问题

根据official documentation的说法，Trainer类“为大多数标准用例提供了PyTorch中功能完整训练的API”。
然而，当我尝试在实践中实际使用Trainer时，我得到了以下错误消息，似乎表明TensorFlow目前正在幕后使用。

tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

那么它是哪一个呢？HuggingFace transformers库使用PyTorch还是TensorFlow来实现Trainer的内部实现？是否可以切换到只使用PyTorch？我似乎无法在TrainingArguments中找到相关参数。

为什么我的脚本总是打印出TensorFlow相关的错误？Trainer不应该只使用PyTorch吗？

源码

from transformers import GPT2Tokenizer
from transformers import GPT2LMHeadModel
from transformers import TextDataset
from transformers import DataCollatorForLanguageModeling
from transformers import Trainer
from transformers import TrainingArguments

import torch

# Load the GPT-2 tokenizer and LM head model
tokenizer    = GPT2Tokenizer.from_pretrained('gpt2')
lmhead_model = GPT2LMHeadModel.from_pretrained('gpt2')

# Load the training dataset and divide blocksize
train_dataset = TextDataset(
    tokenizer=tokenizer,
    file_path='./datasets/tinyshakespeare.txt',
    block_size=64
)

# Create a data collator for preprocessing batches
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False
)

# Defining the training arguments
training_args = TrainingArguments(
    output_dir='./models/tinyshakespeare', # output directory for checkpoints
    overwrite_output_dir=True,             # overwrite any existing content

    per_device_train_batch_size=4,         # sample batch size for training
    dataloader_num_workers=1,              # number of workers for dataloader
    max_steps=100,                         # maximum number of training steps
    save_steps=50,                         # after # steps checkpoints are saved
    save_total_limit=5,                    # maximum number of checkpoints to save

    prediction_loss_only=True,             # only compute loss during prediction
    learning_rate=3e-4,                    # learning rate
    fp16=False,                            # use 16-bit (mixed) precision

    optim='adamw_torch',                   # define the optimizer for training
    lr_scheduler_type='linear',            # define the learning rate scheduler

    logging_steps=5,                       # after # steps logs are printed
    report_to='none',                      # report to wandb, tensorboard, etc.
)

if __name__ == '__main__':
    torch.multiprocessing.freeze_support()

    trainer = Trainer(
        model=lmhead_model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset,
    )

    trainer.train()