To enable gradient checkpointing in theTrainerwe only need to pass it as a flag to theTrainingArguments. Everything else is handled under the hood: training_args=TrainingArguments(per_device_train_batch_size=1,gradient_accumulation_steps=4,gradient_checkpointing=True,**default_args)trainer=Trainer...
由于sentiment-analysis管道的默认检查点是distilbert-base-uncased-finetuned-sst-2-english(您可以在此处查看其模型卡),我们运行以下命令: fromtransformersimportAutoTokenizercheckpoint="distilbert-base-uncased-finetuned-sst-2-english"tokenizer=AutoTokenizer.from_pretrained(checkpoint)raw_inputs=["I've been wa...
training_args=TrainingArguments(per_device_train_batch_size=1,gradient_accumulation_steps=4,gradient_checkpointing=True,**default_args)trainer=Trainer(model=model,args=training_args,train_dataset=ds)result=trainer.train()print_summary(result) 输出结果: GPU Memory进一步降低 (4169MB --> 3706MB), 吞...
collator = DataCollatorForCompletionOnlyLM(response_template_ids, tokenizer=tokenizer, padding_free=True) trainer = SFTTrainer( model, train_dataset=dataset, args=SFTConfig( output_dir="./tmp", gradient_checkpointing=True, per_device_train_batch_size=8 ), formatting_func=formatting_prompts_func, ...
你还可以使用gradient_checkpointing来减少激活值所需的内存。这一技术在计算梯度时,会重新计算一遍前向过程,而不是在前向过程中保存用于计算梯度的中间结果。需要使用时,设置gradient_checkpointing=True即可。 完整训练代码 一切就绪,我们可以开始训练了。下面是我们的完整训练代码。除了上面提到的部分外,我们还设置了da...
注意,当我们设置了显存重计算的功能,则eval steps之类的参数自动进行相应的调整,比如我们设置这个参数前,256的batch,我们希望10个batch评估一次,即10个steps进行一次eval,当时改为batch size=32并且 gradient_accumulation_steps=8,则默认trainer会 8*10=80个steps 进行一次eval。
We currently have a few issues like #831 and #480 where gradient checkpointing + DDP does not work with the RewardTrainer. Let's use this issue to collect the various training modes we'd like to support and track the status of their fixe...
{ "_name_or_path": "bert-base-cased", "architectures": [ "BertModel" ], "attention_probs_dropout_prob": 0.1, "gradient_checkpointing": false, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_nor...
For reference, here is how I set up LoRA for LongT5 before calling trainer.train(): model=AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-local-base")# for gradient checkpointingmodel.config.use_cache=Falsemodel.enable_input_require_grads()lora_config=LoraConfig(task_type=TaskType.SEQ...
( per_device_train_batch_size=args.per_device_train_batch_size, per_device_eval_batch_size=args.per_device_train_batch_size, gradient_checkpointing=True, warmup_steps=args.warmup_steps, learning_rate=float(args.learn_rate), bf16=True, logging_steps=1, optim="adamw_torch", evaluation_...