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from __future__ import annotations |
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import sys |
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from pathlib import Path |
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import numpy as np |
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import torch |
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import lm_eval.tasks as tasks |
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from lm_eval.api.instance import Instance |
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from lm_eval.models.huggingface import HFLM |
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task_manager = tasks.TaskManager() |
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class Test_HFLM: |
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torch.use_deterministic_algorithms(True) |
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task_list = task_manager.load_task_or_group(["arc_easy", "gsm8k", "wikitext"]) |
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version_minor = sys.version_info.minor |
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multiple_choice_task = task_list["arc_easy"] |
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multiple_choice_task.build_all_requests(limit=10, rank=0, world_size=1) |
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MULTIPLE_CH: list[Instance] = multiple_choice_task.instances |
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generate_until_task = task_list["gsm8k"] |
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generate_until_task._config.generation_kwargs["max_gen_toks"] = 10 |
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generate_until_task.build_all_requests(limit=10, rank=0, world_size=1) |
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generate_until: list[Instance] = generate_until_task.instances |
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rolling_task = task_list["wikitext"] |
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rolling_task.build_all_requests(limit=10, rank=0, world_size=1) |
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ROLLING: list[Instance] = rolling_task.instances |
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MULTIPLE_CH_RES = [ |
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-41.902435302734375, |
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-42.939308166503906, |
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-33.914180755615234, |
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-37.07139205932617, |
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-22.95258331298828, |
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-20.342208862304688, |
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-14.818366050720215, |
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-27.942853927612305, |
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-15.80704116821289, |
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-15.936427116394043, |
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-13.052018165588379, |
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-18.04828453063965, |
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-13.345029830932617, |
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-13.366025924682617, |
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-12.127134323120117, |
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-11.872495651245117, |
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-47.10598373413086, |
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-47.76410675048828, |
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-36.4406852722168, |
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-50.0289421081543, |
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-16.72093963623047, |
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-18.535587310791016, |
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-26.46993637084961, |
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-20.355995178222656, |
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-17.757919311523438, |
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-21.80595588684082, |
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-33.1990852355957, |
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-39.28636932373047, |
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-14.759679794311523, |
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-16.753942489624023, |
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-11.486852645874023, |
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-15.42177677154541, |
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-13.15798282623291, |
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-15.887393951416016, |
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-15.28614616394043, |
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-12.339089393615723, |
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-44.59441375732422, |
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-55.40888214111328, |
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-52.70050811767578, |
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-56.25089645385742, |
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] |
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generate_until_RES = [ |
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" The average of $2.50 each is $", |
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" A robe takes 2 bolts of blue fiber and half", |
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" $50,000 in repairs.\n\nQuestion", |
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" He runs 1 sprint 3 times a week.", |
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" They feed each of her chickens three cups of mixed", |
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" The price of the glasses is $5, but", |
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" The total percentage of students who said they like to", |
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" Carla is downloading a 200 GB file. Normally", |
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" John drives for 3 hours at a speed of 60", |
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" Eliza sells 4 tickets to 5 friends so she", |
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] |
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ROLLING_RES = [ |
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-3603.6328125, |
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-19779.23974609375, |
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-8834.16455078125, |
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-27967.591796875, |
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-7636.794982910156, |
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-9491.93505859375, |
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-41043.4248046875, |
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-8397.689819335938, |
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-45969.47155761719, |
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-7158.90625, |
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] |
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LM = HFLM(pretrained="EleutherAI/pythia-70m", device="cpu", dtype="float32") |
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def test_logliklihood(self) -> None: |
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res = self.LM.loglikelihood(self.MULTIPLE_CH) |
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_RES, _res = self.MULTIPLE_CH_RES, [r[0] for r in res] |
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dir_path = Path("test_logs") |
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dir_path.mkdir(parents=True, exist_ok=True) |
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file_path = dir_path / f"outputs_log_{self.version_minor}.txt" |
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file_path = file_path.resolve() |
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with open(file_path, "w") as f: |
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f.write("\n".join(str(x) for x in _res)) |
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assert np.allclose(_res, _RES, atol=1e-2) |
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argmax_RES, argmax_res = ( |
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np.argmax(np.array(_RES).reshape(-1, 4), axis=1), |
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np.argmax(np.array(_res).reshape(-1, 4), axis=1), |
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) |
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assert (argmax_RES == argmax_res).all() |
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def test_generate_until(self) -> None: |
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res = self.LM.generate_until(self.generate_until) |
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assert res == self.generate_until_RES |
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def test_logliklihood_rolling(self) -> None: |
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res = self.LM.loglikelihood_rolling(self.ROLLING) |
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assert np.allclose(res, self.ROLLING_RES, atol=1e-1) |
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def test_toc_encode(self) -> None: |
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res = self.LM.tok_encode("foo bar") |
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assert res == [12110, 2534] |
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def test_toc_decode(self) -> None: |
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res = self.LM.tok_decode([12110, 2534]) |
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assert res == "foo bar" |
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def test_batch_encode(self) -> None: |
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res = self.LM.tok_batch_encode(["foo bar", "bar foo"])[0].tolist() |
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assert res == [[12110, 2534], [2009, 17374]] |
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def test_model_generate(self) -> None: |
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context = self.LM.tok_batch_encode(["foo bar"])[0] |
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res = self.LM._model_generate(context, max_length=10, stop=["\n\n"]) |
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res = self.LM.tok_decode(res[0]) |
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assert res == "foo bar\n<bazhang>!info bar" |
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