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score_utils.py
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score_utils.py
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from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import string
from pyemd import emd
def padding(arr, pad_token, dtype=torch.long):
lens = torch.LongTensor([len(a) for a in arr])
max_len = lens.max().item()
padded = torch.ones(len(arr), max_len, dtype=dtype) * pad_token
mask = torch.zeros(len(arr), max_len, dtype=torch.long)
for i, a in enumerate(arr):
padded[i, :lens[i]] = torch.tensor(a, dtype=dtype)
mask[i, :lens[i]] = 1
return padded, lens, mask
def bert_encode(model, x, attention_mask):
model.eval()
with torch.no_grad():
output, _, x_encoded_layers, _ = model(input_ids = x, token_type_ids = None, attention_mask = attention_mask)
return x_encoded_layers
def collate_idf(arr, tokenize, numericalize, idf_dict,
pad="[PAD]", device='cuda:0'):
tokens = [["[CLS]"]+tokenize(a)+["[SEP]"] for a in arr]
arr = [numericalize(a) for a in tokens]
idf_weights = [[idf_dict[i] for i in a] for a in arr]
pad_token = numericalize([pad])[0]
padded, lens, mask = padding(arr, pad_token, dtype=torch.long)
padded_idf, _, _ = padding(idf_weights, pad_token, dtype=torch.float)
padded = padded.to(device=device)
mask = mask.to(device=device)
lens = lens.to(device=device)
return padded, padded_idf, lens, mask, tokens
def get_bert_embedding(all_sens, model, tokenizer, idf_dict,
batch_size=-1, device='cuda:0'):
padded_sens, padded_idf, lens, mask, tokens = collate_idf(all_sens,
tokenizer.tokenize, tokenizer.convert_tokens_to_ids,
idf_dict,
device=device)
if batch_size == -1: batch_size = len(all_sens)
embeddings = []
with torch.no_grad():
for i in range(0, len(all_sens), batch_size):
batch_embedding = bert_encode(model, padded_sens[i:i+batch_size],
attention_mask=mask[i:i+batch_size])
batch_embedding = torch.stack(batch_embedding)
embeddings.append(batch_embedding)
del batch_embedding
total_embedding = torch.cat(embeddings, dim=-3)
return total_embedding, lens, mask, padded_idf, tokens
def pairwise_distances(x, y=None):
x_norm = (x**2).sum(1).view(-1, 1)
y_norm = (y**2).sum(1).view(1, -1)
y_t = torch.transpose(y, 0, 1)
dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t)
return torch.clamp(dist, 0.0, np.inf)
def slide_window(input_, w = 3, o = 2):
if input_.size - w + 1 <= 0:
w = input_.size
sh = (input_.size - w + 1, w)
st = input_.strides * 2
view = np.lib.stride_tricks.as_strided(input_, strides = st, shape = sh)[0::o]
return view.copy().tolist()
def _safe_divide(numerator, denominator):
return numerator / (denominator + 1e-30)
def load_ngram(ids, embedding, idf, n, o, device='cuda:0'):
new_a = []
new_idf = []
slide_wins = slide_window(np.array(ids), w=n, o=o)
for slide_win in slide_wins:
new_idf.append(idf[slide_win].sum().item())
scale = _safe_divide(idf[slide_win], idf[slide_win].sum(0)).unsqueeze(-1).to(device)
tmp = (scale * embedding[slide_win]).sum(0)
new_a.append(tmp)
new_a = torch.stack(new_a, 0).to(device)
return new_a, new_idf
from collections import defaultdict
def cross_lingual_mapping(mapping, embedding, projection, bias):
batch_size = embedding.shape[0]
n_tokens = embedding.shape[1]
if mapping == 'CLP':
embedding = torch.matmul(embedding, projection)
if mapping == 'UMD':
embedding = embedding - (embedding * bias).sum(2, keepdim=True) * bias.repeat(batch_size, n_tokens, 1)
return embedding
def lm_perplexity(model, hyps, tokenizer, batch_size=1, device='cuda:0'):
preds = []
model.eval()
for batch_start in range(0, len(hyps), batch_size):
batch_hyps = hyps[batch_start:batch_start+batch_size]
tokenize_input = tokenizer.tokenize(batch_hyps[0])
if len(tokenize_input) <=1:
preds.append(0)
else:
if len(tokenize_input) > 1024:
tokenize_input = tokenize_input[:1024]
arr = tokenizer.convert_tokens_to_ids(tokenize_input)
input_ids = torch.tensor([arr])
input_ids = input_ids.to(device=device)
score = model(input_ids, labels=input_ids)[0]
preds.append(-score.item())
return preds
def word_mover_score(mapping, projection, bias, model, tokenizer, src, hyps, n_gram=1, batch_size=256, device='cuda:0'):
idf_dict_src = defaultdict(lambda: 1.)
idf_dict_hyp = defaultdict(lambda: 1.)
preds = []
for batch_start in range(0, len(src), batch_size):
batch_src = src[batch_start:batch_start+batch_size]
batch_hyps = hyps[batch_start:batch_start+batch_size]
src_embedding, src_lens, src_masks, src_idf, src_tokens = get_bert_embedding(batch_src, model, tokenizer, idf_dict_src,
device=device)
hyp_embedding, hyp_lens, hyp_masks, hyp_idf, hyp_tokens = get_bert_embedding(batch_hyps, model, tokenizer, idf_dict_hyp,
device=device)
src_embedding = src_embedding[-1]
hyp_embedding = hyp_embedding[-1]
src_embedding = cross_lingual_mapping(mapping, src_embedding, projection, bias[0])
batch_size = len(src_embedding)
for i in range(batch_size):
src_ids = [k for k, w in enumerate(src_tokens[i]) if w not in set(string.punctuation) and '##' not in w]
hyp_ids = [k for k, w in enumerate(hyp_tokens[i]) if w not in set(string.punctuation) and '##' not in w]
src_embedding_i, src_idf_i = load_ngram(src_ids, src_embedding[i], src_idf[i], n_gram, 1)
hyp_embedding_i, hyp_idf_i = load_ngram(hyp_ids, hyp_embedding[i], hyp_idf[i], n_gram, 1)
embeddings = torch.cat([src_embedding_i, hyp_embedding_i], 0)
embeddings.div_(torch.norm(embeddings, dim=-1).unsqueeze(-1) + 1e-30)
distance_matrix = pairwise_distances(embeddings, embeddings)
c1 = np.zeros(len(src_idf_i) + len(hyp_idf_i))
c2 = np.zeros_like(c1)
c1[:len(src_idf_i)] = src_idf_i
c2[-len(hyp_idf_i):] = hyp_idf_i
score = emd(_safe_divide(c1, np.sum(c1)),
_safe_divide(c2, np.sum(c2)),
distance_matrix.double().cpu().numpy())
preds.append(1 - score)
return preds