mirror of
https://github.com/wassname/openai-transformer-lm-gutenberg-erotic.git
synced 2026-06-27 16:10:19 +08:00
thomwolf
290 lines
11 KiB
Python
290 lines
11 KiB
Python
import re
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import os
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import time
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import math
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import json
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import joblib
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import random
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import argparse
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from tqdm import tqdm
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from functools import partial
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from sklearn.utils import shuffle
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from sklearn.metrics import accuracy_score
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from model_py import Model, LMHead, ClfHead, load_openai_pretrained_model
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from opt import OpenAIAdam
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from datasets import rocstories
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from analysis import rocstories as rocstories_analysis
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from text_utils import TextEncoder
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from utils import (encode_dataset, flatten, iter_data,
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ResultLogger, make_path)
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class LossCompute:
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"A Loss compute and train function."
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def __init__(self, lm_criterion, clf_criterion, lm_coef, opt=None):
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self.lm_criterion = lm_criterion
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self.clf_criterion = clf_criterion
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self.lm_coef = lm_coef
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self.opt = opt
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def __call__(self, X, Y, M, clf_logits, lm_logits=None, only_return_losses=False):
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# Language modeling loss
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if lm_logits is not None:
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x_shifted = X[:, :, 1:, 0].contiguous().view(-1) # Shape: 252
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M = M.view(-1, M.size(2))
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lm_losses = self.lm_criterion(lm_logits, x_shifted)
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lm_losses = lm_losses.view(X.size(0) * X.size(1), X.size(2)-1)
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lm_losses = lm_losses * M[:, 1:]
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lm_losses = lm_losses.sum(1) / torch.sum(M[:, 1:], 1)
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# Classification loss
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clf_losses = self.clf_criterion(clf_logits, Y)
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if only_return_losses:
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return (clf_losses, lm_losses) if lm_logits is not None else clf_losses
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if self.lm_coef > 0 and lm_logits is not None:
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train_loss = clf_losses.sum() + self.lm_coef * lm_losses.sum()
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else:
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train_loss = clf_losses.sum()
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train_loss.backward()
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if self.opt is not None:
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self.opt.step()
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self.opt.zero_grad()
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return train_loss.item()
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def transform_roc(X1, X2, X3):
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n_batch = len(X1)
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xmb = np.zeros((n_batch, 2, n_ctx, 2), dtype=np.int32)
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mmb = np.zeros((n_batch, 2, n_ctx), dtype=np.float32)
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start = encoder['_start_']
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delimiter = encoder['_delimiter_']
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for i, (x1, x2, x3), in enumerate(zip(X1, X2, X3)):
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x12 = [start]+x1[:max_len]+[delimiter]+x2[:max_len]+[clf_token]
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x13 = [start]+x1[:max_len]+[delimiter]+x3[:max_len]+[clf_token]
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l12 = len(x12)
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l13 = len(x13)
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xmb[i, 0, :l12, 0] = x12
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xmb[i, 1, :l13, 0] = x13
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mmb[i, 0, :l12] = 1
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mmb[i, 1, :l13] = 1
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xmb[:, :, :, 1] = np.arange(n_vocab+n_special, n_vocab+n_special+n_ctx)
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return xmb, mmb
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def iter_apply(Xs, Ms, Ys):
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fns = [lambda x: np.concatenate(x, 0), lambda x: float(np.sum(x))]
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results = []
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with torch.no_grad():
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model.eval()
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for xmb, mmb, ymb in iter_data(Xs, Ms, Ys, n_batch=n_batch_train, truncate=False, verbose=True):
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n = len(xmb)
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XMB = torch.tensor(xmb, dtype=torch.long).to(device)
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YMB = torch.tensor(ymb, dtype=torch.long).to(device)
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MMB = torch.tensor(mmb).to(device)
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h = model(XMB)
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clf_logits = clf_head(h, XMB)
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clf_losses = compute_loss(XMB, YMB, MMB, clf_logits, only_return_losses=True)
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res = (clf_logits.numpy()*n, clf_losses.numpy()*n)
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results.append(res)
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results = zip(*results)
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return [fn(res) for res, fn in zip(results, fns)]
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def iter_predict(Xs, Ms):
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logits = []
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with torch.no_grad():
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model.eval()
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for xmb, mmb in iter_data(Xs, Ms, n_batch=n_batch_train, truncate=False, verbose=True):
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n = len(xmb)
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XMB = torch.tensor(xmb, dtype=torch.long).to(device)
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MMB = torch.tensor(mmb).to(device)
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h = model(XMB)
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clf_logits = clf_head(h, XMB)
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logits.append(clf_logits.numpy())
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logits = np.concatenate(logits, 0)
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return logits
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def log():
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global best_score
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tr_logits, tr_cost = iter_apply(trX[:n_valid], trM[:n_valid], trY[:n_valid])
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va_logits, va_cost = iter_apply(vaX, vaM, vaY)
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tr_cost = tr_cost/len(trY[:n_valid])
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va_cost = va_cost/n_valid
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tr_acc = accuracy_score(trY[:n_valid], np.argmax(tr_logits, 1))*100.
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va_acc = accuracy_score(vaY, np.argmax(va_logits, 1))*100.
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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)
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print('%d %d %.3f %.3f %.2f %.2f'%(n_epochs, n_updates, tr_cost, va_cost, tr_acc, va_acc))
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if submit:
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score = va_acc
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if score > best_score:
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best_score = score
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path = os.path.join(save_dir, desc, 'best_params')
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torch.save(model.state_dict(), make_path(path))
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def predict():
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filename = filenames[dataset]
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pred_fn = pred_fns[dataset]
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label_decoder = label_decoders[dataset]
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predictions = pred_fn(iter_predict(teX, teM))
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if label_decoder is not None:
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predictions = [label_decoder[prediction] for prediction in predictions]
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path = os.path.join(submission_dir, filename)
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os.makedirs(os.path.dirname(path), exist_ok=True)
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with open(path, 'w') as f:
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f.write('{}\t{}\n'.format('index', 'prediction'))
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for i, prediction in enumerate(predictions):
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f.write('{}\t{}\n'.format(i, prediction))
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def run_epoch():
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for xmb, mmb, ymb in iter_data(*shuffle(trX, trM, trYt, random_state=np.random),
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n_batch=n_batch_train, truncate=True, verbose=True):
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global n_updates
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XMB = torch.tensor(xmb, dtype=torch.long).to(device)
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YMB = torch.tensor(ymb, dtype=torch.long).to(device)
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MMB = torch.tensor(mmb).to(device)
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model.train()
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h = model(XMB)
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lm_logits = lm_head(h)
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clf_logits = clf_head(h, XMB)
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compute_loss(XMB, YMB, MMB, clf_logits, lm_logits)
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n_updates += 1
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if n_updates in [1000, 2000, 4000, 8000, 16000, 32000] and n_epochs == 0:
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log()
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argmax = lambda x:np.argmax(x, 1)
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pred_fns = {
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'rocstories':argmax,
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}
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filenames = {
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'rocstories':'ROCStories.tsv',
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}
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label_decoders = {
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'rocstories':None,
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}
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--desc', type=str)
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parser.add_argument('--dataset', type=str)
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parser.add_argument('--log_dir', type=str, default='log/')
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parser.add_argument('--save_dir', type=str, default='save/')
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parser.add_argument('--data_dir', type=str, default='data/')
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parser.add_argument('--submission_dir', type=str, default='submission/')
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parser.add_argument('--submit', action='store_true')
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parser.add_argument('--analysis', action='store_true')
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parser.add_argument('--seed', type=int, default=42)
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parser.add_argument('--n_iter', type=int, default=3)
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parser.add_argument('--n_batch', type=int, default=8)
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parser.add_argument('--max_grad_norm', type=int, default=1)
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parser.add_argument('--lr', type=float, default=6.25e-5)
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parser.add_argument('--lr_warmup', type=float, default=0.002)
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parser.add_argument('--n_ctx', type=int, default=512)
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parser.add_argument('--n_embd', type=int, default=768)
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parser.add_argument('--n_head', type=int, default=12)
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parser.add_argument('--n_layer', type=int, default=12)
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parser.add_argument('--embd_pdrop', type=float, default=0.1)
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parser.add_argument('--attn_pdrop', type=float, default=0.1)
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parser.add_argument('--resid_pdrop', type=float, default=0.1)
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parser.add_argument('--clf_pdrop', type=float, default=0.1)
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parser.add_argument('--l2', type=float, default=0.01)
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parser.add_argument('--vector_l2', action='store_true')
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parser.add_argument('--n_gpu', type=int, default=4)
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parser.add_argument('--opt', type=str, default='adam')
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parser.add_argument('--afn', type=str, default='gelu')
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parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
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parser.add_argument('--encoder_path', type=str, default='model/encoder_bpe_40000.json')
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parser.add_argument('--bpe_path', type=str, default='model/vocab_40000.bpe')
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parser.add_argument('--n_transfer', type=int, default=12)
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parser.add_argument('--lm_coef', type=float, default=0.5)
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parser.add_argument('--b1', type=float, default=0.9)
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parser.add_argument('--b2', type=float, default=0.999)
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parser.add_argument('--e', type=float, default=1e-8)
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args = parser.parse_args()
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print(args)
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globals().update(args.__dict__) #TODO remove gobal
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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# torch.device object used throughout this script TODO add gpu setting
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device = torch.device("cpu") #"cuda" if use_cuda else "cpu")
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logger = ResultLogger(path=os.path.join(log_dir, '{}.jsonl'.format(desc)), **args.__dict__)
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text_encoder = TextEncoder(encoder_path, bpe_path)
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encoder = text_encoder.encoder
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n_vocab = len(text_encoder.encoder)
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(trX1, trX2, trX3, trY), (vaX1, vaX2, vaX3, vaY), (teX1, teX2, teX3) = encode_dataset(rocstories(data_dir), encoder=text_encoder)
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n_y = 2
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encoder['_start_'] = len(encoder)
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encoder['_delimiter_'] = len(encoder)
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encoder['_classify_'] = len(encoder)
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clf_token = encoder['_classify_']
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n_special = 3
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max_len = n_ctx//2-2
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n_ctx = min(
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max(
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[len(x1[:max_len])+max(len(x2[:max_len]), len(x3[:max_len])) for x1, x2, x3 in zip(trX1, trX2, trX3)]
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+[len(x1[:max_len])+max(len(x2[:max_len]), len(x3[:max_len])) for x1, x2, x3 in zip(vaX1, vaX2, vaX3)]
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+[len(x1[:max_len])+max(len(x2[:max_len]), len(x3[:max_len])) for x1, x2, x3 in zip(teX1, teX2, teX3)]
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)+3, n_ctx
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)
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vocab = n_vocab + n_special + n_ctx
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trX, trM = transform_roc(trX1, trX2, trX3)
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vaX, vaM = transform_roc(vaX1, vaX2, vaX3)
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if submit:
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teX, teM = transform_roc(teX1, teX2, teX3)
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n_train = len(trY)
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n_valid = len(vaY)
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n_batch_train = n_batch*n_gpu
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n_updates_total = (n_train//n_batch_train)*n_iter
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model = Model(vocab, args)
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lm_head = LMHead(model, args)
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clf_head = ClfHead(clf_token, args)
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criterion = nn.CrossEntropyLoss(reduce=False) # TODO check loss functions
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model_opt = OpenAIAdam(model.parameters(), lr=lr, schedule=lr_schedule,
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warmup=lr_warmup, t_total=n_updates_total, b1=b1,
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b2=b2, e=e, l2=l2, vector_l2=vector_l2,
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max_grad_norm=max_grad_norm)
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compute_loss = LossCompute(criterion, criterion, lm_coef, model_opt)
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# TODO add train() and eval()
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load_openai_pretrained_model(model, n_ctx, n_special, args)
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model.to(device)
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lm_head.to(device)
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clf_head.to(device)
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n_updates = 0
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n_epochs = 0
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if dataset != 'stsb':
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trYt = trY
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if submit:
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path = os.path.join(save_dir, desc, 'best_params')
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torch.save(model.state_dict(), make_path(path))
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best_score = 0
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log()
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for i in range(n_iter):
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run_epoch()
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n_epochs += 1
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log()
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if submit:
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path = os.path.join(save_dir, desc, 'best_params')
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model.load_state_dict(torch.load(path))
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predict()
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if analysis:
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rocstories_analysis(data_dir, os.path.join(submission_dir, 'ROCStories.tsv'),
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os.path.join(log_dir, 'rocstories.jsonl'))
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