model_path, device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time() # divide the prompt list onto the available GPUs with accelerator.sp...
device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time() # divide the prompt list onto the available GPUs with accelerator.split_between_p...
load_checkpoint_in_model(unwrapped_model, save_directory, device_map={"": device}) 也可以使用load_checkpoint_and_dispatch() 函数在空模型中加载完整检查点或分片检查点,它还会自动在您可用的设备(GPU、CPU RAM)上分配这些权重。完整的模型分片推理过程见此YouTube视频。 load_checkpoint_and_dispatch函数常...
device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) tokenizer.pad_token = tokenizer.eos_token# batch, left pad (for inference), and tokenizedefprepare_prompts(prompts, tokenizer, batch_size=16):batches=[prompts[i:i ...
device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path)# sync GPUs and start the timeraccelerator.wait_for_everyone() start=time.time()# divide the prompt list onto the available GPUswithaccelerator.split_between_processes...
device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time() # divide the prompt list onto the available GPUs ...
device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) # sync GPUs and start the timer accelerator.wait_for_everyone() start=time.time() # divide the prompt list onto the available GPUs ...
device_map={"": accelerator.process_index}, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained(model_path) tokenizer.pad_token = tokenizer.eos_token# batch, left pad (for inference), and tokenizedefprepare_prompts(prompts, tokenizer, batch_size=16): ...
accelerate.hooks.attach_align_device_hook_on_blocks(module: Module, execution_device: typing.Union[torch.device, typing.Dict[str, torch.device], NoneType] = None, offload: typing.Union[bool, typing.Dict[str, bool]] = False, weights_map: typing.Mapping = None, offload_buffers: bool = False...
Big Model Inference功能,或者说是device_map="auto": 这使得用户能够在多种不同硬件设备上进行大模型推理,同时现在可以通过诸如高效参数微调 (PEFT) 等技术以较小计算量来训练大模型。 这三方面的贡献,使得 Accelerate 成为了几乎所有 Hugging Face 代码库的基础依赖,其中包括transformers、diffusers、peft、trl。