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5cdb27e091
* examples/finetune -opt SGD (stochastic gradient descent) memory opt
add unit tested GGML_OPT_OPTIMIZER_SGD to ggml - avoids allocating
m, v tensors.
support finetune.cpp arg -opt SGD (or sgd). (default adamw as before)
llama 3.2-1b-F32 result: observed 11gb gpu ram (41 sec/epoch)
when using SGD instead of 19gb (55 sec/epoch) using adamw.
(wikipedia 100 lines finetune)
(
using the same GPU memory, adamw can only do before OOM 512
batch/context, reaching:
train: [███████▉] data=0000140/0000140 loss=0.02575±0.00099 acc=99.52±0.03% t=00:00:47 ETA=00:00:00
val: [███████▉] data=0000008/0000008 loss=4.76565±0.28810 acc=41.46±0.77% t=00:00:00 ETA=00:00:00
SGD is superior, though it converges slower, with max before OOM 1728
batch/context (esp see the better validation perf):
train: [███████▉] data=0000039/0000039 loss=0.00371±0.00010 acc=99.96±0.01% t=00:00:41 ETA=00:00:00
val: [███████▉] data=0000003/0000003 loss=5.11406±0.76034 acc=48.01±0.69% t=00:00:01 ETA=00:00:00
)
note: when finetuning long enough (or w/ enough -lr),
validation accuracy *eventually* drops ('catastrophic forgetting')
-lr-half (halflife) option useful for SGD to avoid oscillation or
super slow underdamped learning (makes setting -lr more forgiving).
terminal -lr for now is set by lr-halvings i.e. if you want at most
1/8 the inital -lr you set -lr-halvings 3.
note: objective loss not directly comparable between adamw, sgd? -
check perplexity or accuracy or consider relative improvements
for convergence
new finetune args -wd 1e-9 to enable weight decay in sgd or adamw,
and max -epochs N (default 2 as before)
cache (1 - wd*alpha) in 'adamw' opt struct -
no noticeable perf benefit, disabled (still done
for new SGD though)
since opt. memory is pre-allocated, the ggml_opt_get_optimizer_params
would probably be able to change between SGD and AdamW with each epoch
but would need to use adamw for the first (unconfirmed - no cmdline arg
to set such a policy yet)
test-opt checks adamw as before and now sgd (except for a few disabled
tests for sgd only; probably just needs logging values and adding
alternate reference values); tolerance on the 'regression'
test is broader for sgd (so we don't need many more epochs)
* Vulkan: Implement GGML_OP_OPT_STEP_SGD
* tests: Fix OPT_STEP_SGD test-backend-ops
* SGD op param store weight-decay and not 1-alpha*wd
* minor + cosmetic changes
* fix vulkan sgd
* try CI fix
---------
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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|---|---|---|
| .. | ||
| batched | ||
| batched.swift | ||
| convert-llama2c-to-ggml | ||
| deprecation-warning | ||
| diffusion | ||
| embedding | ||
| eval-callback | ||
| gen-docs | ||
| gguf | ||
| gguf-hash | ||
| gritlm | ||
| jeopardy | ||
| llama.android | ||
| llama.swiftui | ||
| lookahead | ||
| lookup | ||
| parallel | ||
| passkey | ||
| retrieval | ||
| save-load-state | ||
| simple | ||
| simple-chat | ||
| simple-cmake-pkg | ||
| speculative | ||
| speculative-simple | ||
| sycl | ||
| training | ||
| chat-13B.bat | ||
| chat-13B.sh | ||
| chat-persistent.sh | ||
| chat-vicuna.sh | ||
| chat.sh | ||
| CMakeLists.txt | ||
| convert_legacy_llama.py | ||
| json_schema_pydantic_example.py | ||
| json_schema_to_grammar.py | ||
| llama.vim | ||
| llm.vim | ||
| Miku.sh | ||
| pydantic_models_to_grammar.py | ||
| pydantic_models_to_grammar_examples.py | ||
| reason-act.sh | ||
| regex_to_grammar.py | ||
| server-llama2-13B.sh | ||
| server_embd.py | ||
| ts-type-to-grammar.sh | ||