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models_rlhf.py
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models_rlhf.py
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"""
classes for ChatGLM RLHF
Critic model
Action model is ChatGLM, 所以可省略
Reward model
"""
import torch
import sys, os
# sys.path.append("/home/faith/chatglm2-6b")
now_dir = os.path.dirname(os.path.abspath(__file__))
project_dir = os.path.dirname(now_dir)
sys.path.append(project_dir)
from chatglm2_6b.modeling_chatglm import ChatGLMModel
# from chatglm_local.modeling_chatglm import ChatGLMModel
from torch import nn
from transformers import BertTokenizer, BertModel
import numpy as np
from functools import partial
from copy import deepcopy
"""
critic 的词表最好和action模型的词表一样这样便于对每个生成的token进行打分，
不一致的词表会导致打分不对齐，所以选择用一样的模型但是加一下打分的输出
为了减小打分模型的大小，可以把原来的模型的layers缩减层数。
这样直接继承了，原来的token embedding
"""
class Critic(nn.Module):
def __init__(self, device="cpu_float", m=None) -> None:
super().__init__()
if m is None:
model = ChatGLMModel.from_pretrained("/home/faith/chatglm2-6b", trust_remote_code=True)
else:
# model = m
model = deepcopy(m)
# 检查参数是否相等
# for p1, p2 in zip(model.parameters(), m.parameters()):
# print(torch.equal(p1, p2))
# model = ChatGLMModel()
# model.load_state_dict(m.state_dic())
layers_keep = len(model.encoder.layers)//9
layers_keep = 1
model.encoder.num_layers = layers_keep
model.encoder.layers = model.encoder.layers[:layers_keep]
# solve RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
if "cuda" in device:
model = model.half().cuda(device) # half for gpu only
elif "cpu" == device:
model = model.bfloat16()
else:
model = model.float()
self.model = model
self.output_linear = nn.Linear(self.model.hidden_size, 1, device=self.model.device, dtype=self.model.dtype)
self.dtype = self.model.dtype
self.device = self.model.device
def forward(self, **kwargs):
output = self.model(**kwargs)
values = torch.tanh(self.output_linear(output.last_hidden_state))
return values.transpose(0, 1).squeeze(-1)
"""
一样的原因，不需要再把生成的token ids转成文字在再转到目标ids，
所以也用chatglm直接做基模型，
只是这里只取最后的token算出对整句生成的奖励分数，具体取哪个位置可以
后续在代码里面指定，比如用torch.gather
"""
Reward = Critic
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def jaccard(s1, s2):
"""
可能有字符串重合但是语义不一致问题，
TODO 可以用多阶的jaccard来解决
"""
assert len(s1)+len(s2)>0
s1 = set(s1)
s2 = set(s2)
s_or = s1 | s2
s_and = s1 & s2
jaccard_distance = len(s_and)/len(s_or)
return jaccard_distance
# 基于和good_answers和bad_answers比较的Reward模型，具有通用性和易学习，基本上都是基于Bert余弦值
class RewardBySimilarity(nn.Module):
def __init__(self, device="cpu") -> None:
super().__init__()
# Load model from HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained('/data/text2vec-base-chinese')
model = BertModel.from_pretrained('/data/text2vec-base-chinese')
model.eval()
self.model = model.to(device)
self.tokenizer = tokenizer
self.device = device
def forward(self, gen_texts=["中大好吗"],
good_answers=['好吗好吗好吗好吗好吗', "真的好"],
bad_answers=['表要', '这吗'],
weight_for_cos_and_jaccard = [0.5, 0.5]):
examples = good_answers + bad_answers
example_num = len(examples)
assert len(gen_texts)>0 and example_num>0
reward_direction = torch.ones(example_num, device=self.model.device)
reward_direction[len(good_answers):] = -1
sentences = gen_texts + examples
# Tokenize sentences
encoded_input = self.tokenizer(sentences, padding=True, return_tensors='pt')
ids = self.tokenizer.batch_encode_plus(sentences, add_special_tokens=False)["input_ids"]
# temporary truncate position_ids
batch_size, max_seq_len = encoded_input["input_ids"].shape
if max_seq_len > self.model.config.max_position_embeddings:
encoded_input["position_ids"] = torch.arange(max_seq_len).expand((1, -1)).repeat(batch_size, 1)
encoded_input["position_ids"] = encoded_input["position_ids"]/max_seq_len*self.model.config.max_position_embeddings
encoded_input["position_ids"] = encoded_input["position_ids"].floor().long()
# Compute token embeddings
with torch.no_grad():
encoded_input = encoded_input.to(self.model.device)
model_output = self.model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
gen_text_vecs = sentence_embeddings[:len(gen_texts)]
answers_vecs = sentence_embeddings[len(gen_texts):]
reward_ = []
for i in range(gen_text_vecs.shape[0]):
gen_text_vecs_ = gen_text_vecs[i:i+1]
# 用一下广播计算cos
coses = torch.cosine_similarity(gen_text_vecs_, answers_vecs, dim=1)
# 余弦截断
coses[(coses<0)] = 0
# 计算 jaccard距离
jaccard_s1 = partial(jaccard, ids[i])
jaccards = torch.tensor(np.vectorize(jaccard_s1)(np.array(ids[-len(examples):], dtype=object)), dtype=coses.dtype, device=coses.device)
similarity = weight_for_cos_and_jaccard[0]*coses + weight_for_cos_and_jaccard[1]*jaccards
value, index = similarity.max(dim=-1)
print(value, index)
reward_.append(value*reward_direction[index])
reward = torch.stack(reward_)
return reward
def test_reward_by_similarity():
reward_model = RewardBySimilarity()
reward = reward_model()
print(reward)
def test_critic():
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("/home/faith/chatglm2-6b", trust_remote_code=True)
critic = Critic()
input_ids = torch.tensor(tokenizer.encode("你好"), dtype=torch.long).unsqueeze(0)
input_ids = input_ids.repeat(2,1)
output = critic(input_ids=input_ids)
print(output.shape)
def test_reward():
# with torch.no_grad():
# input_ids_RM = sequences.to(RM_device)
# rewards_ = reward_model(input_ids = input_ids_RM)
# # 由于只对最后的整句进行reward，所以只有最后一个action后的state有reward
# rewards = torch.zeros_like( sequences, dtype=rewards_.dtype, device=rewards_.device)
# pad_id = tokenizer.convert_tokens_to_ids("<pad>")
# masks = ( sequences!=pad_id).long().to(RM_device)
# final_position = masks.sum(dim=-1)-1
# index=final_position.unsqueeze(-1)
# reward = rewards_.gather(dim=1, index=index)
# rewards.scatter_(dim=1, index=index, src=reward)
pass
if __name__ == "__main__":
# test_reward_by_similarity()
test_critic()