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Unverified Commit 54999199 authored by Stella Biderman's avatar Stella Biderman Committed by GitHub
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Merge pull request #2 from cjlovering/master 

Pulling eval harness updates
parents 6caa0afd 18af502b
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Showing with 1506 additions and 1019 deletions
lm_eval/base.py
View file @ 54999199
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lm_eval/evaluator.py
View file @ 54999199
......@@ -2,25 +2,38 @@ import collections
import itertools
import pathlib
import random
import lm_eval.metrics
import lm_eval.models
import lm_eval.tasks
import lm_eval.base
import promptsource
import numpy as np
from promptsource.templates import DatasetTemplates
from lm_eval.utils import positional_deprecated, run_task_tests
@positional_deprecated
def simple_evaluate(model, model_args=None, tasks=[],
num_fewshot=0, batch_size=None, device=None,
no_cache=False, limit=None, bootstrap_iters=100000,
description_dict=None, check_integrity=False):
def simple_evaluate(
model,
model_args=None,
tasks=[],
num_fewshot=0,
batch_size=None,
device=None,
no_cache=False,
limit=None,
bootstrap_iters=100000,
description_dict=None,
check_integrity=False,
):
"""Instantiate and evaluate a model on a list of tasks.
:param model: Union[str, LM]
Name of model or LM object, see lm_eval.models.get_model
:param model_args: Optional[str]
String arguments for each model class, see LM.create_from_arg_string.
String arguments for each model class, see LM.create_from_arg_string.
Ignored if `model` argument is a LM object.
:param tasks: list[Union[str, Task]]
List of task names or Task objects. Task objects will be taken to have name task.EVAL_HARNESS_NAME if defined and type(task).__name__ otherwise.
......@@ -37,7 +50,7 @@ def simple_evaluate(model, model_args=None, tasks=[],
: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`
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
......@@ -49,20 +62,28 @@ def simple_evaluate(model, model_args=None, tasks=[],
assert tasks != [], "No tasks specified"
if isinstance(model, str):
if model_args is None: model_args = ""
lm = lm_eval.models.get_model(model).create_from_arg_string(model_args, {
'batch_size': batch_size, 'device': device
})
if model_args is None:
model_args = ""
lm = lm_eval.models.get_model(model).create_from_arg_string(
model_args, {"batch_size": batch_size, "device": device}
)
else:
assert isinstance(model, lm_eval.base.LM)
lm = model
# TODO: Hard-code turning off cache while testing. Remove once testing is completed.
no_cache = True
if not no_cache:
lm = lm_eval.base.CachingLM(
lm, 'lm_cache/' + model + '_' + model_args.replace('=', '-').replace(',', '_').replace('/', '-') + '.db'
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_promptsource(tasks)
if check_integrity:
run_task_tests(task_list=tasks)
......@@ -72,7 +93,7 @@ def simple_evaluate(model, model_args=None, tasks=[],
task_dict=task_dict,
num_fewshot=num_fewshot,
limit=limit,
description_dict=description_dict
description_dict=description_dict,
)
# add info about the model and few shot config
......@@ -85,14 +106,22 @@ def simple_evaluate(model, model_args=None, tasks=[],
"no_cache": no_cache,
"limit": limit,
"bootstrap_iters": bootstrap_iters,
"description_dict": description_dict
"description_dict": description_dict,
}
return results
@positional_deprecated
def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None, bootstrap_iters=100000, description_dict=None):
def evaluate(
lm,
task_dict,
provide_description=None,
num_fewshot=0,
limit=None,
bootstrap_iters=100000,
description_dict=None,
):
"""Instantiate and evaluate a model on a list of tasks.
:param lm: obj
......@@ -108,7 +137,7 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
: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`
Dictionary of custom task descriptions of the form: `task_name: description`
:return
Dictionary of results
"""
......@@ -118,12 +147,14 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
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")
print(
"WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
)
task_dict_items = [
(name, task)
for name, task in task_dict.items()
if(task.has_validation_docs() or task.has_test_docs())
if (task.has_validation_docs() or task.has_test_docs())
]
results = collections.defaultdict(dict)
......@@ -141,8 +172,12 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
docs = {}
# get lists of each type of request
for task_name, task in task_dict_items:
versions[task_name] = task.VERSION
for task_prompt_name, task in task_dict_items:
# if task.is_generation_task():
# print(f"WARNING: Skipping generation prompt {task.prompt.name}.")
# continue
versions[task_prompt_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():
......@@ -158,15 +193,19 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
rnd.seed(42)
rnd.shuffle(task_docs)
description = description_dict[task_name] if description_dict and task_name in description_dict else ""
description = (
description_dict[task_prompt_name]
if description_dict and task_prompt_name in description_dict
else ""
)
for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
docs[(task_name, doc_id)] = doc
if task.invalid_doc_for_prompt(doc):
continue
docs[(task_prompt_name, doc_id)] = doc
ctx = task.fewshot_context(
doc=doc,
num_fewshot=num_fewshot,
rnd=rnd,
description=description
doc=doc, num_fewshot=num_fewshot, rnd=rnd, description=description
)
reqs = task.construct_requests(doc, ctx)
if not isinstance(reqs, (list, tuple)):
......@@ -175,7 +214,9 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
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))
requests_origin[req.request_type].append(
(i, task_prompt_name, doc, doc_id)
)
# all responses for each (task, doc)
process_res_queue = collections.defaultdict(list)
......@@ -189,43 +230,49 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
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)]
resps = [
x if req.index is None else x[req.index] for x, req in zip(resps, reqs)
]
for resp, (i, task_prompt_name, doc, doc_id) in zip(
resps, requests_origin[reqtype]
):
process_res_queue[(task_prompt_name, doc_id)].append((i, resp))
for resp, (i, task_name, doc, doc_id) in zip(resps, requests_origin[reqtype]):
process_res_queue[(task_name, doc_id)].append((i, resp))
vals = collections.defaultdict(list)
# unpack results and sort back in order and return control to Task
for (task_name, doc_id), requests in process_res_queue.items():
for (task_prompt_name, doc_id), requests in process_res_queue.items():
requests.sort(key=lambda x: x[0])
requests = [x[1] for x in requests]
task = task_dict[task_name]
doc = docs[(task_name, doc_id)]
task = task_dict[task_prompt_name]
doc = docs[(task_prompt_name, doc_id)]
metrics = task.process_results(doc, requests)
for metric, value in metrics.items():
vals[(task_name, metric)].append(value)
vals[(task_prompt_name, metric)].append(value)
# aggregate results
for (task_name, metric), items in vals.items():
task = task_dict[task_name]
results[task_name][metric] = task.aggregation()[metric](items)
for (task_prompt_name, metric), items in vals.items():
task_name, prompt_name = task_prompt_name.split("+")
results[task_prompt_name]["task_name"] = task_name
results[task_prompt_name]["prompt_name"] = prompt_name
task = task_dict[task_prompt_name]
results[task_prompt_name][metric] = 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()[metric],
bootstrap_iters=min(bootstrap_iters, 1000) if metric in ["bleu", "chrf", "ter"] else bootstrap_iters,
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)
return {
"results": dict(results),
"versions": dict(versions)
}
results[task_prompt_name][metric + "_stderr"] = stderr(items)
return {"results": dict(results), "versions": dict(versions)}
def make_table(result_dict):
......@@ -234,22 +281,50 @@ def make_table(result_dict):
md_writer = MarkdownTableWriter()
latex_writer = LatexTableWriter()
md_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
latex_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
md_writer.headers = ["Task", "Prompt", "Version", "Metric", "Value", "", "Stderr"]
latex_writer.headers = [
"Task",
"Prompt",
"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 "_name" in m:
continue
if m + "_stderr" in dic:
se = dic[m + "_stderr"]
values.append([k, version, m, '%.4f' % v, '±', '%.4f' % se])
values.append(
[
dic["task_name"],
dic["prompt_name"],
version,
m,
"%.4f" % v,
"±",
"%.4f" % se,
]
)
else:
values.append([k, version, m, '%.4f' % v, '', ''])
values.append(
[
dic["task_name"],
dic["prompt_name"],
version,
m,
"%.4f" % v,
"",
"",
]
)
k = ""
version = ""
md_writer.value_matrix = values
......
lm_eval/metrics.py
View file @ 54999199
import typing
import math
from collections.abc import Iterable
import numpy as np
import sacrebleu
from rouge_score import rouge_scorer
import sklearn.metrics
import random
......@@ -184,6 +186,74 @@ def _sacreformat(refs, preds):
return refs, preds
def rouge(
refs: typing.List[str],
pred: str,
rouge_types: typing.List[str] = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
):
""" ROUGE with multi-reference support
Implementation based on GEM-metrics:
https://github.com/GEM-benchmark/GEM-metrics/blob/431a8174bd6b3637e8d6118bfad2983e39e99733/gem_metrics/rouge.py
:param refs:
A `list` of reference `str`s.
:param pred:
A single prediction `str`s.
"""
# Add newlines between sentences to correctly compute `rougeLsum`.
if "rougeLsum" in rouge_types:
# TODO: Adapt this to handle languages that do not support sentence endings by `.`.
# See GEM-metrics implementation with lang specific `nltk` tokenizers to
# split sentences.
pred = pred.replace(".", ".\n")
refs = [ref.replace(".", ".\n") for ref in refs]
scorer = rouge_scorer.RougeScorer(rouge_types=rouge_types, use_stemmer=True)
# ROUGE multi-ref jackknifing
if len(refs) > 1:
cur_scores = [scorer.score(ref, pred) for ref in refs]
# get best score for all leave-one-out sets
best_scores = []
for leave in range(len(refs)):
cur_scores_leave_one = [
cur_scores[s] for s in range(len(refs)) if s != leave
]
best_scores.append(
{
rouge_type: max(
[s[rouge_type] for s in cur_scores_leave_one],
key=lambda s: s.fmeasure,
)
for rouge_type in rouge_types
}
)
# average the leave-one-out bests to produce the final score
score = {
rouge_type: rouge_scorer.scoring.Score(
np.mean([b[rouge_type].precision for b in best_scores]),
np.mean([b[rouge_type].recall for b in best_scores]),
np.mean([b[rouge_type].fmeasure for b in best_scores]),
)
for rouge_type in rouge_types
}
else:
score = scorer.score(refs[0], pred)
# convert the named tuples to plain nested dicts
score = {
rouge_type: {
"precision": score[rouge_type].precision,
"recall": score[rouge_type].recall,
"fmeasure": score[rouge_type].fmeasure,
}
for rouge_type in rouge_types
}
return score
# stderr stuff
class _bootstrap_internal:
......
lm_eval/models/__init__.py
View file @ 54999199
from . import gpt2
from . import gptj
from . import gpt3
from . import t5
from . import t0
from . import dummy
MODEL_REGISTRY = {
"hf": gpt2.HFLM,
"gpt2": gpt2.GPT2LM,
"gptj": gptj.GPTJLM,
"gpt3": gpt3.GPT3LM,
"t5": t5.T5LM,
"mt5": t5.T5LM,
"t0": t0.T0LM,
"dummy": dummy.DummyLM,
}
......
lm_eval/models/gpt2.py
View file @ 54999199
......@@ -4,8 +4,15 @@ from lm_eval.base import BaseLM
class HFLM(BaseLM):
def __init__(self, device='cuda', pretrained='gpt2', revision='main', subfolder=None, tokenizer=None, batch_size=1):
def __init__(
self,
device="cuda",
pretrained="gpt2",
revision="main",
subfolder=None,
tokenizer=None,
batch_size=1,
):
super().__init__()
assert isinstance(device, str)
......@@ -15,28 +22,47 @@ class HFLM(BaseLM):
if device:
self._device = torch.device(device)
else:
self._device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self._device = (
torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
# TODO: update this to be less of a hack once subfolder is fixed in HF
self.gpt2 = transformers.AutoModelForCausalLM.from_pretrained(
pretrained, revision=revision + ("/" + subfolder if subfolder is not None else "")
pretrained,
revision=revision + ("/" + subfolder if subfolder is not None else ""),
).to(self.device)
self.gpt2.eval()
# pretrained tokenizer for neo is broken for now so just hard-coding this to gpt2
self.tokenizer = transformers.AutoTokenizer.from_pretrained(
pretrained if tokenizer is None else tokenizer, revision=revision, subfolder=subfolder)
pretrained if tokenizer is None else tokenizer,
revision=revision,
subfolder=subfolder,
)
assert isinstance(self.tokenizer, (
transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast,
transformers.T5Tokenizer, transformers.T5TokenizerFast,
)), "this tokenizer has not been checked for compatibility yet!"
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')
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
......@@ -75,7 +101,7 @@ class HFLM(BaseLM):
def tok_encode(self, string: str):
return self.tokenizer.encode(string, add_special_tokens=False)
def tok_decode(self, tokens):
return self.tokenizer.decode(tokens)
......@@ -89,14 +115,42 @@ class HFLM(BaseLM):
"""
with torch.no_grad():
return self.gpt2(inps)[0][:, :, :50257]
def _model_generate(self, context, max_length, eos_token_id):
def _get_stopping_criteria(self, stopping_criteria_ids):
class MultitokenEOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
self.eos_seq = tokenizer.decode(eos_seq_id)
self.eos_seq_id = eos_seq_id
self.eos_seq_len = len(eos_seq_id) + 1
self.tokenizer = tokenizer
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
last_token_id = input_ids[0, -self.eos_seq_len:]
last_tokens = self.tokenizer.decode(last_token_id)
is_stopped = self.eos_seq in last_tokens
return is_stopped
class EOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_token_id: torch.LongTensor):
self.eos_token_id = eos_token_id
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids[0,-1] == self.eos_token_id
return transformers.StoppingCriteriaList([
MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
EOSCriteria(self.tokenizer.eos_token)
])
def _model_generate(self, context, max_length, stopping_criteria_ids):
stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
return self.gpt2.generate(
context,
max_length=max_length,
eos_token_id=eos_token_id,
do_sample=False
context,
max_length=max_length,
stopping_criteria=stopping_criteria,
do_sample=False,
)
# for backwards compatibility
......
lm_eval/models/gptj.py 0 → 100644
View file @ 54999199
import transformers
import torch
from lm_eval.base import BaseLM
class GPTJLM(BaseLM):
def __init__(
self,
device="cuda",
batch_size=1,
):
super().__init__()
assert isinstance(device, str)
assert isinstance(batch_size, int)
if device:
self._device = torch.device(device)
else:
self._device = (
torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
pretrained = "EleutherAI/gpt-j-6B"
self.gptj = transformers.AutoModelForCausalLM.from_pretrained(pretrained).to(self.device)
self.gptj.eval()
# pretrained tokenizer for neo is broken for now so just hard-coding this to gptj
self.tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)
self.vocab_size = self.tokenizer.vocab_size
# multithreading and batching
self.batch_size_per_gpu = batch_size # todo: adaptive batch size
# TODO: fix multi-gpu
# gpus = torch.cuda.device_count()
# if gpus > 1:
# self.gptj = nn.DataParallel(self.gptj)
@property
def eot_token_id(self):
# we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
return self.tokenizer.eos_token_id
@property
def max_length(self):
try:
return self.gptj.config.n_ctx
except AttributeError:
# gptneoconfig doesn't have n_ctx apparently
return self.gptj.config.max_position_embeddings
@property
def max_gen_toks(self):
return 256
@property
def batch_size(self):
# TODO: fix multi-gpu
return self.batch_size_per_gpu # * gpus
@property
def device(self):
# TODO: fix multi-gpu
return self._device
def tok_encode(self, string: str):
return self.tokenizer.encode(string, add_special_tokens=False)
def tok_decode(self, tokens):
return self.tokenizer.decode(tokens)
def _model_call(self, inps):
"""
inps: a torch tensor of shape [batch, sequence]
the size of sequence may vary from call to call
returns: a torch tensor of shape [batch, sequence, vocab] with the
logits returned from the model
"""
with torch.no_grad():
return self.gptj(inps)[0][:, :, :50257]
def _get_stopping_criteria(self, stopping_criteria_ids):
class MultitokenEOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
self.eos_seq = tokenizer.decode(eos_seq_id)
self.eos_seq_id = eos_seq_id
self.eos_seq_len = len(eos_seq_id) + 1
self.tokenizer = tokenizer
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
last_token_id = input_ids[0, -self.eos_seq_len:]
last_tokens = self.tokenizer.decode(last_token_id)
is_stopped = self.eos_seq in last_tokens
return is_stopped
class EOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_token_id: torch.LongTensor):
self.eos_token_id = eos_token_id
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids[0,-1] == self.eos_token_id
return transformers.StoppingCriteriaList([
MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
EOSCriteria(self.tokenizer.eos_token)
])
def _model_generate(self, context, max_length, stopping_criteria_ids):
stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
return self.gptj.generate(
context,
max_length=max_length,
stopping_criteria=stopping_criteria,
do_sample=False,
)
lm_eval/models/t0.py 0 → 100644
View file @ 54999199
import transformers
import torch
import torch.nn as nn
import torch.nn.functional as F
from lm_eval.base import LM
from lm_eval import utils
from tqdm import tqdm
import numpy as np
import math
class T0LM(LM):
MAX_GEN_TOKS = 256
MAX_INP_LENGTH = 512
VOCAB_SIZE = 32100
EOT_TOKEN_ID = 1
def __init__(self, device='cuda', parallelize=False, pretrained='t0', batch_size=1):
super().__init__()
if device:
self.device = torch.device(device)
else:
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print(pretrained)
self.t0 = transformers.AutoModelForSeq2SeqLM.from_pretrained(pretrained)
self.t0.eval()
if parallelize == "True":
print(parallelize)
self.t0.parallelize()
self.device = torch.device('cuda:0')
else:
self.t0.to(self.device)
self.tokenizer = transformers.AutoTokenizer.from_pretrained(pretrained)
self.max_length = self.MAX_INP_LENGTH
self.batch_size = int(batch_size)
@classmethod
def create_from_arg_string(cls, arg_string, additional_config={}):
args = utils.simple_parse_args_string(arg_string)
args2 = {k: v for k, v in additional_config.items() if v is not None}
return cls(**args, **args2)
def loglikelihood(self, requests):
res = []
for chunk in tqdm(utils.chunks(requests, self.batch_size), total=math.ceil(len(requests)/self.batch_size)):
inputs, targets = zip(*chunk)
inputs_tok = self.tokenizer(
list(inputs),
max_length=self.max_length,
padding=True,
# truncation=True,
add_special_tokens=False,
return_tensors="pt"
).to(self.device)
for key in inputs_tok:
inputs_tok[key] = inputs_tok[key][:, -(self.max_length - 1) :]
targets_tok = self.tokenizer(
list(targets),
max_length=self.MAX_GEN_TOKS,
padding=True,
# truncation=True,
add_special_tokens=False,
return_tensors="pt"
).to(self.device)
for key in targets_tok:
targets_tok[key] = targets_tok[key][:, -(self.max_length - 1) :]
with torch.no_grad():
outputs = self.t0(
**inputs_tok,
labels=targets_tok["input_ids"]
)
log_softmaxes = F.log_softmax(outputs.logits, dim=-1)
output_iterator = zip(
chunk,
log_softmaxes,
targets_tok["input_ids"],
targets_tok["attention_mask"],
)
for cache_key, log_softmax, target_tok, target_mask in output_iterator:
length = target_mask.sum()
log_softmax = log_softmax[:length]
target_tok = target_tok[:length]
greedy_tokens = log_softmax.argmax(dim=-1)
max_equal = (greedy_tokens == target_tok).all()
target_logits = torch.gather(
log_softmax, 1, target_tok.unsqueeze(-1)
).squeeze(-1)
answer = (float(target_logits.sum()), bool(max_equal))
if cache_key is not None:
self.cache_hook.add_partial("loglikelihood", cache_key, answer)
res.append(answer)
return res
def loglikelihood_rolling(self, requests):
raise NotImplementedError
def _get_stopping_criteria(self, stopping_criteria_ids):
class MultitokenEOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
self.eos_seq = tokenizer.decode(eos_seq_id)
self.eos_seq_id = eos_seq_id
self.eos_seq_len = len(eos_seq_id) + 1
self.tokenizer = tokenizer
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
last_token_id = input_ids[0, -self.eos_seq_len:]
last_tokens = self.tokenizer.decode(last_token_id)
is_stopped = self.eos_seq in last_tokens
return is_stopped
class EOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_token_id: torch.LongTensor):
self.eos_token_id = eos_token_id
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids[0,-1] == self.eos_token_id
return transformers.StoppingCriteriaList([
MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
EOSCriteria(self.tokenizer.eos_token)
])
def greedy_until(self, requests):
res = []
for context, until in tqdm(requests):
if isinstance(until, str): until = [until]
context_enc = self.tokenizer(context, return_tensors="pt").to(self.device).input_ids
stopping_criteria_ids = self.tokenizer.encode(until[0])
stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
cont = self.t0.generate(
context_enc,
max_length=self.MAX_GEN_TOKS,
stopping_criteria=stopping_criteria,
do_sample=False
)
s = self.tokenizer.decode(cont[0].tolist())
self.cache_hook.add_partial("greedy_until", (context, until), s)
res.append(s)
return res
\ No newline at end of file
lm_eval/models/t5.py 0 → 100644
View file @ 54999199
import transformers
import torch
import torch.nn as nn
import torch.nn.functional as F
from lm_eval.base import LM
from lm_eval import utils
from tqdm import tqdm
import numpy as np
import math
class T5LM(LM):
MAX_GEN_TOKS = 256
MAX_INP_LENGTH = 512
VOCAB_SIZE = 32128
EOT_TOKEN_ID = 1
def __init__(self, device='cuda', parallelize=False, pretrained='t5', batch_size=1):
super().__init__()
if device:
self.device = torch.device(device)
else:
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print(pretrained)
self.t5 = transformers.AutoModelForSeq2SeqLM.from_pretrained(pretrained)
self.t5.eval()
if parallelize == "True":
print(parallelize)
self.t5.parallelize()
self.device = torch.device('cuda:0')
else:
self.t5.to(self.device)
self.tokenizer = transformers.T5TokenizerFast.from_pretrained(pretrained)
self.max_length = self.MAX_INP_LENGTH
self.batch_size = int(batch_size)
@classmethod
def create_from_arg_string(cls, arg_string, additional_config={}):
args = utils.simple_parse_args_string(arg_string)
args2 = {k: v for k, v in additional_config.items() if v is not None}
return cls(**args, **args2)
def loglikelihood(self, requests):
res = []
for chunk in tqdm(utils.chunks(requests, self.batch_size), total=math.ceil(len(requests)/self.batch_size)):
inputs, targets = zip(*chunk)
inputs_tok = self.tokenizer(
list(inputs),
max_length=self.max_length,
padding=True,
# truncation=True,
add_special_tokens=False,
return_tensors="pt"
).to(self.device)
for key in inputs_tok:
inputs_tok[key] = inputs_tok[key][:, -(self.max_length - 1) :]
targets_tok = self.tokenizer(
list(targets),
max_length=self.MAX_GEN_TOKS,
padding=True,
# truncation=True,
add_special_tokens=False,
return_tensors="pt"
).to(self.device)
for key in targets_tok:
targets_tok[key] = targets_tok[key][:, -(self.max_length - 1) :]
with torch.no_grad():
outputs = self.t5(
**inputs_tok,
labels=targets_tok["input_ids"]
)
log_softmaxes = F.log_softmax(outputs.logits, dim=-1)
output_iterator = zip(
chunk,
log_softmaxes,
targets_tok["input_ids"],
targets_tok["attention_mask"],
)
for cache_key, log_softmax, target_tok, target_mask in output_iterator:
length = target_mask.sum()
log_softmax = log_softmax[:length]
target_tok = target_tok[:length]
greedy_tokens = log_softmax.argmax(dim=-1)
max_equal = (greedy_tokens == target_tok).all()
target_logits = torch.gather(
log_softmax, 1, target_tok.unsqueeze(-1)
).squeeze(-1)
answer = (float(target_logits.sum()), bool(max_equal))
if cache_key is not None:
self.cache_hook.add_partial("loglikelihood", cache_key, answer)
res.append(answer)
return res
def loglikelihood_rolling(self, requests):
raise NotImplementedError
def _get_stopping_criteria(self, stopping_criteria_ids):
class MultitokenEOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_seq_id: torch.LongTensor, tokenizer):
self.eos_seq = tokenizer.decode(eos_seq_id)
self.eos_seq_id = eos_seq_id
self.eos_seq_len = len(eos_seq_id) + 1
self.tokenizer = tokenizer
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
last_token_id = input_ids[0, -self.eos_seq_len:]
last_tokens = self.tokenizer.decode(last_token_id)
is_stopped = self.eos_seq in last_tokens
return is_stopped
class EOSCriteria(transformers.StoppingCriteria):
def __init__(self, eos_token_id: torch.LongTensor):
self.eos_token_id = eos_token_id
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return input_ids[0,-1] == self.eos_token_id
return transformers.StoppingCriteriaList([
MultitokenEOSCriteria(stopping_criteria_ids, self.tokenizer),
EOSCriteria(self.tokenizer.eos_token)
])
def greedy_until(self, requests):
res = []
for context, until in tqdm(requests):
if isinstance(until, str): until = [until]
context_enc = self.tokenizer(context, return_tensors="pt").to(self.device).input_ids
stopping_criteria_ids = self.tokenizer.encode(until[0])
stopping_criteria = self._get_stopping_criteria(stopping_criteria_ids)
cont = self.t5.generate(
context_enc,
max_length=self.MAX_GEN_TOKS,
stopping_criteria=stopping_criteria,
do_sample=False
)
s = self.tokenizer.decode(cont[0].tolist())
self.cache_hook.add_partial("greedy_until", (context, until), s)
res.append(s)
return res
\ No newline at end of file
lm_eval/tasks/__init__.py
View file @ 54999199
from promptsource.templates import DatasetTemplates
from pprint import pprint
from typing import List, Union
......@@ -51,6 +52,9 @@ from . import blimp
from . import asdiv
from . import gsm8k
from . import storycloze
from . import hans
# from . import e2e_nlg_cleaned
########################################
# Translation tasks
......@@ -58,8 +62,8 @@ from . import storycloze
# 6 total
gpt3_translation_benchmarks = {
"wmt14": ['en-fr', 'fr-en'], # French
"wmt16": ['en-ro', 'ro-en', 'de-en', 'en-de'], # German, Romanian
"wmt14": ["en-fr", "fr-en"], # French
"wmt16": ["en-ro", "ro-en", "de-en", "en-de"], # German, Romanian
}
......@@ -67,7 +71,7 @@ gpt3_translation_benchmarks = {
selected_translation_benchmarks = {
**gpt3_translation_benchmarks,
"wmt20": sacrebleu.get_langpairs_for_testset("wmt20"),
"iwslt17": ['en-ar', 'ar-en'] # Arabic
"iwslt17": ["en-ar", "ar-en"], # Arabic
}
# 319 total
......@@ -91,7 +95,7 @@ TASK_REGISTRY = {
"rte": glue.RTE,
"qnli": glue.QNLI,
"qqp": glue.QQP,
#"stsb": glue.STSB, # not implemented yet
# "stsb": glue.STSB, # not implemented yet
"sst": glue.SST,
"wnli": glue.WNLI,
# SuperGLUE
......@@ -102,34 +106,27 @@ TASK_REGISTRY = {
"record": superglue.ReCoRD,
"wic": superglue.WordsInContext,
"wsc": superglue.SGWinogradSchemaChallenge,
# Order by benchmark/genre?
"coqa": coqa.CoQA,
"drop": drop.DROP,
"lambada": lambada.LAMBADA,
"lambada_cloze": lambada_cloze.LAMBADA_cloze,
# multilingual lambada
**lambada_multilingual.construct_tasks(),
"wikitext": wikitext.WikiText,
# "cbt-cn": cbt.CBTCN, # disabled pending context length fix
# "cbt-ne": cbt.CBTNE, # disabled pending context length fix
"piqa": piqa.PiQA,
"prost": prost.PROST,
"mc_taco": mc_taco.MCTACO,
# Science related
"pubmedqa" : pubmedqa.Pubmed_QA,
"sciq" : sciq.SciQ,
"pubmedqa": pubmedqa.Pubmed_QA,
"sciq": sciq.SciQ,
# "e2e_nlg_cleaned": e2e_nlg_cleaned.E2E_NLG_Cleaned,
"qasper": qasper.QASPER,
"qa4mre_2011" : qa4mre.QA4MRE_2011,
"qa4mre_2012" : qa4mre.QA4MRE_2012,
"qa4mre_2013" : qa4mre.QA4MRE_2013,
"qa4mre_2011": qa4mre.QA4MRE_2011,
"qa4mre_2012": qa4mre.QA4MRE_2012,
"qa4mre_2013": qa4mre.QA4MRE_2013,
"triviaqa": triviaqa.TriviaQA,
"arc_easy": arc.ARCEasy,
"arc_challenge": arc.ARCChallenge,
......@@ -140,7 +137,7 @@ TASK_REGISTRY = {
"squad2": squad.SQuAD2,
"race": race.RACE,
# "naturalqs": naturalqs.NaturalQs, # not implemented yet
"headqa": headqa.HeadQAEsDeprecated, # for backwards compat - headqa used to default to es
"headqa": headqa.HeadQAEsDeprecated, # for backwards compat - headqa used to default to es
"headqa_es": headqa.HeadQAEs,
"headqa_en": headqa.HeadQAEn,
"mathqa": mathqa.MathQA,
......@@ -150,21 +147,18 @@ TASK_REGISTRY = {
"anli_r1": anli.ANLIRound1,
"anli_r2": anli.ANLIRound2,
"anli_r3": anli.ANLIRound3,
"hans": hans.HANS,
"ethics_cm": hendrycks_ethics.EthicsCM,
"ethics_deontology": hendrycks_ethics.EthicsDeontology,
"ethics_justice": hendrycks_ethics.EthicsJustice,
"ethics_utilitarianism_original": hendrycks_ethics.EthicsUtilitarianismOriginal,
"ethics_utilitarianism": hendrycks_ethics.EthicsUtilitarianism,
"ethics_virtue": hendrycks_ethics.EthicsVirtue,
"truthfulqa_mc": truthfulqa.TruthfulQAMultipleChoice,
"truthfulqa_gen": truthfulqa.TruthfulQAGeneration,
"truthfulqa_mc": truthfulqa.TruthfulQAMultipleChoice,
"truthfulqa_gen": truthfulqa.TruthfulQAGeneration,
# dialogue
"mutual": mutual.MuTual,
"mutual_plus": mutual.MuTualPlus,
# math
"math_algebra": hendrycks_math.MathAlgebra,
"math_counting_and_prob": hendrycks_math.MathCountingAndProbability,
......@@ -175,7 +169,6 @@ TASK_REGISTRY = {
"math_precalc": hendrycks_math.MathPrecalculus,
"math_asdiv": asdiv.Asdiv,
"gsm8k": gsm8k.GradeSchoolMath8K,
# arithmetic
"arithmetic_2da": arithmetic.Arithmetic2DPlus,
"arithmetic_2ds": arithmetic.Arithmetic2DMinus,
......@@ -189,22 +182,18 @@ TASK_REGISTRY = {
"arithmetic_1dc": arithmetic.Arithmetic1DComposite,
# TODO Perhaps make these groups of tasks
# e.g. anli, arithmetic, openai_translations, harness_translations
# hendrycksTest (57 tasks)
**hendrycks_test.create_all_tasks(),
# e.g. wmt14-fr-en
**translation.create_tasks_from_benchmarks(gpt3_translation_benchmarks),
# chef's selection, mostly wmt20
**translation.create_tasks_from_benchmarks(selected_translation_benchmarks),
# Word Scrambling and Manipulation Tasks
"anagrams1": unscramble.Anagrams1,
"anagrams2": unscramble.Anagrams2,
"cycle_letters": unscramble.CycleLetters,
"random_insertion": unscramble.RandomInsertion,
"reversed_words": unscramble.ReversedWords,
# Pile
"pile_arxiv": pile.PileArxiv,
"pile_books3": pile.PileBooks3,
......@@ -228,7 +217,6 @@ TASK_REGISTRY = {
"pile_ubuntu-irc": pile.PileUbuntuIrc,
"pile_wikipedia": pile.PileWikipedia,
"pile_youtubesubtitles": pile.PileYoutubeSubtitles,
# BLiMP
"blimp_adjunct_island": blimp.BlimpAdjunctIsland,
"blimp_anaphor_gender_agreement": blimp.BlimpAnaphorGenderAgreement,
......@@ -297,7 +285,6 @@ TASK_REGISTRY = {
"blimp_wh_vs_that_no_gap_long_distance": blimp.BlimpWhVsThatNoGapLongDistance,
"blimp_wh_vs_that_with_gap": blimp.BlimpWhVsThatWithGap,
"blimp_wh_vs_that_with_gap_long_distance": blimp.BlimpWhVsThatWithGapLongDistance,
# Requires manual download of data.
# "storycloze_2016": storycloze.StoryCloze2016,
# "storycloze_2018": storycloze.StoryCloze2018,
......@@ -321,19 +308,51 @@ def get_task_name_from_object(task_object):
for name, class_ in TASK_REGISTRY.items():
if class_ is task_object:
return name
# this gives a mechanism for non-registered tasks to have a custom name anyways when reporting
return task_object.EVAL_HARNESS_NAME if hasattr(task_object, "EVAL_HARNESS_NAME") else type(task_object).__name__
return (
task_object.EVAL_HARNESS_NAME
if hasattr(task_object, "EVAL_HARNESS_NAME")
else type(task_object).__name__
)
def get_task_dict(task_name_list: List[Union[str, lm_eval.base.Task]]):
task_name_dict = {
task_name: get_task(task_name)()
for task_name in task_name_list if isinstance(task_name, str)
for task_name in task_name_list
if isinstance(task_name, str)
}
task_name_from_object_dict = {
get_task_name_from_object(task_object): task_object
for task_object in task_name_list if not isinstance(task_object, str)
for task_object in task_name_list
if not isinstance(task_object, str)
}
assert set(task_name_dict.keys()).isdisjoint(set(task_name_from_object_dict.keys()))
return {**task_name_dict, **task_name_from_object_dict}
def get_task_dict_promptsource(task_name_list: List[str]):
"""Loads a task instance for each prompt written for that task."""
task_name_dict = {}
for task_name in task_name_list:
assert isinstance(task_name, str)
# Static version of the Task Use this to get HF dataset path / name.
static_task_obj = get_task(task_name)
# Create the proper task name arg for DatasetTemplates.
sub_task = (
f"/{static_task_obj.DATASET_NAME}" if static_task_obj.DATASET_NAME else ""
)
ps_task_name = f"{static_task_obj.DATASET_PATH}{sub_task}"
task_prompts = DatasetTemplates(ps_task_name)
for prompt_name in task_prompts.all_template_names:
prompt = task_prompts[prompt_name]
# NOTE: We choose a sep that can be easily split.
task_name_dict[f"{task_name}+{prompt_name}"] = get_task(task_name)(
prompt=prompt
)
return task_name_dict
lm_eval/tasks/anli.py
View file @ 54999199
......@@ -10,7 +10,7 @@ provided explanations.
Homepage: "https://github.com/facebookresearch/anli"
"""
import numpy as np
from lm_eval.base import rf, Task
from lm_eval.base import rf, PromptSourceTask
from lm_eval.metrics import mean
......@@ -30,7 +30,7 @@ _CITATION = """
"""
class ANLIBase(Task):
class ANLIBase(PromptSourceTask):
VERSION = 0
DATASET_PATH = "anli"
DATASET_NAME = None
......@@ -59,51 +59,6 @@ class ANLIBase(Task):
if self.has_test_docs():
return self.dataset["test_r" + str(self.SPLIT)]
def doc_to_text(self, doc):
# OA does this a bit weirdly: they prepend "anli 1: anli 1: " to the beginning
# of the prompt (yes, repeating it!). also, " True, False, or Neither?" is directly
# appended onto the question, with no "Answer:" or even a newline. Do we *really*
# want to do it exactly as OA did?
return doc['premise'] + '\nQuestion: ' + doc['hypothesis'] + ' True, False, or Neither?\nAnswer:'
def doc_to_target(self, doc):
# True = entailment
# False = contradiction
# Neither = neutral
return " " + ["True", "Neither", "False"][doc['label']]
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_neither, _ = rf.loglikelihood(ctx, " Neither")
ll_false, _ = rf.loglikelihood(ctx, " False")
return ll_true, ll_neither, ll_false
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param results:
The results of the requests created in construct_requests.
"""
gold = doc["label"]
pred = np.argmax(results)
return {
"acc": pred == gold
}
def aggregation(self):
"""
:returns: {str: [float] -> float}
......
lm_eval/tasks/arithmetic.py
View file @ 54999199
......@@ -58,10 +58,11 @@ class Arithmetic(Task):
def construct_requests(self, doc, ctx):
ll, is_prediction = rf.loglikelihood(ctx, doc["completion"])
return is_prediction
return ll, is_prediction
def process_results(self, doc, results):
is_prediction, = results
print(results)
results = results
return {
"acc": is_prediction
}
......
lm_eval/tasks/coqa.py
View file @ 54999199
......@@ -12,7 +12,7 @@ Homepage: https://stanfordnlp.github.io/coqa/
import inspect
import transformers.data.metrics.squad_metrics as squad_metrics
import lm_eval.datasets.coqa.coqa
from lm_eval.base import Task, rf, mean
from lm_eval.base import PromptSourceTask, Task, rf, mean
from itertools import zip_longest
......@@ -28,9 +28,9 @@ _CITATION = """
"""
class CoQA(Task):
class CoQA(PromptSourceTask):
VERSION = 1
DATASET_PATH = inspect.getfile(lm_eval.datasets.coqa.coqa)
DATASET_PATH = "coqa"
DATASET_NAME = None
def has_training_docs(self):
......@@ -51,44 +51,21 @@ class CoQA(Task):
def test_docs(self):
pass
def doc_to_text(self, doc):
# Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
# and a question qi, the task is to predict the answer ai
doc_text = doc["story"] + '\n\n'
for (q, a) in zip_longest(doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]): # omit target answer ai
question = f"Q: {q}\n\n"
answer = f"A: {a}\n\n" if a is not None else "A:"
doc_text += question + answer
return doc_text
@classmethod
def get_answers(cls, doc, turn_id):
# Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
answers = []
answer_forturn = doc["answers"]["input_text"][turn_id - 1]
answers.append(answer_forturn)
additional_answers = doc.get("additional_answers")
if additional_answers:
for key in additional_answers:
additional_answer_for_turn = additional_answers[key]["input_text"][turn_id - 1]
if additional_answer_for_turn.lower() not in map(str.lower, answers):
answers.append(additional_answer_for_turn)
return answers
@classmethod
def get_answer_choice(self, raw_text):
# Function maps answers to CoQA answer categories
# ~ 1/5 of the CoQA answers are Yes/No
# ~ 2/3 of the CoQA answers are span-based
# (answers overlap with the passage ignoring punctuation and case mismatch)
if raw_text == "unknown":
return '0'
if squad_metrics.normalize_answer(raw_text) == "yes":
return '1'
if squad_metrics.normalize_answer(raw_text) == "no":
return '2'
return '3' # Not a yes/no question
# @classmethod
# def get_answers(cls, doc, turn_id):
# # Returns unique answers and valid alternatives (Some questions in CoQA have multiple valid answers).
# answers = []
# answer_forturn = doc["answers"]["input_text"][turn_id - 1]
# answers.append(answer_forturn)
# additional_answers = doc.get("additional_answers")
# if additional_answers:
# for key in additional_answers:
# additional_answer_for_turn = additional_answers[key]["input_text"][
# turn_id - 1
# ]
# if additional_answer_for_turn.lower() not in map(str.lower, answers):
# answers.append(additional_answer_for_turn)
# return answers
@staticmethod
def compute_scores(gold_list, pred):
......@@ -98,40 +75,40 @@ class CoQA(Task):
em_sum = 0.0
if len(gold_list) > 1:
for i in range(len(gold_list)):
gold_answers = gold_list[0:i] + gold_list[i + 1:]
gold_answers = gold_list[0:i] + gold_list[i + 1 :]
# predictions compared against (n) golds and take maximum
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
em_sum += max(
squad_metrics.compute_exact(a, pred) for a in gold_answers
)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
else:
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
return {'em': em_sum / max(1, len(gold_list)), 'f1': f1_sum / max(1, len(gold_list))}
return {
"em": em_sum / max(1, len(gold_list)),
"f1": f1_sum / max(1, len(gold_list)),
}
def doc_to_target(self, doc, turnid=None):
# Default to prediction of last turn.
if turnid is None:
turnid = len(doc["questions"]["input_text"])
raw_text = doc['answers']["input_text"][turnid - 1]
return " " + raw_text
def stopping_criteria(self):
return "\n\n"
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
# def construct_requests(self, doc, ctx):
# """Uses RequestFactory to construct Requests and returns an iterable of
# Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
cont_request = rf.greedy_until(ctx, ['\nQ:'])
return cont_request
# :param doc:
# The document as returned from training_docs, validation_docs, or test_docs.
# :param ctx: str
# The context string, generated by fewshot_context. This includes the natural
# language description, as well as the few shot examples, and the question
# part of the document for `doc`.
# """
# return cont_request
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -139,15 +116,25 @@ class CoQA(Task):
:param results:
The results of the requests created in construct_requests.
"""
turn_id = len(doc["questions"]["input_text"])
gold_list = self.get_answers(doc, turn_id)
pred = results[0].strip().split('\n')[0]
scores = self.compute_scores(gold_list, pred)
target = self.doc_to_target(doc).strip()
pred = results[0].strip().split("\n")[0]
print("*" * 80)
print(f"DOC: {doc}")
# print(f"PS: {self.prompt.apply(doc)}")
print(f"TEXT: {self.doc_to_text(doc)}")
print(f"TARGET: {target} END TARGET")
print(f"PRED: {pred} END PRED")
print("*" * 80)
# turn_id = len(doc["questions"]["input_text"])
# gold_list = self.get_answers(doc, turn_id)
# TODO: Add HF metrics mapped from promptsource metadata.
scores = self.compute_scores([target], pred)
return {
"f1": scores['f1'],
"em": scores['em'],
"f1": scores["f1"],
"em": scores["em"],
}
def higher_is_better(self):
......
lm_eval/tasks/drop.py
View file @ 54999199
......@@ -18,7 +18,7 @@ import re
import string
import lm_eval.datasets.drop.drop
from scipy.optimize import linear_sum_assignment
from lm_eval.base import Task, rf
from lm_eval.base import PromptSourceTask, rf
from lm_eval.metrics import mean
......@@ -37,9 +37,9 @@ _CITATION = """
_ARTICLES = re.compile(r"\b(a|an|the)\b", re.UNICODE)
class DROP(Task):
class DROP(PromptSourceTask):
VERSION = 1
DATASET_PATH = inspect.getfile(lm_eval.datasets.drop.drop)
DATASET_PATH = "drop" # inspect.getfile(lm_eval.datasets.drop.drop)
DATASET_NAME = None
def has_training_docs(self):
......@@ -52,46 +52,13 @@ class DROP(Task):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(map(self._process_doc, self.dataset["train"]))
return self._training_docs
# if self._training_docs is None:
# self._training_docs = list()
# return self._training_docs
return self.dataset["train"]
def validation_docs(self):
return map(self._process_doc, self.dataset["validation"])
def _process_doc(self, doc):
return {
"id": doc["query_id"],
"passage": doc["passage"],
"question": doc["question"],
"answers": self.get_answers(doc),
}
@classmethod
def get_answers(cls, qa):
def _flatten_validated_answers(validated_answers):
""" Flattens a dict of lists of validated answers.
{"number": ['1', '8'], ...}
-> [{"number": ['1'], ...}, {"number": ['8'], ...}]
"""
vas = []
for i in range(len(validated_answers["number"])):
vas.append({
"number": validated_answers["number"][i],
"date": validated_answers["date"][i],
"spans": validated_answers["spans"][i],
})
return vas
answers = []
answers_set = set()
candidates = [qa["answer"]] + _flatten_validated_answers(qa["validated_answers"])
for candidate in candidates:
answer = cls.parse_answer(candidate)
if answer in answers_set:
continue
answers_set.add(answer)
answers.append(answer)
return answers
return self.dataset["validation"]
@classmethod
def parse_answer(cls, answer):
......@@ -100,29 +67,33 @@ class DROP(Task):
return (str(answer["number"]),)
if answer["spans"] != []:
return tuple(answer["spans"])
return (" ".join([answer["date"]["day"],
answer["date"]["month"],
answer["date"]["year"]]).strip(),)
return (
" ".join(
[answer["date"]["day"], answer["date"]["month"], answer["date"]["year"]]
).strip(),
)
def doc_to_text(self, doc):
return f"Passage: {doc['passage']}\nQuestion: {doc['question']}\nAnswer:"
# def doc_to_text(self, doc):
# return f"Passage: {doc['passage']}\nQuestion: {doc['question']}\nAnswer:"
def doc_to_target(self, doc):
return " " + ", ".join(doc["answers"][0])
# def doc_to_target(self, doc):
# return " " + ", ".join(doc["answers"][0])
def construct_requests(self, doc, ctx):
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
# def construct_requests(self, doc, ctx):
# """Uses RequestFactory to construct Requests and returns an iterable of
# Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
conts = [rf.greedy_until(ctx, ["."])]
return conts
# :param doc:
# The document as returned from training_docs, validation_docs, or test_docs.
# :param ctx: str
# The context string, generated by fewshot_context. This includes the natural
# language description, as well as the few shot examples, and the question
# part of the document for `doc`.
# """
# conts = [rf.greedy_until(ctx, ["."])]
# return conts
def stopping_criteria(self):
return "."
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
......@@ -134,7 +105,21 @@ class DROP(Task):
:param results:
The results of the requests created in construct_requests.
"""
preds, golds = results, doc["answers"]
pred = results[0].strip()
target = self.doc_to_target(doc).strip()
print("*" * 80)
print(f"DOC: {doc}")
print(f"PS: {self.prompt.apply(doc)}")
print(f"TEXT: {self.doc_to_text(doc)}")
print(f"TARGET: {target} END TARGET")
print(f"PRED: {pred} END PRED")
print("*" * 80)
preds = [pred]
golds = [target]
max_em = 0
max_f1 = 0
for gold_answer in golds:
......@@ -142,10 +127,7 @@ class DROP(Task):
if gold_answer[0].strip():
max_em = max(max_em, exact_match)
max_f1 = max(max_f1, f1_score)
return {
"em": max_em,
"f1": max_f1
}
return {"em": max_em, "f1": max_f1}
def get_metrics(self, predicted, gold):
"""
......@@ -158,7 +140,9 @@ class DROP(Task):
predicted_bags = self._answer_to_bags(predicted)
gold_bags = self._answer_to_bags(gold)
if set(predicted_bags[0]) == set(gold_bags[0]) and len(predicted_bags[0]) == len(gold_bags[0]):
if set(predicted_bags[0]) == set(gold_bags[0]) and len(
predicted_bags[0]
) == len(gold_bags[0]):
exact_match = 1.0
else:
exact_match = 0.0
......@@ -190,7 +174,9 @@ class DROP(Task):
for gold_index, gold_item in enumerate(gold):
for pred_index, pred_item in enumerate(predicted):
if self._match_numbers_if_present(gold_item, pred_item):
scores[gold_index, pred_index] = self._compute_f1(pred_item, gold_item)
scores[gold_index, pred_index] = self._compute_f1(
pred_item, gold_item
)
row_ind, col_ind = linear_sum_assignment(-scores)
max_scores = np.zeros([max(len(gold), len(predicted))])
......@@ -256,7 +242,11 @@ class DROP(Task):
def _normalize(self, answer):
tokens = [
self._white_space_fix(self._remove_articles(self._fix_number(self._remove_punc(token.lower()))))
self._white_space_fix(
self._remove_articles(
self._fix_number(self._remove_punc(token.lower()))
)
)
for token in self._tokenize(answer)
]
tokens = [token for token in tokens if token.strip()]
......@@ -269,10 +259,7 @@ class DROP(Task):
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"em": mean,
"f1": mean
}
return {"em": mean, "f1": mean}
def higher_is_better(self):
"""
......@@ -280,7 +267,4 @@ class DROP(Task):
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"em": True,
"f1": True
}
return {"em": True, "f1": True}
lm_eval/tasks/glue.py
View file @ 54999199
......@@ -14,7 +14,7 @@ respect to a wide range of linguistic phenomena found in natural language.
Homepage: https://gluebenchmark.com/
"""
import numpy as np
from lm_eval.base import rf, Task
from lm_eval.base import PromptSourceTask, rf, Task
from lm_eval.metrics import mean, matthews_corrcoef, f1_score, yesno
from lm_eval.utils import general_detokenize
......@@ -45,7 +45,7 @@ _CITATION = """
# Single-Sentence Tasks
class CoLA(Task):
class CoLA(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "cola"
......@@ -67,37 +67,20 @@ class CoLA(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: Does this sentence make sense?\nAnswer:".format(doc["sentence"])
def doc_to_target(self, doc):
return " {}".format({1: "yes", 0: "no"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_true, _ = rf.loglikelihood(ctx, " yes")
ll_false, _ = rf.loglikelihood(ctx, " no")
return ll_true, ll_false
def process_results(self, doc, results):
ll_true, ll_false = results
pred = ll_true > ll_false
gold = doc["label"]
return {
"mcc": (gold, pred)
}
# def process_results(self, doc, results):
# answer_choices_list = self.prompt.get_answer_choices_list(doc)
# pred = np.argmax(results)
# target = answer_choices_list.index(self.doc_to_target(doc).strip())
# return {"mcc": (target, pred)}
def higher_is_better(self):
return {
"mcc": True
}
# def higher_is_better(self):
# return {"mcc": True}
def aggregation(self):
return {
"mcc": matthews_corrcoef
}
# def aggregation(self):
# return {"mcc": matthews_corrcoef}
class SST(Task):
class SST(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "sst2"
......@@ -119,42 +102,11 @@ class SST(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: Is this sentence positive or negative?\nAnswer:".format(
general_detokenize(doc["sentence"]),
)
def doc_to_target(self, doc):
return " {}".format({1: "positive", 0: "negative"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_positive, _ = rf.loglikelihood(ctx, " positive")
ll_negative, _ = rf.loglikelihood(ctx, " negative")
return ll_positive, ll_negative
def process_results(self, doc, results):
ll_positive, ll_negative = results
pred = ll_positive > ll_negative
gold = doc["label"]
return {
"acc": pred == gold
}
def higher_is_better(self):
return {
"acc": True
}
def aggregation(self):
return {
"acc": mean
}
# Inference Tasks
class MNLI(Task):
class MNLI(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "mnli"
......@@ -181,41 +133,6 @@ class MNLI(Task):
if self.has_test_docs():
return self.dataset["test_matched"]
def doc_to_text(self, doc):
return "{}\nQuestion: {} True, False or Neither?\nAnswer:".format(
doc["premise"],
doc["hypothesis"].strip() + ('' if doc["hypothesis"].strip().endswith('.') else '.'),
)
def doc_to_target(self, doc):
# True = entailment
# False = contradiction
# Neither = neutral
return " {}".format({0: "True", 1: "Neither", 2: "False"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_neither, _ = rf.loglikelihood(ctx, " Neither")
ll_false, _ = rf.loglikelihood(ctx, " False")
return ll_true, ll_neither, ll_false
def process_results(self, doc, results):
gold = doc["label"]
pred = np.argmax(results)
return {
"acc": pred == gold
}
def higher_is_better(self):
return {
"acc": True
}
def aggregation(self):
return {
"acc": mean
}
class MNLIMismatched(MNLI):
VERSION = 0
......@@ -229,7 +146,7 @@ class MNLIMismatched(MNLI):
return self.dataset["test_mismatched"]
class QNLI(Task):
class QNLI(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "qnli"
......@@ -251,42 +168,8 @@ class QNLI(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\n{}\nQuestion: Does this response answer the question?\nAnswer:".format(
doc["question"],
doc["sentence"],
)
def doc_to_target(self, doc):
# True = entailment
# False = not entailment
return " {}".format({0: "yes", 1: "no"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
pred = ll_no > ll_yes
gold = doc["label"]
return {
"acc": pred == gold
}
def higher_is_better(self):
return {
"acc": True
}
def aggregation(self):
return {
"acc": mean
}
class WNLI(Task):
class WNLI(PromptSourceTask):
VERSION = 1
DATASET_PATH = "glue"
DATASET_NAME = "wnli"
......@@ -301,49 +184,13 @@ class WNLI(Task):
return False
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
return self.dataset["train"]
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: {} True or False?\nAnswer:".format(
doc["sentence1"],
doc["sentence2"],
)
def doc_to_target(self, doc):
# True = entailment
# False = not_entailment
return " {}".format({0: "False", 1: "True"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_false, _ = rf.loglikelihood(ctx, " False")
return ll_true, ll_false
def process_results(self, doc, results):
ll_true, ll_false = results
pred = ll_true > ll_false
gold = doc["label"]
return {
"acc": pred == gold
}
def higher_is_better(self):
return {
"acc": True
}
def aggregation(self):
return {
"acc": mean
}
class RTE(Task):
class RTE(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "rte"
......@@ -365,45 +212,17 @@ class RTE(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: {} True or False?\nAnswer:".format(
doc["sentence1"],
doc["sentence2"],
)
def doc_to_target(self, doc):
# 0 = entailment
# 1 = not_entailment
return " {}".format({0: "True", 1: "False"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_false, _ = rf.loglikelihood(ctx, " False")
return ll_true, ll_false
def process_results(self, doc, results):
ll_true, ll_false = results
pred = ll_false > ll_true
gold = doc["label"]
return {
"acc": pred == gold
}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
# Similarity and Paraphrase Tasks
class MRPC(Task):
class MRPC(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "mrpc"
......@@ -417,6 +236,9 @@ class MRPC(Task):
def has_test_docs(self):
return False
def stopping_criteria(self):
return "\n"
def training_docs(self):
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
......@@ -425,43 +247,8 @@ class MRPC(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "Sentence 1: {}\nSentence 2: {}\nQuestion: Do both sentences mean the same thing?\nAnswer:".format(
general_detokenize(doc["sentence1"]),
general_detokenize(doc["sentence2"]),
)
def doc_to_target(self, doc):
return " {}".format(yesno(doc["label"]))
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
pred = ll_yes > ll_no
return {
"acc": pred == gold,
"f1": (gold, pred),
}
def higher_is_better(self):
return {
"acc": True,
"f1": True
}
def aggregation(self):
return {
"acc": mean,
"f1": f1_score
}
class QQP(Task):
class QQP(PromptSourceTask):
VERSION = 0
DATASET_PATH = "glue"
DATASET_NAME = "qqp"
......@@ -483,41 +270,6 @@ class QQP(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "Question 1: {}\nQuestion 2: {}\nQuestion: Do both questions ask the same thing?\nAnswer:".format(
doc["question1"],
doc["question2"],
)
def doc_to_target(self, doc):
return " {}".format(yesno(doc["label"]))
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, " yes")
ll_no, _ = rf.loglikelihood(ctx, " no")
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
pred = ll_yes > ll_no
return {
"acc": pred == gold,
"f1": (gold, pred),
}
def higher_is_better(self):
return {
"acc": True,
"f1": True
}
def aggregation(self):
return {
"acc": mean,
"f1": f1_score
}
class STSB(Task):
VERSION = 0
......@@ -554,22 +306,22 @@ class STSB(Task):
return " {}".format(doc["label"])
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
part of the document for `doc`.
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -578,22 +330,22 @@ class STSB(Task):
The results of the requests created in construct_requests.
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
# TODO: implement evaluation.
raise NotImplementedError('Evaluation not implemented')
raise NotImplementedError("Evaluation not implemented")
lm_eval/tasks/hans.py 0 → 100644
View file @ 54999199
"""
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
https://arxiv.org/abs/1902.01007
A controlled evaluation set called HANS (Heuristic Analysis for NLI Systems),
which contains many examples where the heuristics fail.
Homepage: https://github.com/tommccoy1/hans
"""
from lm_eval.base import PromptSourceTask
_CITATION = """
@inproceedings{mccoy-etal-2019-right,
title = "Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference",
author = "McCoy, Tom and
Pavlick, Ellie and
Linzen, Tal",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1334",
doi = "10.18653/v1/P19-1334",
pages = "3428--3448",
abstract = "A machine learning system can score well on a given test set by relying on heuristics that are effective for frequent example types but break down in more challenging cases. We study this issue within natural language inference (NLI), the task of determining whether one sentence entails another. We hypothesize that statistical NLI models may adopt three fallible syntactic heuristics: the lexical overlap heuristic, the subsequence heuristic, and the constituent heuristic. To determine whether models have adopted these heuristics, we introduce a controlled evaluation set called HANS (Heuristic Analysis for NLI Systems), which contains many examples where the heuristics fail. We find that models trained on MNLI, including BERT, a state-of-the-art model, perform very poorly on HANS, suggesting that they have indeed adopted these heuristics. We conclude that there is substantial room for improvement in NLI systems, and that the HANS dataset can motivate and measure progress in this area.",
}
"""
class HANS(PromptSourceTask):
VERSION = 0
DATASET_PATH = "hans"
DATASET_NAME = None
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if self.has_training_docs():
# We cache training documents in `self._training_docs` for faster
# few-shot processing. If the data is too large to fit in memory,
# return the training data as a generator instead of a list.
if self._training_docs is None:
self._training_docs = list(self.dataset["train"])
return self._training_docs
def validation_docs(self):
if self.has_validation_docs():
return self.dataset["validation"]
def test_docs(self):
if self.has_test_docs():
return self.dataset["test"]
lm_eval/tasks/race.py
View file @ 54999199
......@@ -12,7 +12,7 @@ Homepage: https://www.cs.cmu.edu/~glai1/data/race/
import collections
import datasets
import numpy as np
from lm_eval.base import rf, Task
from lm_eval.base import PromptSourceTask, rf
from lm_eval.metrics import mean
......@@ -34,13 +34,13 @@ class each:
return list(map(self.f, other))
class RACE(Task):
class RACE(PromptSourceTask):
VERSION = 1
DATASET_PATH = "race"
DATASET_NAME = "high"
cache = {}
letter_to_num = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
letter_to_num = {"A": 0, "B": 1, "C": 2, "D": 3}
def has_training_docs(self):
return True
......@@ -51,83 +51,92 @@ class RACE(Task):
def has_test_docs(self):
return True
def _collate_data(self, set):
if set in self.cache:
return self.cache[set]
# One big issue with HF's implementation of this dataset: it makes a
# separate document for each question; meanwhile, in the GPT3 paper it
# is shown that one document is made per passage.
r = collections.defaultdict(list)
for item in datasets.load_dataset(path=self.DATASET_PATH, name=self.DATASET_NAME)[set]:
r[item['article']].append(item)
res = list(r.values() >> each(lambda x: {
'article': x[0]['article'],
'problems': x >> each(lambda y: {
'question': y['question'],
'answer': y['answer'],
'options': y['options'],
})
}))
self.cache[set] = res
return res
# def _collate_data(self, set):
# if set in self.cache:
# return self.cache[set]
# # One big issue with HF's implementation of this dataset: it makes a
# # separate document for each question; meanwhile, in the GPT3 paper it
# # is shown that one document is made per passage.
# r = collections.defaultdict(list)
# for item in datasets.load_dataset(
# path=self.DATASET_PATH, name=self.DATASET_NAME
# )[set]:
# r[item["article"]].append(item)
# res = list(
# r.values()
# >> each(
# lambda x: {
# "article": x[0]["article"],
# "problems": x
# >> each(
# lambda y: {
# "question": y["question"],
# "answer": y["answer"],
# "options": y["options"],
# }
# ),
# }
# )
# )
# self.cache[set] = res
# return res
def training_docs(self):
return self._collate_data("train")
return self.dataset["train"]
def validation_docs(self):
return self._collate_data("validation")
return self.dataset["validation"]
def test_docs(self):
return self._collate_data("test")
return self.dataset["test"]
@classmethod
def get_answer_option(cls, problem):
answer = cls.letter_to_num[problem['answer']]
return problem['options'][answer]
answer = cls.letter_to_num[problem["answer"]]
return problem["options"][answer]
@classmethod
def last_problem(cls, doc):
return doc['problems'][-1]
def doc_to_text(self, doc):
text = 'Article: ' + doc['article'] + '\n\n'
for problem in doc['problems'][:-1]:
if problem['question'][-6:] == ' _ .':
text += problem['question'][-5:] + self.get_answer_option(problem) + '\n'
else:
question = 'Question: ' + problem['question'] + '\n'
answer = 'Answer: ' + self.get_answer_option(problem) + '\n'
text += question + answer
text += self.last_problem(doc)['question']
return text
def doc_to_target(self, doc):
return " " + self.get_answer_option(self.last_problem(doc))
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
problem = self.last_problem(doc)
ll_choices = [
rf.loglikelihood(ctx, " " + problem['options'][i])[0]
for i in range(4)
]
return ll_choices
return doc["problems"][-1]
# def doc_to_text(self, doc):
# text = 'Article: ' + doc['article'] + '\n\n'
# for problem in doc['problems'][:-1]:
# if problem['question'][-6:] == ' _ .':
# text += problem['question'][-5:] + self.get_answer_option(problem) + '\n'
# else:
# question = 'Question: ' + problem['question'] + '\n'
# answer = 'Answer: ' + self.get_answer_option(problem) + '\n'
# text += question + answer
# text += self.last_problem(doc)['question']
# return text
# def doc_to_target(self, doc):
# return " " + self.get_answer_option(self.last_problem(doc))
# def construct_requests(self, doc, ctx):
# """Uses RequestFactory to construct Requests and returns an iterable of
# Requests which will be sent to the LM.
# :param doc:
# The document as returned from training_docs, validation_docs, or test_docs.
# :param ctx: str
# The context string, generated by fewshot_context. This includes the natural
# language description, as well as the few shot examples, and the question
# part of the document for `doc`.
# """
# problem = self.last_problem(doc)
# ll_choices = [
# rf.loglikelihood(ctx, " " + problem["options"][i])[0] for i in range(4)
# ]
# return ll_choices
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -135,28 +144,24 @@ class RACE(Task):
:param results:
The results of the requests created in construct_requests.
"""
gold = self.letter_to_num[self.last_problem(doc)['answer']]
#
gold = self.letter_to_num[self.doc_to_target(doc)]
# gold = self.letter_to_num[self.last_problem(doc)["answer"]]
pred = np.argmax(results)
return {
"acc": int(pred == gold)
}
return {"acc": int(pred == gold)}
def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"acc": mean
}
return {"acc": mean}
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"acc": True
}
return {"acc": True}
lm_eval/tasks/superglue.py
View file @ 54999199
......@@ -12,7 +12,7 @@ TODO: WSC requires free-form generation.
import numpy as np
import sklearn
import transformers.data.metrics.squad_metrics as squad_metrics
from lm_eval.base import rf, Task
from lm_eval.base import rf, PromptSourceTask
from lm_eval.metrics import mean, acc_all, metric_max_over_ground_truths, yesno
from lm_eval.utils import general_detokenize
......@@ -32,7 +32,7 @@ _CITATION = """
"""
class BoolQ(Task):
class BoolQ(PromptSourceTask):
VERSION = 1
DATASET_PATH = "super_glue"
DATASET_NAME = "boolq"
......@@ -54,41 +54,8 @@ class BoolQ(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return f"{doc['passage']}\nQuestion: {doc['question']}?\nAnswer:"
def doc_to_target(self, doc):
return " " + yesno(doc['label'])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, ' yes')
ll_no, _ = rf.loglikelihood(ctx, ' no')
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
acc = 1. if (ll_yes > ll_no) == gold else 0.
return {
"acc": acc
}
def higher_is_better(self):
return {
"acc": True
}
def aggregation(self):
return {
"acc": mean
}
class CommitmentBank(Task):
class CommitmentBank(PromptSourceTask):
VERSION = 1
DATASET_PATH = "super_glue"
DATASET_NAME = "cb"
......@@ -110,40 +77,15 @@ class CommitmentBank(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "{}\nQuestion: {}. True, False or Neither?\nAnswer:".format(
doc["premise"],
doc["hypothesis"],
)
def doc_to_target(self, doc):
# True = entailment
# False = contradiction
# Neither = neutral
return " {}".format({0: "True", 1: "False", 2: "Neither"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_true, _ = rf.loglikelihood(ctx, ' True')
ll_false, _ = rf.loglikelihood(ctx, ' False')
ll_neither, _ = rf.loglikelihood(ctx, ' Neither')
return ll_true, ll_false, ll_neither
def process_results(self, doc, results):
gold = doc["label"]
pred = np.argmax(results)
acc = 1. if pred == gold else 0.
acc = 1.0 if pred == gold else 0.0
return {"acc": acc, "f1": (pred, gold)}
return {
"acc": acc,
"f1": (pred, gold)
}
def higher_is_better(self):
return {
"acc": True,
"f1": True
}
return {"acc": True, "f1": True}
@classmethod
def cb_multi_fi(cls, items):
......@@ -155,7 +97,7 @@ class CommitmentBank(Task):
f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)
avg_f1 = mean([f11, f12, f13])
return avg_f1
def aggregation(self):
return {
"acc": mean,
......@@ -163,7 +105,7 @@ class CommitmentBank(Task):
}
class Copa(Task):
class Copa(PromptSourceTask):
VERSION = 0
DATASET_PATH = "super_glue"
DATASET_NAME = "copa"
......@@ -185,53 +127,25 @@ class Copa(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
# Drop the period
connector = {
"cause": "because",
"effect": "therefore",
}[doc["question"]]
return doc["premise"].strip()[:-1] + f" {connector}"
def doc_to_target(self, doc):
correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]
# Connect the sentences
return " " + self.convert_choice(correct_choice)
def construct_requests(self, doc, ctx):
choice1 = " " + self.convert_choice(doc["choice1"])
choice2 = " " + self.convert_choice(doc["choice2"])
ll_choice1, _ = rf.loglikelihood(ctx, choice1)
ll_choice2, _ = rf.loglikelihood(ctx, choice2)
return ll_choice1, ll_choice2
def process_results(self, doc, results):
gold = doc["label"]
pred = np.argmax(results)
acc = 1. if pred == gold else 0.
acc = 1.0 if pred == gold else 0.0
return {"acc": acc}
return {
"acc": acc
}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
@staticmethod
def convert_choice(choice):
return choice[0].lower() + choice[1:]
class MultiRC(Task):
class MultiRC(PromptSourceTask):
VERSION = 1
DATASET_PATH = "super_glue"
DATASET_NAME = "multirc"
......@@ -253,45 +167,19 @@ class MultiRC(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return f"{doc['paragraph']}\nQuestion: {doc['question']}\nAnswer:"
def doc_to_target(self, doc):
return " " + self.format_answer(answer=doc["answer"], label=doc["label"])
@staticmethod
def format_answer(answer, label):
label_str = "yes" if label else "no"
return f"{answer}\nIs the answer correct? {label_str}"
def construct_requests(self, doc, ctx):
true_choice = self.format_answer(answer=doc["answer"], label=True)
false_choice = self.format_answer(answer=doc["answer"], label=False)
ll_true_choice, _ = rf.loglikelihood(ctx, f' {true_choice}')
ll_false_choice, _ = rf.loglikelihood(ctx, f' {false_choice}')
return ll_true_choice, ll_false_choice
def process_results(self, doc, results):
ll_true_choice, ll_false_choice = results
pred = ll_true_choice > ll_false_choice
return {
"acc": (pred, doc)
}
return {"acc": (pred, doc)}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": acc_all
}
return {"acc": acc_all}
class ReCoRD(Task):
class ReCoRD(PromptSourceTask):
VERSION = 0
DATASET_PATH = "super_glue"
DATASET_NAME = "record"
......@@ -311,56 +199,31 @@ class ReCoRD(Task):
if self._training_docs is None:
self._training_docs = []
for doc in self.dataset["train"]:
self._training_docs.append(self._process_doc(doc))
self._training_docs.append(doc)
return self._training_docs
def validation_docs(self):
# See: training_docs
for doc in self.dataset["validation"]:
yield self._process_doc(doc)
@classmethod
def _process_doc(cls, doc):
return {
"passage": doc["passage"],
"query": doc["query"],
"entities": sorted(list(set(doc["entities"]))),
"answers": sorted(list(set(doc["answers"]))),
}
def doc_to_text(self, doc):
initial_text, *highlights = doc["passage"].strip().split("\n@highlight\n")
text = initial_text + "\n\n"
for highlight in highlights:
text += f" - {highlight}.\n"
return text
@classmethod
def format_answer(cls, query, entity):
return f' - {query}'.replace("@placeholder", entity)
def doc_to_target(self, doc):
# We only output the first correct entity in a doc
return self.format_answer(query=doc["query"], entity=doc["answers"][0])
def construct_requests(self, doc, ctx):
requests = [
rf.loglikelihood(ctx, self.format_answer(query=doc["query"], entity=entity))
for entity in doc["entities"]
]
return requests
yield doc
def process_results(self, doc, results):
# ReCoRD's evaluation is actually deceptively simple:
# - Pick the maximum likelihood prediction entity
# - Evaluate the accuracy and token F1 PER EXAMPLE
# - Average over all examples
# TODO (jon-tow): Look at result
max_idx = np.argmax(np.array([result[0] for result in results]))
prediction = doc["entities"][max_idx]
gold_label_set = doc["answers"]
f1 = metric_max_over_ground_truths(squad_metrics.compute_f1, prediction, gold_label_set)
em = metric_max_over_ground_truths(squad_metrics.compute_exact, prediction, gold_label_set)
f1 = metric_max_over_ground_truths(
squad_metrics.compute_f1, prediction, gold_label_set
)
em = metric_max_over_ground_truths(
squad_metrics.compute_exact, prediction, gold_label_set
)
return {
"f1": f1,
......@@ -380,7 +243,7 @@ class ReCoRD(Task):
}
class WordsInContext(Task):
class WordsInContext(PromptSourceTask):
VERSION = 0
DATASET_PATH = "super_glue"
DATASET_NAME = "wic"
......@@ -402,50 +265,19 @@ class WordsInContext(Task):
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
return "Sentence 1: {}\nSentence 2: {}\nQuestion: Is the word '{}' used in the same way in the" \
" two sentences above?\nAnswer:".format(
doc["sentence1"],
doc["sentence2"],
doc["sentence1"][doc["start1"]:doc["end1"]],
)
def doc_to_target(self, doc):
return " {}".format({0: "no", 1: "yes"}[doc["label"]])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, ' yes')
ll_no, _ = rf.loglikelihood(ctx, ' no')
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
acc = 1. if (ll_yes > ll_no) == gold else 0.
return {
"acc": acc
}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
class SGWinogradSchemaChallenge(Task):
class SGWinogradSchemaChallenge(PromptSourceTask):
VERSION = 0
# Note: This implementation differs from Fig G.32 because this is the SuperGLUE,
# binary version of the task.
DATASET_PATH = "super_glue"
DATASET_NAME = "wsc"
DATASET_NAME = "wsc.fixed"
def has_training_docs(self):
return True
......@@ -461,56 +293,15 @@ class SGWinogradSchemaChallenge(Task):
if self._training_docs is None:
# GPT-3 Paper's format only uses positive examples for fewshot "training"
self._training_docs = [
doc for doc in
self.dataset["train"]
if doc["label"]
doc for doc in self.dataset["train"] if doc["label"]
]
return self._training_docs
def validation_docs(self):
return self.dataset["validation"]
def doc_to_text(self, doc):
raw_passage = doc["text"]
# NOTE: HuggingFace span indices are word-based not character-based.
pre = " ".join(raw_passage.split()[:doc["span2_index"]])
post = raw_passage[len(pre) + len(doc["span2_text"]) + 1:]
passage = general_detokenize(pre + " *{}*".format(doc['span2_text']) + post)
noun = doc["span1_text"]
pronoun = doc["span2_text"]
text = (
f"Passage: {passage}\n"
+ f"Question: In the passage above, does the pronoun \"*{pronoun}*\" refer to \"*{noun}*\"?\n"
+ "Answer:"
)
return text
def doc_to_target(self, doc):
return " " + yesno(doc['label'])
def construct_requests(self, doc, ctx):
ll_yes, _ = rf.loglikelihood(ctx, ' yes')
ll_no, _ = rf.loglikelihood(ctx, ' no')
return ll_yes, ll_no
def process_results(self, doc, results):
ll_yes, ll_no = results
gold = doc["label"]
acc = 1. if (ll_yes > ll_no) == gold else 0.
return {
"acc": acc
}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
def aggregation(self):
return {
"acc": mean
}
return {"acc": mean}
lm_eval/utils.py
View file @ 54999199
......@@ -146,6 +146,19 @@ class Reorderer:
return res
def flatten(d, parent_key='', sep='_'):
# From: https://stackoverflow.com/a/6027615
items = []
for k, v in d.items():
new_key = parent_key + sep + k if parent_key else k
if isinstance(v, collections.MutableMapping):
items.extend(flatten(v, new_key, sep=sep).items())
else:
items.append((new_key, v))
return dict(items)
def positional_deprecated(fn):
"""
A decorator to nudge users into passing only keyword args (`kwargs`) to the
......
scripts/write_out.py
View file @ 54999199
......@@ -30,7 +30,7 @@ def main():
task_names = tasks.ALL_TASKS
else:
task_names = args.tasks.split(",")
task_dict = tasks.get_task_dict(task_names)
task_dict = tasks.get_task_dict_promptsource(task_names)
description_dict = {}
if args.description_dict_path:
......
setup.py
View file @ 54999199
......@@ -18,8 +18,12 @@ setuptools.setup(
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
python_requires=">=3.6",
install_requires=[
"promptsource @ git+https://github.com/bigscience-workshop/promptsource@eval-hackathon",
"wrapt",
"nltk",
"jinja2",
"black",
"datasets==2.0.0",
"click>=7.1",
......@@ -42,9 +46,9 @@ setuptools.setup(
"openai==0.6.4",
"jieba==0.42.1",
"nagisa==0.2.7",
"bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt"
"bleurt@https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt",
],
dependency_links=[
"https://github.com/google-research/bleurt/archive/b610120347ef22b494b6d69b4316e303f5932516.zip#egg=bleurt",
]
],
)
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