tokenizer = AutoTokenizer.from_pretrained('./qwen/Qwen1.5-7B-Chat/', use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained('./qwen/Qwen1.5-7B-Chat/', device_map="auto",torch_dtype=torch.bfloat16) model.enable_input_require_grads() # 开启梯度检查点时,要...
model.enable_input_require_grads() # Loading Dataset random_state = np.random.RandomState(args.dataset_seed) train_dataset, val_dataset = get_dataset( Expand All @@ -107,6 +116,9 @@ def llm_dpo(args: DPOArguments) -> str: logger.info(f'val_dataset_sample: {val_dataset_sample}') ...
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...
make_inputs_require_grad) if hasattr(self.visual_encoder, 'enable_input_require_grads'): self.visual_encoder.enable_input_require_grads() else: self.visual_encoder.get_input_embeddings( ).register_forward_hook(make_inputs_require_grad) self.projector.enable_input_require_grads() ...
保存keras模型错误："RuntimeError:不匹配的ReplicaContext."，"ValueError:跟踪SavedModel梯度时的错误“。
enable_high_availability and hasattr(optimizer, "set_current_step"): optimizer.set_current_step(argument.iteration) return model, optimizer, opt_param_scheduler return wrapper def build_train_args(*input_args): args, timers, train_valid_test_dataset_provider, model_provider, model_type, ...
Taken together, these factors make it clear that weather/climate models require faster data throughput in all layers of a computer system. NICAM has been tactically designed to achieve efficient throughput at the 1) memory layer, 2) network layer, and 3) file input/output (I/O) layer. ...
- `gradient_checkpointing`:梯度检查,这个一旦开启,模型就必须执行`model.enable_input_require_grads()`,这个原理大家可以自行探索,这里就不细说了。 ```python args = TrainingArguments( output_dir="./output/BlueLM", per_device_train_batch_size=8, gradient_accumulation_steps=2, logging_steps=10, nu...
(model, "enable_input_require_grads"): model.enable_input_require_grads() elif hasattr(model, "get_input_embeddings"): def make_inputs_require_grad(module, input, output): output.requires_grad_(True) model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) return model...
if hasattr(self.visual_encoder, 'enable_input_require_grads'): self.visual_encoder.enable_input_require_grads() else: self.visual_encoder.get_input_embeddings( ).register_forward_hook(make_inputs_require_grad) self.projector.enable_input_require_grads() ...