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lm_eval/datasets/sat_analogies/sat_analogies.py deleted 100644 → 0
View file @ 814940e8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""SAT Analogy Questions dataset."""
import os
import datasets
_CITATION = """\
@article{article,
author = {Turney, Peter},
year = {2006},
month = {09},
pages = {379-416},
title = {Similarity of Semantic Relations},
volume = {32},
journal = {Computational Linguistics},
doi = {10.1162/coli.2006.32.3.379}
}
"""
_DESCRIPTION = """\
SAT (Scholastic Aptitude Test) Analogy Questions is a dataset comprising 374
multiple-choice analogy questions; 5 choices per question.
"""
_HOMEPAGE = "https://aclweb.org/aclwiki/SAT_Analogy_Questions_(State_of_the_art)"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
class SatAnalogies(datasets.GeneratorBasedBuilder):
"""SAT (Scholastic Aptitude Test) Analogy Questions is a dataset comprising 374 multiple-choice analogy questions."""
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="sat_analogies",
version=VERSION,
description="The SAT Analogy Questions dataset",
),
]
@property
def manual_download_instructions(self):
return (
"To use SAT Analogy Questions you have to download it manually. Please "
"email Peter Turney to request the data (https://www.apperceptual.com). "
"Once you receive a download link for the dataset, supply the local path "
"as the `data_dir` arg: "
"`datasets.load_dataset('sat_analogies', data_dir='path/to/folder/folder_name')`"
)
def _info(self):
features = datasets.Features(
{
"source": datasets.Value("string"),
"stem": datasets.Value("string"),
"choices": datasets.features.Sequence(datasets.Value("string")),
"solution": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
if not os.path.exists(data_dir):
raise FileNotFoundError(
f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('matinf', data_dir=...)` that includes SAT-package-V3.txt. Manual download instructions: {self.manual_download_instructions}"
)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "SAT-package-V3.txt"),
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
data = []
with open(filepath, "r", encoding="utf-8") as f:
record = []
for line in f:
line = line.strip()
if len(line) == 0 and record:
data.append(record)
record = []
elif len(line) > 0 and line[0] == "#":
# Skip comments.
continue
else:
record.append(line)
data.append(record)
for key, record in enumerate(data):
source = record[-8]
stem = record[-7]
choices = record[-6:-1]
solution = record[-1]
yield key, {
"source": source,
"stem": stem,
"choices": choices,
"solution": solution,
}
lm_eval/datasets/triviaqa/README.md deleted 100644 → 0
View file @ 814940e8
---
dataset_info:
features:
- name: question_id
dtype: string
- name: question_source
dtype: string
- name: question
dtype: string
- name: answer
struct:
- name: aliases
sequence: string
- name: value
dtype: string
- name: search_results
sequence:
- name: description
dtype: string
- name: filename
dtype: string
- name: rank
dtype: int32
- name: title
dtype: string
- name: url
dtype: string
- name: search_context
dtype: string
config_name: triviaqa
splits:
- name: train
num_bytes: 1270894387
num_examples: 87622
- name: validation
num_bytes: 163755044
num_examples: 11313
download_size: 632549060
dataset_size: 1434649431
---
lm_eval/datasets/triviaqa/dataset_infos.json deleted 100644 → 0
View file @ 814940e8
{"triviaqa": {"description": "TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence\ntriples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts\nand independently gathered evidence documents, six per question on average, that provide\nhigh quality distant supervision for answering the questions.\n", "citation": "@InProceedings{JoshiTriviaQA2017,\n author = {Joshi, Mandar and Choi, Eunsol and Weld, Daniel S. and Zettlemoyer, Luke},\n title = {TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension},\n booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},\n month = {July},\n year = {2017},\n address = {Vancouver, Canada},\n publisher = {Association for Computational Linguistics},\n}\n", "homepage": "https://nlp.cs.washington.edu/triviaqa/", "license": "Apache License 2.0", "features": {"question_id": {"dtype": "string", "id": null, "_type": "Value"}, "question_source": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"aliases": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "value": {"dtype": "string", "id": null, "_type": "Value"}}, "search_results": {"feature": {"description": {"dtype": "string", "id": null, "_type": "Value"}, "filename": {"dtype": "string", "id": null, "_type": "Value"}, "rank": {"dtype": "int32", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "search_context": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "triviaqa", "config_name": "triviaqa", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 1271393601, "num_examples": 87622, "dataset_name": "triviaqa"}, "validation": {"name": "validation", "num_bytes": 163819509, "num_examples": 11313, "dataset_name": "triviaqa"}}, "download_checksums": {"http://eaidata.bmk.sh/data/triviaqa-unfiltered.tar.gz": {"num_bytes": 546481381, "checksum": "adc19b42769062d241a8fbe834c56e58598d9322eb6c614e9f33a68a2cf5523e"}}, "download_size": 546481381, "post_processing_size": null, "dataset_size": 1435213110, "size_in_bytes": 1981694491}}
lm_eval/datasets/triviaqa/triviaqa.py deleted 100644 → 0
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Custom TriviaQA because HF version sanitizes the dataset differently.
# https://github.com/huggingface/datasets/blob/9977ade72191ff0b6907ec63935448c6269a91a1/datasets/trivia_qa/trivia_qa.py#L285
"""TriviaQA (Unfiltered Raw) dataset."""
import json
import os
import datasets
_CITATION = """\
@InProceedings{JoshiTriviaQA2017,
author = {Joshi, Mandar and Choi, Eunsol and Weld, Daniel S. and Zettlemoyer, Luke},
title = {TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
}
"""
_DESCRIPTION = """\
TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence
triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts
and independently gathered evidence documents, six per question on average, that provide
high quality distant supervision for answering the questions.
"""
_HOMEPAGE = "https://nlp.cs.washington.edu/triviaqa/"
_LICENSE = "Apache License 2.0"
_URLS = "https://nlp.cs.washington.edu/triviaqa/data/triviaqa-unfiltered.tar.gz"
class Triviaqa(datasets.GeneratorBasedBuilder):
"""TriviaQA is a reading comprehension dataset containing over 650K question-answer-evidence triples"""
VERSION = datasets.Version("0.0.2")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="triviaqa", version=VERSION, description="The TriviaQA dataset"
),
]
def _info(self):
features = datasets.Features(
{
"question_id": datasets.Value("string"),
"question_source": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": {
"aliases": datasets.features.Sequence(
datasets.Value("string"),
),
"value": datasets.Value("string"),
},
"search_results": datasets.features.Sequence(
{
"description": datasets.Value("string"),
"filename": datasets.Value("string"),
"rank": datasets.Value("int32"),
"title": datasets.Value("string"),
"url": datasets.Value("string"),
"search_context": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "triviaqa-unfiltered", "unfiltered-web-train.json"
),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(
data_dir, "triviaqa-unfiltered", "unfiltered-web-dev.json"
),
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
json_data = json.load(f)["Data"]
for key, data in enumerate(json_data):
search_results = []
for search_result in data["SearchResults"]:
search_results.append(
{
"description": search_result["Description"]
if "Description" in search_result
else "",
"filename": search_result["Filename"]
if "Filename" in search_result
else "",
"rank": search_result["Rank"]
if "Rank" in search_result
else -1,
"title": search_result["Title"]
if "Title" in search_result
else "",
"url": search_result["Url"]
if "Url" in search_result
else "",
"search_context": search_result["SearchContext"]
if "SearchContext" in search_result
else "",
}
)
yield key, {
"question_id": data["QuestionId"],
"question_source": data["QuestionSource"],
"question": data["Question"],
"answer": {
"aliases": data["Answer"]["Aliases"],
"value": data["Answer"]["Value"],
},
"search_results": search_results,
}
lm_eval/datasets/unscramble/dataset_infos.json deleted 100644 → 0
View file @ 814940e8
{"mid_word_1_anagrams": {"description": "Unscramble is a small battery of 5 \u201ccharacter manipulation\u201d tasks. Each task\ninvolves giving the model a word distorted by some combination of scrambling,\naddition, or deletion of characters, and asking it to recover the original word.\n", "citation": "@inproceedings{NEURIPS2020_1457c0d6,\n author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},\n pages = {1877--1901},\n publisher = {Curran Associates, Inc.},\n title = {Language Models are Few-Shot Learners},\n url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},\n volume = {33},\n year = {2020}\n}\n", "homepage": "https://github.com/openai/gpt-3/tree/master/data", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "completion": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "unscramble", "config_name": "mid_word_1_anagrams", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 271516, "num_examples": 10000, "dataset_name": "unscramble"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/gpt-3/master/data/mid_word_1_anagrams.jsonl.gz": {"num_bytes": 106533, "checksum": "6768a86896083199de4815d4964cb2f6f1046476cfd80c2a562784f182905979"}}, "download_size": 106533, "post_processing_size": null, "dataset_size": 271516, "size_in_bytes": 378049}, "mid_word_2_anagrams": {"description": "Unscramble is a small battery of 5 \u201ccharacter manipulation\u201d tasks. Each task\ninvolves giving the model a word distorted by some combination of scrambling,\naddition, or deletion of characters, and asking it to recover the original word.\n", "citation": "@inproceedings{NEURIPS2020_1457c0d6,\n author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},\n pages = {1877--1901},\n publisher = {Curran Associates, Inc.},\n title = {Language Models are Few-Shot Learners},\n url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},\n volume = {33},\n year = {2020}\n}\n", "homepage": "https://github.com/openai/gpt-3/tree/master/data", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "completion": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "unscramble", "config_name": "mid_word_2_anagrams", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 282654, "num_examples": 10000, "dataset_name": "unscramble"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/gpt-3/master/data/mid_word_2_anagrams.jsonl.gz": {"num_bytes": 109091, "checksum": "c3d839d09a7954b78a27cd2cd75d4ed0488656c56ef4dbd741a005343826cb01"}}, "download_size": 109091, "post_processing_size": null, "dataset_size": 282654, "size_in_bytes": 391745}, "cycle_letters_in_word": {"description": "Unscramble is a small battery of 5 \u201ccharacter manipulation\u201d tasks. Each task\ninvolves giving the model a word distorted by some combination of scrambling,\naddition, or deletion of characters, and asking it to recover the original word.\n", "citation": "@inproceedings{NEURIPS2020_1457c0d6,\n author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},\n pages = {1877--1901},\n publisher = {Curran Associates, Inc.},\n title = {Language Models are Few-Shot Learners},\n url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},\n volume = {33},\n year = {2020}\n}\n", "homepage": "https://github.com/openai/gpt-3/tree/master/data", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "completion": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "unscramble", "config_name": "cycle_letters_in_word", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 282654, "num_examples": 10000, "dataset_name": "unscramble"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/gpt-3/master/data/cycle_letters_in_word.jsonl.gz": {"num_bytes": 98451, "checksum": "1689c9002bb8c5988bf5f05e977c9db92f57932c1b5a38998c29ac0dd71e1d42"}}, "download_size": 98451, "post_processing_size": null, "dataset_size": 282654, "size_in_bytes": 381105}, "random_insertion_in_word": {"description": "Unscramble is a small battery of 5 \u201ccharacter manipulation\u201d tasks. Each task\ninvolves giving the model a word distorted by some combination of scrambling,\naddition, or deletion of characters, and asking it to recover the original word.\n", "citation": "@inproceedings{NEURIPS2020_1457c0d6,\n author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},\n pages = {1877--1901},\n publisher = {Curran Associates, Inc.},\n title = {Language Models are Few-Shot Learners},\n url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},\n volume = {33},\n year = {2020}\n}\n", "homepage": "https://github.com/openai/gpt-3/tree/master/data", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "completion": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "unscramble", "config_name": "random_insertion_in_word", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 353981, "num_examples": 10000, "dataset_name": "unscramble"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/gpt-3/master/data/random_insertion_in_word.jsonl.gz": {"num_bytes": 143626, "checksum": "72e65d83da53d15752ee0c47379509de149ddbad32d61184e5991df29616b78a"}}, "download_size": 143626, "post_processing_size": null, "dataset_size": 353981, "size_in_bytes": 497607}, "reversed_words": {"description": "Unscramble is a small battery of 5 \u201ccharacter manipulation\u201d tasks. Each task\ninvolves giving the model a word distorted by some combination of scrambling,\naddition, or deletion of characters, and asking it to recover the original word.\n", "citation": "@inproceedings{NEURIPS2020_1457c0d6,\n author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},\n booktitle = {Advances in Neural Information Processing Systems},\n editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},\n pages = {1877--1901},\n publisher = {Curran Associates, Inc.},\n title = {Language Models are Few-Shot Learners},\n url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},\n volume = {33},\n year = {2020}\n}\n", "homepage": "https://github.com/openai/gpt-3/tree/master/data", "license": "", "features": {"context": {"dtype": "string", "id": null, "_type": "Value"}, "completion": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "unscramble", "config_name": "reversed_words", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 282654, "num_examples": 10000, "dataset_name": "unscramble"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/gpt-3/master/data/reversed_words.jsonl.gz": {"num_bytes": 91917, "checksum": "133a08f875cd6c1ef8608a3233571a773881cc27b1c707de738cc6543439332a"}}, "download_size": 91917, "post_processing_size": null, "dataset_size": 282654, "size_in_bytes": 374571}}
lm_eval/datasets/unscramble/unscramble.py deleted 100644 → 0
View file @ 814940e8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Unscramble dataset."""
import json
import os
import datasets
_CITATION = """\
@inproceedings{NEURIPS2020_1457c0d6,
author = {Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winter, Clemens and Hesse, Chris and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {1877--1901},
publisher = {Curran Associates, Inc.},
title = {Language Models are Few-Shot Learners},
url = {https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf},
volume = {33},
year = {2020}
}
"""
_DESCRIPTION = """\
Unscramble is a small battery of 5 “character manipulation” tasks. Each task
involves giving the model a word distorted by some combination of scrambling,
addition, or deletion of characters, and asking it to recover the original word.
"""
_HOMEPAGE = "https://github.com/openai/gpt-3/tree/master/data"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
_BASE_URL = "https://raw.githubusercontent.com/openai/gpt-3/master/data"
_DESCRIPTIONS = {
"mid_word_1_anagrams": "Anagrams of all but the first and last letter.",
"mid_word_2_anagrams": "Anagrams of all but the first and last 2 letters.",
"cycle_letters_in_word": "Cycle letters in the word.",
"random_insertion_in_word": "Random insertions in the word that must be removed.",
"reversed_words": "Words spelled backwards that must be reversed.",
}
_NAMES = _DESCRIPTIONS.keys()
class Unscramble(datasets.GeneratorBasedBuilder):
"""Unscramble is a small battery of 5 “character manipulation” tasks."""
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=name, version=version, description=_DESCRIPTIONS[name]
)
for name, version in zip(_NAMES, [VERSION] * len(_NAMES))
]
def _info(self):
features = datasets.Features(
{
"context": datasets.Value("string"),
"completion": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = os.path.join(_BASE_URL, f"{self.config.name}.jsonl.gz")
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "validation",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
yield key, {
"context": data["context"],
"completion": data["completion"],
}
lm_eval/evaluator.py
View file @ d2a9b759
......@@ -2,11 +2,11 @@ import collections
import itertools
import numpy as np
import random
import lm_eval.metrics
import lm_eval.api.metrics
import lm_eval.models
import lm_eval.tasks
import lm_eval.base
from lm_eval.utils import positional_deprecated, run_task_tests
import lm_eval.api
from lm_eval.utils import positional_deprecated, run_task_tests, make_table
@positional_deprecated
......@@ -20,7 +20,6 @@ def simple_evaluate(
no_cache=False,
limit=None,
bootstrap_iters=100000,
description_dict=None,
check_integrity=False,
decontamination_ngrams_path=None,
):
......@@ -46,8 +45,6 @@ def simple_evaluate(
Limit the number of examples per task (only use this for testing)
:param bootstrap_iters:
Number of iterations for bootstrap statistics
:param description_dict: dict[str, str]
Dictionary of custom task descriptions of the form: `task_name: description`
:param check_integrity: bool
Whether to run the relevant part of the test suite for the tasks
:return
......@@ -65,20 +62,10 @@ def simple_evaluate(
model_args, {"batch_size": batch_size, "device": device}
)
else:
assert isinstance(model, lm_eval.base.LM)
assert isinstance(model, lm_eval.api.model.LM)
lm = model
if not no_cache:
lm = lm_eval.base.CachingLM(
lm,
"lm_cache/"
+ model
+ "_"
+ model_args.replace("=", "-").replace(",", "_").replace("/", "-")
+ ".db",
)
task_dict = lm_eval.tasks.get_task_dict(tasks)
task_dict = lm_eval.tasks.get_task_dict(tasks, num_fewshot=num_fewshot)
if check_integrity:
run_task_tests(task_list=tasks)
......@@ -89,7 +76,6 @@ def simple_evaluate(
num_fewshot=num_fewshot,
limit=limit,
bootstrap_iters=bootstrap_iters,
description_dict=description_dict,
decontamination_ngrams_path=decontamination_ngrams_path,
)
......@@ -103,7 +89,6 @@ def simple_evaluate(
"no_cache": no_cache,
"limit": limit,
"bootstrap_iters": bootstrap_iters,
"description_dict": description_dict,
}
return results
......@@ -116,11 +101,9 @@ decontaminate_suffix = "_decontaminate"
def evaluate(
lm,
task_dict,
provide_description=None,
num_fewshot=0,
limit=None,
bootstrap_iters=100000,
description_dict=None,
decontamination_ngrams_path=None,
):
"""Instantiate and evaluate a model on a list of tasks.
......@@ -129,202 +112,99 @@ def evaluate(
Language Model
:param task_dict: dict[str, Task]
Dictionary of tasks. Tasks will be taken to have name task.EVAL_HARNESS_NAME if defined and type(task).__name__ otherwise.
:param provide_description: bool
Not implemented, and this option is deprecated and will be removed in a future version in favor of a different description providing method
:param num_fewshot: int
Number of examples in few-shot context
:param limit: int, optional
Limit the number of examples per task (only use this for testing)
:param bootstrap_iters:
Number of iterations for bootstrap statistics
:param description_dict: dict[str, str]
Dictionary of custom task descriptions of the form: `task_name: description`
:return
Dictionary of results
"""
# TODO: completely refactor this entire function to not be a huge mess, ideally breaking it down into smaller pieces
# TODO: todo: implement proper description-providing system
assert not provide_description # not implemented.
if provide_description is not None:
# nudge people to not specify it at all
print(
"WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
)
decontaminate = decontamination_ngrams_path is not None
task_dict_items = [
(name, task)
for name, task in task_dict.items()
if (task.has_validation_docs() or task.has_test_docs())
]
results = collections.defaultdict(dict)
versions = collections.defaultdict(dict)
requests = collections.defaultdict(list)
requests_origin = collections.defaultdict(list)
overlaps = collections.defaultdict(list) # {task_name: contaminated_docs}
# If we ever run into issues where the eval tasks don't fit in memory and we can't afford a machine with bigger
# memory, we can always modify this plumbing to support that, but I didn't want to include it just yet because
# over-engineering is bad (or we could make it write the requests to disk and then read them back out again
# - probably using an sqlite db because of all the moving parts we have
# TODO: we need unit tests & sanity checks or something to ensure that the return of `validation_docs` is stable
docs = {}
docs_for_decontamination = collections.defaultdict(list)
# get lists of each type of request
for task_name, task in task_dict_items:
for task_name, task in task_dict.items():
versions[task_name] = task.VERSION
# default to test doc, fall back to val doc if validation unavailable
# TODO: the test-fallback-to-val system isn't final, we should revisit it at some point
if task.has_test_docs():
task_doc_func = task.test_docs
task_set = "test" # Required for caching in the decontamination
elif task.has_validation_docs():
task_set = "val" # Required for caching in the decontamination
task_doc_func = task.validation_docs
else:
raise RuntimeError("Task has neither test_docs nor validation_docs")
# deterministically shuffle docs and chop off the first `limit` because sometimes docs are in some kind of order
task_docs = list(task_doc_func())
rnd = random.Random()
rnd.seed(42)
rnd.shuffle(task_docs)
description = (
description_dict[task_name]
if description_dict and task_name in description_dict
else ""
)
for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
if decontaminate and task.should_decontaminate():
docs_for_decontamination[(task_name, task_set)].append(
task.doc_to_decontamination_query(doc)
)
# task_docs = list(task_doc_func())
# rnd = random.Random()
# rnd.seed(42)
# rnd.shuffle(task_docs)
docs[(task_name, doc_id)] = doc
ctx = task.fewshot_context(
doc=doc, num_fewshot=num_fewshot, rnd=rnd, description=description
)
reqs = task.construct_requests(doc, ctx)
if not isinstance(reqs, (list, tuple)):
reqs = [reqs]
for i, req in enumerate(reqs):
requests[req.request_type].append(req)
# i: index in requests for a single task instance
# doc_id: unique id that we can get back to a doc using `docs`
requests_origin[req.request_type].append((i, task_name, doc, doc_id))
# Compare all tasks/sets at once to ensure a single training set scan
if decontaminate:
from lm_eval.decontamination.decontaminate import get_train_overlap
print("Finding train/test overlap, please wait...")
overlaps = get_train_overlap(
docs_for_decontamination, decontamination_ngrams_path, limit
)
# all responses for each (task, doc)
process_res_queue = collections.defaultdict(list)
# for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
task.build_all_requests(limit=limit)
# aggregate Instances by LM method requested to get output.
requests[task.OUTPUT_TYPE].extend(task.instances)
### Run LM on inputs, get all outputs ###
# execute each type of request
for reqtype, reqs in requests.items():
# TODO: right now, this code runs multiple separate LM requests for multiple Requests differing
# only in index. We could implement some kind of caching, but that would be more of a band-aid
# solution. we could also implement some kind of auto-grouping here;
# they should end up next to each other.
print("Running", reqtype, "requests")
resps = getattr(lm, reqtype)([req.args for req in reqs])
resps = [
x if req.index is None else x[req.index] for x, req in zip(resps, reqs)
]
# create `K` copies of each request `req` based off `K = req.repeats`
cloned_reqs = []
for req in reqs:
cloned_reqs.extend([req] * req.repeats)
for resp, (i, task_name, doc, doc_id) in zip(resps, requests_origin[reqtype]):
process_res_queue[(task_name, doc_id)].append((i, resp))
# run requests through model
resps = getattr(lm, reqtype)(cloned_reqs)
vals = collections.defaultdict(list)
# put responses from model into a list of length K for each request.
for x, req in zip(resps, cloned_reqs):
req.resps.append(x)
# unpack results and sort back in order and return control to Task
for (task_name, doc_id), requests in process_res_queue.items():
requests.sort(key=lambda x: x[0])
requests = [x[1] for x in requests]
### Postprocess outputs ###
# TODO: del model here, maybe (idea: allow user to specify device of e.g. reward model separately)
for task_name, task in task_dict.items():
task.apply_filters()
task = task_dict[task_name]
doc = docs[(task_name, doc_id)]
metrics = task.process_results(doc, requests)
### Collect values of metrics on all datapoints ###
# TODO: make metric configurable, add metric registry
vals = collections.defaultdict(list)
# unpack results and sort back in order and return control to Task
for task_name, task in task_dict.items():
# calculate values for each filter setup (TODO: make getting list of keys cleaner)
# TODO: make it possible to use a different metric per key
for key in task.instances[0].filtered_resps.keys():
for doc_id, doc in enumerate(itertools.islice(task.test_docs(), 0, limit) if task.has_test_docs() else task.validation_docs()):
# subset instances to only this document id ; sort by idx
requests = list(filter(lambda x: x.doc_id == doc_id, task.instances))
requests.sort(key=lambda x: x.id_)
metrics = task.process_results(doc, [req.filtered_resps[key] for req in requests])
for metric, value in metrics.items():
vals[(task_name, metric)].append(value)
vals[(task_name, key, metric)].append(value)
# Re-use the evaluation for the decontaminated set by just ignoring the overlaps
if decontaminate and task_name in overlaps:
if doc_id not in overlaps[task_name]:
vals[(task_name, metric + decontaminate_suffix)].append(value)
# aggregate results
for (task_name, metric), items in vals.items():
### Aggregate results over all datapoints ###
# aggregate results ; run bootstrap CIs
for (task_name, key, metric), items in vals.items():
task = task_dict[task_name]
real_metric = metric # key when looking up the metric with task.aggregation
if metric.endswith(decontaminate_suffix):
real_metric = metric.replace(
decontaminate_suffix, ""
) # decontaminated still uses the same metric
results[task_name][metric] = task.aggregation()[real_metric](items)
results[task_name][metric + " - filter=" + key] = task.aggregation()[metric](items)
# hotfix: bleu, chrf, ter seem to be really expensive to bootstrap
# so we run them less iterations. still looking for a cleaner way to do this
stderr = lm_eval.metrics.stderr_for_metric(
metric=task.aggregation()[real_metric],
stderr = lm_eval.api.metrics.stderr_for_metric(
metric=task.aggregation()[metric],
bootstrap_iters=min(bootstrap_iters, 1000)
if metric in ["bleu", "chrf", "ter"]
else bootstrap_iters,
)
if stderr is not None:
results[task_name][metric + "_stderr"] = stderr(items)
results[task_name][metric + " - filter=" + key + "_stderr"] = stderr(items)
return {"results": dict(results), "versions": dict(versions)}
def make_table(result_dict):
"""Generate table of results."""
from pytablewriter import MarkdownTableWriter, LatexTableWriter
md_writer = MarkdownTableWriter()
latex_writer = LatexTableWriter()
md_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
latex_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
values = []
for k, dic in result_dict["results"].items():
version = result_dict["versions"][k]
for m, v in dic.items():
if m.endswith("_stderr"):
continue
if m + "_stderr" in dic:
se = dic[m + "_stderr"]
values.append([k, version, m, "%.4f" % v, "±", "%.4f" % se])
else:
values.append([k, version, m, "%.4f" % v, "", ""])
k = ""
version = ""
md_writer.value_matrix = values
latex_writer.value_matrix = values
# todo: make latex table look good
# print(latex_writer.dumps())
return md_writer.dumps()
lm_eval/filters/__init__.py 0 → 100644
View file @ d2a9b759
from lm_eval.api.filter import FilterEnsemble
from . import selection
from . import extraction
FILTER_REGISTRY = {
"take_first": selection.TakeFirstFilter,
"regex": extraction.RegexFilter,
# TODO: implement this filter. either it should take in an arbitrary "scoring"/reward function
# that takes an input and returns a scalar and then should select the max reward,
# or should implement different filters for different ways of handling a reward model's inference.
#"arg_max": selection.ArgMaxFilter,
}
def get_filter(filter_name):
return FILTER_REGISTRY[filter_name]
def build_filter_ensemble(name, components):
"""
Create a filtering pipeline.
"""
filters = []
for step in components:
# create a filter given its name in the registry
f = get_filter(step)() # TODO: pass kwargs to filters properly
# add the filter as a pipeline step
filters.append(f)
return FilterEnsemble(name=name, filters=filters)
lm_eval/filters/decontamination.py 0 → 100644
View file @ d2a9b759
from lm_eval.api.filter import Filter
class DecontaminationFilter(Filter):
"""
A filter which evaluates
"""
name = "track_decontamination"
def __init__(self, path):
"""
TODO: make sure only ever run one time on the train set (should this be cached as a class var? keyed by value for "path").
should further cache result on a given (task_name, doc_id)
"""
self._decontam_results = None
def apply(self, reps):
"""
Return {"no_contamination", "only_contamination"} keys for the 2 different subsets
"""
pass
\ No newline at end of file
lm_eval/filters/extraction.py 0 → 100644
View file @ d2a9b759
import re
from lm_eval.api.filter import Filter
class RegexFilter(Filter):
"""
"""
def __init__(self, regex=r"#### (\-?[0-9\.\,]+)", fallback="[invalid]"):
"""
pass a string `regex` to run `re.compile(r"regex")` on.
`fallback` defines the output returned if no matches for the regex are located.
"""
self.regex_pattern = regex
self.regex = re.compile(regex)
self.fallback = fallback
def apply(self, resps):
# here, we assume we have a list, in which each element is
# a list of model responses for some particular input/target pair.
# so we process each of these (same input/target response sets)
# independently (and keep them a list.)
def filter_set(inst):
filtered = []
for resp in inst:
match = self.regex.search(resp)
if match:
match = match.group(1).strip()
match_str.replace(",", "")
# TODO: should we assume any other filtering is performed?
else:
match = self.fallback
filtered.append(match)
return filtered
# print(resps)
filtered_resps = list(map(lambda x: filter_set(x), resps))
# print(filtered_resps)
return filtered_resps
lm_eval/filters/selection.py 0 → 100644
View file @ d2a9b759
from lm_eval.api.filter import Filter
class TakeFirstFilter:
def __init__(self):
"""
Can define custom behavior here, if an individual instantiation of a Filter class should have state.
"""
def apply(self, resps):
"""
Assuming each entry of `resps` is a list of model responses, we discard all but the first response.
"""
return map(lambda r: r[0], resps)
\ No newline at end of file
lm_eval/models/__init__.py
View file @ d2a9b759
from . import gpt2
from . import gpt3
from . import huggingface
from . import textsynth
from . import dummy
MODEL_REGISTRY = {
"hf": gpt2.HFLM,
"hf-causal": huggingface.AutoCausalLM,
"hf-seq2seq": huggingface.AutoSeq2SeqLM,
"gpt2": gpt2.GPT2LM,
"gpt3": gpt3.GPT3LM,
"hf-causal": gpt2.HFLM,
"openai": gpt3.GPT3LM,
"textsynth": textsynth.TextSynthLM,
"dummy": dummy.DummyLM,
}
......
lm_eval/models/dummy.py
View file @ d2a9b759
import random
from lm_eval.base import LM
from lm_eval.api.model import LM
class DummyLM(LM):
......
lm_eval/models/gpt2.py
View file @ d2a9b759
import torch
import transformers
from lm_eval.base import BaseLM
from tqdm import tqdm
class HFLM(BaseLM):
import torch.nn.functional as F
from lm_eval import utils
from lm_eval.api.model import LM
class HFLM(LM):
def __init__(
self,
device="cuda",
......@@ -47,28 +53,8 @@ class HFLM(BaseLM):
revision=revision,
)
assert isinstance(
self.tokenizer,
(
transformers.GPT2Tokenizer,
transformers.GPT2TokenizerFast,
transformers.T5Tokenizer,
transformers.T5TokenizerFast,
),
), "this tokenizer has not been checked for compatibility yet!"
self.vocab_size = self.tokenizer.vocab_size
if isinstance(
self.tokenizer, (transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast)
):
assert self.tokenizer.encode("hello\n\nhello") == [
31373,
198,
198,
31373,
], self.tokenizer.encode("hello\n\nhello")
# multithreading and batching
self.batch_size_per_gpu = batch_size # todo: adaptive batch size
......@@ -123,9 +109,196 @@ class HFLM(BaseLM):
def _model_generate(self, context, max_length, eos_token_id):
return self.gpt2.generate(
context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False
context, max_length=max_length, pad_token_id=eos_token_id, eos_token_id=eos_token_id, do_sample=False
)
def loglikelihood(self, requests):
new_reqs = []
for context, continuation in [req.args for req in requests]:
if context == "":
# end of text as context
context_enc = [self.eot_token_id]
else:
context_enc = self.tok_encode(context)
continuation_enc = self.tok_encode(continuation)
new_reqs.append(((context, continuation), context_enc, continuation_enc))
return self._loglikelihood_tokens(new_reqs)
def loglikelihood_rolling(self, requests):
# TODO: Implement caching once we've confirmed the perplexity implementation
# TODO: automatic batch size detection for vectorization
loglikelihoods = []
for (string,) in tqdm([req.args for req in requests]):
rolling_token_windows = list(
map(
utils.make_disjoint_window,
utils.get_rolling_token_windows(
token_list=self.tok_encode(string),
prefix_token=self.eot_token_id,
max_seq_len=self.max_length,
context_len=1,
),
)
)
rolling_token_windows = [(None,) + x for x in rolling_token_windows]
# TODO: extract out this call so it only gets called once and also somehow figure out partial caching for
# that
string_nll = self._loglikelihood_tokens(
rolling_token_windows, disable_tqdm=True
)
# discard is_greedy
string_nll = [x[0] for x in string_nll]
string_nll = sum(string_nll)
loglikelihoods.append(string_nll)
return loglikelihoods
def _loglikelihood_tokens(self, requests, disable_tqdm=False):
# TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
res = []
def _collate(x):
# the negative sign on len(toks) sorts descending - this has a few advantages:
# - time estimates will always be over not underestimates, which is more useful for planning
# - to know the size of a batch when going through the list, you know the first one is always the batch
# padded context length. this is useful to simplify the batching logic and more importantly to make
# automatic adaptive batches much much easier to implement
# - any OOMs will happen right away rather than near the end
toks = x[1] + x[2]
return -len(toks), tuple(toks)
# TODO: automatic (variable) batch size detection for vectorization
re_ord = utils.Reorderer(requests, _collate)
for chunk in utils.chunks(
tqdm(re_ord.get_reordered(), disable=disable_tqdm), self.batch_size
):
inps = []
cont_toks_list = []
inplens = []
padding_length = None
# because vectorizing is annoying, we first convert each (context, continuation) pair to padded
# tensors, then we pack them together into a batch, call the model, and then pick it all apart
# again because vectorizing is annoying
for _, context_enc, continuation_enc in chunk:
# sanity check
assert len(context_enc) > 0
assert len(continuation_enc) > 0
assert len(continuation_enc) <= self.max_length
# how this all works:
# CTX CONT
# inp 0 1 2 3|4 5 6 7 8 9 <- last token is deleted by inp[:, :-1]
# gpt2 \ \
# logits 1 2 3|4 5 6 7 8 9 <- the ctx half gets tossed out by the
# cont_toks 4 5 6 7 8 9 [:, -len(continuation_enc):, :self.vocab_size] slice
# when too long to fit in context, truncate from the left
inp = torch.tensor(
(context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
dtype=torch.long,
).to(self.device)
(inplen,) = inp.shape
cont = continuation_enc
# since in _collate we make sure length is descending, the longest is always the first one.
padding_length = (
padding_length if padding_length is not None else inplen
)
# pad length from seq to padding_length
inp = torch.cat(
[
inp, # [seq]
torch.zeros(padding_length - inplen, dtype=torch.long).to(
inp.device
), # [padding_length - seq]
],
dim=0,
)
inps.append(inp.unsqueeze(0)) # [1, padding_length]
cont_toks_list.append(cont)
inplens.append(inplen)
batched_inps = torch.cat(inps, dim=0) # [batch, padding_length
multi_logits = F.log_softmax(
self._model_call(batched_inps), dim=-1
).cpu() # [batch, padding_length, vocab]
for (cache_key, _, _), logits, inp, inplen, cont_toks in zip(
chunk, multi_logits, inps, inplens, cont_toks_list
):
# Slice to original seq length
contlen = len(cont_toks)
logits = logits[inplen - contlen : inplen].unsqueeze(
0
) # [1, seq, vocab]
# Check if per-token argmax is exactly equal to continuation
greedy_tokens = logits.argmax(dim=-1)
cont_toks = torch.tensor(cont_toks, dtype=torch.long).unsqueeze(
0
) # [1, seq]
max_equal = (greedy_tokens == cont_toks).all()
# Obtain log-probs at the corresponding continuation token indices
# last_token_slice = logits[:, -1, :].squeeze(0).tolist()
logits = torch.gather(logits, 2, cont_toks.unsqueeze(-1)).squeeze(
-1
) # [1, seq]
# Answer: (log prob, is-exact-match)
answer = (float(logits.sum()), bool(max_equal))
res.append(answer)
return re_ord.get_original(res)
def greedy_until(self, requests):
# TODO: implement fully general `until` that handles until that are
# multiple tokens or that span multiple tokens correctly
res = []
def _collate(x):
toks = self.tok_encode(x[0])
return len(toks), x[0]
re_ord = utils.Reorderer([req.args for req in requests], _collate)
for context, until in tqdm(re_ord.get_reordered()):
if isinstance(until, str):
until = [until]
(primary_until,) = self.tok_encode(until[0])
context_enc = torch.tensor(
[self.tok_encode(context)[self.max_gen_toks - self.max_length :]]
).to(self.device)
cont = self._model_generate(
context_enc, context_enc.shape[1] + self.max_gen_toks, primary_until
)
s = self.tok_decode(cont[0].tolist()[context_enc.shape[1] :])
for term in until:
s = s.split(term)[0]
res.append(s)
# for backwards compatibility
GPT2LM = HFLM
return re_ord.get_original(res)
lm_eval/models/gpt3.py
View file @ d2a9b759
import os
import numpy as np
import transformers
from lm_eval.base import BaseLM
from lm_eval.api.model import LM
from lm_eval import utils
from tqdm import tqdm
import time
......@@ -54,7 +54,7 @@ def oa_completion(**kwargs):
backoff_time *= 1.5
class GPT3LM(BaseLM):
class GPT3LM(LM):
REQ_CHUNK_SIZE = 20
def __init__(self, engine, truncate=False):
......
lm_eval/models/huggingface.py deleted 100644 → 0
View file @ 814940e8
This diff is collapsed.
lm_eval/models/textsynth.py
View file @ d2a9b759
......@@ -16,7 +16,7 @@ import os
import requests as _requests
import time
from tqdm import tqdm
from lm_eval.base import BaseLM
from lm_eval.api.model import LM
logger = logging.getLogger(__name__)
......@@ -38,7 +38,7 @@ def textsynth_completion(**kwargs):
backoff_time *= 1.5
class TextSynthLM(BaseLM):
class TextSynthLM(LM):
def __init__(self, engine, truncate=False):
"""
:param engine: str
......
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