* Adjust to pytorch
* Add antialiasing upscale
* Increase number of patches to 1024
* Handle default marker insertion for LFM2
* Switch to flag
* Reformat
* Cuda implementation of antialias kernel
* Change placement in ops.cpp
* consistent float literals
* Pad only for LFM2
* Address PR feedback
* Rollback default marker placement changes
* Fallback to CPU implementation for antialias implementation of upscale
This change limits progress updates to approximately every 0.1% of the
file size to minimize stdio overhead.
Also fixes compiler warnings regarding __func__ in lambdas.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* git mv
* add server-context.h
* add server-context.h
* clean up headers
* cont : cleanup
* also expose server_response_reader (to be used by CLI)
* fix windows build
* decouple server_routes and server_http
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
As [1] explained, the real debug message will be like:
"res operator(): operator() : queue result stop"
Set the name explicitly, the message is easy for debugging:
"res operator(): recv : queue result stop"
The left "operator()" is generated by 'RES_DBG() ... __func__'
[1]: https://clang.llvm.org/extra/clang-tidy/checks/bugprone/lambda-function-name.html
Signed-off-by: Haiyue Wang <haiyuewa@163.com>
gguf_new_metadata.py reads data from reader.
Reader doesn't byteswap tensors to native endianness.
But writer does expect tensors in native endianness to convert them
into requested endianness.
There are two ways to fix this: update reader and do conversion to native endianness and back,
or skip converting endianness in writer in this particular USE-case.
gguf_editor_gui.py doesn't allow editing or viewing tensor data.
Let's go with skipping excessive byteswapping.
If eventually capability to view or edit tensor data is added,
tensor data should be instead byteswapped when reading it.
* ggml : add GGML_SCHED_NO_REALLOC option to disable reallocations in ggml_backend_sched
Enabled in ggml-ci for testing.
* llama : update worst-case graph for unified cache
* ci : disable op offload in some tests
* fix spelling
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : add Anthropic Messages API support
* remove -@pytest.mark.slow from tool calling/jinja tests
* server : remove unused code and slow/skip on test_anthropic_vision_base64_with_multimodal_model in test_anthropic_api.py
* server : removed redundant n field logic in anthropic_params_from_json
* server : use single error object instead of error_array in streaming response handler for /v1/chat/completions and use unordered_set instead of set in to_json_anthropic_stream()
* server : refactor Anthropic API to use OAI conversion
* make sure basic test always go first
* clean up
* clean up api key check, add test
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Qwen3 Next - cleaned up version
* Whitespaces and stuff
* Correct minor errors
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Misc. fixes.
* Clean up code, add missing hybrid qualifier
* Did someone transpose the SOLVE_TRI result matrix? Perhaps...
* Whitespace
* Proper tensors for cb calls
* Use llama-graph.h vertical alignment
* BROKEN: chunking
* Set new tensors as inputs.
* Proper chunk logic
* It's the circle of life...
* More shenanigans for n_seq > 1
* Nail in the coffin?
* Fix Windows build
* Eh, one fails on Windows, the other fails on Mac... just use general capture.
* quant : cleanup
* model : cleanup
* qwen3 : cleanup
* cont : cleanup
* cont : cleanup
* ggml : revert change
* qwen3 : cleanup
* cont : cleanup
* Readd cmath
* qwen3 : fix typo
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Usual suspects
* fix my bad suggestion
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Store the last computed graph and reuse it when possible.
Also do not return response from GRAPH_COMPUTE and assume it always
completes successfully. If this this is not the case, the server closes
the connection. This saves us a network round trip to the server.
* enable mmf for rdna4
* move some mmvf to mmf
* revert lds128 for wmma loading
* Revert "revert lds128 for wmma loading"
This reverts commit db9ae8b6b4.
* Revert "enable mmf for rdna4"
This reverts commit 698c9f2418.
* Revert "move some mmvf to mmf"
This reverts commit 99b92bd665.
* enable mul_mat for rdna4
---------
Co-authored-by: zhang hui <you@example.com>
* Enabled q4_K_4x8 path
* Fixed generic Q4_K 8x4 implementation
* wip: dotprod gemm
* Working arm q4_K dotprod gemm
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Undo acc rename
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Q4_K arm dotprod gemm
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Fix: q4_qs reinterpret from uint to int
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
* Removed comments
* Fixed macro guards
* Fixed unused vars in generic implementation
* Fixed unused vars in 8x4 repack
* Fixed unused vars in generic implementation, unneeded comment
* Missing arch fallback for x86
* minor : style
---------
Signed-off-by: Alberto Cabrera <alberto.cabrera@liquid.ai>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* vulkan: Implement top-k
Each pass launches workgroups that each sort 2^N elements (where N is usually 7-10)
and discards all but the top K. Repeat until only K are left. And there's a fast
path when K==1 to just find the max value rather than sorting.
* fix pipeline selection
* vulkan: Add N-ary search algorithm for topk
* microoptimizations
Aman Gupta
Xuan-Son Nguyen
Adrien Gallouët
Tarek Dakhran
Aaron Teo
Gilad S.
ddh0
Mahekk Shaikh
Acly
Haiyue Wang
Igor Smirnov
Neo Zhang
ixgbe
Ruben Ortlam
Jeff Bolz
Aleksei Nikiforov
DAN™
o7si
Diego Devesa
R0CKSTAR
Fredrik Hultin
Piotr Wilkin (ilintar)
Johannes Gäßler
Radoslav Gerganov
yulo
Neo Zhang Jianyu
Georgi Gerganov
matt23654
Alberto Cabrera Pérez
Eric Curtin
lhez
Han Qingzhe
xctan