classLlamaPreTrainedModel(PreTrainedModel):config_class=LlamaConfigbase_model_prefix="model"supports_gradient_checkpointing=True_no_split_modules=["LlamaDecoderLayer"]_skip_keys_device_placement="past_key_values"_supports_flash_attn_2=Truedef_init_weights(self,module):std=self.config.initializer_rangei...
supports_gradient_checkpointing 表示该模型是否支持梯度检查点技术(Gradient Checkpointing)。Baichuan 开发的模型支持梯度检查点技术,因此将其设置为 True。_no_split_modules 列表表示需要忽略哪些模块进行分割,_keys_to_ignore_on_load_unexpected 列表表示在加载模型时应该忽略哪些键值。这些列表是为了兼容一些特定的 ...
classLlamaPreTrainedModel(PreTrainedModel):config_class = LlamaConfigbase_model_prefix ="model"supports_gradient_checkpointing =True_no_split_modules = ["LlamaDecoderLayer"]_skip_keys_device_placement ="past_key_values"_supports_flash_attn_2 =Truedef_init_weights(self, module):std = self.config....
base_model_prefix="model"supports_gradient_checkpointing=True _no_split_modules=["LlamaDecoderLayer"]_skip_keys_device_placement="past_key_values"_supports_flash_attn_2=True def_init_weights(self,module):std=self.config.initializer_rangeifisinstance(module,nn.Linear):module.weight.data.normal_(me...
supports_gradient_checkpointing = True _no_split_modules = ["LlamaDecoderLayer"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): ...
However, when we uncommentmodel.gradient_checkpointing_enable(), we get this error: (venv) username@server:~/project$ accelerate launch --use_fsdp -m train_multi The following values were not passed to`accelerate launch`and had defaults used instead:`--num_processes`wassetto a value of`2`...
🐛 Describe the bug Hello, when I am using DDP to train a model, I found that using multi-task loss and gradient checkpointing at the same time can lead to gradient synchronization failure between GPUs, which in turn causes the parameters...
supports_gradient_checkpointing = True _no_split_modules = ["DecoderLayer"] _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std...
from transformers import AutoModelForCausalLM, TraininArgumentsmodel = AutoModelForCausalLM.from_pretrained( model_id, use_cache=False, # False if gradient_checkpointing=True **default_args)model.gradient_checkpointing_enable()LoRA LoRA是微软团队开发的一种技术，用于加速大型语言模型的微调。他...
Enable Gradient checkpointBooleanyesUse gradient checkpointing. The is recommended to save memory. Learning rateFloat0.0002The initial learning rate for AdamW. Max stepsInteger-1If set to a positive number, the total number of training steps to perform. This overrides num_train_epochs. In case of...