Berk Idem [Fri, 10 Apr 2026 09:49:14 +0000 (05:49 -0400)]
common : enable reasoning budget sampler for gemma4 (#21697)
* fix: enable reasoning budget sampler for gemma4
Add thinking_start_tag and thinking_end_tag to
common_chat_params_init_gemma4(). Without these, the reasoning
budget sampler never activates for gemma4.
Make the newline after "thought" optional in the PEG parser to
handle budget=0 (sampler forces end tag before the newline).
Add test case for empty thinking block.
Fixes #21487
* use p.space() instead of p.optional(p.literal("\n")) in gemma4 thought parser
- vendors/hip.h: Add CDNA4 preprocessor define for __gfx950__
- common.cuh: Add GGML_CUDA_CC_CDNA4 and GGML_CUDA_CC_IS_CDNA4 macros
- mma.cuh: Route CDNA4 to compatible MFMA instructions:
* f32 matmul: mfma_f32_16x16x4f32 (xf32 variant unavailable on gfx950)
* bf16 matmul: mfma_f32_16x16x16bf16_1k (same as CDNA3)
* int8 matmul: mfma_i32_16x16x32_i8/32x32x16 (same as CDNA3)
- mmq.cuh: Include CDNA4 in stream-k kernel dispatch
CDNA4 is largely compatible with CDNA3 except:
- No xf32 MFMA (mfma_f32_16x16x8_xf32) — routes to f32 path
- Different FP8 format (e4m3fn vs e4m3_fnuz) — not changed here
Tested on AMD Instinct MI355X (gfx950), ROCm 7.0.1:
- Build: compiles cleanly with -DAMDGPU_TARGETS=gfx950
- llama-bench (Qwen2.5-1.5B Q4_K_M, single GPU):
* f16+FA: 40,013 tok/s prefill, 254 tok/s decode
* q8_0+FA: functional
- Flash attention: works correctly
- MMQ: works correctly with stream-k dispatch
* Remove shfl and AllReduce from backend interface
* move allocation workaround out of ggml-alloc.c
* 2d tensor set/get support
* Fix the seg fault without NCCL
* Apply suggestion from JohannesGaessler
* support for tensor dims % n_devs != 0
* fix view_offs scaling
* arbitrary num. of GPUs/tensor split
* fix compilation
* better granularity estimate
* Support device-specific host buffer types if all underlying backends expose the same type. This allows using pinned memory instead of pageable memory for CUDA.
Fix compilation errors.
* partial Qwen 3 Next support
* Fix qwen3 30b (#8)
* Fix crash with Qwen-30B-A3B Q4_0
Qwen-30B-A3B Q4_0 has an intermediate dimension of 768. Using a granularity of 256 forces an uneven split between GPUs, which is not supported by the current implementation.
* Decide block size based on tensor quantization type
* Fix crashes due to KV cache serialization (#9)
KV cache serialization requires non-zero offsets on the tensor. Add support in the meta backend to set/get a tensor with a non-zero offset.
* metal : fix build (#7)
* static memory allocations, fix usage count
* fix tensor granularity
* more even memory distribution
* use BF16 for allreduce
* rebase fixup
* better error message for unsupported architectures
* Fix device mismatch during scatter of allReduce. (#11)
There is a mismatch between the dst buffer device and the backend device, causing the use of sync copies
* Enable the previous allreduce implementation. It is better in both perf and stability (#12)
* delay AllReduce for Moe for less I/O
* build : clean-up compile warnings
* backend : move most of the meta backend API to ggml-backend-impl.h
* cont : hide unused public API in the implementation
* llama : use llama_device + remove ggml_backend_dev_is_meta()
* ggml-backend : remove unused alloc include
* minor : remove regex include
* ggml : introduce ggml-ext.h for staging new APIs
* rebase fixup
* fix tests
* llama : more robust logic for determining Meta devices (#16)
* llama : more robust logic for determining Meta devices
* cont : fix devs size check
Co-authored-by: Johannes Gäßler <redacted>
* cont : fix log type
Co-authored-by: Johannes Gäßler <redacted>
---------
Co-authored-by: Johannes Gäßler <redacted>
* disable roundtrip for meta backend
* fix arch selection
* Qwen 3.5 support
* fix Gemma 4 MoE
* fix OpenVino, SYCL
* fix test-llama-archs for CPU-only builds
* Fix Qwen 3.5 MoE
* disable meta backend tests for WebGPU
* tests : filter CPU-based devices from the Meta backend tests (#17)
* meta : formatting, naming, indentation (#18)
* formatting : llama-model.cpp
* formatting : ggml-ext.h
* formatting : ggml-backend-meta.cpp
* meta : add TODO
* add documentation
* better error messages
* fix GPT-OSS
---------
Co-authored-by: Carl Philipp Klemm <redacted> Co-authored-by: Gaurav Garg <redacted> Co-authored-by: Georgi Gerganov <redacted>
Daniel Bevenius [Thu, 9 Apr 2026 10:36:29 +0000 (12:36 +0200)]
requirements : update transformers to 5.5.1 (#21617)
* requirements : update transformers to 5.5.0
This commit updates the transformers dependency to version 5.5.0.
The motivation for this is that transformers 5.5.0 includes support for
Gemma4 and is required to be able to convert Gemma4 models. This is also
causing issues for user of gguf-my-repo.
* convert : add ty ignore directives to convert_hf_to_gguf.py
This commit adds `ty: ignore` directives to transformers tokenizers
field/methods to avoid type check errors. There might be better ways to
handle this and perhaps this can be done in a follow up commit.
The motivation for this is that it looks like in transformers 5.5.0
AutoTokenizer.from_pretrained can return generic tokenizer types or None
and the type checker now produces an error when the conversion script
accesses field like tokenizer.vocab.
* convert : add ty ignore to suppress type check errors
* convert : remove incorrect type ignores
* convert : fix remaining python checks
I was running a newer version of ty locally but I've switched to
version 0.0.26 which is what CI uses and I was then able to reproduce
the errors. Sorry about the noise.
Kwa Jie Hao [Thu, 9 Apr 2026 09:28:33 +0000 (17:28 +0800)]
jinja : support ensure_ascii=true, string repetition and int/float self-filtering (#21623)
* feat: jinja engine improvements for reka-edge
Port three Jinja engine improvements needed for the reka-edge model:
1. Python-style string repetition ("ab" * 3 → "ababab")
2. ensure_ascii=true support for tojson filter (escapes non-ASCII to \uXXXX)
3. int() builtin on value_int_t (identity, needed for Reka Edge template)
* fix: escape invalid utf8 bytes when ensure_ascii=true
The json_ensure_ascii_preserving_format function does not correctly
handle an edge case where if UTF-8 parsing fails, it adds the non-ascii
character back to the output as a raw byte.
This commit fixes that by adding the unicode standard replacement
character \\ufffd to the output instead. This is the standard behavior
for various programming languages like Python, Rust, Go, etc.
* chore: address PR comments
1. Add todo comment for supporting string repetition for array/tuples
2. Add support for float identity operation
3. Move invalid ascii test case to test_fuzzing
* chore: accept suggestion for common/jinja/value.cpp
sycl : add flash-attn support for head size 512 (#21654)
* sycl : add flash-attn support for head size 512
This patch extends the SYCL Flash Attention implementation to support head sizes (DKQ/DV) of 512.
Changes:
- Added DKQ/DV 512 cases to both tile and vector Flash Attention kernels.
- Updated kernel selection logic to allow vector kernels for head sizes up to 512 (previously 256).
- Removed unused/redundant AMD and RDNA-specific configuration functions in `fattn-tile.hpp`.
- Refactored `ggml_backend_sycl_buffer_init_tensor` to use a switch statement for clearer tensor extra buffer initialization.
- Added necessary template instances for the new 512 head size across various quantization types.
* remove defunct mxfp4 reorder from setting buffer type
ci: drop v5 `all:` composition from labeler.yml (#21627)
actions/labeler@v6 removed the `all:` / `any:` composition keys.
The `server/webui` and `server` entries used `all:` to combine
`any-glob-to-any-file` with negated `all-globs-to-all-files`,
which now errors on every PR with:
Unknown config options were under "changed-files": all
Flatten both entries to a single `any-glob-to-any-file`. PRs
touching both webui and other server files will now receive both
labels instead of only `server/webui`.
fix: free ctx_copy in ggml_opt_free to plug per-training-session leak (#21592)
* fix: free ctx_copy in ggml_opt_free to plug per-training-session leak
ggml_opt_alloc populates opt_ctx->ctx_copy via a free+init pair every
time the allocated graph shape changes. The last ctx_copy from the
final ggml_opt_alloc call survives until ggml_opt_free is invoked,
but ggml_opt_free was only freeing ctx_static and ctx_cpu, never
ctx_copy. Each opt_ctx lifetime therefore leaks the final per-batch
context — ~900 KB for a typical GNN training session in
sindarin-pkg-tensor, surfaced via AddressSanitizer.
ctx_copy is nullptr-initialized and ggml_free() handles NULL safely,
so the new release is guard-free.
* Update ggml/src/ggml-opt.cpp
Co-authored-by: Johannes Gäßler <redacted>
---------
Co-authored-by: realorko <redacted> Co-authored-by: Johannes Gäßler <redacted>
Daniel Bevenius [Wed, 8 Apr 2026 12:10:33 +0000 (14:10 +0200)]
examples : disable cb_eval callback for --save-logits (#21553)
This commit updates the debug example to not create the
base_callback_data.
The motivation for this is when using `--save-logits`, which is used by
examples/model-conversion scripts, we often don't care about the tensor
outputs and they just add noise to the output. This changes is quiet by
default we can always remove --save-logits to get the tensor outputs
when debugging.
webui: fix syntax highlighting lost after streaming for non-common languages (#21206)
* webui: fix syntax highlighting lost for non-common languages after streaming
rehype-highlight uses lowlight internally, which only bundles 37 "common"
languages. The streaming code path uses highlight.js directly (192 languages),
so languages like Haskell highlight correctly while streaming but lose all
color once the code block closes. Pass the full lowlight language set to
rehype-highlight so both paths support the same languages.
ggml-cuda: ds_read_b128 for q4_0 and q4_1 mmq kernels (#21168)
* ds_read_b128 for q4_0 and q4_1 mmq kernels
Current for loop generates ds_read_b32 instructions with hip compiler, the new solution generates ds_read_b128 instructions for the same operation, saving some LDS bandwidth. Tested on MI50 and RX6800XT, its faster on both.
* Vectorized lds load update: used ggml_cuda_get_max_cpy_bytes and ggml_cuda_memcpy_1 functions for generic implementation
* Explicit for loop in mmq, renamed vec into tmp
* Fixed max_cpy usage in the loading loop
* Fixed typo in q4_1 kernel
* Update ggml/src/ggml-cuda/mmq.cuh
Co-authored-by: Johannes Gäßler <redacted>
* Update ggml/src/ggml-cuda/mmq.cuh
Co-authored-by: Johannes Gäßler <redacted>
* Update ggml/src/ggml-cuda/mmq.cuh
Co-authored-by: Johannes Gäßler <redacted>
* Renoved trailing white line 500
* Update mmq.cuh removed other whitelines
* Remove trailing whitespaces
---------
Co-authored-by: iacopPBK <redacted> Co-authored-by: Johannes Gäßler <redacted> Co-authored-by: iacopPBK <redacted>
Daniel Bevenius [Tue, 7 Apr 2026 19:24:25 +0000 (21:24 +0200)]
gguf-py : fix missing comma after bad merge in tensor-mapping (#21558)
This commit adds a missing comma in the vision encoder attention qkv
block.
The motivation for this change is that without the comma there will be
a string concatenation of the Kimi-K2.5 and the Nemotron Nano v2 VL
tensor mappings which will be broken.
Son H. Nguyen [Tue, 7 Apr 2026 13:13:38 +0000 (22:13 +0900)]
unicode : add custom Qwen2 regex handler to fix segfault on long input (#21257)
* unicode : add custom Qwen2 regex handler to fix segfault on long input
std::regex uses recursive backtracking internally, which causes a stack
overflow (segfault) when tokenizing long sequences of repeated characters
(e.g. 43K 'A's). The Qwen2 tokenizer regex differs from Llama3 only in
the digit pattern (\p{N} vs \p{N}{1,3}), so it was falling through to
the std::regex fallback path instead of using a custom handler.
Add unicode_regex_split_custom_qwen2() following the established pattern
used by gpt2, llama3, kimi_k2, and afmoe custom handlers.
Closes: https://github.com/ggml-org/llama.cpp/issues/21113
* cont : remove TODO comment
* cont : update comment to reflect original regex
* use the correct regex in the comment this time... [no ci]
vulkan: add FA dequant for q4_1, q5_0, q5_1, iq4_nl (#21029)
Add dequantize4() implementations for Q4_1, Q5_0, Q5_1, and IQ4_NL
in the flash attention base shader. Register them in the shader
generator, pipeline creation, and enable in the scalar/coopmat1 FA
support check.
- Add dir='auto' attributes to markdown containers and blocks
- Implement post-processing to add dir='auto' to all text elements
- Replace directional CSS properties with logical properties for proper RTL list alignment
- Ensure bidirectional text support for mixed Arabic/English content
* Clean up commented duplicate function
Remove the commented-out duplicate transformMdastNode function
that was left over from refactoring.
* Fix Arabic RTL text rendering in web UI
- Add dir='auto' attributes to markdown containers and blocks
- Implement post-processing to add dir='auto' to all text elements
- Replace directional CSS properties with logical properties for proper RTL list alignment
- Minor code formatting improvements
This ensures bidirectional text support for mixed Arabic/English content in the llama.cpp web UI.
* Implement rehype plugin for comprehensive RTL text support
- Add rehypeRtlSupport plugin that applies dir='auto' to all elements with children
- Replace DOMParser-based approach with efficient HAST tree processing
- Remove hardcoded element lists for better maintainability
- Ensure proper bidirectional text rendering for mixed RTL/LTR content
* Fix RTL text rendering with rehype plugin and cleanup
Extend the existing reorder optimization to Q8_0. The reorder
separates scale factors from weight data for coalesced memory
access -- was implemented for Q4_0/Q4_K/Q6_K but Q8_0 was missing.
On Arc Pro B70 (Xe2), Q8_0 tg goes from 4.88 to 15.24 t/s (3.1x)
on Qwen3.5-27B. BW utilization: 21% -> 66%.
The key fix beyond the kernels: Q8_0 was missing from the type
check in ggml_backend_sycl_buffer_init_tensor() that allocates
the extra struct carrying the reorder flag -- so the optimization
was silently skipped.
AI (Claude) was used to assist with root cause investigation and
writing the kernel code. All code was human-reviewed and tested
on real hardware.
[CUDA ] Write an optimized flash_attn_stream_k_fixup kernel (#21159)
* Write an optimized flash_attn_stream_k_fixup kernel
Write a specialized and more optimized kernel for cases where nblocks_stream_k is multiple of ntiles_dst.
Make nblocks_stream_k to multiple of ntiles_dst if nblocks_stream_k > 2 * ntiles_dst
* Use the new kernel only for nblocks_stream_k_raw > 4 * ntiles_dst to make sure we have enough concurrency on GPUs
server : handle unsuccessful sink.write in chunked stream provider (#21478)
Check the return value of sink.write() in the chunked content provider
and return false when the write fails, matching cpp-httplib's own
streaming contract. This prevents logging chunks as sent when the sink
rejected them and properly aborts the stream on connection failure.
server : fix logging of build + system info (#21460)
This PR changes the logging that occurs at startup of llama-server.
Currently, it is redundant (including CPU information twice) and it is
missing the build + commit info.
common : fix tool call type detection for nullable and enum schemas (#21327)
* common : fix tool call type detection for nullable and enum schemas
* common, tests : fix grammar delegation for nullable/enum schemas and add tests
Fix enum type inference to scan all enum values (not just index 0) so
schemas like {"enum": [0, "celsius"]} correctly detect string type.
Fix schema_delegates in peg-parser to handle nullable type arrays
(["string", "null"]) and typeless enum schemas in raw mode, allowing
the tagged parser to use raw text instead of JSON-formatted strings.
Add test cases for Qwen3-Coder (TAG_WITH_TAGGED format):
- nullable string ["string", "null"]
- nullable string with null first ["null", "string"]
- nullable integer ["integer", "null"]
- enum without explicit type key
The `HSA_OVERRIDE_GFX_VERSION` variable can be used in ROCm to override an unsupported target architecture with a similar but supported target architecture.
This does not and has never worked on Windows. I think the clarification could avoid driving Windows people towards this solution that does not work.
ggml-zendnn : add MUL_MAT_ID op support for MoE models (#21315)
* ggml-zendnn : add MUL_MAT_ID op support for MoE models
- Add MUL_MAT_ID op acceleration for Mixture-of-Experts models
- MUL_MAT_ID op fallback to CPU backend if total experts > 32
- Point ZenDNN lib to latest bits ZenDNN-2026-WW13
* ggml-zendnn : add braces to sgemm failure condition for consistency
overview / pkg / ggml / sources / llama.cpp / log