1098 lines
47 KiB
Python
1098 lines
47 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning the library models for question answering.
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"""
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# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
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import json
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import logging
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import math
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import os
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import random
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import sys
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import time
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import warnings
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from dataclasses import asdict, dataclass, field
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from enum import Enum
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from pathlib import Path
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from typing import Any, Callable, Dict, Optional, Tuple
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import datasets
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import evaluate
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import jax
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import jax.numpy as jnp
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import numpy as np
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import optax
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from datasets import load_dataset
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from flax import struct, traverse_util
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from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
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from flax.training import train_state
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from flax.training.common_utils import get_metrics, onehot, shard
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from huggingface_hub import Repository, create_repo
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from tqdm import tqdm
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from utils_qa import postprocess_qa_predictions
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import transformers
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from transformers import (
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AutoConfig,
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AutoTokenizer,
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EvalPrediction,
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FlaxAutoModelForQuestionAnswering,
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HfArgumentParser,
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PreTrainedTokenizerFast,
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is_tensorboard_available,
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)
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from transformers.utils import check_min_version, send_example_telemetry
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logger = logging.getLogger(__name__)
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.38.0.dev0")
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Array = Any
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Dataset = datasets.arrow_dataset.Dataset
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PRNGKey = Any
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# region Arguments
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@dataclass
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class TrainingArguments:
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output_dir: str = field(
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metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
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)
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overwrite_output_dir: bool = field(
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default=False,
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metadata={
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"help": (
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"Overwrite the content of the output directory. "
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"Use this to continue training if output_dir points to a checkpoint directory."
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)
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},
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)
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do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
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do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
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do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
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per_device_train_batch_size: int = field(
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default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
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)
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per_device_eval_batch_size: int = field(
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default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
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)
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learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
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weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
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adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
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adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
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adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
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adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
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num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
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warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
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logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
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save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
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eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
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seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
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push_to_hub: bool = field(
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default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
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)
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hub_model_id: str = field(
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default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
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)
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hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
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def __post_init__(self):
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if self.output_dir is not None:
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self.output_dir = os.path.expanduser(self.output_dir)
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def to_dict(self):
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"""
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Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
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the token values by removing their value.
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"""
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d = asdict(self)
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for k, v in d.items():
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if isinstance(v, Enum):
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d[k] = v.value
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if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
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d[k] = [x.value for x in v]
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if k.endswith("_token"):
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d[k] = f"<{k.upper()}>"
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return d
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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)
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},
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)
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use_auth_token: bool = field(
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default=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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},
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)
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dtype: Optional[str] = field(
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default="float32",
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metadata={
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"help": (
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"Floating-point format in which the model weights should be initialized and trained. Choose one of"
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" `[float32, float16, bfloat16]`."
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)
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},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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test_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_seq_length: int = field(
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default=384,
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metadata={
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when"
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" batching to the maximum length in the batch (which can be faster on GPU but will be slower on TPU)."
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)
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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)
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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)
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},
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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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)
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},
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)
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version_2_with_negative: bool = field(
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default=False, metadata={"help": "If true, some of the examples do not have an answer."}
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)
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null_score_diff_threshold: float = field(
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default=0.0,
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metadata={
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"help": (
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"The threshold used to select the null answer: if the best answer has a score that is less than "
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"the score of the null answer minus this threshold, the null answer is selected for this example. "
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"Only useful when `version_2_with_negative=True`."
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)
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},
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)
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doc_stride: int = field(
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default=128,
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metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
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)
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n_best_size: int = field(
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default=20,
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metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
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)
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max_answer_length: int = field(
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default=30,
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metadata={
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"help": (
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"The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another."
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)
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},
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)
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def __post_init__(self):
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if (
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self.dataset_name is None
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and self.train_file is None
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and self.validation_file is None
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and self.test_file is None
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):
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raise ValueError("Need either a dataset name or a training/validation file/test_file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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if self.test_file is not None:
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extension = self.test_file.split(".")[-1]
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assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
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# endregion
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# region Create a train state
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def create_train_state(
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model: FlaxAutoModelForQuestionAnswering,
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learning_rate_fn: Callable[[int], float],
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num_labels: int,
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training_args: TrainingArguments,
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) -> train_state.TrainState:
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"""Create initial training state."""
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class TrainState(train_state.TrainState):
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"""Train state with an Optax optimizer.
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The two functions below differ depending on whether the task is classification
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or regression.
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Args:
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logits_fn: Applied to last layer to obtain the logits.
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loss_fn: Function to compute the loss.
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"""
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logits_fn: Callable = struct.field(pytree_node=False)
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loss_fn: Callable = struct.field(pytree_node=False)
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# We use Optax's "masking" functionality to not apply weight decay
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# to bias and LayerNorm scale parameters. decay_mask_fn returns a
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# mask boolean with the same structure as the parameters.
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# The mask is True for parameters that should be decayed.
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def decay_mask_fn(params):
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flat_params = traverse_util.flatten_dict(params)
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# find out all LayerNorm parameters
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layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
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layer_norm_named_params = {
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layer[-2:]
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for layer_norm_name in layer_norm_candidates
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for layer in flat_params.keys()
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if layer_norm_name in "".join(layer).lower()
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}
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flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
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return traverse_util.unflatten_dict(flat_mask)
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tx = optax.adamw(
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learning_rate=learning_rate_fn,
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b1=training_args.adam_beta1,
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b2=training_args.adam_beta2,
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eps=training_args.adam_epsilon,
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weight_decay=training_args.weight_decay,
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mask=decay_mask_fn,
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)
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def cross_entropy_loss(logits, labels):
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start_loss = optax.softmax_cross_entropy(logits[0], onehot(labels[0], num_classes=num_labels))
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end_loss = optax.softmax_cross_entropy(logits[1], onehot(labels[1], num_classes=num_labels))
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xentropy = (start_loss + end_loss) / 2.0
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return jnp.mean(xentropy)
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return TrainState.create(
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apply_fn=model.__call__,
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params=model.params,
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tx=tx,
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logits_fn=lambda logits: logits,
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loss_fn=cross_entropy_loss,
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)
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# endregion
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# region Create learning rate function
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def create_learning_rate_fn(
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train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
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) -> Callable[[int], jnp.ndarray]:
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"""Returns a linear warmup, linear_decay learning rate function."""
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steps_per_epoch = train_ds_size // train_batch_size
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num_train_steps = steps_per_epoch * num_train_epochs
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warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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decay_fn = optax.linear_schedule(
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init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
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)
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schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
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return schedule_fn
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# endregion
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# region train data iterator
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def train_data_collator(rng: PRNGKey, dataset: Dataset, batch_size: int):
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"""Returns shuffled batches of size `batch_size` from truncated `train dataset`, sharded over all local devices."""
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steps_per_epoch = len(dataset) // batch_size
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perms = jax.random.permutation(rng, len(dataset))
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perms = perms[: steps_per_epoch * batch_size] # Skip incomplete batch.
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perms = perms.reshape((steps_per_epoch, batch_size))
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for perm in perms:
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batch = dataset[perm]
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batch = {k: np.array(v) for k, v in batch.items()}
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batch = shard(batch)
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yield batch
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# endregion
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# region eval data iterator
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def eval_data_collator(dataset: Dataset, batch_size: int):
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"""Returns batches of size `batch_size` from `eval dataset`. Sharding handled by `pad_shard_unpad` in the eval loop."""
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batch_idx = np.arange(len(dataset))
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steps_per_epoch = math.ceil(len(dataset) / batch_size)
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batch_idx = np.array_split(batch_idx, steps_per_epoch)
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for idx in batch_idx:
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batch = dataset[idx]
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batch = {k: np.array(v) for k, v in batch.items()}
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yield batch
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# endregion
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def main():
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# region Argument parsing
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_qa", model_args, data_args, framework="flax")
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# endregion
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# region Logging
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
)
|
|
# Setup logging, we only want one process per machine to log things on the screen.
|
|
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
|
if jax.process_index() == 0:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
# endregion
|
|
|
|
# Handle the repository creation
|
|
if training_args.push_to_hub:
|
|
# Retrieve of infer repo_name
|
|
repo_name = training_args.hub_model_id
|
|
if repo_name is None:
|
|
repo_name = Path(training_args.output_dir).absolute().name
|
|
# Create repo and retrieve repo_id
|
|
repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
|
|
# Clone repo locally
|
|
repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
|
|
|
|
# region Load Data
|
|
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
|
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
|
# (the dataset will be downloaded automatically from the datasets Hub).
|
|
#
|
|
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
|
# 'text' is found. You can easily tweak this behavior (see below).
|
|
#
|
|
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
|
|
# download the dataset.
|
|
if data_args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
raw_datasets = load_dataset(
|
|
data_args.dataset_name,
|
|
data_args.dataset_config_name,
|
|
cache_dir=model_args.cache_dir,
|
|
token=model_args.token,
|
|
)
|
|
else:
|
|
# Loading the dataset from local csv or json file.
|
|
data_files = {}
|
|
if data_args.train_file is not None:
|
|
data_files["train"] = data_args.train_file
|
|
extension = data_args.train_file.split(".")[-1]
|
|
|
|
if data_args.validation_file is not None:
|
|
data_files["validation"] = data_args.validation_file
|
|
extension = data_args.validation_file.split(".")[-1]
|
|
if data_args.test_file is not None:
|
|
data_files["test"] = data_args.test_file
|
|
extension = data_args.test_file.split(".")[-1]
|
|
raw_datasets = load_dataset(
|
|
extension,
|
|
data_files=data_files,
|
|
field="data",
|
|
cache_dir=model_args.cache_dir,
|
|
token=model_args.token,
|
|
)
|
|
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
|
# https://huggingface.co/docs/datasets/loading_datasets.
|
|
# endregion
|
|
|
|
# region Load pretrained model and tokenizer
|
|
#
|
|
# Load pretrained model and tokenizer
|
|
config = AutoConfig.from_pretrained(
|
|
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
|
cache_dir=model_args.cache_dir,
|
|
revision=model_args.model_revision,
|
|
token=model_args.token,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
)
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
|
cache_dir=model_args.cache_dir,
|
|
use_fast=True,
|
|
revision=model_args.model_revision,
|
|
token=model_args.token,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
)
|
|
# endregion
|
|
|
|
# region Tokenizer check: this script requires a fast tokenizer.
|
|
if not isinstance(tokenizer, PreTrainedTokenizerFast):
|
|
raise ValueError(
|
|
"This example script only works for models that have a fast tokenizer. Checkout the big table of models at"
|
|
" https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
|
|
" this requirement"
|
|
)
|
|
# endregion
|
|
|
|
# region Preprocessing the datasets
|
|
# Preprocessing is slightly different for training and evaluation.
|
|
if training_args.do_train:
|
|
column_names = raw_datasets["train"].column_names
|
|
elif training_args.do_eval:
|
|
column_names = raw_datasets["validation"].column_names
|
|
else:
|
|
column_names = raw_datasets["test"].column_names
|
|
question_column_name = "question" if "question" in column_names else column_names[0]
|
|
context_column_name = "context" if "context" in column_names else column_names[1]
|
|
answer_column_name = "answers" if "answers" in column_names else column_names[2]
|
|
|
|
# Padding side determines if we do (question|context) or (context|question).
|
|
pad_on_right = tokenizer.padding_side == "right"
|
|
|
|
if data_args.max_seq_length > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
|
|
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
|
|
)
|
|
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
|
|
|
|
# Training preprocessing
|
|
def prepare_train_features(examples):
|
|
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
|
|
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
|
|
# left whitespace
|
|
examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
|
|
|
|
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
|
|
# in one example possible giving several features when a context is long, each of those features having a
|
|
# context that overlaps a bit the context of the previous feature.
|
|
tokenized_examples = tokenizer(
|
|
examples[question_column_name if pad_on_right else context_column_name],
|
|
examples[context_column_name if pad_on_right else question_column_name],
|
|
truncation="only_second" if pad_on_right else "only_first",
|
|
max_length=max_seq_length,
|
|
stride=data_args.doc_stride,
|
|
return_overflowing_tokens=True,
|
|
return_offsets_mapping=True,
|
|
padding="max_length",
|
|
)
|
|
|
|
# Since one example might give us several features if it has a long context, we need a map from a feature to
|
|
# its corresponding example. This key gives us just that.
|
|
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
|
|
# The offset mappings will give us a map from token to character position in the original context. This will
|
|
# help us compute the start_positions and end_positions.
|
|
offset_mapping = tokenized_examples.pop("offset_mapping")
|
|
|
|
# Let's label those examples!
|
|
tokenized_examples["start_positions"] = []
|
|
tokenized_examples["end_positions"] = []
|
|
|
|
for i, offsets in enumerate(offset_mapping):
|
|
# We will label impossible answers with the index of the CLS token.
|
|
input_ids = tokenized_examples["input_ids"][i]
|
|
cls_index = input_ids.index(tokenizer.cls_token_id)
|
|
|
|
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
|
|
sequence_ids = tokenized_examples.sequence_ids(i)
|
|
|
|
# One example can give several spans, this is the index of the example containing this span of text.
|
|
sample_index = sample_mapping[i]
|
|
answers = examples[answer_column_name][sample_index]
|
|
# If no answers are given, set the cls_index as answer.
|
|
if len(answers["answer_start"]) == 0:
|
|
tokenized_examples["start_positions"].append(cls_index)
|
|
tokenized_examples["end_positions"].append(cls_index)
|
|
else:
|
|
# Start/end character index of the answer in the text.
|
|
start_char = answers["answer_start"][0]
|
|
end_char = start_char + len(answers["text"][0])
|
|
|
|
# Start token index of the current span in the text.
|
|
token_start_index = 0
|
|
while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
|
|
token_start_index += 1
|
|
|
|
# End token index of the current span in the text.
|
|
token_end_index = len(input_ids) - 1
|
|
while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
|
|
token_end_index -= 1
|
|
|
|
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
|
|
if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
|
|
tokenized_examples["start_positions"].append(cls_index)
|
|
tokenized_examples["end_positions"].append(cls_index)
|
|
else:
|
|
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
|
|
# Note: we could go after the last offset if the answer is the last word (edge case).
|
|
while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
|
|
token_start_index += 1
|
|
tokenized_examples["start_positions"].append(token_start_index - 1)
|
|
while offsets[token_end_index][1] >= end_char:
|
|
token_end_index -= 1
|
|
tokenized_examples["end_positions"].append(token_end_index + 1)
|
|
|
|
return tokenized_examples
|
|
|
|
processed_raw_datasets = {}
|
|
if training_args.do_train:
|
|
if "train" not in raw_datasets:
|
|
raise ValueError("--do_train requires a train dataset")
|
|
train_dataset = raw_datasets["train"]
|
|
if data_args.max_train_samples is not None:
|
|
# We will select sample from whole data if argument is specified
|
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
|
train_dataset = train_dataset.select(range(max_train_samples))
|
|
# Create train feature from dataset
|
|
train_dataset = train_dataset.map(
|
|
prepare_train_features,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
if data_args.max_train_samples is not None:
|
|
# Number of samples might increase during Feature Creation, We select only specified max samples
|
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
|
train_dataset = train_dataset.select(range(max_train_samples))
|
|
processed_raw_datasets["train"] = train_dataset
|
|
|
|
# Validation preprocessing
|
|
def prepare_validation_features(examples):
|
|
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
|
|
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
|
|
# left whitespace
|
|
examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
|
|
|
|
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
|
|
# in one example possible giving several features when a context is long, each of those features having a
|
|
# context that overlaps a bit the context of the previous feature.
|
|
tokenized_examples = tokenizer(
|
|
examples[question_column_name if pad_on_right else context_column_name],
|
|
examples[context_column_name if pad_on_right else question_column_name],
|
|
truncation="only_second" if pad_on_right else "only_first",
|
|
max_length=max_seq_length,
|
|
stride=data_args.doc_stride,
|
|
return_overflowing_tokens=True,
|
|
return_offsets_mapping=True,
|
|
padding="max_length",
|
|
)
|
|
|
|
# Since one example might give us several features if it has a long context, we need a map from a feature to
|
|
# its corresponding example. This key gives us just that.
|
|
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
|
|
|
|
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
|
|
# corresponding example_id and we will store the offset mappings.
|
|
tokenized_examples["example_id"] = []
|
|
|
|
for i in range(len(tokenized_examples["input_ids"])):
|
|
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
|
|
sequence_ids = tokenized_examples.sequence_ids(i)
|
|
context_index = 1 if pad_on_right else 0
|
|
|
|
# One example can give several spans, this is the index of the example containing this span of text.
|
|
sample_index = sample_mapping[i]
|
|
tokenized_examples["example_id"].append(examples["id"][sample_index])
|
|
|
|
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
|
|
# position is part of the context or not.
|
|
tokenized_examples["offset_mapping"][i] = [
|
|
(o if sequence_ids[k] == context_index else None)
|
|
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
|
|
]
|
|
|
|
return tokenized_examples
|
|
|
|
if training_args.do_eval:
|
|
if "validation" not in raw_datasets:
|
|
raise ValueError("--do_eval requires a validation dataset")
|
|
eval_examples = raw_datasets["validation"]
|
|
if data_args.max_eval_samples is not None:
|
|
# We will select sample from whole data
|
|
max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
|
|
eval_examples = eval_examples.select(range(max_eval_samples))
|
|
# Validation Feature Creation
|
|
eval_dataset = eval_examples.map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
if data_args.max_eval_samples is not None:
|
|
# During Feature creation dataset samples might increase, we will select required samples again
|
|
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
|
|
eval_dataset = eval_dataset.select(range(max_eval_samples))
|
|
processed_raw_datasets["validation"] = eval_dataset
|
|
|
|
if training_args.do_predict:
|
|
if "test" not in raw_datasets:
|
|
raise ValueError("--do_predict requires a test dataset")
|
|
predict_examples = raw_datasets["test"]
|
|
if data_args.max_predict_samples is not None:
|
|
# We will select sample from whole data
|
|
predict_examples = predict_examples.select(range(data_args.max_predict_samples))
|
|
# Predict Feature Creation
|
|
predict_dataset = predict_examples.map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
if data_args.max_predict_samples is not None:
|
|
# During Feature creation dataset samples might increase, we will select required samples again
|
|
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
|
|
predict_dataset = predict_dataset.select(range(max_predict_samples))
|
|
processed_raw_datasets["test"] = predict_dataset
|
|
# endregion
|
|
|
|
# region Metrics and Post-processing:
|
|
def post_processing_function(examples, features, predictions, stage="eval"):
|
|
# Post-processing: we match the start logits and end logits to answers in the original context.
|
|
predictions = postprocess_qa_predictions(
|
|
examples=examples,
|
|
features=features,
|
|
predictions=predictions,
|
|
version_2_with_negative=data_args.version_2_with_negative,
|
|
n_best_size=data_args.n_best_size,
|
|
max_answer_length=data_args.max_answer_length,
|
|
null_score_diff_threshold=data_args.null_score_diff_threshold,
|
|
output_dir=training_args.output_dir,
|
|
prefix=stage,
|
|
)
|
|
# Format the result to the format the metric expects.
|
|
if data_args.version_2_with_negative:
|
|
formatted_predictions = [
|
|
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
|
|
]
|
|
else:
|
|
formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
|
|
|
|
references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
|
|
return EvalPrediction(predictions=formatted_predictions, label_ids=references)
|
|
|
|
metric = evaluate.load(
|
|
"squad_v2" if data_args.version_2_with_negative else "squad", cache_dir=model_args.cache_dir
|
|
)
|
|
|
|
def compute_metrics(p: EvalPrediction):
|
|
return metric.compute(predictions=p.predictions, references=p.label_ids)
|
|
|
|
# Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
|
|
def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
|
|
"""
|
|
Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor
|
|
|
|
Args:
|
|
start_or_end_logits(:obj:`tensor`):
|
|
This is the output predictions of the model. We can only enter either start or end logits.
|
|
eval_dataset: Evaluation dataset
|
|
max_len(:obj:`int`):
|
|
The maximum length of the output tensor. ( See the model.eval() part for more details )
|
|
"""
|
|
|
|
step = 0
|
|
# create a numpy array and fill it with -100.
|
|
logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float64)
|
|
# Now since we have create an array now we will populate it with the outputs of the model.
|
|
for i, output_logit in enumerate(start_or_end_logits): # populate columns
|
|
# We have to fill it such that we have to take the whole tensor and replace it on the newly created array
|
|
# And after every iteration we have to change the step
|
|
|
|
batch_size = output_logit.shape[0]
|
|
cols = output_logit.shape[1]
|
|
|
|
if step + batch_size < len(dataset):
|
|
logits_concat[step : step + batch_size, :cols] = output_logit
|
|
else:
|
|
logits_concat[step:, :cols] = output_logit[: len(dataset) - step]
|
|
|
|
step += batch_size
|
|
|
|
return logits_concat
|
|
|
|
# endregion
|
|
|
|
# region Training steps and logging init
|
|
train_dataset = processed_raw_datasets["train"]
|
|
eval_dataset = processed_raw_datasets["validation"]
|
|
|
|
# Log a few random samples from the training set:
|
|
for index in random.sample(range(len(train_dataset)), 3):
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
|
|
# Define a summary writer
|
|
has_tensorboard = is_tensorboard_available()
|
|
if has_tensorboard and jax.process_index() == 0:
|
|
try:
|
|
from flax.metrics.tensorboard import SummaryWriter
|
|
|
|
summary_writer = SummaryWriter(training_args.output_dir)
|
|
summary_writer.hparams({**training_args.to_dict(), **vars(model_args), **vars(data_args)})
|
|
except ImportError as ie:
|
|
has_tensorboard = False
|
|
logger.warning(
|
|
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
|
)
|
|
else:
|
|
logger.warning(
|
|
"Unable to display metrics through TensorBoard because the package is not installed: "
|
|
"Please run pip install tensorboard to enable."
|
|
)
|
|
|
|
def write_train_metric(summary_writer, train_metrics, train_time, step):
|
|
summary_writer.scalar("train_time", train_time, step)
|
|
|
|
train_metrics = get_metrics(train_metrics)
|
|
for key, vals in train_metrics.items():
|
|
tag = f"train_{key}"
|
|
for i, val in enumerate(vals):
|
|
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
|
|
|
def write_eval_metric(summary_writer, eval_metrics, step):
|
|
for metric_name, value in eval_metrics.items():
|
|
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
|
|
|
num_epochs = int(training_args.num_train_epochs)
|
|
rng = jax.random.PRNGKey(training_args.seed)
|
|
dropout_rngs = jax.random.split(rng, jax.local_device_count())
|
|
|
|
train_batch_size = int(training_args.per_device_train_batch_size) * jax.local_device_count()
|
|
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
|
|
eval_batch_size = per_device_eval_batch_size * jax.local_device_count()
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|
# endregion
|
|
|
|
# region Load model
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|
model = FlaxAutoModelForQuestionAnswering.from_pretrained(
|
|
model_args.model_name_or_path,
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|
config=config,
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|
cache_dir=model_args.cache_dir,
|
|
revision=model_args.model_revision,
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|
token=model_args.token,
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|
trust_remote_code=model_args.trust_remote_code,
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|
seed=training_args.seed,
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|
dtype=getattr(jnp, model_args.dtype),
|
|
)
|
|
|
|
learning_rate_fn = create_learning_rate_fn(
|
|
len(train_dataset),
|
|
train_batch_size,
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|
training_args.num_train_epochs,
|
|
training_args.warmup_steps,
|
|
training_args.learning_rate,
|
|
)
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|
|
|
state = create_train_state(model, learning_rate_fn, num_labels=max_seq_length, training_args=training_args)
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|
# endregion
|
|
|
|
# region Define train step functions
|
|
def train_step(
|
|
state: train_state.TrainState, batch: Dict[str, Array], dropout_rng: PRNGKey
|
|
) -> Tuple[train_state.TrainState, float]:
|
|
"""Trains model with an optimizer (both in `state`) on `batch`, returning a pair `(new_state, loss)`."""
|
|
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
|
|
start_positions = batch.pop("start_positions")
|
|
end_positions = batch.pop("end_positions")
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|
targets = (start_positions, end_positions)
|
|
|
|
def loss_fn(params):
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|
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)
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|
loss = state.loss_fn(logits, targets)
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|
return loss
|
|
|
|
grad_fn = jax.value_and_grad(loss_fn)
|
|
loss, grad = grad_fn(state.params)
|
|
grad = jax.lax.pmean(grad, "batch")
|
|
new_state = state.apply_gradients(grads=grad)
|
|
metrics = jax.lax.pmean({"loss": loss, "learning_rate": learning_rate_fn(state.step)}, axis_name="batch")
|
|
return new_state, metrics, new_dropout_rng
|
|
|
|
p_train_step = jax.pmap(train_step, axis_name="batch", donate_argnums=(0,))
|
|
# endregion
|
|
|
|
# region Define eval step functions
|
|
def eval_step(state, batch):
|
|
logits = state.apply_fn(**batch, params=state.params, train=False)
|
|
return state.logits_fn(logits)
|
|
|
|
p_eval_step = jax.pmap(eval_step, axis_name="batch")
|
|
# endregion
|
|
|
|
# region Define train and eval loop
|
|
logger.info(f"===== Starting training ({num_epochs} epochs) =====")
|
|
train_time = 0
|
|
|
|
# make sure weights are replicated on each device
|
|
state = replicate(state)
|
|
|
|
train_time = 0
|
|
step_per_epoch = len(train_dataset) // train_batch_size
|
|
total_steps = step_per_epoch * num_epochs
|
|
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
|
for epoch in epochs:
|
|
train_start = time.time()
|
|
train_metrics = []
|
|
|
|
# Create sampling rng
|
|
rng, input_rng = jax.random.split(rng)
|
|
|
|
# train
|
|
for step, batch in enumerate(
|
|
tqdm(
|
|
train_data_collator(input_rng, train_dataset, train_batch_size),
|
|
total=step_per_epoch,
|
|
desc="Training...",
|
|
position=1,
|
|
),
|
|
1,
|
|
):
|
|
state, train_metric, dropout_rngs = p_train_step(state, batch, dropout_rngs)
|
|
train_metrics.append(train_metric)
|
|
|
|
cur_step = epoch * step_per_epoch + step
|
|
|
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
|
|
# Save metrics
|
|
train_metric = unreplicate(train_metric)
|
|
train_time += time.time() - train_start
|
|
if has_tensorboard and jax.process_index() == 0:
|
|
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
|
|
|
|
epochs.write(
|
|
f"Step... ({cur_step}/{total_steps} | Training Loss: {train_metric['loss']}, Learning Rate:"
|
|
f" {train_metric['learning_rate']})"
|
|
)
|
|
|
|
train_metrics = []
|
|
|
|
if (
|
|
training_args.do_eval
|
|
and (cur_step % training_args.eval_steps == 0 or cur_step % step_per_epoch == 0)
|
|
and cur_step > 0
|
|
):
|
|
eval_metrics = {}
|
|
all_start_logits = []
|
|
all_end_logits = []
|
|
# evaluate
|
|
for batch in tqdm(
|
|
eval_data_collator(eval_dataset, eval_batch_size),
|
|
total=math.ceil(len(eval_dataset) / eval_batch_size),
|
|
desc="Evaluating ...",
|
|
position=2,
|
|
):
|
|
_ = batch.pop("example_id")
|
|
_ = batch.pop("offset_mapping")
|
|
predictions = pad_shard_unpad(p_eval_step)(
|
|
state, batch, min_device_batch=per_device_eval_batch_size
|
|
)
|
|
start_logits = np.array(predictions[0])
|
|
end_logits = np.array(predictions[1])
|
|
all_start_logits.append(start_logits)
|
|
all_end_logits.append(end_logits)
|
|
|
|
max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor
|
|
|
|
# concatenate the numpy array
|
|
start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len)
|
|
end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len)
|
|
|
|
# delete the list of numpy arrays
|
|
del all_start_logits
|
|
del all_end_logits
|
|
outputs_numpy = (start_logits_concat, end_logits_concat)
|
|
prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
|
|
eval_metrics = compute_metrics(prediction)
|
|
|
|
logger.info(f"Step... ({cur_step}/{total_steps} | Evaluation metrics: {eval_metrics})")
|
|
|
|
if has_tensorboard and jax.process_index() == 0:
|
|
write_eval_metric(summary_writer, eval_metrics, cur_step)
|
|
|
|
if (cur_step % training_args.save_steps == 0 and cur_step > 0) or (cur_step == total_steps):
|
|
# save checkpoint after each epoch and push checkpoint to the hub
|
|
if jax.process_index() == 0:
|
|
params = jax.device_get(unreplicate(state.params))
|
|
model.save_pretrained(training_args.output_dir, params=params)
|
|
tokenizer.save_pretrained(training_args.output_dir)
|
|
if training_args.push_to_hub:
|
|
repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
|
|
epochs.desc = f"Epoch ... {epoch + 1}/{num_epochs}"
|
|
# endregion
|
|
|
|
# Eval after training
|
|
if training_args.do_eval:
|
|
eval_metrics = {}
|
|
all_start_logits = []
|
|
all_end_logits = []
|
|
|
|
eval_loader = eval_data_collator(eval_dataset, eval_batch_size)
|
|
for batch in tqdm(
|
|
eval_loader, total=math.ceil(len(eval_dataset) / eval_batch_size), desc="Evaluating ...", position=2
|
|
):
|
|
_ = batch.pop("example_id")
|
|
_ = batch.pop("offset_mapping")
|
|
predictions = pad_shard_unpad(p_eval_step)(state, batch, min_device_batch=per_device_eval_batch_size)
|
|
start_logits = np.array(predictions[0])
|
|
end_logits = np.array(predictions[1])
|
|
all_start_logits.append(start_logits)
|
|
all_end_logits.append(end_logits)
|
|
|
|
max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor
|
|
|
|
# concatenate the numpy array
|
|
start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len)
|
|
end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len)
|
|
|
|
# delete the list of numpy arrays
|
|
del all_start_logits
|
|
del all_end_logits
|
|
outputs_numpy = (start_logits_concat, end_logits_concat)
|
|
prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy)
|
|
eval_metrics = compute_metrics(prediction)
|
|
|
|
if jax.process_index() == 0:
|
|
eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()}
|
|
path = os.path.join(training_args.output_dir, "eval_results.json")
|
|
with open(path, "w") as f:
|
|
json.dump(eval_metrics, f, indent=4, sort_keys=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|