Akarshan Biswas b66df9d9c9 CUDA: fix build error from ambiguous __half conversions in conv2d (#15690) ...

* CUDA: fix build error from ambiguous __half conversions in conv2d

Building conv2d with half precision failed because `__half` defines
multiple implicit conversion operators (to float, int, short, etc.),
causing ambiguous overload resolution when multiplying with float.

Introduce a templated `to_float` helper that explicitly converts
`__half` via `__half2float`, while passing through float unchanged.
Use this helper in conv2d accumulation to ensure unambiguous and
correct promotion to float.

Fixes some build errors with half-precision kernels on CUDA.

ggml-ci

* CUDA: Replace custom to_float helper with unified ggml_cuda_cast and add half‑>float conversion

* CUDA: Add missing convert.cuh header

* CUDA: remove unnecessary extension in ggml_cuda_cast

* CUDA: Address review comment, remove second type template argument

2025-09-01 06:55:06 +05:30

..

template-instances

llama : add gpt-oss (#15091)

2025-08-05 22:10:36 +03:00

vendors

CUDA: MoE helper in device code, better tile sizes (#15525)

2025-08-25 17:23:40 +02:00

acc.cu

llama/ggml: add LLM training support (#10544)

2025-05-12 14:44:49 +02:00

acc.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

add-id.cu

musa: fix build warnings (#15258)

2025-08-20 10:17:37 +08:00

add-id.cuh

llama : add gpt-oss (#15091)

2025-08-05 22:10:36 +03:00

arange.cu

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

arange.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

argmax.cu

cuda : optimize argmax (#10441)

2024-11-21 18:18:50 +01:00

argmax.cuh

ggml/ex: calculate accuracy in graph, adapt MNIST (ggml/980)

2024-10-03 21:17:26 +03:00

argsort.cu

ggml : reduce hash table reset cost (#8698)

2024-07-27 04:41:55 +02:00

argsort.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

binbcast.cu

CUDA: fix bug in rms_norm fusion (#15660)

2025-08-29 21:30:06 +08:00

binbcast.cuh

CUDA: fuse adds, fuse add with rms norm (#15631)

2025-08-29 11:35:58 +08:00

clamp.cu

cuda: unary ops as float + de-duplicate (ggml/1130)

2025-03-03 18:18:11 +02:00

clamp.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

CMakeLists.txt

cuda: Add cublasLt_static linking when GGML_STATIC is enabled (#15622)

2025-08-28 02:32:36 +02:00

common.cuh

CUDA: return -1 for nonexistent compiled arch (#15587)

2025-08-26 16:01:20 +02:00

concat.cu

musa: fix all warnings, re-enable -DLLAMA_FATAL_WARNINGS=ON in ci and update doc (#12611)

2025-03-30 10:59:38 +02:00

concat.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

conv2d-dw.cu

CUDA: add conv_2d_dw (#14265)

2025-06-20 09:50:24 +08:00

conv2d-dw.cuh

CUDA: add conv_2d_dw (#14265)

2025-06-20 09:50:24 +08:00

conv2d-transpose.cu

CUDA: add conv_2d_transpose (#14287)

2025-06-20 22:48:24 +08:00

conv2d-transpose.cuh

CUDA: add conv_2d_transpose (#14287)

2025-06-20 22:48:24 +08:00

conv2d.cu

CUDA: fix build error from ambiguous __half conversions in conv2d (#15690)

2025-09-01 06:55:06 +05:30

conv2d.cuh

CUDA: add conv2d (#15635)

2025-08-28 20:33:03 +02:00

conv-transpose-1d.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

conv-transpose-1d.cuh

feat: cuda implementation for ggml_conv_transpose_1d (ggml/854)

2024-07-08 12:23:00 +03:00

convert.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

convert.cuh

HIP: Cleanup hipification header (#15285)

2025-08-14 16:23:56 +02:00

count-equal.cu

ggml: fix zero division in ‘dne’ calculation in CUDA COUNT_EQUAL operator when ‘ne’ is small (#10213)

2024-11-09 08:35:46 +01:00

count-equal.cuh

ggml/ex: calculate accuracy in graph, adapt MNIST (ggml/980)

2024-10-03 21:17:26 +03:00

cp-async.cuh

CUDA: FA support for Deepseek (Ampere or newer) (#13306)

2025-05-09 13:34:58 +02:00

cpy-utils.cuh

HIP: Cleanup hipification header (#15285)

2025-08-14 16:23:56 +02:00

cpy.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

cpy.cuh

ggml: Re-enable CUDA graphs in presence of CONT and DUP nodes (#12970)

2025-04-17 15:19:42 +02:00

cross-entropy-loss.cu

CUDA: add dynamic shared mem to softmax, refactor general usage (#14497)

2025-07-03 07:45:11 +08:00

cross-entropy-loss.cuh

ggml/examples: add backend support for numerical optimization (ggml/949)

2024-09-20 21:15:05 +03:00

dequantize.cuh

CUDA: replace GGML_CUDA_F16 with CUDA arch checks (#15433)

2025-08-20 16:58:49 +02:00

diagmask.cu

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

diagmask.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

fattn-common.cuh

CUDA: refactor FA support/selection code (#15454)

2025-08-20 23:14:14 +02:00

fattn-mma-f16.cuh

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

fattn-tile-f16.cu

CUDA: fix half2 -> half conversion for HIP (#15529)

2025-08-23 21:37:06 +02:00

fattn-tile-f16.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

fattn-tile-f32.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

fattn-tile-f32.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

fattn-vec-f16.cuh

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

fattn-vec-f32.cuh

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

fattn-wmma-f16.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

fattn-wmma-f16.cuh

CUDA: use mma PTX instructions for FlashAttention (#11583)

2025-02-02 19:31:09 +01:00

fattn.cu

CUDA: refactor FA support/selection code (#15454)

2025-08-20 23:14:14 +02:00

fattn.cuh

CUDA: refactor FA support/selection code (#15454)

2025-08-20 23:14:14 +02:00

getrows.cu

CUDA: replace GGML_CUDA_F16 with CUDA arch checks (#15433)

2025-08-20 16:58:49 +02:00

getrows.cuh

CUDA: batched+noncont MMQ, refactor bs>1 MoE code (#13199)

2025-04-30 23:12:59 +02:00

ggml-cuda.cu

CUDA: fix bug in rms_norm fusion (#15660)

2025-08-29 21:30:06 +08:00

gla.cu

llama: add support for QRWKV6 model architecture (#11001)

2025-01-10 09:58:08 +08:00

gla.cuh

llama: add support for QRWKV6 model architecture (#11001)

2025-01-10 09:58:08 +08:00

im2col.cu

llama : add gpt-oss (#15091)

2025-08-05 22:10:36 +03:00

im2col.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

mean.cu

cuda : fix GGML_CUDA_GRAPHS=OFF (#15300)

2025-08-14 13:22:07 +03:00

mean.cuh

CUDA: add mean operation (#14313)

2025-06-22 12:39:54 +08:00

mma.cuh

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

mmf.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

mmf.cuh

CUDA: GEMM for FP32/FP16/BF16 and ne11 <= 16 (#15131)

2025-08-07 10:53:21 +02:00

mmq.cu

CUDA: MoE helper in device code, better tile sizes (#15525)

2025-08-25 17:23:40 +02:00

mmq.cuh

CUDA: MoE helper in device code, better tile sizes (#15525)

2025-08-25 17:23:40 +02:00

mmvf.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

mmvf.cuh

CUDA: GEMM for FP32/FP16/BF16 and ne11 <= 16 (#15131)

2025-08-07 10:53:21 +02:00

mmvq.cu

musa: add GGML_UNUSED_VARS (#15446)

2025-08-21 11:06:05 +08:00

mmvq.cuh

CUDA: noncont MMVQ + batched bs1 MUL_MAT_ID (#13014)

2025-04-22 21:27:40 +02:00

norm.cu

CUDA: fix bug in rms_norm fusion (#15660)

2025-08-29 21:30:06 +08:00

norm.cuh

CUDA: fuse adds, fuse add with rms norm (#15631)

2025-08-29 11:35:58 +08:00

opt-step-adamw.cu

ggml: new optimization interface (ggml/988)

2024-11-17 08:30:29 +02:00

opt-step-adamw.cuh

ggml/examples: add backend support for numerical optimization (ggml/949)

2024-09-20 21:15:05 +03:00

opt-step-sgd.cu

finetune: SGD optimizer, more CLI args (#13873)

2025-08-14 12:03:57 +02:00

opt-step-sgd.cuh

finetune: SGD optimizer, more CLI args (#13873)

2025-08-14 12:03:57 +02:00

out-prod.cu

CPU/CUDA: fix (GQA) mul mat back, add CUDA support (#11380)

2025-01-24 12:38:31 +01:00

out-prod.cuh

ggml/examples: add backend support for numerical optimization (ggml/949)

2024-09-20 21:15:05 +03:00

pad_reflect_1d.cu

cuda : add Pad Reflect 1D support (#14659)

2025-08-22 13:06:29 +02:00

pad_reflect_1d.cuh

cuda : add Pad Reflect 1D support (#14659)

2025-08-22 13:06:29 +02:00

pad.cu

musa: fix all warnings, re-enable -DLLAMA_FATAL_WARNINGS=ON in ci and update doc (#12611)

2025-03-30 10:59:38 +02:00

pad.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

pool2d.cu

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

pool2d.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

quantize.cu

CUDA: fix crash on large batch size for quant. MoE (#13537)

2025-05-14 16:41:02 +02:00

quantize.cuh

CUDA: batched+noncont MMQ, refactor bs>1 MoE code (#13199)

2025-04-30 23:12:59 +02:00

reduce_rows.cuh

musa: fix build warnings (#15258)

2025-08-20 10:17:37 +08:00

roll.cu

CUDA: add roll (#14919)

2025-07-29 14:45:18 +08:00

roll.cuh

CUDA: add roll (#14919)

2025-07-29 14:45:18 +08:00

rope.cu

cuda : fix rope with partial rotation and non-cont src (#14580)

2025-07-08 10:15:21 +03:00

rope.cuh

RoPE: fix back, CUDA support for back + noncont. (#11240)

2025-01-15 12:51:37 +01:00

scale.cu

ggml : add ggml_scale_bias (#14417)

2025-07-09 18:16:12 +02:00

scale.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

set-rows.cu

HIP: Cleanup hipification header (#15285)

2025-08-14 16:23:56 +02:00

set-rows.cuh

CUDA: add set rows for f32 and f16 (#14551)

2025-07-12 16:31:38 +03:00

softcap.cu

cuda : add softcap fusion (#14907)

2025-07-29 14:22:03 +02:00

softcap.cuh

cuda : add softcap fusion (#14907)

2025-07-29 14:22:03 +02:00

softmax.cu

llama : add gpt-oss (#15091)

2025-08-05 22:10:36 +03:00

softmax.cuh

CUDA: backwards pass for misc. ops, add tests (#11257)

2025-01-16 16:43:38 +01:00

ssm-conv.cu

model : support LiquidAI LFM2 hybrid family (#14620)

2025-07-11 20:27:01 +02:00

ssm-conv.cuh

ggml : faster ssm scan (#10558)

2025-03-31 18:05:13 +02:00

ssm-scan.cu

ggml : fix SSM_SCAN for n_groups > 1 (#15625)

2025-08-28 10:11:36 -04:00

ssm-scan.cuh

ggml : faster ssm scan (#10558)

2025-03-31 18:05:13 +02:00

sum.cu

CUDA: Optimize reduce_rows_f32 kernel, leading up to 25x perf improvement on kernel-level and 10% perf increase for Gemma3n (#15132)

2025-08-13 10:04:46 +02:00

sum.cuh

tests: add gradient tests for all backends (ggml/932)

2024-09-08 11:05:55 +03:00

sumrows.cu

CUDA: Optimize reduce_rows_f32 kernel, leading up to 25x perf improvement on kernel-level and 10% perf increase for Gemma3n (#15132)

2025-08-13 10:04:46 +02:00

sumrows.cuh

CUDA: add mean operation (#14313)

2025-06-22 12:39:54 +08:00

tsembd.cu

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

tsembd.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

unary.cu

llama : add gpt-oss (#15091)

2025-08-05 22:10:36 +03:00

unary.cuh

llama : add gpt-oss (#15091)

2025-08-05 22:10:36 +03:00

upscale.cu

CUDA: add bilinear interpolation for upscale (#14563)

2025-07-08 10:11:18 +08:00

upscale.cuh

llama : reorganize source code + improve CMake (#8006)

2024-06-26 18:33:02 +03:00

vecdotq.cuh

CUDA: Accelerate MXFP4 table lookup using __byte_perm (#15451)

2025-08-25 23:21:22 +02:00

wkv.cu

llama: Add support for RWKV v7 architecture (#12412)

2025-03-18 07:27:50 +08:00

wkv.cuh

llama: Add support for RWKV v7 architecture (#12412)

2025-03-18 07:27:50 +08:00