* Add block interleaving support for Q4_K quantization
* Remove whitespaces and fix CI/CD issues
* Update pointer of bsums from int16_t to const int16_t
* Add vector version of quantize_q8_K_4x8 function
* Update code formatting based on review comments
tokenizer.added_tokens_decoder returns a fresh dict every time relatively slowly (~0.04s on average) which results in massive slowdowns when we have a huge number of added tokens
- Find out active blocks per SM using cudaOccupancyMaxActiveBlocksPerMultiprocessor API. Use this value to determine the optimal parallel_blocks value.
- Prefer vector flash attention kernels over MMA kernel for BS=1
Fixes Issue: #12182
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Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* ci: add visionOS build workflow
Add a new GitHub Actions workflow for building on visionOS with CMake and Xcode.
* ggml: Define _DARWIN_C_SOURCE for visionOS to fix missing u_xxx typedefs
* ci: remove define hacks for u_xxx system types
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Co-authored-by: Giovanni Petrantoni <7008900+sinkingsugar@users.noreply.github.com>
* Add support for GPT2, Bloom and CodeShell tied word embeddings
* Deduplicate tied word embeddings weights
* Workaround for incorrect weight map
It appears transformer.wte.weight is in the weight map even though the weights are not there, remove it if output weights are encountered first.
* check++
* fatfingers--
I've been seeing significantly worse performance for tg with flash attention
enabled vs disabled, and it seems to be related to the submit heuristic.
Change the heuristic to check how many bytes worth of weight matrix are
used and flush every 100MB, and ramp up after the first few submits.
This seems to resolve the issue, and also increases perf for non-FA a bit.
* opencl: more profiling timing
* opencl: generate trace for profiling
* opencl: reduce profiling overhead
* Populate profiling timing info at the end rather than after each
kernel run
* opencl: fix for chrome tracing
* graph : normalize Q, K, V shapes and add comments
ggml-ci
* context : synchronize before getting cross attention data
* model : fix command-r attention norm check
* Enable CUDA Graph on CTK < 12.x
`cudaGraphExecUpdate` API was changed on 12.x. For this reason CUDA graph support was disabled on older CUDA toolkit. This change enables CUDA support in CTK version < 12.x by using older API if CTK < 12.x.
* Fix compilation errors with MUSA
* Disable CUDA Graph for MUSA
* cmake: Factor out compiler flag function from ggml
llama.cpps's build requires it, too, and we may want to make use of it
without add_subdirectory(ggml).
* cmake: Enable building against system ggml
This facilitates package maintenance for Linux distributions, where the
libggml library most likely will be shipped as an individual package
upon which a llama.cpp package depends.
This commit adds the --symlinks option to the zip command used to create
the xcframework zip file. This is necessary to create symlinks in the
zip file. Without this option, the Versions symlink is stored as a
regular directory entry in the zip file, rather than as a symlink in the
zip which causes the followig error in xcode:
```console
Couldn't resolve framework symlink for '/Users/danbev/work/ai/llama.cpp/tmp_1/build-apple/llama.xcframework/macos-arm64_x86_64/llama.framework/Versions/Current': readlink(/Users/danbev/work/ai/llama.cpp/tmp_1/build-apple/llama.xcframework/macos-arm64_x86_64/llama.framework/Versions/Current): Invalid argument (22)
```
Refs: https://github.com/ggml-org/llama.cpp/pull/11996#issuecomment-2727026377
* added -o option to specify an output file name
* llama-tts returns ENOENT in case of file write error
note : PR #12042 is closed as superseded with this one.
* llama : introduce llama_set_warmup() API call that controls warmup mode; use all MoE experts during warmup
* common : use new API to enable warmup mode during model warmup
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
