File size: 2,467 Bytes
53c02a9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import random
import numpy as np
import torch
from transformers import StoppingCriteria, StoppingCriteriaList
def auto_configure_device_map(num_gpus):
# visual_encoder 算4层
# internlm_model.model.embed_tokens 占用1层
# norm 和 lm_head 占用1层
# transformer.layers 占用 32 层
# 总共34层分配到num_gpus张卡上
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) # For multi-GPU.
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 |