Pass ThreadingContext instead of Pools/Profiler individually, for access to Zones
Add GCPP_ZONE helper
Add Caller argument to pool.Run to enable new stats
Remove most direct dependencies on ThreadPool, prefer ParallelFor
PiperOrigin-RevId: 822934530
Previously, this happened concurrently with the matmul autotune, which could lead to incorrect outcomes.
threading: de-singleton Pinning (no longer stores affinity); pass PoolWorkerMapping; fix Pool dtor order
Also enable SPR target (Zen4 is AMD-only),
update Highway version for renamed Thread()->GlobalIdx().
PiperOrigin-RevId: 816223017
Lift DecompressA out of main autotuner to prevent interference
Also use kMaxNR / kNR constants instead of extra args
Fix: only require vector alignment, not cache alignment
PiperOrigin-RevId: 804333769
remove MatMul f32 special case (smaller code),
types: Add u32/u64 for use by Activations
move renamed ParallelismStrategy to threading_context so can pass ctx
ensure worker index is unique across clusters
matmul.h: const member functions for renamed policy classes (easier to call)
PiperOrigin-RevId: 802848086
use new ThreadingContext2 instead of monostate/init in each frontend
Add ThreadingArgs(replaces AppArgs)
backprop: use Packed() accessor and MakePacked factory and row-based access to allow for stride
compress_weights: remove, moving to py-only exporter instead
Move MatPtr to mat.h and revise interface:
- Generic MatOwner
- rename accessors to Packed*
- support stride/row accessors, fix RowPtr stride
Add TypeBits(Type)
Move GenerateMat to test_util-inl for sharing between matmul test/bench
Move internal init to gemma.cc to avoid duplication
Rename GemmaEnv model_ to gemma_ for disambiguating vs upcoming ModelStorage
Remove --compressed_weights, use --weights instead.
tensor_index: add ExtentsFromInfo and TensorIndexLLM/Img
Allocator: use normal unique_ptr for AllocBytes so users can call directly
threading: use -> because AlignedPtr no longer assumes arrays
PiperOrigin-RevId: 745918637
Jan Wassenberg
The gemma.cpp Authors