| Age | Commit message (Collapse) | Author |
|---|
|
* examples : add passkey test
* passkey : better prints
* passkey : select pass key pos from CLI
* passkey : simplify n_past logic
* make : add passkey target
* passkey : add "self-extend"-like context extension (#4810)
* llama : "self-extend"-like context extension
* passkey : add comment
* passkey : add readme
|
|
* cmake : add helper for faster CUDA builds
* batched : add NGL arg
* ggml : skip nops in compute_forward
* cuda : minor indentation
* cuda : batched cuBLAS GEMMs for src0 F16 and src1 F32 (attention ops)
* Apply suggestions from code review
These changes plus:
```c++
#define cublasGemmBatchedEx hipblasGemmBatchedEx
```
are needed to compile with ROCM. I haven't done performance testing, but it seems to work.
I couldn't figure out how to propose a change for lines outside what the pull changed, also this is the first time trying to create a multi-part review so please forgive me if I mess something up.
* cuda : add ROCm / hipBLAS cublasGemmBatchedEx define
* cuda : add cublasGemmStridedBatchedEx for non-broadcasted cases
* cuda : reduce mallocs in cublasGemmBatchedEx branch
* cuda : add TODO for calling cublas from kernel + using mem pool
---------
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
|
|
* added `llama_model_token_*` variants to all the `llama_token_*` functions.
* added `LLAMA_API`
* formatting
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* removed old `llama_token` functions
* changed 3 more functions to take in model
- `llama_token_get_text`
- `llama_token_get_score`
- `llama_token_get_type`
* added back docs
* fixed main.cpp
* changed token functions to use new model variants
* changed token functions to use new model variants
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
|
|
|
|
* sampling : one sequence per sampling context
ggml-ci
* speculative : add tree-based sampling support
ggml-ci
* speculative : reuse the n_parallel CLI param
* speculative : refactor sampling
* examples : fix build after sampling refactoring
ggml-ci
* batched : fix n_seq_id
* sampling : fix malloc
ggml-ci
* swift : fix build
ggml-ci
* swift : try to fix build
ggml-ci
* prompts : add assistant.txt
* common : add llama_batch_add() and llama_batch_clear() helpers
* speculative : minor refactor
ggml-ci
* minor : comments + rename
ggml-ci
* speculative : fix off-by-one for n_drafted
* speculative : fix the n_drafted fix + p constants
|
|
* batched : add bench tool
* batched : minor fix table
* batched-bench : add readme + n_kv_max is now configurable
* batched-bench : init warm-up batch
* batched-bench : pass custom set of PP, TG and PL
* batched-bench : add mmq CLI arg
|
|
* llama.cpp : split llama_context_params into model and context params
ggml-ci
* fix metal build
* fix freq_base/scale default to model value
* llama-bench : keep the same model between tests when possible
* move n_threads to llama_context_params, add n_threads_batch
* fix mpi build
* remove kv_size(), cuda scratch fixes
* remove low-vram option
* add n_threads_batch to system info, refactor to get_system_info()
* add documentation about --threads-batch to the READMEs
* llama-bench fix
* main : fix rope freq/scale warning
* llama.cpp : add llama_get_model
common : add llama_tokenize from model
* remove duplicated ctx/model functions
ggml-ci
* cuda : print total VRAM used
|
|
* tests : verify that RoPE is "additive"
* llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask)
* ggml : ggml_rope now takes a vector with positions instead of n_past
* metal : add rope_f16 kernel + optimize cpy kernels
* llama : unified KV cache + batch inference API
* llama : add new llama_decode() API that works with llama_batch
* llama : add cell_max heuristic for more efficient kv_cache
* llama : extend llama_kv_cache API
* llama : more robust cell_max heuristic + wip shift
* metal : disable concurrency optimization
* llama : add llama_kv_cache_shift_seq + no more context swaps
* llama : apply K-cache roping for Falcon and Baichuan
* speculative : fix KV cache management
* parallel : example for serving multiple users in parallel
* parallel : disable hot-plug to avoid cache fragmentation
* fixes : speculative KV cache + llama worst-case graph
* llama : extend batch API to select which logits to output
* llama : fix worst case graph build
* ggml-cuda : update rope implementation for parallel decoding (#3254)
* ggml-cuda : update rope implementation for parallel decoding
* better solution for p0 computation
* fix rope
* simpler rope implementation
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* make : add parallel to build + fix static functions in llama.cpp
* simple : fix token counting
* parallel : various improvements
* llama : fix cell_max logic + rename functions
* parallel : try smaller batches when the KV cache is fragmented
* parallel : fix sequence termination criteria
* llama : silence errors KV cache errors
* parallel : remove new line from prompt
* parallel : process system prompt once + configurable paramters + llama API
* parallel : remove question with short answers
* parallel : count cache misses
* parallel : print misses on each request
* parallel : minor
* llama : fix n_kv to never become 0
* parallel : rename hot-plug to continuous-batching
* llama : improve llama_batch API + simplify parallel example
* simple : add parallel decoding support
* simple : improve comments + free batch
* ggml-cuda : add rope f16, restore performance with parallel decoding (#3272)
* ggml-cuda : add rope f16, restore performance
* offload KQ_mask with all models
* fix rope shift
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : disable MPI for now
ggml-ci
* train : make KQ_pos memory buffer permanent via dummy scale op
* ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275)
ggml-ci
* parallel : fix bug (extra BOS) + smaller token_prev array
* parallel : fix cases where the input prompts can overflow the batch
* parallel : add disabled experimental batch chunking in powers of two
* llama : llama.h formatting + comments
* simple : add README.md
* llama : fix kv cache heuristic when context is less than 32
* parallel : fix crash when `-n -1`
* llama : simplify returns if/else branches
* metal : use mm kernels for batch size > 2
* examples : utilize new llama_get_logits_ith()
* examples : add example for batched decoding
* examples : do not eval prompt 2 times (close #3348)
* server : clear the KV cache beyond n_past before llama_decode
* server : avoid context swaps by shifting the KV cache
---------
Co-authored-by: slaren <slarengh@gmail.com>
|
