logging.warning("Using LoRA") ifmodel.is_gradient_checkpointingortraining_args.gradient_checkpointing: # https://github.com/huggingface/peft/issues/137 model.enable_input_require_grads() model=get_peft_model( model, LoraConfig( Expand Down
["GLM6BBlock"]def__init__(self,*inputs,**kwargs):super().__init__(*inputs,**kwargs)def_init_weights(self,module:nn.Module):"""Initialize the weights."""return# add thisdef_set_gradient_checkpointing(self,module,value=False):ifisinstance(module,ChatGLMForConditionalGeneration):module....
or int4 parameters to fp32for param in model.parameters(): if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16): param.data = param.data.to(torch.float32)if use_gradient_checkpointing: # For backward compatibility model.enable_input_require_grads()在最新的pe...
Currently, this mode gives a warning that gradients areNoneon the inputs (i.e. model doesn't learn): /fsx/lewis/miniconda/envs/trl/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn("No...
() model.enable_input_require_grads() class CastOutputToFloat(nn.Sequential): def forward(self, x): return super().forward(x).to(torch.float32) model.lm_head = CastOutputToFloat(model.lm_head) config = LoraConfig( r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"], lora...
// before working with the grads in any capacity. const auto opt_parent_stream = (*func).stream(c10::DeviceType::CUDA); auto opt_parent_stream = (*func).stream(c10::DeviceType::CUDA); if (!opt_parent_stream.has_value()) { opt_parent_stream = (*func).stream(c10::DeviceType::Pr...