update readme

examples/run_openai_gpt.py

-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# 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.
-" Run OpenAI GPT on RocStories"
-import argparse
-import os
-import random
-import logging
-
-from sklearn.metrics import accuracy_score
-from sklearn.utils import shuffle
-
-# from analysis import rocstories as rocstories_analysis
-# from datasets import rocstories
-# from model_pytorch import DoubleHeadModel, load_openai_pretrained_model
-# from opt import OpenAIAdam
-# from text_utils import TextEncoder
-# from utils import (encode_dataset, iter_data,
-#                    ResultLogger, make_path)
-# from loss import MultipleChoiceLossCompute
-
-import numpy as np
-import torch
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-from torch.utils.data.distributed import DistributedSampler
-
-from pytorch_pretrained_bert.tokenization_openai import OpenAIGPTTokenizer
-from pytorch_pretrained_bert.modeling_openai import OpenAIGPTDoubleHeadsModel
-from pytorch_pretrained_bert.optimization_openai import OpenAIAdam
-from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
-
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
-logger = logging.getLogger(__name__)
-
-def transform_roc(X1, X2, X3):
-    n_batch = len(X1)
-    xmb = np.zeros((n_batch, 2, n_ctx, 2), dtype=np.int32)
-    mmb = np.zeros((n_batch, 2, n_ctx), dtype=np.float32)
-    start = encoder['_start_']
-    delimiter = encoder['_delimiter_']
-    for i, (x1, x2, x3), in enumerate(zip(X1, X2, X3)):
-        x12 = [start] + x1[:max_len] + [delimiter] + x2[:max_len] + [clf_token]
-        x13 = [start] + x1[:max_len] + [delimiter] + x3[:max_len] + [clf_token]
-        l12 = len(x12)
-        l13 = len(x13)
-        xmb[i, 0, :l12, 0] = x12
-        xmb[i, 1, :l13, 0] = x13
-        mmb[i, 0, :l12] = 1
-        mmb[i, 1, :l13] = 1
-    # Position information that is added to the input embeddings in the TransformerModel
-    xmb[:, :, :, 1] = np.arange(n_vocab + n_special, n_vocab + n_special + n_ctx)
-    return xmb, mmb
-
-
-def iter_apply(Xs, Ms, Ys):
-    # fns = [lambda x: np.concatenate(x, 0), lambda x: float(np.sum(x))]
-    logits = []
-    cost = 0
-    with torch.no_grad():
-        dh_model.eval()
-        for xmb, mmb, ymb in iter_data(Xs, Ms, Ys, n_batch=n_batch_train, truncate=False, verbose=True):
-            n = len(xmb)
-            XMB = torch.tensor(xmb, dtype=torch.long).to(device)
-            YMB = torch.tensor(ymb, dtype=torch.long).to(device)
-            MMB = torch.tensor(mmb).to(device)
-            _, clf_logits = dh_model(XMB)
-            clf_logits *= n
-            clf_losses = compute_loss_fct(XMB, YMB, MMB, clf_logits, only_return_losses=True)
-            clf_losses *= n
-            logits.append(clf_logits.to("cpu").numpy())
-            cost += clf_losses.sum().item()
-        logits = np.concatenate(logits, 0)
-    return logits, cost
-
-
-def iter_predict(Xs, Ms):
-    logits = []
-    with torch.no_grad():
-        dh_model.eval()
-        for xmb, mmb in iter_data(Xs, Ms, n_batch=n_batch_train, truncate=False, verbose=True):
-            n = len(xmb)
-            XMB = torch.tensor(xmb, dtype=torch.long).to(device)
-            MMB = torch.tensor(mmb).to(device)
-            _, clf_logits = dh_model(XMB)
-            logits.append(clf_logits.to("cpu").numpy())
-    logits = np.concatenate(logits, 0)
-    return logits
-
-
-def log(save_dir, desc):
-    global best_score
-    print("Logging")
-    tr_logits, tr_cost = iter_apply(trX[:n_valid], trM[:n_valid], trY[:n_valid])
-    va_logits, va_cost = iter_apply(vaX, vaM, vaY)
-    tr_cost = tr_cost / len(trY[:n_valid])
-    va_cost = va_cost / n_valid
-    tr_acc = accuracy_score(trY[:n_valid], np.argmax(tr_logits, 1)) * 100.
-    va_acc = accuracy_score(vaY, np.argmax(va_logits, 1)) * 100.
-    logger.log(n_epochs=n_epochs, n_updates=n_updates, tr_cost=tr_cost, va_cost=va_cost, tr_acc=tr_acc, va_acc=va_acc)
-    print('%d %d %.3f %.3f %.2f %.2f' % (n_epochs, n_updates, tr_cost, va_cost, tr_acc, va_acc))
-    if submit:
-        score = va_acc
-        if score > best_score:
-            best_score = score
-            path = os.path.join(save_dir, desc, 'best_params')
-            torch.save(dh_model.state_dict(), make_path(path))
-
-
-def predict(dataset, submission_dir):
-    filename = filenames[dataset]
-    pred_fn = pred_fns[dataset]
-    label_decoder = label_decoders[dataset]
-    predictions = pred_fn(iter_predict(teX, teM))
-    if label_decoder is not None:
-        predictions = [label_decoder[prediction] for prediction in predictions]
-    path = os.path.join(submission_dir, filename)
-    os.makedirs(os.path.dirname(path), exist_ok=True)
-    with open(path, 'w') as f:
-        f.write('{}\t{}\n'.format('index', 'prediction'))
-        for i, prediction in enumerate(predictions):
-            f.write('{}\t{}\n'.format(i, prediction))
-
-
-def run_epoch():
-    for xmb, mmb, ymb in iter_data(*shuffle(trX, trM, trYt, random_state=np.random),
-                                   n_batch=n_batch_train, truncate=True, verbose=True):
-        global n_updates
-        dh_model.train()
-        XMB = torch.tensor(xmb, dtype=torch.long).to(device)
-        YMB = torch.tensor(ymb, dtype=torch.long).to(device)
-        MMB = torch.tensor(mmb).to(device)
-        lm_logits, clf_logits = dh_model(XMB)
-        compute_loss_fct(XMB, YMB, MMB, clf_logits, lm_logits)
-        n_updates += 1
-        if n_updates in [1000, 2000, 4000, 8000, 16000, 32000] and n_epochs == 0:
-            log(save_dir, desc)
-
-
-argmax = lambda x: np.argmax(x, 1)
-
-pred_fns = {
-    'rocstories': argmax,
-}
-
-filenames = {
-    'rocstories': 'ROCStories.tsv',
-}
-
-label_decoders = {
-    'rocstories': None,
-}
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--desc', type=str, help="Description")
-    parser.add_argument('--dataset', type=str)
-    parser.add_argument('--log_dir', type=str, default='log/')
-    parser.add_argument('--save_dir', type=str, default='save/')
-    parser.add_argument('--data_dir', type=str, default='data/')
-    parser.add_argument('--submission_dir', type=str, default='submission/')
-    parser.add_argument('--submit', action='store_true')
-    parser.add_argument('--analysis', action='store_true')
-    parser.add_argument('--seed', type=int, default=42)
-    parser.add_argument('--n_iter', type=int, default=3)
-    parser.add_argument('--n_batch', type=int, default=8)
-    parser.add_argument('--max_grad_norm', type=int, default=1)
-    parser.add_argument('--lr', type=float, default=6.25e-5)
-    parser.add_argument('--lr_warmup', type=float, default=0.002)
-    parser.add_argument('--n_ctx', type=int, default=512)
-    parser.add_argument('--n_embd', type=int, default=768)
-    parser.add_argument('--n_head', type=int, default=12)
-    parser.add_argument('--n_layer', type=int, default=12)
-    parser.add_argument('--embd_pdrop', type=float, default=0.1)
-    parser.add_argument('--attn_pdrop', type=float, default=0.1)
-    parser.add_argument('--resid_pdrop', type=float, default=0.1)
-    parser.add_argument('--clf_pdrop', type=float, default=0.1)
-    parser.add_argument('--l2', type=float, default=0.01)
-    parser.add_argument('--vector_l2', action='store_true')
-    parser.add_argument('--opt', type=str, default='adam')
-    parser.add_argument('--afn', type=str, default='gelu')
-    parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
-    parser.add_argument('--encoder_path', type=str, default='model/encoder_bpe_40000.json')
-    parser.add_argument('--bpe_path', type=str, default='model/vocab_40000.bpe')
-    parser.add_argument('--n_transfer', type=int, default=12)
-    parser.add_argument('--lm_coef', type=float, default=0.5)
-    parser.add_argument('--b1', type=float, default=0.9)
-    parser.add_argument('--b2', type=float, default=0.999)
-    parser.add_argument('--e', type=float, default=1e-8)
-    parser.add_argument('--n_valid', type=int, default=374)
-
-    args = parser.parse_args()
-    print(args)
-
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    torch.cuda.manual_seed_all(args.seed)
-
-    # Constants
-    submit = args.submit
-    dataset = args.dataset
-    n_ctx = args.n_ctx
-    save_dir = args.save_dir
-    desc = args.desc
-    data_dir = args.data_dir
-    log_dir = args.log_dir
-    submission_dir = args.submission_dir
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    n_gpu = torch.cuda.device_count()
-    print("device", device, "n_gpu", n_gpu)
-
-    logger = ResultLogger(path=os.path.join(log_dir, '{}.jsonl'.format(desc)), **args.__dict__)
-    text_encoder = TextEncoder(args.encoder_path, args.bpe_path)
-    encoder = text_encoder.encoder
-    n_vocab = len(text_encoder.encoder)
-
-    print("Encoding dataset...")
-    ((trX1, trX2, trX3, trY),
-     (vaX1, vaX2, vaX3, vaY),
-     (teX1, teX2, teX3)) = encode_dataset(*rocstories(data_dir, n_valid=args.n_valid),
-                                          encoder=text_encoder)
-    encoder['_start_'] = len(encoder)
-    encoder['_delimiter_'] = len(encoder)
-    encoder['_classify_'] = len(encoder)
-    clf_token = encoder['_classify_']
-    n_special = 3
-    max_len = n_ctx // 2 - 2
-    n_ctx = min(max(
-        [len(x1[:max_len]) + max(len(x2[:max_len]),
-                                 len(x3[:max_len])) for x1, x2, x3 in zip(trX1, trX2, trX3)]
-        + [len(x1[:max_len]) + max(len(x2[:max_len]),
-                                   len(x3[:max_len])) for x1, x2, x3 in zip(vaX1, vaX2, vaX3)]
-        + [len(x1[:max_len]) + max(len(x2[:max_len]),
-                                   len(x3[:max_len])) for x1, x2, x3 in zip(teX1, teX2, teX3)]
-        ) + 3, n_ctx)
-    vocab = n_vocab + n_special + n_ctx
-    trX, trM = transform_roc(trX1, trX2, trX3)
-    vaX, vaM = transform_roc(vaX1, vaX2, vaX3)
-    if submit:
-        teX, teM = transform_roc(teX1, teX2, teX3)
-
-    n_train = len(trY)
-    n_valid = len(vaY)
-    n_batch_train = args.n_batch * max(n_gpu, 1)
-    n_updates_total = (n_train // n_batch_train) * args.n_iter
-
-    dh_model = DoubleHeadModel(args, clf_token, 'multiple_choice', vocab, n_ctx)
-
-    criterion = nn.CrossEntropyLoss(reduce=False)
-    model_opt = OpenAIAdam(dh_model.parameters(),
-                           lr=args.lr,
-                           schedule=args.lr_schedule,
-                           warmup=args.lr_warmup,
-                           t_total=n_updates_total,
-                           b1=args.b1,
-                           b2=args.b2,
-                           e=args.e,
-                           l2=args.l2,
-                           vector_l2=args.vector_l2,
-                           max_grad_norm=args.max_grad_norm)
-    compute_loss_fct = MultipleChoiceLossCompute(criterion,
-                                                 criterion,
-                                                 args.lm_coef,
-                                                 model_opt)
-    load_openai_pretrained_model(dh_model.transformer, n_ctx=n_ctx, n_special=n_special)
-
-    dh_model.to(device)
-    dh_model = nn.DataParallel(dh_model)
-
-    n_updates = 0
-    n_epochs = 0
-    if dataset != 'stsb':
-        trYt = trY
-    if submit:
-        path = os.path.join(save_dir, desc, 'best_params')
-        torch.save(dh_model.state_dict(), make_path(path))
-    best_score = 0
-    for i in range(args.n_iter):
-        print("running epoch", i)
-        run_epoch()
-        n_epochs += 1
-        log(save_dir, desc)
-    if submit:
-        path = os.path.join(save_dir, desc, 'best_params')
-        dh_model.load_state_dict(torch.load(path))
-        predict(dataset, args.submission_dir)
-        if args.analysis:
-            rocstories_analysis(data_dir, os.path.join(args.submission_dir, 'ROCStories.tsv'),
-                                os.path.join(log_dir, 'rocstories.jsonl'))