mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-01 15:40:21 +01:00
1c641e6aac
* `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew
* server: update refs -> llama-server
gitignore llama-server
* server: simplify nix package
* main: update refs -> llama
fix examples/main ref
* main/server: fix targets
* update more names
* Update build.yml
* rm accidentally checked in bins
* update straggling refs
* Update .gitignore
* Update server-llm.sh
* main: target name -> llama-cli
* Prefix all example bins w/ llama-
* fix main refs
* rename {main->llama}-cmake-pkg binary
* prefix more cmake targets w/ llama-
* add/fix gbnf-validator subfolder to cmake
* sort cmake example subdirs
* rm bin files
* fix llama-lookup-* Makefile rules
* gitignore /llama-*
* rename Dockerfiles
* rename llama|main -> llama-cli; consistent RPM bin prefixes
* fix some missing -cli suffixes
* rename dockerfile w/ llama-cli
* rename(make): llama-baby-llama
* update dockerfile refs
* more llama-cli(.exe)
* fix test-eval-callback
* rename: llama-cli-cmake-pkg(.exe)
* address gbnf-validator unused fread warning (switched to C++ / ifstream)
* add two missing llama- prefixes
* Updating docs for eval-callback binary to use new `llama-` prefix.
* Updating a few lingering doc references for rename of main to llama-cli
* Updating `run-with-preset.py` to use new binary names.
Updating docs around `perplexity` binary rename.
* Updating documentation references for lookup-merge and export-lora
* Updating two small `main` references missed earlier in the finetune docs.
* Update apps.nix
* update grammar/README.md w/ new llama-* names
* update llama-rpc-server bin name + doc
* Revert "update llama-rpc-server bin name + doc"
This reverts commit
|
||
|---|---|---|
| .. | ||
| CMakeLists.txt | ||
| eval-callback.cpp | ||
| README.md | ||
llama.cpp/examples/eval-callback
A simple example which demonstrates how to use callback during the inference. It simply prints to the console all operations and tensor data.
Usage:
llama-eval-callback \
--hf-repo ggml-org/models \
--hf-file phi-2/ggml-model-q4_0.gguf \
--model phi-2-q4_0.gguf \
--prompt hello \
--seed 42 \
-ngl 33
Will print:
llm_load_tensors: offloaded 33/33 layers to GPU
...
llama_new_context_with_model: n_ctx = 512
...
llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB
llama_new_context_with_model: graph nodes = 1225
llama_new_context_with_model: graph splits = 2
ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1}
[
[
[ -0.0181, 0.0272, 0.0272, ...],
],
]
ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
[
[
[ -0.6989, 1.0636, 1.0636, ...],
],
]
ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
[
[
[ -0.1800, 0.2817, 0.2632, ...],
],
]
ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
[
[
[ -0.1863, 0.2970, 0.2604, ...],
],
]
ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1}
[
[
[ -1.1238, 1.2876, -1.8086, ...],
],
]
ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
],
]
ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
],
]
ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
],
]
ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
[ -0.3608, 0.5076, -1.8866, ...],
[ 1.7643, 0.0273, -2.1065, ...],
...
],
]
ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1}
[
[
[ -1.1135, 1.4604, -1.9226, ...],
[ -0.3608, 0.5076, -1.8866, ...],
[ 1.7643, 0.0273, -2.1065, ...],
...
],
]