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Unverified Commit a2e33148 authored by Cagri Eryilmaz's avatar Cagri Eryilmaz Committed by GitHub
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BERT SQuAD Example - Updates (#815) 



* update to readme for pip3 upgrade, needed for tf2.4. requirement file change. initial commit for bert example update

* remove tokenization.py file

* added tokenizers and bertwordpiecetokenizer to main file

* added tokenizers to requirements file

* changes to run_onnx_squad after importing tokenizers module, to replace py file

* additional post processing tokenizer change in run_onnx_squad.py

* changes to notebook after tokenizers

* cleanup notebook output cells

* typo in readme

* formatting on py file
Co-authored-by: default avatarmvermeulen <5479696+mvermeulen@users.noreply.github.com>
parent e5bfdd72
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Showing with 35 additions and 422 deletions
examples/python_bert_squad_example/BERT-Squad.ipynb
View file @ a2e33148
......@@ -43,7 +43,7 @@
"from os import path\n",
"import sys\n",
"\n",
"import tokenization\n",
"import tokenizers\n",
"from run_onnx_squad import *\n",
"\n",
"import migraphx"
......@@ -137,8 +137,7 @@
"outputs": [],
"source": [
"vocab_file = os.path.join('uncased_L-12_H-768_A-12', 'vocab.txt')\n",
"tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,\n",
" do_lower_case=True)"
"tokenizer = tokenizers.BertWordPieceTokenizer(vocab_file)"
]
},
{
......
examples/python_bert_squad_example/README.md
View file @ a2e33148
......@@ -7,21 +7,25 @@ There are two ways to run the example:
# Steps
1) Install MIGraphX to your environment. Please follow the steps to build MIGraphX given at https://github.com/ROCmSoftwarePlatform/AMDMIGraphX
2) Install the requirements file
2) Upgrade your pip3 to latest version
```
pip3 install --upgrade pip
```
3) Install the requirements file
```
pip3 install -r requirements_bertsquad.txt
```
3) Install `unzip` and fetch the uncased file (vocabulary):
4) Install `unzip` and fetch the uncased file (vocabulary):
```
apt-get install unzip
wget -q https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip
unzip uncased_L-12_H-768_A-12.zip
```
4) Get BERT ONNX model (bertsquad-10.onnx):
5) Get BERT ONNX model (bertsquad-10.onnx):
```
wget https://github.com/onnx/models/raw/master/text/machine_comprehension/bert-squad/model/bertsquad-10.onnx
```
5) Run the inference, it will compile and run the model on three questions and small data provided in `inputs.json`:
6) Run the inference, it will compile and run the model on three questions and small data provided in `inputs.json`:
```
python3 bert-squad-migraphx.py
```
......
examples/python_bert_squad_example/bert-squad-migraphx.py
View file @ a2e33148
......@@ -5,7 +5,7 @@ import os.path
from os import path
import sys
import tokenization
import tokenizers
from run_onnx_squad import *
import migraphx
......@@ -30,8 +30,7 @@ n_best_size = 20
max_answer_length = 30
vocab_file = os.path.join('uncased_L-12_H-768_A-12', 'vocab.txt')
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file,
do_lower_case=True)
tokenizer = tokenizers.BertWordPieceTokenizer(vocab_file)
# Use convert_examples_to_features method from run_onnx_squad to get parameters from the input
input_ids, input_mask, segment_ids, extra_data = convert_examples_to_features(
......
examples/python_bert_squad_example/requirements_bertsquad.txt 100644 → 100755
View file @ a2e33148
tensorflow==1.14
onnxruntime
\ No newline at end of file
tensorflow==2.4.0
onnxruntime
tokenizers
\ No newline at end of file
examples/python_bert_squad_example/run_onnx_squad.py 100644 → 100755
View file @ a2e33148
......@@ -38,7 +38,8 @@ from timeit import default_timer as timer
import numpy as np
import onnxruntime as onnxrt
import six
import tokenization
from tokenizers import BertWordPieceTokenizer
from tokenizers import pre_tokenizers
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
......@@ -70,9 +71,8 @@ class SquadExample(object):
def __repr__(self):
s = []
s.append("qas_id: %s" % (tokenization.printable_text(self.qas_id)))
s.append("question_text: %s" %
(tokenization.printable_text(self.question_text)))
s.append("qas_id: %s" % (self.qas_id))
s.append("question_text: %s" % (self.question_text))
s.append("doc_tokens: [%s]" % (" ".join(self.doc_tokens)))
if self.start_position:
s.append("start_position: %d" % (self.start_position))
......@@ -130,7 +130,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
unique_id = 0
for (example_index, example) in enumerate(examples):
query_tokens = tokenizer.tokenize(example.question_text)
query_tokens = tokenizer.encode(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
......@@ -140,8 +140,8 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
sub_tokens = tokenizer.encode(token, add_special_tokens=False)
for sub_token in sub_tokens.tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
......@@ -172,7 +172,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in query_tokens:
for token in query_tokens.tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
......@@ -192,7 +192,9 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = []
for token in tokens:
input_ids.append(tokenizer.token_to_id(token))
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
......@@ -437,9 +439,15 @@ def get_final_text(pred_text, orig_text, do_lower_case):
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
tokenizer = pre_tokenizers.Sequence(
[pre_tokenizers.Whitespace(),
pre_tokenizers.Punctuation()])
tok_text = " ".join(tokenizer.tokenize(orig_text))
tok_text = []
for item in tokenizer.pre_tokenize_str(orig_text):
tok_text.append(item[0])
tok_text = " ".join(tok_text)
start_position = tok_text.find(pred_text)
if start_position == -1:
......@@ -559,8 +567,7 @@ def main():
sess_options = onnxrt.SessionOptions()
sess_options.session_log_verbosity_level = args.log
tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=True)
tokenizer = BertWordPieceTokenizer(vocab_file)
eval_examples = read_squad_examples(input_file=args.predict_file)
input_ids, input_mask, segment_ids, extra_data = \
......
examples/python_bert_squad_example/tokenization.py deleted 100644 → 0
View file @ e5bfdd72
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Tokenization classes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
"""Checks whether the casing config is consistent with the checkpoint name."""
# The casing has to be passed in by the user and there is no explicit check
# as to whether it matches the checkpoint. The casing information probably
# should have been stored in the bert_config.json file, but it's not, so
# we have to heuristically detect it to validate.
if not init_checkpoint:
return
m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
if m is None:
return
model_name = m.group(1)
lower_models = [
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
]
cased_models = [
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
"multi_cased_L-12_H-768_A-12"
]
is_bad_config = False
if model_name in lower_models and not do_lower_case:
is_bad_config = True
actual_flag = "False"
case_name = "lowercased"
opposite_flag = "True"
if model_name in cased_models and do_lower_case:
is_bad_config = True
actual_flag = "True"
case_name = "cased"
opposite_flag = "False"
if is_bad_config:
raise ValueError(
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
"However, `%s` seems to be a %s model, so you "
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
"how the model was pre-training. If this error is wrong, please "
"just comment out this check." %
(actual_flag, init_checkpoint, model_name, case_name,
opposite_flag))
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with tf.gfile.GFile(vocab_file, "r") as reader:
while True:
token = convert_to_unicode(reader.readline())
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class FullTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = convert_to_unicode(text)
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer.
Returns:
A list of wordpiece tokens.
"""
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat in ("Cc", "Cf"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64)
or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False
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