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dino/scripts/imdb/run_unsupervised.py
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# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" | |
This script can be used to evaluate unsupervised models on the IMDb dataset using prompts. | |
""" | |
import argparse | |
import math | |
import openai | |
import torch | |
from tqdm import tqdm | |
from datasets import load_dataset | |
from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoModelForCausalLM | |
from utils import DatasetEntry | |
class CausalLMWrapper: | |
"""A wrapper for a causal language model (like GPT-2)""" | |
def __init__(self, model_name: str, use_cuda: bool = True): | |
self._device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" | |
self._tokenizer = AutoTokenizer.from_pretrained(model_name) | |
self._model = AutoModelForCausalLM.from_pretrained(model_name) | |
if use_cuda: | |
self._model.parallelize() | |
def get_token_probabilities(self, input_text: str, prompt: str) -> torch.Tensor: | |
input_text = input_text + prompt | |
inputs = self._tokenizer.batch_encode_plus([input_text], truncation=True, return_tensors='pt') | |
inputs = {key: val.to(self._device) for key, val in inputs.items()} | |
output = self._model(**inputs)['logits'] | |
return output[:, -1, :] | |
class MaskedLMWrapper: | |
"""A wrapper for a masked language model (like BERT)""" | |
def __init__(self, model_name: str, use_cuda: bool = True): | |
self._device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" | |
self._tokenizer = AutoTokenizer.from_pretrained(model_name) | |
self._model = AutoModelForMaskedLM.from_pretrained(model_name).to(self._device) | |
def get_token_probabilities(self, input_text: str, prompt: str) -> torch.Tensor: | |
text_ids = self._tokenizer.encode(input_text, truncation=True, add_special_tokens=False) | |
prompt_ids = self._tokenizer.encode(prompt, truncation=False, add_special_tokens=False) | |
max_len = self._tokenizer.model_max_length | |
max_len_for_text_ids = max_len - len(prompt_ids) - self._tokenizer.num_special_tokens_to_add(False) | |
text_ids = text_ids[:max_len_for_text_ids] | |
input_ids = text_ids + prompt_ids | |
input_ids = torch.tensor([self._tokenizer.build_inputs_with_special_tokens(input_ids)], device=self._device) | |
assert sum(1 for id_ in input_ids[0] if id_ == self._tokenizer.mask_token_id) == 1, \ | |
f"Input text must contain exactly one mask token ('{self._tokenizer.mask_token}'). Got '{input_text}'." | |
scores = self._model(input_ids)['logits'] | |
mask_positions = (input_ids == self._tokenizer.mask_token_id) | |
return scores[mask_positions] | |
class GPT3Wrapper: | |
"""A wrapper around OpenAI's GPT-3 API""" | |
def __init__(self, engine: str): | |
self.engine = engine | |
def get_scores(self, prompt: str): | |
response = openai.Completion.create(engine=self.engine, prompt=prompt, max_tokens=1, logprobs=100) | |
top_logprobs = response['choices'][0]['logprobs']['top_logprobs'][0] | |
positive_score = max([top_logprobs.get(" good", -math.inf)]) | |
negative_score = max([top_logprobs.get(" bad", -math.inf)]) | |
return positive_score, negative_score | |
if __name__ == '__main__': | |
parser = argparse.ArgumentParser() | |
parser.add_argument("--model_type", choices=["causal", "masked", "gpt3"], required=True, | |
help="The type of the model to evaluate. One of 'causal' (for causal language models like GPT-2), 'masked' (for " | |
"masked language models like BERT), and 'gpt3' (for GPT-3 models accessed via OpenAI's API)") | |
parser.add_argument("--model_name", type=str, required=True, | |
help="The name of the pretrained model to use (e.g., 'roberta-large')") | |
parser.add_argument("--openai_api_key", type=str, | |
help="An optional key for OpenAI's API (only if --model_type is gpt3)") | |
parser.add_argument("--test_file", type=str, | |
help="An optional path to a jsonl file of dataset entries. If not given, the entire IMDb dataset is used.") | |
parser.add_argument("--no_cuda", action='store_true', | |
help="If set to true, inference is done on CPU only") | |
args = parser.parse_args() | |
if args.test_file: | |
print(f"Evaluating on entries from '{args.test_file}'") | |
dataset = DatasetEntry.read_list(args.test_file) | |
print(f"Done loading {len(dataset)} examples from '{args.test_file}'") | |
else: | |
print("Evaluating on the entire IMDb test set") | |
dataset = load_dataset('imdb')['test'] | |
dataset = [DatasetEntry(text_a=text, text_b=None, label=label) for text, label in zip(dataset['text'], dataset['label'])] | |
print(f"Done loading {len(dataset)} examples") | |
if args.openai_api_key: | |
openai.api_key = args.openai_api_key | |
predictions, labels = [], [] | |
if args.model_type == "causal": | |
model = CausalLMWrapper(args.model_name, use_cuda=not args.no_cuda) | |
prompt = "\nQuestion: Is this movie good or bad?\nAnswer: It is" | |
elif args.model_type == "masked": | |
model = MaskedLMWrapper(args.model_name, use_cuda=not args.no_cuda) | |
prompt = "\nQuestion: Is this movie good or bad?\nAnswer: It is <mask>." | |
elif args.model_type == "gpt3": | |
model = GPT3Wrapper(args.model_name) | |
prompt = "\nQuestion: Is this movie good or bad?\nAnswer: It is" | |
else: | |
raise ValueError() | |
dataset_iterator = tqdm(dataset) | |
for ds_entry in dataset_iterator: | |
if args.model_type == "gpt3": | |
instance_prompt = ds_entry.text_a + prompt | |
positive_score, negative_score = model.get_scores(instance_prompt) | |
else: | |
token_probabilities = model.get_token_probabilities(input_text=ds_entry.text_a, prompt=prompt)[0].detach() | |
positive_score = token_probabilities[model._tokenizer.convert_tokens_to_ids("Ġgood")] | |
negative_score = token_probabilities[model._tokenizer.convert_tokens_to_ids("Ġbad")] | |
labels.append(int(ds_entry.label)) | |
predictions.append(1 if positive_score > negative_score else 0) | |
dataset_iterator.set_description(f"Texts (acc={100 * sum(1 for x, y in zip(labels, predictions) if x == y) / len(labels):5.2f})") | |
dataset_iterator.refresh() | |
print(f"Final accuracy: {sum(1 for x, y in zip(labels, predictions) if x == y) / len(labels)} (total: {len(labels)})") |