We accept an array of strings and numbers representing tokens,
in addition to the current string valued prompt or content.
This allows direct token input, so that any special tokens
can be processed and used at the frontend during the construction
of the json data, before sending to the server. And the server
does not need to know or parse special tokens from textual input.
With this, we can use EOS and BOS used in llama-2-chat models.
* metal: use uint16_t instead of uint8_t.
Apple GPU doesn't like uint8_t. For every operation on uint8_t
the gpu need to copy the uint8_t to an empty 16 bit register, then
it can issue other instructions.
For the matrix-vector multiplication kernel only, we observed a
340~350 GB/s memory read speed on M1 Max after this commit, which is
very close to the reported hardware limit.
* metal: update rms_norm kernel
This commit double the speed of rms_norm operations by using 512 threads
per threadgroup, combining with SIMD primitives to minimize the need for
thread group barriers.
* metal: use template to reduce size
Revert modifications on block_q4_0 and block_q4_1.
* ci : run ctest
ggml-ci
* ci : add open llama 3B-v2 tests
ggml-ci
* ci : disable wget progress output
ggml-ci
* ci : add open llama 3B-v2 tg tests for q4 and q5 quantizations
ggml-ci
* tests : try to fix tail free sampling test
ggml-ci
* ci : add K-quants
ggml-ci
* ci : add short perplexity tests
ggml-ci
* ci : add README.md
* ppl : add --chunks argument to limit max number of chunks
ggml-ci
* ci : update README
* Implement customizable RoPE
The original RoPE has pre-defined parameters
theta_i = 10000^(−2(i−1)/d), for i in [1, 2, ..., d/2]
Our customizable RoPE, ggml_rope_custom_inplace, uses
theta_i = scale * base^(−2(i−1)/d), for i in [1, 2, ..., d/2]
with the default matches the original
scale = 1.0
base = 10000
The new command line arguments
--rope-freq-base
--rope-freq-scale
set the two new RoPE parameter.
Recent researches show changing these two parameters extends the context limit with minimal loss.
1. Extending Context to 8K
kaiokendev
https://kaiokendev.github.io/til#extending-context-to-8k
2. Extending Context Window of Large Language Models via Positional Interpolation
Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian
https://arxiv.org/abs/2306.15595
3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation.
https://www.reddit.com/user/bloc97https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
For the bold, try adding the following command line parameters to your favorite model:
-c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5
* ggml-metal: fix custom rope
* common: fix argument names in help
* llama: increase MEM_REQ_EVAL for MODEL_3B
It avoids crashing for quantized weights on CPU.
Better ways to calculate the required buffer size would be better.
* llama: make MEM_REQ_EVAL depend on n_ctx
* server: use proper Content-Type in curl examples
Without the header Content-Type: application/json, curl will POST with
Content-Type: application/x-www-form-urlencoded
Though our simple server doesn't care, the httplib.h used has a limit
with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192
With Content-Type: application/json, we can send large json data.
* style : minor fixes, mostly indentations
* ggml : fix asserts
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* 3-5% faster Q4_0 on Metal
* 7-25% faster Q4_1 on Metal
* Oops, forgot to delete the original Q4_1 kernel
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Initial implementation
* Remove debug print
* Restore signature of llama_init_from_gpt_params
* Free guidance context
* Make freeing of guidance_ctx conditional
* Make Classifier-Free Guidance a sampling function
* Correct typo. CFG already means context-free grammar.
* Record sampling time in llama_sample_classifier_free_guidance
* Shift all values by the max value before applying logsoftmax
* Fix styling based on review
* This allows LLAMA models that were previously incompatible with K quants to function mostly as normal. This happens when a model has a vocab != 32000, e.g 32001 which means it's not divisible by 256 or 64. Since the problematic dimensions only apply for `tok_embeddings.weight` and `output.weight` (dimentions 4096 x n_vocab), we can simply quantize these layers to Q8_0 whereas the majority of the hidden layers are still K-quanted since they have compatible dimensions.
* Fix indentation
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* As an alternative, to avoid failing on Metal due to lack of Q8_0 support, instead quantize tok_embeddings.weight to Q4_0 and retain output.weight as F16. This results in a net gain of about 55mb for a 7B model compared to previous approach, but should minimize adverse impact to model quality.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* MPI support, first cut
* fix warnings, update README
* fixes
* wrap includes
* PR comments
* Update CMakeLists.txt
* Add GH workflow, fix test
* Add info to README
* mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099)
* mpi : add names for layer inputs + prep ggml_mpi_graph_compute()
* mpi : move all MPI logic into ggml-mpi
Not tested yet
* mpi : various fixes - communication now works but results are wrong
* mpi : fix output tensor after MPI compute (still not working)
* mpi : fix inference
* mpi : minor
* Add OpenMPI to GH action
* [mpi] continue-on-error: true
* mpi : fix after master merge
* [mpi] Link MPI C++ libraries to fix OpenMPI
* tests : fix new llama_backend API
* [mpi] use MPI_INT32_T
* mpi : factor out recv / send in functions and reuse
* mpi : extend API to allow usage with outer backends (e.g. Metal)
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
The file pathing is significant when running models inside of Termux on Android devices. llama.cpp performance is improved with loading a .bin from the $HOME directory.
