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corpipe24.py
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corpipe24.py
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#!/usr/bin/env python3
# This file is part of CorPipe <https://github.com/ufal/crac2024-corpipe>.
#
# Copyright 2024 Institute of Formal and Applied Linguistics, Faculty of
# Mathematics and Physics, Charles University in Prague, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
from __future__ import annotations
import argparse
import asyncio
import contextlib
import datetime
import functools
import json
import os
import pickle
import shutil
import re
os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "2") # Report only TF errors by default
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
import transformers
import udapi
import udapi.block.corefud.movehead
import udapi.block.corefud.removemisc
parser = argparse.ArgumentParser()
parser.add_argument("--adafactor", default=False, action="store_true", help="Use Adafactor.")
parser.add_argument("--batch_size", default=8, type=int, help="Batch size.")
parser.add_argument("--beta_2", default=0.999, type=float, help="Beta2.")
parser.add_argument("--debug", default=False, action="store_true", help="Debug mode.")
parser.add_argument("--depth", default=5, type=int, help="Constrained decoding depth.")
parser.add_argument("--dev", default=None, nargs="*", type=str, help="Predict dev (treebanks).")
parser.add_argument("--encoder", default="google/mt5-large", type=str, help="MLM encoder model.")
parser.add_argument("--epochs", default=15, type=int, help="Number of epochs.")
parser.add_argument("--exp", default="", type=str, help="Exp name.")
parser.add_argument("--label_smoothing", default=0.2, type=float, help="Label smoothing.")
parser.add_argument("--lazy_adam", default=False, action="store_true", help="Use Lazy Adam.")
parser.add_argument("--learning_rate", default=5e-4, type=float, help="Learning rate.")
parser.add_argument("--learning_rate_decay", default=False, action="store_true", help="Decay LR.")
parser.add_argument("--load", default=[], type=str, nargs="*", help="Models to load.")
parser.add_argument("--max_links", default=None, type=int, help="Max antecedent links to train on.")
parser.add_argument("--resample", default=[], nargs="*", type=float, help="Train data resample ratio.")
parser.add_argument("--right", default=50, type=int, help="Reserved space for right context, if any.")
parser.add_argument("--seed", default=42, type=int, help="Random seed.")
parser.add_argument("--segment", default=512, type=int, help="Segment size")
parser.add_argument("--test", default=None, nargs="*", type=str, help="Predict test (treebanks).")
parser.add_argument("--threads", default=8, type=int, help="Maximum number of threads to use.")
parser.add_argument("--train", default=False, action="store_true", help="Perform training.")
parser.add_argument("--treebanks", default=[], nargs="+", type=str, help="Data.")
parser.add_argument("--treebank_id", default=False, action="store_true", help="Use treebank id.")
parser.add_argument("--warmup", default=0.1, type=float, help="Warmup ratio.")
parser.add_argument("--zeros_per_parent", default=2, type=int, help="Zeros per parent.")
class Dataset:
TOKEN_EMPTY = "[TOKEN_EMPTY]"
TOKEN_CLS = "[TOKEN_CLS]"
TOKEN_TREEBANK = "[TOKEN_TREEBANK{}]"
ZDEPREL_PAD = 0
ZDEPREL_NONE = 1
def __init__(self, path: str, tokenizer: transformers.PreTrainedTokenizerFast, treebank_id: int) -> None:
self._cls = tokenizer.cls_token_id
self._sep = tokenizer.sep_token_id if tokenizer.sep_token_id is not None else tokenizer.eos_token_id
self._path = path
self._treebank_token = []
if treebank_id: # 0 is deliberately considered as no treebank id
treebank_token = tokenizer.vocab[self.TOKEN_TREEBANK.format(treebank_id - 1)]
if self._cls is None:
self._cls = treebank_token
else:
self._treebank_token = [treebank_token]
if self._cls is None:
self._cls = tokenizer.vocab[self.TOKEN_CLS]
# Create the tokenized documents if they do not exist
cache_path = f"{path}.mentions.{os.path.basename(tokenizer.name_or_path)}"
if not os.path.exists(cache_path) or os.path.getmtime(cache_path) <= os.path.getmtime(path):
# Parse with Udapi
if not os.path.exists(f"{path}.mentions") or os.path.getmtime(f"{path}.mentions") <= os.path.getmtime(path):
docs, new_doc = [], []
for doc in udapi.block.read.conllu.Conllu(files=[path]).read_documents():
for tree in doc.trees:
if tree.newdoc is not None and new_doc:
docs.append(new_doc)
new_doc = []
words, coref_mentions = [], set()
for node in tree.descendants:
words.append(node.form)
coref_mentions.update(node.coref_mentions)
for enode in tree.empty_nodes:
coref_mentions.update(enode.coref_mentions)
dense_mentions = []
for mention in [mention for mention in coref_mentions if not mention.head.is_empty()]:
span = [word for word in mention.words if not word.is_empty()]
start = end = span.index(mention.head)
while start > 0 and span[start - 1].ord + 1 == span[start].ord: start -= 1
while end < len(span) - 1 and span[end].ord + 1== span[end + 1].ord: end += 1
dense_mentions.append(((span[start].ord - 1, span[end].ord - 1), mention.entity.eid, start > 0 or end + 1 < len(span)))
dense_mentions = sorted(dense_mentions, key=lambda x:(x[0][0], -x[0][1], x[2]))
mentions = []
for i, mention in enumerate(dense_mentions):
if i and dense_mentions[i-1][0] == mention[0]:
print(f"Multiple same mentions {mention[2]}/{dense_mentions[i-1][2]} in sent_id {tree.sent_id}: {tree.get_sentence()}", flush=True)
continue
mentions.append((mention[0][0], mention[0][1], mention[1]))
zero_mentions = []
for mention in [mention for mention in coref_mentions if mention.head.is_empty()]:
if len(mention.words) > 1:
print(f"A empty-node-head mention with multiple words {mention.words} in sent_id {tree.sent_id}: {tree.get_sentence()}", flush=True)
assert len(mention.head.deps) >= 1
zero_mentions.append((mention.head.deps[0]["parent"].ord - 1, mention.head.deps[0]["deprel"], mention.entity.eid))
zero_mentions = sorted(zero_mentions)
new_doc.append((words, mentions, zero_mentions))
if new_doc:
docs.append(new_doc)
with open(f"{path}.mentions", "wb") as cache_file:
pickle.dump(docs, cache_file, protocol=3)
with open(f"{path}.mentions", "rb") as cache_file:
docs = pickle.load(cache_file)
# Tokenize the data, generate stack operations and subword mentions
self.docs = []
for doc in docs:
new_doc = []
for words, mentions, zero_mentions in doc:
subwords, word_indices, word_tags, subword_mentions, stack = [], [], [], [], []
for i in range(len(words)):
word_indices.append(len(subwords))
word = (" " if "robeczech" in tokenizer.name_or_path else "") + words[i]
subword = tokenizer.encode(word, add_special_tokens=False)
assert len(subword) > 0
if subword[0] == 6 and "xlm-r" in tokenizer.name_or_path: # Hack: remove the space-only token in XLM-R
subword = subword[1:]
assert len(subword) > 0
subwords.extend(subword)
tag = [str(len(stack))]
for _ in range(2):
for j in reversed(range(len(stack))):
start, end, eid = stack[j]
if end == i:
tag.append(f"POP:{len(stack)-j}")
subword_mentions.append((start, word_indices[-1], eid))
stack.pop(j)
while mentions and mentions[0][0] == i:
tag.append("PUSH")
stack.append((word_indices[-1], mentions[0][1], mentions[0][2]))
mentions = mentions[1:]
word_tags.append(",".join(tag))
assert len(stack) == 0
word_zdeprels = [[] for _ in range(len(words))]
for parent, deprel, eid in zero_mentions:
word_zdeprels[parent].append(deprel)
subword_mentions.append((word_indices[parent], -len(word_zdeprels[parent]), eid))
subword_mentions = sorted(subword_mentions, key=lambda x:(x[0], -x[1]))
new_doc.append((subwords, word_indices, word_tags, word_zdeprels, subword_mentions))
self.docs.append(new_doc)
with open(cache_path, "wb") as cache_file:
pickle.dump(self.docs, cache_file, protocol=3)
with open(cache_path, "rb") as cache_file:
self.docs = pickle.load(cache_file)
for doc in self.docs:
for _, _, word_tags, _, _ in doc:
for i in range(len(word_tags)):
word_tags[i] = ",".join(word_tags[i].split(",")[1:])
@staticmethod
def create_tags(trains: list[Dataset]) -> list[str]:
tags = set()
for train in trains:
for doc in train.docs:
for _, _, word_tags, _, _ in doc:
tags.update(word_tags)
return sorted(tags)
@staticmethod
def create_zdeprels(trains: list[Dataset]) -> list[str]:
zdeprels = set()
for train in trains:
for doc in train.docs:
for _, _, _, word_zdeprels, _ in doc:
zdeprels.update(zdeprel for zdeprels in word_zdeprels for zdeprel in zdeprels)
return ["[PAD]", "[NONE]"] + sorted(zdeprels) # Respect ZDEPREL_PAD and ZDEPREL_NONE
@staticmethod
def allowed_tag_transitions(tags: list[str], depth: int) -> np.array:
tags = [f"{d}{',' if tag else ''}{tag}" for d in range(depth) for tag in tags]
allowed = np.zeros([len(tags), len(tags)], np.float32)
for i, tag_i in enumerate(tags):
for j, tag_j in enumerate(tags):
i_parts = tag_i.split(",")
i_depth = int(i_parts[0])
j_depth = int(tag_j.split(",")[0])
for command in i_parts[1:]:
i_depth += 1 if command == "PUSH" else -1
allowed[i, j] = i_depth == j_depth
return allowed
def pipeline(self, tags_map: dict[str, int], zdeprels_map: dict[str, int], train: bool, args: argparse.Namespace) -> tf.data.Dataset:
def generator():
tid = len(self._treebank_token)
for doc in self.docs:
p_subwords, p_subword_mentions = [], []
for doc_i, (subwords, word_indices, word_tags, word_zdeprels, subword_mentions) in enumerate(doc):
subword_mentions = [(s, e, eid) for s, e, eid in subword_mentions if e >= -args.zeros_per_parent]
if not train and len(subwords) + 4 + tid > args.segment:
print("Truncating a long sentence during prediction")
subwords = subwords[:args.segment - 4 - tid]
assert train or len(subwords) + 4 + tid <= args.segment
if len(subwords) + 4 + tid <= args.segment:
right_reserve = min((args.segment - 4 - tid - len(subwords)) // 2, args.right or 0)
context = min(args.segment - 4 - tid - len(subwords) - right_reserve, len(p_subwords))
word_indices = [context + 2 + tid + i for i in word_indices + [len(subwords)]]
e_subwords = [self._cls, *self._treebank_token, *p_subwords[-context:], self._sep, *subwords, self._sep]
if args.right is not None:
i = doc_i + 1
while i < len(doc) and len(e_subwords) + 1 < args.segment:
e_subwords.extend(doc[i][0][:args.segment - len(e_subwords) - 1])
i += 1
e_subwords.append(self._sep)
output = (e_subwords, word_indices)
if train:
offset = len(p_subwords) - context
prev = [(s - offset + 1 + tid, e if e < 0 else e - offset + 1 + tid, eid) for s, e, eid in p_subword_mentions if s >= offset]
prev_pos = np.array([[s, e] for s, e, _ in prev], dtype=np.int32).reshape([-1, 2])
prev_eid = np.array([eid for _, _, eid in prev], dtype=str)
ment = [(context + 2 + tid + s, e if e < 0 else context + 2 + tid + e, eid) for s, e, eid in subword_mentions]
ment_pos = np.array([[s, e] for s, e, _ in ment], dtype=np.int32).reshape([-1, 2])
ment_eid = np.array([eid for _, _, eid in ment], dtype=str)
mask = ment_pos[:, 0, None] > np.concatenate([prev_pos[:, 0], ment_pos[:, 0]])[None, :]
diag = np.pad(np.eye(len(ment_pos)), [[0, 0], [len(prev_pos), 0]])
gold = (ment_eid[:, None] == np.concatenate([prev_eid, ment_eid])[None, :]) * mask
gold = np.where(np.sum(gold, axis=1, keepdims=True) > 0, gold, diag)
if args.max_links is not None:
max_link_mask = np.cumsum(gold, axis=1)
gold *= (max_link_mask > max_link_mask[:, -1:] - args.max_links)
gold /= np.sum(gold, axis=1, keepdims=True)
mask = mask + diag
if args.label_smoothing:
gold = (1 - args.label_smoothing) * gold + args.label_smoothing * (mask / np.sum(mask, axis=1, keepdims=True))
word_tags = [tags_map[tag] for tag in word_tags]
word_zdeprels_padded = np.zeros([len(word_tags), args.zeros_per_parent], np.int32)
for zdeprels_padded, zdeprels in zip(word_zdeprels_padded, word_zdeprels):
zdeprels_padded[:min(args.zeros_per_parent, len(zdeprels) + 1)] = (
[zdeprels_map[zdeprel] for zdeprel in zdeprels] + [self.ZDEPREL_NONE])[:args.zeros_per_parent]
output = (output, (word_tags, word_zdeprels_padded, prev_pos, ment_pos, mask, gold))
yield output
p_subword_mentions.extend((s + len(p_subwords), e if e < 0 else e + len(p_subwords), eid) for s, e, eid in subword_mentions)
p_subwords.extend(subwords)
output_signature=(tf.TensorSpec([None], tf.int32), tf.TensorSpec([None], tf.int32))
if train:
output_signature = (output_signature, (
tf.TensorSpec([None], tf.int32), tf.TensorSpec([None, args.zeros_per_parent], tf.int32), tf.TensorSpec([None, 2], tf.int32),
tf.TensorSpec([None, 2], tf.int32), tf.TensorSpec([None, None], tf.bool), tf.TensorSpec([None, None], tf.float32),
))
pipeline = tf.data.Dataset.from_generator(generator, output_signature=output_signature)
pipeline = pipeline.cache()
pipeline = pipeline.apply(tf.data.experimental.assert_cardinality(sum(1 for _ in pipeline)))
return pipeline
def save_mentions(self, path: str, mentions: list[list[tuple[int, int, int]]], zero_mentions: list[list[tuple[int, str, int]]]) -> None:
doc = udapi.block.read.conllu.Conllu(files=[self._path]).read_documents()[0]
udapi.block.corefud.removemisc.RemoveMisc(attrnames="Entity,SplitAnte,Bridge").apply_on_document(doc)
entities = {}
for i, tree in enumerate(doc.trees):
tree.empty_nodes = [] # Drop existing empty nodes
for node in tree.descendants: # Remove references to empty nodes also from DEPS, by replacing them by the main dependency edge
if "." in node.raw_deps:
node.raw_deps = f"{node.parent.ord}:{node.deprel}"
ords = {}
for parent, deprel, eid in zero_mentions[i]: # Add predicted empty nodes
tree.create_empty_child()
ords[parent] = ords.get(parent, 0) + 1
tree.empty_nodes[-1].ord = f"{parent+1}.{ords[parent]}"
tree.empty_nodes[-1].raw_deps = f"{parent+1}:{deprel}"
if not eid in entities:
entities[eid] = udapi.core.coref.CorefEntity(f"c{eid}")
udapi.core.coref.CorefMention([tree.empty_nodes[-1]], entity=entities[eid])
nodes = tree.descendants_and_empty
for start, end, eid in mentions[i]:
if not eid in entities:
entities[eid] = udapi.core.coref.CorefEntity(f"c{eid}")
udapi.core.coref.CorefMention([node for node in nodes if start <= node.ord - 1 <= end], entity=entities[eid])
doc._eid_to_entity = {entity._eid: entity for entity in sorted(entities.values())}
udapi.block.corefud.movehead.MoveHead(bugs='ignore').apply_on_document(doc)
udapi.block.write.conllu.Conllu(files=[path]).apply_on_document(doc)
class Model(tf.keras.Model):
def __init__(self, tokenizer: transformers.PreTrainedTokenizer, tags: list[str], zdeprels: list[str], args: argparse.Namespace) -> None:
super().__init__()
self._tags = tags
self._zdeprels = zdeprels
self._args = args
assert tags[0] == "" # Used as a boundary tag in CRF
self._allowed_tag_transitions = tf.constant(Dataset.allowed_tag_transitions(tags, args.depth + 1))
self._boundary_logits = tf.cast(tf.range(self._allowed_tag_transitions.shape[0]) > 0, tf.float32) * -1e6
self._encoder = transformers.TFMT5EncoderModel if "mt5" in args.encoder.lower() else transformers.TFAutoModel
if not args.load:
self._encoder = self._encoder.from_pretrained(
args.encoder, from_pt=any(m in args.encoder.lower() for m in ["rubert", "herbert", "flaubert", "litlat", "roberta-base-ca", "spanbert", "xlm-v", "infoxlm"]))
else:
self._encoder = self._encoder.from_config(transformers.AutoConfig.from_pretrained(args.encoder))
self._encoder(tf.constant([[0]], dtype=tf.int32), attention_mask=tf.constant([[1.]], dtype=tf.float32))
self._encoder.resize_token_embeddings(len(tokenizer.vocab))
self._dense_hidden_q = tf.keras.layers.Dense(4 * self._encoder.config.hidden_size, activation=tf.nn.relu, name="dense_hidden_q")
self._dense_hidden_k = tf.keras.layers.Dense(4 * self._encoder.config.hidden_size, activation=tf.nn.relu, name="dense_hidden_k")
self._dense_hidden_tags = tf.keras.layers.Dense(4 * self._encoder.config.hidden_size, activation=tf.nn.relu, name="dense_hidden_tags")
self._dense_q = tf.keras.layers.Dense(self._encoder.config.hidden_size, use_bias=False, name="dens_q")
self._dense_k = tf.keras.layers.Dense(self._encoder.config.hidden_size, use_bias=False, name="dens_k")
self._dense_tags = tf.keras.layers.Dense(len(tags), name="dense_tags")
self._dense_hidden_zeros = [
tf.keras.layers.Dense(4 * self._encoder.config.hidden_size, activation=tf.nn.relu, name=f"dense_hidden_zeros_{i}") for i in range(args.zeros_per_parent)]
self._dense_zeros = [tf.keras.layers.Dense(self._encoder.config.hidden_size, name=f"dense_zeros_{i}") for i in range(args.zeros_per_parent)]
self._dense_hidden_zdeprels = [
tf.keras.layers.Dense(4 * self._encoder.config.hidden_size, activation=tf.nn.relu, name=f"dense_hidden_zdeprels_{i}") for i in range(args.zeros_per_parent)]
self._dense_zdeprels = [tf.keras.layers.Dense(len(zdeprels), name=f"dense_zdeprels_{i}") for i in range(args.zeros_per_parent)]
if args.load:
self.compute_tags(tf.ragged.constant([[0]]), tf.ragged.constant([[0]]))
self.compute_antecedents(tf.zeros([1, 1, self._encoder.config.hidden_size]), tf.zeros([1, args.zeros_per_parent, self._encoder.config.hidden_size]),
*[tf.ragged.constant([[[0, 0]]], dtype=tf.int32, ragged_rank=1, inner_shape=(2,))] * 2)
self.built = True
self.load_weights(args.load[0])
def compile(self, train: tf.data.Dataset) -> None:
args = self._args
warmup_steps = int(args.warmup * args.epochs * len(train))
learning_rate = tf.optimizers.schedules.PolynomialDecay(
args.learning_rate, args.epochs * len(train) - warmup_steps, 0. if args.learning_rate_decay else args.learning_rate)
if warmup_steps:
class LinearWarmup(tf.optimizers.schedules.LearningRateSchedule):
def __init__(self, warmup_steps, following_schedule):
self._warmup_steps = warmup_steps
self._warmup = tf.optimizers.schedules.PolynomialDecay(0., warmup_steps, following_schedule(0))
self._following = following_schedule
def __call__(self, step):
return tf.cond(step < self._warmup_steps,
lambda: self._warmup(step),
lambda: self._following(step - self._warmup_steps))
learning_rate = LinearWarmup(warmup_steps, learning_rate)
if args.adafactor:
optimizer = tf.optimizers.Adafactor(learning_rate=learning_rate)
elif args.lazy_adam:
optimizer = tfa.optimizers.LazyAdam(learning_rate=learning_rate, beta_2=args.beta_2)
else:
optimizer = tf.optimizers.Adam(learning_rate=learning_rate, beta_2=args.beta_2)
super().compile(optimizer=optimizer)
def crf_decode(self, logits: tf.RaggedTensor, crf_weights: tf.Tensor) -> tf.RaggedTensor:
boundary_logits = tf.broadcast_to(self._boundary_logits, [logits.bounding_shape(0), 1, len(self._boundary_logits)])
logits = tf.concat([boundary_logits, logits, boundary_logits], axis=1)
predictions, _ = tfa.text.crf_decode(logits.to_tensor(), crf_weights, logits.row_lengths())
predictions = tf.RaggedTensor.from_tensor(predictions, logits.row_lengths())
predictions = predictions[:, 1:-1]
return predictions
@tf.function(experimental_relax_shapes=True)
def compute_tags(self, subwords, word_indices, training=False) -> tuple[tf.RaggedTensor, tf.RaggedTensor, tf.RaggedTensor, tf.RaggedTensor]:
if training or subwords.bounding_shape(0) > 0:
embeddings = self._encoder(subwords.to_tensor(), attention_mask=tf.sequence_mask(subwords.row_lengths(), dtype=tf.float32),
training=training).last_hidden_state
else:
# During prediction, we need to correctly handle batches of size 0 when using multiple GPUs
embeddings = tf.zeros([0, 0, self._encoder.config.hidden_size])
words = tf.gather(embeddings, word_indices[:, :-1], batch_dims=1)
tag_logits = self._dense_tags(self._dense_hidden_tags(words))
zero_embeddings = []
for i in range(self._args.zeros_per_parent):
zero_embeddings.append(self._dense_zeros[i](self._dense_hidden_zeros[i](tf.concat([embeddings] + zero_embeddings, axis=-1))))
zdeprel_logits = []
for i in range(self._args.zeros_per_parent):
zdeprel_logits.append(self._dense_zdeprels[i](self._dense_hidden_zdeprels[i](tf.gather(zero_embeddings[i], word_indices[:, :-1], batch_dims=1))))
return embeddings, tf.concat(zero_embeddings, axis=1), tag_logits, tf.stack(zdeprel_logits, axis=-2)
@tf.function(experimental_relax_shapes=True)
def compute_antecedents(self, embeddings, zero_embeddings, previous, mentions) -> tf.RaggedTensor:
mentions_embedded = tf.gather(embeddings, tf.math.maximum(mentions, 0), batch_dims=1).values
mentions_embedded = tf.reshape(mentions_embedded, [-1, np.prod(mentions_embedded.shape[-2:])])
zero_mentions_embedded = tf.gather(zero_embeddings, self._args.zeros_per_parent * mentions[..., 0] + tf.math.maximum(-mentions[..., 1] - 1, 0), batch_dims=1).values
zero_mentions_embedded = tf.tile(zero_mentions_embedded, [1, 2])
mentions_embedded = tf.where(mentions[..., 1:].values >= 0, mentions_embedded, zero_mentions_embedded)
queries = mentions.with_values(self._dense_q(self._dense_hidden_q(mentions_embedded)))
keys_mentions = mentions.with_values(self._dense_k(self._dense_hidden_k(mentions_embedded)))
previous_embedded = tf.gather(embeddings, tf.math.maximum(previous, 0), batch_dims=1).values
previous_embedded = tf.reshape(previous_embedded, [-1, mentions_embedded.shape[-1]])
zero_previous_embedded = tf.gather(zero_embeddings, self._args.zeros_per_parent * previous[..., 0] + tf.math.maximum(-previous[..., 1] - 1, 0), batch_dims=1).values
zero_previous_embedded = tf.tile(zero_previous_embedded, [1, 2])
previous_embedded = tf.where(previous[..., 1:].values >= 0, previous_embedded, zero_previous_embedded)
keys_previous = previous.with_values(self._dense_k(self._dense_hidden_k(previous_embedded)))
keys = tf.concat([keys_previous, keys_mentions], axis=1)
weights = tf.matmul(queries.to_tensor(), keys.to_tensor(), transpose_b=True) / (self._dense_q.units ** 0.5)
return weights
def train_step(self, data: tuple) -> dict[str, tf.Tensor]:
(subwords, word_indices), (tags, zdeprels, previous, mentions, mask, antecedents) = data
with tf.GradientTape() as tape:
# Tagging part
embeddings, zero_embeddings, tag_logits, zdeprel_logits = self.compute_tags(subwords, word_indices, training=True)
tags_loss = tf.losses.CategoricalCrossentropy(
from_logits=True, label_smoothing=self._args.label_smoothing, reduction=tf.losses.Reduction.SUM)(
tf.one_hot(tags.values, len(self._tags)), tag_logits.values) / tf.cast(tf.shape(tag_logits.values)[0], tf.float32)
zdeprels_mask = tf.cast(zdeprels.values != Dataset.ZDEPREL_PAD, tf.float32)
zdeprels_loss = tf.losses.CategoricalCrossentropy(
from_logits=True, label_smoothing=self._args.label_smoothing, reduction=tf.losses.Reduction.SUM)(
tf.one_hot(zdeprels.values, len(self._zdeprels)), zdeprel_logits.values, zdeprels_mask) / tf.math.reduce_sum(zdeprels_mask)
# Antecedents part
def antecedent_loss():
weights = self.compute_antecedents(embeddings, zero_embeddings, previous, mentions)
mask_dense = tf.cast(mask.to_tensor(), tf.float32)
weights = weights[:, :, :tf.shape(mask_dense)[-1]] # Happens when the largest number of mentions have 0 queries
weights = mask_dense * weights + (1 - mask_dense) * -1e9
return tf.losses.CategoricalCrossentropy(from_logits=True, reduction=tf.losses.Reduction.SUM)(
antecedents.values.to_tensor(), tf.RaggedTensor.from_tensor(weights, antecedents.row_lengths()).values
) / tf.cast(tf.math.reduce_sum(antecedents.row_lengths()), tf.float32)
antecedent_loss = tf.cond(tf.math.reduce_sum(antecedents.row_lengths()) != 0, antecedent_loss, lambda: 0.)
loss = tags_loss + zdeprels_loss + antecedent_loss
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
return {"tags_loss": tags_loss, "zdeprels_loss": zdeprels_loss, "antecedent_loss": antecedent_loss, "loss": loss,
"lr": self.optimizer.learning_rate(self.optimizer.iterations)
if callable(self.optimizer.learning_rate) else self.optimizer.learning_rate}
def predict(self, dataset: Dataset, pipeline: tf.data.Dataset) -> tuple[list[list[tuple[int, int, int]]], list[list[tuple[int, str, int]]]]:
tid = len(dataset._treebank_token)
results, results_zeros, entities = [], [], 0
doc_mentions, doc_subwords = [], 0
for b_subwords, b_word_indices in pipeline:
b_embeddings, b_zero_embeddings, b_tag_logits, b_zdeprel_logits = self.compute_tags(b_subwords, b_word_indices)
b_size = b_word_indices.shape[0]
b_tag_logits = b_tag_logits.with_values(tf.math.log_softmax(tf.tile(b_tag_logits.values, [1, self._args.depth + 1]), axis=-1))
b_tags = self.crf_decode(b_tag_logits, (1 - self._allowed_tag_transitions) * -1e6)
b_zdeprels = b_zdeprel_logits.with_values(tf.argmax(b_zdeprel_logits.values, axis=-1))
b_previous, b_mentions, b_refs = [], [], []
for b in range(b_size):
word_indices, tags, zdeprels = b_word_indices[b].numpy(), b_tags[b].numpy(), b_zdeprels[b].numpy()
if word_indices[0] == 2 + tid:
doc_mentions, doc_subwords = [], 0
# Decode mentions
mentions, stack = [], []
for i, tag in enumerate(self._tags[tag % len(self._tags)] for tag in tags):
for command in tag.split(","):
if command == "PUSH":
stack.append(i)
elif command.startswith("POP:"):
j = int(command.removeprefix("POP:"))
if len(stack):
j = len(stack) - (j if j <= len(stack) else 1)
mentions.append((stack.pop(j), i, None))
elif command:
raise ValueError(f"Unknown command '{command}'")
while len(stack):
mentions.append((stack.pop(), len(tags) - 1, None))
# Decode zero mentions
for i, zdeprel in enumerate(zdeprels):
for j in range(self._args.zeros_per_parent):
if zdeprel[j] == Dataset.ZDEPREL_PAD or zdeprel[j] == Dataset.ZDEPREL_NONE:
break
mentions.append((i, -j - 1, self._zdeprels[zdeprel[j]]))
# Prepare inputs for antecedent prediction
mentions = sorted(set(mentions), key=lambda x: (x[0], -x[1]))
offset = doc_subwords - (word_indices[0] - 2 - tid)
results.append([]), results_zeros.append([]), b_previous.append([]), b_mentions.append([]), b_refs.append([])
for doc_mention in doc_mentions:
if doc_mention[0] < offset: continue
b_previous[-1].append([doc_mention[0] - offset + 1 + tid, doc_mention[1] if doc_mention[1] < 0 else doc_mention[1] - offset + 1 + tid])
b_refs[-1].append(doc_mention[2])
for mention in mentions:
if mention[2] is not None:
result_mention = [mention[0], mention[2], None]
results_zeros[-1].append(result_mention)
else:
result_mention = [mention[0], mention[1], None]
results[-1].append(result_mention)
b_refs[-1].append(result_mention)
b_mentions[-1].append([word_indices[mention[0]], mention[1] if mention[1] < 0 else word_indices[mention[1]]])
doc_mentions.append([doc_subwords + word_indices[mention[0]] - word_indices[0],
mention[1] if mention[1] < 0 else doc_subwords + word_indices[mention[1]] - word_indices[0], result_mention])
doc_subwords += word_indices[-1] - word_indices[0]
# Decode antecedents
if sum(len(mentions) for mentions in b_mentions) == 0: continue
b_antecedents = self.compute_antecedents(
b_embeddings, b_zero_embeddings, tf.ragged.constant(b_previous, dtype=tf.int32, ragged_rank=1, inner_shape=(2,)),
tf.ragged.constant(b_mentions, dtype=tf.int32, ragged_rank=1, inner_shape=(2,)))
for b in range(b_size):
len_prev, mentions, refs, antecedents = len(b_previous[b]), b_mentions[b], b_refs[b], b_antecedents[b].numpy()
for i in range(len(mentions)):
j = i - 1
while j >= 0 and mentions[j][0] == mentions[i][0]:
antecedents[i, j + len_prev] = antecedents[i, i + len_prev] - 1
j -= 1
j = np.argmax(antecedents[i, :i + len_prev + 1])
if j == i + len_prev:
entities += 1
refs[i + len_prev][2] = entities
else:
refs[i + len_prev][2] = refs[j][2]
return results, results_zeros
def callback(self, epoch: int, datasets: list[tuple[Dataset, tf.data.Dataset]], evaluate: bool) -> None:
for dataset, pipeline in datasets:
mentions, zero_mentions = self.predict(dataset, pipeline)
path = os.path.join(self._args.logdir, f"{os.path.splitext(os.path.basename(dataset._path))[0]}.{epoch:02d}.conllu")
dataset.save_mentions(path, mentions, zero_mentions)
if evaluate:
os.system(f"sbatch -p cpu-troja -o /dev/null run ./corefud-score.sh '{dataset._path}' '{path}'")
class ModelEnsemble:
def __init__(self, tokenizer: transformers.PreTrainedTokenizer, tags: list[str], zdeprels: list[str], args: argparse.Namespace) -> None:
assert len(tf.config.list_physical_devices("GPU")) >= len (args.load)
self._tags = tags
self._zdeprels = zdeprels
self._args = args
self._models = []
for i, model in enumerate(args.load):
with open(os.path.join(os.path.dirname(model), "options.json"), mode="r") as options_file:
model_args = argparse.Namespace(**vars(args))
model_args.load = [model]
model_args.encoder = json.load(options_file)["encoder"]
with tf.device(f"/gpu:{i}"):
self._models.append(Model(tokenizer, tags, zdeprels, model_args))
def np_softmax(self, x):
x = np.exp(x - np.max(x, axis=-1, keepdims=True))
return x / np.sum(x, axis=-1, keepdims=True)
def predict(self, dataset: Dataset, pipeline: tf.data.Dataset) -> tuple[list[list[tuple[int, int, int]]], list[list[tuple[int, str, int]]]]:
tid = len(dataset._treebank_token)
results, results_zeros, entities = [], [], 0
doc_mentions, doc_subwords = [], 0
for b_subwords, b_word_indices in pipeline:
async def do_compute_tags():
def compute_tags(i):
with tf.device(f"/gpu:{i}"):
return self._models[i].compute_tags(b_subwords, b_word_indices)
return await asyncio.gather(*[asyncio.to_thread(compute_tags, i) for i in range(len(self._models))])
b_embeddings, b_zero_embeddings, b_tag_logits, b_zdeprel_logits = zip(*asyncio.run(do_compute_tags()))
b_tag_logits = tf.math.log(sum(tf.nn.softmax(logits, axis=-1) for logits in b_tag_logits))
b_zdeprel_logits = sum(logits for logits in b_zdeprel_logits)
b_size = b_word_indices.shape[0]
b_tag_logits = b_tag_logits.with_values(tf.math.log_softmax(tf.tile(b_tag_logits.values, [1, self._args.depth + 1]), axis=-1))
b_tags = self._models[0].crf_decode(b_tag_logits, (1 - self._models[0]._allowed_tag_transitions) * -1e6)
b_zdeprels = b_zdeprel_logits.with_values(tf.argmax(b_zdeprel_logits.values, axis=-1))
b_previous, b_mentions, b_refs = [], [], []
for b in range(b_size):
word_indices, tags, zdeprels = b_word_indices[b].numpy(), b_tags[b].numpy(), b_zdeprels[b].numpy()
if word_indices[0] == 2 + tid:
doc_mentions, doc_subwords = [], 0
# Decode mentions
mentions, stack = [], []
for i, tag in enumerate(self._tags[tag % len(self._tags)] for tag in tags):
for command in tag.split(","):
if command == "PUSH":
stack.append(i)
elif command.startswith("POP:"):
j = int(command.removeprefix("POP:"))
if len(stack):
j = len(stack) - (j if j <= len(stack) else 1)
mentions.append((stack.pop(j), i, None))
elif command:
raise ValueError(f"Unknown command '{command}'")
while len(stack):
mentions.append((stack.pop(), len(tags) - 1, None))
# Decode zero mentions
for i, zdeprel in enumerate(zdeprels):
for j in range(self._args.zeros_per_parent):
if zdeprel[j] == Dataset.ZDEPREL_PAD or zdeprel[j] == Dataset.ZDEPREL_NONE:
break
mentions.append((i, -j - 1, self._zdeprels[zdeprel[j]]))
# Prepare inputs for antecedent prediction
mentions = sorted(set(mentions), key=lambda x: (x[0], -x[1]))
offset = doc_subwords - (word_indices[0] - 2 - tid)
results.append([]), results_zeros.append([]), b_previous.append([]), b_mentions.append([]), b_refs.append([])
for doc_mention in doc_mentions:
if doc_mention[0] < offset: continue
b_previous[-1].append([doc_mention[0] - offset + 1 + tid, doc_mention[1] if doc_mention[1] < 0 else doc_mention[1] - offset + 1 + tid])
b_refs[-1].append(doc_mention[2])
for mention in mentions:
if mention[2] is not None:
result_mention = [mention[0], mention[2], None]
results_zeros[-1].append(result_mention)
else:
result_mention = [mention[0], mention[1], None]
results[-1].append(result_mention)
b_refs[-1].append(result_mention)
b_mentions[-1].append([word_indices[mention[0]], mention[1] if mention[1] < 0 else word_indices[mention[1]]])
doc_mentions.append([doc_subwords + word_indices[mention[0]] - word_indices[0],
mention[1] if mention[1] < 0 else doc_subwords + word_indices[mention[1]] - word_indices[0], result_mention])
doc_subwords += word_indices[-1] - word_indices[0]
# Decode antecedents
if sum(len(mentions) for mentions in b_mentions) == 0: continue
async def do_compute_antecedents():
def compute_antecedents(i):
with tf.device(f"/gpu:{i}"):
return self._models[i].compute_antecedents(
b_embeddings[i], b_zero_embeddings[i], tf.ragged.constant(b_previous, dtype=tf.int32, ragged_rank=1, inner_shape=(2,)),
tf.ragged.constant(b_mentions, dtype=tf.int32, ragged_rank=1, inner_shape=(2,)))
return await asyncio.gather(*[asyncio.to_thread(compute_antecedents, i) for i in range(len(self._models))])
b_antecedents = asyncio.run(do_compute_antecedents())
for b in range(b_size):
len_prev, mentions, refs, antecedents = len(b_previous[b]), b_mentions[b], b_refs[b], [a[b].numpy() for a in b_antecedents]
for i in range(len(mentions)):
j = i - 1
while j >= 0 and mentions[j][0] == mentions[i][0]:
for a in antecedents:
a[i, j + len_prev] = a[i, i + len_prev] - 1
j -= 1
j = np.argmax(sum(self.np_softmax(a[i, :i + len_prev + 1]) for a in antecedents))
if j == i + len_prev:
entities += 1
refs[i + len_prev][2] = entities
else:
refs[i + len_prev][2] = refs[j][2]
return results, results_zeros
def callback(self, epoch: int, datasets: list[tuple[Dataset, tf.data.Dataset]], evaluate: bool) -> None:
return Model.callback(self, epoch, datasets, evaluate)
def main(params: list[str] | None = None) -> None:
args = parser.parse_args(params)
# If supplied, load configuration from a trained model
if args.load:
with open(os.path.join(os.path.dirname(args.load[0]), "options.json"), mode="r") as options_file:
args = argparse.Namespace(**{k: v for k, v in json.load(options_file).items() if k in [
"batch_size", "depth", "encoder", "right", "segment", "seed", "treebanks", "treebank_id"]})
args = parser.parse_args(params, namespace=args)
args.logdir = args.exp if args.exp else os.path.dirname(args.load[0])
else:
if not args.train:
raise ValueError("Either --load or --train must be set.")
args.logdir = os.path.join("logs", "{}{}-{}-{}-{}".format(
args.exp + (args.exp and "-"),
os.path.splitext(os.path.basename(globals().get("__file__", "notebook")))[0],
os.environ.get("SLURM_JOB_ID", ""),
datetime.datetime.now().strftime("%y%m%d_%H%M%S"),
",".join(("{}={}".format(
re.sub("(.)[^_]*_?", r"\1", k),
",".join(re.sub(r"^.*/", "", str(x)) for x in ((v if len(v) <= 1 else [v[0], "..."]) if isinstance(v, list) else [v])),
) for k, v in sorted(vars(args).items()) if k not in ["debug", "exp", "load", "threads"]))
))
print(json.dumps(vars(args), sort_keys=True, ensure_ascii=False, indent=2))
# Set the random seed and the number of threads
tf.keras.utils.set_random_seed(args.seed)
tf.config.threading.set_inter_op_parallelism_threads(args.threads)
tf.config.threading.set_intra_op_parallelism_threads(args.threads)
if args.debug:
tf.config.run_functions_eagerly(True)
# Load the data
tokenizer = transformers.AutoTokenizer.from_pretrained(args.encoder)
tokenizer.add_special_tokens({"additional_special_tokens": [Dataset.TOKEN_EMPTY] +
[Dataset.TOKEN_TREEBANK.format(i) for i in range(len(args.treebanks))] +
([Dataset.TOKEN_CLS] if tokenizer.cls_token_id is None and not args.treebank_id else [])})
trains = [Dataset(path, tokenizer, args.treebank_id * i) for i, path in enumerate(args.treebanks, 1)] if args.train else []
if args.dev and args.treebank_id:
print("When --treebank_id is set and you pass explicit --dev treebanks, they MUST correspond to --treebanks.")
devs = [Dataset(path.replace("-train.conllu", "-dev.conllu"), tokenizer, args.treebank_id * i)
for i, path in enumerate([] if args.dev is None else (args.dev or args.treebanks), 1) if path]
if args.test and args.treebank_id:
print("When --treebank_id is set and you pass explicit --test treebanks, they MUST correspond to --treebanks.")
tests = [Dataset(path.replace("-train.conllu", "-test.conllu"), tokenizer, args.treebank_id * i)
for i, path in enumerate([] if args.test is None else (args.test or args.treebanks), 1) if path]
if args.load:
with open(os.path.join(os.path.dirname(args.load[0]), "tags.txt"), mode="r") as tags_file:
tags = [line.rstrip("\r\n") for line in tags_file]
with open(os.path.join(os.path.dirname(args.load[0]), "zdeprels.txt"), mode="r") as zdeprels_file:
zdeprels = [line.rstrip("\r\n") for line in zdeprels_file]
else:
tags = Dataset.create_tags(trains)
zdeprels = Dataset.create_zdeprels(trains)
tags_map = {tag: i for i, tag in enumerate(tags)}
zdeprels_map = {zdeprel: i for i, zdeprel in enumerate(zdeprels)}
strategy_scope = None
if len(tf.config.list_physical_devices("GPU")) > 1 and len(args.load) <= 1:
strategy_scope = tf.distribute.MirroredStrategy().scope()
with strategy_scope or contextlib.nullcontext():
# Create pipelines
if args.train:
trains = [train.pipeline(tags_map, zdeprels_map, True, args) for train in trains]
if args.resample:
steps, *ratios = args.resample
assert len(ratios) == len(trains)
ratios = [ratio / sum(ratios) for ratio in ratios]
trains = [train.shuffle(len(train)).repeat().take(1 + int(steps * args.batch_size * ratio))
for train, ratio in zip(trains, ratios)]
train = functools.reduce(lambda x, y: x.concatenate(y), trains)
train = train.shuffle(len(train), seed=args.seed).ragged_batch(args.batch_size, True).prefetch(tf.data.AUTOTUNE)
devs = [(dev, dev.pipeline(tags_map, zdeprels_map, False, args).ragged_batch(args.batch_size).prefetch(tf.data.AUTOTUNE)) for dev in devs]
tests = [(test, test.pipeline(tags_map, zdeprels_map, False, args).ragged_batch(args.batch_size).prefetch(tf.data.AUTOTUNE)) for test in tests]
model = (ModelEnsemble if len(args.load) > 1 else Model)(tokenizer, tags, zdeprels, args)
if args.train:
# Create logdir with the source, options, and tags
os.makedirs(args.logdir)
shutil.copy2(__file__, os.path.join(args.logdir, os.path.basename(__file__)))
with open(os.path.join(args.logdir, "options.json"), "w") as json_file:
json.dump(vars(args), json_file, sort_keys=True, ensure_ascii=False, indent=2)
with open(os.path.join(args.logdir, "tags.txt"), "w") as tags_file:
for tag in tags:
print(tag, file=tags_file)
with open(os.path.join(args.logdir, "zdeprels.txt"), "w") as zdeprels_file:
for zdeprel in zdeprels:
print(zdeprel, file=zdeprels_file)
# Compile the model and train
model.compile(train)
model.fit(train, epochs=args.epochs, verbose=int(os.environ.get("VERBOSE", "2")), callbacks=[
tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, _: model.save_weights(f"{args.logdir}/model{epoch+1:02d}.h5")),
tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, _: model.callback(epoch + 1, devs, evaluate=True)),
tf.keras.callbacks.LambdaCallback(on_epoch_end=lambda epoch, _: model.callback(epoch + 1, tests, evaluate=False)),
])
elif args.dev is not None or args.test is not None:
os.makedirs(args.logdir, exist_ok=True)
if args.dev is not None:
model.callback(args.epochs, devs, evaluate=True)
if args.test is not None:
model.callback(args.epochs, tests, evaluate=False)
if __name__ == "__main__":
main([] if "__file__" not in globals() else None)