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Commit 18c0fa29 authored by cardy20's avatar cardy20
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conflict solved

parents 09915adf 0542d35d
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Showing with 705 additions and 464 deletions
tests/test_evaluator.py
View file @ 18c0fa29
......@@ -10,20 +10,21 @@ import pytest
# TODO: more fine grained unit tests rather than this big honking integration
# test once we break evaluator into smaller, more manageable pieces
@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
def test_evaluator(taskname, task_class):
task_dict = tasks.get_task_dict([taskname])
os.system("rm test_cache.db")
lm = base.CachingLM(models.get_model('dummy')(), "test_cache.db")
lm = base.CachingLM(models.get_model("dummy")(), "test_cache.db")
def ll_fn(reqs):
for ctx, cont in reqs:
if len(ctx) == 0:
continue
# space convention
assert ctx[-1] != ' '
assert cont[0] == ' ' or ctx[-1] == '\n'
assert ctx[-1] != " "
assert cont[0] == " " or ctx[-1] == "\n"
res = []
......@@ -34,7 +35,7 @@ def test_evaluator(taskname, task_class):
return res
def ll_perp_fn(reqs):
for string, in reqs:
for (string,) in reqs:
assert isinstance(string, str)
res = []
......@@ -54,7 +55,7 @@ def test_evaluator(taskname, task_class):
num_fewshot=0,
limit=limit,
bootstrap_iters=10,
description_dict=None
description_dict=None,
)
e2 = evaluator.evaluate(
lm=lm,
......@@ -62,7 +63,7 @@ def test_evaluator(taskname, task_class):
num_fewshot=0,
limit=limit,
bootstrap_iters=10,
description_dict=None
description_dict=None,
)
# check that caching is working
......
tests/test_generate_13_grams.py
View file @ 18c0fa29
......@@ -3,12 +3,16 @@ from collections import Counter
import shutil
import glob
from scripts.clean_training_data.janitor import *
from lm_eval.decontamination.janitor import Janitor, word_ngrams
from scripts.clean_training_data.generate_13_grams import do_ngrams_in_buckets
from scripts.clean_training_data.archiver import Archive, TextReader
from lm_eval.decontamination.archiver import Archive, TextReader
import logging
def test_generate_13_grams_1():
logger = logging.getLogger(__name__)
def test_generate_13_grams_1(caplog):
data = """A goose (plural geese) is a bird of any of several waterfowl species in the family Anatidae.
This group comprises the genera Anser (the grey geese and white geese) and Branta (the black geese).
Some other birds, mostly related to the shelducks, have "goose" as part of their names.
......@@ -22,6 +26,7 @@ def test_generate_13_grams_1():
data = data + data
# Simple Generation
print("simple generation")
n = 13
janitor = Janitor()
ngrams = word_ngrams(janitor.normalize_string(data), n)
......@@ -31,23 +36,29 @@ def test_generate_13_grams_1():
# print(comparison)
# Generating into buckets
print("bucket generation")
test_working_directory = "test_generate_13_grams"
output_directory = os.path.join(test_working_directory, "output")
try:
shutil.rmtree(output_directory)
shutil.rmtree(test_working_directory)
except FileNotFoundError:
pass
os.makedirs(test_working_directory, exist_ok=True)
archive = Archive(os.path.join(test_working_directory, "test.jsonl.zst"))
os.makedirs(test_working_directory)
assert not os.path.exists("pile")
os.makedirs("pile")
archive = Archive(os.path.join("pile", "test.jsonl.zst"))
archive.add_data(data)
archive.commit()
bucket_count = 4
do_ngrams_in_buckets(n, test_working_directory, bucket_count)
# Rebuild from buckets
print("rebuild")
rebuilt_ngrams = []
bucket_file_paths = glob.glob(os.path.join(test_working_directory, "output", f"*.bkt.txt"))
bucket_file_paths = glob.glob(
os.path.join(test_working_directory, "output", f"*.bkt.txt")
)
for bucket_file_path in bucket_file_paths:
reader = TextReader(bucket_file_path)
......@@ -56,10 +67,11 @@ def test_generate_13_grams_1():
rebuilt_ngrams.append(ngram)
# Compare
print("compare")
result_counter = Counter(rebuilt_ngrams)
# print(len(result_counter))
# print(len(comparison_counter))
assert(len(result_counter) == len(comparison_counter))
assert len(result_counter) == len(comparison_counter)
# print(result_counter)
# print(comparison_counter)
assert(comparison_counter == result_counter)
\ No newline at end of file
assert comparison_counter == result_counter
tests/test_gpt3.py deleted 100644 → 0
View file @ 09915adf
import lm_eval.models as models
import pytest
import os
import json
import openai
import mock
import pickle
import hashlib
def mock_completion(**kwargs):
# Mock completion function
# Loads from a cached+pickled response if it exists, otherwise it will actually try to ping
os.makedirs("tests/testdata", exist_ok=True)
hash = hashlib.sha256(json.dumps(kwargs, sort_keys=True).encode('utf-8')).hexdigest()
fname = f"tests/testdata/gpt3_test_{hash}.pkl"
if os.path.exists(fname):
with open(fname, 'rb') as fh:
return pickle.load(fh)
ret = openai.Completion.create(**kwargs)
ret.api_key = ""
with open(fname, 'wb') as fh:
pickle.dump(ret, fh)
return ret
@mock.patch("lm_eval.models.gpt3.oa_completion", new=mock_completion)
def test_gpt3():
if "OPENAI_API_SECRET_KEY" not in os.environ: os.environ["OPENAI_API_SECRET_KEY"] = ""
gpt3 = models.get_model('gpt3').create_from_arg_string("engine=ada")
(ll_dog, ig_dog), (ll_cat, ig_cat), (_, ll_max_0), (_, ll_max_1), (_, ll_max_2), *vals = gpt3.loglikelihood([
('The quick brown fox jumps over the lazy', ' dog'),
('The quick brown fox jumps over the lazy', ' cat'),
('The quick brown fox jumps over the lazy', ', lazy dog'),
('The quick brown fox jumps over the lazy', ', lazy fox'),
('The quick brown fox jumps over the lazy', ', lazy fox and they both fall to the ground'),
("""A mult""", """ilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)"""),
("""The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons""", """ (with threshold activation); see § Terminology"""),
("""Multilayer perceptrons are sometimes coll""", """oquially referred to as "vanilla" neural networks, especially when they have a single hidden layer.[1]"""),
("""An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear""", """ activation function."""),
("""MLP utilizes a supervised""", """ learning technique called backpropagation for training.[2][3] Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.[4]"""),
("""Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic""", """ in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. """),
("""Specifically, we train GPT-3, an autoregressive language model with 175""", """ billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."""),
("""A mult""", """ilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)"""),
("""Hello""", """ World"""),
])
assert ll_dog > ll_cat
assert not ig_cat
assert ig_dog
assert not ll_max_0
assert not ll_max_1
assert not ll_max_2
# test empty context
gpt3.loglikelihood([('', 'test')])
gen, = gpt3.greedy_until([
('The quick brown fox jumps over the lazy', ['.', '\n'])
])
assert gen == ' dog'
print([x[0] for x in vals])
targets = [
-34.848301606999996, -47.148329679999996, -45.44380149599999, -5.285246016, -133.97821690686004,
-321.2616693239001, -658.0299524401041, -34.848301606999996, -7.525115,
]
for (pred, _), tgt in zip(vals, targets):
assert pred == pytest.approx(tgt, rel=1e-3)
@mock.patch("lm_eval.models.gpt3.oa_completion", new=mock_completion)
def test_gpt3_perplexity():
if "OPENAI_API_SECRET_KEY" not in os.environ: os.environ["OPENAI_API_SECRET_KEY"] = ""
gpt3 = models.get_model('gpt3').create_from_arg_string("engine=ada")
test_string = "We study empirical scaling laws for language model performance on the cross-entropy loss."
perplexity = gpt3.loglikelihood_rolling([(test_string,)])[0]
tgt = -84.38819608
assert perplexity == pytest.approx(tgt, rel=1e-3)
# Hack: modify gpt3 to have shorter context length to induce rolling windows
with mock.patch.object(models.gpt3.GPT3LM, 'max_length', new_callable=mock.PropertyMock) as mock_max_length:
mock_max_length.return_value = 5
gpt3 = models.get_model('gpt3').create_from_arg_string("engine=ada")
perplexity = gpt3.loglikelihood_rolling([(test_string,)])[0]
tgt = -101.81967209999999
assert perplexity == pytest.approx(tgt, rel=1e-3)
tests/test_janitor.py
View file @ 18c0fa29
import re
from collections import defaultdict
from scripts.clean_training_data.janitor import *
from lm_eval.decontamination.janitor import (
Janitor,
form_ngrams,
word_ngrams,
split_indices,
word_ngrams_indices,
)
def simple_ngram(sequence, n):
ngrams = list()
......@@ -16,8 +23,10 @@ def simple_ngram(sequence, n):
def test_form_ngrams():
sequence = "Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some" \
sequence = (
"Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some"
" more salad but it's so booooring. I just... like eating pizza, chicken, chips and ice cream so much."
)
n_values = [1, 2, 3, 5, 13]
for n in n_values:
......@@ -26,9 +35,12 @@ def test_form_ngrams():
assert len(comparison) == len(result_to_test)
assert comparison == result_to_test
def test_word_ngrams():
sequence = "Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some" \
sequence = (
"Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some"
" more salad but it's so booooring. I just... like eating pizza, chicken, chips and ice cream so much."
)
words = sequence.split()
......@@ -40,9 +52,12 @@ def test_word_ngrams():
assert len(comparison) == len(result_to_test)
assert result_to_test == comparison
def test_split_indices():
sequence = "Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some" \
sequence = (
"Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some"
" more salad but it's so booooring. I just... like eating pizza, chicken, chips and ice cream so much."
)
comparison = []
current_word = ""
......@@ -55,17 +70,22 @@ def test_split_indices():
current_word = ""
if current_word:
comparison.append((current_word, (len(sequence) - len(current_word), len(sequence) - 1)))
comparison.append(
(current_word, (len(sequence) - len(current_word), len(sequence) - 1))
)
current_word = ""
result_to_test = list(split_indices(sequence))
assert len(comparison) == len(result_to_test)
assert(comparison == result_to_test)
assert comparison == result_to_test
def test_word_ngrams_indices():
sequence = "Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some" \
sequence = (
"Hello my name is Bob, I like eating pizza, chicken, chips and ice cream. Maybe I should eat some"
" more salad but it's so booooring. I just... like eating pizza, chicken, chips and ice cream so much."
)
n_values = [1, 2, 3, 5, 13]
......@@ -82,8 +102,9 @@ def test_word_ngrams_indices():
tracker[ngram] = end + 1
# ignore partial word matches
if (start != 0 and sequence[start - 1] != " ") or \
(end != len(sequence) - 1 and sequence[end + 1] != " "):
if (start != 0 and sequence[start - 1] != " ") or (
end != len(sequence) - 1 and sequence[end + 1] != " "
):
pass
else:
break
......@@ -94,6 +115,7 @@ def test_word_ngrams_indices():
assert len(result_to_test) == len(comparison)
assert result_to_test == comparison
# Assumptions from GPT3 Paper:
# the 200 characters to remove include punctuation and is actually a half-window
......@@ -103,28 +125,33 @@ def test_janitor1():
# First test using a 1gram and expected the first block before the filth to have some remaining
# characters, but the second block should be completely removed.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filth = "filth"
expected_result = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
expected_result = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing "
)
janitor = Janitor(ngram_n=1, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=1, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -136,39 +163,44 @@ def test_janitor1():
result = "".join(result)
assert result == expected_result
def test_janitor2():
# Second test using a 1gram and expected the first block before the filth to have some remaining
# characters, and the second block is longer then 200 characters so should also have some remaining.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filth = "filth"
expected_result = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing " \
" characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
expected_result = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing "
" characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
janitor = Janitor(ngram_n=1, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=1, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -180,37 +212,43 @@ def test_janitor2():
result = "".join(result)
assert result == expected_result
def test_janitor3():
# Same test as above but with a 6gram.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of dirty filtHy FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of dirty filtHy FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filth = "filth lots of dirty filthy filth"
expected_result = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing " \
" characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
expected_result = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing "
" characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
janitor = Janitor(ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -222,45 +260,51 @@ def test_janitor3():
result = "".join(result)
assert result == expected_result
def test_janitor4():
# This test adds another block to that from the previous. The middle block should be entirely
# removed as the 200 characters are removed from each side.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of dirty filtHy FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of dirty filtHy FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of dirty filtHy FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of dirty filtHy FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filth = "filth lots of dirty filthy filth"
expected_result = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing " \
" characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
expected_result = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing "
" characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
janitor = Janitor(ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -272,44 +316,50 @@ def test_janitor4():
result = "".join(result)
assert result == expected_result
def test_janitor5():
# Same as above but using multiple different filth 6grams.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of dirty filtHy FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of filtHy dirty FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of dirty filtHy FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of filtHy dirty FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filths = ["filth lots of dirty filthy filth", "filth lots of filthy dirty filth"]
expected_result = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing " \
" characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
expected_result = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing "
" characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
janitor = Janitor(ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -322,52 +372,58 @@ def test_janitor5():
result = "".join(result)
assert result == expected_result
def test_janitor6():
# Same as above but now we add 10 filths and expect the same result, the following test does 11.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of filtHy dirty FIlTh " \
"FILTH. lots of filtHy dirty FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of filtHy dirty FIlTh "
"FILTH. lots of filtHy dirty FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filths = ["filth lots of dirty filthy filth", "filth lots of filthy dirty filth"]
expected_result = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing " \
" characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
expected_result = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing "
" characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
janitor = Janitor(ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -380,46 +436,50 @@ def test_janitor6():
result = "".join(result)
assert result == expected_result
def test_janitor7():
# Same as above but now we add 9 filths and expect the same result, the following test does 10.
sequence = "This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"FILTH. lots of dirty filtHy FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"FILTH. lots of filtHy dirty FIlTh " \
"FILTH. lots of filtHy dirty FIlTh " \
"FILTH. lots of filtHy dirty FIlTh " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
"This is a @line #containing a certain number of characters, 76 to be exact. " \
sequence = (
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"FILTH. lots of dirty filtHy FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"FILTH. lots of filtHy dirty FIlTh "
"FILTH. lots of filtHy dirty FIlTh "
"FILTH. lots of filtHy dirty FIlTh "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
"This is a @line #containing a certain number of characters, 76 to be exact. "
)
filths = ["filth lots of dirty filthy filth", "filth lots of filthy dirty filth"]
expected_result = ""
janitor = Janitor(ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200)
janitor = Janitor(
ngram_n=6, window_to_remove=200, too_dirty_cutoff=10, minimum_slice_length=200
)
result = janitor.clean_python(sequence)
result = "".join(result)
assert result == sequence
......@@ -453,23 +513,3 @@ def test_janitor8():
# cleaned = " ".join(jan.clean(source))
# for contam in jan.dirt_ngrams:
# assert contam not in cleaned, contam
tests/test_models.py
View file @ 18c0fa29
import hashlib
import json
import openai
import os
import pickle
import pytest
import unittest.mock as mock
import lm_eval.models as models
def test_gpt2():
gpt2 = models.get_model('gpt2').create_from_arg_string("device=cpu")
(ll_dog, ig_dog), (ll_cat, ig_cat), (_, ll_max_0), (_, ll_max_1), (_, ll_max_2), *vals = gpt2.loglikelihood([
('The quick brown fox jumps over the lazy', ' dog'),
('The quick brown fox jumps over the lazy', ' cat'),
('The quick brown fox jumps over the lazy', ', lazy dog'),
('The quick brown fox jumps over the lazy', ', lazy fox'),
('The quick brown fox jumps over the lazy', ', lazy fox and they both fall to the ground'),
("""A mult""", """ilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)"""),
("""The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons""", """ (with threshold activation); see § Terminology"""),
("""Multilayer perceptrons are sometimes coll""", """oquially referred to as "vanilla" neural networks, especially when they have a single hidden layer.[1]"""),
("""An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear""", """ activation function."""),
("""MLP utilizes a supervised""", """ learning technique called backpropagation for training.[2][3] Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.[4]"""),
("""Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic""", """ in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. """),
("""Specifically, we train GPT-3, an autoregressive language model with 175""", """ billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general."""),
("""A mult""", """ilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)"""),
LOGLIKELIHOOD_TEST_CASES = [
("The quick brown fox jumps over the lazy", " dog"),
("The quick brown fox jumps over the lazy", " cat"),
("The quick brown fox jumps over the lazy", ", lazy dog"),
("The quick brown fox jumps over the lazy", ", lazy fox"),
(
"The quick brown fox jumps over the lazy",
", lazy fox and they both fall to the ground",
),
(
"""A mult""",
"""ilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)""",
),
(
"""The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons""",
""" (with threshold activation); see § Terminology""",
),
(
"""Multilayer perceptrons are sometimes coll""",
"""oquially referred to as "vanilla" neural networks, especially when they have a single hidden layer.[1]""",
),
(
"""An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear""",
""" activation function.""",
),
(
"""MLP utilizes a supervised""",
""" learning technique called backpropagation for training.[2][3] Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.[4]""",
),
(
"""Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic""",
""" in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. """,
),
(
"""Specifically, we train GPT-3, an autoregressive language model with 175""",
""" billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.""",
),
(
"""A mult""",
"""ilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN)""",
),
("""Hello""", """ World"""),
])
]
# Test HuggingFace Models (GPT-2)
def test_gpt2():
gpt2 = models.get_model("gpt2").create_from_arg_string("device=cpu")
(
(ll_dog, ig_dog),
(ll_cat, ig_cat),
(_, ll_max_0),
(_, ll_max_1),
(_, ll_max_2),
*vals,
) = gpt2.loglikelihood(LOGLIKELIHOOD_TEST_CASES)
assert ll_dog > ll_cat
assert not ig_cat
......@@ -31,17 +76,24 @@ def test_gpt2():
assert ll_max_2
# test empty context
gpt2.loglikelihood([('', 'test')])
gpt2.loglikelihood([("", "test")])
gen, = gpt2.greedy_until([
('The quick brown fox jumps over the lazy', ['.', '\n'])
])
(gen,) = gpt2.greedy_until(
[("The quick brown fox jumps over the lazy", [".", "\n"])]
)
assert gen == ', lazy fox and they both fall to the ground'
assert gen == ", lazy fox and they both fall to the ground"
targets = [
-61.60536193847656, -56.57843780517578, -62.131004333496094, -9.799489974975586, -153.96334838867188,
-341.222900390625, -731.1475830078125, -61.60536193847656, -8.682319641113281
-61.60536193847656,
-56.57843780517578,
-62.131004333496094,
-9.799489974975586,
-153.96334838867188,
-341.222900390625,
-731.1475830078125,
-61.60536193847656,
-8.682319641113281,
]
for (pred, _), tgt in zip(vals, targets):
......@@ -49,21 +101,224 @@ def test_gpt2():
def test_gpt2_perplexity():
gpt2 = models.get_model('gpt2').create_from_arg_string("device=cpu")
gpt2 = models.get_model("gpt2").create_from_arg_string("device=cpu")
test_string = "We study empirical scaling laws for language model performance on the cross-entropy loss."
perplexity = gpt2.loglikelihood_rolling([(test_string,)])[0]
tgt = sum([
-4.9599953, -8.069298, -8.308624, -10.178513, -8.906924, -1.9318912, -7.745445, -7.146077, -5.2072,
-3.5882986, -1.9957212, -8.044922, -0.20841774, -5.1096807, -0.099879116, -8.888423, -4.6180487,
])
tgt = sum(
[
-4.9599953,
-8.069298,
-8.308624,
-10.178513,
-8.906924,
-1.9318912,
-7.745445,
-7.146077,
-5.2072,
-3.5882986,
-1.9957212,
-8.044922,
-0.20841774,
-5.1096807,
-0.099879116,
-8.888423,
-4.6180487,
]
)
assert perplexity == pytest.approx(tgt, rel=1e-3)
with mock.patch.object(models.gpt2.HFLM, 'max_length', new_callable=mock.PropertyMock) as mock_max_length:
with mock.patch.object(
models.gpt2.HFLM, "max_length", new_callable=mock.PropertyMock
) as mock_max_length:
mock_max_length.return_value = 5
gpt2 = models.get_model('gpt2').create_from_arg_string("device=cpu")
gpt2 = models.get_model("gpt2").create_from_arg_string("device=cpu")
perplexity = gpt2.loglikelihood_rolling([(test_string,)])[0]
tgt = sum([
-4.96001, -8.069275, -8.308612, -10.178482, -8.90691, -4.037338, -8.09261, -11.662385, -10.206891,
-4.425003, -2.2563353, -7.909143, -1.9304147, -7.3610134, -2.3120654, -7.3229, -2.1643813,
])
tgt = sum(
[
-4.96001,
-8.069275,
-8.308612,
-10.178482,
-8.90691,
-4.037338,
-8.09261,
-11.662385,
-10.206891,
-4.425003,
-2.2563353,
-7.909143,
-1.9304147,
-7.3610134,
-2.3120654,
-7.3229,
-2.1643813,
]
)
assert perplexity == pytest.approx(tgt, rel=1e-3)
# Test OpenAI Models (GPT-3)
def openai_mock_completion(**kwargs):
# Mock completion function
# Loads from a cached+pickled response if it exists, otherwise it will actually try to ping
os.makedirs("tests/testdata", exist_ok=True)
hash = hashlib.sha256(
json.dumps(kwargs, sort_keys=True).encode("utf-8")
).hexdigest()
fname = f"tests/testdata/gpt3_test_{hash}.pkl"
if os.path.exists(fname):
with open(fname, "rb") as fh:
return pickle.load(fh)
ret = openai.Completion.create(**kwargs)
ret.api_key = ""
with open(fname, "wb") as fh:
pickle.dump(ret, fh)
return ret
@mock.patch("lm_eval.models.gpt3.oa_completion", new=openai_mock_completion)
def test_gpt3():
if "OPENAI_API_SECRET_KEY" not in os.environ:
os.environ["OPENAI_API_SECRET_KEY"] = ""
gpt3 = models.get_model("gpt3").create_from_arg_string("engine=ada")
(
(ll_dog, ig_dog),
(ll_cat, ig_cat),
(_, ll_max_0),
(_, ll_max_1),
(_, ll_max_2),
*vals,
) = gpt3.loglikelihood(LOGLIKELIHOOD_TEST_CASES)
assert ll_dog > ll_cat
assert not ig_cat
assert ig_dog
assert not ll_max_0
assert not ll_max_1
assert not ll_max_2
# test empty context
gpt3.loglikelihood([("", "test")])
(gen,) = gpt3.greedy_until(
[("The quick brown fox jumps over the lazy", [".", "\n"])]
)
assert gen == " dog"
print([x[0] for x in vals])
targets = [
-34.848301606999996,
-47.148329679999996,
-45.44380149599999,
-5.285246016,
-133.97821690686004,
-321.2616693239001,
-658.0299524401041,
-34.848301606999996,
-7.525115,
]
for (pred, _), tgt in zip(vals, targets):
assert pred == pytest.approx(tgt, rel=1e-3)
@mock.patch("lm_eval.models.gpt3.oa_completion", new=openai_mock_completion)
def test_gpt3_perplexity():
if "OPENAI_API_SECRET_KEY" not in os.environ:
os.environ["OPENAI_API_SECRET_KEY"] = ""
gpt3 = models.get_model("gpt3").create_from_arg_string("engine=ada")
test_string = "We study empirical scaling laws for language model performance on the cross-entropy loss."
perplexity = gpt3.loglikelihood_rolling([(test_string,)])[0]
tgt = -84.38819608
assert perplexity == pytest.approx(tgt, rel=1e-3)
# Hack: modify gpt3 to have shorter context length to induce rolling windows
with mock.patch.object(
models.gpt3.GPT3LM, "max_length", new_callable=mock.PropertyMock
) as mock_max_length:
mock_max_length.return_value = 5
gpt3 = models.get_model("gpt3").create_from_arg_string("engine=ada")
perplexity = gpt3.loglikelihood_rolling([(test_string,)])[0]
tgt = -101.81967209999999
assert perplexity == pytest.approx(tgt, rel=1e-3)
# Test TextSynth Models (GPT-J)
def textsynth_mock_completion(**kwargs):
# Mock completion function
# Loads from a cached+pickled response if it exists, otherwise it will actually try to ping
import requests
os.makedirs("tests/testdata", exist_ok=True)
hash_kwargs = {k: v for k, v in kwargs.items() if k != "headers"}
hash = hashlib.sha256(
json.dumps(hash_kwargs, sort_keys=True).encode("utf-8")
).hexdigest()
fname = f"tests/testdata/textsynth_test_{hash}.pkl"
if os.path.exists(fname):
with open(fname, "rb") as fh:
return pickle.load(fh)
ret = requests.post(**kwargs)
with open(fname, "wb") as fh:
pickle.dump(ret, fh)
return ret
@mock.patch(
"lm_eval.models.textsynth.textsynth_completion", new=textsynth_mock_completion
)
def test_textsynth():
if "TEXTSYNTH_API_SECRET_KEY" not in os.environ:
os.environ["TEXTSYNTH_API_SECRET_KEY"] = ""
textsynth = models.get_model("textsynth").create_from_arg_string("engine=gptj_6B")
(
(ll_dog, ig_dog),
(ll_cat, ig_cat),
(_, ll_max_0),
(_, ll_max_1),
(_, ll_max_2),
*vals,
) = textsynth.loglikelihood(LOGLIKELIHOOD_TEST_CASES)
assert ll_dog > ll_cat
assert not ig_cat
assert ig_dog
assert not ll_max_0
assert not ll_max_1
assert not ll_max_2
# test empty context
textsynth.loglikelihood([("", "test")])
(gen,) = textsynth.greedy_until(
[("The quick brown fox jumps over the lazy", [".", "\n"])]
)
assert gen == " dog"
print([x[0] for x in vals])
targets = [
-17.90513712817,
-41.83518912287,
-33.82445643841,
-2.377361565302,
-99.53018069754,
-243.5642283598,
-528.6862613790,
-17.90513712817,
-5.041000672142,
]
for (pred, _), tgt in zip(vals, targets):
assert pred == pytest.approx(tgt, rel=1e-3)
tests/test_tasks.py
View file @ 18c0fa29
......@@ -6,11 +6,8 @@ from itertools import islice
@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
def test_basic_interface(taskname, task_class):
print('Evaluating task', taskname)
# dl = task_class.download
# task_class.download = MagicMock()
print("Evaluating task", taskname)
task = task_class()
# task_class.download = dl
assert task.has_training_docs() in [True, False]
assert task.has_validation_docs() in [True, False]
......@@ -70,7 +67,7 @@ def test_basic_interface(taskname, task_class):
@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
def test_documents_and_requests(taskname, task_class):
print('Evaluating task', taskname)
print("Evaluating task", taskname)
task = task_class()
fns = []
if task.has_training_docs():
......@@ -93,8 +90,8 @@ def test_documents_and_requests(taskname, task_class):
# space convention
# allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
if len(txt) != 0:
assert txt[-1] != ' '
assert tgt[0] == ' ' or txt[-1] == '\n'
assert txt[-1] != " "
assert tgt[0] == " " or txt[-1] == "\n"
reqs = task.construct_requests(doc, txt)
......
tests/test_utils.py
View file @ 18c0fa29
......@@ -5,8 +5,14 @@ from lm_eval.utils import get_rolling_token_windows, make_disjoint_window
def test_get_rolling_token_windows_v1():
gold = [
([-100, 0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
([9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
([19, 20, 21, 22, 23, 24, 25, 26, 27, 28], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),
(
[9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
),
(
[19, 20, 21, 22, 23, 24, 25, 26, 27, 28],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
),
([23, 24, 25, 26, 27, 28, 29, 30, 31, 32], [30, 31, 32, 33]),
]
x = list(range(34))
......@@ -123,7 +129,6 @@ def test_get_rolling_token_windows_v4():
([17, 18, 19, 20, 21, 22, 23, 24, 25, 26], [27]),
([18, 19, 20, 21, 22, 23, 24, 25, 26, 27], [28]),
([19, 20, 21, 22, 23, 24, 25, 26, 27, 28], [29]),
]
x = list(range(30))
generator = get_rolling_token_windows(
......@@ -145,8 +150,14 @@ def test_get_rolling_token_windows_v4():
def test_get_rolling_token_windows_v5():
gold = [
([-100, 0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
([9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]),
([19, 20, 21, 22, 23, 24, 25, 26, 27, 28], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),
(
[9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
),
(
[19, 20, 21, 22, 23, 24, 25, 26, 27, 28],
[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
),
]
x = list(range(30))
generator = get_rolling_token_windows(
......@@ -203,5 +214,9 @@ def test_get_rolling_token_windows_empty():
def test_make_disjoint_window():
assert make_disjoint_window(([1,2,3,4,5], [2,3,4,5,6])) == ([1], [2,3,4,5,6])
assert make_disjoint_window(([1,2,3,4,5], [4,5,6])) == ([1,2,3], [4,5,6])
\ No newline at end of file
assert make_disjoint_window(([1, 2, 3, 4, 5], [2, 3, 4, 5, 6])) == (
[1],
[2, 3, 4, 5, 6],
)
assert make_disjoint_window(([1, 2, 3, 4, 5], [4, 5, 6])) == ([1, 2, 3], [4, 5, 6])
assert make_disjoint_window(([1, 2, 3, 4, 5], [6])) == ([1, 2, 3, 4, 5], [6])
tests/test_version_stable.py
View file @ 18c0fa29
......@@ -20,9 +20,10 @@ def assert_target(name, ob):
# assuming most metrics work on `float32` values, which is the common
# default floating type across popular libraries (PyTorch, Tensorflow, and JAX).
assert flatten(json.load(fh)) == pytest.approx(
flatten(json.loads(json.dumps(ob, sort_keys=True))), rel=1e-5, abs=1e-8)
flatten(json.loads(json.dumps(ob, sort_keys=True))), rel=1e-5, abs=1e-8
)
else:
with open(fname, 'w') as fh:
with open(fname, "w") as fh:
json.dump(ob, fh, sort_keys=True)
......@@ -30,37 +31,48 @@ def assert_target_hashed(name, ob):
fname = f"tests/testdata/{name}"
if os.path.exists(fname):
with open(fname) as fh:
assert fh.read() == hashlib.sha256(json.dumps(ob, sort_keys=True).encode('utf-8')).hexdigest()
assert (
fh.read()
== hashlib.sha256(
json.dumps(ob, sort_keys=True).encode("utf-8")
).hexdigest()
)
else:
with open(fname, 'w') as fh:
fh.write(hashlib.sha256(json.dumps(ob, sort_keys=True).encode('utf-8')).hexdigest())
with open(fname, "w") as fh:
fh.write(
hashlib.sha256(
json.dumps(ob, sort_keys=True).encode("utf-8")
).hexdigest()
)
# from https://stackoverflow.com/a/6027615
def flatten(d, parent_key='', sep='.'):
def flatten(d, parent_key="", sep="."):
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, collections.MutableMapping):
if isinstance(v, collections.abc.MutableMapping):
items.extend(flatten(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
# make sure eval results for a task version are stable
@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
def test_versions_stable(taskname, task_class):
task_dict = tasks.get_task_dict([taskname])
lm = models.get_model('dummy')()
lm = models.get_model("dummy")()
def ll_fn(reqs):
for ctx, cont in reqs:
if len(ctx) == 0:
continue
# space convention
assert ctx[-1] != ' '
assert cont[0] == ' ' or ctx[-1] == '\n'
assert ctx[-1] != " "
assert cont[0] == " " or ctx[-1] == "\n"
assert_target_hashed(f"{taskname}-v{task_class.VERSION}-loglikelihood", reqs)
res = []
......@@ -72,10 +84,12 @@ def test_versions_stable(taskname, task_class):
return res
def ll_perp_fn(reqs):
for string, in reqs:
for (string,) in reqs:
assert isinstance(string, str)
assert_target_hashed(f"{taskname}-v{task_class.VERSION}-loglikelihood_rolling", reqs)
assert_target_hashed(
f"{taskname}-v{task_class.VERSION}-loglikelihood_rolling", reqs
)
res = []
random.seed(42)
......@@ -90,7 +104,7 @@ def test_versions_stable(taskname, task_class):
for ctx, _ in reqs:
res.append("lol")
assert ctx.strip() != ''
assert ctx.strip() != ""
return res
......@@ -105,7 +119,7 @@ def test_versions_stable(taskname, task_class):
num_fewshot=0,
limit=limit,
bootstrap_iters=10,
description_dict=None
description_dict=None,
)
assert_target(f"{taskname}-v{task_class.VERSION}-res", result)
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