993 lines
43 KiB
Python
993 lines
43 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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Pretraining the library models for T5-like span-masked language modeling on a text file or a dataset.
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Here is the full list of checkpoints on the hub that can be pretrained by this script:
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https://huggingface.co/models?filter=t5
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"""
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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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# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
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from enum import Enum
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from itertools import chain
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from pathlib import Path
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from typing import Dict, List, Optional
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import numpy as np
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from datasets import load_dataset
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from tqdm import tqdm
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import flax
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import jax
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import jax.numpy as jnp
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import optax
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from flax import jax_utils, traverse_util
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from flax.jax_utils import pad_shard_unpad
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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
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from transformers import (
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CONFIG_MAPPING,
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FLAX_MODEL_FOR_MASKED_LM_MAPPING,
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AutoTokenizer,
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BatchEncoding,
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FlaxT5ForConditionalGeneration,
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HfArgumentParser,
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PreTrainedTokenizerBase,
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T5Config,
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is_tensorboard_available,
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set_seed,
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)
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from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
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from transformers.utils import get_full_repo_name, send_example_telemetry
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_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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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, 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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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": (
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"Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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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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train_ref_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input train ref data file for whole word masking in Chinese."},
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)
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validation_ref_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."},
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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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validation_split_percentage: Optional[int] = field(
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default=5,
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metadata={
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"help": "The percentage of the train set used as validation set in case there's no validation split"
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},
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)
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max_seq_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 input sequence length after tokenization and masking. Sequences longer than this"
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" will be truncated. Default to the max input length of the model."
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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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mlm_probability: float = field(
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default=0.15, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"}
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)
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mean_noise_span_length: float = field(
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default=3.0,
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metadata={"help": "Mean span length of masked tokens"},
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation 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", "txt"], "`train_file` should be a csv, a json or a txt 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", "txt"], "`validation_file` should be a csv, a json or a txt file."
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def compute_input_and_target_lengths(inputs_length, noise_density, mean_noise_span_length):
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"""This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2466>`__ .
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Training parameters to avoid padding with random_spans_noise_mask.
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When training a model with random_spans_noise_mask, we would like to set the other
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training hyperparmeters in a way that avoids padding.
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This function helps us compute these hyperparameters.
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We assume that each noise span in the input is replaced by extra_tokens_per_span_inputs sentinel tokens,
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and each non-noise span in the targets is replaced by extra_tokens_per_span_targets sentinel tokens.
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This function tells us the required number of tokens in the raw example (for split_tokens())
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as well as the length of the encoded targets. Note that this function assumes
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the inputs and targets will have EOS appended and includes that in the reported length.
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Args:
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inputs_length: an integer - desired length of the tokenized inputs sequence
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noise_density: a float
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mean_noise_span_length: a float
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Returns:
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tokens_length: length of original text in tokens
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targets_length: an integer - length in tokens of encoded targets sequence
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"""
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def _tokens_length_to_inputs_length_targets_length(tokens_length):
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num_noise_tokens = int(round(tokens_length * noise_density))
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num_nonnoise_tokens = tokens_length - num_noise_tokens
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num_noise_spans = int(round(num_noise_tokens / mean_noise_span_length))
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# inputs contain all nonnoise tokens, sentinels for all noise spans
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# and one EOS token.
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_input_length = num_nonnoise_tokens + num_noise_spans + 1
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_output_length = num_noise_tokens + num_noise_spans + 1
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return _input_length, _output_length
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tokens_length = inputs_length
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while _tokens_length_to_inputs_length_targets_length(tokens_length + 1)[0] <= inputs_length:
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tokens_length += 1
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inputs_length, targets_length = _tokens_length_to_inputs_length_targets_length(tokens_length)
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# minor hack to get the targets length to be equal to inputs length
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# which is more likely to have been set to a nice round number.
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if noise_density == 0.5 and targets_length > inputs_length:
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tokens_length -= 1
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targets_length -= 1
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return tokens_length, targets_length
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@flax.struct.dataclass
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class FlaxDataCollatorForT5MLM:
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"""
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Data collator used for T5 span-masked language modeling.
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It is made sure that after masking the inputs are of length `data_args.max_seq_length` and targets are also of fixed length.
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For more information on how T5 span-masked language modeling works, one can take a look
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at the `official paper <https://arxiv.org/pdf/1910.10683.pdf>`__
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or the `official code for preprocessing <https://github.com/google-research/text-to-text-transfer-transformer/blob/master/t5/data/preprocessors.py>`__ .
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Args:
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tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
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The tokenizer used for encoding the data.
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noise_density (:obj:`float`):
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The probability with which to (randomly) mask tokens in the input.
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mean_noise_span_length (:obj:`float`):
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The average span length of the masked tokens.
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input_length (:obj:`int`):
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The expected input length after masking.
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target_length (:obj:`int`):
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The expected target length after masking.
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pad_token_id: (:obj:`int`):
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The pad token id of the model
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decoder_start_token_id: (:obj:`int):
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The decoder start token id of the model
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"""
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tokenizer: PreTrainedTokenizerBase
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noise_density: float
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mean_noise_span_length: float
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input_length: int
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target_length: int
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pad_token_id: int
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decoder_start_token_id: int
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def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]:
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# convert list to dict and tensorize input
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batch = BatchEncoding(
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{k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
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)
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input_ids = batch["input_ids"]
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batch_size, expandend_input_length = input_ids.shape
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mask_indices = np.asarray([self.random_spans_noise_mask(expandend_input_length) for i in range(batch_size)])
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labels_mask = ~mask_indices
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input_ids_sentinel = self.create_sentinel_ids(mask_indices.astype(np.int8))
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labels_sentinel = self.create_sentinel_ids(labels_mask.astype(np.int8))
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batch["input_ids"] = self.filter_input_ids(input_ids, input_ids_sentinel)
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batch["labels"] = self.filter_input_ids(input_ids, labels_sentinel)
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if batch["input_ids"].shape[-1] != self.input_length:
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raise ValueError(
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f"`input_ids` are incorrectly preprocessed. `input_ids` length is {batch['input_ids'].shape[-1]}, but"
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f" should be {self.target_length}."
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)
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if batch["labels"].shape[-1] != self.target_length:
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raise ValueError(
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f"`labels` are incorrectly preprocessed. `labels` length is {batch['labels'].shape[-1]}, but should be"
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f" {self.target_length}."
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)
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# to check that tokens are correctly preprocessed, one can run `self.tokenizer.batch_decode(input_ids)` and `self.tokenizer.batch_decode(labels)` here...
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batch["decoder_input_ids"] = shift_tokens_right(
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batch["labels"], self.pad_token_id, self.decoder_start_token_id
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)
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return batch
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def create_sentinel_ids(self, mask_indices):
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"""
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Sentinel ids creation given the indices that should be masked.
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The start indices of each mask are replaced by the sentinel ids in increasing
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order. Consecutive mask indices to be deleted are replaced with `-1`.
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"""
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start_indices = mask_indices - np.roll(mask_indices, 1, axis=-1) * mask_indices
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start_indices[:, 0] = mask_indices[:, 0]
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sentinel_ids = np.where(start_indices != 0, np.cumsum(start_indices, axis=-1), start_indices)
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sentinel_ids = np.where(sentinel_ids != 0, (len(self.tokenizer) - sentinel_ids), 0)
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sentinel_ids -= mask_indices - start_indices
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return sentinel_ids
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||
|
|
||
|
def filter_input_ids(self, input_ids, sentinel_ids):
|
||
|
"""
|
||
|
Puts sentinel mask on `input_ids` and fuse consecutive mask tokens into a single mask token by deleting.
|
||
|
This will reduce the sequence length from `expanded_inputs_length` to `input_length`.
|
||
|
"""
|
||
|
batch_size = input_ids.shape[0]
|
||
|
|
||
|
input_ids_full = np.where(sentinel_ids != 0, sentinel_ids, input_ids)
|
||
|
# input_ids tokens and sentinel tokens are >= 0, tokens < 0 are
|
||
|
# masked tokens coming after sentinel tokens and should be removed
|
||
|
input_ids = input_ids_full[input_ids_full >= 0].reshape((batch_size, -1))
|
||
|
input_ids = np.concatenate(
|
||
|
[input_ids, np.full((batch_size, 1), self.tokenizer.eos_token_id, dtype=np.int32)], axis=-1
|
||
|
)
|
||
|
return input_ids
|
||
|
|
||
|
def random_spans_noise_mask(self, length):
|
||
|
|
||
|
"""This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .
|
||
|
|
||
|
Noise mask consisting of random spans of noise tokens.
|
||
|
The number of noise tokens and the number of noise spans and non-noise spans
|
||
|
are determined deterministically as follows:
|
||
|
num_noise_tokens = round(length * noise_density)
|
||
|
num_nonnoise_spans = num_noise_spans = round(num_noise_tokens / mean_noise_span_length)
|
||
|
Spans alternate between non-noise and noise, beginning with non-noise.
|
||
|
Subject to the above restrictions, all masks are equally likely.
|
||
|
|
||
|
Args:
|
||
|
length: an int32 scalar (length of the incoming token sequence)
|
||
|
noise_density: a float - approximate density of output mask
|
||
|
mean_noise_span_length: a number
|
||
|
|
||
|
Returns:
|
||
|
a boolean tensor with shape [length]
|
||
|
"""
|
||
|
|
||
|
orig_length = length
|
||
|
|
||
|
num_noise_tokens = int(np.round(length * self.noise_density))
|
||
|
# avoid degeneracy by ensuring positive numbers of noise and nonnoise tokens.
|
||
|
num_noise_tokens = min(max(num_noise_tokens, 1), length - 1)
|
||
|
num_noise_spans = int(np.round(num_noise_tokens / self.mean_noise_span_length))
|
||
|
|
||
|
# avoid degeneracy by ensuring positive number of noise spans
|
||
|
num_noise_spans = max(num_noise_spans, 1)
|
||
|
num_nonnoise_tokens = length - num_noise_tokens
|
||
|
|
||
|
# pick the lengths of the noise spans and the non-noise spans
|
||
|
def _random_segmentation(num_items, num_segments):
|
||
|
"""Partition a sequence of items randomly into non-empty segments.
|
||
|
Args:
|
||
|
num_items: an integer scalar > 0
|
||
|
num_segments: an integer scalar in [1, num_items]
|
||
|
Returns:
|
||
|
a Tensor with shape [num_segments] containing positive integers that add
|
||
|
up to num_items
|
||
|
"""
|
||
|
mask_indices = np.arange(num_items - 1) < (num_segments - 1)
|
||
|
np.random.shuffle(mask_indices)
|
||
|
first_in_segment = np.pad(mask_indices, [[1, 0]])
|
||
|
segment_id = np.cumsum(first_in_segment)
|
||
|
# count length of sub segments assuming that list is sorted
|
||
|
_, segment_length = np.unique(segment_id, return_counts=True)
|
||
|
return segment_length
|
||
|
|
||
|
noise_span_lengths = _random_segmentation(num_noise_tokens, num_noise_spans)
|
||
|
nonnoise_span_lengths = _random_segmentation(num_nonnoise_tokens, num_noise_spans)
|
||
|
|
||
|
interleaved_span_lengths = np.reshape(
|
||
|
np.stack([nonnoise_span_lengths, noise_span_lengths], axis=1), [num_noise_spans * 2]
|
||
|
)
|
||
|
span_starts = np.cumsum(interleaved_span_lengths)[:-1]
|
||
|
span_start_indicator = np.zeros((length,), dtype=np.int8)
|
||
|
span_start_indicator[span_starts] = True
|
||
|
span_num = np.cumsum(span_start_indicator)
|
||
|
is_noise = np.equal(span_num % 2, 1)
|
||
|
|
||
|
return is_noise[:orig_length]
|
||
|
|
||
|
|
||
|
def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray:
|
||
|
"""Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by
|
||
|
the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned."""
|
||
|
num_samples = len(samples_idx)
|
||
|
if drop_last:
|
||
|
samples_to_remove = num_samples % batch_size
|
||
|
if samples_to_remove != 0:
|
||
|
samples_idx = samples_idx[:-samples_to_remove]
|
||
|
sections_split = num_samples // batch_size
|
||
|
samples_idx = samples_idx.reshape((sections_split, batch_size))
|
||
|
else:
|
||
|
sections_split = math.ceil(num_samples / batch_size)
|
||
|
samples_idx = np.array_split(samples_idx, sections_split)
|
||
|
return samples_idx
|
||
|
|
||
|
|
||
|
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)
|
||
|
|
||
|
|
||
|
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_t5_mlm", 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."
|
||
|
)
|
||
|
|
||
|
# Setup logging
|
||
|
logging.basicConfig(
|
||
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||
|
level=logging.INFO,
|
||
|
datefmt="[%X]",
|
||
|
)
|
||
|
|
||
|
# Log on each process the small summary:
|
||
|
logger = logging.getLogger(__name__)
|
||
|
|
||
|
# Set the verbosity to info of the Transformers logger (on main process only):
|
||
|
logger.info(f"Training/evaluation parameters {training_args}")
|
||
|
|
||
|
# Set seed before initializing model.
|
||
|
set_seed(training_args.seed)
|
||
|
|
||
|
# Handle the repository creation
|
||
|
if training_args.push_to_hub:
|
||
|
if training_args.hub_model_id is None:
|
||
|
repo_name = get_full_repo_name(
|
||
|
Path(training_args.output_dir).absolute().name, token=training_args.hub_token
|
||
|
)
|
||
|
else:
|
||
|
repo_name = training_args.hub_model_id
|
||
|
repo = Repository(training_args.output_dir, clone_from=repo_name)
|
||
|
|
||
|
# 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).
|
||
|
if data_args.dataset_name is not None:
|
||
|
# Downloading and loading a dataset from the hub.
|
||
|
datasets = load_dataset(
|
||
|
data_args.dataset_name,
|
||
|
data_args.dataset_config_name,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
|
||
|
if "validation" not in datasets.keys():
|
||
|
datasets["validation"] = load_dataset(
|
||
|
data_args.dataset_name,
|
||
|
data_args.dataset_config_name,
|
||
|
split=f"train[:{data_args.validation_split_percentage}%]",
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
datasets["train"] = load_dataset(
|
||
|
data_args.dataset_name,
|
||
|
data_args.dataset_config_name,
|
||
|
split=f"train[{data_args.validation_split_percentage}%:]",
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
else:
|
||
|
data_files = {}
|
||
|
if data_args.train_file is not None:
|
||
|
data_files["train"] = data_args.train_file
|
||
|
if data_args.validation_file is not None:
|
||
|
data_files["validation"] = data_args.validation_file
|
||
|
extension = data_args.train_file.split(".")[-1]
|
||
|
if extension == "txt":
|
||
|
extension = "text"
|
||
|
datasets = load_dataset(
|
||
|
extension,
|
||
|
data_files=data_files,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
|
||
|
if "validation" not in datasets.keys():
|
||
|
datasets["validation"] = load_dataset(
|
||
|
extension,
|
||
|
data_files=data_files,
|
||
|
split=f"train[:{data_args.validation_split_percentage}%]",
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
datasets["train"] = load_dataset(
|
||
|
extension,
|
||
|
data_files=data_files,
|
||
|
split=f"train[{data_args.validation_split_percentage}%:]",
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
# 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.html.
|
||
|
|
||
|
# Load pretrained model and tokenizer
|
||
|
|
||
|
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,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
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,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
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.config_name:
|
||
|
config = T5Config.from_pretrained(
|
||
|
model_args.config_name,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
vocab_size=len(tokenizer),
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
elif model_args.model_name_or_path:
|
||
|
config = T5Config.from_pretrained(
|
||
|
model_args.model_name_or_path,
|
||
|
cache_dir=model_args.cache_dir,
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
else:
|
||
|
config = CONFIG_MAPPING[model_args.model_type]()
|
||
|
logger.warning("You are instantiating a new config instance from scratch.")
|
||
|
|
||
|
# Preprocessing the datasets.
|
||
|
# First we tokenize all the texts.
|
||
|
if training_args.do_train:
|
||
|
column_names = datasets["train"].column_names
|
||
|
else:
|
||
|
column_names = datasets["validation"].column_names
|
||
|
text_column_name = "text" if "text" in column_names else column_names[0]
|
||
|
|
||
|
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
|
||
|
|
||
|
# Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
|
||
|
# Since we make sure that all sequences are of the same length, no attention_mask is needed.
|
||
|
def tokenize_function(examples):
|
||
|
return tokenizer(examples[text_column_name], return_attention_mask=False)
|
||
|
|
||
|
tokenized_datasets = datasets.map(
|
||
|
tokenize_function,
|
||
|
batched=True,
|
||
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
remove_columns=column_names,
|
||
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
)
|
||
|
|
||
|
# T5-like span masked language modeling will fuse consecutively masked tokens to a single sentinel token.
|
||
|
# To ensure that the input length is `max_seq_length`, we need to increase the maximum length
|
||
|
# according to `mlm_probability` and `mean_noise_span_length`. We can also define the label length accordingly.
|
||
|
expanded_inputs_length, targets_length = compute_input_and_target_lengths(
|
||
|
inputs_length=max_seq_length,
|
||
|
noise_density=data_args.mlm_probability,
|
||
|
mean_noise_span_length=data_args.mean_noise_span_length,
|
||
|
)
|
||
|
|
||
|
# Main data processing function that will concatenate all texts from our dataset and generate chunks of expanded_inputs_length.
|
||
|
def group_texts(examples):
|
||
|
# Concatenate all texts.
|
||
|
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
|
||
|
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
||
|
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
||
|
# customize this part to your needs.
|
||
|
if total_length >= expanded_inputs_length:
|
||
|
total_length = (total_length // expanded_inputs_length) * expanded_inputs_length
|
||
|
# Split by chunks of max_len.
|
||
|
result = {
|
||
|
k: [t[i : i + expanded_inputs_length] for i in range(0, total_length, expanded_inputs_length)]
|
||
|
for k, t in concatenated_examples.items()
|
||
|
}
|
||
|
return result
|
||
|
|
||
|
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
|
||
|
# remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
|
||
|
# might be slower to preprocess.
|
||
|
#
|
||
|
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
||
|
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
||
|
tokenized_datasets = tokenized_datasets.map(
|
||
|
group_texts,
|
||
|
batched=True,
|
||
|
num_proc=data_args.preprocessing_num_workers,
|
||
|
load_from_cache_file=not data_args.overwrite_cache,
|
||
|
)
|
||
|
|
||
|
# 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)
|
||
|
dropout_rngs = jax.random.split(rng, jax.local_device_count())
|
||
|
|
||
|
if model_args.model_name_or_path:
|
||
|
model = FlaxT5ForConditionalGeneration.from_pretrained(
|
||
|
model_args.model_name_or_path,
|
||
|
config=config,
|
||
|
seed=training_args.seed,
|
||
|
dtype=getattr(jnp, model_args.dtype),
|
||
|
use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
else:
|
||
|
config.vocab_size = len(tokenizer)
|
||
|
model = FlaxT5ForConditionalGeneration(
|
||
|
config,
|
||
|
seed=training_args.seed,
|
||
|
dtype=getattr(jnp, model_args.dtype),
|
||
|
#use_auth_token=True if model_args.use_auth_token else None,
|
||
|
)
|
||
|
|
||
|
# Data collator
|
||
|
# This one will take care of randomly masking the tokens.
|
||
|
data_collator = FlaxDataCollatorForT5MLM(
|
||
|
tokenizer=tokenizer,
|
||
|
noise_density=data_args.mlm_probability,
|
||
|
mean_noise_span_length=data_args.mean_noise_span_length,
|
||
|
input_length=max_seq_length,
|
||
|
target_length=targets_length,
|
||
|
pad_token_id=model.config.pad_token_id,
|
||
|
decoder_start_token_id=model.config.decoder_start_token_id,
|
||
|
)
|
||
|
|
||
|
# 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()
|
||
|
|
||
|
num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs
|
||
|
|
||
|
num_of_hosts = jax.process_count()
|
||
|
current_host_idx = jax.process_index()
|
||
|
|
||
|
# Create learning rate schedule
|
||
|
warmup_fn = optax.linear_schedule(
|
||
|
init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps
|
||
|
)
|
||
|
decay_fn = optax.linear_schedule(
|
||
|
init_value=training_args.learning_rate,
|
||
|
end_value=0,
|
||
|
transition_steps=num_train_steps - training_args.warmup_steps,
|
||
|
)
|
||
|
linear_decay_lr_schedule_fn = optax.join_schedules(
|
||
|
schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps]
|
||
|
)
|
||
|
|
||
|
# 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 = set(
|
||
|
[
|
||
|
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
|
||
|
if training_args.adafactor:
|
||
|
# We use the default parameters here to initialize adafactor,
|
||
|
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
|
||
|
optimizer = optax.adafactor(
|
||
|
learning_rate=linear_decay_lr_schedule_fn,
|
||
|
)
|
||
|
else:
|
||
|
optimizer = optax.adamw(
|
||
|
learning_rate=linear_decay_lr_schedule_fn,
|
||
|
b1=training_args.adam_beta1,
|
||
|
b2=training_args.adam_beta2,
|
||
|
weight_decay=training_args.weight_decay,
|
||
|
mask=decay_mask_fn,
|
||
|
)
|
||
|
|
||
|
# Setup train state
|
||
|
state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer)
|
||
|
|
||
|
# Define gradient update step fn
|
||
|
def train_step(state, batch, dropout_rng):
|
||
|
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng)
|
||
|
|
||
|
def loss_fn(params):
|
||
|
labels = batch.pop("labels")
|
||
|
|
||
|
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
||
|
|
||
|
# compute loss
|
||
|
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])).mean()
|
||
|
|
||
|
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": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
|
||
|
)
|
||
|
|
||
|
return new_state, metrics, new_dropout_rng
|
||
|
|
||
|
# Create parallel version of the train step
|
||
|
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
|
||
|
|
||
|
# Define eval fn
|
||
|
def eval_step(params, batch):
|
||
|
labels = batch.pop("labels")
|
||
|
|
||
|
logits = model(**batch, params=params, train=False)[0]
|
||
|
|
||
|
# compute loss
|
||
|
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))
|
||
|
|
||
|
# compute accuracy
|
||
|
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels)
|
||
|
|
||
|
# summarize metrics
|
||
|
metrics = {"loss": loss.mean(), "accuracy": accuracy.mean()}
|
||
|
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
||
|
|
||
|
return metrics
|
||
|
|
||
|
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,))
|
||
|
|
||
|
# Replicate the train state on each device
|
||
|
state = jax_utils.replicate(state)
|
||
|
|
||
|
train_time = 0
|
||
|
epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0)
|
||
|
for epoch in epochs:
|
||
|
# ======================== Training ================================
|
||
|
train_start = time.time()
|
||
|
train_metrics = []
|
||
|
|
||
|
# Create sampling rng
|
||
|
rng, input_rng = jax.random.split(rng)
|
||
|
|
||
|
# Generate an epoch by shuffling sampling indices from the train dataset
|
||
|
num_train_samples = len(tokenized_datasets["train"])
|
||
|
# Avoid using jax.numpy here in case of TPU training
|
||
|
train_samples_idx = np.random.permutation(np.arange(num_train_samples))
|
||
|
train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size)
|
||
|
|
||
|
# Gather the indexes for creating the batch and do a training step
|
||
|
for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)):
|
||
|
samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx]
|
||
|
model_inputs = data_collator(samples)
|
||
|
|
||
|
local_host_model_inputs = {
|
||
|
key: np.split(model_inputs.data[key], num_of_hosts, axis=0)[current_host_idx]
|
||
|
for key, value in model_inputs.data.items()
|
||
|
}
|
||
|
|
||
|
# Model forward
|
||
|
model_inputs = shard(local_host_model_inputs)
|
||
|
state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
|
||
|
train_metrics.append(train_metric)
|
||
|
|
||
|
cur_step = epoch * (num_train_samples // train_batch_size) + step
|
||
|
|
||
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
|
||
|
# Save metrics
|
||
|
train_metric = jax_utils.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} | Loss: {train_metric['loss'].mean()}, Learning Rate:"
|
||
|
f" {train_metric['learning_rate'].mean()})"
|
||
|
)
|
||
|
|
||
|
train_metrics = []
|
||
|
|
||
|
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
|
||
|
# ======================== Evaluating ==============================
|
||
|
num_eval_samples = len(tokenized_datasets["validation"])
|
||
|
# Avoid using jax.numpy here in case of TPU training
|
||
|
eval_samples_idx = np.arange(num_eval_samples)
|
||
|
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
|
||
|
|
||
|
eval_metrics = []
|
||
|
for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
|
||
|
samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
|
||
|
model_inputs = data_collator(samples)
|
||
|
|
||
|
# Model forward
|
||
|
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
|
||
|
state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
|
||
|
)
|
||
|
eval_metrics.append(metrics)
|
||
|
|
||
|
# get eval metrics
|
||
|
eval_metrics = get_metrics(eval_metrics)
|
||
|
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
||
|
|
||
|
# Update progress bar
|
||
|
epochs.write(f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})")
|
||
|
|
||
|
# Save 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:
|
||
|
# save checkpoint after each epoch and push checkpoint to the hub
|
||
|
if jax.process_index() == 0:
|
||
|
params = jax.device_get(jax.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:
|
||
|
repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)
|
||
|
|
||
|
# Eval after training
|
||
|
if training_args.do_eval:
|
||
|
num_eval_samples = len(tokenized_datasets["validation"])
|
||
|
# Avoid using jax.numpy here in case of TPU training
|
||
|
eval_samples_idx = np.arange(num_eval_samples)
|
||
|
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
|
||
|
|
||
|
eval_metrics = []
|
||
|
for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
|
||
|
samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx]
|
||
|
model_inputs = data_collator(samples)
|
||
|
|
||
|
# Model forward
|
||
|
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
|
||
|
state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size
|
||
|
)
|
||
|
eval_metrics.append(metrics)
|
||
|
|
||
|
# get eval metrics
|
||
|
eval_metrics = get_metrics(eval_metrics)
|
||
|
eval_metrics = jax.tree_map(lambda metric: jnp.mean(metric).item(), eval_metrics)
|
||
|
|
||
|
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()
|