| import random |
| import numpy as np |
| import torch |
| from transformers import StoppingCriteria, StoppingCriteriaList |
|
|
| def auto_configure_device_map(num_gpus): |
| |
| |
| |
| |
| |
| num_trans_layers = 32 |
| per_gpu_layers = 38 / num_gpus |
|
|
| device_map = { |
| 'vit': 0, |
| 'vision_proj': 0, |
| 'model.tok_embeddings': 0, |
| 'plora_glb_GN': 0, |
| 'plora_sub_GN': 0, |
| 'model.norm': num_gpus - 1, |
| 'output': num_gpus - 1, |
| } |
|
|
| used = 3 |
| gpu_target = 0 |
| for i in range(num_trans_layers): |
| if used >= per_gpu_layers: |
| gpu_target += 1 |
| used = 0 |
| assert gpu_target < num_gpus |
| device_map[f'model.layers.{i}'] = gpu_target |
| used += 1 |
|
|
| return device_map |
|
|
| class StoppingCriteriaSub(StoppingCriteria): |
|
|
| def __init__(self, stops=[], encounters=1): |
| super().__init__() |
| self.stops = stops |
|
|
| def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor): |
| for stop in self.stops: |
| if torch.all((stop == input_ids[:, -len(stop):])).item(): |
| return True |
| return False |
|
|
|
|
| def get_stopping_criteria(stop_words_ids): |
| stop_words_ids = [torch.tensor([i]).cuda() for i in stop_words_ids] |
| stopping_criteria = StoppingCriteriaList( |
| [StoppingCriteriaSub(stops=stop_words_ids)]) |
| return stopping_criteria |
|
|
|
|
| meta_instruction = """You are an AI assistant whose name is InternLM (书生·浦语). |
| - InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless. |
| - InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文. |
| """ |
|
|
|
|
| def set_random_seed(seed, set_cudnn=False): |
| """Set the random seed for reproducibility. |
| |
| Parameters: |
| seed (int): The seed to use for generating random numbers. |
| """ |
| torch.manual_seed(seed) |
| if torch.cuda.is_available(): |
| torch.cuda.manual_seed_all(seed) |
| np.random.seed(seed) |
| random.seed(seed) |
| if set_cudnn and torch.backends.cudnn.is_available(): |
| torch.backends.cudnn.deterministic = True |
| torch.backends.cudnn.benchmark = False |
