     2048 kB/run -   13.46 GB/s
  SOFT_MAX(type=f32,ne=[262144,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 8194 runs -   155.46 us/run -     8192 kB/run -   50.26 GB/s
  SOFT_MAX(type=f32,ne=[262144,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                7175 runs -   160.70 us/run -    32768 kB/run -  194.56 GB/s
  SOFT_MAX(type=f32,ne=[524288,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 8191 runs -   285.81 us/run -     4096 kB/run -   13.67 GB/s
  SOFT_MAX(type=f32,ne=[524288,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 4098 runs -   306.91 us/run -    16384 kB/run -   50.92 GB/s
  SOFT_MAX(type=f32,ne=[524288,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                3591 runs -   317.06 us/run -    65536 kB/run -  197.32 GB/s

-- After
Backend 1/2: CUDA0
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96691 MB free)

  SOFT_MAX(type=f32,ne=[4096,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                2236 runs -   450.67 us/run -   655360 kB/run - 1400.15 GB/s
  SOFT_MAX(type=f32,ne=[12888,256,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               17748 runs -    56.97 us/run -   128880 kB/run - 2161.50 GB/s
  SOFT_MAX(type=f32,ne=[77,4096,5,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 57204 runs -    18.35 us/run -    12320 kB/run -  640.36 GB/s
  SOFT_MAX(type=f32,ne=[1024,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               9840 runs -   102.46 us/run -    81920 kB/run -  763.42 GB/s
  SOFT_MAX(type=f32,ne=[77,1024,10,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98064 runs -    10.25 us/run -     6160 kB/run -  573.43 GB/s
  SOFT_MAX(type=f32,ne=[256,256,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98310 runs -    10.25 us/run -    10240 kB/run -  953.21 GB/s
  SOFT_MAX(type=f32,ne=[64,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 147438 runs -     7.00 us/run -      640 kB/run -   87.26 GB/s
  SOFT_MAX(type=f32,ne=[77,64,20,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 147438 runs -     6.99 us/run -      770 kB/run -  105.05 GB/s
  SOFT_MAX(type=f32,ne=[8192,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 172011 runs -     6.02 us/run -       64 kB/run -   10.13 GB/s
  SOFT_MAX(type=f32,ne=[8192,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 163820 runs -     6.12 us/run -      256 kB/run -   39.87 GB/s
  SOFT_MAX(type=f32,ne=[8192,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                147438 runs -     6.91 us/run -     1024 kB/run -  141.40 GB/s
  SOFT_MAX(type=f32,ne=[16384,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                122865 runs -     8.20 us/run -      128 kB/run -   14.89 GB/s
  SOFT_MAX(type=f32,ne=[16384,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                114674 runs -     8.79 us/run -      512 kB/run -   55.54 GB/s
  SOFT_MAX(type=f32,ne=[16384,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98292 runs -    10.24 us/run -     2048 kB/run -  190.82 GB/s
  SOFT_MAX(type=f32,ne=[32768,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                131056 runs -     8.11 us/run -      256 kB/run -   30.12 GB/s
  SOFT_MAX(type=f32,ne=[32768,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 49146 runs -    22.54 us/run -     1024 kB/run -   43.33 GB/s
  SOFT_MAX(type=f32,ne=[32768,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                49146 runs -    23.32 us/run -     4096 kB/run -  167.50 GB/s
  SOFT_MAX(type=f32,ne=[65536,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                122865 runs -     8.19 us/run -      512 kB/run -   59.63 GB/s
  SOFT_MAX(type=f32,ne=[65536,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                 40955 runs -    24.59 us/run -     2048 kB/run -   79.43 GB/s
  SOFT_MAX(type=f32,ne=[65536,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                24582 runs -    43.21 us/run -     8192 kB/run -  180.84 GB/s
  SOFT_MAX(type=f32,ne=[131072,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               122865 runs -     8.19 us/run -     1024 kB/run -  119.25 GB/s
  SOFT_MAX(type=f32,ne=[131072,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                40955 runs -    24.59 us/run -     4096 kB/run -  158.87 GB/s
  SOFT_MAX(type=f32,ne=[131072,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               12294 runs -    82.37 us/run -    16384 kB/run -  189.74 GB/s
  SOFT_MAX(type=f32,ne=[262144,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):               122865 runs -     8.20 us/run -     2048 kB/run -  238.28 GB/s
  SOFT_MAX(type=f32,ne=[262144,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                36873 runs -    28.66 us/run -     8192 kB/run -  272.61 GB/s
  SOFT_MAX(type=f32,ne=[262144,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                9225 runs -   108.51 us/run -    32768 kB/run -  288.13 GB/s
  SOFT_MAX(type=f32,ne=[524288,1,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                98292 runs -    10.24 us/run -     4096 kB/run -  381.65 GB/s
  SOFT_MAX(type=f32,ne=[524288,4,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                32784 runs -    31.74 us/run -    16384 kB/run -  492.43 GB/s
  SOFT_MAX(type=f32,ne=[524288,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0):                8721 runs -   121.20 us/run -    65536 kB/run -  516.19 GB/s
```
 2025-12-09 12:58:56 +01:00
Oliver Simons a84dfd3e10 CUDA: Add Cooperative-Groups-based parallelization of ncols in softmax 
Old implementation parallelizes rows across SMs, which does not fit the
needs of backend-sampling (where we have ncols >> nrows and thus want to
parallelize ncols across SMs)
 2025-12-09 12:58:56 +01:00
Sigbjørn Skjæret 86a3f0fad8
ggml : allow fill node alloc inplace (#17870) 2025-12-09 12:23:47 +01:00
Georgi Gerganov 7ab6f51b97
Revert "ggml : remove redundant src in ggml_cast" 
This reverts commit 62d1b0082d.
 2025-12-09 12:52:59 +02:00
Chenguang Li ca709e427b
CANN: add support for partial RoPE and Vision mode (#17543) 
* cann: add support for partial RoPE and Vision mode

Add support for two important RoPE variants: partial rotation (rope_dims < ne0)
and Vision mode rotation.

1. Support for partial RoPE (rope_dims < ne0):
   - Split tensor into head (first rope_dims dimensions) and tail portions
   - Apply rotation only to head portion using RotaryPositionEmbedding operator
   - Copy unrotated tail portion directly from source to destination
   - Handle both contiguous and non-contiguous tensor layouts

2. Support for Vision mode (GGML_ROPE_TYPE_VISION):
   - Set rope_dims = ne0 for Vision mode to rotate entire tensor
   - Vision mode pairs dimension i with dimension i+n_dims (where n_dims = ne0/2)
   - No tail handling needed since entire tensor is rotated

Implementation details:
   - Use has_tail flag to determine execution path: head/tail splitting when
     rope_dims < ne0, or full tensor rotation when rope_dims == ne0
   - Support both F32 and F16 data types with intermediate F32 conversion
   - Copy non-contiguous tensors to contiguous buffers before calling
     RotaryPositionEmbedding operator for compatibility
   - Improve cache invalidation logic to include rope_dims and indep_sects
     parameters

These enhancements enable CANN backend to handle various RoPE configurations
used in modern vision-language models and models with partial rotation.

* cann: fix review comment
 2025-12-09 17:53:23 +08:00
Georgi Gerganov 9f6681c3a4
ggml-alloc : fix reuse-parent logic for misaligned sizes 2025-12-09 11:13:44 +02:00
Georgi Gerganov 62d1b0082d
ggml : remove redundant src in ggml_cast 2025-12-09 10:58:06 +02:00
Georgi Gerganov d62b5804e1
metal : print node names for debugging 2025-12-09 10:55:54 +02:00
Johannes Gäßler 0cdce38a97
CUDA: fix FP16 overflow in tile FA kernel (#17875) 2025-12-09 09:34:02 +01:00
Georgi Gerganov 6d38db5dfe
Merge branch 'master' into HEAD 2025-12-08 17:55:24 +02:00
Jay Zenith 51e0c2d917
cuda : add FILL op support (#17851) 
* cuda : add FILL op support

* cuda : add missing FILL op files
 2025-12-08 21:10:12 +08:00
wsbagnsv1 5814b4dce1
cuda: optimize SOLVE_TRI using registers and FMAF (#17703) 
* ggml-cuda: optimize solve_tri_f32_fast and fix stride handling

- Switch from using shared memory for the RHS/solution matrix to a register-based approach (x_low, x_high), reducing shared memory pressure and bank conflicts.
- Implement explicit `fmaf` instructions for the reduction loop.
- Update kernel arguments to pass strides in bytes rather than elements to align with standard ggml tensor arithmetic (casting to `char *` before addition).
- Remove unused `MAX_K_FAST` definition.

* Small cleanup

* Remove comments in solve_tri.cu

* Update ggml/src/ggml-cuda/solve_tri.cu

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

* Update ggml/src/ggml-cuda/solve_tri.cu

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

* Update ggml/src/ggml-cuda/solve_tri.cu

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

* Use const for variables in solve_tri.cu

* Replace fmaf with more readable code

* remove last fmaf

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
 2025-12-08 10:41:08 +01:00
ixgbe 79d61896d3
ggml-cpu: add ggml_thread_cpu_relax with Zihintpause support (#17784) 
* ggml-cpu: add ggml_thread_cpu_relax with Zihintpause support

Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>

* cmake: enable RISC-V zihintpause extension for Spacemit builds

* readme : add ZIHINTPAUSE support for RISC-V

---------

Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
 2025-12-08 10:41:34 +02:00
lovedheart 08f9d3cc1d
Vulkan: improve mul_mat_vec_iq1_m (#16907) 
* Optimize Vulkan shader for matrix-vector multiplication

* Revert changes on compute_outputs and main

Refactor compute_outputs to handle remaining rows correctly.

* Fix trailing whitespace
 2025-12-07 18:40:42 +01:00
Georgi Gerganov 8ef5f900db
cont : fixes 2025-12-07 15:45:00 +02:00
Law Po Ying d9e03db1e7
sycl: add missing BF16 conversion support for Intel oneAPI (#17780) 
* sycl: add missing BF16 conversion support for Intel oneAPI

* Fix Line 645: Trailing whitespace
 2025-12-07 09:18:18 +08:00
Jeff Bolz db97837385
vulkan: perf_logger improvements (#17672) 
* vulkan: perf_logger improvements

- Move perf_logger from device to ctx.
- Add an env var to control the frequency we dump the stats. If you set a very
large value, it just dumps when the ctx is destroyed.
- Add a fusion info string to the tracking, only log one item per fused op.
- Fix MUL_MAT_ID flops calculation.

* fix vector sizes
 2025-12-06 18:46:46 +01:00
Vishal Singh 017761daf5
ggml-zendnn : add ZenDNN backend for AMD CPUs (#17690) 
* ggml-zennn: add ZenDNN backend support

* ggml-zendnn : address ZenDNN backend review fixes and suggestions

* docs : apply blockquote syntax to ZenDNN docs

---------

Co-authored-by: Manoj Kumar <mkumar@zettabolt.com>
 2025-12-07 00:13:33 +08:00
Georgi Gerganov fdac9686f7
Merge branch 'master' into HEAD 2025-12-06 16:55:33 +02:00
Phylliida Dev 09c7c50e64
ggml : add circular tiling support to pad, for Vulkan, CUDA, and CPU (used for making seamless textures) (#16985) 
* Feat: Added vulkan circular tiling support

* Feat: Added cpu circular

* Feat: Added cuda kernels

* Added tests

* Added tests

* Removed non-pad operations

* Removed unneded changes

* removed backend non pad tests

* Update test-backend-ops.cpp

* Fixed comment on pad test

* removed trailing whitespace

* Removed unneded test in test-backend-ops

* Removed removed test from calls

* Update ggml/src/ggml-vulkan/vulkan-shaders/pad.comp

Co-authored-by: Ruben Ortlam <picard12@live.de>

* Fixed alignment

* Formatting

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* Format pad

* Format

* Clang format

* format

* format

* don't change so much stuff

* clang format and update to bool

* fix duplicates

* don't need to fix the padding

* make circular bool

* duplicate again

* rename vulkan to wrap around

* Don't need indent

* moved to const expr

* removed unneded extra line break

* More readable method calls

* Minor wording changes

* Added final newline

* Update ggml/include/ggml.h

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

* Update ggml/include/ggml.h

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

* Added circular pad ext tests

* Gate non circular pad devices

* Cleaned gating of non-circular pad devices

---------

Co-authored-by: Phylliida <phylliidadev@gmail.com>
Co-authored-by: Ruben Ortlam <picard12@live.de>
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
 2025-12-06 15:07:02 +01:00
Johannes Gäßler f334b79494
HIP: fix RDNA3 FP16/BF16 matrix multiplication (#17817) 2025-12-06 13:45:36 +01:00
Sky 7b43f55753
ggml : improve error handling for search path existence checks (#17653) 
* Improve error handling for search path existence checks

Refactor existence checks for search paths using std::error_code to handle potential errors.

* Improve cache file existence check with error code 

Update fs::exists to use std::error_code for error handling.

* Simplify existence check for search paths

Simplify existence check for search paths

* Fix logging path in error message for posix_stat

* Update ggml/src/ggml-backend-reg.cpp

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* Adapt to the coding standard

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
 2025-12-06 12:28:16 +01:00
Jeff Bolz 2960eb2975
vulkan: Use one row per workgroup for f32 mmv (#17711) 
The MoE models have a mul_mat_vec with very small m (32, 64, 128) right before
the topk_moe selection. Running multiple rows per wg doesn't utilize the SMs
well. I think even for larger m, f32 is so bandwidth-limited that running
multiple rows doesn't help.
 2025-12-06 11:12:26 +01:00
Jeff Bolz c6c5e85979
vulkan: support solve_tri with larger N/K values (#17781) 
Split N into chunks to fit into shared memory.
If K > 128, use a larger workgroup with enough invocations.
Add perf tests matching qwen3next.
 2025-12-06 08:56:45 +01:00
Georgi Gerganov 8ce774a102
metal : fix build(#17799) 
* metal : fix build

* tests : fix context destruction
 2025-12-06 09:33:59 +02:00
Masato Nakasaka 67788f6846
vulkan: Replace deprecated VK_EXT_validation_features (#17637) 
* replaced deprecated VK_EXT_validation_features

* forgot to remove old code
 2025-12-06 06:39:42 +01:00
Masato Nakasaka d8c0a7b085
vulkan: Fix mismatch in TOPK_MOE unit test (#17541) 
* Fix shader to support 2D workgroup mapping to a single subgroup

* Set required_subgroup_size

topk_moe shader requires static WARP_SIZE and actual subgroup size to match
 2025-12-06 06:23:30 +01:00
Jeff Bolz 933414c0b6
vulkan: add more num_blocks instantiations in rms_norm (#17701) 2025-12-05 22:08:56 +01:00
Jeff Bolz a0f3897d53
vulkan: fix top_k bug when there are ties in the input (#17659) 
* vulkan: Reduce temporary memory usage for TOP_K

- Compute row size for the temp buffer based on the output of the first pass.
- Update shader addressing math to use the output row size
- Pass the output row size as "ncols_output", what used to be "ncols_output" is now "k"

For the common case of K=40 and src0=(200000,1,1,1), this reduces the temporary buffer
from about 3.2MB to 500KB.

* vulkan: fix top_k bug when there are ties in the input

I noticed by inspection a bug in the vulkan top_k shader where if the least
value in the top_k appears multiple times we could end up writing those extra
copies out rather than some larger values (if the larger values are on higher
numbered threads).

I rewrote the test verification to handle this case, where the final index set
is not necessarily the same.

* Update tests/test-backend-ops.cpp

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

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
 2025-12-05 22:03:19 +01:00
Acly e15cd06a94
vulkan : support conv-2d with large output size (#17685) 2025-12-05 21:46:39 +01:00
Reese Levine fd57b24c0f
ggml webgpu: unary op suppport, code refactoring, ops support (#17764) 
* Squashed commit of the following:

commit b3c6bf4b0450d8d452b934df27a0fb7cb53cd755
Author: Abhijit Ramesh <abhijitramesh2k@gmail.com>
Date:   Mon Dec 1 18:29:00 2025 -0800

    ggml webgpu: fix xielu parameter passing (#11)

    The XIELU operation was incorrectly using static_cast to convert
    float parameters to uint32_t, which converted numeric values instead
    of preserving IEEE 754 bit patterns. This caused incorrect values
    to be interpreted by the GPU shader.

    * Use reinterpret_cast to preserve float bit patterns when passing
      through uint32_t params buffer
    * Update WGSL shader parameter types from u32 to f32
    * Re-enable XIELU support (was disabled due to numerical issues)

    Fixes NMSE test failures for XIELU operation on WebGPU backend.

commit 5ca9b5e49ea7cddc9ab7c8b43a11a9c76a4dff4a
Author: neha-ha <137219201+neha-ha@users.noreply.github.com>
Date:   Tue Nov 18 12:17:00 2025 -0800

    Refactored pipelines and workgroup calculations (#10)

    * refactored pipelines

    * refactored workgroup calculation

    * removed commented out block of prior maps

    * Clean up ceiling division pattern

    ---------

    Co-authored-by: Neha Abbas <nehaabbas@eduroam-169-233-141-223.ucsc.edu>
    Co-authored-by: Reese Levine <reeselevine1@gmail.com>

Author: James Contini <jamescontini@gmail.com>
Date:   Wed Oct 29 23:13:06 2025 -0700

    formatted embed wgsl and ggml-webgpu.cpp

commit e1f6baea31645e5d96ad53664acae856f74b96f4
Author: James Contini <jamescontini@gmail.com>
Date:   Wed Oct 29 23:08:37 2025 -0700

    implemented REPL_Template support and removed bug in unary operators kernel

commit 8c70b8fece445cdc9a8c660dbddbf201e52da2bb
Author: James Contini <jamescontini@gmail.com>
Date:   Wed Oct 15 16:14:20 2025 -0700

    responded and dealt with PR comments

commit f9282c660c10dec4487d434549bdb707a9cd9f37
Author: James Contini <jamescontini@gmail.com>
Date:   Sun Oct 12 13:41:41 2025 -0700

    removed unnecesarry checking if node->src[1] exists for unary operators

commit 4cf28d7dec41c29186d66152735b244c5699f9dc
Author: James Contini <jamescontini@gmail.com>
Date:   Sun Oct 12 13:32:45 2025 -0700

    All operators (inlcluding xielu) working

commit 74c6add1761a59d2c2ff60b60e8ad3c8300f6d3e
Author: James Contini <jamescontini@gmail.com>
Date:   Fri Oct 10 13:16:48 2025 -0700

    fixed autoconfig

commit 362749910be4f0120c8ffb21ceddeb7d2c088e51
Author: James Contini <jamescontini@gmail.com>
Date:   Fri Oct 10 13:10:46 2025 -0700

    removed vestigial files

commit cb0858333785757804c5104e59c4981843207c16
Author: James Contini <jamescontini@gmail.com>
Date:   Fri Oct 10 12:59:32 2025 -0700

    abides by editor-config

commit 5360e2852a4b51197d7d67d0a5d42e908b02d7ed
Author: James Contini <jamescontini@gmail.com>
Date:   Fri Oct 10 12:45:57 2025 -0700

    rms_norm double declaration bug atoned

commit 7b09baa4aa53711be5a126043670cc182c78bfcd
Merge: 8a6ec843 74b8fc17
Author: James Contini <jamescontini@gmail.com>
Date:   Fri Oct 10 11:50:03 2025 -0700

    resolving merge conflicts

commit 8a6ec843a50ab82f8cef59b4558eb63f318ba02d
Author: James Contini <jamescontini@gmail.com>
Date:   Wed Oct 8 18:06:47 2025 -0700

    unary operators pass ggml tests

commit c3ae38278a2db236adc5912c9140e4f0d63f2c19
Author: James Contini <jamescontini@gmail.com>
Date:   Wed Oct 1 16:22:40 2025 -0700

    neg passes backend test

commit aa1c9b2f8877a405470ca56709c42a1fd43713de
Author: James Contini <jamescontini@gmail.com>
Date:   Tue Sep 30 23:55:27 2025 -0700

    neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though

Co-authored-by: James Contini <jamescontini@gmail.com>
Co-authored-by: Neha Abbas <neabbas@ucsc.edu>
Co-authored-by: Abhijit Ramesh <abhijitramesh2k@gmail.com>

* Remove extra code and format

* Add ops documentation (finally)

* Update ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: James Contini <jamescontini@gmail.com>
Co-authored-by: Neha Abbas <neabbas@ucsc.edu>
Co-authored-by: Abhijit Ramesh <abhijitramesh2k@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 2025-12-05 12:25:51 -08:00
Jeff Bolz 6ab0d64960
vulkan: enable mmvq for q2_k on NVIDIA (#17675) 2025-12-05 21:21:57 +01:00
Jeff Bolz 93bb92664e
vulkan: set all memory allocations to high priority (#17624) 
* vulkan: set all memory allocations to high priority

* gate by env var
 2025-12-05 21:21:04 +01:00
Georgi Gerganov 8160b38a5f
rpc : fix alloc size logic (#17116) 
* rpc : fix alloc size logic

* rpc : bump version
 2025-12-05 19:39:04 +02:00
Georgi Gerganov c41bde6fbd
metal : add residency sets keep-alive heartbeat (#17766) 
* examples : add idle

* metal : attach residency sets to queue

* idle : add link

* idle : adjust intervals

* metal : add residency sets keep-alive heartbeat

* cont : adjust default keep-alive time
 2025-12-05 19:38:54 +02:00
Oliver Simons e652566139 Readd `cub::DeviceScan::InclusiveSum`-based CumSum 
For single rows and large columns doing a for-loop over the function
`cub::DeviceScan::InclusiveSum` offered by CUB outperforms the
`cumsum_cub_kernel` where `cub::BlockScan` is used.

Numbers before this change

  Backend 1/3: CUDA0
  Device description: NVIDIA RTX 6000 Ada Generation
  Device memory: 48510 MB (48039 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  311258 runs -     3.26 us/run -     2048 kB/run -  599.76 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  229390 runs -     4.40 us/run -     5120 kB/run - 1110.23 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  37583 runs -    29.63 us/run -     6250 kB/run -  201.18 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    892819 runs -     1.12 us/run -        1 kB/run -    0.85 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   450505 runs -     2.25 us/run -        8 kB/run -    3.39 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   155629 runs -     6.61 us/run -       32 kB/run -    4.62 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                    81910 runs -    12.60 us/run -       64 kB/run -    4.85 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                   49146 runs -    23.99 us/run -      128 kB/run -    5.09 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                   24573 runs -    47.10 us/run -      256 kB/run -    5.18 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                   16382 runs -    93.57 us/run -      512 kB/run -    5.22 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                   8191 runs -   184.79 us/run -     1024 kB/run -    5.29 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                   8191 runs -   280.43 us/run -     1562 kB/run -    5.31 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                  2148 runs -  2771.23 us/run -    15625 kB/run -    5.38 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    458696 runs -     2.21 us/run -        4 kB/run -    1.73 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   360404 runs -     2.82 us/run -       32 kB/run -   10.83 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   147438 runs -     7.12 us/run -      128 kB/run -   17.15 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    81910 runs -    12.90 us/run -      256 kB/run -   18.92 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   49146 runs -    24.32 us/run -      512 kB/run -   20.08 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   24573 runs -    47.28 us/run -     1024 kB/run -   20.66 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   16382 runs -    93.21 us/run -     2048 kB/run -   20.96 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                   8191 runs -   185.04 us/run -     4096 kB/run -   21.11 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                   5369 runs -   282.08 us/run -     6250 kB/run -   21.13 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                   537 runs -  2806.46 us/run -    62500 kB/run -   21.26 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    458696 runs -     2.20 us/run -        8 kB/run -    3.47 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   360404 runs -     2.82 us/run -       64 kB/run -   21.66 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   147438 runs -     7.12 us/run -      256 kB/run -   34.28 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    81910 runs -    12.90 us/run -      512 kB/run -   37.84 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   49146 runs -    24.32 us/run -     1024 kB/run -   40.15 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    47.28 us/run -     2048 kB/run -   41.31 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -    93.20 us/run -     4096 kB/run -   41.92 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                   8194 runs -   185.05 us/run -     8192 kB/run -   42.22 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                   5370 runs -   282.15 us/run -    12500 kB/run -   42.26 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                   269 runs -  4067.61 us/run -   125000 kB/run -   29.36 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   303067 runs -     3.32 us/run -       16 kB/run -    4.60 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  303067 runs -     3.32 us/run -      128 kB/run -   36.76 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  147438 runs -     7.17 us/run -      512 kB/run -   68.13 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   81910 runs -    12.90 us/run -     1024 kB/run -   75.68 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  49146 runs -    24.33 us/run -     2048 kB/run -   80.28 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    47.30 us/run -     4096 kB/run -   82.59 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -    93.24 us/run -     8192 kB/run -   83.80 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                  6147 runs -   185.07 us/run -    16384 kB/run -   84.45 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                  4029 runs -   282.40 us/run -    25000 kB/run -   84.46 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                  270 runs -  4118.40 us/run -   250000 kB/run -   58.11 GB/s
  Backend CUDA0: OK
Backend 2/3: CUDA1
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96677 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  368595 runs -     2.73 us/run -     2048 kB/run -  715.83 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  216282 runs -     4.72 us/run -     5120 kB/run - 1035.32 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  32214 runs -    34.33 us/run -     6250 kB/run -  173.64 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    810909 runs -     1.24 us/run -        1 kB/run -    0.77 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   401359 runs -     2.52 us/run -        8 kB/run -    3.03 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   139247 runs -     7.44 us/run -       32 kB/run -    4.10 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                    73719 runs -    14.27 us/run -       64 kB/run -    4.28 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                   40955 runs -    27.24 us/run -      128 kB/run -    4.48 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                   24573 runs -    53.46 us/run -      256 kB/run -    4.57 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                   16382 runs -   105.29 us/run -      512 kB/run -    4.64 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                   8191 runs -   210.15 us/run -     1024 kB/run -    4.65 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                   8191 runs -   318.22 us/run -     1562 kB/run -    4.68 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                  2148 runs -  3142.23 us/run -    15625 kB/run -    4.74 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    303067 runs -     3.34 us/run -        4 kB/run -    1.14 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   253921 runs -     4.03 us/run -       32 kB/run -    7.58 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   122865 runs -     8.20 us/run -      128 kB/run -   14.89 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    73719 runs -    14.96 us/run -      256 kB/run -   16.32 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   40955 runs -    28.66 us/run -      512 kB/run -   17.04 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   24573 runs -    54.21 us/run -     1024 kB/run -   18.01 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   16382 runs -   106.49 us/run -     2048 kB/run -   18.34 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                   8191 runs -   210.88 us/run -     4096 kB/run -   18.52 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                   5369 runs -   321.77 us/run -     6250 kB/run -   18.53 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                   537 runs -  3191.79 us/run -    62500 kB/run -   18.69 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    376786 runs -     2.67 us/run -        8 kB/run -    2.86 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   245730 runs -     4.10 us/run -       64 kB/run -   14.90 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   122865 runs -     8.20 us/run -      256 kB/run -   29.79 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    65528 runs -    16.38 us/run -      512 kB/run -   29.82 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   40955 runs -    28.69 us/run -     1024 kB/run -   34.04 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    55.28 us/run -     2048 kB/run -   35.33 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -   108.50 us/run -     4096 kB/run -   36.00 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                   8194 runs -   213.75 us/run -     8192 kB/run -   36.55 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                   5370 runs -   326.31 us/run -    12500 kB/run -   36.54 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                   538 runs -  3252.68 us/run -   125000 kB/run -   36.72 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   303067 runs -     3.32 us/run -       16 kB/run -    4.60 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  253921 runs -     4.06 us/run -      128 kB/run -   30.09 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  122865 runs -     8.20 us/run -      512 kB/run -   59.57 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   65528 runs -    16.38 us/run -     1024 kB/run -   59.63 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  40955 runs -    28.69 us/run -     2048 kB/run -   68.09 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    55.28 us/run -     4096 kB/run -   70.67 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -   108.50 us/run -     8192 kB/run -   72.02 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                  6147 runs -   213.60 us/run -    16384 kB/run -   73.17 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                  4029 runs -   326.04 us/run -    25000 kB/run -   73.15 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                  270 runs -  5458.69 us/run -   250000 kB/run -   43.84 GB/s

----
Numbers after:

Backend 1/3: CUDA0
  Device description: NVIDIA RTX 6000 Ada Generation
  Device memory: 48510 MB (48039 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  311258 runs -     3.25 us/run -     2048 kB/run -  601.62 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  229390 runs -     4.40 us/run -     5120 kB/run - 1110.14 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  37583 runs -    29.67 us/run -     6250 kB/run -  200.89 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    892819 runs -     1.12 us/run -        1 kB/run -    0.85 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   458696 runs -     2.21 us/run -        8 kB/run -    3.45 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   376786 runs -     2.66 us/run -       32 kB/run -   11.46 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                   393168 runs -     2.59 us/run -       64 kB/run -   23.57 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                  393168 runs -     2.59 us/run -      128 kB/run -   47.15 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                  376786 runs -     2.69 us/run -      256 kB/run -   90.69 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                  327640 runs -     3.06 us/run -      512 kB/run -  159.65 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                 311258 runs -     3.28 us/run -     1024 kB/run -  297.77 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                 270303 runs -     3.74 us/run -     1562 kB/run -  398.14 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                137472 runs -     7.35 us/run -    15625 kB/run - 2026.94 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    876437 runs -     1.14 us/run -        4 kB/run -    3.33 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   442314 runs -     2.28 us/run -       32 kB/run -   13.39 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   155629 runs -     6.69 us/run -      128 kB/run -   18.24 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    81910 runs -    12.53 us/run -      256 kB/run -   19.49 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   49146 runs -    24.18 us/run -      512 kB/run -   20.20 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   65528 runs -    15.34 us/run -     1024 kB/run -   63.66 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   73719 runs -    14.76 us/run -     2048 kB/run -  132.35 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                  65528 runs -    16.01 us/run -     4096 kB/run -  244.07 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                  64428 runs -    16.51 us/run -     6250 kB/run -  360.97 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                 33831 runs -    29.59 us/run -    62500 kB/run - 2016.08 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    868246 runs -     1.16 us/run -        8 kB/run -    6.59 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   442314 runs -     2.28 us/run -       64 kB/run -   26.76 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   155629 runs -     6.69 us/run -      256 kB/run -   36.48 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    81910 runs -    12.53 us/run -      512 kB/run -   38.97 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   49146 runs -    24.17 us/run -     1024 kB/run -   40.41 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    47.53 us/run -     2048 kB/run -   41.10 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -    61.25 us/run -     4096 kB/run -   63.77 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                  32776 runs -    31.79 us/run -     8192 kB/run -  245.82 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                  32220 runs -    32.90 us/run -    12500 kB/run -  362.35 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                  6725 runs -   151.99 us/run -   125000 kB/run -  785.77 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   851864 runs -     1.18 us/run -       16 kB/run -   12.97 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  442314 runs -     2.30 us/run -      128 kB/run -   53.13 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  155629 runs -     6.68 us/run -      512 kB/run -   73.13 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   81910 runs -    12.68 us/run -     1024 kB/run -   77.00 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  40955 runs -    24.56 us/run -     2048 kB/run -   79.53 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    47.52 us/run -     4096 kB/run -   82.21 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -    93.44 us/run -     8192 kB/run -   83.62 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                 16392 runs -    63.36 us/run -    16384 kB/run -  246.68 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                 16116 runs -    65.25 us/run -    25000 kB/run -  365.53 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                 3375 runs -   304.46 us/run -   250000 kB/run -  785.98 GB/s
  Backend CUDA0: OK
Backend 2/3: CUDA1
  Device description: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
  Device memory: 97250 MB (96677 MB free)

  CUMSUM(type=f32,ne=[128,128,4,4]):                  376786 runs -     2.69 us/run -     2048 kB/run -  727.04 GB/s
  CUMSUM(type=f32,ne=[2048,16,5,4]):                  216282 runs -     4.64 us/run -     5120 kB/run - 1053.30 GB/s
  CUMSUM(type=f32,ne=[20000,10,4,1]):                  32214 runs -    34.21 us/run -     6250 kB/run -  174.27 GB/s
  CUMSUM(type=f32,ne=[128,1,1,1]):                    819100 runs -     1.22 us/run -        1 kB/run -    0.78 GB/s
  CUMSUM(type=f32,ne=[1024,1,1,1]):                   409550 runs -     2.47 us/run -        8 kB/run -    3.09 GB/s
  CUMSUM(type=f32,ne=[4096,1,1,1]):                   303067 runs -     3.31 us/run -       32 kB/run -    9.21 GB/s
  CUMSUM(type=f32,ne=[8192,1,1,1]):                   237539 runs -     4.33 us/run -       64 kB/run -   14.08 GB/s
  CUMSUM(type=f32,ne=[16384,1,1,1]):                  237539 runs -     4.33 us/run -      128 kB/run -   28.17 GB/s
  CUMSUM(type=f32,ne=[32768,1,1,1]):                  188393 runs -     5.37 us/run -      256 kB/run -   45.47 GB/s
  CUMSUM(type=f32,ne=[65536,1,1,1]):                  188393 runs -     5.41 us/run -      512 kB/run -   90.20 GB/s
  CUMSUM(type=f32,ne=[131072,1,1,1]):                 188393 runs -     5.41 us/run -     1024 kB/run -  180.41 GB/s
  CUMSUM(type=f32,ne=[200000,1,1,1]):                 188393 runs -     5.41 us/run -     1562 kB/run -  275.27 GB/s
  CUMSUM(type=f32,ne=[2000000,1,1,1]):                128880 runs -     7.76 us/run -    15625 kB/run - 1920.33 GB/s
  CUMSUM(type=f32,ne=[128,4,1,1]):                    802718 runs -     1.26 us/run -        4 kB/run -    3.03 GB/s
  CUMSUM(type=f32,ne=[1024,4,1,1]):                   401359 runs -     2.51 us/run -       32 kB/run -   12.18 GB/s
  CUMSUM(type=f32,ne=[4096,4,1,1]):                   139247 runs -     7.51 us/run -      128 kB/run -   16.26 GB/s
  CUMSUM(type=f32,ne=[8192,4,1,1]):                    73719 runs -    14.17 us/run -      256 kB/run -   17.23 GB/s
  CUMSUM(type=f32,ne=[16384,4,1,1]):                   40955 runs -    27.37 us/run -      512 kB/run -   17.84 GB/s
  CUMSUM(type=f32,ne=[32768,4,1,1]):                   40955 runs -    26.33 us/run -     1024 kB/run -   37.10 GB/s
  CUMSUM(type=f32,ne=[65536,4,1,1]):                   40955 runs -    26.19 us/run -     2048 kB/run -   74.59 GB/s
  CUMSUM(type=f32,ne=[131072,4,1,1]):                  40955 runs -    26.35 us/run -     4096 kB/run -  148.26 GB/s
  CUMSUM(type=f32,ne=[200000,4,1,1]):                  42952 runs -    24.18 us/run -     6250 kB/run -  246.51 GB/s
  CUMSUM(type=f32,ne=[2000000,4,1,1]):                 32757 runs -    31.01 us/run -    62500 kB/run - 1923.68 GB/s
  CUMSUM(type=f32,ne=[128,8,1,1]):                    786336 runs -     1.28 us/run -        8 kB/run -    5.95 GB/s
  CUMSUM(type=f32,ne=[1024,8,1,1]):                   393168 runs -     2.57 us/run -       64 kB/run -   23.73 GB/s
  CUMSUM(type=f32,ne=[4096,8,1,1]):                   131056 runs -     7.67 us/run -      256 kB/run -   31.82 GB/s
  CUMSUM(type=f32,ne=[8192,8,1,1]):                    73719 runs -    14.43 us/run -      512 kB/run -   33.84 GB/s
  CUMSUM(type=f32,ne=[16384,8,1,1]):                   40955 runs -    27.90 us/run -     1024 kB/run -   35.01 GB/s
  CUMSUM(type=f32,ne=[32768,8,1,1]):                   24573 runs -    54.63 us/run -     2048 kB/run -   35.75 GB/s
  CUMSUM(type=f32,ne=[65536,8,1,1]):                   16382 runs -    72.24 us/run -     4096 kB/run -   54.08 GB/s
  CUMSUM(type=f32,ne=[131072,8,1,1]):                  20485 runs -    52.66 us/run -     8192 kB/run -  148.37 GB/s
  CUMSUM(type=f32,ne=[200000,8,1,1]):                  21480 runs -    48.00 us/run -    12500 kB/run -  248.42 GB/s
  CUMSUM(type=f32,ne=[2000000,8,1,1]):                 16140 runs -    61.99 us/run -   125000 kB/run - 1926.51 GB/s
  CUMSUM(type=f32,ne=[128,16,1,1]):                   786336 runs -     1.28 us/run -       16 kB/run -   11.90 GB/s
  CUMSUM(type=f32,ne=[1024,16,1,1]):                  393168 runs -     2.57 us/run -      128 kB/run -   47.57 GB/s
  CUMSUM(type=f32,ne=[4096,16,1,1]):                  131056 runs -     7.65 us/run -      512 kB/run -   63.83 GB/s
  CUMSUM(type=f32,ne=[8192,16,1,1]):                   73719 runs -    14.42 us/run -     1024 kB/run -   67.74 GB/s
  CUMSUM(type=f32,ne=[16384,16,1,1]):                  40955 runs -    27.87 us/run -     2048 kB/run -   70.09 GB/s
  CUMSUM(type=f32,ne=[32768,16,1,1]):                  24573 runs -    54.54 us/run -     4096 kB/run -   71.63 GB/s
  CUMSUM(type=f32,ne=[65536,16,1,1]):                  12291 runs -   107.53 us/run -     8192 kB/run -   72.66 GB/s
  CUMSUM(type=f32,ne=[131072,16,1,1]):                 10245 runs -   105.10 us/run -    16384 kB/run -  148.70 GB/s
  CUMSUM(type=f32,ne=[200000,16,1,1]):                 10744 runs -    95.36 us/run -    25000 kB/run -  250.11 GB/s
  CUMSUM(type=f32,ne=[2000000,16,1,1]):                 5400 runs -   186.97 us/run -   250000 kB/run - 1279.90 GB/s
 2025-12-05 16:26:18 +01:00
Oliver Simons 7668999518 Merge branch 'master' into gpu-sampling 
Let's keep `master's` cumsum implementation for it's likely better AMD
perf and add back pure-CUB-implementation in follow-up commit
 2025-12-05 14:41:08 +01:00
Johannes Gäßler 6016d0bd41
HIP : fix RDNA4 build (#17792) 2025-12-05 13:47:52 +01:00
shalinib-ibm 3a0d10533a
Q4/Q8 Tiled Gemm Optimization. (#16999) 2025-12-05 19:41:51 +08:00
Johannes Gäßler e95d0bc8fd
CUDA: fix FA VKQ accumulator overflow (#17746) 2025-12-05 09:18:10 +01:00