mirror of
https://github.com/huggingface/transformers.git
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571 lines
22 KiB
Python
571 lines
22 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Pre-training/Fine-tuning ViT for image classification .
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=vit
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"""
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import logging
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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 pathlib import Path
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from typing import Callable, Optional
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# for dataset and preprocessing
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import torch
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import torchvision
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import torchvision.transforms as transforms
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from tqdm import tqdm
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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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import transformers
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from flax import jax_utils
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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 Repository
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from transformers import (
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CONFIG_MAPPING,
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FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
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AutoConfig,
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FlaxAutoModelForImageClassification,
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HfArgumentParser,
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is_tensorboard_available,
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set_seed,
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)
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from transformers.utils import get_full_repo_name, send_example_telemetry
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_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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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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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 `huggingface-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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train_dir: str = field(
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metadata={"help": "Path to the root training directory which contains one subdirectory per class."}
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)
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validation_dir: str = field(
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metadata={"help": "Path to the root validation directory which contains one subdirectory per class."},
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)
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image_size: Optional[int] = field(default=224, metadata={"help": " The size (resolution) of each image."})
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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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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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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 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
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) -> Callable[[int], jnp.array]:
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"""Returns a linear warmup, linear_decay learning rate function."""
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steps_per_epoch = train_ds_size // train_batch_size
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num_train_steps = steps_per_epoch * num_train_epochs
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warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
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decay_fn = optax.linear_schedule(
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init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
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)
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schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
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return schedule_fn
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_image_classification", model_args, data_args, framework="flax")
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if (
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os.path.exists(training_args.output_dir)
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and os.listdir(training_args.output_dir)
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and training_args.do_train
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and not training_args.overwrite_output_dir
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):
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty."
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"Use --overwrite_output_dir to overcome."
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)
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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# Setup logging, we only want one process per machine to log things on the screen.
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logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
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if jax.process_index() == 0:
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transformers.utils.logging.set_verbosity_info()
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else:
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transformers.utils.logging.set_verbosity_error()
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# Set the verbosity to info of the Transformers logger (on main process only):
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logger.info(f"Training/evaluation parameters {training_args}")
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# set seed for random transforms and torch dataloaders
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set_seed(training_args.seed)
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# Handle the repository creation
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if training_args.push_to_hub:
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if training_args.hub_model_id is None:
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repo_name = get_full_repo_name(
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Path(training_args.output_dir).absolute().name, token=training_args.hub_token
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)
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else:
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repo_name = training_args.hub_model_id
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repo = Repository(training_args.output_dir, clone_from=repo_name)
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# Initialize datasets and pre-processing transforms
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# We use torchvision here for faster pre-processing
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# Note that here we are using some default pre-processing, for maximum accuray
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# one should tune this part and carefully select what transformations to use.
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normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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train_dataset = torchvision.datasets.ImageFolder(
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data_args.train_dir,
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transforms.Compose(
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[
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transforms.RandomResizedCrop(data_args.image_size),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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normalize,
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]
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),
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)
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eval_dataset = torchvision.datasets.ImageFolder(
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data_args.validation_dir,
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transforms.Compose(
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[
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transforms.Resize(data_args.image_size),
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transforms.CenterCrop(data_args.image_size),
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transforms.ToTensor(),
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normalize,
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]
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),
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)
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# Load pretrained model and tokenizer
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if model_args.config_name:
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config = AutoConfig.from_pretrained(
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model_args.config_name,
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num_labels=len(train_dataset.classes),
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image_size=data_args.image_size,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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num_labels=len(train_dataset.classes),
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image_size=data_args.image_size,
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cache_dir=model_args.cache_dir,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.model_name_or_path:
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model = FlaxAutoModelForImageClassification.from_pretrained(
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model_args.model_name_or_path,
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config=config,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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use_auth_token=True if model_args.use_auth_token else None,
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)
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else:
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model = FlaxAutoModelForImageClassification.from_config(
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config,
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seed=training_args.seed,
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dtype=getattr(jnp, model_args.dtype),
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)
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# Store some constant
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num_epochs = int(training_args.num_train_epochs)
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train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
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per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
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eval_batch_size = per_device_eval_batch_size * jax.device_count()
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steps_per_epoch = len(train_dataset) // train_batch_size
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total_train_steps = steps_per_epoch * num_epochs
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def collate_fn(examples):
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pixel_values = torch.stack([example[0] for example in examples])
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labels = torch.tensor([example[1] for example in examples])
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batch = {"pixel_values": pixel_values, "labels": labels}
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batch = {k: v.numpy() for k, v in batch.items()}
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return batch
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# Create data loaders
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train_loader = torch.utils.data.DataLoader(
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train_dataset,
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batch_size=train_batch_size,
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shuffle=True,
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num_workers=data_args.preprocessing_num_workers,
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persistent_workers=True,
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drop_last=True,
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collate_fn=collate_fn,
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)
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eval_loader = torch.utils.data.DataLoader(
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eval_dataset,
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batch_size=eval_batch_size,
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shuffle=False,
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num_workers=data_args.preprocessing_num_workers,
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persistent_workers=True,
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drop_last=False,
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collate_fn=collate_fn,
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)
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# Enable tensorboard only on the master node
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has_tensorboard = is_tensorboard_available()
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if has_tensorboard and jax.process_index() == 0:
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try:
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from flax.metrics.tensorboard import SummaryWriter
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summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
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except ImportError as ie:
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has_tensorboard = False
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logger.warning(
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f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
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)
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else:
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logger.warning(
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"Unable to display metrics through TensorBoard because the package is not installed: "
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"Please run pip install tensorboard to enable."
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)
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# Initialize our training
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rng = jax.random.PRNGKey(training_args.seed)
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rng, dropout_rng = jax.random.split(rng)
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# Create learning rate schedule
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linear_decay_lr_schedule_fn = create_learning_rate_fn(
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len(train_dataset),
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train_batch_size,
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training_args.num_train_epochs,
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training_args.warmup_steps,
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training_args.learning_rate,
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)
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# create adam optimizer
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adamw = optax.adamw(
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learning_rate=linear_decay_lr_schedule_fn,
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b1=training_args.adam_beta1,
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b2=training_args.adam_beta2,
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eps=training_args.adam_epsilon,
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weight_decay=training_args.weight_decay,
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)
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# Setup train state
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state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
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def loss_fn(logits, labels):
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loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))
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return loss.mean()
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# Define gradient update step fn
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def train_step(state, batch):
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dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
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def compute_loss(params):
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labels = batch.pop("labels")
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logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
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loss = loss_fn(logits, labels)
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return loss
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grad_fn = jax.value_and_grad(compute_loss)
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loss, grad = grad_fn(state.params)
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grad = jax.lax.pmean(grad, "batch")
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new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
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|
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metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
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metrics = jax.lax.pmean(metrics, axis_name="batch")
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|
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return new_state, metrics
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|
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# Define eval fn
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def eval_step(params, batch):
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labels = batch.pop("labels")
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logits = model(**batch, params=params, train=False)[0]
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loss = loss_fn(logits, labels)
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|
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# summarize metrics
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accuracy = (jnp.argmax(logits, axis=-1) == labels).mean()
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metrics = {"loss": loss, "accuracy": accuracy}
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metrics = jax.lax.pmean(metrics, axis_name="batch")
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return metrics
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|
|
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# Create parallel version of the train and eval step
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p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
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p_eval_step = jax.pmap(eval_step, "batch")
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|
|
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# Replicate the train state on each device
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|
state = state.replicate()
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|
|
|
logger.info("***** Running training *****")
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|
logger.info(f" Num examples = {len(train_dataset)}")
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|
logger.info(f" Num Epochs = {num_epochs}")
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logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
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logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
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|
logger.info(f" Total optimization steps = {total_train_steps}")
|
|
|
|
train_time = 0
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|
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 = []
|
|
|
|
steps_per_epoch = len(train_dataset) // train_batch_size
|
|
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
|
|
# train
|
|
for batch in train_loader:
|
|
batch = shard(batch)
|
|
state, train_metric = p_train_step(state, batch)
|
|
train_metrics.append(train_metric)
|
|
|
|
train_step_progress_bar.update(1)
|
|
|
|
train_time += time.time() - train_start
|
|
|
|
train_metric = unreplicate(train_metric)
|
|
|
|
train_step_progress_bar.close()
|
|
epochs.write(
|
|
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate:"
|
|
f" {train_metric['learning_rate']})"
|
|
)
|
|
|
|
# ======================== Evaluating ==============================
|
|
eval_metrics = []
|
|
eval_steps = len(eval_dataset) // eval_batch_size
|
|
eval_step_progress_bar = tqdm(total=eval_steps, desc="Evaluating...", position=2, leave=False)
|
|
for batch in eval_loader:
|
|
# Model forward
|
|
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)
|
|
|
|
eval_step_progress_bar.update(1)
|
|
|
|
# normalize eval metrics
|
|
eval_metrics = get_metrics(eval_metrics)
|
|
eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
|
|
|
|
# Print metrics and update progress bar
|
|
eval_step_progress_bar.close()
|
|
desc = (
|
|
f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {round(eval_metrics['loss'].item(), 4)} | "
|
|
f"Eval Accuracy: {round(eval_metrics['accuracy'].item(), 4)})"
|
|
)
|
|
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)
|
|
if training_args.push_to_hub:
|
|
repo.push_to_hub(commit_message=f"Saving weights and logs of epoch {epoch}", blocking=False)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|