mirror of
https://github.com/huggingface/transformers.git
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654 lines
27 KiB
Python
654 lines
27 KiB
Python
import functools
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import math
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import os # noqa: F401
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from random import choice, randint
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from time import time
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import numpy as np
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import pandas as pd
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import torch
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import torch.utils.checkpoint as checkpoint
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from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
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from tqdm import tqdm
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import faiss # noqa: F401
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import nlp # noqa: F401
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from elasticsearch import Elasticsearch # noqa: F401
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from elasticsearch.helpers import bulk, streaming_bulk # noqa: F401
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from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
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pd.set_option("display.max_colwidth", None)
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###############
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# Sparse index
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###############
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def make_es_index_snippets(es_client, passages_dset, index_name="english_wiki_kilt_snippets_100w"):
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index_config = {
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"settings": {
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"number_of_shards": 1,
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"analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}},
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},
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"mappings": {
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"properties": {
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"article_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
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"section_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
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"passage_text": {"type": "text", "analyzer": "standard", "similarity": "BM25"},
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}
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},
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}
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es_client.indices.create(index=index_name, body=index_config)
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number_of_docs = passages_dset.num_rows
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progress = tqdm(unit="docs", total=number_of_docs)
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successes = 0
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def passage_generator():
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for passage in passages_dset:
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yield passage
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# create the ES index
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for ok, action in streaming_bulk(client=es_client, index=index_name, actions=passage_generator(),):
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progress.update(1)
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successes += ok
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print("Indexed %d documents" % (successes,))
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def query_es_index(question, es_client, index_name="english_wiki_kilt_snippets_100w", n_results=10, min_length=20):
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q = question.lower()
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banned = ["how", "why", "what", "where", "which", "do", "does", "is", "?", "eli5", "eli5:"]
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q = " ".join([w for w in q.split() if w not in banned])
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response = es_client.search(
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index=index_name,
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body={
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"query": {
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"multi_match": {
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"query": q,
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"fields": ["article_title", "section_title", "passage_text^2"],
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"type": "cross_fields",
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}
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},
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"size": 2 * n_results,
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},
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)
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hits = response["hits"]["hits"]
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support_doc = "<P> " + " <P> ".join([hit["_source"]["passage_text"] for hit in hits])
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res_list = [dict([(k, hit["_source"][k]) for k in hit["_source"] if k != "passage_text"]) for hit in hits]
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for r, hit in zip(res_list, hits):
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r["passage_id"] = hit["_id"]
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r["score"] = hit["_score"]
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r["passage_text"] = hit["_source"]["passage_text"]
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res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
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return support_doc, res_list
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###############
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# ELI5 retriever training
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###############
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class ELI5DatasetQARetriver(Dataset):
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def __init__(self, examples_array, extra_answer_threshold=3, min_answer_length=64, training=True, n_samples=None):
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self.data = examples_array
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self.answer_thres = extra_answer_threshold
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self.min_length = min_answer_length
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self.training = training
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self.n_samples = self.data.num_rows if n_samples is None else n_samples
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def __len__(self):
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return self.n_samples
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def make_example(self, idx):
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example = self.data[idx]
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question = example["title"]
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if self.training:
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answers = [a for i, (a, sc) in enumerate(zip(example["answers"]["text"], example["answers"]["score"]))]
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answer_tab = choice(answers).split(" ")
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start_idx = randint(0, max(0, len(answer_tab) - self.min_length))
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answer_span = " ".join(answer_tab[start_idx:])
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else:
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answer_span = example["answers"]["text"][0]
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return (question, answer_span)
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def __getitem__(self, idx):
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return self.make_example(idx % self.data.num_rows)
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class RetrievalQAEmbedder(torch.nn.Module):
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def __init__(self, sent_encoder, dim):
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super(RetrievalQAEmbedder, self).__init__()
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self.sent_encoder = sent_encoder
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self.output_dim = 128
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self.project_q = torch.nn.Linear(dim, self.output_dim, bias=False)
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self.project_a = torch.nn.Linear(dim, self.output_dim, bias=False)
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self.ce_loss = torch.nn.CrossEntropyLoss(reduction="mean")
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def embed_sentences_checkpointed(self, input_ids, attention_mask, checkpoint_batch_size=-1):
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# reproduces BERT forward pass with checkpointing
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if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
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return self.sent_encoder(input_ids, attention_mask=attention_mask)[1]
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else:
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# prepare implicit variables
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device = input_ids.device
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input_shape = input_ids.size()
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token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
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head_mask = [None] * self.sent_encoder.config.num_hidden_layers
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extended_attention_mask: torch.Tensor = self.sent_encoder.get_extended_attention_mask(
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attention_mask, input_shape, device
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)
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# define function for checkpointing
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def partial_encode(*inputs):
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encoder_outputs = self.sent_encoder.encoder(inputs[0], attention_mask=inputs[1], head_mask=head_mask,)
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sequence_output = encoder_outputs[0]
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pooled_output = self.sent_encoder.pooler(sequence_output)
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return pooled_output
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# run embedding layer on everything at once
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embedding_output = self.sent_encoder.embeddings(
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input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None
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)
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# run encoding and pooling on one mini-batch at a time
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pooled_output_list = []
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for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
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b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
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b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
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pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask)
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pooled_output_list.append(pooled_output)
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return torch.cat(pooled_output_list, dim=0)
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def embed_questions(self, q_ids, q_mask, checkpoint_batch_size=-1):
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q_reps = self.embed_sentences_checkpointed(q_ids, q_mask, checkpoint_batch_size)
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return self.project_q(q_reps)
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def embed_answers(self, a_ids, a_mask, checkpoint_batch_size=-1):
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a_reps = self.embed_sentences_checkpointed(a_ids, a_mask, checkpoint_batch_size)
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return self.project_a(a_reps)
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def forward(self, q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=-1):
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device = q_ids.device
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q_reps = self.embed_questions(q_ids, q_mask, checkpoint_batch_size)
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a_reps = self.embed_answers(a_ids, a_mask, checkpoint_batch_size)
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compare_scores = torch.mm(q_reps, a_reps.t())
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loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device))
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loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device))
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loss = (loss_qa + loss_aq) / 2
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return loss
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def make_qa_retriever_model(model_name="google/bert_uncased_L-8_H-512_A-8", from_file=None, device="cuda:0"):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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bert_model = AutoModel.from_pretrained(model_name).to(device)
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# run bert_model on a dummy batch to get output dimension
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d_ids = torch.LongTensor(
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[[bert_model.config.bos_token_id if bert_model.config.bos_token_id is not None else 1]]
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).to(device)
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d_mask = torch.LongTensor([[1]]).to(device)
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sent_dim = bert_model(d_ids, attention_mask=d_mask)[1].shape[-1]
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qa_embedder = RetrievalQAEmbedder(bert_model, sent_dim).to(device)
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if from_file is not None:
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param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states
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qa_embedder.load_state_dict(param_dict["model"])
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return tokenizer, qa_embedder
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def make_qa_retriever_batch(qa_list, tokenizer, max_len=64, device="cuda:0"):
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q_ls = [q for q, a in qa_list]
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a_ls = [a for q, a in qa_list]
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q_toks = tokenizer.batch_encode_plus(q_ls, max_length=max_len, pad_to_max_length=True)
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q_ids, q_mask = (
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torch.LongTensor(q_toks["input_ids"]).to(device),
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torch.LongTensor(q_toks["attention_mask"]).to(device),
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)
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a_toks = tokenizer.batch_encode_plus(a_ls, max_length=max_len, pad_to_max_length=True)
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a_ids, a_mask = (
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torch.LongTensor(a_toks["input_ids"]).to(device),
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torch.LongTensor(a_toks["attention_mask"]).to(device),
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)
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return (q_ids, q_mask, a_ids, a_mask)
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def train_qa_retriever_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0):
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model.train()
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# make iterator
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train_sampler = RandomSampler(dataset)
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model_collate_fn = functools.partial(
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make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
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)
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data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
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epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
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# accumulate loss since last print
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loc_steps = 0
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loc_loss = 0.0
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st_time = time()
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for step, batch in enumerate(epoch_iterator):
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q_ids, q_mask, a_ids, a_mask = batch
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pre_loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size)
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loss = pre_loss.sum()
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# optimizer
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loss.backward()
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optimizer.step()
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scheduler.step()
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model.zero_grad()
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# some printing within the epoch
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loc_loss += loss.item()
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loc_steps += 1
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if step % args.print_freq == 0 or step == 1:
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print(
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"{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
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e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time,
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)
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)
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loc_loss = 0
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loc_steps = 0
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def train_qa_retriever_joint_epoch(model, dataset_list, tokenizer, optimizer, scheduler, args, e=0):
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model.train()
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model_collate_fn = functools.partial(
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make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
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)
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# make iterator
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train_samplers = [RandomSampler(dataset) for dataset in dataset_list]
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data_loaders = [
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DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
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for dataset, train_sampler in zip(dataset_list, train_samplers)
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]
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iterators = [iter(dloader) for dloader in data_loaders]
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joint_iter = zip(*iterators)
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# accumulate loss since last print
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loc_steps = 0
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loc_loss = 0.0
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st_time = time()
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for step, (batches,) in enumerate(zip(joint_iter)):
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for batch in batches:
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q_ids, q_mask, a_ids, a_mask = batch
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loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size)
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# optimizer
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loss.backward()
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optimizer.step()
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scheduler.step()
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model.zero_grad()
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# some printing within the epoch
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loc_loss += loss.item()
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loc_steps += 1
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if step % args.print_freq == 0:
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print(
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"{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
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e, step, len(dataset_list[0]) // args.batch_size, loc_loss / loc_steps, time() - st_time,
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)
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)
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loc_loss = 0
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loc_steps = 0
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def evaluate_qa_retriever(model, dataset, tokenizer, args):
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model.eval()
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# make iterator
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eval_sampler = SequentialSampler(dataset)
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model_collate_fn = functools.partial(
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make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
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)
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data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=eval_sampler, collate_fn=model_collate_fn)
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epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
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tot_loss = 0.0
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with torch.no_grad():
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for step, batch in enumerate(epoch_iterator):
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q_ids, q_mask, a_ids, a_mask = batch
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loss = model(q_ids, q_mask, a_ids, a_mask)
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tot_loss += loss.item()
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return tot_loss / (step + 1)
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def train_qa_retriever(qar_model, qar_tokenizer, qar_train_dset, qar_valid_dset, qar_args):
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qar_optimizer = AdamW(qar_model.parameters(), lr=qar_args.learning_rate, eps=1e-8)
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qar_scheduler = get_linear_schedule_with_warmup(
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qar_optimizer,
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num_warmup_steps=100,
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num_training_steps=(qar_args.num_epochs + 1) * math.ceil(len(qar_train_dset) / qar_args.batch_size),
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)
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for e in range(qar_args.num_epochs):
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train_qa_retriever_epoch(qar_model, qar_train_dset, qar_tokenizer, qar_optimizer, qar_scheduler, qar_args, e)
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m_save_dict = {
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"model": qar_model.state_dict(),
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"optimizer": qar_optimizer.state_dict(),
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"scheduler": qar_scheduler.state_dict(),
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}
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print("Saving model {}".format(qar_args.model_save_name))
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torch.save(m_save_dict, "{}_{}.pth".format(qar_args.model_save_name, e))
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eval_loss = evaluate_qa_retriever(qar_model, qar_valid_dset, qar_tokenizer, qar_args)
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print("Evaluation loss epoch {:4d}: {:.3f}".format(e, eval_loss))
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###############
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# ELI5 seq2seq model training
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###############
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class ELI5DatasetS2S(Dataset):
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def __init__(
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self, examples_array, make_doc_fun=None, extra_answer_threshold=3, document_cache=None, training=True
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):
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self.training = training
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self.data = examples_array
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self.make_doc_function = make_doc_fun
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self.document_cache = {} if document_cache is None else document_cache
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assert not (make_doc_fun is None and document_cache is None)
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# make index of specific question-answer pairs from multi-answers
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if self.training:
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self.qa_id_list = [
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(i, j)
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for i, qa in enumerate(self.data)
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for j, (a, sc) in enumerate(zip(qa["answers"]["text"], qa["answers"]["score"]))
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if j == 0 or sc >= extra_answer_threshold
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]
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else:
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self.qa_id_list = [(i, 0) for i in range(self.data.num_rows)]
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def __len__(self):
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return len(self.qa_id_list)
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def make_example(self, idx):
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i, j = self.qa_id_list[idx]
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example = self.data[i]
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question = example["title"] + " " + example["selftext"]
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answer = example["answers"]["text"][j]
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q_id = example["q_id"]
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if self.make_doc_function is not None:
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self.document_cache[q_id] = self.document_cache.get(q_id, self.make_doc_function(example["title"]))
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document = self.document_cache[q_id]
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in_st = "question: {} context: {}".format(
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question.lower().replace(" --t--", "").strip(), document.lower().strip(),
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)
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out_st = answer
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return (in_st, out_st)
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def __getitem__(self, idx):
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return self.make_example(idx)
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def make_qa_s2s_model(model_name="facebook/bart-large", from_file=None, device="cuda:0"):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
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if from_file is not None:
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param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states
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model.load_state_dict(param_dict["model"])
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return tokenizer, model
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def make_qa_s2s_batch(qa_list, tokenizer, max_len=64, max_a_len=360, device="cuda:0"):
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q_ls = [q for q, a in qa_list]
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a_ls = [a for q, a in qa_list]
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q_toks = tokenizer.batch_encode_plus(q_ls, max_length=max_len, pad_to_max_length=True)
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q_ids, q_mask = (
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torch.LongTensor(q_toks["input_ids"]).to(device),
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torch.LongTensor(q_toks["attention_mask"]).to(device),
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)
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a_toks = tokenizer.batch_encode_plus(a_ls, max_length=min(max_len, max_a_len), pad_to_max_length=True)
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a_ids, a_mask = (
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torch.LongTensor(a_toks["input_ids"]).to(device),
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torch.LongTensor(a_toks["attention_mask"]).to(device),
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)
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lm_labels = a_ids[:, 1:].contiguous().clone()
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lm_labels[a_mask[:, 1:].contiguous() == 0] = -100
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model_inputs = {
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"input_ids": q_ids,
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"attention_mask": q_mask,
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"decoder_input_ids": a_ids[:, :-1].contiguous(),
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"lm_labels": lm_labels,
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}
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return model_inputs
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def train_qa_s2s_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0, curriculum=False):
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model.train()
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# make iterator
|
|
if curriculum:
|
|
train_sampler = SequentialSampler(dataset)
|
|
else:
|
|
train_sampler = RandomSampler(dataset)
|
|
model_collate_fn = functools.partial(
|
|
make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
|
|
)
|
|
data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
|
|
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
|
|
# accumulate loss since last print
|
|
loc_steps = 0
|
|
loc_loss = 0.0
|
|
st_time = time()
|
|
for step, batch_inputs in enumerate(epoch_iterator):
|
|
pre_loss = model(**batch_inputs)[0]
|
|
loss = pre_loss.sum() / pre_loss.shape[0]
|
|
loss.backward()
|
|
# optimizer
|
|
if step % args.backward_freq == 0:
|
|
optimizer.step()
|
|
scheduler.step()
|
|
model.zero_grad()
|
|
# some printing within the epoch
|
|
loc_loss += loss.item()
|
|
loc_steps += 1
|
|
if step % args.print_freq == 0 or step == 1:
|
|
print(
|
|
"{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
|
|
e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time,
|
|
)
|
|
)
|
|
loc_loss = 0
|
|
loc_steps = 0
|
|
|
|
|
|
def eval_qa_s2s_epoch(model, dataset, tokenizer, args):
|
|
model.eval()
|
|
# make iterator
|
|
train_sampler = SequentialSampler(dataset)
|
|
model_collate_fn = functools.partial(
|
|
make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0"
|
|
)
|
|
data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn)
|
|
epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True)
|
|
# accumulate loss since last print
|
|
loc_steps = 0
|
|
loc_loss = 0.0
|
|
st_time = time()
|
|
with torch.no_grad():
|
|
for step, batch_inputs in enumerate(epoch_iterator):
|
|
pre_loss = model(**batch_inputs)[0]
|
|
loss = pre_loss.sum() / pre_loss.shape[0]
|
|
loc_loss += loss.item()
|
|
loc_steps += 1
|
|
if step % args.print_freq == 0:
|
|
print(
|
|
"{:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format(
|
|
step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time,
|
|
)
|
|
)
|
|
print("Total \t L: {:.3f} \t -- {:.3f}".format(loc_loss / loc_steps, time() - st_time,))
|
|
|
|
|
|
def train_qa_s2s(qa_s2s_model, qa_s2s_tokenizer, s2s_train_dset, s2s_valid_dset, s2s_args):
|
|
s2s_optimizer = AdamW(qa_s2s_model.parameters(), lr=s2s_args.learning_rate, eps=1e-8)
|
|
s2s_scheduler = get_linear_schedule_with_warmup(
|
|
s2s_optimizer,
|
|
num_warmup_steps=400,
|
|
num_training_steps=(s2s_args.num_epochs + 1) * math.ceil(len(s2s_train_dset) / s2s_args.batch_size),
|
|
)
|
|
for e in range(s2s_args.num_epochs):
|
|
train_qa_s2s_epoch(
|
|
qa_s2s_model,
|
|
s2s_train_dset,
|
|
qa_s2s_tokenizer,
|
|
s2s_optimizer,
|
|
s2s_scheduler,
|
|
s2s_args,
|
|
e,
|
|
curriculum=(e == 0),
|
|
)
|
|
m_save_dict = {
|
|
"model": qa_s2s_model.state_dict(),
|
|
"optimizer": s2s_optimizer.state_dict(),
|
|
"scheduler": s2s_scheduler.state_dict(),
|
|
}
|
|
print("Saving model {}".format(s2s_args.model_save_name))
|
|
eval_qa_s2s_epoch(qa_s2s_model, s2s_valid_dset, qa_s2s_tokenizer, s2s_args)
|
|
torch.save(m_save_dict, "{}_{}.pth".format(s2s_args.model_save_name, e))
|
|
|
|
|
|
# generate answer from input "question: ... context: <p> ..."
|
|
def qa_s2s_generate(
|
|
question_doc,
|
|
qa_s2s_model,
|
|
qa_s2s_tokenizer,
|
|
num_answers=1,
|
|
num_beams=None,
|
|
min_len=64,
|
|
max_len=256,
|
|
do_sample=False,
|
|
temp=1.0,
|
|
top_p=None,
|
|
top_k=None,
|
|
max_input_length=512,
|
|
device="cuda:0",
|
|
):
|
|
model_inputs = make_qa_s2s_batch([(question_doc, "A")], qa_s2s_tokenizer, max_input_length, device=device,)
|
|
n_beams = num_answers if num_beams is None else max(num_beams, num_answers)
|
|
generated_ids = qa_s2s_model.generate(
|
|
input_ids=model_inputs["input_ids"],
|
|
attention_mask=model_inputs["attention_mask"],
|
|
min_length=min_len,
|
|
max_length=max_len,
|
|
do_sample=do_sample,
|
|
early_stopping=True,
|
|
num_beams=1 if do_sample else n_beams,
|
|
temperature=temp,
|
|
top_k=top_k,
|
|
top_p=top_p,
|
|
eos_token_id=qa_s2s_tokenizer.eos_token_id,
|
|
no_repeat_ngram_size=3,
|
|
num_return_sequences=num_answers,
|
|
decoder_start_token_id=qa_s2s_tokenizer.bos_token_id,
|
|
)
|
|
return [qa_s2s_tokenizer.decode(ans_ids, skip_special_tokens=True).strip() for ans_ids in generated_ids]
|
|
|
|
|
|
###############
|
|
# ELI5-trained retrieval model usage
|
|
###############
|
|
def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=128, device="cuda:0"):
|
|
a_toks = tokenizer.batch_encode_plus(passages, max_length=max_length, pad_to_max_length=True)
|
|
a_ids, a_mask = (
|
|
torch.LongTensor(a_toks["input_ids"]).to(device),
|
|
torch.LongTensor(a_toks["attention_mask"]).to(device),
|
|
)
|
|
with torch.no_grad():
|
|
a_reps = qa_embedder.embed_answers(a_ids, a_mask).cpu().type(torch.float)
|
|
return a_reps.numpy()
|
|
|
|
|
|
def embed_questions_for_retrieval(q_ls, tokenizer, qa_embedder, device="cuda:0"):
|
|
q_toks = tokenizer.batch_encode_plus(q_ls, max_length=128, pad_to_max_length=True)
|
|
q_ids, q_mask = (
|
|
torch.LongTensor(q_toks["input_ids"]).to(device),
|
|
torch.LongTensor(q_toks["attention_mask"]).to(device),
|
|
)
|
|
with torch.no_grad():
|
|
q_reps = qa_embedder.embed_questions(q_ids, q_mask).cpu().type(torch.float)
|
|
return q_reps.numpy()
|
|
|
|
|
|
def make_qa_dense_index(
|
|
qa_embedder,
|
|
tokenizer,
|
|
passages_dset,
|
|
batch_size=512,
|
|
max_length=128,
|
|
index_name="kilt_passages_reps.dat",
|
|
dtype="float32",
|
|
device="cuda:0",
|
|
):
|
|
st_time = time()
|
|
fp = np.memmap(index_name, dtype=dtype, mode="w+", shape=(passages_dset.num_rows, 128))
|
|
n_batches = math.ceil(passages_dset.num_rows / batch_size)
|
|
for i in range(n_batches):
|
|
passages = [p for p in passages_dset[i * batch_size : (i + 1) * batch_size]["passage_text"]]
|
|
reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length, device)
|
|
fp[i * batch_size : (i + 1) * batch_size] = reps
|
|
if i % 50 == 0:
|
|
print(i, time() - st_time)
|
|
|
|
|
|
def evaluate_retriever(qa_list, retriever_func, scoring_func, n_ret=10, verbose=False):
|
|
total_retriever_time = 0.0
|
|
total_retriever_score = 0.0
|
|
st_time = time()
|
|
for i, (question, answer) in enumerate(qa_list):
|
|
r_time = time()
|
|
retrieved_passages = retriever_func(question, n_ret)
|
|
total_retriever_time += time() - r_time
|
|
total_retriever_score += scoring_func(retrieved_passages, answer)
|
|
if verbose and ((i + 1) % 500 == 0 or i <= 1):
|
|
print(
|
|
"{:03d}: S-{:.4f} T-{:.4f} | {:.2f}".format(
|
|
i + 1, total_retriever_score / (i + 1), total_retriever_time / (i + 1), time() - st_time
|
|
)
|
|
)
|
|
return {"idf_recall": total_retriever_score / (i + 1), "retrieval_time": total_retriever_time / (i + 1)}
|
|
|
|
|
|
# build a support document for the question out of Wikipedia snippets
|
|
def query_qa_dense_index(
|
|
question, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20, device="cuda:0"
|
|
):
|
|
q_rep = embed_questions_for_retrieval([question], tokenizer, qa_embedder, device=device)
|
|
D, I = wiki_index.search(q_rep, 2 * n_results)
|
|
res_passages = [wiki_passages[int(i)] for i in I[0]]
|
|
support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages])
|
|
res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
|
|
res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
|
|
for r, sc in zip(res_list, D[0]):
|
|
r["score"] = float(sc)
|
|
return support_doc, res_list
|
|
|
|
|
|
def batch_query_qa_dense_index(questions, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10):
|
|
q_rep = embed_questions_for_retrieval(questions, tokenizer, qa_embedder)
|
|
D, I = wiki_index.search(q_rep, n_results)
|
|
res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I]
|
|
support_doc_lst = [
|
|
"<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst
|
|
]
|
|
all_res_lists = []
|
|
for (res_passages, dl) in zip(res_passages_lst, D):
|
|
res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
|
|
for r, sc in zip(res_list, dl):
|
|
r["score"] = float(sc)
|
|
all_res_lists += [res_list[:]]
|
|
return support_doc_lst, all_res_lists
|
|
|
|
|
|
# find nearest neighbors of an answer or declarative text in Wikipedia snippets
|
|
def query_qa_dense_index_nn(passage, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20):
|
|
a_rep = embed_passages_for_retrieval([passage], tokenizer, qa_embedder)
|
|
D, I = wiki_index.search(a_rep, 2 * n_results)
|
|
res_passages = [wiki_passages[int(i)] for i in I[0]]
|
|
support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages])
|
|
res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
|
|
res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results]
|
|
for r, sc, i in zip(res_list, D[0], I[0]):
|
|
r["passage_id"] = int(i)
|
|
r["score"] = float(sc)
|
|
return support_doc, res_list
|
|
|
|
|
|
def batch_query_qa_dense_index_nn(passages, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10):
|
|
a_reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder)
|
|
D, I = wiki_index.search(a_reps, n_results)
|
|
res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I]
|
|
support_doc_lst = [
|
|
"<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst
|
|
]
|
|
all_res_lists = []
|
|
for (res_passages, dl, il) in zip(res_passages_lst, D, I):
|
|
res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages]
|
|
for r, sc, i in zip(res_list, dl, il):
|
|
r["passage_id"] = int(i)
|
|
r["score"] = float(sc)
|
|
all_res_lists += [res_list[:]]
|
|
return support_doc_lst, all_res_lists
|