Hangs were reported on Jetson Orin AGX if we set CUDA_SCALE_LAUNCH_QUEUES=4x. Reverting the previous PR (#19042) and updating the document to consider setting CUDA_SCALE_LAUNCH_QUEUES=4x for faster throughput on multi-GPU systems.
* jinja : add missing 'in' test to template engine (#19004)
The jinja template parser was missing the 'in' test from
global_builtins(), causing templates using reject("in", ...),
select("in", ...), or 'x is in(y)' to fail with
"selectattr: unknown test 'in'".
This broke tool-calling for Qwen3-Coder and any other model
whose chat template uses the 'in' test.
Added test_is_in supporting array, string, and object containment
checks, mirroring the existing 'in' operator logic in runtime.cpp.
Includes test cases for all three containment types plus
reject/select filter usage.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* reuse test_is_in in binary op
---------
Co-authored-by: Sid Mohan <sidmohan0@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Experimenting with AI, my environment gets messy fast and it's not
always easy to know what model my software is trying to load. This helps
with troubleshooting.
before:
Error: {
code = 400,
message = "model not found",
type = "invalid_request_error"
}
After:
Error: {
code = 400,
message = "model 'toto' not found",
type = "invalid_request_error"
}
This commit adds a debug option to the model conversion script to enable
using the Python debugger (pdb) during model conversion.
The motivation for this is that I've found myself adding this a few
times now and it would be quicker to have this flag as an option and a
makefile target/recipe for it.
* Remove mutex for pipeline caches, since they are now per-thread.
* Add comment
* Run clang-format
* Cleanup
* Run CI again
* Run CI once more
* Run clang-format
The allowUnfreePredicate in pkgsCuda was wrapping p.meta.license in a
list unconditionally. This fails when meta.license is already a list
of licenses, as it creates a nested list and then tries to access
.free and .shortName on the inner list.
Use lib.toList instead, which correctly handles both cases:
- Single license attrset -> wraps in list
- List of licenses -> returns unchanged
Without this I get:
> * Getting build dependencies for wheel...
> * Building wheel...
> Successfully built gguf-0.17.1-py3-none-any.whl
> Finished creating a wheel...
> Finished executing pypaBuildPhase
> Running phase: pythonRuntimeDepsCheckHook
> Executing pythonRuntimeDepsCheck
> Checking runtime dependencies for gguf-0.17.1-py3-none-any.whl
> - requests not installed
For full logs, run:
nix log /nix/store/x0c4a251l68bvdgang9d8v2fsmqay8a4-python3.12-gguf-0.0.0.drv
I changed a bit the style to make it more terse ~> more elegant in my
opinion.
* wip
* ggml-hexagon: add vectorized dot product function for FP32 and FP16 accumulation
* ggml-hexagon: optimize dot product functions for FP16 and FP32 with new vectorized implementations
* wip
* ggml-hexagon: optimize hvx_vec_dump_f32_n and hvx_vec_reduce_sum_qf32x2 functions for improved performance
* ggml-hexagon: refactor dot product functions to use a common loading function for improved readability
* optimize vector dot product functions to use unified reduction for improved performance
* wip
* ggml-hexagon: add vectorized dot product function for FP32 and FP16 accumulation
* ggml-hexagon: optimize dot product functions for FP16 and FP32 with new vectorized implementations
* wip
* ggml-hexagon: optimize hvx_vec_dump_f32_n and hvx_vec_reduce_sum_qf32x2 functions for improved performance
* ggml-hexagon: refactor dot product functions to use a common loading function for improved readability
* optimize vector dot product functions to use unified reduction for improved performance
* hexagon: optimize reduce-sum for v75+
* hexagon: always keep row_sums in sf/fp32
* ggml-hexagon: enhance directory checks for HEXAGON_SDK_ROOT and HEXAGON_TOOLS_ROOT
* fix compiling error after rebase
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* add option --tensor-type-file to llama-quantize, but it raises an error.
* add error message when file not found
* quantize: update help menu, fix CI
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
---------
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Aaron Teo <aaron.teo1@ibm.com>
* lookup, lookahead: fix crash when n_ctx not specified
Since PR #16653 (Dec 15, 2025), the default n_ctx is 0 to enable automatic
GPU memory fitting. This causes llama-lookup and llama-lookahead to crash
when run without explicit -c flag:
GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded")
Root cause: Both examples use params.n_ctx directly for batch initialization,
but params.n_ctx remains 0 even after the context is properly initialized
to n_ctx_train internally.
Bug history:
- Nov 2023: lookahead.cpp created (PR #4207) with params.n_ctx pattern
- Dec 2023: lookup.cpp created (PR #4484) with same pattern
- Nov 2024: default n_ctx changed to 4096 (PR #10136) - bug dormant
- Dec 2025: default n_ctx changed to 0 (PR #16653) - bug activated
The bug was dormant for 2+ years because params.n_ctx defaulted to 512,
then 4096. PR #16653 changed it to 0 for GPU auto-fitting, triggering
the crash.
Fix: Use llama_n_ctx(ctx) to get the actual runtime context size, matching
the pattern already used elsewhere in lookup.cpp (line 72) and in
speculative.cpp/speculative-simple.cpp.
Tested: llama-lookup now works without -c flag (12.5% acceptance on
Gemma-3-1B).
Note: llama-lookahead has a separate pre-existing issue with sequence
initialization (n_seq_max=1 vs W+G+1 needed) that is unrelated to this fix.
* lookahead: fix n_seq_max and kv_unified configuration
Lookahead decoding requires:
- W + G + 1 = 31 sequences for parallel Jacobi decoding
- Unified KV cache for coupled sequences in batch splitting
These requirements were broken after PR #14482 changed validation logic.
Consolidates fix from PR #18730 per maintainer request.
Commit message drafted with Claude.
* Add Q8_0 OpenCL kernel
Co-authored-by: yunjie <yunjie@qti.qualcomm.com>
* opencl: fix build for non-adreno
* opencl: refactor q8_0
* opencl: enforce subgroup size of 64 for adreno for q8_0
* For A750 and older generations, subgroup size can be 64 or 128.
This kernel assumes subgroup size 64.
* opencl: suppress warning when adreno kernels are disabled
---------
Co-authored-by: yunjie <yunjie@qti.qualcomm.com>
Co-authored-by: Li He <lih@qti.qualcomm.com>
On macos Sequoia 15.7.3, x86_64, the build has recently started failing with
```
In file included from .../code/cpp/llama.cpp/common/jinja/string.cpp:2:
.../code/cpp/llama.cpp/common/./jinja/value.h:478:10: error: no template named 'unordered_map' in namespace 'std'
478 | std::unordered_map<value, value, value_hasher, value_equivalence> unordered;
| ~~~~~^
In file included from .../code/cpp/llama.cpp/common/jinja/caps.cpp:1:
.../code/cpp/llama.cpp/common/jinja/value.h:478:10: error: no template named 'unordered_map' in namespace 'std'
478 | std::unordered_map<value, value, value_hasher, value_equivalence> unordered;
| ~~~~~^
In file included from .../code/cpp/llama.cpp/common/jinja/value.cpp:1:
In file included from .../code/cpp/llama.cpp/common/jinja/runtime.h:4:
.../code/cpp/llama.cpp/common/jinja/value.h:478:10: error: no template named 'unordered_map' in namespace 'std'
478 | std::unordered_map<value, value, value_hasher, value_equivalence> unordered;
[...]
```
After a bit of digging to make sure all the appropriate flags were used, I notifced that the necessary header was not included. This fixes the build for me and should not affect negatively other builds that for some reasons were already succeeding
