File size: 5,540 Bytes
20cc109 04b6315 20cc109 9a93a48 20cc109 04b6315 20cc109 04b6315 20cc109 eda9db4 20cc109 eda9db4 20cc109 b7dea2d eda9db4 |
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
#import gradio as gr
#from transformers import pipeline
#sentiment = pipeline("sentiment-analysis")
#def get_sentiment(input_text):
# return sentiment(input_text)
#iface = gr.Interface(fn = get_sentiment,
# inputs = "text",
# outputs = ["text"],
# title = "Sentiment Analysis",
# description = "Ciao!!!")
#
#iface.launch(inline = False)
import gradio as gr
from typing import *
import torch
import transformers
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
device_map="cpu",
)
def evaluate(question):
prompt = f"The conversation between human and AI assistant.\n[|Human|] {question}.\n[|AI|] "
inputs = tokenizer(question, return_tensors="pt")
input_ids = inputs["input_ids"].cuda()
generation_output = model.generate(
input_ids=input_ids,
generation_config=GenerationConfig(
temperature=1,
top_p=0.95,
num_beams=4,
max_context_length_tokens=2048,
),
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=512
)
output = tokenizer.decode(generation_output.sequences[0]).split("[|AI|]")[1]
return output
def generate_prompt_with_history(text:str, history: str, tokenizer, max_length=2048):
history = ["\n[|Human|]{}\n[|AI|]{}".format(x[0],x[1]) for x in history]
history.append("\n[|Human|]{}\n[|AI|]".format(text))
history_text = ""
for x in history[::-1]:
if tokenizer(history_text + x, return_tensors="pt")['input_ids'].size(-1) <= max_length:
history_text = x + history_text
flag = True
if flag:
return history_text, tokenizer(history_text, return_tensors="pt")
else:
return False
def is_stop_word_or_prefix(s: str, stop_words: list) -> bool:
for stop_word in stop_words:
if s.endswith(stop_word):
return True
for i in range(1, len(stop_word)):
if s.endswith(stop_word[:i]):
return True
return False
def greedy_search(input_ids: torch.Tensor,
model: torch.nn.Module,
tokenizer: transformers.PreTrainedTokenizer,
stop_words: list,
max_length: int,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = 25) -> Iterator[str]:
generated_tokens = []
past_key_values = None
current_length = 1
for i in range(max_length):
with torch.no_grad():
if past_key_values is None:
outputs = model(input_ids)
else:
outputs = model(input_ids[:, -1:], past_key_values=past_key_values)
logits = outputs.logits[:, -1, :]
past_key_values = outputs.past_key_values
logits /= temperature
probs = torch.softmax(logits, dim=-1)
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > top_p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
input_ids = torch.cat((input_ids, next_token), dim=-1)
generated_tokens.append(next_token[0].item())
text = tokenizer.decode(generated_tokens)
yield text
if any([x in text for x in stop_words]):
return
@torch.no_grad()
def predict(text:str,
chatbot,
history:str = "",
top_p:float = 0.95,
temperature:float = 1.0,
max_length_tokens:int = 512,
max_context_length_tokens:int = 2048):
if text=="":
return ""
inputs = generate_prompt_with_history(text, history, tokenizer, max_length=max_context_length_tokens)
prompt,inputs=inputs
begin_length = len(prompt)
input_ids = inputs["input_ids"].to(chatbot.device)
output = []
for x in greedy_search(input_ids,model,tokenizer,stop_words=["[|Human|]", "[|AI|]"],max_length=max_length_tokens,temperature=temperature,top_p=top_p):
if is_stop_word_or_prefix(x,["[|Human|]", "[|AI|]"]) is False:
if "[|Human|]" in x:
x = x[:x.index("[|Human|]")].strip()
elif "[| Human |]" in x:
x = x[:x.index("[| Human |]")].strip()
if "[|AI|]" in x:
x = x[:x.index("[|AI|]")].strip()
x = x.strip(" ")
output.append(x)
return output[-1]
#text = "Can you give a more formal definition?"
#print(predict(text, model))
#sentiment = pipeline("sentiment-analysis")
#def get_sentiment(input_text):
# return sentiment(input_text)
#iface = gr.Interface(fn = get_sentiment,
# inputs = "text",
# outputs = ["text"],
# title = "Sentiment Analysis",
# description = "Ciao!!!")
#
#iface.launch(inline = False)
iface = gr.Interface(fn = predict,
inputs = "text",
outputs = ["text"],
title = "Learn with ChadGPT",
description = "Ciao!!!")
iface.launch(inline = False) |