1021 lines
42 KiB
Python
1021 lines
42 KiB
Python
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#!/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 summarization.
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"""
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# You can also adapt this script on your own sequence to sequence 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 sys
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import time
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from dataclasses import asdict, dataclass, field
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from enum import Enum
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from functools import partial
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from pathlib import Path
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from typing import Callable, Optional
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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 nltk # Here to have a nice missing dependency error message early on
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import numpy as np
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import optax
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from datasets import Dataset, load_dataset
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from filelock import FileLock
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from flax import jax_utils, traverse_util
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from flax.jax_utils import pad_shard_unpad, 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, shard_prng_key
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from huggingface_hub import HfApi
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from tqdm import tqdm
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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AutoConfig,
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AutoTokenizer,
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FlaxAutoModelForSeq2SeqLM,
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HfArgumentParser,
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is_tensorboard_available,
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)
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from transformers.utils import is_offline_mode, send_example_telemetry
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logger = logging.getLogger(__name__)
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try:
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nltk.data.find("tokenizers/punkt")
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except (LookupError, OSError):
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if is_offline_mode():
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raise LookupError(
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"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
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)
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with FileLock(".lock") as lock:
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nltk.download("punkt", quiet=True)
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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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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label_smoothing_factor: float = field(
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default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
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)
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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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gradient_checkpointing: bool = field(
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default=False,
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metadata={
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"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
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},
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)
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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, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": (
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"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
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)
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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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, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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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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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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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 to trust the execution of code from datasets/models defined on the Hub."
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" This option should only be set to `True` for repositories you trust and in which you have read the"
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" code, as it will 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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@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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text_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
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)
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summary_column: Optional[str] = field(
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default=None,
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metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
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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 predict data file to do prediction on (a text file)."},
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)
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max_source_length: Optional[int] = field(
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default=1024,
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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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max_target_length: Optional[int] = field(
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default=128,
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metadata={
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"help": (
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"The maximum total sequence length for target text 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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val_max_target_length: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"The maximum total sequence length for validation target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`. "
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"This argument is also used to override the `max_length` param of `model.generate`, which is used "
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"during evaluation."
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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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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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source_prefix: Optional[str] = field(
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default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
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)
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predict_with_generate: bool = field(
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default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
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)
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num_beams: Optional[int] = field(
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default=1,
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metadata={
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"help": (
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"Number of beams to use for evaluation. This argument will be passed to `model.generate`, "
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"which is used during evaluation."
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)
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},
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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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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, or 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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if self.val_max_target_length is None:
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self.val_max_target_length = self.max_target_length
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summarization_name_mapping = {
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"amazon_reviews_multi": ("review_body", "review_title"),
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"big_patent": ("description", "abstract"),
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"cnn_dailymail": ("article", "highlights"),
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"orange_sum": ("text", "summary"),
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"pn_summary": ("article", "summary"),
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"psc": ("extract_text", "summary_text"),
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"samsum": ("dialogue", "summary"),
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"thaisum": ("body", "summary"),
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"xglue": ("news_body", "news_title"),
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"xsum": ("document", "summary"),
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"wiki_summary": ("article", "highlights"),
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}
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class TrainState(train_state.TrainState):
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dropout_rng: jnp.ndarray
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def replicate(self):
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return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
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def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False, drop_last=True):
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"""
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Returns batches of size `batch_size` from `dataset`. If `drop_last` is set to `False`, the final batch may be incomplete,
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and range in size from 1 to `batch_size`. Shuffle batches if `shuffle` is `True`.
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"""
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if shuffle:
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batch_idx = jax.random.permutation(rng, len(dataset))
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batch_idx = np.asarray(batch_idx)
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else:
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batch_idx = np.arange(len(dataset))
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if drop_last:
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steps_per_epoch = len(dataset) // batch_size
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batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
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batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
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else:
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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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def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
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summary_writer.scalar("train_time", train_time, step)
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train_metrics = get_metrics(train_metrics)
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for key, vals in train_metrics.items():
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tag = f"train_{key}"
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for i, val in enumerate(vals):
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summary_writer.scalar(tag, val, step - len(vals) + i + 1)
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for metric_name, value in eval_metrics.items():
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summary_writer.scalar(f"eval_{metric_name}", value, step)
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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
|
||
|
) -> Callable[[int], jnp.ndarray]:
|
||
|
"""Returns a linear warmup, linear_decay learning rate function."""
|
||
|
steps_per_epoch = train_ds_size // train_batch_size
|
||
|
num_train_steps = steps_per_epoch * num_train_epochs
|
||
|
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
||
|
decay_fn = optax.linear_schedule(
|
||
|
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
||
|
)
|
||
|
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
||
|
return schedule_fn
|
||
|
|
||
|
|
||
|
def main():
|
||
|
# See all possible arguments in src/transformers/training_args.py
|
||
|
# or by passing the --help flag to this script.
|
||
|
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||
|
|
||
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
||
|
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||
|
# If we pass only one argument to the script and it's the path to a json file,
|
||
|
# let's parse it to get our arguments.
|
||
|
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||
|
else:
|
||
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||
|
|
||
|
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
|
||
|
# information sent is the one passed as arguments along with your Python/PyTorch versions.
|
||
|
send_example_telemetry("run_summarization", model_args, data_args, framework="flax")
|
||
|
|
||
|
if (
|
||
|
os.path.exists(training_args.output_dir)
|
||
|
and os.listdir(training_args.output_dir)
|
||
|
and training_args.do_train
|
||
|
and not training_args.overwrite_output_dir
|
||
|
):
|
||
|
raise ValueError(
|
||
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
||
|
"Use --overwrite_output_dir to overcome."
|
||
|
)
|
||
|
|
||
|
# Make one log on every process with the configuration for debugging.
|
||
|
logging.basicConfig(
|
||
|
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()
|
||
|
|
||
|
# Set the verbosity to info of the Transformers logger (on main process only):
|
||
|
logger.info(f"Training/evaluation parameters {training_args}")
|
||
|
|
||
|
# 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
|
||
|
api = HfApi()
|
||
|
repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
|
||
|
|
||
|
# Get the datasets: you can either provide your own CSV/JSON 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 first column for the full texts and the second column for the
|
||
|
# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
|
||
|
#
|
||
|
if data_args.dataset_name is not None:
|
||
|
# Downloading and loading a dataset from the hub.
|
||
|
dataset = load_dataset(
|
||
|
data_args.dataset_name,
|
||
|
data_args.dataset_config_name,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
keep_in_memory=False,
|
||
|
token=model_args.token,
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
else:
|
||
|
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]
|
||
|
dataset = load_dataset(
|
||
|
extension,
|
||
|
data_files=data_files,
|
||
|
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.
|
||
|
|
||
|
# Load pretrained model and tokenizer
|
||
|
|
||
|
if model_args.config_name:
|
||
|
config = AutoConfig.from_pretrained(
|
||
|
model_args.config_name,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
token=model_args.token,
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
elif model_args.model_name_or_path:
|
||
|
config = AutoConfig.from_pretrained(
|
||
|
model_args.model_name_or_path,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
token=model_args.token,
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
else:
|
||
|
config = CONFIG_MAPPING[model_args.model_type]()
|
||
|
logger.warning("You are instantiating a new config instance from scratch.")
|
||
|
|
||
|
if model_args.tokenizer_name:
|
||
|
tokenizer = AutoTokenizer.from_pretrained(
|
||
|
model_args.tokenizer_name,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_fast=model_args.use_fast_tokenizer,
|
||
|
token=model_args.token,
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
elif model_args.model_name_or_path:
|
||
|
tokenizer = AutoTokenizer.from_pretrained(
|
||
|
model_args.model_name_or_path,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_fast=model_args.use_fast_tokenizer,
|
||
|
token=model_args.token,
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
|
||
|
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
||
|
)
|
||
|
|
||
|
if model_args.model_name_or_path:
|
||
|
model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
|
||
|
model_args.model_name_or_path,
|
||
|
config=config,
|
||
|
seed=training_args.seed,
|
||
|
dtype=getattr(jnp, model_args.dtype),
|
||
|
token=model_args.token,
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
else:
|
||
|
model = FlaxAutoModelForSeq2SeqLM.from_config(
|
||
|
config,
|
||
|
seed=training_args.seed,
|
||
|
dtype=getattr(jnp, model_args.dtype),
|
||
|
trust_remote_code=model_args.trust_remote_code,
|
||
|
)
|
||
|
|
||
|
if training_args.gradient_checkpointing:
|
||
|
model.enable_gradient_checkpointing()
|
||
|
|
||
|
if model.config.decoder_start_token_id is None:
|
||
|
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
|
||
|
|
||
|
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
|
||
|
|
||
|
# Preprocessing the datasets.
|
||
|
# We need to tokenize inputs and targets.
|
||
|
if training_args.do_train:
|
||
|
if "train" not in dataset:
|
||
|
raise ValueError("--do_train requires a train dataset")
|
||
|
column_names = dataset["train"].column_names
|
||
|
elif training_args.do_eval:
|
||
|
if "validation" not in dataset:
|
||
|
raise ValueError("--do_eval requires a validation dataset")
|
||
|
column_names = dataset["validation"].column_names
|
||
|
elif training_args.do_predict:
|
||
|
if "test" not in dataset:
|
||
|
raise ValueError("--do_predict requires a test dataset")
|
||
|
column_names = dataset["test"].column_names
|
||
|
else:
|
||
|
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
|
||
|
return
|
||
|
|
||
|
# Get the column names for input/target.
|
||
|
dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
|
||
|
if data_args.text_column is None:
|
||
|
text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
|
||
|
else:
|
||
|
text_column = data_args.text_column
|
||
|
if text_column not in column_names:
|
||
|
raise ValueError(
|
||
|
f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
|
||
|
)
|
||
|
if data_args.summary_column is None:
|
||
|
summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
|
||
|
else:
|
||
|
summary_column = data_args.summary_column
|
||
|
if summary_column not in column_names:
|
||
|
raise ValueError(
|
||
|
f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
|
||
|
)
|
||
|
|
||
|
# Temporarily set max_target_length for training.
|
||
|
max_target_length = data_args.max_target_length
|
||
|
|
||
|
# In Flax, for seq2seq models we need to pass `decoder_input_ids`
|
||
|
# as the Flax models don't accept `labels`, we need to prepare the decoder_input_ids here
|
||
|
# for that dynamically import the `shift_tokens_right` function from the model file
|
||
|
model_module = __import__(model.__module__, fromlist=["shift_tokens_tight"])
|
||
|
shift_tokens_right_fn = getattr(model_module, "shift_tokens_right")
|
||
|
|
||
|
# Setting padding="max_length" as we need fixed length inputs for jitted functions
|
||
|
def preprocess_function(examples):
|
||
|
inputs = examples[text_column]
|
||
|
targets = examples[summary_column]
|
||
|
inputs = [prefix + inp for inp in inputs]
|
||
|
model_inputs = tokenizer(
|
||
|
inputs, max_length=data_args.max_source_length, padding="max_length", truncation=True, return_tensors="np"
|
||
|
)
|
||
|
|
||
|
# Setup the tokenizer for targets
|
||
|
labels = tokenizer(
|
||
|
text_target=targets,
|
||
|
max_length=max_target_length,
|
||
|
padding="max_length",
|
||
|
truncation=True,
|
||
|
return_tensors="np",
|
||
|
)
|
||
|
|
||
|
model_inputs["labels"] = labels["input_ids"]
|
||
|
decoder_input_ids = shift_tokens_right_fn(
|
||
|
labels["input_ids"], config.pad_token_id, config.decoder_start_token_id
|
||
|
)
|
||
|
model_inputs["decoder_input_ids"] = np.asarray(decoder_input_ids)
|
||
|
|
||
|
# We need decoder_attention_mask so we can ignore pad tokens from loss
|
||
|
model_inputs["decoder_attention_mask"] = labels["attention_mask"]
|
||
|
|
||
|
return model_inputs
|
||
|
|
||
|
if training_args.do_train:
|
||
|
train_dataset = dataset["train"]
|
||
|
if data_args.max_train_samples is not None:
|
||
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
||
|
train_dataset = train_dataset.select(range(max_train_samples))
|
||
|
train_dataset = train_dataset.map(
|
||
|
preprocess_function,
|
||
|
batched=True,
|
||
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
remove_columns=column_names,
|
||
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
desc="Running tokenizer on train dataset",
|
||
|
)
|
||
|
|
||
|
if training_args.do_eval:
|
||
|
max_target_length = data_args.val_max_target_length
|
||
|
eval_dataset = dataset["validation"]
|
||
|
if data_args.max_eval_samples is not None:
|
||
|
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
|
||
|
eval_dataset = eval_dataset.select(range(max_eval_samples))
|
||
|
eval_dataset = eval_dataset.map(
|
||
|
preprocess_function,
|
||
|
batched=True,
|
||
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
remove_columns=column_names,
|
||
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
desc="Running tokenizer on validation dataset",
|
||
|
)
|
||
|
|
||
|
if training_args.do_predict:
|
||
|
max_target_length = data_args.val_max_target_length
|
||
|
predict_dataset = dataset["test"]
|
||
|
if data_args.max_predict_samples is not None:
|
||
|
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
|
||
|
predict_dataset = predict_dataset.select(range(max_predict_samples))
|
||
|
predict_dataset = predict_dataset.map(
|
||
|
preprocess_function,
|
||
|
batched=True,
|
||
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
remove_columns=column_names,
|
||
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
desc="Running tokenizer on prediction dataset",
|
||
|
)
|
||
|
|
||
|
# Metric
|
||
|
metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
|
||
|
|
||
|
def postprocess_text(preds, labels):
|
||
|
preds = [pred.strip() for pred in preds]
|
||
|
labels = [label.strip() for label in labels]
|
||
|
|
||
|
# rougeLSum expects newline after each sentence
|
||
|
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
|
||
|
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
|
||
|
|
||
|
return preds, labels
|
||
|
|
||
|
def compute_metrics(preds, labels):
|
||
|
decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
|
||
|
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
|
||
|
|
||
|
# Some simple post-processing
|
||
|
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
|
||
|
|
||
|
result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
|
||
|
result = {k: round(v * 100, 4) for k, v in result.items()}
|
||
|
prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
|
||
|
result["gen_len"] = np.mean(prediction_lens)
|
||
|
return result
|
||
|
|
||
|
# Enable tensorboard only on the master node
|
||
|
has_tensorboard = is_tensorboard_available()
|
||
|
if has_tensorboard and jax.process_index() == 0:
|
||
|
try:
|
||
|
from flax.metrics.tensorboard import SummaryWriter
|
||
|
|
||
|
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
||
|
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."
|
||
|
)
|
||
|
|
||
|
# Initialize our training
|
||
|
rng = jax.random.PRNGKey(training_args.seed)
|
||
|
rng, dropout_rng = jax.random.split(rng)
|
||
|
|
||
|
# Store some constant
|
||
|
num_epochs = int(training_args.num_train_epochs)
|
||
|
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
|
||
|
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
|
||
|
eval_batch_size = per_device_eval_batch_size * jax.device_count()
|
||
|
steps_per_epoch = len(train_dataset) // train_batch_size
|
||
|
total_train_steps = steps_per_epoch * num_epochs
|
||
|
|
||
|
# Create learning rate schedule
|
||
|
linear_decay_lr_schedule_fn = create_learning_rate_fn(
|
||
|
len(train_dataset),
|
||
|
train_batch_size,
|
||
|
training_args.num_train_epochs,
|
||
|
training_args.warmup_steps,
|
||
|
training_args.learning_rate,
|
||
|
)
|
||
|
|
||
|
# We use Optax's "masking" functionality to not apply weight decay
|
||
|
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
||
|
# mask boolean with the same structure as the parameters.
|
||
|
# The mask is True for parameters that should be decayed.
|
||
|
def decay_mask_fn(params):
|
||
|
flat_params = traverse_util.flatten_dict(params)
|
||
|
# find out all LayerNorm parameters
|
||
|
layer_norm_candidates = ["layernorm", "layer_norm", "ln"]
|
||
|
layer_norm_named_params = {
|
||
|
layer[-2:]
|
||
|
for layer_norm_name in layer_norm_candidates
|
||
|
for layer in flat_params.keys()
|
||
|
if layer_norm_name in "".join(layer).lower()
|
||
|
}
|
||
|
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params}
|
||
|
return traverse_util.unflatten_dict(flat_mask)
|
||
|
|
||
|
# create adam optimizer
|
||
|
adamw = optax.adamw(
|
||
|
learning_rate=linear_decay_lr_schedule_fn,
|
||
|
b1=training_args.adam_beta1,
|
||
|
b2=training_args.adam_beta2,
|
||
|
eps=training_args.adam_epsilon,
|
||
|
weight_decay=training_args.weight_decay,
|
||
|
mask=decay_mask_fn,
|
||
|
)
|
||
|
|
||
|
# Setup train state
|
||
|
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
|
||
|
|
||
|
# label smoothed cross entropy
|
||
|
def loss_fn(logits, labels, padding_mask, label_smoothing_factor=0.0):
|
||
|
"""
|
||
|
The label smoothing implementation is adapted from Flax's official example:
|
||
|
https://github.com/google/flax/blob/87a211135c6a377c8f29048a1cac3840e38b9da4/examples/wmt/train.py#L104
|
||
|
"""
|
||
|
vocab_size = logits.shape[-1]
|
||
|
confidence = 1.0 - label_smoothing_factor
|
||
|
low_confidence = (1.0 - confidence) / (vocab_size - 1)
|
||
|
normalizing_constant = -(
|
||
|
confidence * jnp.log(confidence) + (vocab_size - 1) * low_confidence * jnp.log(low_confidence + 1e-20)
|
||
|
)
|
||
|
soft_labels = onehot(labels, vocab_size, on_value=confidence, off_value=low_confidence)
|
||
|
|
||
|
loss = optax.softmax_cross_entropy(logits, soft_labels)
|
||
|
loss = loss - normalizing_constant
|
||
|
|
||
|
# ignore padded tokens from loss
|
||
|
loss = loss * padding_mask
|
||
|
loss = loss.sum()
|
||
|
num_labels = padding_mask.sum()
|
||
|
return loss, num_labels
|
||
|
|
||
|
# Define gradient update step fn
|
||
|
def train_step(state, batch, label_smoothing_factor=0.0):
|
||
|
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
||
|
|
||
|
def compute_loss(params):
|
||
|
labels = batch.pop("labels")
|
||
|
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
||
|
loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
|
||
|
return loss, num_labels
|
||
|
|
||
|
grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
|
||
|
(loss, num_labels), grad = grad_fn(state.params)
|
||
|
num_labels = jax.lax.psum(num_labels, "batch")
|
||
|
|
||
|
# true loss = total loss / total samples
|
||
|
loss = jax.lax.psum(loss, "batch")
|
||
|
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
|
||
|
|
||
|
# true grad = total grad / total samples
|
||
|
grad = jax.lax.psum(grad, "batch")
|
||
|
grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
|
||
|
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
||
|
|
||
|
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
|
||
|
return new_state, metrics
|
||
|
|
||
|
# Define eval fn
|
||
|
def eval_step(params, batch, label_smoothing_factor=0.0):
|
||
|
labels = batch.pop("labels")
|
||
|
logits = model(**batch, params=params, train=False)[0]
|
||
|
|
||
|
loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
|
||
|
num_labels = jax.lax.psum(num_labels, "batch")
|
||
|
|
||
|
# true loss = total loss / total samples
|
||
|
loss = jax.lax.psum(loss, "batch")
|
||
|
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
|
||
|
|
||
|
metrics = {"loss": loss}
|
||
|
return metrics
|
||
|
|
||
|
# Define generation function
|
||
|
max_length = (
|
||
|
data_args.val_max_target_length if data_args.val_max_target_length is not None else model.config.max_length
|
||
|
)
|
||
|
num_beams = data_args.num_beams if data_args.num_beams is not None else model.config.num_beams
|
||
|
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
|
||
|
|
||
|
def generate_step(params, batch):
|
||
|
model.params = params
|
||
|
output_ids = model.generate(batch["input_ids"], attention_mask=batch["attention_mask"], **gen_kwargs)
|
||
|
return output_ids.sequences
|
||
|
|
||
|
# Create parallel version of the train and eval step
|
||
|
p_train_step = jax.pmap(
|
||
|
partial(train_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch", donate_argnums=(0,)
|
||
|
)
|
||
|
p_eval_step = jax.pmap(partial(eval_step, label_smoothing_factor=training_args.label_smoothing_factor), "batch")
|
||
|
p_generate_step = jax.pmap(generate_step, "batch")
|
||
|
|
||
|
# Replicate the train state on each device
|
||
|
state = state.replicate()
|
||
|
|
||
|
logger.info("***** Running training *****")
|
||
|
logger.info(f" Num examples = {len(train_dataset)}")
|
||
|
logger.info(f" Num Epochs = {num_epochs}")
|
||
|
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
||
|
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
|
||
|
logger.info(f" Total optimization steps = {total_train_steps}")
|
||
|
|
||
|
train_time = 0
|
||
|
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
||
|
for epoch in epochs:
|
||
|
# ======================== Training ================================
|
||
|
train_start = time.time()
|
||
|
|
||
|
# Create sampling rng
|
||
|
rng, input_rng = jax.random.split(rng)
|
||
|
train_metrics = []
|
||
|
|
||
|
# Generate an epoch by shuffling sampling indices from the train dataset
|
||
|
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
|
||
|
steps_per_epoch = len(train_dataset) // train_batch_size
|
||
|
# train
|
||
|
for _ in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
|
||
|
batch = next(train_loader)
|
||
|
batch = shard(batch)
|
||
|
state, train_metric = p_train_step(state, batch)
|
||
|
train_metrics.append(train_metric)
|
||
|
|
||
|
train_time += time.time() - train_start
|
||
|
|
||
|
train_metric = unreplicate(train_metric)
|
||
|
|
||
|
epochs.write(
|
||
|
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate:"
|
||
|
f" {train_metric['learning_rate']})"
|
||
|
)
|
||
|
|
||
|
# ======================== Evaluating ==============================
|
||
|
eval_metrics = []
|
||
|
eval_preds = []
|
||
|
eval_labels = []
|
||
|
|
||
|
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size, drop_last=False)
|
||
|
eval_steps = math.ceil(len(eval_dataset) / eval_batch_size)
|
||
|
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
||
|
# Model forward
|
||
|
batch = next(eval_loader)
|
||
|
labels = batch["labels"]
|
||
|
|
||
|
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
|
||
|
state.params, batch, min_device_batch=per_device_eval_batch_size
|
||
|
)
|
||
|
eval_metrics.append(metrics)
|
||
|
|
||
|
# generation
|
||
|
if data_args.predict_with_generate:
|
||
|
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch)
|
||
|
eval_preds.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
||
|
eval_labels.extend(labels)
|
||
|
|
||
|
# normalize eval metrics
|
||
|
eval_metrics = get_metrics(eval_metrics)
|
||
|
eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
|
||
|
|
||
|
# compute ROUGE metrics
|
||
|
rouge_desc = ""
|
||
|
if data_args.predict_with_generate:
|
||
|
rouge_metrics = compute_metrics(eval_preds, eval_labels)
|
||
|
eval_metrics.update(rouge_metrics)
|
||
|
rouge_desc = " ".join([f"Eval {key}: {value} |" for key, value in rouge_metrics.items()])
|
||
|
|
||
|
# Print metrics and update progress bar
|
||
|
desc = f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {eval_metrics['loss']} | {rouge_desc})"
|
||
|
epochs.write(desc)
|
||
|
epochs.desc = desc
|
||
|
|
||
|
# Save metrics
|
||
|
if has_tensorboard and jax.process_index() == 0:
|
||
|
cur_step = epoch * (len(train_dataset) // train_batch_size)
|
||
|
write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
|
||
|
|
||
|
# save checkpoint after each epoch and push checkpoint to the hub
|
||
|
if jax.process_index() == 0:
|
||
|
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
|
||
|
model.save_pretrained(training_args.output_dir, params=params)
|
||
|
tokenizer.save_pretrained(training_args.output_dir)
|
||
|
if training_args.push_to_hub:
|
||
|
api.upload_folder(
|
||
|
commit_message=f"Saving weights and logs of epoch {epoch}",
|
||
|
folder_path=training_args.output_dir,
|
||
|
repo_id=repo_id,
|
||
|
repo_type="model",
|
||
|
token=training_args.hub_token,
|
||
|
)
|
||
|
|
||
|
# ======================== Prediction loop ==============================
|
||
|
if training_args.do_predict:
|
||
|
logger.info("*** Predict ***")
|
||
|
|
||
|
pred_metrics = []
|
||
|
pred_generations = []
|
||
|
pred_labels = []
|
||
|
|
||
|
pred_loader = data_loader(input_rng, predict_dataset, eval_batch_size, drop_last=False)
|
||
|
pred_steps = math.ceil(len(predict_dataset) / eval_batch_size)
|
||
|
for _ in tqdm(range(pred_steps), desc="Predicting...", position=2, leave=False):
|
||
|
# Model forward
|
||
|
batch = next(pred_loader)
|
||
|
labels = batch["labels"]
|
||
|
|
||
|
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
|
||
|
state.params, batch, min_device_batch=per_device_eval_batch_size
|
||
|
)
|
||
|
pred_metrics.append(metrics)
|
||
|
|
||
|
# generation
|
||
|
if data_args.predict_with_generate:
|
||
|
generated_ids = pad_shard_unpad(p_generate_step)(state.params, batch)
|
||
|
pred_generations.extend(jax.device_get(generated_ids.reshape(-1, gen_kwargs["max_length"])))
|
||
|
pred_labels.extend(labels)
|
||
|
|
||
|
# normalize prediction metrics
|
||
|
pred_metrics = get_metrics(pred_metrics)
|
||
|
pred_metrics = jax.tree_util.tree_map(jnp.mean, pred_metrics)
|
||
|
|
||
|
# compute ROUGE metrics
|
||
|
rouge_desc = ""
|
||
|
if data_args.predict_with_generate:
|
||
|
rouge_metrics = compute_metrics(pred_generations, pred_labels)
|
||
|
pred_metrics.update(rouge_metrics)
|
||
|
rouge_desc = " ".join([f"Predict {key}: {value} |" for key, value in rouge_metrics.items()])
|
||
|
|
||
|
# Print metrics
|
||
|
desc = f"Predict Loss: {pred_metrics['loss']} | {rouge_desc})"
|
||
|
logger.info(desc)
|
||
|
|
||
|
# save final metrics in json
|
||
|
if jax.process_index() == 0:
|
||
|
rouge_metrics = {f"test_{metric_name}": value for metric_name, value in rouge_metrics.items()}
|
||
|
path = os.path.join(training_args.output_dir, "test_results.json")
|
||
|
with open(path, "w") as f:
|
||
|
json.dump(rouge_metrics, f, indent=4, sort_keys=True)
|
||
|
|
||
|
|
||
|
if __name__ == "__main__":
|
||
|
main()
|