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import os |
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import pytest |
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from tests.utils import wrap_test_forked |
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@pytest.mark.skipif(not os.getenv('BENCHMARK'), |
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reason="Only valid on sufficiently large system and not normal part of testing." |
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" Instead used to get eval scores for all models.") |
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@pytest.mark.parametrize( |
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"base_model", |
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[ |
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"h2oai/h2ogpt-oasst1-falcon-40b", |
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"h2oai/h2ogpt-oig-oasst1-512-6_9b", |
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"h2oai/h2ogpt-oig-oasst1-512-12b", |
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"h2oai/h2ogpt-oig-oasst1-512-20b", |
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"h2oai/h2ogpt-oasst1-512-12b", |
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"h2oai/h2ogpt-oasst1-512-20b", |
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"h2oai/h2ogpt-gm-oasst1-en-1024-20b", |
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"databricks/dolly-v2-12b", |
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"h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2", |
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"ehartford/WizardLM-7B-Uncensored", |
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"ehartford/WizardLM-13B-Uncensored", |
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"AlekseyKorshuk/vicuna-7b", |
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"TheBloke/stable-vicuna-13B-HF", |
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"decapoda-research/llama-7b-hf", |
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"decapoda-research/llama-13b-hf", |
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"decapoda-research/llama-30b-hf", |
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"junelee/wizard-vicuna-13b", |
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"openaccess-ai-collective/wizard-mega-13b", |
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] |
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) |
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@wrap_test_forked |
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def test_score_eval(base_model): |
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from src.gen import main |
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main( |
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base_model=base_model, |
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chat=False, |
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stream_output=False, |
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gradio=False, |
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eval_prompts_only_num=500, |
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eval_as_output=False, |
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num_beams=2, |
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use_gpu_id=False, |
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) |
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@pytest.mark.skipif(not os.getenv('FALCONS'), reason="download purpose") |
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@pytest.mark.parametrize( |
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"base_model", |
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[ |
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"OpenAssistant/falcon-7b-sft-top1-696", |
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"OpenAssistant/falcon-7b-sft-mix-2000", |
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"h2oai/h2ogpt-oasst1-falcon-40b", |
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"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1", |
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"h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2", |
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"h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b", |
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"OpenAssistant/falcon-40b-sft-top1-560", |
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"OpenAssistant/falcon-40b-sft-mix-1226", |
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] |
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) |
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@wrap_test_forked |
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def test_get_falcons(base_model): |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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t = AutoTokenizer.from_pretrained(base_model, |
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use_fast=False, |
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padding_side="left", |
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trust_remote_code=True, |
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token=True, |
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) |
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assert t is not None |
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m = AutoModelForCausalLM.from_pretrained(base_model, |
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trust_remote_code=True, |
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torch_dtype=torch.float16, |
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token=True, |
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) |
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assert m is not None |
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@pytest.mark.skipif(not os.getenv('LLAMA'), reason="LLaMa conversion") |
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@wrap_test_forked |
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def test_get_landmark_llama(): |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from transformers import LlamaForCausalLM, LlamaTokenizer |
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m = LlamaForCausalLM.from_pretrained("epfml/landmark-attention-llama7b-wdiff") |
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t = LlamaTokenizer.from_pretrained("epfml/landmark-attention-llama7b-wdiff") |
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assert m is not None and t is not None |
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os.system(""" |
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# |
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# step 1, convert llama to HF format |
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pip install protobuf==3.19.0 |
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source ~/.bashrc.mamba |
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mamba create -n trans |
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conda activate trans |
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conda install python=3.10 -y |
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git clone https://github.com/epfml/landmark-attention.git |
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pip install fire datasets |
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git clone https://github.com/huggingface/transformers.git |
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cd transformers |
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pip install . |
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pip install torch accelerate sentencepiece protobuf==3.19.0 |
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# below requires LLaMa weights |
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python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir /data/jon/LLaMA --model_size 7B --output_dir llama_7B |
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# |
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# step 2, make landmark model (change hash if updated) |
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mkdir -p epfml/landmark-attention-llama7b-wdiff |
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cd epfml/landmark-attention-llama7b-wdiff |
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ln -s ~/.cache/huggingface/hub/models--epfml--landmark-attention-llama7b-wdiff/snapshots/050562871ac72723b4ab674f0392b02cd9609842/* . |
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cd ../../ |
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python ../landmark-attention/llama/weight_diff.py recover --path_raw llama_7B --path_diff epfml/landmark-attention-llama7b-wdiff --path_tuned landmark_llama_7b |
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""") |
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